diff --git "a/6338.jsonl" "b/6338.jsonl"
new file mode 100644--- /dev/null
+++ "b/6338.jsonl"
@@ -0,0 +1,602 @@
+{"seq_id":"580145016","text":"import RPi.GPIO as GPIO\n\nimport os\n\nfrom globaldefs import *\nfrom platformdefs import *\n\nfrom ledscanner import *\nfrom ledcontroller import *\nfrom buttonmonitor import *\n\nimport time\nimport subprocess\nfrom threading import Thread\n\nclass CommandSwitch(object):\n\n    terminate = False\n\n    def __init__(self, prompts_dir, termination_event, sound_player, led_scanner):\n        self.__command_underway = False\n        self.__prompts_dir = prompts_dir\n        self.__termination_event = termination_event\n        self.__sound_player = sound_player\n        self.__led_scanner = led_scanner\n\n\n    def process_switch_events(self):\n        try:\n            while not CommandSwitch.terminate:\n                GPIO.wait_for_edge(ROTARY_SWITCH_PIN, GPIO.FALLING)\n                print('control button pressed')\n\n                if self.__sound_player.is_active():\n                    # sound player is active, meaning a sound is playing\n\n                    if self.__sound_player.is_paused():\n                        # if paused and the button remains pressed...\n                        time.sleep(1.0)\n                        if GPIO.input(ROTARY_SWITCH_PIN) == GPIO.LOW:\n                            # button was held down while playing/paused,\n                            # stop this sound file and resume scanning\n                            # awaiting a new sound choice\n                            # sound player is paused, quit playing\n                            self.__sound_player.quit_playing()\n                        else:\n                            p = self.__sound_player.toggle_playback()\n                    else:\n                        # sound playing, not paused\n                        p = self.__sound_player.toggle_playback()\n                else:\n                    # Player is not active (leds are scanning)...\n                    # Wait 2 seconds to see if command button remains pressed\n                    time.sleep(2.0)\n                    if GPIO.input(ROTARY_SWITCH_PIN) == GPIO.LOW:\n                        # we are ending one way or another.\n                        # prevent the main thread from starting a sound\n                        self.__termination_event.clear()\n\n                        # command button held down for 2 sec\n                        # see if we shut down or go to configuration restart\n                        turnoff_all_leds()\n                        self.__led_scanner.stop_scanning()\n                        turnoff_all_leds()\n\n                        # shutdown-admin-resume.wav\n                        #\n                        # Soundbox is shutting down.\n                        # Choose one of the three following options\n                        # Press the red button to power down.\n                        # or\n                        # Press the green button to configure Soundbox and\n                        # restart it\n                        # or\n                        # Press any other button to restart Soundbox\n                        subprocess.Popen(['omxplayer',\n                                        '-o','alsa:'+ALSA_DEVICE_NAME,\n                                        self.__prompts_dir+'shutdown-admin-resume.wav'],\n                                        stdin=subprocess.PIPE,\n                                        stdout=None,stderr=None)\n\n                        led_controller = LEDController(1000, (LED_GREEN, LED_RED))\n                        led_thread = Thread(target=led_controller.run)\n                        led_thread.start()\n\n\n                        button_monitor = ButtonMonitor(1)\n                        button_pattern = button_monitor.get_button_presses()\n\n                        led_controller.go_dark()\n\n                        if button_pattern[0] == BUTTON_GREEN:\n\n                            led_controller.light_up(BUTTON_GREEN)\n                            time.sleep(1.0)\n                            print('restarting in config mode')\n                            self.__sound_player.close()\n                            os.system('sudo /etc/init.d/soundbox restart')\n\n                        else:\n                            if button_pattern[0] == BUTTON_RED:\n\n                                print('shutting down now')\n                                led_controller.light_up(BUTTON_RED)\n                                time.sleep(1.0)\n                                os.system('sudo shutdown now')\n\n                            else:\n                                os.system('sudo /etc/init.d/soundbox restart2')\n\n\n\n#\n\n                # sleep just a bit in case of switch bounce. this is because\n                # we are using edge detection here, and if the switch bounces,\n                # one press could cause two or more detectable edges.\n                time.sleep(0.2)\n        except Exception as ex:\n            print('CommandSwitch: exception: ', ex)\n","sub_path":"commandswitch.py","file_name":"commandswitch.py","file_ext":"py","file_size_in_byte":4874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"333815184","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Feb 28 19:46:22 2021\r\n\r\n@author: maeng\r\n\"\"\"\r\nimport os\r\nimport numpy as np\r\nimport datetime\r\nimport pickle\r\nimport gc\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom PIL import Image\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torchvision\r\nimport torchvision.transforms as T\r\n\r\nos.chdir(\"D:/masked-CNN\")\r\nfrom cam import scorecam_vggnet_batch\r\nfrom cam import scorecam_resnet_batch\r\nfrom cam import scorecam_vggnet_original\r\n\r\ndef load_image(image_path):   \r\n    return Image.open(image_path).convert('RGB')\r\n\r\ndef softmax(x):\r\n    return F.softmax(torch.tensor(x), dim = 0).numpy()\r\n\r\n##################################################################################################################\r\n#                                                                                                                #\r\n#                                               Parameter                                                        #\r\n#                                                                                                                #\r\n################################################################################################################## \r\n\r\ntransform_test = T.Compose([\r\n    T.Resize(256),\r\n    T.CenterCrop(224),\r\n    T.ToTensor(),\r\n    T.Normalize([0.4831, 0.4918, 0.4248], [0.1839, 0.1833, 0.1943]),\r\n])\r\n\r\nUSE_CUDA = torch.cuda.is_available()\r\nDEVICE = torch.device(\"cuda\" if USE_CUDA else \"cpu\")\r\n\r\nalias = \"VGG16\"\r\n\r\nDATA_PATH  = \"D:/data/CUB_200_2011\"\r\nLOAD_PATH  = \"D:/masked-CNN/CUB_prop/\"\r\nSAVE_PATH  = os.path.join(\"D:/masked-CNN/CUB_mask\", alias)\r\nMODEL_PATH = os.path.join(DATA_PATH, \"record\", alias)\r\n\r\nTARGET_LAYER = 42\r\nNUM_CLASS = 200\r\nBATCH_SIZE = 50\r\n\r\n##################################################################################################################\r\n#                                                                                                                #\r\n#                                                    Data Load                                                   #\r\n#                                                                                                                #\r\n################################################################################################################## \r\n\r\n#Prop Data\r\ndata = pd.read_csv(os.path.join(LOAD_PATH, \"CUB_img_\" + alias + \"_prop.csv\"), encoding = \"UTF-8-sig\")\r\n\r\ndata[\"top1_correct\"] = data.top1_pred == data.target\r\ndata[\"top5_correct\"] = data.apply(lambda x : x.target in eval(x.top5_pred), axis = 1)\r\n\r\nsum(data.top1_correct) / len(data)\r\nsum(data.top5_correct) / len(data)\r\n\r\nidx = [str(i) for i in range(0, NUM_CLASS)]\r\n\r\n#Best epoch\r\nlast = str(max(set([int(i.split(\".\")[0].split(\"_\")[1]) for i in os.listdir(os.path.join(MODEL_PATH))])))\r\ntest_acc  = eval(open(os.path.join(MODEL_PATH, alias + \"_\" + last + \"_test_acc.txt\"), 'r').read())\r\nepoch = max(test_acc.items(), key = lambda x : x[1])[0]\r\nprint(\"Best All   Epoch : \" + str(epoch) + \"  Acc : \" + str(test_acc[epoch]))\r\n\r\nepoch = max([i for i in test_acc.items() if i[0] % 5 == 0], key = lambda x : x[1])[0]\r\nprint(\"Best Save  Epoch : \" + str(epoch) + \"  Acc : \" + str(test_acc[epoch]))\r\n\r\n#VGG\r\nmodel = torchvision.models.vgg16_bn(pretrained = False)\r\nmodel.classifier[-1] = nn.Linear(in_features = model.classifier[-1].in_features, out_features = 200, bias = True)\r\nmodel.load_state_dict(torch.load(os.path.join(MODEL_PATH, alias + \"_\" + str(epoch) + \".pth\")))\r\nmodel = model.to(DEVICE)\r\n\r\n#RESNET\r\n# model = torchvision.models.resnet50(pretrained = False)\r\n# model.fc = nn.Linear(in_features = model.fc.in_features, out_features = NUM_CLASS, bias = True)\r\n# model.load_state_dict(torch.load(os.path.join(MODEL_PATH, alias + \"_\" + str(epoch) + \".pth\")))\r\n# model = model.to(DEVICE)\r\n\r\nmodel.eval()\r\n##################################################################################################################\r\n#                                                                                                                #\r\n#                                                    Mask image                                                  #\r\n#                                                                                                                #\r\n################################################################################################################## \r\nif not os.path.exists(SAVE_PATH):\r\n    os.makedirs(SAVE_PATH)\r\n\r\ni = 0\r\nfor i in range(0, len(data)):\r\n\r\n    start = datetime.datetime.now()\r\n\r\n    picture = load_image(data.path[i])\r\n    img     = transform_test(picture).unsqueeze(0).to(DEVICE)\r\n    label   = torch.tensor(eval(data.topk_pred[i]))\r\n    \r\n    #score_cam1    = scorecam_vggnet_original.ScoreCam(model, TARGET_LAYER)\r\n    #saliency_map1 = score_cam1.generate_cam(img, label[0])\r\n\r\n    score_cam    = scorecam_vggnet_batch.ScoreCam(model, TARGET_LAYER)\r\n    saliency_map = score_cam.generate_cam(img, label, BATCH_SIZE)\r\n    \r\n    meta = dict()\r\n    meta[\"img\"]         = data.img[i]\r\n    meta[\"path\"]        = data.path[i]\r\n    meta[\"target\"]      = data.target[i]\r\n    meta[\"origin_pred\"] = data.top1_pred[i]\r\n    meta[\"mask_label\"]  = label.cpu().numpy()\r\n    meta[\"saliency_map\"] = saliency_map\r\n    \r\n    with open(os.path.join(SAVE_PATH, \"CUB_\" + alias + \"_img_\" + str(i) + \".pickle\"), 'wb') as file:\r\n        pickle.dump(meta, file)\r\n        file.close()\r\n    \r\n    del meta, file\r\n    gc.collect()\r\n    \r\n    end = datetime.datetime.now()\r\n    \r\n    print(i, (end - start).total_seconds())\r\n\r\n\r\n","sub_path":"CUB_saliency_map.py","file_name":"CUB_saliency_map.py","file_ext":"py","file_size_in_byte":5639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"64235011","text":"__author__ = 'jingxuan'\n\nimport unittest\nfrom genomeapi.api import ODThroughLink\nimport json\n\nclass TestODThroughLink(unittest.TestCase):\n  def test_od_through_link(self):\n    od_thr_link = ODThroughLink(\"URL\",\"token\")\n    od_thr_link.dates(begin_date=\"2018-07-11\")\n    od_thr_link.time_series_reference(\"arrival\")\n    od_thr_link.link(\"NSW513133313\")\n    od_thr_link.dimension_facets(\"mode\")\n    od_thr_link.granularity(period=\"PT6H\")\n    od_thr_link.aggregate(metric=\"unique_agents\", described_as=\"unique_agents\")\n    od_thr_link.aggregate(metric=\"total_records\", described_as=\"total_records\")\n    od_thr_link.dumps()\n    res = od_thr_link.json\n    expected ={\n      \"dates\": {\n      \"type\": \"single\",\n      \"value\": \"2018-07-11\"\n      },\n      \"timeSeriesReference\": \"arrival\",\n      \"links\": [\n          \"NSW513133313\"\n      ],\n      \"dimensionFacets\": [\n          \"mode\"\n      ],\n      \"queryGranularity\": {\n          \"type\": \"period\",\n          \"period\": \"PT6H\",\n          \"timeZone\": \"Australia/Sydney\"\n      },\n      \"aggregations\": [\n      {\n        \"metric\": \"unique_agents\",\n        \"type\": \"hyperUnique\",\n        \"describedAs\": \"unique_agents\"\n      },\n      {\n        \"metric\": \"total_records\",\n        \"type\": \"longSum\",\n        \"describedAs\": \"total_records\"\n      }]\n    }\n\n    self.assertEqual(json.loads(res), expected)\n\nif __name__ == '__main__':\n    unittest.main()","sub_path":"testing/api/test_od_through_link.py","file_name":"test_od_through_link.py","file_ext":"py","file_size_in_byte":1385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"560348475","text":"# view created 11:33 19.07.2018\n\nimport pandas as pd\nfrom providers import getProvider\n\nprovider = getProvider(\n    name = 'daily-minimum-temperatures'\n)\n\nview = pd.DataFrame(provider.getData());\nview.set_index('date');\n","sub_path":"app/electron/git/flaff/example-temperature-analysis.view/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"483014755","text":"from datetime import timedelta\nfrom requests.auth import HTTPBasicAuth\nfrom django.core.exceptions import ValidationError\nfrom django.utils import timezone\nfrom django.core.cache import cache\nfrom ..settings import (\n    WFRS_GATEWAY_COMPANY_ID,\n    WFRS_GATEWAY_ENTITY_ID,\n    WFRS_GATEWAY_API_HOST,\n    WFRS_GATEWAY_CONSUMER_KEY,\n    WFRS_GATEWAY_CONSUMER_SECRET,\n    WFRS_GATEWAY_CLIENT_CERT_PATH,\n    WFRS_GATEWAY_PRIV_KEY_PATH,\n)\nfrom ..security import encrypt_pickle, decrypt_pickle\nimport requests\nimport logging\nimport uuid\n\nlogger = logging.getLogger(__name__)\n\n\nclass BearerTokenAuth(requests.auth.AuthBase):\n    def __init__(self, api_key):\n        self.api_key = api_key\n\n    def __call__(self, request):\n        request.headers[\"Authorization\"] = \"Bearer %s\" % self.api_key\n        return request\n\n\nclass WFRSAPIKey:\n    def __init__(self, api_key, expires_on):\n        self.api_key = api_key\n        self.expires_on = expires_on\n\n    @property\n    def is_expired(self):\n        # Force key rotation 10 minutes before it actually expires\n        expires_on = self.expires_on - timedelta(minutes=10)\n        now = timezone.now()\n        return now >= expires_on\n\n    @property\n    def ttl(self):\n        return int((self.expires_on - timezone.now()).total_seconds())\n\n    def __str__(self):\n        return \"<WFRSAPIKey expires_on=[%s]>\" % self.expires_on\n\n\nclass WFRSGatewayAPIClient:\n    company_id = WFRS_GATEWAY_COMPANY_ID\n    entity_id = WFRS_GATEWAY_ENTITY_ID\n    api_host = WFRS_GATEWAY_API_HOST\n    consumer_key = WFRS_GATEWAY_CONSUMER_KEY\n    consumer_secret = WFRS_GATEWAY_CONSUMER_SECRET\n    client_cert_path = WFRS_GATEWAY_CLIENT_CERT_PATH\n    priv_key_path = WFRS_GATEWAY_PRIV_KEY_PATH\n    scopes = [\n        \"PLCCA-Prequalifications\",\n        \"PLCCA-Applications\",\n        \"PLCCA-Payment-Calculations\",\n        \"PLCCA-Transactions-Authorization\",\n        \"PLCCA-Transactions-Charge\",\n        \"PLCCA-Transactions-Authorization-Charge\",\n        \"PLCCA-Transactions-Return\",\n        \"PLCCA-Transactions-Cancel-Authorization\",\n        \"PLCCA-Transactions-Void-Return\",\n        \"PLCCA-Transactions-Void-Sale\",\n        \"PLCCA-Transactions-Timeout-Authorization-Charge\",\n        \"PLCCA-Transactions-Timeout-Return\",\n        \"PLCCA-Account-Details\",\n    ]\n\n    cache_version = 1\n\n    @property\n    def cache_key(self):\n        return \"wfrs-gateway-api-key-{api_host}-{consumer_key}\".format(\n            api_host=self.api_host, consumer_key=self.consumer_key\n        )\n\n    def api_get(self, path, **kwargs):\n        return self.make_api_request(\"get\", path, **kwargs)\n\n    def api_post(self, path, **kwargs):\n        return self.make_api_request(\"post\", path, **kwargs)\n\n    def make_api_request(self, method, path, client_request_id=None, **kwargs):\n        url = \"https://{host}{path}\".format(host=self.api_host, path=path)\n        # Setup authentication\n        auth = BearerTokenAuth(self.get_api_key().api_key)\n        cert = None\n        if self.client_cert_path and self.priv_key_path:\n            cert = (self.client_cert_path, self.priv_key_path)\n        # Build headers\n        request_id = (\n            str(uuid.uuid4()) if client_request_id is None else str(client_request_id)\n        )\n        headers = {\n            \"request-id\": request_id,\n            \"gateway-company-id\": self.company_id,\n            \"gateway-entity-id\": self.entity_id,\n        }\n        if client_request_id is not None:\n            headers[\"client-request-id\"] = str(client_request_id)\n        # Send request\n        logger.info(\n            \"Sending WFRS Gateway API request. URL=[%s], RequestID=[%s]\",\n            url,\n            request_id,\n        )\n        request_fn = getattr(requests, method)\n        resp = request_fn(url, auth=auth, cert=cert, headers=headers, **kwargs)\n        logger.info(\n            \"WFRS Gateway API request returned. URL=[%s], RequestID=[%s], Status=[%s]\",\n            url,\n            request_id,\n            resp.status_code,\n        )\n        # Check response for errors\n        if resp.status_code == 400:\n            resp_data = resp.json()\n            errors = []\n            for err in resp_data.get(\"errors\", []):\n                exc = ValidationError(err[\"description\"], code=err[\"error_code\"])\n                errors.append(exc)\n            raise ValidationError(errors)\n        # Return response\n        return resp\n\n    def get_api_key(self):\n        # Check for a cached key\n        key_obj = self.get_cached_api_key()\n        if key_obj is None:\n            key_obj = self.generate_api_key()\n            self.store_cached_api_key(key_obj)\n        return key_obj\n\n    def get_cached_api_key(self):\n        # Try to get an API key from cache\n        encrypted_obj = cache.get(self.cache_key, version=self.cache_version)\n        if encrypted_obj is None:\n            return None\n        # Try to decrypt the object we got from cache\n        try:\n            key_obj = decrypt_pickle(encrypted_obj)\n        except Exception as e:\n            logger.exception(e)\n            return None\n        # Check if the key is expired\n        if key_obj.is_expired:\n            return None\n        # Return the key\n        return key_obj\n\n    def store_cached_api_key(self, key_obj):\n        # Pickle and encrypt the key object\n        encrypted_obj = encrypt_pickle(key_obj)\n        # Store it in Django's cache for later\n        cache.set(\n            self.cache_key, encrypted_obj, key_obj.ttl, version=self.cache_version\n        )\n\n    def generate_api_key(self):\n        url = \"https://{host}/oauth2/v1/token\".format(host=self.api_host)\n        auth = HTTPBasicAuth(self.consumer_key, self.consumer_secret)\n        cert = (self.client_cert_path, self.priv_key_path)\n        req_data = {\n            \"grant_type\": \"client_credentials\",\n            \"scope\": \" \".join(self.scopes),\n        }\n        resp = requests.post(url, auth=auth, cert=cert, data=req_data)\n        resp.raise_for_status()\n        resp_data = resp.json()\n        expires_on = timezone.now() + timedelta(seconds=resp_data[\"expires_in\"])\n        logger.info(\"Generated new WFRS API Key. ExpiresIn=[%s]\", expires_on)\n        key_obj = WFRSAPIKey(api_key=resp_data[\"access_token\"], expires_on=expires_on)\n        return key_obj\n","sub_path":"src/wellsfargo/connector/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":6238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"383065308","text":"\"\"\"empty message\n\nRevision ID: d03ba82d2923\nRevises: 48ac60350ca4\nCreate Date: 2020-04-10 04:59:10.477213\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'd03ba82d2923'\ndown_revision = '48ac60350ca4'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    with op.batch_alter_table('user', schema=None) as batch_op:\n        batch_op.add_column(sa.Column('email', sa.String(length=80), nullable=True))\n        batch_op.create_unique_constraint(None, ['email'])\n\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    with op.batch_alter_table('user', schema=None) as batch_op:\n        batch_op.drop_constraint(None, type_='unique')\n        batch_op.drop_column('email')\n\n    # ### end Alembic commands ###\n","sub_path":"migrations/versions/d03ba82d2923_.py","file_name":"d03ba82d2923_.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"489007770","text":"import codecs\nimport re\n\nf = codecs.open(\"python_text.rtf\", 'r')\nfile1 = f.read()\n\npythoninputbychunksbylinesbykeywords = []\n\npythoninputbychunksbylinesbykeywords = [x.split('\\n') for x in file1.split('\\n\\n\\n')] # this is still just lines\n\n#print(pythoninputbychunksbylinesbykeywords)\n\npythoninputbychunksbylinesbykeywords = [[x for x in y if x != \"\"] for y in pythoninputbychunksbylinesbykeywords] # no need for \"\\n\" just \"\" (see visualizer)\n\n#print(pythoninputbychunksbylinesbykeywords)\n\npythonbinarybychunksbylinesbykeywords = [[0 for x in y] for y in pythoninputbychunksbylinesbykeywords]\n\n#print(pythonbinarybychunksbylinesbykeywords)\n\n# Python keywords\n# Removed:\n# 'elif ' because of \"if\"\n# 'for ' because of \"or\"\n# 'not ' because always compounded with another\n# Changes:\n# ' ' added to end of all but \"print\", \"yield\", \"None\", \"True\", and \"False\"\n# 'else:' used instead of 'else '\nkeywords = [\n    'and ', 'assert ', 'break ', 'class ', 'continue ', 'def ',\n    'del ', 'else:', 'except ', 'exec ', 'finally ',\n    'from ', 'global ', 'if ', 'import ', 'in ',\n    'is ', 'lambda ', 'or ', 'pass ', 'print',\n    'raise ', 'return ', 'try ', 'while ' , 'yield',\n    'None', 'True', 'False',\n    ]\n\nfor chunk in pythoninputbychunksbylinesbykeywords:\n    chunkindex = pythoninputbychunksbylinesbykeywords.index(chunk)\n    for line in chunk:\n        lineindex = pythoninputbychunksbylinesbykeywords[chunkindex].index(line)\n        q = []\n        for x in keywords:\n            if re.findall(x, line) != []:\n                q.append(x)\n        print(q)\n        if (len(q)) == 1:\n            pythonbinarybychunksbylinesbykeywords[chunkindex][lineindex] = [0, 1]\n        elif (len(q)) == 2:\n            pythonbinarybychunksbylinesbykeywords[chunkindex][lineindex] = [0, 1, 0, 1] # line was previously: 0; now it is: [0, 1, 0, 1]\n        elif \"=\" in line:\n            pythonbinarybychunksbylinesbykeywords[chunkindex][lineindex] = 1\n\nprint(pythoninputbychunksbylinesbykeywords)\nprint(pythonbinarybychunksbylinesbykeywords)","sub_path":"python_keywords.py","file_name":"python_keywords.py","file_ext":"py","file_size_in_byte":2022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"535910333","text":"# ./crackme.hooked XXXXXXXXXXXXXXXXXXXXX\n# Hook add\n# Damn_YoU_Got_The_Flag\n# XXXXXXXXXXXXXXXXXXXXX\n# You got it !!\nimport lief\n\ncrackme = lief.parse(\"crackme.bin\")\nhook    = lief.parse(\"hook\")\n\nsegment_added  = crackme.add(hook.segments[0])\n\nmy_memcmp      = hook.get_symbol(\"my_memcmp\")\nmy_memcmp_addr = segment_added.virtual_address + my_memcmp.value\n\ncrackme.patch_pltgot('memcmp', my_memcmp_addr)\ncrackme.write(\"crackme.hooked\")\n","sub_path":"05_ELF_infect_plt-got/hook_pltgot.py","file_name":"hook_pltgot.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"57786523","text":"# Copyright (C) 2012 Robert Lanfear and Brett Calcott\n#\n# This program is free software: you can redistribute it and/or modify it under\n# the terms of the GNU General Public License as published by the Free Software\n# Foundation, either version 3 of the License, or (at your option) any later\n# version.\n#\n# This program is distributed in the hope that it will be useful, but WITHOUT\n# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n# FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more\n# details. You should have received a copy of the GNU General Public License\n# along with this program.  If not, see <http://www.gnu.org/licenses/>.\n# PartitionFinder also includes the PhyML program, the RAxML program, and the\n# PyParsing library, all of which are protected by their own licenses and\n# conditions, using PartitionFinder implies that you agree with those licences\n# and conditions as well.\n\nimport hashlib\nimport cPickle as pickle\nimport subset\n\nimport logging\nlog = logging.getLogger(\"subset_ops\")\n\n\ndef subset_unique_name(subset):\n    \"\"\"Return a unique string based on the subsets columns (which are unique)\"\"\"\n\n    # Use pickle to give us a string from the object\n    pickled_columns = pickle.dumps(subset.column_set, -1)\n\n    # Now get an md5 hash from this. There is some vanishingly small chance that\n    # we'll get the same thing. Google \"MD5 Hash Collision\"\n    return hashlib.md5(pickled_columns).hexdigest()\n\n\ndef merge_subsets(subset_list):\n    \"\"\"Take a set of subsets and merge them together\"\"\"\n    columns = set()\n\n    # We just need the columns\n    for sub in subset_list:\n        columns |= sub.column_set\n\n    return subset.Subset(sub.cfg, columns)\n\n\ndef subsets_overlap(subset_list):\n    columns = set()\n\n    for sub in subset_list:\n        # If the intersection is non-empty...\n        if sub.column_set & columns:\n            return True\n        columns |= sub.column_set\n\n    return False\n\n\ndef has_missing(subset_list):\n    return False\n\n\ndef split_subset(a_subset, cluster_list):\n    \"\"\"Takes a subset and splits it according to a cluster list,\n     then returns the subsets resulting from the split\"\"\"\n    # Take each site from the first list and add it to a new\n    \n    subset_list = a_subset.columns\n    likelihood_list = a_subset.site_lnls_GTRG\n    subset_columns = []\n    site_likelihoods = []\n    list_of_subsets = []\n    for cluster in cluster_list:\n        list_of_sites = []\n        likelihood_for_site = []\n        for site in cluster:\n            list_of_sites.append(subset_list[site - 1])\n            likelihood_for_site += (likelihood_list[site - 1])\n        subset_columns.append(set(list_of_sites))\n        site_likelihoods.append(likelihood_for_site)\n        # print subset_columns\n    tracker = 0\n    for column_set in subset_columns:\n        new_subset = subset.Subset(a_subset.cfg, column_set)\n        if new_subset.best_lnl > 0:\n            new_subset.fabricated = True\n        list_of_subsets.append(new_subset)\n        new_subset.site_lnls_GTRG = site_likelihoods[tracker]\n        tracker += 1\n\n    return list_of_subsets\n\ndef merge_fabricated_subsets(subset_list):\n    '''Allows the merging of fabricated subsets and the preservation of their\n    centroids and lnls'''\n    columns = set()\n    lnl = 0\n    centroid = []\n\n    # Figure out how many dimensions the centroid is\n    centroid_dim = len(subset_list[0].centroid)\n    for i in range(centroid_dim):\n        centroid.append(0)\n\n    for sub in subset_list:\n        columns |= sub.column_set\n        lnl += sub.best_lnl\n        number = 0\n        for observation in centroid:\n            observation += sub.centroid[number]\n            number += 1\n\n    # Now just take the average of each centroid to be the centroid of the new\n    # subset\n    centroid = [x/len(subset_list) for x in centroid]\n\n    new_sub = subset.Subset(sub.cfg, columns)\n\n    # Add the centroid and sum of the lnls to the subset. TODO: create\n    # functions to update these variables in the subset rather than messing\n    # with them directly\n    new_sub.centroid = centroid\n    new_sub.lnl = lnl\n    return new_sub\n","sub_path":"partfinder/subset_ops.py","file_name":"subset_ops.py","file_ext":"py","file_size_in_byte":4144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"13100540","text":"from collections import Counter\nfrom heapq import heappush, heappop\n\nclass Solution(object):\n    def topKFrequent(self, nums, k):\n        \"\"\"\n        :type nums: List[int]\n        :type k: int\n        :rtype: List[int]\n        \"\"\"\n\n        result = []\n\n        h = []\n        for i, n in Counter( nums ).iteritems():\n            heappush( h, ( -n, i ) )\n\n        for j in xrange( k ):\n            _, i = heappop( h )\n            result.append( i )\n\n        return result\n","sub_path":"347.py","file_name":"347.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"312663901","text":"from typing import List\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\nclass Solution:\n    def isCousins(self, root: TreeNode, x: int, y: int) -> bool:\n\n        res = [[root.val, 0]] if root.val == x or root.val == y else []\n        res = self.search(root.left, x, y, 1, root.val, res)\n        res = self.search(root.right, x, y, 1, root.val, res)\n        print(res)\n        if res[0][1] == res[1][1] and res[0][0] != res[1][0]: return True\n        return False\n\n\n    def search(self, root: TreeNode, x: int, y: int, depth:int, ancestor_val: int, val_list: List[List[int]]):\n        if not root: return val_list\n\n        if root.val == x or root.val == y:\n            val_list.append([ancestor_val, depth])\n\n        val_list = self.search(root.left, x, y, depth + 1, root.val, val_list)\n        val_list = self.search(root.right, x, y, depth + 1, root.val, val_list)\n        return val_list","sub_path":"leetcode/data_structure/tree/cousins_in_binary_tree.py","file_name":"cousins_in_binary_tree.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"578556663","text":"import cv2\nimport numpy as np\n\n\nimg = cv2.imread('10.jpg')\nnewImage=np.zeros(img.shape,np.uint8)\ngray=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\ni, j, k = img.shape\n\nfor x in xrange(i):\n  for y in xrange(j):\n    R = img[x,y,2] * 0.256 + img[x,y,1] * 0.145 + img[x,y,0] * 0.342\n    G = img[x,y,2] * 0.349 + img[x,y,1] * 0.686 + img[x,y,0] * 0.168\n    B = img[x,y,2] * 0.272 + img[x,y,1] * 0.534 + img[x,y,0] * 0.131\n    if R > 255:\n        newImage[x,y,2] = 255\n    else:\n        newImage[x,y,2] = R\n    if G > 255:\n        newImage[x,y,1] = 255\n    else:\n        newImage[x,y,1] = G\n    if B > 255:\n        newImage[x,y,0] = 255\n    else:\n        newImage[x,y,0] = B\n\ncv2.imwrite('/home/vishnu/sepia_bw/12.jpg',newImage)\ncv2.imwrite('/home/vishnu/sepia_bw/13.jpg',gray)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","sub_path":"color/bw_sep.py","file_name":"bw_sep.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"183780479","text":"import unittest\n\nfrom unittest.mock import patch, MagicMock\nfrom requests import Request\nfrom tekore.sender import (\n    Sender, TransientSender, SingletonSender, PersistentSender, RetryingSender\n)\n\n\nclass TestSender(unittest.TestCase):\n    def test_sender_cannot_be_instantiated(self):\n        with self.assertRaises(TypeError):\n            Sender()\n\n\nclass MockSessionFactory:\n    prepare_return = 'prepared'\n\n    def __init__(self):\n        self.instances = []\n\n    def __call__(self, *args, **kwargs):\n        mock = MagicMock()\n        mock.__enter__ = MagicMock(return_value=mock)\n        mock.prepare_request = MagicMock(return_value=self.prepare_return)\n\n        self.instances.append(mock)\n        return mock\n\n\nclass TestSingletonSender(unittest.TestCase):\n    def test_instances_share_session(self):\n        s1 = SingletonSender()\n        s2 = SingletonSender()\n        self.assertTrue(s1.session is s2.session)\n\n    def test_request_prepared(self):\n        mock = MockSessionFactory()\n        with patch('tekore.sender.SingletonSender.session', mock()):\n            s = SingletonSender()\n            r = Request()\n            s.send(r)\n            mock.instances[0].prepare_request.assert_called_with(r)\n\n    def test_keywords_passed_to_session(self):\n        mock = MockSessionFactory()\n        kwargs = dict(k1='k1', k2='k2')\n        with patch('tekore.sender.SingletonSender.session', mock()):\n            s = SingletonSender(**kwargs)\n            r = Request()\n            s.send(r)\n            mock.instances[0].send.assert_called_with(\n                mock.prepare_return,\n                **kwargs\n            )\n\n\ndef test_request_prepared(sender_type):\n    mock = MockSessionFactory()\n    with patch('tekore.sender.Session', mock):\n        s = sender_type()\n        r = Request()\n        s.send(r)\n        mock.instances[0].prepare_request.assert_called_with(r)\n\n\ndef test_keywords_passed_to_session(sender_type):\n    mock = MockSessionFactory()\n    kwargs = dict(k1='k1', k2='k2')\n    with patch('tekore.sender.Session', mock):\n        s = sender_type(**kwargs)\n        s.send(Request())\n        mock.instances[0].send.assert_called_with(mock.prepare_return, **kwargs)\n\n\nclass TestPersistentSender(unittest.TestCase):\n    @patch('tekore.sender.Session', MagicMock)\n    def test_session_is_reused(self):\n        s = PersistentSender()\n        sess1 = s.session\n        s.send(Request())\n        s.send(Request())\n        sess2 = s.session\n        self.assertTrue(sess1 is sess2)\n\n    def test_instances_dont_share_session(self):\n        s1 = PersistentSender()\n        s2 = PersistentSender()\n        self.assertTrue(s1.session is not s2.session)\n\n    def test_request_prepared(self):\n        test_request_prepared(PersistentSender)\n\n    def test_keywords_passed_to_session(self):\n        test_keywords_passed_to_session(PersistentSender)\n\n\nclass TestTransientSender(unittest.TestCase):\n    def test_session_is_not_reused(self):\n        mock = MockSessionFactory()\n        with patch('tekore.sender.Session', mock):\n            s = TransientSender()\n            s.send(Request())\n            s.send(Request())\n            self.assertEqual(len(mock.instances), 2)\n\n    def test_request_prepared(self):\n        test_request_prepared(TransientSender)\n\n    def test_keywords_passed_to_session(self):\n        test_keywords_passed_to_session(TransientSender)\n\n\ndef ok_response() -> MagicMock:\n    response = MagicMock()\n    response.status_code = 200\n    return response\n\n\ndef rate_limit_response(retry_after: int = 1) -> MagicMock:\n    response = MagicMock()\n    response.status_code = 429\n    response.headers = {'Retry-After': retry_after}\n    return response\n\n\ndef failed_response() -> MagicMock:\n    response = MagicMock()\n    response.status_code = 500\n    return response\n\n\nclass TestRetryingSender(unittest.TestCase):\n    def test_rate_limited_request_retried_after_set_seconds(self):\n        time = MagicMock()\n        sender = MagicMock()\n\n        fail = rate_limit_response()\n        success = ok_response()\n        sender.send.side_effect = [fail, success]\n\n        s = RetryingSender(sender=sender)\n        with patch('tekore.sender.time', time):\n            s.send(Request())\n            time.sleep.assert_called_once_with(1)\n\n    def test_failing_request_but_no_retries_returns_failed(self):\n        sender = MagicMock()\n        fail = failed_response()\n        success = ok_response()\n        sender.send.side_effect = [fail, success]\n\n        s = RetryingSender(sender=sender)\n        r = s.send(Request())\n        self.assertTrue(r is fail)\n\n    def test_failing_request_retried_max_times(self):\n        sender = MagicMock()\n        fail = failed_response()\n        success = ok_response()\n        sender.send.side_effect = [fail, fail, fail, success]\n\n        s = RetryingSender(retries=2, sender=sender)\n        with patch('tekore.sender.time', MagicMock()):\n            s.send(Request())\n        self.assertEqual(sender.send.call_count, 3)\n\n    def test_retry_returns_on_first_success(self):\n        sender = MagicMock()\n        fail = failed_response()\n        success = ok_response()\n        sender.send.side_effect = [fail, fail, success, fail, success]\n\n        s = RetryingSender(retries=5, sender=sender)\n        with patch('tekore.sender.time', MagicMock()):\n            s.send(Request())\n        self.assertEqual(sender.send.call_count, 3)\n\n    def test_rate_limited_retry_doesnt_decrease_retry_count(self):\n        sender = MagicMock()\n\n        fail = failed_response()\n        rate = rate_limit_response()\n        success = ok_response()\n        sender.send.side_effect = [fail, rate, fail, success]\n\n        s = RetryingSender(retries=2, sender=sender)\n        with patch('tekore.sender.time', MagicMock()):\n            s.send(Request())\n\n        self.assertEqual(sender.send.call_count, 4)\n\n\nclass TestSenderDefaults(unittest.TestCase):\n    def setUp(self):\n        from tekore import sender\n        self.old_default_type = sender.default_sender_type\n        self.old_default_instance = sender.default_sender_instance\n        self.old_default_kwargs = sender.default_requests_kwargs\n\n    def test_modify_default_sender_type(self):\n        instance = MagicMock()\n        type_mock = MagicMock(return_value=instance)\n\n        from tekore import sender, Spotify\n        sender.default_sender_type = type_mock\n\n        s = Spotify()\n        self.assertIs(s.sender, instance)\n\n    def test_modify_default_sender_instance(self):\n        instance = MagicMock()\n\n        from tekore import sender, Spotify\n        sender.default_sender_instance = instance\n\n        s = Spotify()\n        self.assertIs(s.sender, instance)\n\n    def test_instance_has_precedence_over_type(self):\n        instance = MagicMock()\n        type_mock = MagicMock(return_value=MagicMock())\n\n        from tekore import sender, Spotify\n        sender.default_sender_type = type_mock\n        sender.default_sender_instance = instance\n\n        s = Spotify()\n        self.assertIs(s.sender, instance)\n\n    def test_retrying_sender_as_default_type_recurses(self):\n        from tekore import sender\n        sender.default_sender_type = sender.RetryingSender\n\n        with self.assertRaises(RecursionError):\n            RetryingSender()\n\n    def test_default_kwargs_used_if_none_specified(self):\n        kwargs = {'arg': 'val'}\n\n        from tekore import sender\n        sender.default_requests_kwargs = kwargs\n        s = sender.TransientSender()\n        self.assertDictEqual(s.requests_kwargs, kwargs)\n\n    def test_default_kwargs_ignored_if_kwargs_specified(self):\n        kwargs = {'arg': 'val'}\n\n        from tekore import sender\n        sender.default_requests_kwargs = kwargs\n        s = sender.TransientSender(kw='value')\n        self.assertNotIn('arg', s.requests_kwargs)\n\n    def tearDown(self):\n        from tekore import sender\n        sender.default_sender_type = self.old_default_type\n        sender.default_sender_instance = self.old_default_instance\n        sender.default_requests_kwargs = self.old_default_kwargs\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests/sender.py","file_name":"sender.py","file_ext":"py","file_size_in_byte":8083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"512263060","text":"# Purpose\n#       Takes in one or multiple .fastq.gz files, unzips, quality filters, and reports unique reads + associated read counts.\n#       Output is tsv file named <input>_collapsed.txt. column 1 = read. column 2 = read count.\n\n\n\n\n\n# Usage\n#\tpython x1.collapseSeq.py </dir/to/input/data/basename_gz> </dir/to/output/> <memory_gigs> <hrs_requested> <email>\t \n\n# Arguments\n#\t</dir/to/output/>\t\tdirectory of the outputted files\n#\t<memory_gigs> \t\t\tinteger. Number of gigabytes of memory requested for computation\n#\t<hrs_requested> \t\tinteger. Maximum computation time requested \n#\t<email>\t\t\t\temail to send alerts to  \n\n# Inputs\n#       </dir/to/input/basename_gz>     directory and basename of the .fastq.gz files. Make sure you omit the .fastq.gz suffix.\n\n# Outputs\n#\tbasename_collapsed_filtered.fastq\tunzipped fastq file from all .fastq.gz files that matched the basename of input\n#\t\t\t\t\t\tfiltered for quality\n\n\n\n\n\n\n\nimport os\nimport sys\n\n# ensure all arguments passed\ntry:\n        basename=sys.argv[1] # /dir/to/data/input.fastq\n        basename_noDir=basename.split('/')[-1] # input.fastq\n        dataDir=basename[:basename.rfind('/')+1] # /dir/to/data/\n        outputDir=sys.argv[2]\n        mem=sys.argv[3]\n        jobHours=sys.argv[4]\n        email=sys.argv[5]\nexcept IndexError:\n        print(\"Error: Not all arguments passed\")\n\n# Make dirs for submission scripts and batch stderr/stdout files to be saved\nos.system(\"mkdir \"+outputDir+\"stdout 2>/dev/null\")\nos.system(\"mkdir \"+outputDir+\"stderr 2>/dev/null\")\n\n# Script version to use\ncollapseSeq='A.reads2collapsed.sh'\ncollapseSeqPy='0.collapseSeq.py'\n\n# Define Job Name\njobName='collapseSeq_'+basename_noDir\n\n# Create batch submit script\nline_out=\"#!/bin/bash\\n\"\nline_out+=\"#SBATCH --partition=shared\\n\"\nline_out+=\"#SBATCH --job-name=\"+jobName+\"\\n\"\nline_out+=\"#SBATCH --nodes=1\\n\"\nline_out+=\"#SBATCH --ntasks-per-node=1\\n\"\nline_out+=\"#SBATCH --mem=\"+mem+\"G\\n\"\nline_out+=\"#SBATCH --time=\"+jobHours+\":00:00\\n\"\nline_out+=\"#SBATCH --output=\"+outputDir+\"stdout/\"+jobName+\".out.txt\\n\"\nline_out+=\"#SBATCH --error=\"+outputDir+\"stderr/\"+jobName+\".err.txt\\n\"\nline_out+=\"#SBATCH --export=ALL\\n\"\nline_out+=\"#SBATCH --mail-user=\"+email+\"\\n\"\nline_out+=\"#SBATCH --mail-type=ALL\\n\"\nline_out+=\"module load python\\n\" # load package numpy\nline_out+=\"module load scipy\\n\" # load package numpy\nline_out+=\"module load biopython\\n\" # load package biopython\nline_out+=\" \".join([\"bash\",collapseSeq,dataDir,outputDir,basename_noDir]) # reads2BcDict $dataDir $basenam$\n\n# Write and submit batch script\nwith open(\"submit_collapseSeq.sh\",\"w\") as fn:\n        fn.write(line_out)\nos.system(\"sbatch submit_collapseSeq.sh\")\n\n# Copy submit script\nos.system(\"mkdir \"+outputDir+\"submit-scripts 2>/dev/null\")\nos.system(\"cp submit_collapseSeq.sh \"+outputDir+\"submit-scripts/\"+jobName+\".submit.sh\")\n\n# Copy script to data file\nos.system(\"mkdir \"+outputDir+\"scripts 2>/dev/null\")\nos.system(' '.join([\"cp\",collapseSeq,outputDir+\"scripts/\"+collapseSeq]))\nos.system(' '.join([\"cp\",collapseSeqPy,outputDir+\"scripts/\"+collapseSeqPy]))\n\n","sub_path":"RNA_library/x1.collapseSeq.py","file_name":"x1.collapseSeq.py","file_ext":"py","file_size_in_byte":3046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"109469906","text":"#!/usr/bin/env python3\n\nimport argparse\nimport logging\nimport os\nimport shutil\nimport subprocess\nimport sys\nimport tempfile\n\n\n__ENV_VOL_WIKI          = \"VOL_WIKI\"\n__ENV_VOL_WIKI_SETTINGS = \"VOL_WIKI_SETTINGS\"\n__ENV_VOL_WIKI_IMAGES   = \"VOL_WIKI_IMAGES\"\n__ENV_VOL_DATABASE      = \"VOL_WIKI_DATABASE\"\n__ENV_VOL_BACKUP        = \"VOL_WIKI_BACKUP\"\n\n__ENV_OUTPUT_FILE = \"wiki.env\"\n__ENV_OUTPUT_PATH = os.path.normpath(os.path.join(os.getcwd(), __ENV_OUTPUT_FILE))\n\n\n# -----------------------------------------------------------------------------\n#\ndef write_config(config: argparse.Namespace) -> None:\n\n    data = \"--wiki\\n{}\\n\".format(config.vol_wiki)\n    data += \"--database\\n{}\\n\".format(config.vol_database)\n    data += \"--backup\\n{}\\n\".format(config.vol_backup)\n\n    # Simply write the array to the output file\n    with open(__ENV_OUTPUT_PATH, \"w+\") as fd:\n        fd.writelines(data)\n\n\n# -----------------------------------------------------------------------------\n#\ndef gather_args():\n\n    # If there are arguments, use those\n    if len(sys.argv) > 1:\n        return sys.argv[1:]\n\n    # Next check for a config\n    elif os.path.exists(__ENV_OUTPUT_PATH):\n        with open(__ENV_OUTPUT_PATH, \"r\") as fd:\n            result = [x.strip() for x in fd.readlines()]\n            logging.debug(result)\n            return result\n\n    # Arguments will always fall through but expect to be empty\n    return sys.argv[1:]\n\n\n# -----------------------------------------------------------------------------\n#\ndef parse_args(args):\n    \"\"\"\n    Parse a list of arguments into a configuration object\n\n    :param args:    <list> of arguments to parse\n    :return:        <object> struct containing parsed arguments\n    \"\"\"\n\n    parser = argparse.ArgumentParser(\"Deploy\")\n\n    parser.add_argument(\"--wiki\", dest=\"vol_wiki\",\n                        type=str, help=\"Location on host machine for wiki\")\n    parser.add_argument(\"--database\", dest=\"vol_database\",\n                        type=str, help=\"Location on host machine for wiki's database\")\n    parser.add_argument(\"--backup\", dest=\"vol_backup\",\n                        type=str, help=\"Location on host machine for wiki's database backup output\")\n    parser.add_argument(\"--env\", dest=\"env\",\n                        type=argparse.FileType('r'), help=\"Location of environment arguments\")\n\n    parser.add_argument(\"--down\", dest=\"down\", action=\"store_true\", default=False,\n                        help=\"Optional argument to take down the containers\")\n\n    config = parser.parse_args(args)\n\n    if config.env is None and config.vol_wiki is None and config.vol_database is None and config.vol_backup is None:\n        parser.error(\"'--env' is required or the explicit use of '--wiki', '--database', and '--env'\")\n\n    if config.env is not None:\n        # Gather the arguments from the file\n        env_args = [x.strip() for x in config.env.readlines()]\n        logging.debug(env_args)\n\n        # Walk through each argument and assign them to the appropriate namespace\n        i = 0\n        while i < len(env_args):\n            if env_args[i] == \"--wiki\":\n                config.vol_wiki = env_args[i+1]\n            if env_args[i] == \"--database\":\n                config.vol_database = env_args[i+1]\n            if env_args[i] == \"--backup\":\n                config.vol_backup = env_args[i+1]\n            i += 2\n\n    return config\n\n\n# -----------------------------------------------------------------------------\n#\ndef post_deploy_hacky_stuff(config: argparse.Namespace, env: dict) -> None:\n\n    # Hack - 1\n    # Copy the LocalSettings.php file into the wiki container if it exists\n    if not config.down:\n\n        # Define variables\n        local_settings_php_path = os.path.join(env[__ENV_VOL_WIKI_SETTINGS], \"LocalSettings.php\")\n\n        # Only do this if the file exists (we could be on our initialization step)\n        if os.path.exists(local_settings_php_path):\n            cmd = [\"docker\", \"exec\", \"-it\", \"wiki_web\", \"cp\", \"/var/www/wiki_settings/LocalSettings.php\",\n                   \"/var/www/html/LocalSettings.php\"]\n            logging.info(\"CMD: {}\".format(cmd))\n            subprocess.run(cmd, check=True, env=env)\n\n    # Hack - 2\n    # This hack is to change the ownership of the mounted volume for the images uploaded to the wiki\n    if not config.down:\n        cmd = [\"docker\", \"exec\", \"-it\", \"wiki_web\", \"chown\", \"-R\", \"www-data:www-data\", \"/var/www/html\"]\n        logging.info(\"CMD: {}\".format(cmd))\n        subprocess.run(cmd, check=True, env=env)\n\n\n# -----------------------------------------------------------------------------\n#\ndef get_volumes(config: argparse.Namespace, make_dirs: bool = True) -> list:\n    vol_wiki = os.path.normpath(os.path.abspath(config.vol_wiki))\n    vol_wiki_images = os.path.join(vol_wiki, \"images\")\n    vol_wiki_settings = os.path.join(vol_wiki, \"wiki_settings\")\n    vol_database = os.path.normpath(os.path.abspath(config.vol_database))\n    vol_backup = os.path.normpath(os.path.abspath(config.vol_backup))\n\n    volumes = [vol_wiki, vol_wiki_images, vol_wiki_settings, vol_database, vol_backup]\n\n    # Create the directories if necessary\n    if make_dirs:\n        for vol in volumes:\n            if not os.path.exists(vol):\n                os.makedirs(vol)\n\n    return volumes\n\n\n# -----------------------------------------------------------------------------\n#\ndef deploy_containers():\n\n    # Parse them into a runtime config\n    config = parse_args(sys.argv[1:])\n\n    # Get the absolute paths\n    vol_wiki, vol_wiki_images, vol_wiki_settings, vol_database, vol_backup = get_volumes(config)\n\n    # Make the directories if they do not exist\n    vols = [vol_wiki, vol_database, vol_backup]\n    logging.debug(\"Vols: {}\".format(vols))\n    for vol in vols:\n        if not os.path.exists(vol):\n            os.makedirs(vol)\n\n    # Build our target environment\n    env = os.environ.copy()\n    env[__ENV_VOL_WIKI] = vol_wiki\n    env[__ENV_VOL_WIKI_IMAGES] = vol_wiki_images\n    env[__ENV_VOL_WIKI_SETTINGS] = vol_wiki_settings\n    env[__ENV_VOL_DATABASE] = vol_database\n    env[__ENV_VOL_BACKUP] = vol_backup\n\n    # Locate the compose file\n    working_dir = os.path.dirname(os.path.abspath(__file__))\n    compose_file = os.path.join(working_dir, \"docker-compose.yml\")\n\n    # Root command to run\n    exe = [\"docker-compose\"]\n\n    # Arguments for the compose file\n    file_arg = [\"--file\", compose_file]\n\n    # Final docker commands\n    build_cmd = [\"build\"]\n    deploy_cmd = [\"up\", \"-d\"]\n    down_cmd = [\"down\"]\n\n    # Then perform the desired action\n    if config.down:\n        cmd = exe + file_arg + down_cmd\n        logging.info(\"CMD: {}\".format(cmd))\n        subprocess.run(cmd, check=True, env=env)\n    else:\n        cmd = exe + file_arg + build_cmd\n        logging.info(\"CMD: {}\".format(cmd))\n        subprocess.run(cmd, check=True, env=env)\n        cmd = exe + file_arg + deploy_cmd\n        logging.info(\"CMD: {}\".format(cmd))\n        subprocess.run(cmd, check=True, env=env)\n\n    # Do hacky things that should probably be handled by the compose.yaml file\n    post_deploy_hacky_stuff(config, env)\n\n    # Save the configs so the file can be ran again\n    write_config(config)\n\n\n# -----------------------------------------------------------------------------\n#\nif __name__ == \"__main__\":\n\n    # Configure the logger\n    logging.basicConfig(level=logging.DEBUG)\n    deploy_containers()\n\n\n","sub_path":"deploy.py","file_name":"deploy.py","file_ext":"py","file_size_in_byte":7363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"445228199","text":"#!/usr/bin/python2.5\n# Copyright 2010 Google Inc.\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\nfrom datetime import datetime\nimport simplejson\nimport sys\n\nfrom model import *\nfrom utils import *\nimport reveal\n\n\nclass Admin(Handler):\n    # After a subdomain is deactivated, we still need the admin page to be\n    # accessible so we can edit its settings.\n    ignore_deactivation = True\n\n    def get(self):\n        user = users.get_current_user()\n        simplejson.encoder.FLOAT_REPR = str\n        encoder = simplejson.encoder.JSONEncoder(ensure_ascii=False)\n        config_json = dict((name, encoder.encode(self.config[name]))\n                           for name in self.config.keys())\n        self.render('templates/admin.html', user=user,\n                    subdomains=Subdomain.all(),\n                    config=self.config, config_json=config_json,\n                    start_url=self.get_start_url(),\n                    login_url=users.create_login_url(self.request.url),\n                    logout_url=users.create_logout_url(self.request.url),\n                    id=self.env.domain + '/person.')\n\n    def post(self):\n        if self.params.operation == 'delete':\n            # Redirect to the deletion handler with a valid signature.\n            action = ('delete', str(self.params.id))\n            self.redirect('/delete', id=self.params.id,\n                          signature=reveal.sign(action))\n\n        elif self.params.operation == 'subdomain_create':\n            Subdomain(key_name=self.params.subdomain_new).put()\n            config.set_for_subdomain(  # Provide some defaults.\n                self.params.subdomain_new,\n                language_menu_options=['en', 'fr'],\n                subdomain_titles={'en': 'Earthquake', 'fr': u'S\\xe9isme'},\n                keywords='person finder, people finder, person, people, ' +\n                    'crisis, survivor, family',\n                use_family_name=True,\n                use_postal_code=True,\n                min_query_word_length=2,\n                map_default_zoom=6,\n                map_default_center=[0, 0],\n                map_size_pixels=[400, 280],\n                read_auth_key_required=True,\n                search_auth_key_required=True,\n                deactivated=False,\n                deactivation_message_html='',\n                main_page_custom_html='',\n                results_page_custom_html='',\n                view_page_custom_html='',\n            )\n            self.redirect('/admin', subdomain=self.params.subdomain_new)\n\n        elif self.params.operation == 'subdomain_save':\n            values = {}\n            for name in [  # These settings are all entered in JSON.\n                'language_menu_options', 'subdomain_titles',\n                'use_family_name', 'family_name_first', 'use_postal_code',\n                'min_query_word_length', 'map_default_zoom',\n                'map_default_center', 'map_size_pixels',\n                'read_auth_key_required', 'search_auth_key_required',\n                'deactivated'\n            ]:\n                try:\n                    values[name] = simplejson.loads(self.request.get(name))\n                except:\n                    return self.error(\n                        400, 'The setting for %s was not valid JSON.' % name)\n\n            for name in ['keywords',\n                         'deactivation_message_html',\n                         'main_page_custom_html',\n                         'results_page_custom_html',\n                         'view_page_custom_html']:\n                # These settings are literal strings (not JSON).\n                values[name] = self.request.get(name)\n\n            config.set_for_subdomain(self.subdomain, **values)\n            self.redirect('/admin', subdomain=self.subdomain)\n\nif __name__ == '__main__':\n    run(('/admin', Admin))\n","sub_path":"app/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":4325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"148309472","text":"\nimport os\nimport pydicom\nimport pandas as pd\nfrom skimage.color import rgb2gray\nfrom skimage import exposure\nfrom skimage import img_as_float\nfrom skimage import transform\n# import imageio\n# from scipy.misc import imsave\n# import matplotlib.pyplot as plt\nimport argparse\nimport numpy as np\nfrom PIL import Image\n\n# Image crop params\nIMAGE_PARAMS = {0: [2.1, 4, 4],\n                1: [2.5, 3.2, 3.2],\n                2: [1.7, 4.5, 4.5]  # [2.5, 3.2, 3.2] # [1.7, 4.5, 4.5]\n                }  # factor to crop images by: crop ratio, translate_x, translate_y\n\n# loads all image paths to array\ndef load_file_names(cab_dir):\n    dcm_files = []\n    for subdir, dirs, files in os.walk(cab_dir):\n        for file in files:\n            if file.lower()[-4:] == \".dcm\":\n                dcm_files.append(os.path.join(subdir, file))\n    return sorted(dcm_files)\n\n# CROP IMAGES #\ndef crop_image(image, param_num):\n    width = image.shape[1]\n    height = image.shape[0]\n\n    params = IMAGE_PARAMS[int(param_num)]\n\n    new_dim = int(width // params[0])  # final image will be square of dim width/2 * width/2\n\n    start_col = int(width // params[1])  # column (width position) to start from\n    start_row = int(height // params[2])  # row (height position) to start from\n\n    cropped_image = image[start_row:start_row + new_dim, start_col:start_col + new_dim]\n    return cropped_image\n\n# function to set the image contrast\ndef set_contrast(image, contrast):\n    if contrast == 0:\n        out_img = image\n    elif contrast == 1:\n        out_img = exposure.equalize_hist(image)\n    elif contrast == 2:\n        out_img = exposure.equalize_adapthist(image)\n    elif contrast == 3:\n        out_img = exposure.rescale_intensity(image)\n\n    return out_img\n\ndef get_filename(opt, linking_log):\n    mrn_usnum = opt.dcm_dir[1:]\n    my_mrn = mrn_usnum.split(\"_\")[0]\n    deid = linking_log.loc[linking_log['mrn'] == np.int64(my_mrn)]['deid']\n    my_usnum = mrn_usnum.split(\"_\")[1]\n    filename = '_'.join([str(int(deid)), my_usnum])\n\n    return filename\n\ndef format_np_output(np_arr):\n    \"\"\"\n        From: https://github.com/utkuozbulak/pytorch-cnn-visualizations/blob/master/src/misc_functions.py\n\n        This is a (kind of) bandaid fix to streamline saving procedure.\n        It converts all the outputs to the same format which is 3xWxH\n        with using sucecssive if clauses.\n    Args:\n        im_as_arr (Numpy array): Matrix of shape 1xWxH or WxH or 3xWxH\n    \"\"\"\n    # Phase/Case 1: The np arr only has 2 dimensions\n    # Result: Add a dimension at the beginning\n    if len(np_arr.shape) == 2:\n        np_arr = np.expand_dims(np_arr, axis=0)\n    # Phase/Case 2: Np arr has only 1 channel (assuming first dim is channel)\n    # Result: Repeat first channel and convert 1xWxH to 3xWxH\n    if np_arr.shape[0] == 1:\n        np_arr = np.repeat(np_arr, 3, axis=0)\n    # Phase/Case 3: Np arr is of shape 3xWxH\n    # Result: Convert it to WxHx3 in order to make it saveable by PIL\n    if np_arr.shape[0] == 3:\n        np_arr = np_arr.transpose(1, 2, 0)\n    # Phase/Case 4: NP arr is normalized between 0-1\n    # Result: Multiply with 255 and change type to make it saveable by PIL\n    if np.max(np_arr) <= 1:\n        np_arr = (np_arr*255).astype(np.uint8)\n    return np_arr\n\n\ndef save_image(im, path):\n    \"\"\"\n        Saves a numpy matrix or PIL image as an image\n    Args:\n        im_as_arr (Numpy array): Matrix of shape DxWxH\n        path (str): Path to the image\n    \"\"\"\n    if isinstance(im, (np.ndarray, np.generic)):\n        im = format_np_output(im)\n        im = Image.fromarray(im)\n    im.save(path)\n\ndef load_images(dcm_files, link_log, lab_file, opt):\n\n    img_num = 0\n    img_creation_time = []\n    manu = []\n    img_id = []\n    img_label = []\n\n    ## debug\n    # image_path = dcm_files[0]\n\n    for image_path in dcm_files:\n        tokens = image_path.split(\"/\")\n        # print(tokens)\n\n        rootdir_tokens = opt.cabs_rootdir.split(\"/\")\n        # print(rootdir_tokens)\n\n        # get mrn and sample number\n        # print(tokens[len(rootdir_tokens) - 1])\n        sample_name = tokens[len(rootdir_tokens) - 1][1:].split(\"_\")\n        # print(sample_name)\n        mrn = sample_name[0]\n        # print(mrn)\n\n        ## debug\n        # mrn = '2455689'\n\n        float_mrn = float(mrn)\n        # print(float_mrn)\n\n        sample_id = float_mrn\n        print(\"sample id: \" + str(int(sample_id)))\n\n        # print(link_log.head)\n        deid = str(int(link_log.loc[link_log['mrn'] == np.int64(mrn)]['deid']))\n\n        try:\n            ds = pydicom.dcmread(image_path)\n            print(\"Image read in from dicom\")\n\n        except:\n            print(\"IMAGE PATH ERROR\")\n            print(image_path)\n\n        try:\n            inst_num = ds.InstanceNumber\n            img_creation_time.append(inst_num)\n        except:\n            print(\"Error grabbing instance number\")\n\n        img = ds.pixel_array\n        print(\"Image transferred to pixel array\")\n        img = img_as_float(rgb2gray(img))\n        cropped_image = crop_image(img, opt.params)  # crop image with i-th params\n        resized_image = transform.resize(cropped_image, output_shape=(opt.output_dim, opt.output_dim))  # reduce\n        final_img = set_contrast(resized_image, opt.contrast)\n\n        mrn_img_id_val = str(int(mrn)) + \"_\" + sample_name[1] + \"_\" + str(inst_num)\n        # print(lab_file.head)\n        print(\"Image ID: \" + mrn_img_id_val)\n\n        try:\n            label = lab_file.loc[lab_file['img_id'] == mrn_img_id_val, 'revised_labels'].values[0]\n            img_label.append(label)\n            print(\"Image label: \" + label)\n\n            img_id_val = str(int(deid)) + \"_\" + sample_name[1] + \"_\" + str(inst_num)\n            img_id.append(img_id_val)\n            print(\"De-id image ID: \" + img_id_val)\n\n            img_file_name = img_id_val + \".jpg\"\n            print(\"Image file name:\" + img_file_name)\n\n            manufacturer = ds.Manufacturer\n            manu.append(manufacturer)\n            print(\"Manufacturer name: \" + manufacturer)\n\n            save_image(final_img, os.path.join(opt.jpg_dump_dir, img_file_name))\n            print('Image file written to' + opt.jpg_dump_dir + img_file_name)\n\n        except:\n            print(\"Image ID not in label file\")\n\n        img_num = img_num + 1\n\n    # print(\"Image IDs: \")\n    # print(img_id)\n    # print(\"Image labels: \")\n    # print(img_label)\n    # print(\"Image manufacturers: \")\n    # print(manu)\n    df_dict = {'image_ids' : img_id, 'image_label': img_label, 'image_manu': manu}\n    out_csv = pd.DataFrame(df_dict)\n    return out_csv\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-params', default=1, help=\"parameter settings to crop images \"\n                                                   \"(see IMAGE_PARAMS_1 at top of file)\")\n    parser.add_argument('-output_dim', default=256, help=\"dimension of output image\")\n    parser.add_argument('-rootdir', default='/hpf/largeprojects/agoldenb/lauren/Hydronephrosis/data/load_training_test_sets/',\n                        help=\"directory of US sequence dicoms\")\n    parser.add_argument('-cabs_rootdir', default='/hpf/largeprojects/agoldenb/lauren/Hydronephrosis/all-cabs/',\n                        help=\"directory of US sequence dicoms\")\n    parser.add_argument('-dcm_dir', default='D5048003_1',\n                        help=\"directory of US sequence dicoms\")\n    parser.add_argument('-jpg_dump_dir', default='/hpf/largeprojects/agoldenb/lauren/Hydronephrosis/label_img/',\n                        help=\"directory of US sequence dicoms\")\n    parser.add_argument('-csv_out_dir', default='/hpf/largeprojects/agoldenb/lauren/Hydronephrosis/label_csv/',\n                        help=\"directory of US sequence dicoms\")\n    parser.add_argument('-label_filename', default='view_label_df_20200120.csv',\n                        help=\"directory of US sequence dicoms\")\n    parser.add_argument('-id_linking_filename', default='linking_log_20191120.csv',\n                        help=\"directory of US sequence dicoms\")\n    parser.add_argument(\"--contrast\", default=1, type=int, help=\"Image contrast to train on\")\n\n    opt = parser.parse_args() ## comment for debug\n    opt.output_dim = int(opt.output_dim)\n\n    cab_dir = os.path.join(opt.cabs_rootdir, opt.dcm_dir + \"/\")\n    print(\"cab_dir: \" + cab_dir)\n\n    my_dcm_files = load_file_names(cab_dir=cab_dir)\n    my_linking_log = pd.read_csv(opt.rootdir + '/' + opt.id_linking_filename)\n    my_lab_file = pd.read_csv(opt.rootdir + '/' + opt.label_filename)\n\n    csv_out = load_images(dcm_files=my_dcm_files, link_log=my_linking_log, lab_file=my_lab_file, opt=opt)\n    csv_filename = get_filename(opt, linking_log=my_linking_log) + \".csv\"\n\n    print(\"Writing csv file to: \" + opt.csv_out_dir + \"/\" + csv_filename)\n    csv_out.to_csv(opt.csv_out_dir + \"/\" + csv_filename)\n\n\n\nif __name__ == \"__main__\":\n    main()\n\n\n\n####\n####\n####\n    # parser.add_argument('-params', default=1, help=\"parameter settings to crop images \"\n    #                                                \"(see IMAGE_PARAMS_1 at top of file)\")\n    # parser.add_argument('-output_dim', default=256, help=\"dimension of output image\")\n    # parser.add_argument('-rootdir', default='C:/Users/larun/Desktop/Data Science Core/Projects/Urology/Front-end-test-files/',\n    #                     help=\"directory of US sequence dicoms\")\n    # parser.add_argument('-dcm_dir', default='D5048003_1',\n    #                     help=\"directory of US sequence dicoms\")\n    # parser.add_argument('-jpg_dump_dir', default='C:/Users/larun/Desktop/Data Science Core/Projects/Urology/Front-end-test-files/',\n    #                     help=\"directory of US sequence dicoms\")\n    # parser.add_argument('-csv_out_dir', default='C:/Users/larun/Desktop/Data Science Core/Projects/Urology/Front-end-test-files/',\n    #                     help=\"directory of US sequence dicoms\")\n    # parser.add_argument('-label_filename', default='linking_log_20191120.csv',\n    #                     help=\"directory of US sequence dicoms\")\n    # parser.add_argument('-id_linking_filename', default='view_label_df_20191120.csv',\n    #                     help=\"directory of US sequence dicoms\")\n    # parser.add_argument(\"--contrast\", default=1, type=int, help=\"Image contrast to train on\")\n\n\n    ## comment out after debug\n# class make_opt():\n#     def __init__(self):\n#         self.params = 1\n#         self.output_dim = 256\n#         self.rootdir = 'C:/Users/larun/Desktop/Data Science Core/Projects/Urology/Image organization Nov 2019/'\n#         self.cabs_rootdir = 'C:/Users/larun/Desktop/Data Science Core/Projects/Urology/Front-end-test-files/'\n#         self.dcm_dir = 'D5048003_1'\n#         self.jpg_dump_dir = 'C:/Users/larun/Desktop/Data Science Core/Projects/Urology/Front-end-test-files/'\n#         self.csv_out_dir = 'C:/Users/larun/Desktop/Data Science Core/Projects/Urology/Front-end-test-files/'\n#         self.label_filename = 'view_label_df_20200120.csv'\n#         self.id_linking_filename = 'linking_log_20191120.csv'\n#         self.contrast = 1\n#\n# opt = make_opt()\n\n","sub_path":"Kidney labels/Preprocessing/preprocess-us-seq-DA.py","file_name":"preprocess-us-seq-DA.py","file_ext":"py","file_size_in_byte":11044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"600835581","text":"from mock import MagicMock\nfrom mock import patch\nimport os\nimport shutil\nimport tempfile\nimport unittest\n\nfrom chainerui import app\nfrom chainerui import db\nfrom chainerui.models.project import Project\n\n\nclass TestApp(unittest.TestCase):\n\n    def setUp(self):\n        temp_dir = tempfile.mkdtemp(prefix='chainerui_test_app_')\n        self._dir = temp_dir\n        self._db_path = os.path.join(temp_dir, 'test_app.db')\n        self._db_url = 'sqlite:///' + self._db_path\n\n    def tearDown(self):\n        if db._session is not None:\n            db.session.remove()\n        db.remove_db()\n        if os.path.exists(self._dir):\n            shutil.rmtree(self._dir)\n\n    def test_server(self):\n        args = app.create_parser().parse_args(\n            ['--db', self._db_url, 'server', '-H', 'test.domain',\n             '-p', '5001', '-d'])\n        assert args.host == 'test.domain'\n        assert args.port == 5001\n        assert args.debug\n        assert args.db == self._db_url\n\n        mock_app = MagicMock()\n        mock_app_creator = MagicMock(return_value=mock_app)\n\n        with patch('chainerui.app.create_app', mock_app_creator) as f:\n            args.handler(args)\n            f.assert_not_called()\n\n            db.upgrade()\n            args.handler(args)\n            f.assert_called_once()\n            mock_app.run.assert_called_with(\n                host='test.domain', port=5001, debug=True, threaded=True)\n\n    def test_project_create(self):\n        args = app.create_parser().parse_args(\n            ['--db', self._db_url, 'project', 'create', '-d', self._dir,\n             '-n', 'test'])\n        assert args.project_dir == self._dir\n        assert args.project_name == 'test'\n        assert args.db == self._db_url\n\n        args.handler(args)\n        db.upgrade()\n        p = db.session.query(Project).filter_by(path_name=self._dir).first()\n        assert p is None\n        # on Windows/Python2, another session is create, need to remove\n        # this session externally (*)\n        db._session.remove()\n\n        args.handler(args)\n        p = db.session.query(Project).filter_by(path_name=self._dir).first()\n        assert p is not None\n        assert p.path_name == self._dir\n        assert p.name == 'test'\n        db._session.remove()  # same as (*)\n\n        args.handler(args)  # already registered, confirm not occur error\n        ps = db.session.query(Project).filter_by(path_name=self._dir).all()\n        assert len(ps) == 1\n\n    def test_db_create(self):\n        args = app.create_parser().parse_args(\n            ['--db', self._db_url, 'db', 'create'])\n        assert args.type == 'create'\n        assert args.db == self._db_url\n        args.handler(args)\n        assert os.path.isdir(db._sqlite_default_db_dir())\n        assert not db._initialized\n\n    def test_db_status(self):\n        args = app.create_parser().parse_args(\n            ['--db', self._db_url, 'db', 'status'])\n        assert args.type == 'status'\n        assert args.db == self._db_url\n        args.handler(args)\n        assert db._initialized\n        assert db._external_db\n\n    def test_db_upgrade_drop(self):\n        args = app.create_parser().parse_args(\n            ['--db', self._db_url, 'db', 'upgrade'])\n        assert args.type == 'upgrade'\n        assert args.db == self._db_url\n        args.handler(args)\n        assert db._initialized\n        assert db._external_db\n\n        args = app.create_parser().parse_args(\n            ['--db', self._db_url, 'db', 'drop'])\n        assert args.type == 'drop'\n        assert args.db == self._db_url\n        args.handler(args)\n        assert not db._initialized\n        assert not db._external_db\n\n    def test_db_revision(self):\n        args = app.create_parser().parse_args(\n            ['--db', self._db_url, 'db', 'revision'])\n        assert args.type == 'revision'\n        assert args.db == self._db_url\n        with patch('chainerui.app.db_revision.new_revision') as f:\n            args.handler(args)\n            assert f.called\n        assert db._initialized\n        assert db._external_db\n","sub_path":"tests/test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":4039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"566624146","text":"import numpy as np\n\n\n\ndef in_field(field):\n    formato = ('%s %s %s %s %s %s %s %s %s %s %s %s %s %s  %s %s %s %s %s %s %s %s %s \\\n                %s %s %s %s %s %s %s %s %s  %s %s %s %s %s %s %s %s %s %s %s \\\n                %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s  %s %s\\\n\t\t  %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s')\n    # This catalogue is created by cutting_catalogue_liners.in_field\n    filename = 'F'+str(field)+'_catalogue_master_hst_liners.txt'\n    f = np.loadtxt(filename,dtype='float')\n\n    field_number,ID_5,Prob_posiew_5,prob_agn5,prob_sf5,prob_retired5,prob_weak_agn5,\\\n    cont_maxprob_5,cont_median_5,rel_error_cont_median_5,rel_error_cont_maxlik_5,\\\n    z_maxprob_5,z_median_5,rel_error_z_median_5,\\\n    fha_maxprob_5,fha_median_5,rel_error_fha_median_5,rel_error_fha_maxlik_5,lha_median_5,\\\n    fnii_maxprob_5,fnii_median_5,rel_error_fnii_median_5,rel_error_fnii_maxlik_5,\\\n    ewha_median_5,ewha_maxlik_5,error_ewha_median_5,error_ewha_maxlik_5,lineratio_median_5,lineratio_maxlik_5,error_lineratio_median_5,error_lineratio_maxlik_5,\\\n    chisq_5,twolines_5,oneline_5,\\\n    ID_total,Prob_posiew_total,prob_agn_total,prob_sf_total,prob_retiredtotal,prob_weak_agntotal,\\\n    cont_maxprob_total,cont_median_total,rel_error_cont_median_total,rel_error_cont_maxlik_total,\\\n    z_maxprob_total,z_median_total,rel_error_z_median_total,\\\n    fha_maxprob_total,fha_median_total,rel_error_fha_median_total,rel_error_fha_maxlik_total,lha_median_total,\\\n    fnii_maxprob_total,fnii_median_total,rel_error_fnii_median_total,rel_error_fnii_maxlik_total,\\\n    ewha_median_total,ewha_maxlik_total,error_ewha_median_total,error_ewha_maxlik_total,lineratio_median_total,lineratio_maxlik_total,error_lineratio_median_total,error_lineratio_maxlik_total,\\\n    chisq_total,twolines_total,oneline_total,\\\n    ST_A_IMAGE,ra,dec,Rmag,combo_flag,stages_flag,MC_z,logmass,logmass_cl,flux24,tir,tuv,tir_cl,tuv_cl,\\\n    sfr_det,sfr_lo,sfr_hi,sfr_det_cl,sfr_lo_cl,sfr_hi_cl,sed_type = f[:].T\n\n\n    ### One line aper5\n    cut_one_line_5 = np.where((error_ewha_median_5/(ewha_median_5) < 0.15) & (Prob_posiew_5 > 99.7) & (oneline_5 == 1))[0]\n\n    ### One line total aper\n    cut_one_line_total = np.where((error_ewha_median_total/(ewha_median_total) < 0.15) & (Prob_posiew_total  > 99.7) & (oneline_total  == 1))[0]\n\n    ###Two lines aper5\n    cut_two_lines_5 = np.where((error_ewha_median_5/(ewha_median_5) < 0.15) & (Prob_posiew_5  > 99.7) & (twolines_5  == 1))[0]\n\n    ###Two lines total aper\n    cut_two_lines_total = np.where((error_ewha_median_total/(ewha_median_total) < 0.15) & (Prob_posiew_total  > 99.7) & (twolines_total  == 1))[0]\n\n    ###Two lines total aper more restricted\n    cut_two_lines_total_01 = np.where((error_ewha_median_total/(ewha_median_total) < 0.1) & (Prob_posiew_total  > 99.7) & (twolines_total  == 1))[0]\n\n    ### Two lines and AGN\n    cut_two_lines_agn_5 = np.where((error_ewha_median_5/(ewha_median_5) < 0.15) & (Prob_posiew_5  > 99.7) & (twolines_5  == 1) & (prob_agn5  > 99.7))[0]\n\n    ### Two lines and SF\n    cut_two_lines_sf_5 = np.where((error_ewha_median_5/(ewha_median_5) < 0.15) & (Prob_posiew_5  > 99.7) & (twolines_5  == 1) & (prob_sf5  > 99.7))[0]\n\n\n    ### Catalogue with all the galaxies within the z cut in the total aperture measurement\n    np.savetxt('F'+str(field)+'_catalogue_total_master_hst_liners_nozcut.txt',f,fmt=formato)\n\n\n    ### Catalogue with all the galaxies within the two lines cut in the total aperture measurement\n    np.savetxt('F'+str(field)+'_catalogue_cut_two_lines_total_master_hst_liners_nozcut.txt',f[cut_two_lines_total],fmt=formato)\n\n    ### Catalogue with all the galaxies within the two lines cut in the total aperture measurement MORE RESTRICTED\n    np.savetxt('F'+str(field)+'_catalogue_cut_two_lines_total_01_master_hst_liners_nozcut.txt',f[cut_two_lines_total_01],fmt=formato)\n\n\n    ### Catalogue with all the galaxies within the two lines cut in the 5 pixel aperture measurement\n    np.savetxt('F'+str(field)+'_catalogue_cut_two_lines_aper5_master_hst_liners_nozcut.txt',f[cut_two_lines_5],fmt=formato)\n\n    ### Catalogue with all the galaxies within the one line cut in the total aperture measurement\n    np.savetxt('F'+str(field)+'_catalogue_cut_one_line_total_master_hst_liners_nozcut.txt',f[cut_one_line_total],fmt=formato)\n\n\n\n    f1 = open('number_of_galaxies_hst_liners_nozcut.txt','a+')\n    f1.write('%2i %3i %3i %3i %3i %3i \\n'%(field,len(field_number), len(field_number),len(cut_one_line_total),len(cut_two_lines_total), len(cut_two_lines_5)))\n    f1.close()\n","sub_path":"cutting_catalogue_liners_nozcut.py","file_name":"cutting_catalogue_liners_nozcut.py","file_ext":"py","file_size_in_byte":4587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"92110384","text":"'''\nPrograma: URI - problema 1071\nProgramador: Paulo Henrique M. Leite\nData: 21/06/2019\nLinguagem: Python\nAlgoritmo: \nStatus: ACEITO\n'''\n\n# inicializando informação\n\nini = input()\nend = input()\n\naux = 0\nsoma = 0\n\n# processando informação\n\nif ini > end:\n\taux = ini\n\tini = end\n\tend = aux\n\ni = int(ini)\nf = int(end)\n\ni += 1\nf -= 1\nwhile(i <= f):\n\tif i % 2 != 0:\n\t\tsoma = soma + i\n\ti = i + 1\n\n\n# imprimindo informação\nprint(soma)\n\n","sub_path":"uri/1071.py","file_name":"1071.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"495182073","text":"# coding=utf-8\n\n'''\n批量梯度下降\n正确结果\n[[ 0.7502]\n [ 0.0639]]\n'''\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Training data set\n# each element in x represents (x0,x1,x2)\n#x = [(1, 0., 3), (1, 1., 3), (1, 2., 3), (1, 3., 2), (1, 4., 4)]\na = np.loadtxt(\"ex2Data/ex2x.dat\",unpack='true')\nm=len(a)\n#往数组中加1列的值为 1\nx=np.column_stack((np.ones((m,1)), a))\n# y[i] is the output of y = theta0 * x[0] + theta1 * x[1] +theta2 * x[2]\n#y = [95.364, 97.217205, 75.195834, 60.105519, 49.342380]\ny = np.loadtxt(\"ex2Data/ex2y.dat\",unpack='true')\n'''\nplt.xlabel(\"age\")\nplt.ylabel(\"height\")\nplt.title(\"house price distribution\")\n#绘制散列点\nplt.scatter(a, y)\n# 绘制线条\n# plt.plot(x,y)\nplt.legend()\nplt.show()\n'''\nepsilon = 0.000001\n# learning rate\nalpha = 0.001\ndiff = [0, 0]\nerror1 = 0\nerror0 = 0\nm = len(x)\n\n# init the parameters to zero\ntheta0 = 0\ntheta1 = 0\ntheta2 = 0\nsum0 = 0\nsum1 = 0\nsum2 = 0\nmaxCycles=1500\nfor k in range(maxCycles):\n# while true:\n    # calculate the parameters\n    for i in range(m):\n        # begin batch gradient descent\n        diff[0] = y[i] - (theta0 + theta1 * x[i][1] )\n        sum0 = sum0 + alpha * diff[0] * x[i][0]\n        sum1 = sum1 + alpha * diff[0] * x[i][1]\n        # sum2 = sum2 + alpha * diff[0] * x[i][2]\n        # end  batch gradient descent\n    theta0 = sum0;\n    theta1 = sum1;\n    # theta2 = sum2;\n    # calculate the cost function\n    # error1 = 0\n    # for lp in range(len(x)):\n    #     error1 += (y[i] - (theta0 + theta1 * x[i][1])) ** 2 / 2\n    #\n    # if abs(error1 - error0) < epsilon:\n    #     break\n    # else:\n    #     error0 = error1\n    #\n    # print(' theta0 : %f, theta1 : %f  error1 : %f' % (theta0, theta1, error1))\n\nprint('Done: theta0 : %f, theta1 : %f' % (theta0, theta1))\n\n\nh1=theta0+theta1*3\nh2=theta0+theta1*7\nprint(h1)\nprint(\"\\n\")\nprint(h2)","sub_path":"code/1.Linear_Regression/batchGD.py","file_name":"batchGD.py","file_ext":"py","file_size_in_byte":1843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"607489581","text":"#!/usr/bin/env python\n\n\nimport logging\nfrom flask import Flask, jsonify\nfrom flask_cors import CORS\nfrom flask_ngrok import run_with_ngrok\n\napp = Flask(__name__)\nlogging.basicConfig(level=logging.INFO)\nlogging.getLogger('flask_cors').level = logging.DEBUG\nCORS(app)\nrun_with_ngrok(app)\n\n\n@app.route(\"/return_json\", methods=[\"POST\"])\ndef returnJson():\n    return jsonify({\n        'folder_id': 'yoyo',\n        'img': 'adad',\n        'imgname': 'bibi',\n        'imguniquename': 'kaka'\n    })\n\n\n@app.route(\"/\", methods=[\"GET\"])\ndef index():\n    return jsonify({'msg': 'success'})\n\n\nif __name__ == '__main__':\n    app.run()","sub_path":"apiget/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"406641413","text":"# Class: XYZFile\n# used for getUncertaintyDEM\n# by Whyjay Zheng, Jul 28 2016\n\nimport numpy as np\n\nclass XYZFile:\n\n\tdef __init__(self, fpath=None, refpts_path=None, dem_path=None):\n\t\tself.fpath = fpath\n\t\tself.refpts_path = refpts_path\n\t\tself.dem_path = dem_path\n\t\tself.data = None\n\n\tdef Read(self):\n\n\t\t\"\"\"\n\t\tself.data will be usually a 3- or 4-column np.array\n\t\tcolumn 1: easting\n\t\tcolumn 2: northing\n\t\tcolumn 3: height of the 1st group (usually reference points)\n\t\tcolumn 4: height of the 2nd group (usually DEM points made from grdtrack)\n\t\t\"\"\"\n\n\t\tself.data = np.loadtxt(self.fpath)\n\n\tdef StatisticOutput(self):\n\t\tmad = lambda x : 1.482 * np.median(abs(x - np.median(x)))\n\t\tif self.data.size == 0:\n\t\t\tprint('NOTE: ' + self.dem_path + ' does not cover any ref points.')\n\t\t\treturn [self.dem_path, '', '', '', '', '', '', self.refpts_path]\n\t\telif self.data.shape[1] == 4:\n\t\t\tidx = ~np.isnan(self.data[:, 3])\n\t\t\tdiffval = self.data[idx, 3] - self.data[idx, 2]\n\t\t\toffset_median = np.median(diffval)\n\t\t\toffset_mad = mad(diffval)\n\t\t\tlbound = offset_median - 3. * offset_mad\n\t\t\tubound = offset_median + 3. * offset_mad\n\t\t\tidx2 = np.logical_and(diffval > lbound, diffval < ubound)\n\t\t\tdiffval_trimmed = diffval[idx2]\n\t\t\t# The return value is ready for CsvTable.SaveData method.\n\t\t\t# ['filename', 'date', 'uncertainty', 'mean_offset_wrt_refpts', \\\n\t\t\t#  'trimmed_N', 'trimming_lb', 'trimming_up', 'refpts_file']\n\t\t\t# 'date' is an empty string since we don't specify any date string in .xyz file.\n\t\t\treturn [self.dem_path, '', diffval_trimmed.std(ddof=1), diffval_trimmed.mean(), \\\n\t\t\t          len(diffval_trimmed), lbound, ubound, self.refpts_path]\n\t\telif self.data.shape[1] == 3:\n\t\t\tprint(\"Not yet designed.\")\n\t\t\treturn []\n\t\telse:\n\t\t\tprint(\"This program currently doesn't support the xyz file whose column number is not 3 or 4.\")\n\t\t\treturn []\n","sub_path":"dhdt/UtilXYZ.py","file_name":"UtilXYZ.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"200335711","text":"from timeit import timeit\nfrom random import random\n\nrandomList = [random() for num in range(600_000)]\n\ndef qsort(values):\n    if len(values) < 2: return values\n    return qsort([val for val in values if val < values[0]]) +  \\\n    [values[0]] + \\\n    qsort([val for val in values if val > values[0]])\n\ntime1 = timeit('qsort(randomList)', globals=globals(), number=100)\nprint('super-simple qsort: ' + str(time1))\n","sub_path":"QuickSort/time_quick.py","file_name":"time_quick.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"419530674","text":"#   Copyright 2017 Steven E. Lamberson, Jr. <steven.lamberson@gmail.com>\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\nimport logging\n\n\n\nclass Command(object):\n    \"\"\"Maps a string (the command) to a function that should be called\n    when the string is present.\n    \"\"\"\n\n    def __init__(self, string=None, function=None):\n        \"\"\"Creates the Command object.\"\"\"\n        self.function = None\n        self.string = ''\n        self.subcommands = {}\n        if function is not None:\n            self.function = function\n        if string is not None:\n            self.string = string\n\n\n    def add_subcommands(self, subcommands, function):\n        \"\"\"Create subcommands for this command.\"\"\"\n        this_subcommand = subcommands[0]\n        if len(subcommands) == 1:\n            self.subcommands[this_subcommand] = Command(this_subcommand,\n                                                        function)\n        else:\n            new_subcommands = subcommands[1:]\n            self.subcommands[this_subcommand] = Command(this_subcommand)\n            self.subcommands[this_subcommand].add_subcommands(new_subcommands,\n                                                              function)\n\n\n\nclass Interpreter(object):\n    \"\"\"Interprets a string as a command with possible subcommands.\"\"\"\n\n    def __init__(self):\n        \"\"\"Creates the Interpreter object.\"\"\"\n        self._log = logging.getLogger('__name__')\n","sub_path":"acolytegm/interpreter.py","file_name":"interpreter.py","file_ext":"py","file_size_in_byte":1929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"601470894","text":"from ethereum.tools import tester as t\nfrom ethereum import utils, abi\nfrom ethereum.hybrid_casper.casper_initiating_transactions import \\\n    viper_rlp_decoder_address, sig_hasher_address, purity_checker_address, casper_abi, purity_checker_abi\nfrom ethereum.hybrid_casper.casper_utils import mk_prepare, mk_commit, mk_logout, induct_validator\nfrom ethereum.hybrid_casper import casper_utils\nfrom viper import compiler\n# from ethereum.slogging import configure_logging, set_level\n# config_string = ':info,eth.vm.log:trace,eth.vm.op:trace,eth.vm.stack:trace,eth.vm.exit:trace,eth.pb.msg:trace,eth.pb.tx:debug'\n# configure_logging(config_string=config_string)\nalloc = {}\nfor i in range(9):\n    alloc[utils.int_to_addr(i)] = {'balance': 1}\nalloc[t.a0] = {'balance': 10**22}\nalloc[t.a1] = {'balance': 10**22}\ngenesis = casper_utils.make_casper_genesis(alloc, 5, 100, 0.02, 0.002)\nc = t.Chain(genesis=genesis)\nt.languages['viper'] = compiler.Compiler()\nt.gas_limit = 9999999\nt.STARTGAS = 2000000\nc.mine(1)\n\nEPOCH_LENGTH = c.chain.config['EPOCH_LENGTH']\n\nct = abi.ContractTranslator(purity_checker_abi)\n# Check that the RLP decoding library and the sig hashing library are \"pure\"\nassert utils.big_endian_to_int(c.tx(t.k0, purity_checker_address, 0, ct.encode('submit', [viper_rlp_decoder_address]))) == 1\nassert utils.big_endian_to_int(c.tx(t.k0, purity_checker_address, 0, ct.encode('submit', [sig_hasher_address]))) == 1\n\ncasper = t.ABIContract(c, casper_abi, c.chain.config['CASPER_ADDRESS'])\nc.mine(1)\n\n# Begin the test\n\nprint(\"Starting tests\")\n# Initialize the first epoch\nc.mine(EPOCH_LENGTH - c.head_state.block_number)\n# casper.initialize_epoch(1)\nassert casper.get_nextValidatorIndex() == 1\nassert casper.get_current_epoch() == 1\nprint(\"Epoch initialized\")\n\n# Deposit one validator\ninduct_validator(c, casper, t.k1, 200 * 10**18)\n# Mine two epochs\nc.mine(EPOCH_LENGTH * 3 - c.head_state.block_number)\n# casper.initialize_epoch(2)\n# casper.initialize_epoch(3)\nassert casper.get_total_curdyn_deposits() == 200 * 10**18\nassert casper.get_total_prevdyn_deposits() == 0\n\n_e, _a, _se, _sa = \\\n    casper.get_current_epoch(), casper.get_recommended_ancestry_hash(), \\\n    casper.get_recommended_source_epoch(), casper.get_recommended_source_ancestry_hash()\nprint(\"Penalty factor: %.8f\" % (casper.get_current_penalty_factor()))\n# Send a prepare message\nprint('pre deposit', casper.get_deposit_size(1), casper.get_total_curdyn_deposits())\nassert casper.get_deposit_size(1) == casper.get_total_curdyn_deposits()\ncasper.prepare(mk_prepare(1, _e, _a, _se, _sa, t.k1))\nprint('Gas consumed for a prepare: %d' % c.last_gas_used(with_tx=True))\nsourcing_hash = utils.sha3(utils.encode_int32(_e) + _a + utils.encode_int32(_se) + _sa)\nassert casper.get_consensus_messages__ancestry_hash_justified(_e, _a)\nassert casper.get_main_hash_justified()\nprint(\"Prepare message processed\")\ntry:\n    casper.prepare(mk_prepare(1, 1, '\\x35' * 32, '\\x00' * 32, 0, '\\x00' * 32, t.k0))\n    success = True\nexcept:\n    success = False\nassert not success\nprint(\"Prepare message fails the second time\")\n# Send a commit message\ncasper.commit(mk_commit(1, _e, _a, 0, t.k1))\nprint('post deposit', casper.get_deposit_size(1))\nprint('Gas consumed for a commit: %d' % c.last_gas_used(with_tx=True))\n# Check that we committed\nassert casper.get_main_hash_finalized()\nprint(\"Commit message processed\")\n# Initialize the fourth epoch\nc.mine(EPOCH_LENGTH * 4 - c.head_state.block_number)\n# casper.initialize_epoch(4)\n# Check that the dynasty increased as expected\nassert casper.get_dynasty() == 4\nprint(casper.get_total_prevdyn_deposits(), casper.get_total_curdyn_deposits())\nprint(\"Second epoch initialized, dynasty increased as expected\")\n_e, _a, _se, _sa = \\\n    casper.get_current_epoch(), casper.get_recommended_ancestry_hash(), \\\n    casper.get_recommended_source_epoch(), casper.get_recommended_source_ancestry_hash()\n# Send a prepare message\nprint('pre deposit', casper.get_deposit_size(1), casper.get_total_curdyn_deposits())\nassert casper.get_deposit_size(1) == casper.get_total_curdyn_deposits()\ncasper.prepare(mk_prepare(1, _e, _a, _se, _sa, t.k1))\nassert casper.get_main_hash_justified()\n# Send a commit message\nepoch_4_commit = mk_commit(1, _e, _a, 3, t.k1)\ncasper.commit(epoch_4_commit)\nprint('post deposit', casper.get_deposit_size(1))\n# Check that we committed\nassert casper.get_main_hash_finalized()\n# Initialize the fifth epoch\nc.mine(EPOCH_LENGTH * 5 - c.head_state.block_number)\n# casper.initialize_epoch(5)\nprint(casper.get_latest_npf(), casper.get_latest_ncf(), casper.get_latest_resize_factor())\nprint('pre deposit', casper.get_deposit_size(1))\nassert casper.get_total_curdyn_deposits() == casper.get_deposit_size(1)\nprint(\"Fourth epoch prepared and committed, fifth epoch initialized\")\n_e, _a, _se, _sa = \\\n    casper.get_current_epoch(), casper.get_recommended_ancestry_hash(), \\\n    casper.get_recommended_source_epoch(), casper.get_recommended_source_ancestry_hash()\n# Test the NO_DBL_PREPARE slashing condition\np1 = mk_prepare(1, _e, _a, _se, _sa, t.k1)\np2 = mk_prepare(1, _e, _sa, _se, _sa, t.k1)\nsnapshot = c.snapshot()\ncasper.double_prepare_slash(p1, p2)\nc.revert(snapshot)\nprint(\"NO_DBL_PREPARE slashing condition works\")\n# Test the PREPARE_COMMIT_CONSISTENCY slashing condition\np3 = mk_prepare(1, _e, _a, 0, casper.get_ancestry_hashes(0), t.k1)\nsnapshot = c.snapshot()\ncasper.prepare_commit_inconsistency_slash(p3, epoch_4_commit)\nc.revert(snapshot)\nprint(\"PREPARE_COMMIT_CONSISTENCY slashing condition works\")\n# Finish the fifth epoch\ncasper.prepare(p1)\ncasper.commit(mk_commit(1, _e, _a, 4, t.k1))\nassert casper.get_main_hash_justified()\nassert casper.get_main_hash_finalized()\nds_0_non_finalized = casper.get_deposit_size(1)\nc.mine(EPOCH_LENGTH * 6 - c.head_state.block_number)\n# casper.initialize_epoch(6)\nds_1_non_finalized = casper.get_deposit_size(1)\nprint(\"Non-finalization losses (first epoch): %.4f\" % (1 - ds_1_non_finalized / ds_0_non_finalized))\nassert ds_1_non_finalized < ds_0_non_finalized\nc.mine(EPOCH_LENGTH * 7 - c.head_state.block_number)\n# casper.initialize_epoch(7)\nds_2_non_finalized = casper.get_deposit_size(1)\nprint(\"Non-finalization losses (second epoch): %.4f\" % (1 - ds_2_non_finalized / ds_1_non_finalized))\nc.mine(EPOCH_LENGTH * 8 - c.head_state.block_number)\n# casper.initialize_epoch(8)\nds_3_non_finalized = casper.get_deposit_size(1)\nprint(\"Non-finalization losses (third epoch): %.4f\" % (1 - ds_3_non_finalized / ds_2_non_finalized))\nassert (ds_2_non_finalized - ds_3_non_finalized) > (ds_0_non_finalized - ds_1_non_finalized)\n_e, _a, _se, _sa = \\\n    casper.get_current_epoch(), casper.get_recommended_ancestry_hash(), \\\n    casper.get_recommended_source_epoch(), casper.get_recommended_source_ancestry_hash()\nprint(casper.get_deposit_size(1))\np4 = mk_prepare(1, _e, _a, _se, _sa, t.k1)\ncasper.prepare(p4)\nprint(casper.get_deposit_size(1))\np4 = mk_prepare(1, _e, _a, _se, _sa, t.k1)\nc4 = mk_commit(1, _e, _a, 5, t.k1)\ncasper.commit(c4)\nprint(casper.get_deposit_size(1))\np4 = mk_prepare(1, _e, _a, _se, _sa, t.k1)\nassert casper.get_main_hash_finalized()\nc.mine(EPOCH_LENGTH * 9 - c.head_state.block_number)\n# casper.initialize_epoch(9)\nprint(casper.get_latest_npf(), casper.get_latest_ncf(), casper.get_latest_resize_factor())\nprint(casper.get_deposit_size(1), casper.get_current_penalty_factor())\nds_after_finalize = casper.get_deposit_size(1)\nassert casper.get_latest_npf() < 0.1 and casper.get_latest_ncf() < 0.1\nassert ds_after_finalize > ds_3_non_finalized\nprint(\"Finalization gains: %.4f\" % (ds_after_finalize / ds_3_non_finalized - 1))\ninduct_validator(c, casper, t.k2, 200 * 10**18)\ninduct_validator(c, casper, t.k3, 200 * 10**18)\ninduct_validator(c, casper, t.k4, 200 * 10**18)\ninduct_validator(c, casper, t.k5, 200 * 10**18)\nc.mine(1)\nassert casper.get_deposit_size(1) == casper.get_total_curdyn_deposits()\n_e, _a, _se, _sa = \\\n    casper.get_current_epoch(), casper.get_recommended_ancestry_hash(), \\\n    casper.get_recommended_source_epoch(), casper.get_recommended_source_ancestry_hash()\np4 = mk_prepare(1, _e, _a, _se, _sa, t.k1)\ncasper.prepare(p4)\nc4 = mk_commit(1, _e, _a, 8, t.k1)\ncasper.commit(c4)\nc.mine(EPOCH_LENGTH * 10 - c.head_state.block_number)\n# casper.initialize_epoch(10)\n_e, _a, _se, _sa = \\\n    casper.get_current_epoch(), casper.get_recommended_ancestry_hash(), \\\n    casper.get_recommended_source_epoch(), casper.get_recommended_source_ancestry_hash()\np4 = mk_prepare(1, _e, _a, _se, _sa, t.k1)\ncasper.prepare(p4)\nc4 = mk_commit(1, _e, _a, 9, t.k1)\ncasper.commit(c4)\nc.mine(EPOCH_LENGTH * 11 - c.head_state.block_number)\n# casper.initialize_epoch(11)\nassert abs(sum(map(casper.get_deposit_size, range(1, 6))) - casper.get_total_curdyn_deposits()) < 5\nprint(\"Validator induction works\")\n_e, _a, _se, _sa = \\\n    casper.get_current_epoch(), casper.get_recommended_ancestry_hash(), \\\n    casper.get_recommended_source_epoch(), casper.get_recommended_source_ancestry_hash()\nfor prepare in [mk_prepare(i, _e, _a, _se, _sa, k) for i, k in zip([1, 2, 3, 4], [t.k1, t.k2, t.k3, t.k4])]:\n    casper.prepare(prepare)\nassert casper.get_main_hash_justified()\nc.mine(1)\nfor commit in [mk_commit(i, _e, _a, casper.get_validators__prev_commit_epoch(i), k) for i, k in zip([1, 2, 3, 4], [t.k1, t.k2, t.k3, t.k4])]:\n    casper.commit(commit)\nassert casper.get_main_hash_finalized()\nprint(\"Epoch 11 finalized with 4/5 prepares/commits\")\ncasper.logout(mk_logout(1, 11, t.k1))\n\nc.mine(EPOCH_LENGTH * 12 - c.head_state.block_number)\n# casper.initialize_epoch(12)\nassert casper.get_deposit_size(5) < \\\n    casper.get_deposit_size(2) == casper.get_deposit_size(3) == casper.get_deposit_size(4)\n\n_e, _a, _se, _sa = \\\n    casper.get_current_epoch(), casper.get_recommended_ancestry_hash(), \\\n    casper.get_recommended_source_epoch(), casper.get_recommended_source_ancestry_hash()\nfor prepare in [mk_prepare(i, _e, _a, _se, _sa, k) for i, k in zip([1, 2, 3, 4], [t.k1, t.k2, t.k3, t.k4])]:\n    casper.prepare(prepare)\nassert casper.get_main_hash_justified()\nc.mine(1)\nfor commit in [mk_commit(i, _e, _a, casper.get_validators__prev_commit_epoch(i), k) for i, k in zip([2, 3, 4, 5], [t.k2, t.k3, t.k4, t.k5])]:\n    casper.commit(commit)\nassert casper.get_main_hash_finalized()\n\nprint(\"Epoch 12 finalized with 4/5 prepares/commits\")\nc.mine(EPOCH_LENGTH * 13 - c.head_state.block_number)\n# casper.initialize_epoch(13)\nassert abs(sum(map(casper.get_deposit_size, range(2, 6))) - casper.get_total_curdyn_deposits()) < 5\nassert abs(sum(map(casper.get_deposit_size, range(1, 6))) - casper.get_total_prevdyn_deposits()) < 5\n\n_e, _a, _se, _sa = \\\n    casper.get_current_epoch(), casper.get_recommended_ancestry_hash(), \\\n    casper.get_recommended_source_epoch(), casper.get_recommended_source_ancestry_hash()\nfor prepare in [mk_prepare(i, _e, _a, _se, _sa, k) for i, k in zip([1, 2, 3, 4], [t.k1, t.k2, t.k3, t.k4])]:\n    casper.prepare(prepare)\nassert casper.get_main_hash_justified()\nc.mine(1)\nfor commit in [mk_commit(i, _e, _a, casper.get_validators__prev_commit_epoch(i), k) for i, k in zip([2, 3, 4, 5], [t.k2, t.k3, t.k4, t.k5])]:\n    casper.commit(commit)\nassert casper.get_main_hash_finalized()\nprint(\"Epoch 13 finalized with 4/5 prepares/commits\")\n\nc.mine(EPOCH_LENGTH * 14 - c.head_state.block_number)\n# casper.initialize_epoch(14)\nassert abs(sum(map(casper.get_deposit_size, range(2, 6))) - casper.get_total_curdyn_deposits()) < 5\nassert abs(sum(map(casper.get_deposit_size, range(2, 6))) - casper.get_total_prevdyn_deposits()) < 5\n\nprint(\"Verified post-deposit logouts\")\nfor i in range(15, 100):\n    c.mine(EPOCH_LENGTH * i - c.head_state.block_number)\n    # casper.initialize_epoch(i)\n    _e, _a, _se, _sa = \\\n        casper.get_current_epoch(), casper.get_recommended_ancestry_hash(), \\\n        casper.get_recommended_source_epoch(), casper.get_recommended_source_ancestry_hash()\n    for prepare in [mk_prepare(i, _e, _a, _se, _sa, k) for i, k in zip([2, 3], [t.k2, t.k3])]:\n        casper.prepare(prepare)\n    print(casper.get_main_hash_prepared_frac())\n    assert abs(sum(map(casper.get_deposit_size, range(2, 6))) - casper.get_total_curdyn_deposits()) < 5\n    assert abs(sum(map(casper.get_deposit_size, range(2, 6))) - casper.get_total_prevdyn_deposits()) < 5\n    ovp = (casper.get_deposit_size(2) + casper.get_deposit_size(3)) / casper.get_total_curdyn_deposits()\n    print(\"Epoch %d, online validator portion %.4f\" % (i, ovp))\n    if ovp >= 0.7:\n        assert casper.get_main_hash_justified()\n        break\n\nfor commit in [mk_commit(i, _e, _a, casper.get_validators__prev_commit_epoch(i), k) for i, k in zip([2, 3], [t.k2, t.k3])]:\n    casper.commit(commit)\n\nassert casper.get_main_hash_finalized()\nassert casper.get_main_hash_committed_frac() >= 0.667\nprint(\"Deposits of remaining validators: %d %d\" % (casper.get_deposit_size(2), casper.get_deposit_size(3)))\nprint(\"Deposits of offline validators: %d %d\" % (casper.get_deposit_size(4), casper.get_deposit_size(5)))\nprint(\"Tests passed\")\n","sub_path":"ethereum/tests/hybrid_casper/test_casper.py","file_name":"test_casper.py","file_ext":"py","file_size_in_byte":12931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"506151001","text":"# -*- coding: utf-8 -*-\n##############################################################################\n#\n#    Author: Jordi Ballester (Eficent)\n#    Copyright 2015 Eficent\n#\n#    This program is free software: you can redistribute it and/or modify\n#    it under the terms of the GNU Affero General Public License as\n#    published by the Free Software Foundation, either version 3 of the\n#    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 Affero General Public License for more details.\n#\n#    You should have received a copy of the GNU Affero General Public License\n#    along with this program.  If not, see <http://www.gnu.org/licenses/>.\n#\n##############################################################################\n\nfrom openerp import api, fields, models, _\nfrom openerp.exceptions import Warning\n\n\nclass CrossoveredBudget(models.Model):\n    _inherit = \"crossovered.budget\"\n\n    operating_unit_id = fields.Many2one(\n        'operating.unit',\n        string='Operating Unit',\n        default=lambda self: self.env['res.users'].\n        operating_unit_default_get(self._uid),\n    )\n\n    @api.one\n    @api.constrains('operating_unit_id', 'company_id')\n    def _check_company_operating_unit(self):\n        if self.company_id and self.operating_unit_id and \\\n                self.company_id != self.operating_unit_id.company_id:\n            raise Warning(_('The Company in the Move Line and in the '\n                            'Operating Unit must be the same.'))\n\n\nclass CrossoveredBudgetLines(models.Model):\n    _inherit = \"crossovered.budget.lines\"\n\n    operating_unit_id = fields.Many2one(\n        'operating.unit',\n        related='crossovered_budget_id.operating_unit_id',\n        string='Operating Unit', readonly=True, store=True,\n    )\n\n    # DONE but NOT USED YET\n#     @api.one\n#     @api.constrains('operating_unit_id', 'analytic_account_id')\n#     def _check_analytic_account_operating_unit(self):\n#         if self.type not in ('payment', 'receipt'):\n#             return True\n#         if (\n#             self.analytic_account_id and self.operating_unit_id and\n#             self.analytic_account_id.operating_unit_id and\n#             self.analytic_account_id.operating_unit_idid !=\n#                 self.operating_unit_id.id\n#         ):\n#             raise Warning(_('The Analytic Account must have the same '\n#                             'Operating Unit as the one indicated in the '\n#                             'budget plan'))\n#         return True\n","sub_path":"account_budget_operating_unit/models/account_budget.py","file_name":"account_budget.py","file_ext":"py","file_size_in_byte":2695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"372378603","text":"import pytest\nfrom datetime import datetime\n\nfrom app.models.task import Task\n\ndef test_task_constructor():\n    desc = \"My task\"\n    t = Task(desc)\n    assert t.description == desc\n    assert t.completed == False\n\n\ndef test_task_properties():\n    desc = \"Test task properties\"\n    t = Task(desc)\n    assert t.description == desc\n    \n    rename = \"Test task description set\"\n    t.description = rename\n    assert t.description == rename\n\n    assert t.completed == False\n    t.completed = 1\n    assert t.completed == True\n\n\n# This type of testing is NOT required, but shown for example purposes\ndef test_task_properties_exceptions():\n    t = Task(\"Foobar\")\n\n    # Since we cannot set completed > 1 we can use pytest\n    #   to check for python errors using the following syntax.\n    with pytest.raises(ValueError):\n        t.completed = 2\n    \n    # Cannot set completed < 0\n    with pytest.raises(ValueError):\n        t.completed = -1\n\n    # Cannot use set property on creation_datetime\n    with pytest.raises(AttributeError):\n        t.creation_datetime = datetime.now()\n","sub_path":"TheBlackBookMap/tests/test_models/test_task.py","file_name":"test_task.py","file_ext":"py","file_size_in_byte":1072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"21294958","text":"###############################################################################\n### GUI to display hand rom                                                 ###\n### Input: Hand rom                                                         ###\n### Output: Realtime display of hand rom                                    ###\n###############################################################################\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass GuiHand:\n    def __init__(self, max_len=50):\n        # Use ggplot style for more sophisticated visuals\n        plt.style.use('ggplot')\n        \n        # Line plot for MCP joint of five digits\n        self.line_mcp = [[] for i in range(5)] \n        self.line_pip = [[] for i in range(5)]\n        self.line_dip = [[] for i in range(5)]\n        # To store the joint angles for each digit\n        self.y_mcp = [np.zeros(max_len) for i in range(5)]\n        self.y_pip = [np.zeros(max_len) for i in range(5)]\n        self.y_dip = [np.zeros(max_len) for i in range(5)]\n        # x axis value [0, 0.01, ..., 0.99]\n        self.x_vec = np.linspace(0,1,max_len+1)[0:-1] \n\n        # Call to matplotlib to allow dynamic plotting\n        plt.ion() \n        \n        fig = plt.figure(figsize=(6,3.7))\n        ax = fig.add_subplot(111)\n        for i in range(5): \n            self.line_mcp[i], = ax.plot(self.x_vec, self.y_mcp[i], '-o', markersize=2)        \n            self.line_pip[i], = ax.plot(self.x_vec, self.y_pip[i], '-o', markersize=2)        \n            self.line_dip[i], = ax.plot(self.x_vec, self.y_dip[i], '-o', markersize=2)\n        \n        plt.show()\n\n\n    def live_plotter(self, data, mode=0, pause_time=0.001):\n        # Extract the data into different types\n        data_mcp = [data[9],data[12],data[16],data[20],data[24]]\n        data_pip = [data[10],data[13],data[17],data[21],data[25]]\n        data_dip = [data[10],data[14],data[18],data[22],data[26]] # Note data[10] is thumb IP so considered dip and pip\n\n        ###########\n        ### MCP ###\n        ###########\n        label = ['Thumb', 'Index', 'Middle', 'Ring', 'Little']\n        for i, d in enumerate(data_mcp):\n            self.y_mcp[i][-1] = d\n            # Update the y-data\n            self.line_mcp[i].set_ydata(self.y_mcp[i])\n            if mode==0: \n                self.line_mcp[i].set_label('%s %.2f'%(label[i], d))\n            else:\n                self.line_mcp[i].set_label('')\n            self.y_mcp[i] = np.append(self.y_mcp[i][1:], 0.0)             \n\n        ###########\n        ### PIP ###\n        ###########\n        for i, d in enumerate(data_pip):\n            self.y_pip[i][-1] = d\n            # Update the y-data\n            self.line_pip[i].set_ydata(self.y_pip[i])\n            if mode==1: \n                self.line_pip[i].set_label('%s %.2f'%(label[i], d))\n            else:\n                self.line_pip[i].set_label('')\n            self.y_pip[i] = np.append(self.y_pip[i][1:], 0.0)           \n\n\n        ###########\n        ### DIP ###\n        ###########\n        for i, d in enumerate(data_dip):\n            self.y_dip[i][-1] = d\n            # Update the y-data\n            self.line_dip[i].set_ydata(self.y_dip[i])\n            if mode==2: \n                self.line_dip[i].set_label('%s %.2f'%(label[i], d))\n            else: \n                self.line_dip[i].set_label('')\n            self.y_dip[i] = np.append(self.y_dip[i][1:], 0.0)           \n\n        # Adjust the plot visibility according \n        if mode==0:\n            for i in range(5):\n                self.line_mcp[i].set_visible(True)\n                self.line_pip[i].set_visible(False)\n                self.line_dip[i].set_visible(False)\n                # Adjust limits if new data goes beyond bounds\n                ylim_min = self.line_mcp[i].axes.get_ylim()[0] \n                ylim_max = self.line_mcp[i].axes.get_ylim()[1] \n                if np.min(self.y_mcp[i])<=ylim_min or np.max(self.y_mcp[i])>=ylim_max:\n                    plt.ylim([np.min(self.y_mcp[i])-np.std(self.y_mcp[i]),np.max(self.y_mcp[i])+np.std(self.y_mcp[i])])                    \n            plt.ylabel('MCP angle (deg)')\n        elif mode==1:\n            for i in range(5):\n                self.line_mcp[i].set_visible(False)\n                self.line_pip[i].set_visible(True)\n                self.line_dip[i].set_visible(False)   \n                # Adjust limits if new data goes beyond bounds\n                ylim_min = self.line_pip[i].axes.get_ylim()[0] \n                ylim_max = self.line_pip[i].axes.get_ylim()[1] \n                if np.min(self.y_pip[i])<=ylim_min or np.max(self.y_pip[i])>=ylim_max:\n                    plt.ylim([np.min(self.y_pip[i])-np.std(self.y_pip[i]),np.max(self.y_pip[i])+np.std(self.y_pip[i])])                                   \n            plt.ylabel('PIP angle (deg)')\n        elif mode==2:\n            for i in range(5):\n                self.line_mcp[i].set_visible(False)\n                self.line_pip[i].set_visible(False)\n                self.line_dip[i].set_visible(True)   \n                # Adjust limits if new data goes beyond bounds\n                ylim_min = self.line_dip[i].axes.get_ylim()[0] \n                ylim_max = self.line_dip[i].axes.get_ylim()[1] \n                if np.min(self.y_dip[i])<=ylim_min or np.max(self.y_dip[i])>=ylim_max:\n                    plt.ylim([np.min(self.y_dip[i])-np.std(self.y_dip[i]),np.max(self.y_dip[i])+np.std(self.y_dip[i])])                                   \n            plt.ylabel('DIP angle (deg)')\n\n        # Replot the legend to update the data\n        plt.legend(loc='upper left')\n        \n        # Pauses the data so the figure/axis can catch up\n        plt.pause(pause_time)\n        \n\n###############################################################################\n### Simple example to test the program                                      ###\n###############################################################################\nif __name__ == '__main__':\n    import time\n    import keyboard\n    gui = GuiHand(max_len=100)\n\n    mode = 0\n    while True:\n        data = np.random.randn(27)\n        print(data)\n        gui.live_plotter(data, mode)\n\n        if keyboard.is_pressed('esc'): # Press escape to end the program\n            print('Quitting...')\n            break\n        if keyboard.is_pressed('m'):\n            mode = (mode+1)%3\n            print('mode', mode)\n            time.sleep(0.3)\n","sub_path":"code/utils_gui.py","file_name":"utils_gui.py","file_ext":"py","file_size_in_byte":6389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"420843685","text":"from microkinetic_toolkit.reactions import Reactions\nfrom ase.build import fcc111\n\n# preparation\nT = 800  # temperature [K]\nP = 100  # total pressure [bar]\nratio = {\"H2\": 1.1, \"CO2\": 1.0}  # partial pressure ratio\n\n\n## read reactions from file and set Reactions\nreactions = Reactions.from_csv(\"test.csv\")\n\n## define surface if you like\n#surf = fcc111(\"Ni\", size=[3, 3, 4], vacuum=10.0)\n\n## if you have pre-calculated ase.db\n#reactions.ase_db = \"ase.db\"\n\n# reaction energy evaluation\nreactions.calculator = \"emt\"\ndeltaEs = reactions.get_reaction_energies()\n\n# microkinetic analysis\n## set parameters for kinetics\nreactions.set_kinetic_parameters(alpha=0.5, beta=2.5, sden=1.0e-5, v0=1.0e-5, wcat=1.0e-3, phi=0.5, rho_b=1.0e3)\n\n## calculate rate constant from reaction energies\nks = reactions.get_rate_constants(deltaEs=deltaEs, T=T)\n\n## do_microkinetics\nreactions.do_microkinetics(deltaEs=deltaEs, ks=ks, T=T, P=P, ratio=ratio)\n\n## draw graph\n#reactions.draw_network(rate)\n","sub_path":"example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"544910329","text":"import tkinter as tk\nfrom tkinter import messagebox\nfrom tkinter.filedialog import askopenfilename\nimport os\nimport vlc\nfrom datetime import datetime\n\n\nclass PlayQueueWindow(tk.Frame):\n\n    def __init__(self, parent, my_controller):\n        \"\"\" Initialize the popup queue window \"\"\"\n        tk.Frame.__init__(self, parent)\n\n        self._songs_in_queue = []\n        self.main_controller = my_controller\n\n        self._vlc_instance = vlc.Instance()\n        self._player = self._vlc_instance.media_player_new()\n\n        self._current_title = None\n\n        parent.title('Play Queue')\n\n        self.top_frame = tk.Frame(self.master)\n        self.bot_frame = tk.Frame(self.master)\n        self.mid_frame = tk.Frame(self.master)\n        self.top_frame.grid(row=0, padx=30, pady=10)\n        self.bot_frame.grid(row=2, padx=30, pady=10)\n        self.mid_frame.grid(row=1, padx=30, pady=10)\n\n        self.song_listbox = tk.Listbox(self.top_frame, width=40,\n                                       selectmode=tk.BROWSE)\n        self.song_scrollbar = tk.Scrollbar(self.top_frame, orient='vertical')\n        self.song_scrollbar.config(command=self.song_listbox.yview)\n        self.song_listbox.config(yscrollcommand=self.song_scrollbar.set)\n\n        self.details = tk.Label(self.mid_frame, text='')\n        self.details.grid(row=0, column=0, sticky=tk.W, padx=5, pady=5)\n\n        play_button = tk.Button(self.bot_frame, text='Play', width=10, bg=\"lightblue\")\n        play_button.grid(row=0, column=0, sticky=tk.E, padx=20, pady=5)\n        play_button.bind(\"<Button-1>\", self.do_play)\n\n        stop_button = tk.Button(self.bot_frame, text='Stop', width=10, bg=\"lightblue\")\n        stop_button.grid(row=0, column=1, sticky=tk.E, padx=20, pady=5)\n        stop_button.bind(\"<Button-1>\", self.do_stop)\n\n        remove_button = tk.Button(self.bot_frame, text='Remove', width=10, bg=\"red\")\n        remove_button.grid(row=2, column=1, sticky=tk.E, padx=20, pady=5)\n        remove_button.bind(\"<Button-1>\", self.do_remove)\n\n        pause_button = tk.Button(self.bot_frame, text='Pause', width=10, bg=\"lightblue\")\n        pause_button.grid(row=1, column=0, sticky=tk.E, padx=20, pady=5)\n        pause_button.bind(\"<Button-1>\", self.do_pause)\n\n        resume_button = tk.Button(self.bot_frame, text='Resume', width=10, bg=\"lightblue\")\n        resume_button.grid(row=1, column=1, sticky=tk.E, padx=20, pady=5)\n        resume_button.bind(\"<Button-1>\", self.do_resume)\n\n        skip_button = tk.Button(self.bot_frame, text='Skip', width=10, bg=\"lightblue\")\n        skip_button.grid(row=2, column=0, sticky=tk.E, padx=20, pady=5)\n        skip_button.bind(\"<Button-1>\", self.do_next)\n\n        self.song_listbox.pack(side=tk.LEFT, fill=tk.BOTH)\n        self.song_scrollbar.pack(side=tk.RIGHT, fill=tk.Y)\n\n\n\n    def add_song_to_queue(self, song):\n        \"\"\" Adds song to the queue\"\"\"\n        song_display = f'{song[\"title\"]} - {song[\"artist\"]}'\n        self._songs_in_queue.append(song)\n        self.song_listbox.insert(tk.END, song_display)\n\n    def do_play(self, event):\n        \"\"\"Play a song specified by index. \"\"\"\n        self.index = self.song_listbox.index(tk.ACTIVE)\n\n        if len(self._songs_in_queue) == 0:\n            msg_str = 'No songs loaded on to the song Que'\n            messagebox.showinfo(title='Play Error', message=msg_str)\n            return\n\n        song = self._songs_in_queue[self.index]\n        song[\"play_count\"] += 1\n        song[\"last_played\"] = datetime.now().strftime(\"%Y-%m-%d\")\n\n        self.details[\"text\"] = f'Last Played: {song[\"last_played\"]}   Count: {song[\"play_count\"]}'\n        file_path = song[\"file_location\"]\n        media = self._vlc_instance.media_new_path(file_path)\n        self._player.set_media(media)\n        self._player.play()\n\n        self.main_controller.update_play(song)\n\n\n    def do_pause(self, event):\n        \"\"\" Pause the player \"\"\"\n        if self._player.get_state() == vlc.State.Playing:\n            self._player.pause()\n\n    def do_resume(self, event):\n        \"\"\" Resume playing \"\"\"\n        if self._player.get_state() == vlc.State.Paused:\n            self._player.pause()\n\n    def do_stop(self, event):\n        \"\"\" Stop the player \"\"\"\n        self._player.stop()\n\n    def do_remove(self, event):\n        \"\"\" Remove song from the player \"\"\"\n        index = self.song_listbox.index(tk.ACTIVE)\n        self._player.stop()\n        self.song_listbox.delete(index)\n        self._songs_in_queue.pop(index)\n\n    def do_next(self, event):\n        \"\"\" Plays next song in the queue\"\"\"\n        self.index += 1\n        if self.index + 1> len(self._songs_in_queue):\n            messagebox.showinfo(title='Next', message=\"No New Songs in Queue\")\n            return\n\n        song = self._songs_in_queue[self.index]\n        song[\"play_count\"] += 1\n        song[\"last_played\"] = datetime.now().strftime(\"%Y-%m-%d\")\n\n        self.details[\"text\"] = f'Last Played: {song[\"last_played\"]}   Count: {song[\"play_count\"]}'\n        file_path = song[\"file_location\"]\n        media = self._vlc_instance.media_new_path(file_path)\n        self._player.set_media(media)\n        self._player.play()\n\n        self.main_controller.update_play(song)\n\n","sub_path":"play_queue_window.py","file_name":"play_queue_window.py","file_ext":"py","file_size_in_byte":5155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"359066515","text":"import pandas as pd\nfrom afinn import Afinn\n\n# Cleaning the data and finding the sentiment score for each review text in the dataset.\n\ndf= pd.read_csv(\"reviewsAndTips .csv\",index_col=0)\nlength=df.__len__()\n\nprint(length)\n\ndf=df.dropna()\n\nafinn = Afinn()\npscore = []\n\nfor text in df['Text']:\n    pscore.append(afinn.score(text))\n\ndf['pscore'] = pscore\n\ndf.to_csv('sentiment_analysis.csv')\n\n","sub_path":"Task 2/SentimentScoreCalculator.py","file_name":"SentimentScoreCalculator.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"429817824","text":"__author__ = 'Azharul'\n\nimport os\nfrom django.conf import settings\nfrom sqlalchemy import orm, Table, Column, Integer, String, DateTime, Boolean\nfrom core import dbconfig\nfrom core.model import Model, GenericRelation, ListProperty\nfrom core.modules.attachment.enums import E_AttachmentType, E_AttachmentStorageType\nfrom core.modules.attachment.models import Attachment\n\n\ncompany_table = Table('companies', dbconfig.metaData,\n                 Column('id', Integer, primary_key=True),\n                 Column('name', String(255)),\n                 Column('primary_contact_name', String(255)),\n                 Column('primary_contact_phone', String(255)),\n                 Column('primary_contact_email', String(255)),\n                 Column('business_address_1', String(255)),\n                 Column('business_address_2', String(255)),\n                 Column('city', String(100)),\n                 Column('state', String(100)),\n                 Column('zip', String(100)),\n                 Column('service_token', String(255)),\n                 Column('inactive', Boolean),\n                 Column('deleted', Boolean),\n                 Column('created_by', Integer),\n                 Column('updated_by', Integer),\n                 Column('created', DateTime),\n                 Column('updated', DateTime))\n\n\nclass CompanyAttachmentRelation(GenericRelation):\n    logo = ListProperty(E_AttachmentType.NavBarLogo.index)\n\n\nclass Company(Model):\n    attachments = CompanyAttachmentRelation(Attachment)\n\n    @property\n    def navbar_logo_attachment(self):\n        return self.attachments.get_property(E_AttachmentType.NavBarLogo.index).default\n\n    @property\n    def navbar_logo_name(self):\n        return self.navbar_logo_attachment.upload_name if self.navbar_logo_attachment else ''\n\n    @property\n    def navbar_logo_url(self):\n        file_path = None\n        default_path = '/static/app/css/images/logo.png'\n        if self.navbar_logo_name:\n            file_path = 'user_data/attachment/companies/'+str(self.id)+'/'+self.navbar_logo_name\n            if self.navbar_logo_attachment.storage_type==E_AttachmentStorageType.Remote.index:\n                return \"http:\" + settings.MEDIA_URL + file_path\n\n        if file_path and os.path.exists(settings.BASE_DIR + settings.MEDIA_URL + file_path):\n            return file_path\n        return default_path\n\n\n    @property\n    def contact_info(self):\n        contact = ''\n        if self.primary_contact_name:\n            contact += \"<b class='text-info'>\"+self.primary_contact_name+\"</b>\"\n        if self.primary_contact_phone:\n            contact += \"<br/><i class='fa fa-phone'></i>  \"+self.primary_contact_phone\n        if self.primary_contact_email:\n            contact += \"<br/><i class='fa fa-envelope'></i>  \"+self.primary_contact_email\n\n        return contact\n\n    @property\n    def address(self):\n        address = ''\n\n        if self.business_address_1:\n            address += self.business_address_1\n        if self.business_address_2:\n            address += \"\\n\"+self.business_address_2\n        if self.city:\n            address += \"\\n\"+self.city\n        if self.state:\n            address += \"\\n\"+self.state\n        if self.zip:\n            address += \" - \"+self.zip\n\n        return address + '.' if address else address\n\n\ncompany_mapper = orm.mapper(Company, company_table\n                               )\n\n","sub_path":"server/core/modules/company/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"461464238","text":"import threading\nimport queue\nfrom LCM import *\nfrom Utils import *\n\ndef sender():\n    input_to_header = {\n        \"score\" : SHEPHERD_HEADER.GOAL_SCORE,\n        \"bid\"   : SHEPHERD_HEADER.GOAL_BID,\n        \"code\"  : SHEPHERD_HEADER.POWERUP_APPLICATION,\n    }\n\n    input_to_alliance = {\n        \"gold\"  : ALLIANCE_COLOR.GOLD,\n        \"blue\"  : ALLIANCE_COLOR.BLUE,\n    }\n\n    input_to_goal = {\n        \"a\"     : GOAL.A,\n        \"b\"     : GOAL.B,\n        \"c\"     : GOAL.C,\n        \"d\"     : GOAL.D,\n        \"e\"     : GOAL.E,\n        \"gold\"  : GOAL.GOLD,\n        \"blue\"  : GOAL.BLUE,\n    }\n\n    while True:\n        new_input = input_to_header.get(input(\"Command: score bid code \"))\n        if new_input in (SHEPHERD_HEADER.GOAL_SCORE, SHEPHERD_HEADER.GOAL_BID):\n            goal_letter = input_to_goal.get(input(\"Goal Letter: a b c d e blue gold \"))\n            alliance = input_to_alliance.get(input(\"Alliance: blue gold \"))\n            if goal_letter is None or alliance is None:\n                print(\"Invalid input\")\n                continue\n            if new_input == SHEPHERD_HEADER.GOAL_SCORE:\n                for _ in range(0):\n                    lcm_send(LCM_TARGETS.SHEPHERD, new_input, {\"alliance\" : alliance,\n                                                               \"goal\" : goal_letter})\n            lcm_send(LCM_TARGETS.SHEPHERD, new_input, {\"alliance\" : alliance, \"goal\" : goal_letter})\n\n        elif new_input == SHEPHERD_HEADER.POWERUP_APPLICATION:\n            goal_letter = input_to_goal.get(input(\"Goal Letter: a b c d e blue gold \"))\n            alliance = input_to_alliance.get(input(\"Alliance: blue gold \"))\n            code = input(\"Code: \")\n            if goal_letter is None or alliance is None or code is None:\n                print(\"Invalid input\")\n                continue\n            lcm_send(LCM_TARGETS.SHEPHERD, new_input, {\"alliance\" : alliance,\n                                                       \"goal\" : goal_letter,\n                                                       \"code\" : code})\n        else:\n            print(\"Invalid input\")\n\ndef receiver():\n    events = queue.Queue()\n    lcm_start_read(LCM_TARGETS.SENSORS, events)\n    while True:\n        event = events.get(True)\n        print(event)\n\nif __name__ == \"__main__\":\n    sender_thread = threading.Thread(target=sender, name=\"DummySensorSender\")\n    recv_thread = threading.Thread(target=receiver, name=\"DummySensorReceiver\")\n    sender_thread.start()\n    recv_thread.start()\n","sub_path":"shepherd/DummySensors.py","file_name":"DummySensors.py","file_ext":"py","file_size_in_byte":2478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"8776450","text":"from django.conf.urls import url\n\nfrom . import views\n\n\napp_name='posts'\n\nurlpatterns = [\n    url(r'^$',views.PostList.as_view(),name='all'),\n    url(r'^new/$',views.CreatePost.as_view(),name='create'),\n    url(r'^by/(?P<username>[-\\w]+)/$',views.UserPosts.as_view(),name='for_user'),\n    url(r'^by/(?P<username>[-\\w]+)/(?P<pk>\\d+)/$',views.PostDetail.as_view(),name='single'),\n    url(r'^delete/(?P<pk>\\d+)/$',views.DeletePost.as_view(),name='delete'),\n    url(r'^by/(?P<pk>\\d+)/comment/$',views.add_comment_to_post,name='add_comment'),\n    \n    url(r'^update/(?P<pk>\\d+)/$',views.UpdatePost.as_view(),name='update'),\n    \n    url(r'^comment/(?P<pk>\\d+)/approve/$',views.comment_approve,name='comment_approve'),\n    url(r'^comment/(?P<pk>\\d+)/remove/$',views.comment_remove,name='comment_remove'),\n    url(r'^comment/(?P<pk>\\d+)/like/$',views.LikePost,name='like'),\n    url(r'^event/$',views.EventList.as_view(),name='event_all'),\n    url(r'^lcc/$',views.LCCList.as_view(),name='lcc_all'),\n\n    ]\n    \n\n\n","sub_path":"posts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"480487372","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nz,T,Ux,Uy,Uz = np.loadtxt('postProcessing/singleGraph/3000/line_T_Ux_Uy_Uz.xy',delimiter='\\t',dtype=np.float,unpack=True)\nzdata,Uueh,Tueh,Ukim,Tkim,Uxie,Txie = np.loadtxt('xie-2006-data.csv',delimiter=',',dtype=np.float,skiprows=1,unpack=True)\n\nTf = 320.82\nTa = 300\nUinf = 0.56\nH = 0.1\ntheta = (T-Tf)/(Ta-Tf)\nz0 = z/H\nU0 = Ux/Uinf\n\nplt.subplot(1,2,1)\nplt.plot(theta,z0,'r',label='This study, Rb = -0.21')\nplt.plot(Tueh,zdata,'bo',label='Uehara et al 2000, Rb = -0.21')\nplt.plot(Tkim,zdata,'k^',label='Kim & Baik 2001, Rb = -0.27')\nplt.plot(Txie,zdata,'gD',label='Xie et al 2006, Rb = -0.21')\nplt.xlabel(r'$\\theta$')\nplt.ylabel('z/H')\nplt.xlim((-0.1,1.2))\nplt.ylim((0.0,2.0))\nplt.legend(loc=2)\n\nplt.subplot(1,2,2)\nplt.plot(U0,z0,'r')\nplt.plot(Uueh,zdata,'bo')\nplt.plot(Ukim,zdata,'k^')\nplt.plot(Uxie,zdata,'gD')\nplt.xlabel('Ux/U0')\nplt.ylabel('z/H')\nplt.ylim((0.0,2.0))\n\nplt.show()\n","sub_path":"Validation/centerlineplot.py","file_name":"centerlineplot.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"613750406","text":"from odoo import api, fields, models, _\nfrom num2words import num2words\n\n\nclass AccountMove(models.Model):\n    _inherit= \"account.move\"\n    \n    line_ids = fields.One2many('account.move.line', 'move_id', string='Journal Items', copy=True, readonly=True,\n        states={'draft': [('readonly', False)]})\n    \n    quantity = fields.Float(string='Poids',default=1.0, related=\"line_ids.quantity\")\n    \n    quantity_poids_total = fields.Float(string='somme des poids', digits='Product Unit of Measure', compute=\"_quantity_poids_total\")\n    \n    \n    \n    @api.onchange('quantity', 'line_ids')\n    def _quantity_poids_total(self):\n        for rec in self:\n            total = sum(rec.line_ids.mapped('quantity'))\n            rec.quantity_poids_total = total\n        \n\n    agrement = fields.Selection(selection=[('001', '001/92/C'), ('0003', '003/17/CE')], string=\"Agrément\")\n    airport = fields.Many2one('optesis.airport', string='Aréoport')\n    lta = fields.Char(string='L.T.A')\n    transit = fields.Many2one('optesis.transit', string='Transitaire')\n    amount_text = fields.Char('Montant en lettres', compute='get_amount_text')\n\n    \n    @api.depends('amount_total', 'currency_id')\n    def get_amount_text(self):\n        number_in_word = num2words(self.amount_total, lang='fr')\n        self.amount_text = number_in_word and number_in_word.capitalize()\n\n\nclass AccountMoveLine(models.Model):\n    _inherit= \"account.move.line\"\n    \n    quantity = fields.Float(string='Poids',\n        default=1.0, digits='Product Unit of Measure',\n        help=\"The optional quantity expressed by this line, eg: number of product sold. \"\n             \"The quantity is not a legal requirement but is very useful for some reports.\")\n    \n\n    nb_colis = fields.Float(string='Nombre de colis')\n    agrement = fields.Selection(selection=[('001', '001/92/C'), ('0003', '003/17/CE')], related='move_id.agrement', store=True, copy=False, index=True, readonly=False)\n    airport = fields.Many2one('optesis.airport', related='move_id.airport', store=True, copy=False, index=True, readonly=False)\n    #lta = fields.Many2one('optesis.lta', related='move_id.lta', store=True, copy=False, index=True, readonly=False)\n    transit = fields.Many2one('optesis.transit', related='move_id.transit', store=True, copy=False, index=True, readonly=False)\n    transformation = fields.Many2one('optesis.transformation', string='Transformation')\n    calibre = fields.Many2one('optesis.calibre', string='Calibre')\n    \n\n    \n    @api.model\n    def _sale_prepare_sale_line_values(self, order, price):\n        \"\"\" Generate the sale.line creation value from the current move line \"\"\"\n        self.ensure_one()\n        last_so_line = self.env['sale.order.line'].search([('order_id', '=', order.id)], order='sequence desc', limit=1)\n        last_sequence = last_so_line.sequence + 1 if last_so_line else 100\n\n        fpos = order.fiscal_position_id or order.partner_id.property_account_position_id\n        taxes = fpos.map_tax(self.product_id.taxes_id, self.product_id, order.partner_id)\n\n        return {\n            'order_id': order.id,\n            'name': self.name,\n            'sequence': last_sequence,\n            'price_unit': price,\n            'tax_id': [x.id for x in taxes],\n            'discount': 0.0,\n            'product_id': self.product_id.id,\n            'transformation': self.transformation_id.id,\n            'calibre': self.calibre_id.id,\n            'product_uom': self.product_uom_id.id,\n            'product_uom_qty': 0.0,\n            'is_expense': True,\n        }\n    \n    @api.model\n    def _prepare_analytic_line(self):\n        \"\"\" Prepare the values used to create() an account.analytic.line upon validation of an account.move.line having\n            an analytic account. This method is intended to be extended in other modules.\n            :return list of values to create analytic.line\n            :rtype list\n        \"\"\"\n        result = []\n        for move_line in self:\n            amount = (move_line.credit or 0.0) - (move_line.debit or 0.0)\n            default_name = move_line.name or (move_line.ref or '/' + ' -- ' + (move_line.partner_id and move_line.partner_id.name or '/'))\n            result.append({\n                'name': default_name,\n                'date': move_line.date,\n                'account_id': move_line.analytic_account_id.id,\n                'group_id': move_line.analytic_account_id.group_id.id,\n                'tag_ids': [(6, 0, move_line._get_analytic_tag_ids())],\n                'unit_amount': move_line.quantity,\n                'calibre':move_line.calibre,\n                'nb_colis':move_line.nb_colis,\n                'product_id': move_line.product_id and move_line.product_id.id or False,\n                'product_uom_id': move_line.product_uom_id and move_line.product_uom_id.id or False,\n                'amount': amount,\n                'general_account_id': move_line.account_id.id,\n                'ref': move_line.ref,\n                'move_id': move_line.id,\n                'user_id': move_line.move_id.invoice_user_id.id or self._uid,\n                'partner_id': move_line.partner_id.id,\n                'company_id': move_line.analytic_account_id.company_id.id or self.env.company.id,\n            })\n        return result\n    @api.model\n    def _copy_data_extend_business_fields(self, values):\n        # OVERRIDE to copy the 'purchase_line_id' field as well.\n        super(AccountMoveLine, self)._copy_data_extend_business_fields(values)\n        values['purchase_line_id'] = self.purchase_line_id.id\n        calibre = self.purchase_line_id.calibre\n    ","sub_path":"delphinus/models/acccount_move.py","file_name":"acccount_move.py","file_ext":"py","file_size_in_byte":5587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"172239424","text":"#!usr/bin/python3\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.stats as st\nfrom aima3.agents import *\nfrom misio.aima import * \n\ncurrent_position = random.choice([0,1])\nloc = [0, 0]\niter, ac = -1, -1\nflag = False\n\ndef MyAgent(): \n    def program(percept): \n        global current_position, loc, iter, ac, flag \n        location, status = percept \n        loc[location[0]] = (1 if status == 'Dirty' else 0) # status\n        iter += 1\n        ac += 1\n\n        if loc[current_position] == 1:\n            loc[current_position] = 0\n            flag = True\n            return 'Suck'\n        elif (ac == 0) or (ac == 1 and flag):\n            if current_position == 0: return 'Right'\n            else: return 'Left'\n        elif sum(loc) == 0 and iter < 8: \n            return 'NoOp'\n        elif loc[current_position] == 0 and current_position == 0:\n            iter = 0\n            current_position = 1\n            return 'Right'\n        elif loc[current_position] == 0 and current_position == 1:\n            iter = 0\n            current_position = 0 \n            return 'Left'\n    \n    return Agent(program)\n\n# params\nno_samples = 50000\nn = 1\nsteps = 50\nconfidence = .95\n\n# default chart\ndef agent_factory_1():\n    return MyAgent()\n\ndef env_factory():\n    return TrivialVacuumEnvironmentWithChildren(random_dirt_prob=0.05)\n\ndef run_agent(EnvFactory, AgentFactory, n=10, steps=1000):\n    envs = [EnvFactory() for i in range(n)]\n    return test_agent(AgentFactory, steps, copy.deepcopy(envs))\n\ndata = [run_agent(env_factory, agent_factory_1, n, steps) for _ in range(no_samples)]\n\nprint(f\"Exp. val. {np.mean(data)} - std. dev. {np.std(data)}\")\nprint(st.norm.interval(confidence, loc=np.mean(data), scale=st.sem(data)))\n\nplt.style.use(\"seaborn-deep\")\nplt.hist(data, density=True, bins=20)\nplt.show()","sub_path":"lab1/agent-histogram.py","file_name":"agent-histogram.py","file_ext":"py","file_size_in_byte":1811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"70147897","text":"import pandas as pd\nimport statsmodels.api as sm \nimport numpy as np\nimport statsmodels\n\nclass Lab3(object):\n    \n    def create_data(self,snp_lines) :\n        '''\n        Input - the snp_lines parsed at the beginning of the notebook\n        Output - You should return the 53 x 3902 dataframe\n        '''\n        #start code here\n        data=np.zeros((53,3902))\n        column_name=[]\n        cur_list=[]\n        for i in range(len(snp_lines)):\n            cur_list=snp_lines[i].split()\n            column_name.append(str(cur_list[0]+':'+cur_list[1]))\n            for j in range(9,len(cur_list)):\n                if (cur_list[j]=='0/0'):\n                    data[j-9][i]=0\n                elif(cur_list[j]=='0/1'or cur_list[j]=='1/0'):\n                    data[j-9][i]=1\n                elif(cur_list[j]=='1/1'):\n                    data[j-9][i]=2\n                else:\n                    data[j-9][i]=np.nan\n        df=pd.DataFrame(data,columns=column_name)\n        return df\n            \n            \n        \n        #end code here\n\n    def create_target(self,header_line) :\n        '''\n        Input - the header_line parsed at the beginning of the notebook\n        Output - a list of values(either 0 or 1)\n        '''\n        #start code here\n        data=header_line.split()[9:]\n        phenotype=[]\n        for i in range(len(data)):\n            if data[i][0:4]=='dark':\n                phenotype.append(0)\n            else:\n                phenotype.append(1)\n        return phenotype\n        #end code here\n    \n    def logistic_reg_per_snp(self,df) :\n        '''\n        Input - snp_data dataframe\n        Output - list of pvalues and list of betavalues\n        '''\n        #start code here\n        p=[]\n        belta=[]\n        head=df.columns[0:-1]\n        for i in range(len(head)):\n            x=sm.add_constant(df[head[i]])\n            y=df[df.columns[-1]]\n            classifier=sm.Logit(y,x,missing=\"drop\").fit(method='bfgs',disp='False')\n            belta.append(round(classifier.params[1],5))\n            p.append(round(classifier.pvalues[1],9))\n        return p,belta\n        #end code here\n    \n    \n    def get_top_snps(self,snp_data,p_values) :\n        '''\n        Input - snp dataframe with target column and p_values calculated previously\n        Output - list of 5 tuples, each with chromosome and position\n        '''\n        #start code here\n        data=snp_data.columns[0:-1]\n        maxium=max(p_values)\n        p=[]\n        output=[]\n        while(len(p)<5):\n            id=np.argmin(p_values)\n            p.append(id)\n            p_values[id]=maxium+1\n        for idx in p:\n            row=data[idx].split(':')\n            output.append((row[0],row[1]))\n        return output\n            \n        \n        #end code here","sub_path":"Genomics_Lab3/.ipynb_checkpoints/main-checkpoint.py","file_name":"main-checkpoint.py","file_ext":"py","file_size_in_byte":2755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"145716408","text":"KAPASITEETTI = 5\nOLETUSKASVATUS = 5\n\n\nclass IntJoukko:\n    def __init__(self, kapasiteetti=None, kasvatuskoko=None):\n        self.kapasiteetti = self.kapasiteetti_kasvatuskoko_tsek(kapasiteetti, kapasiteetti, KAPASITEETTI)\n        self.kasvatuskoko = self.kapasiteetti_kasvatuskoko_tsek(kasvatuskoko, kapasiteetti, OLETUSKASVATUS)\n        self.ljono = [0] * self.kapasiteetti\n        self.alkioiden_lkm = 0\n\n    def kapasiteetti_kasvatuskoko_tsek(self, arvo, kapasiteetti, ifnone):\n        if arvo is None:\n            return ifnone\n        elif not isinstance(kapasiteetti, int) or kapasiteetti < 0:\n            raise Exception(\"Väärä kapasiteetti\")  # heitin vaan jotain :D\n        else:\n            return kapasiteetti\n\n    def kuuluu(self, n):\n        for i in range(0, self.alkioiden_lkm):\n            if n == self.ljono[i]:\n                return True           \n        return False\n\n\n\n    def lisaa(self, n):\n        if self.alkioiden_lkm == 0:\n            self.ljono[0] = n\n            self.alkioiden_lkm += 1\n            return True\n        else:\n            pass\n\n        if not self.kuuluu(n):\n            self.lisaa_n_ei_kuulu(n)\n            return True\n\n        return False\n\n    def lisaa_n_ei_kuulu(self,n):\n        self.ljono[self.alkioiden_lkm] = n\n        self.alkioiden_lkm += 1\n        if self.alkioiden_lkm % len(self.ljono) == 0:\n            taulukko_old = self.ljono\n            self.kopioi_taulukko(self.ljono, taulukko_old)\n            self.ljono = [0] * (self.alkioiden_lkm + self.kasvatuskoko)\n            self.kopioi_taulukko(taulukko_old, self.ljono)      \n\n    def poista(self, n):\n        nIndeksi = self.indeksin_etsinta(n)\n        if nIndeksi != -1:\n            self.listan_lyhentaminen(nIndeksi)\n            return True\n\n        return False\n\n    def indeksin_etsinta(self, n):\n        nIndeksi = -1\n        for i in range(0, self.alkioiden_lkm):\n            if n == self.ljono[i]:\n                nIndeksi = i  # siis luku löytyy tuosta kohdasta :D\n                self.ljono[i] = 0\n                break\n        return nIndeksi\n\n    def listan_lyhentaminen(self, i):\n        apu = 0\n        for j in range(i, self.alkioiden_lkm - 1):\n            apu = self.ljono[j]\n            self.ljono[j] = self.ljono[j + 1]\n            self.ljono[j + 1] = apu\n\n        self.alkioiden_lkm = self.alkioiden_lkm - 1\n\n    def kopioi_taulukko(self, a, b):\n        for i in range(0, len(a)):\n            b[i] = a[i]\n\n    def mahtavuus(self):\n        return self.alkioiden_lkm\n\n    def to_int_list(self):\n        taulu = [0] * self.alkioiden_lkm\n\n        for i in range(0, len(taulu)):\n            taulu[i] = self.ljono[i]\n\n        return taulu\n\n    @staticmethod\n    def yhdiste(a, b):\n        x = IntJoukko()\n        a_taulu = a.to_int_list()\n        b_taulu = b.to_int_list()\n\n        for i in range(0, len(a_taulu)):\n            x.lisaa(a_taulu[i])\n\n        for i in range(0, len(b_taulu)):\n            x.lisaa(b_taulu[i])\n\n        return x\n\n    @staticmethod\n    def leikkaus(a, b):\n        y = IntJoukko()\n        a_taulu = a.to_int_list()\n        b_taulu = b.to_int_list()\n\n        for i in range(0, len(a_taulu)):\n            for j in range(0, len(b_taulu)):\n                if a_taulu[i] == b_taulu[j]:\n                    y.lisaa(b_taulu[j])\n\n        return y\n\n    @staticmethod\n    def erotus(a, b):\n        z = IntJoukko()\n        a_taulu = a.to_int_list()\n        b_taulu = b.to_int_list()\n\n        for i in range(0, len(a_taulu)):\n            z.lisaa(a_taulu[i])\n\n        for i in range(0, len(b_taulu)):\n            z.poista(b_taulu[i])\n\n        return z\n\n    def __str__(self):\n        if self.alkioiden_lkm == 0:\n            return \"{}\"\n        elif self.alkioiden_lkm == 1:\n            return \"{\" + str(self.ljono[0]) + \"}\"\n        else:\n            tuotos = \"{\"\n            for i in range(0, self.alkioiden_lkm - 1):\n                tuotos = tuotos + str(self.ljono[i])\n                tuotos = tuotos + \", \"\n            tuotos = tuotos + str(self.ljono[self.alkioiden_lkm - 1])\n            tuotos = tuotos + \"}\"\n            return tuotos\n","sub_path":"viikko4/int-joukko/src/int_joukko.py","file_name":"int_joukko.py","file_ext":"py","file_size_in_byte":4080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"344657867","text":"from __future__ import division, print_function\n  \n\nimport MDAnalysis\nfrom matplotlib import pyplot as plt\nimport matplotlib as mpl\nfrom matplotlib import cm\nimport numpy as np\n\nimport glob, os\n\nmpl.rcParams.update({'axes.labelsize': 50})\nmpl.rcParams.update({'xtick.labelsize': 40})\nmpl.rcParams.update({'ytick.labelsize': 40})\nmpl.rcParams.update({'axes.titlesize':40})\nmpl.rcParams.update({'legend.fontsize':40})\n\nfnames = glob.glob('*/surf_dat.dat')\nlabels = []\nvals = np.zeros((len(fnames), 14), dtype=float)\n\nfor i, fname in enumerate(fnames):\n    dirname = os.path.dirname(fname)\n    labels.append(dirname)\n    vals[i,...] = np.loadtxt(fname)\nlabels = np.array(labels)\n\nname_lup = {'1brs': 'barnase',\n            '1ubq': 'ubiquitin',\n            '1qgt': 'capsid',\n            '1ubq': 'ubiquitin',\n            '1ycr': 'MDM2',\n            '253l': 'lysozyme',\n            '2b97': 'hydrophobin',\n            '3hhp': 'malate\\ndehydrogenase'}\n\norder = ['hydrophobin', 'capsid', 'MDM2', 'malate\\ndehydrogenase', 'ubiquitin', 'barnase', 'lysozyme']\n\ncolors = cm.rainbow(np.linspace(0,1,len(order)))\n\norder_idx = np.zeros(len(order), dtype=int)\nfor idx, label in enumerate(labels):\n    if name_lup[label] in order:\n        order_idx[order.index(name_lup[label])] = idx\n\nvals = vals[order_idx,...]\nlabels = labels[order_idx]\n\nnames = [name_lup[label] for label in labels]\ndipole = {k:v for k,v in zip(np.loadtxt('dipole.dat', usecols=0, dtype=str), np.loadtxt('dipole.dat', usecols=1))}\n\nn_bars = len(labels)\nindices = np.arange(n_bars)\nwidth = 1\n\ngap = 4\n\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,6))\n\n# upper left\nfor idx, name in enumerate(names):\n    label = labels[idx]\n    n_tot, n_surf, n_res_surf, n_phil_surf, n_phob_surf, n_phob_res, pos_charge_res, neg_charge_res, n_surf_h, n_phob_h, n_hydrophilic_res_atoms, n_hydrophilic_res_atoms_phob, n_hydrophobic_res_atoms, n_hydrophobic_res_atoms_phob = vals[idx] \n    this_dipole = dipole[label]\n\n    ax1.bar(idx, n_hydrophobic_res_atoms_phob/n_hydrophobic_res_atoms, label=label, color=colors[idx], width=width)\n    ax2.bar(idx, n_hydrophilic_res_atoms_phob/n_hydrophilic_res_atoms, label=label, color=colors[idx], width=width)\n\nax1.set_xticks([])\nax1.set_ylim(0.4, 0.9)\nax1.set_title('hydrophobic res')\n\nax2.set_xticks([])\nax2.set_ylim(0.4, 0.9)\nax2.set_title('hydrophilic res')\n\nfig.tight_layout()\nfig.subplots_adjust(hspace=0.3)\nfig.savefig('/Users/nickrego/Desktop/blah.pdf', transparent=True)\n\n\n","sub_path":"scratch/prot_hydrophobicity_comm_scripts/fig_hydrophobic_frac.py","file_name":"fig_hydrophobic_frac.py","file_ext":"py","file_size_in_byte":2461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"314355768","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport json\n\nfrom alipay.aop.api.constant.ParamConstants import *\n\n\nclass LogisticsShopStatusDTO(object):\n\n    def __init__(self):\n        self._audit_desc = None\n        self._logistics_code = None\n        self._logistics_name = None\n        self._status = None\n\n    @property\n    def audit_desc(self):\n        return self._audit_desc\n\n    @audit_desc.setter\n    def audit_desc(self, value):\n        self._audit_desc = value\n    @property\n    def logistics_code(self):\n        return self._logistics_code\n\n    @logistics_code.setter\n    def logistics_code(self, value):\n        self._logistics_code = value\n    @property\n    def logistics_name(self):\n        return self._logistics_name\n\n    @logistics_name.setter\n    def logistics_name(self, value):\n        self._logistics_name = value\n    @property\n    def status(self):\n        return self._status\n\n    @status.setter\n    def status(self, value):\n        self._status = value\n\n\n    def to_alipay_dict(self):\n        params = dict()\n        if self.audit_desc:\n            if hasattr(self.audit_desc, 'to_alipay_dict'):\n                params['audit_desc'] = self.audit_desc.to_alipay_dict()\n            else:\n                params['audit_desc'] = self.audit_desc\n        if self.logistics_code:\n            if hasattr(self.logistics_code, 'to_alipay_dict'):\n                params['logistics_code'] = self.logistics_code.to_alipay_dict()\n            else:\n                params['logistics_code'] = self.logistics_code\n        if self.logistics_name:\n            if hasattr(self.logistics_name, 'to_alipay_dict'):\n                params['logistics_name'] = self.logistics_name.to_alipay_dict()\n            else:\n                params['logistics_name'] = self.logistics_name\n        if self.status:\n            if hasattr(self.status, 'to_alipay_dict'):\n                params['status'] = self.status.to_alipay_dict()\n            else:\n                params['status'] = self.status\n        return params\n\n    @staticmethod\n    def from_alipay_dict(d):\n        if not d:\n            return None\n        o = LogisticsShopStatusDTO()\n        if 'audit_desc' in d:\n            o.audit_desc = d['audit_desc']\n        if 'logistics_code' in d:\n            o.logistics_code = d['logistics_code']\n        if 'logistics_name' in d:\n            o.logistics_name = d['logistics_name']\n        if 'status' in d:\n            o.status = d['status']\n        return o\n\n\n","sub_path":"alipay/aop/api/domain/LogisticsShopStatusDTO.py","file_name":"LogisticsShopStatusDTO.py","file_ext":"py","file_size_in_byte":2458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"466679937","text":"\"\"\"\nCopyright (c) 2014, Samsung Electronics Co.,Ltd.\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice, this\nlist of conditions and the following disclaimer.\n2. Redistributions in binary form must reproduce the above copyright notice,\nthis list of conditions and the following disclaimer in the documentation\nand/or other materials provided with the distribution.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\nANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\nWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR\nANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\nLOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\nON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nThe views and conclusions contained in the software and documentation are those\nof the authors and should not be interpreted as representing official policies,\neither expressed or implied, of Samsung Electronics Co.,Ltd..\n\"\"\"\n\n\"\"\"\nopencl4py - OpenCL cffi bindings and helper classes.\nURL: https://github.com/ajkxyz/opencl4py\nOriginal author: Alexey Kazantsev <a.kazantsev@samsung.com>\n\"\"\"\n\n\"\"\"\nTests some of the api in opencl4py package.\n\"\"\"\nimport unittest\nimport logging\nimport opencl4py as cl\nimport os\n\n\nclass Test(unittest.TestCase):\n    def setUp(self):\n        logging.basicConfig(level=logging.DEBUG)\n        self.old_env = os.environ.get(\"PYOPENCL_CTX\")\n        if self.old_env is None:\n            os.environ[\"PYOPENCL_CTX\"] = \"0:0\"\n        self.src_test = (\n            \"\"\"\n            #include \"test.cl\"\n            \"\"\")\n        self.include_dirs = (\"\", os.path.dirname(__file__), \".\")\n\n    def tearDown(self):\n        if self.old_env is None:\n            del os.environ[\"PYOPENCL_CTX\"]\n        else:\n            os.environ[\"PYOPENCL_CTX\"] = self.old_env\n        del self.old_env\n\n    def test_constants(self):\n        self.assertEqual(cl.CL_DEVICE_TYPE_CPU, 2)\n        self.assertEqual(cl.CL_DEVICE_TYPE_GPU, 4)\n        self.assertEqual(cl.CL_DEVICE_TYPE_ACCELERATOR, 8)\n        self.assertEqual(cl.CL_DEVICE_TYPE_CUSTOM, 16)\n        self.assertEqual(cl.CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, 1)\n        self.assertEqual(cl.CL_MAP_READ, 1)\n        self.assertEqual(cl.CL_MAP_WRITE, 2)\n        self.assertEqual(cl.CL_MAP_WRITE_INVALIDATE_REGION, 4)\n        self.assertEqual(cl.CL_MEM_READ_WRITE, 1)\n        self.assertEqual(cl.CL_MEM_WRITE_ONLY, 2)\n        self.assertEqual(cl.CL_MEM_READ_ONLY, 4)\n        self.assertEqual(cl.CL_MEM_USE_HOST_PTR, 8)\n        self.assertEqual(cl.CL_MEM_ALLOC_HOST_PTR, 16)\n        self.assertEqual(cl.CL_MEM_COPY_HOST_PTR, 32)\n\n    def test_dump_devices(self):\n        platforms = cl.Platforms()\n        s = platforms.dump_devices()\n        del s\n\n    def test_create_context(self):\n        platforms = cl.Platforms()\n        ctx = cl.Context(platforms.platforms[0],\n                         platforms.platforms[0].devices[0:1])\n        del ctx\n\n    def test_create_some_context(self):\n        platforms = cl.Platforms()\n        ctx = platforms.create_some_context()\n        del ctx\n\n    def test_realign_numpy_array(self):\n        try:\n            import numpy\n        except ImportError:  # for pypy\n            try:\n                import numpypy as numpy\n            except ImportError:\n                raise ImportError(\"Could not import numpy\")\n        a = numpy.empty(1000, dtype=numpy.float32)\n        a = cl.realign_array(a, 1056, numpy)\n        self.assertEqual(a.__array_interface__[\"data\"][0] % 1056, 0)\n\n    def test_device_info(self):\n        platforms = cl.Platforms()\n        ctx = platforms.create_some_context()\n        dev = ctx.devices[0]\n        self.assertGreater(dev.max_work_item_dimensions, 0)\n        self.assertEqual(len(dev.max_work_item_sizes),\n                         dev.max_work_item_dimensions)\n        for size in dev.max_work_item_sizes:\n            self.assertGreater(size, 0)\n        self.assertIsInstance(dev.driver_version.encode(\"utf-8\"), bytes)\n        self.assertGreater(len(dev.driver_version), 0)\n        try:\n            self.assertIsInstance(dev.built_in_kernels, list)\n            for krn in dev.built_in_kernels:\n                self.assertIsInstance(krn, str)\n                self.assertGreater(len(krn), 0)\n        except cl.CLRuntimeError as e:\n            if dev.version >= 1.2:\n                raise\n            self.assertEqual(e.code, -30)\n        self.assertIsInstance(dev.extensions, list)\n        for ext in dev.extensions:\n            self.assertIsInstance(ext.encode(\"utf-8\"), bytes)\n            self.assertGreater(len(ext), 0)\n        self.assertGreater(dev.preferred_vector_width_int, 0)\n        self.assertGreater(dev.max_work_group_size, 1)\n        self.assertTrue(dev.available)\n\n    def test_program_info(self):\n        platforms = cl.Platforms()\n        ctx = platforms.create_some_context()\n        prg = ctx.create_program(self.src_test, self.include_dirs)\n        self.assertGreater(prg.reference_count, 0)\n        try:\n            self.assertEqual(prg.num_kernels, 1)\n            names = prg.kernel_names\n            self.assertIsInstance(names, list)\n            self.assertEqual(len(names), 1)\n            self.assertEqual(names[0], \"test\")\n        except cl.CLRuntimeError as e:\n            if prg.devices[0].version >= 1.2:\n                raise\n            self.assertEqual(e.code, -30)\n        bins = prg.binaries\n        self.assertEqual(len(bins), 1)\n        self.assertIsInstance(bins[0], bytes)\n        self.assertGreater(len(bins[0]), 0)\n\n    def test_kernel_info(self):\n        platforms = cl.Platforms()\n        ctx = platforms.create_some_context()\n        prg = ctx.create_program(self.src_test, self.include_dirs)\n        krn = prg.get_kernel(\"test\")\n        self.assertGreater(krn.reference_count, 0)\n        self.assertEqual(krn.num_args, 3)\n        try:\n            self.assertEqual(krn.attributes, \"vec_type_hint(float4)\")\n        except cl.CLRuntimeError as e:\n            self.assertEqual(e.code, -30)\n\n    def test_binary(self):\n        platforms = cl.Platforms()\n        ctx = platforms.create_some_context()\n        prg = ctx.create_program(self.src_test, self.include_dirs)\n        binary = prg.binaries[0]\n        prg = ctx.create_program([binary], binary=True)\n        krn = prg.get_kernel(\"test\")\n\n    def set_kernel_args(self):\n        platforms = cl.Platforms()\n        ctx = platforms.create_some_context()\n        prg = ctx.create_program(self.src_test, self.include_dirs)\n        krn = prg.get_kernel(\"test\")\n        queue = ctx.create_queue(ctx.devices[0])\n        global_size = [a.size]\n        local_size = None\n\n        krn.set_args(cl.skip(3))\n        self.assertRaises(CLRuntimeError,\n                          queue.execute_kernel(krn, global_size, local_size))\n        krn.set_args(cl.skip. cl.skip, cl.skip)\n        self.assertRaises(CLRuntimeError,\n                          queue.execute_kernel(krn, global_size, local_size))\n        krn.set_args(cl.skip(1). cl.skip(1), cl.skip(1))\n        self.assertRaises(CLRuntimeError,\n                          queue.execute_kernel(krn, global_size, local_size))\n        krn.set_args(cl.skip(1000))\n        self.assertRaises(CLRuntimeError,\n                          queue.execute_kernel(krn, global_size, local_size))\n        self.assertRaises(ValueError, cl.skip, 0)\n        self.assertRaises(ValueError, cl.skip, -1)\n\n        c = numpy.array([1.2345], dtype=numpy.float32)\n        krn.set_args(cl.skip(2), c)\n        self.assertRaises(CLRuntimeError,\n                          queue.execute_kernel(krn, global_size, local_size))\n        krn.set_args(cl.skip, cl.skip, c)\n        self.assertRaises(CLRuntimeError,\n                          queue.execute_kernel(krn, global_size, local_size))\n\n    def test_api_numpy(self):\n        try:\n            import numpy\n        except ImportError:  # for pypy\n            try:\n                import numpypy as numpy\n            except ImportError:\n                raise ImportError(\"Could not import numpy\")\n        # Create platform, context, program, kernel and queue\n        platforms = cl.Platforms()\n        ctx = platforms.create_some_context()\n        prg = ctx.create_program(self.src_test, self.include_dirs)\n        krn = prg.get_kernel(\"test\")\n        queue = ctx.create_queue(ctx.devices[0])\n\n        # Create arrays with some values for testing\n        a = numpy.arange(100000, dtype=numpy.float32)\n        b = numpy.cos(a)\n        a = numpy.sin(a)\n        a_copy = a.copy()\n\n        # Prepare arrays for use with map_buffer\n        a = cl.realign_array(a, queue.device.memalign, numpy)\n        b = cl.realign_array(b, queue.device.memalign, numpy)\n        c = numpy.array([1.2345], dtype=numpy.float32)\n        d = a + b * c[0]\n\n        # Create buffers\n        a_ = ctx.create_buffer(cl.CL_MEM_READ_WRITE | cl.CL_MEM_USE_HOST_PTR,\n                               a)\n        b_ = ctx.create_buffer(cl.CL_MEM_READ_WRITE | cl.CL_MEM_USE_HOST_PTR,\n                               b)\n\n        # Set kernel arguments\n        krn.set_args(a_, b_, c[0:1])\n\n        # Execute kernel\n        global_size = [a.size]\n        local_size = None\n        queue.execute_kernel(krn, global_size, local_size, need_event=False)\n\n        # Get results back from the device by map_buffer\n        ev, ptr = queue.map_buffer(a_, cl.CL_MAP_READ, a.nbytes)\n        del ev\n        ev = queue.unmap_buffer(a_, ptr)\n        ev.wait()\n        self.assertLess(numpy.fabs(a - d).max(), 0.0001,\n                        \"Incorrect result after map_buffer\")\n\n        # Get results back from the device by read_buffer\n        aa = numpy.zeros(a.shape, dtype=a.dtype)\n        queue.read_buffer(a_, aa)\n        self.assertLess(numpy.fabs(aa - d).max(), 0.0001,\n                        \"Incorrect result after read_buffer\")\n\n        # Refill buffer with stored copy by map_buffer with event\n        ev, ptr = queue.map_buffer(\n            a_, cl.CL_MAP_WRITE if queue.device.version < 1.1999\n            else cl.CL_MAP_WRITE_INVALIDATE_REGION, a.nbytes,\n            blocking=False, need_event=True)\n        ev.wait()\n        a[:] = a_copy[:]\n        ev = queue.unmap_buffer(a_, ptr)\n\n        # Execute kernel\n        ev = queue.execute_kernel(krn, global_size, local_size, wait_for=(ev,))\n        # Get results back from the device by map_buffer\n        ev, ptr = queue.map_buffer(a_, cl.CL_MAP_READ, a.nbytes,\n                                   wait_for=(ev,), need_event=True)\n        ev.wait()\n        ev = queue.unmap_buffer(a_, ptr)\n        ev.wait()\n        self.assertLess(numpy.fabs(a - d).max(), 0.0001,\n                        \"Incorrect result after map_buffer\")\n\n        # Refill buffer with stored copy by write_buffer\n        ev = queue.write_buffer(a_, a_copy, blocking=False, need_event=True)\n\n        # Execute kernel\n        ev = queue.execute_kernel(krn, global_size, local_size, wait_for=(ev,))\n        # Get results back from the device by map_buffer\n        ev, ptr = queue.map_buffer(a_, cl.CL_MAP_READ, a.nbytes,\n                                   wait_for=(ev,), need_event=True)\n        ev.wait()\n        ev = queue.unmap_buffer(a_, ptr)\n        ev.wait()\n        self.assertLess(numpy.fabs(a - d).max(), 0.0001,\n                        \"Incorrect result after map_buffer\")\n\n    def test_api_nonumpy(self):\n        import math\n        # Create platform, context, program, kernel and queue\n        platforms = cl.Platforms()\n        ctx = platforms.create_some_context()\n        prg = ctx.create_program(self.src_test, self.include_dirs)\n        krn = prg.get_kernel(\"test\")\n        # Create command queue\n        queue = ctx.create_queue(ctx.devices[0])\n\n        # Create arrays with some values for testing\n        N = 100000\n        _a = cl.ffi.new(\"float[]\", N + queue.device.memalign)\n        sz = int(cl.ffi.cast(\"size_t\", _a))\n        if sz % queue.device.memalign != 0:\n            sz += queue.device.memalign - (sz % queue.device.memalign)\n            a = cl.ffi.cast(\"float*\", sz)\n        else:\n            a = _a\n        _b = cl.ffi.new(\"float[]\", N + queue.device.memalign)\n        sz = int(cl.ffi.cast(\"size_t\", _b))\n        if sz % queue.device.memalign != 0:\n            sz += queue.device.memalign - (sz % queue.device.memalign)\n            b = cl.ffi.cast(\"float*\", sz)\n        else:\n            b = _b\n        c = cl.ffi.new(\"float[]\", 1)\n        c[0] = 1.2345\n        d = cl.ffi.new(\"float[]\", N)\n        sz = cl.ffi.sizeof(d)\n        for i, t in enumerate(d):\n            a[i] = math.sin(i)\n            b[i] = math.cos(i)\n            d[i] = a[i] + b[i] * c[0]\n        a_copy = cl.ffi.new(\"float[]\", N)\n        a_copy[0:N] = a[0:N]\n\n        # Create buffers\n        a_ = ctx.create_buffer(cl.CL_MEM_READ_WRITE | cl.CL_MEM_USE_HOST_PTR,\n                               a, size=sz)\n        b_ = ctx.create_buffer(cl.CL_MEM_READ_WRITE | cl.CL_MEM_USE_HOST_PTR,\n                               b, size=sz)\n\n        # Set kernel arguments\n        krn.set_arg(0, a_)\n        krn.set_arg(1, b_)\n        krn.set_arg(2, cl.ffi.cast(\"const void*\", c), cl.ffi.sizeof(c))\n\n        # Execute kernel\n        global_size = [N]\n        local_size = None\n        queue.execute_kernel(krn, global_size, local_size, need_event=False)\n\n        # Get results back from the device by map_buffer\n        ev, ptr = queue.map_buffer(a_, cl.CL_MAP_READ, sz)\n        del ev\n        ev = queue.unmap_buffer(a_, ptr)\n        ev.wait()\n        mx = 0\n        for i, t in enumerate(d):\n            mx = max(mx, math.fabs(a[i] - t))\n        self.assertLess(mx, 0.0001, \"Incorrect result after map_buffer\")\n\n        # Get results back from the device by read_buffer\n        aa = cl.ffi.new(\"float[]\", N)\n        queue.read_buffer(a_, aa, size=sz)\n        mx = 0\n        for i, t in enumerate(d):\n            mx = max(mx, math.fabs(aa[i] - t))\n        self.assertLess(mx, 0.0001, \"Incorrect result after read_buffer\")\n\n        # Refill buffer with stored copy by map_buffer with event\n        ev, ptr = queue.map_buffer(\n            a_, cl.CL_MAP_WRITE if queue.device.version < 1.1999\n            else cl.CL_MAP_WRITE_INVALIDATE_REGION, sz,\n            blocking=False, need_event=True)\n        ev.wait()\n        a[0:N] = a_copy[0:N]\n        ev = queue.unmap_buffer(a_, ptr)\n\n        # Execute kernel\n        ev = queue.execute_kernel(krn, global_size, local_size, wait_for=(ev,))\n        # Get results back from the device by map_buffer\n        ev, ptr = queue.map_buffer(a_, cl.CL_MAP_READ, sz,\n                                   wait_for=(ev,), need_event=True)\n        ev.wait()\n        ev = queue.unmap_buffer(a_, ptr)\n        ev.wait()\n        mx = 0\n        for i, t in enumerate(d):\n            mx = max(mx, math.fabs(a[i] - t))\n        self.assertLess(mx, 0.0001, \"Incorrect result after map_buffer\")\n\n        # Refill buffer with stored copy by write_buffer\n        ev = queue.write_buffer(a_, a_copy, size=sz,\n                                blocking=False, need_event=True)\n\n        # Execute kernel\n        ev = queue.execute_kernel(krn, global_size, local_size, wait_for=(ev,))\n        # Get results back from the device by map_buffer\n        ev, ptr = queue.map_buffer(a_, cl.CL_MAP_READ, sz,\n                                   wait_for=(ev,), need_event=True)\n        ev.wait()\n        ev = queue.unmap_buffer(a_, ptr)\n        ev.wait()\n        mx = 0\n        for i, t in enumerate(d):\n            mx = max(mx, math.fabs(a[i] - t))\n        self.assertLess(mx, 0.0001, \"Incorrect result after map_buffer\")\n\n        del _b\n        del _a\n\n    def test_event_profiling(self):\n        import numpy\n        # Create platform, context, program, kernel and queue\n        platforms = cl.Platforms()\n        ctx = platforms.create_some_context()\n        prg = ctx.create_program(self.src_test, self.include_dirs)\n        krn = prg.get_kernel(\"test\")\n        queue = ctx.create_queue(ctx.devices[0], cl.CL_QUEUE_PROFILING_ENABLE)\n\n        # Create arrays with some values for testing\n        a = numpy.arange(100000, dtype=numpy.float32)\n        b = numpy.cos(a)\n        a = numpy.sin(a)\n        c = numpy.array([1.2345], dtype=numpy.float32)\n\n        # Create buffers\n        a_ = ctx.create_buffer(cl.CL_MEM_READ_WRITE | cl.CL_MEM_COPY_HOST_PTR,\n                               a)\n        b_ = ctx.create_buffer(cl.CL_MEM_READ_ONLY | cl.CL_MEM_COPY_HOST_PTR,\n                               b)\n\n        # Set kernel arguments\n        krn.set_arg(0, a_)\n        krn.set_arg(1, b_)\n        krn.set_arg(2, c[0:1])\n\n        # Execute kernel\n        ev = queue.execute_kernel(krn, [a.size], None)\n        ev.wait()\n\n        try:\n            vles, errs = ev.get_profiling_info()\n            self.assertEqual(vles, ev.profiling_values)\n            self.assertEqual(errs, ev.profiling_errors)\n        except cl.CLRuntimeError:\n            pass\n        for name, vle in ev.profiling_values.items():\n            err = ev.profiling_errors[name]\n            self.assertTrue((vle and not err) or (not vle and err))\n            self.assertEqual(type(vle), float)\n            self.assertEqual(type(err), int)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","sub_path":"tests/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":17687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"253376274","text":"class Solution:\n    def partition(self, nums, p, r):\n        l = p - 1\n        while p < r:\n            if nums[p] >= nums[r]:\n                l += 1\n                nums[l], nums[p] = nums[p], nums[l]\n            p += 1\n        nums[l + 1], nums[r] = nums[r], nums[l + 1]\n        return l + 1\n\n\n    # def quickSort(self, nums, s, e):\n    #     if s < e:\n    #         mid = self.partition(nums, s, e)\n    #         self.quickSort(nums, s, mid - 1)\n    #         self.quickSort(nums, mid + 1, e)\n\n\n    def findKthLargest(self, nums, k):\n        if nums:\n            pos = self.partition(nums, 0, len(nums) - 1)\n            if k < pos + 1:\n                return self.findKthLargest(nums[:pos], k)\n            elif k > pos + 1:\n                return self.findKthLargest(nums[pos + 1:], k - pos - 1)\n            else:\n                return nums[pos]\n\nif __name__ == \"__main__\":\n    s_instance = Solution()\n    nums = [3, 2, 1, 5, 6, 6, 4]\n    print(s_instance.findKthLargest(nums, 4))\n","sub_path":"algorithm/Kth Largest Element in An Array/demo1.py","file_name":"demo1.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"12291919","text":"#Code For City 02: Jaipur\nimport random\ndef randomSolution(tsp):\n    cities = list(range(len(tsp)))\n    solution = []\n    for i in range (len(tsp)):\n        randomCity = cities[random.randint(0,len(cities)-1)]\n        solution.append(randomCity)\n        cities.remove(randomCity)\n        \n        \n    return solution\n\ndef routeLen(tsp,solution):\n    routelen =0\n    for i in range(len(solution)):\n        routelen += tsp[solution[i-1]] [solution[i]]\n        \n    return routelen  \n    \n\ndef getneibours(solution):\n    neibours =[]\n    length =[]\n    for i in range (len(solution)):\n        for j in range (i+1,len(solution)):\n            neibour = solution.copy()\n            neibour[i] = solution[j]\n            neibour[j] =solution[i]\n            neibours.append(neibour)\n            \n    return neibours      \n        \n\ndef getsoln(tsp,neibours):\n    bestlen =routeLen(tsp,neibours[0])\n    bestsoln = neibours[0]\n    for neibour in neibours:\n        \n        currentroutelen =routeLen(tsp,neibour)\n        if currentroutelen <bestlen:\n            \n            bestlen = currentroutelen\n            bestsoln =neibour\n                \n    return bestsoln,bestlen    \n    \n    \n    \n    \n\ndef hillclimbing(tsp):\n    print(\"****Welcome To Jaipur****\")\n    print(\"Top 5 places for Sightseeging in a day are:[0->Amber Fort,1->Nahargarh Fort,2->Hawa Mahal,3->Jal Mahal,4->Jantar Mantar ]\")\n    print(\"Current Optimal(Random) Solution: \")\n    currentsoln = randomSolution(tsp)\n    print(currentsoln)\n    currentroutelen =routeLen(tsp,currentsoln)\n    print(\"Total Distance:\")\n    print(currentroutelen)\n    neibour =getneibours(currentsoln)\n    print(\"All possible Ways for Sightseeing are: \")\n    print(neibour)\n    bestsolution ,bestsolutionlen  = getsoln(tsp,neibour)\n    \n    while bestsolutionlen <currentroutelen:\n        currentsoln = bestsolution\n        currentroutelen = bestsolutionlen\n        bestsolution ,bestsolutionlen  = getsoln(tsp,neibour)\n        print(\"Best Optimal Order for Sightseeing is:\")\n    return  currentsoln,currentroutelen                                \n        \n                                                     \n        \n                  \n                  \ndef main():\n    Data = [ [0,10,8,3,9],\n            [10,0,14,10,14],\n            [8,14,0,5,2],\n            [3,10,5,0,4],\n            [9,14,2,4,0]\n           ]\n    print(hillclimbing(Data))\n    print(\"***Hope you will Enjoy alot***\")\n    \n    \nif __name__ == \"__main__\":\n    main()\n    \n","sub_path":"City02:Jaipur.py","file_name":"City02:Jaipur.py","file_ext":"py","file_size_in_byte":2478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"108710129","text":"import re\nimport requests\nfrom time import sleep\n\nfrom django.conf import settings\n\nfrom common.functions import make_phone\nfrom .interfaces import AbstractSourceRepository\nfrom ..framework import models\nfrom ..adapters.db_repositories import LogDbRepo, TaskDbRepo\nfrom apps.applicants.framework.models import Applicant as ApplicantModel\nfrom apps.applicants.framework.models import Resume as ResumeModel\n\n\nclass ParseZpResumesUsecase:\n\n    SOURCE_ID = 'zp'\n    TASK = None\n\n    def __init__(self, db_repo: AbstractSourceRepository, task_id: int):\n        self.db = db_repo\n        self.task_id = task_id\n        self.TASK = self.db.get_source_task_by_id_and_source(task_id=self.task_id, source_id=self.SOURCE_ID)\n        self.log = LogDbRepo(self.TASK)\n\n    def execute(self):\n\n        headers = {\n            'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.13; rv:60.0) Gecko/20100101 Firefox/60.0',\n            'Accept': '*/*',\n            'Accept-Language': 'ru-RU,ru;q=0.8,en-US;q=0.5,en;q=0.3',\n            'Accept-Encoding': 'gzip, deflate, br',\n            'Referer': 'https://hr.zarplata.ru/?',\n            'content-type': 'application/json; charset=UTF-8',\n            'origin': 'https://hr.zarplata.ru',\n        }\n\n        params = {\n            'login': settings.SOURCE_ZP_USERNAME,\n            'password': settings.SOURCE_ZP_PASSWORD,\n        }\n\n        source_url = 'https://hr.zarplata.ru/resumes?{}'.format(self.TASK.query_uri)\n\n        self.log.mark_begin(url=source_url, source=self.TASK.source_id)\n\n        try:\n            self._proceed(headers=headers, params=params, source_url=source_url)\n        except StopIteration:\n            self.log.mark_task_ok()\n        except Exception as e:\n            self.log.mark_task_failed(message=e.__str__())\n            raise e\n        else:\n            self.log.mark_task_ok()\n\n    def _proceed(self, headers: dict, params: dict, source_url: str):\n        with requests.Session() as session:\n            #\n            # auth\n            #\n\n            r = session.post('https://auth.zarplata.ru/v1/login', headers=headers, json=params)\n            token = r.cookies.get('zp_auth')\n\n            if not token or r.status_code != 200:\n                raise Exception('Failed to authenticate')\n\n            sleep(1)\n\n            #\n            # get resumes list\n            #\n\n            headers['Referer'] = source_url\n            headers['authorization'] = 'token {}'.format(token)\n\n            get_params = {\n                **self.TASK.uri_params,\n                'fields[]': ['*', 'access_status'],\n            }\n\n            url = 'https://api.zp.ru/v1/resumes/'\n\n            for data in self._get_resumes_list(url=url, params=get_params, headers=headers, session=session):\n                for resume in data['resumes']:\n                    self._handle_resume(resume=resume)\n\n    def _handle_resume(self, resume: dict):\n        resume_id = resume.get('id')\n        resume_name = resume.get('header')\n        resume_url = 'https://hr.zarplata.ru/resumes/{}'.format(resume_id)\n\n        self.log.mark_item_started(resume_name=resume_name, source_resume_id=resume_id, url=resume_url)\n\n        try:\n            self._check_if_already_processed(resume_id=resume_id)\n\n            if not resume['contact']['phones']:\n                #todo: log\n                return\n\n            applicant = self._detect_applicant(resume=resume)\n            if not applicant:\n                self.log.mark_item_failed(url=resume_url, resume_name=resume_name,\n                                          message='Нет номера телефона у соискателя')\n                return\n\n            resume = ResumeModel(applicant_id=applicant.id,\n                                 source='ZP',\n                                 source_id=resume_id,\n                                 name=resume_name,\n                                 experience=self._detect_experience(resume),\n                                 cover_letter=self._detect_cover_letter(resume),\n                                 education_desc=self._detect_education(resume),\n                                 education_type=self._detect_education_type(resume),\n                                 known_languages=self._detect_languages(resume),\n                                 salary_from=self._detect_salary_from(resume),\n                                 salary_to=self._detect_salary_from(resume),\n                                 gender=self._detect_gender(resume),\n                                 )\n            resume.save()\n        except StopIteration:\n            self.log.mark_item_failed(url=resume_url, resume_name=resume_name,\n                                      message='Это резюме уже ранее сохранено в базу')\n            raise\n        except Exception as e:\n            self.log.mark_item_failed(url=resume_url, resume_name=resume_name, message=e.__str__())\n            raise e\n        else:\n            self.log.mark_item_ok(url=resume_url, resume_name=resume_name, resume_id=resume_id)\n\n    def _save_resume_scan_log(self, resume_id):\n        model = models.TaskLog(task_id=self.TASK.id, resume_id=resume_id)\n        model.save()\n\n    def _detect_gender(self, resume):\n        return resume.get('sex', 'male')\n\n    def _detect_salary_to(self, resume):\n        # no data in source\n        return None\n\n    def _detect_salary_from(self, resume):\n        string = re.sub('[^\\d]', '', resume['salary'])\n        return int(string) if string else None\n\n    def _detect_languages(self, resume):\n        # not found lang info in source yet\n        return None\n\n    def _detect_education_type(self, resume):\n        try:\n            education_id = resume['education']['id']\n            types_map = dict((\n                (388, 'MID_SCHOOL'),\n                (389, 'MID_SCHOOL'),\n                (390, 'VOCATIONAL'),\n                (391, 'UNCOMPLETE_HIGHER'),\n                (392, 'HIGHER'),\n                (393, 'MULTI_HIGHER'),\n                (3008, 'FULLDAY_EDU'),\n                (3028, 'PART_TIME_EDU'),\n            ))\n            return types_map[education_id]\n        except (KeyError, TypeError):\n            return None\n\n    def _detect_education(self, resume):\n        try:\n            string = ''\n            for row in resume['institutions']:\n                string += row['institution']['title'] + '\\n'\n                spec = 'специальность: ' + row['speciality']['title'] if row['speciality']['title'] else ''\n                string += spec + '\\n'\n                date_from = row['date']['from']['date'] if row['date']['from']['date'] else '-'\n                string += 'с ' + date_from\n                date_to = row['date']['to']['date'] if row['date']['to']['date'] else '-'\n                string += 'по ' + date_to + '\\n'\n            return string\n        except TypeError:\n            return None\n\n    def _detect_cover_letter(self, resume):\n        return resume.get('personal_qualities')\n\n    def _detect_experience(self, resume):\n        return ''\n\n    def _process_detect_experience(self, resume):\n        batches = []\n\n        if resume.get('skills'):\n            batches.append(resume.get('skills'))\n\n        for i in resume['jobs']:\n            string = i['position']['title'] + '\\n'\n            string += '{} ({})\\n'.format(i['company']['title'], i['city']['title'])\n            string += i['description'] + '\\n'\n            string += 'с {} '.format(str(i['date']['from']['date']))\n            till = i['date']['to']['date']\n            if till is None:\n                string += 'по настоящее время\\n'\n            else:\n                string += 'по {}'.format(i['date']['to']['date'])\n\n            batches.append(string)\n\n        return '\\n\\n'.join(batches)\n\n    def _detect_applicant(self, resume):\n        clean_phone = make_phone(phone=resume['contact']['phones'][0]['phone'])\n        if not clean_phone:\n            return None\n        applicant, _ = ApplicantModel.objects.get_or_create(phone=clean_phone, defaults={\n            'name_first': resume['contact']['firstname'],\n            'name_middle': resume['contact']['patronymic'],\n            'name_last': resume['contact']['lastname'],\n            'email': resume['contact']['email'],\n            'skype': resume['contact']['skype'],\n            'birth_day': resume['birthday'],\n            'source': self.TASK.source_id,\n        })\n        return applicant\n\n    def _check_if_already_processed(self, resume_id: str):\n        # If a resume has already been processed, there is not need in continuing\n        check = models.TaskLog.objects.filter(task_id=self.TASK.id, resume_id=resume_id).first()\n        if check:\n            raise StopIteration\n\n    def _get_resumes_list(self, url: str, params: dict, headers: dict, session, limit: int=25, offset: int=0):\n\n        self.log.mark_list_started(url=url)\n\n        try:\n            response = session.get(url, headers=headers, params=params)\n            data = response.json()\n        except Exception as e:\n            self.log.mark_list_failed(message='Ошибка при обращении к источнику')\n            raise e\n        else:\n            self.log.mark_list_ok(url=url)\n\n        # with open('zp_list_{}.json'.format(offset), 'w') as file:\n        #     file.write(json.dumps(data, ensure_ascii=False, indent=4))\n\n        yield data\n\n        new_offset = offset + limit\n        if new_offset < int(data['metadata']['resultset']['count']):\n            params['limit'] = limit\n            params['offset'] = new_offset\n            yield from self._get_resumes_list(url=url, params=params, headers=headers, session=session,\n                                              limit=limit, offset=new_offset)\n","sub_path":"apps/parsing/usecases/zp_resumes.py","file_name":"zp_resumes.py","file_ext":"py","file_size_in_byte":9757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"249177328","text":"from tkinter import *\n\nWINDOW_SIZE = 600\nTILES = 15\nCELL_SIZE = WINDOW_SIZE / TILES\nGRID_LINE_WIDTH = 2\nSYMBOL_SIZE = 2 / (TILES*1.5)\nSYMBOL_WIDTH = WINDOW_SIZE / (TILES*2)\n\nBG_COLOR = 'white'\nTITLE_COLOR = 'black'\nFONT = 'Franklin Gothic'\nGRID_COLOR = 'light grey'\nX_COLOR = 'dodger blue'\nO_COLOR = 'firebrick1'\nDRAW_SCREEN_COLOR = 'light sea green'\n\nEMPTY = 0\nX = 1\nO = 2\n\nFIRST_PLAYER = 1\n\nSTATE_TITLE_SCREEN = 0\nSTATE_X_TURN = 1\nSTATE_O_TURN = 2\nSTATE_GAME_OVER = 3\n\n\nclass Game(Tk):\n    def __init__(self):\n        Tk.__init__(self)\n        self.canvas = Canvas(height=WINDOW_SIZE, width=WINDOW_SIZE, bg=BG_COLOR)\n        self.canvas.pack()\n\n        self.canvas.bind('<Button-1>', self.click)\n        self.bind('<r>', self.restart)\n        self.bind('<q>', self.exit)\n\n        self.gamestate=STATE_TITLE_SCREEN\n        self.title_screen()\n\n        self.board=[[EMPTY] * TILES for i in range(TILES)]\n\n    def title_screen(self):\n        self.canvas.delete('all')\n        self.canvas.create_rectangle(0, 0, WINDOW_SIZE, WINDOW_SIZE, fill=TITLE_COLOR, outline='')\n        self.canvas.create_text(WINDOW_SIZE/2, WINDOW_SIZE/3, text='TIC TAC TOE', fill=BG_COLOR, font=(FONT, int(-WINDOW_SIZE/12), 'bold'))\n        self.canvas.create_text(int(WINDOW_SIZE/2), int(WINDOW_SIZE/2.5), text='[play]', fill=BG_COLOR, font=(FONT, int(-WINDOW_SIZE/25)))\n        self.canvas.create_text(int(WINDOW_SIZE/2), int(WINDOW_SIZE/1.25), text='first move:', fill=BG_COLOR, font=(FONT, int(-WINDOW_SIZE/25)))\n        self.canvas.create_text(int(WINDOW_SIZE/2), int(WINDOW_SIZE/1.15), text=('X' if FIRST_PLAYER==1 else 'O'), \n            fill=X_COLOR if FIRST_PLAYER==1 else O_COLOR, font=(FONT, int(-WINDOW_SIZE/12)))\n\n    def new_board(self):\n        self.canvas.delete('all')\n\n        self.board=[[EMPTY] * TILES for i in range(TILES)]\n\n        for n in range(1, TILES):\n            # vertikálně\n            self.canvas.create_line(CELL_SIZE*n, 0, CELL_SIZE*n, WINDOW_SIZE, width=GRID_LINE_WIDTH, fill=GRID_COLOR)\n            # horizontálně\n            self.canvas.create_line(0, CELL_SIZE*n, WINDOW_SIZE, CELL_SIZE*n, width=GRID_LINE_WIDTH, fill=GRID_COLOR)\n\n    def gameover_screen(self, result):\n        self.canvas.delete('all')\n        if result == 'X WINS':\n            result_text = 'X wins'\n            result_color = X_COLOR\n        elif result == 'O WINS':\n            result_text = 'O wins'\n            result_color = O_COLOR\n        elif result == 'DRAW':\n            result_text = 'Draw'\n            result_color = DRAW_SCREEN_COLOR\n\n        self.canvas.create_rectangle(0, 0, WINDOW_SIZE, WINDOW_SIZE, fill=result_color, outline='')\n        self.canvas.create_text(int(WINDOW_SIZE/2), int(WINDOW_SIZE/2), text=result_text, fill=BG_COLOR, font=(FONT, int(-WINDOW_SIZE/6), 'bold'))\n        self.canvas.create_text(int(WINDOW_SIZE/2), int(WINDOW_SIZE/1.65), text='[click to play again]', fill=BG_COLOR, font=(FONT, int(-WINDOW_SIZE/25)))\n\n    # Logika hry\n    def click(self, event):\n        x = self.pixels_to_grid(event.x)\n        y = self.pixels_to_grid(event.y)\n\n        if self.gamestate == STATE_TITLE_SCREEN:\n            self.new_board()\n            self.gamestate = FIRST_PLAYER\n\n        elif (self.gamestate == STATE_X_TURN and self.board[y][x] == EMPTY):\n            self.new_move(X, x, y)\n\n            if self.has_won(X):\n                self.gamestate = STATE_GAME_OVER\n                self.gameover_screen('X WINS')\n            elif self.is_a_draw():\n                self.gamestate = STATE_GAME_OVER\n                self.gameover_screen('DRAW')\n            else:\n                self.gamestate = STATE_O_TURN\n\n        elif (self.gamestate == STATE_O_TURN and self.board[y][x] == EMPTY):\n            self.new_move(O, x, y)\n\n            if self.has_won(O):\n                self.gamestate = STATE_GAME_OVER\n                self.gameover_screen('O WINS')\n            elif self.is_a_draw():\n                self.gamestate = STATE_GAME_OVER\n                self.gameover_screen('DRAW')\n            else:\n                self.gamestate = STATE_X_TURN\n\n        elif self.gamestate == STATE_GAME_OVER:\n            self.new_board()\n            self.gamestate = FIRST_PLAYER\n\n        for i in self.board:\n            print(i)\n        print(\"---\")\n\n    # Logické vykonání tahu\n    def new_move(self, player, grid_x, grid_y):\n        if player == X:\n            self.board[grid_y][grid_x] = X\n            self.draw_X(grid_x, grid_y)\n\n        elif player == O:\n            self.board[grid_y][grid_x] = O\n            self.draw_O(grid_x, grid_y)\n\n    # Vykreslení daného symbolu do buňky, kterou hráč označil\n    def draw_X(self, grid_x, grid_y):\n        x = self.grid_to_pixels(grid_x)\n        y = self.grid_to_pixels(grid_y)\n        delta = CELL_SIZE/2*SYMBOL_SIZE\n\n        self.canvas.create_line(x-delta, y-delta, x+delta, y+delta, width=SYMBOL_WIDTH, fill=X_COLOR)\n        self.canvas.create_line(x+delta, y-delta, x-delta, y+delta, width=SYMBOL_WIDTH, fill=X_COLOR)\n\n    def draw_O(self, grid_x, grid_y):\n        x = self.grid_to_pixels(grid_x)\n        y = self.grid_to_pixels(grid_y)\n        delta = CELL_SIZE/2*SYMBOL_SIZE\n\n        self.canvas.create_oval(x-delta, y-delta, x+delta, y+delta, width=SYMBOL_WIDTH, outline=O_COLOR)\n\n    # Logika ukončení hry (výhra, remíza)\n    def has_won(self, symbol):\n        for x in range(TILES):\n            for y in range(TILES-4):\n                if self.board[x][y] == self.board[x][y+1] == self.board[x][y+2] == self.board[x][y+3] == self.board[x][y+4] == symbol:\n                    return True\n\n        for y in range(TILES):\n            for x in range(TILES-4):\n                if self.board[x][y] == self.board[x+1][y] == self.board[x+2][y] == self.board[x+3][y] == self.board[x+4][y] == symbol:\n                    return True\n        \n        for x in range(TILES-4):\n            for y in range(TILES-4):\n                if self.board[x][y] == self.board[x+1][y+1] == self.board[x+2][y+2] == self.board[x+3][y+3] == self.board[x+4][y+4] == symbol:\n                    return True\n        \n        for x in range(TILES-4):\n            for y in range(TILES-4):\n                if self.board[x][y+4] == self.board[x+1][y+3] == self.board[x+2][y+2] == self.board[x+3][y+1] == self.board[x+4][y] == symbol:\n                    return True\n        \n        return False\n\n    def is_a_draw(self):\n        for row in self.board:\n            if EMPTY in row:\n                return False\n        return True\n\n    # Funkce umožňující detekci buňky, na kterou zrovna hráč klikl\n    def pixels_to_grid(self, pixel_coord):\n        if pixel_coord >= WINDOW_SIZE:\n            pixel_coord = WINDOW_SIZE - 1    \n\n        grid_coord = int(pixel_coord / CELL_SIZE)\n        return grid_coord\n\n    # Funkce sloužící k úmístění symbolu do středu buňky\n    def grid_to_pixels(self, grid_coord):\n        pixel_coord = grid_coord * CELL_SIZE + CELL_SIZE / 2\n        return pixel_coord\n\n    def restart(self, event):\n        self.gamestate=STATE_TITLE_SCREEN\n        self.title_screen()\n\n    def exit(self, event):\n        self.destroy()\n\ndef main():\n    root = Game()\n    root.mainloop()\n\nmain()","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"614207568","text":"from flask import Flask, render_template, request, json\nimport re\napp = Flask(__name__)\n\ndef isNumber(user_input):\n\tit_is = False\n\ttry:\n\t\tfloat(user_input)\n\t\tit_is = True\n\texcept ValueError:\n\t\tit_is = False\n\treturn it_is\n\ndef swap(arr):\n\taux = 0\n\tn =len(arr)\n\ti = 0\n\tj = n-1\n\n\twhile i < n:\n\t\tif i < j:\n\t\t\taux = arr[i]\n\t\t\tarr[i] = arr[j]\n\t\t\tarr[j] = aux\n\t\tj = j - 1\n\t\ti = i + 1\n\n\treturn arr\n\ndef amplificar(arr,amp):\n\taux = 0\n\tret = []\n\tn =len(arr)\n\ti = 0\n\n\twhile i < n:\n\t\tret.append(float(arr[i])*amp)\n\t\ti = i + 1\n\n\treturn ret\n\n#Donde arr1 y arr2 son arrays, c1 y c2 son los ceros donde comienza cada array y n es la veces que se realizara la suma\ndef suma(arr1,arr2,c1,c2,n):\n\tarrRes=[]\n\ti=0\n\tr=c1-c2\n\tarrLen1 = len(arr1)\n\tarrLen2 = len(arr2)\n\twhile i<n:\n\t\tif i < r:\n\t\t\tarrRes.append(float(arr1[i]))\n\t\telse:\n\t\t\tif i < arrLen1 and i-r < arrLen2:\n\t\t\t\t\n\t\t\t\tarrRes.append(float(arr1[i])+float(arr2[i-r]))\n\t\t\telse:\n\t\t\t\tif i >= arrLen2:\n\t\t\t\t\tarrRes.append(float(arr1[i]))\n\t\t\t\telse:\n\t\t\t\t\tarrRes.append(float(arr2[i-r]))\n\t\ti=i+1\n\treturn arrRes\n\ndef resta(arr1,arr2,c1,c2,n):\n\tarrRes=[]\n\ti=0\n\tr=c1-c2\n\trAbs=0\n\tif r<0:\n\t\trAbs=abs(r)\n\tarrLen1 = len(arr1)\n\tarrLen2 = len(arr2)\n\n\twhile i<n:\n\t\tif i < r:\n\t\t\tarrRes.append(float(arr1[i]))\n\t\telse:\n\t\t\tif r < 0:\n\t\t\t\tif i < rAbs:\n\t\t\t\t\tarrRes.append(float(arr2[i])*(-1))\n\t\t\t\telse:\n\t\t\t\t\tif i-rAbs < arrLen1:\n\t\t\t\t\t\tarrRes.append(float(arr1[i-rAbs])-float(arr2[i]))\n\t\t\t\t\telse:\n\t\t\t\t\t\tarrRes.append(float(arr2[i])*(-1))\n\t\t\telse:\n\t\t\t\tif i < arrLen1 and i-rAbs < arrLen2:\n\t\t\t\t\tarrRes.append(float(arr1[i])-float(arr2[i-rAbs]))\n\t\t\t\telse:\n\t\t\t\t\tif i >= arrLen2:\n\t\t\t\t\t\tarrRes.append(float(arr1[i]))\n\t\t\t\t\telse:\n\t\t\t\t\t\tarrRes.append(float(arr2[i-rAbs])*(-1))\n\t\ti=i+1\n\treturn arrRes\n\n#Donde arr1 y arr2 son arrays, c1 y c2 son los ceros donde comienza cada array y n es la veces que se realizara la suma\ndef multi(arr1,arr2,c1,c2,n):\n\tarrRes=[]\n\ti=0\n\tr=c1-c2\n\tarrLen1 = len(arr1)\n\tarrLen2 = len(arr2)\n\n\twhile i<n:\n\t\tif i < r:\n\t\t\tarrRes.append(float(0))\n\t\telse:\n\t\t\tif i < arrLen1 and i-r < arrLen2:\n\t\t\t\tarrRes.append(float(arr1[i])*float(arr2[i-r]))\n\t\t\telse:\n\t\t\t\tarrRes.append(float(0))\n\t\ti=i+1\n\treturn arrRes\n\ndef division(arr1,arr2,c1,c2,n):\n\tarrRes=[]\n\ti=0\n\tr=c1-c2\n\trAbs=0\n\tif r<0:\n\t\trAbs=abs(r)\n\tarrLen1 = len(arr1)\n\tarrLen2 = len(arr2)\n\taux=0\n\n\twhile i<n:\n\t\tif i < r:\n\t\t\tarrRes.append(0)\n\t\telse:\n\t\t\tif r < 0:\n\t\t\t\tif i < rAbs:\n\t\t\t\t\tarrRes.append(0)\n\t\t\t\telse:\n\t\t\t\t\tif i-rAbs < arrLen1:\n\t\t\t\t\t\tarrRes.append(float(arr1[i-rAbs])/float(arr2[i]))\n\t\t\t\t\telse:\n\t\t\t\t\t\tarrRes.append(0)\n\t\t\telse:\n\t\t\t\tif i < arrLen1 and i-rAbs < arrLen2:\n\t\t\t\t\tarrRes.append(float(arr1[i])/float(arr2[i-rAbs]))\n\t\t\t\telse:\n\t\t\t\t\tarrRes.append(float(0))\n\t\ti=i+1\n\treturn arrRes\n\ndef desplazar(arr,d):\n\ti = 0\n\tret = [float(a) for a in arr]\n\tif d > 0:\n\t\twhile i < d:\n\t\t\tret.append(0)\n\t\t\ti = i + 1\n\telse:\n\t\twhile i > d:\n\t\t\tret.insert(0,0)\n\t\t\ti = i - 1\n\treturn ret\n\ndef diezmar(arr,c,d):\n\ti = 0\n\tn = len(arr)\n\tarrRes = []\n\taux = 0\n\twhile i < n:\n\t\taux = d*i\n\t\tif i < c:\t\n\t\t\tif c - aux > 0:\n\t\t\t\tarrRes.append(float(arr[c - int(aux)]))\n\t\t\telse:\n\t\t\t\tarrRes.append(0)\n\t\telse:\n\t\t\tif i == c:\n\t\t\t\tarrRes.append(float(arr[i]))\n\t\t\telse:\n\t\t\t\tif c + aux < n:\n\t\t\t\t\tarrRes.append(float(arr[c + int(aux)]))\n\t\t\t\telse:\n\t\t\t\t\tarrRes.append(0)\n\t\ti = i + 1\n\treturn arrRes\ndef convolucionar(arr1,arr2):\n\tcLen = len(arr1) + len(arr2) - 1\n\tauxLen1 = 0\n\tres = []\n\tconv = []\n\taux = []\n\twhileAux = 0\n\ti = 0\n\tj = 0\n\n\n\tfor a in arr1:\n\t\taux = []\n\t\tfor b in arr2:\n\t\t\taux.append(float(a)*float(b))\n\t\tconv.append(aux)\n\n\tauxLen1 = len(conv)\n\twhile i < cLen:\n\n\t\twhileAux = 0\n\t\tj = 0\n\t\twhile j <= i:\n\t\t\tif j < auxLen1:\n\t\t\t\tif i-j < len(conv[j]):\n\t\t\t\t\twhileAux += conv[j][i-j]\n\t\t\tj += 1\n\n\t\tres.append(whileAux)\n\t\ti += 1\n\treturn res\n\ndef interpolar(arr,c,n):\n\tres = []\n\tj=0\n\tk=0\n\n\tfor a in arr:\n\t\ti = 0\n\t\tres.append(float(a))\n\t\twhile i < n-1:\n\t\t\tres.append(float(a))\n\t\t\ti += 1\n\t\t\tif k < c:\n\t\t\t\tj += 1\n\t\tk += 0\n\treturn res,j\n\n\"\"\"\ndef interpolar(arr,c,n):\n\tres = []\n\tj=0\n\tk=0\n\taux=0\n\n\tfor a in arr:\n\t\tni = float(a)\n\t\tnf = float(arr[k+1])\n\t\taux = (abs(ni)-abs(nf))/n\n\t\tres.append(ni)\n\n\t\ti = 0\n\t\twhile i < n-1:\n\t\t\tif ni > nf:\n\t\t\t\tres.append(ni-(aux*(i+1)))\n\t\t\telse:\n\t\t\t\tres.append(ni+(aux*(i+1)))\n\n\t\t\ti += 1\n\t\t\tif k < c:\n\t\t\t\tj += 1\n\t\tk += 0\n\treturn res,j\n\"\"\"\n\t\t\n@app.route(\"/\")\ndef main():\n    return render_template('index.html')\n\n@app.route('/showSignUp')\ndef showSignUp():\n    return render_template('signup.html')\n\n@app.route('/signUp',methods=['POST'])\ndef signUp():\n\t# read the posted values from the UI\n\t_secuencia1 = request.form['secuencia1']\n\t_secuencia2 = request.form['secuencia2']\n\t_amp = request.form['amp']\n\t_desp = request.form['desp']\n\t_diezmar = request.form['diezmar']\n\t_interp = request.form['interp']\n\n\tcero1=[]\n\tcero2=[]\n\tsu = []\n\tmult = []\n\tceroR=0\n\tauxLen=0\n\tceroAux=0\n\ti=0\n \n\t# validate the received values\n\tif _secuencia1 and _secuencia2:\n\t\ti=0\n\t\tsecuencia2 = _secuencia2.split(\",\")\n\t\tsecuencia1 = _secuencia1.split(\",\")\n\t\tsLen1 = len(secuencia1)\n\t\tsLen2 = len(secuencia2)\n\t\tfor s1 in secuencia1:\n\t\t\tif not isNumber(s1):\n\t\t\t\tc = re.findall(\"[-+]?[.]?[\\d]+(?:,\\d\\d\\d)*[\\.]?\\d*(?:[eE][-+]?\\d+)?\", s1)\n\t\t\t\tsecuencia1[i]=c[0]\n\t\t\t\tcero1=[i,c[0]]\n\t\t\ti=i+1\n\t\t\t\t\n\t\ti=0\n\t\tfor s2 in secuencia2:\n\t\t\tif not isNumber(s2):\n\t\t\t\tc = re.findall(\"[-+]?[.]?[\\d]+(?:,\\d\\d\\d)*[\\.]?\\d*(?:[eE][-+]?\\d+)?\", s2)\n\t\t\t\tsecuencia2[i]=c[0]\n\t\t\t\tcero2=[i,c[0]]\n\t\t\ti=i+1\n\n\t\tif len(cero1) > 0 and len(cero2) > 0:\n\t\t\tif cero1[0]>cero2[0]:\n\t\t\t\tceroR=cero1[0]-cero2[0]\n\t\n\t\t\t\tif sLen1>sLen2:\n\t\t\t\t\tauxLen = sLen1\n\t\t\t\telse:\n\t\t\t\t\tauxLen = (sLen2-cero2[0])+cero1[0]\n\t\t\t\t\n\t\t\t\tsu = suma(secuencia1,secuencia2,cero1[0],cero2[0],auxLen)\n\t\t\t\tmult = multi(secuencia1,secuencia2,cero1[0],cero2[0],auxLen)\n\t\n\t\t\telse:\n\t\t\t\tceroR=cero2[0]-cero1[0]\n\t\n\t\t\t\tif sLen2>sLen1:\n\t\t\t\t\tauxLen = sLen2\n\t\t\t\telse:\n\t\t\t\t\tauxLen = (sLen1-cero1[0])+cero2[0]\n\t\n\t\t\t\tsu = suma(secuencia2,secuencia1,cero2[0],cero1[0],auxLen)\n\t\t\t\tmult = multi(secuencia2,secuencia1,cero2[0],cero1[0],auxLen)\n\n\t\t\t\n\t\t\tdiv1 = division(secuencia1,secuencia2,cero1[0],cero2[0],auxLen)\n\t\t\tdiv2 = division(secuencia2,secuencia1,cero2[0],cero1[0],auxLen)\n\t\t\trest1 = resta(secuencia1,secuencia2,cero1[0],cero2[0],auxLen)\n\t\t\trest2 = resta(secuencia2,secuencia1,cero2[0],cero1[0],auxLen)\n\n\t\t\tamp1 = amplificar(secuencia1,float(_amp))\n\t\t\tamp2 = amplificar(secuencia2,float(_amp))\n\t\t\tdesp1 = desplazar(secuencia1,float(_desp))\n\t\t\tdesp2 = desplazar(secuencia2,float(_desp))\n\t\t\tdiez1 = diezmar(secuencia1,cero1[0],float(_diezmar))\n\t\t\tdiez2 = diezmar(secuencia2,cero2[0],float(_diezmar))\n\t\t\tconv = convolucionar(secuencia1,secuencia2)\n\t\t\tinterp1,ceroI1 = interpolar(secuencia1,cero1[0],float(_interp))\n\t\t\tinterp2,ceroI2 = interpolar(secuencia2,cero2[0],float(_interp))\n\n\t\t\t#NO PONER NINGUNA FUNCION DEBAJO DEL REFLEJO\n\t\t\trefl1 = swap(secuencia1)\n\t\t\trefl2 = swap(secuencia2)\n\t\t\t\n\n\t\t\t#Saca los valores del eje x\n\t\t\tx = []\n\t\t\tx1 = []\n\t\t\tx2 = []\n\t\t\txC = []\n\t\t\txI1 = []\n\t\t\txI2 = []\n\t\t\ti = 0\n\n\t\t\tfor elem in secuencia1:\n\t\t\t\tx1.append(i-cero1[0])\n\t\t\t\ti = i+1\n\t\t\ti = 0\n\t\t\tfor elem in secuencia2:\n\t\t\t\tx2.append(i-cero2[0])\n\t\t\t\ti = i+1\n\n\t\t\ti = 0\n\t\t\tceroConv = cero1[0]+cero2[0]\n\t\t\tfor elem in conv:\n\t\t\t\txC.append(i-ceroConv)\n\t\t\t\ti = i+1\n\n\t\t\ti = 0\n\t\t\tfor elem in interp1:\n\t\t\t\txI1.append(i-ceroI1)\n\t\t\t\ti = i+1\n\n\t\t\ti = 0\n\t\t\tfor elem in interp2:\n\t\t\t\txI2.append(i-ceroI2)\n\t\t\t\ti = i+1\n\n\t\t\tif sLen1 > sLen2:\n\t\t\t\tceroAux = cero1[0]\n\t\t\t\tx = x1\n\t\t\telse:\n\t\t\t\tceroAux = cero2[0]\n\t\t\t\tx = x2\n\n\n\t\t\tsecuencia1 = [float(a) for a in secuencia1]\n\t\t\tsecuencia2 = [float(a) for a in secuencia2]\n\t\t\trefl1 = [float(a) for a in refl1]\n\t\t\trefl2 = [float(a) for a in refl2]\n\t\t\treturn json.dumps({'grafica1':[secuencia1,x1],'grafica2':[secuencia2,x2],'suma':[su,x],'multiplicacion':[mult,x],'division1':[div1,x],'division2':[div2,x],'resta1':[rest1,x],'resta2':[rest2,x],\"reflejo1\":[refl1,x1],\"reflejo2\":[refl2,x2],\"amplificacion1\":[amp1,x1],\"amplificacion2\":[amp2,x2],\"desplazamiento1\":[amp1,x1],\"desplazamiento2\":[amp2,x2],\"desplazamiento1\":[desp1,x1],\"desplazamiento2\":[desp2,x2],\"diezmacion1\":[diez1,x1],\"diezmacion2\":[diez2,x2],\"interpolacion1\":[interp1,xI1],\"interpolacion2\":[interp2,xI2],\"convolucion\":[conv,xC]}) \n\t\telse:\n\t\t\treturn json.dumps({'html':'No ingreso ceros'})\n\telse:\n\t\treturn json.dumps({'html':'No ingreso todos los campos'})\n\nif __name__ == \"__main__\":\n    app.run()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":8208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"163053846","text":"def ler_matriz(ordem):\n\tmat = []\n\tfor i in range(ordem):\n\t\tlinha = []\n\t\tfor j in range(ordem):\n\t\t\tn = int(input(\"Informe matriz[{}][{}]: \".format(i, j)))\n\t\t\tlinha.append(n)\n\t\tmat.append(linha)\n\treturn mat\n\ndef imprime_matriz(matriz, ordem):\n\tfor i in range(ordem):\n\t\tfor j in range(ordem):\n\t\t\tprint(\"{:3}\".format(matriz[i][j]), end=\" \")\n\t\tprint()\n\ndef multiplica_matriz(matriz, num):\n\tfor i in range(4):\n\t\tfor j in range(4):\n\t\t\tmatriz[i][j] *= num \n\n#Principal....\nprint(\"Leitura da matriz:\")\nmatriz = ler_matriz(4)\n\nnum = int(input(\"Informe o número para mutiplicar: \"))\n\nmultiplica_matriz(matriz, num)\n\nprint(\"\\nImpressão da matriz:\")\nimprime_matriz(matriz, 4)\n","sub_path":"material/respostas_exercicios/lista12_2/exe4.py","file_name":"exe4.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"477082108","text":"from collections import OrderedDict\nimport pandas as pd\nimport numpy as np\n\ndef get_securities_in_sector(sector,df,sector_mapping):\n    secs = list(df[df[sector_mapping] == sector].index.values)\n    return [i for n, i in enumerate(secs) if i not in secs[:n]]\n\ndef get_peer_data(sector,df,sector_mapping):\n\n    peer_univ =  get_securities_in_sector(sector,df,sector_mapping)\n    peer_tbl = df.loc[peer_univ,:]\n    return peer_tbl\n\ndef arrange_data_for_gui(security,tbl,mappings):\n    # df to dictionary for easier access\n    sec_tbl = tbl.loc[[security],:].reset_index()\n    tbl_dict = {c:sec_tbl[c].tolist()[0] for c in sec_tbl.columns}\n\n    gui_data = {}\n    gui_data[\"name\"] = tbl_dict[mappings['id_field']]\n    gui_data[\"des\"] = tbl_dict[mappings['description_field']]\n    gui_data[\"industry\"] = tbl_dict[mappings['sector_field']] if tbl_dict[mappings['id_field']] != 0 else 'Not Covered'\n\n    gui_data[\"internal_score\"] = tbl_dict[mappings['internal_score']]\n    gui_data[\"third_party_score\"] = tbl_dict[mappings['third_party_score']]\n    gui_data[\"third_party_score_date\"] = tbl_dict[mappings['third_party_score_date']].strftime('%Y-%m-%d')\n    gui_data[\"tbl_data\"] = sec_tbl.loc[:,mappings['factor_score_fields']]\n    gui_data[\"peer_data\"] = get_peer_data(gui_data[\"industry\"],tbl,mappings['sector_field']).loc[:,mappings['factor_score_fields']].reset_index()\n    return gui_data\n\ndef get_grid_map_data_from_df(df,group1,group2,weight_type):\n    totals = df.groupby(by=[group1,group2]).sum().unstack().stack(dropna=False).fillna(0)\n    return totals.reset_index().pivot(index=group2,columns=group1,values=weight_type)\n\ndef compute_sector_df(dictionary,date,mappings):\n    return dictionary[date][[mappings['group_field'],mappings['total_score_field']]].groupby(mappings['group_field']).median().sort_values(mappings['total_score_field'],ascending=False)[[mappings['total_score_field']]]\n\ndef compute_score_hist_df(score_df,mappings):\n    return score_df.reset_index().pivot_table(index=mappings['id_field'],columns=mappings['date_field'],values=mappings['total_score_field'],aggfunc = max)\n\ndef highlight_positive():\n    return '''function(params){\n                                if (params.value < 0) {\n                                  return {color: 'red'};\n                                } else {\n                                  return {color: 'forestgreen'};\n                                }\n                               }\n            '''\n\ndef highlight_scores():\n    return '''function(params){\n                                if (params.value > 3) {\n                                  return {color: 'red'};\n                                } else if (params.value===3){\n                                  return {color: '#ff6600'};\n                                } else if (params.value === 1){\n                                  return {color:'#93C02D'};\n                                }\n                                  else{\n                                  return{color: 'DarkOrange'};\n                                }\n                            }'''\n","sub_path":"bqdash/.ipynb_checkpoints/calc_functions-checkpoint.py","file_name":"calc_functions-checkpoint.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"44389089","text":"import requests\nfrom bs4 import BeautifulSoup\n\n# https://buzzorange.com/techorange/?s=AR+VR\n# https://buzzorange.com/techorange/page/2/?s=AR+VR\nf = open('D:\\\\source\\\\BD.txt', 'a', encoding='UTF-8')\nk = 1\nlinks = []\nj = 0\nurl = \"https://buzzorange.com/techorange/?s=%E5%A4%A7%E6%95%B8%E6%93%9A\"\nx = 1\nwhile x < 97:\n    res = requests.get(url, headers={\n        \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36\"})\n    res.encoding = 'utf'\n    soup = BeautifulSoup(res.text, \"lxml\")\n    res.close()\n    for souplink in soup.select(\" header > h4\"):\n        links.append(souplink.a['href'])\n        feedres = requests.get(links[j], headers={\n            \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36\"})\n        feedres.encoding = 'utf'\n        soupfeeds = BeautifulSoup(feedres.text, \"lxml\")\n        feedres.close()\n        print(\"value\"+str(k))\n        dfList=\"\"\n        for soupfeed in soupfeeds.select(\n                '#main p'):  # 用for迴圈取 會按照網頁<p>順序依序取出\n            article = soupfeed.get_text(strip=True)\n            article2 = article.rstrip()\n            dfList = dfList + article2\n        f.write(dfList + \"\\n\\n\")\n        j += 1\n        k += 1\n    x += 1\n    url = \"https://buzzorange.com/techorange/page/\" + str(x) + \"/?s=%E5%A4%A7%E6%95%B8%E6%93%9A\"\n    print(\"page\" + str(x))\nf.close()\n","sub_path":"buzzorangeBD.py","file_name":"buzzorangeBD.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"82834458","text":"import os\r\nimport speech_recognition as sr\r\n\r\nr = sr.Recognizer()\r\nwith sr.Microphone() as source:\r\n    print(\"Say something!\")\r\n    audio = r.listen(source)\r\n\r\n    cmd = r.recognize_sphinx(audio)\r\n    print(cmd)\r\n\r\n    if cmd == \"hello\":\r\n    \tprint(\"Hello\")\r\n    if cmd == \"how are you\":\r\n    \tos.system(\"espeak \\\" Fine thanks\\\" \")","sub_path":"bob_controller_jquery/scripts/speech_rec.py","file_name":"speech_rec.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"361179822","text":"import datetime\nimport json\nimport logging\n\nimport boto3\nfrom botocore.exceptions import ClientError\n\nfrom . import alexa\n\n\nENV_NAMES = {\n    'dev': 'dev',\n    'development': 'dev',\n    'stage': 'stage',\n    'prod': 'prod',\n    'production': 'prod'\n}\n\n\nCF_STATES = [\n    # http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/using-cfn-describing-stacks.html#w1ab2c15c17c21c13\n    'CREATE_COMPLETE',\n    'CREATE_IN_PROGRESS',\n    'CREATE_FAILED',\n    'DELETE_COMPLETE',\n    'DELETE_FAILED',\n    'DELETE_IN_PROGRESS',\n    'REVIEW_IN_PROGRESS',\n    'ROLLBACK_COMPLETE',\n    'ROLLBACK_FAILED',\n    'ROLLBACK_IN_PROGRESS',\n    'UPDATE_COMPLETE',\n    'UPDATE_COMPLETE_CLEANUP_IN_PROGRESS',\n    'UPDATE_IN_PROGRESS',\n    'UPDATE_ROLLBACK_COMPLETE',\n    'UPDATE_ROLLBACK_COMPLETE_CLEANUP_IN_PROGRESS',\n    'UPDATE_ROLLBACK_FAILED',\n    'UPDATE_ROLLBACK_IN_PROGRESS'\n]\n\n\ndef get_timestamp():\n    return '{} UTC'.format(str(datetime.datetime.utcnow().isoformat(sep=' ', timespec='minutes')))\n\n\ndef deploy(request, session, my_env):\n    try:\n        requested_env = ENV_NAMES[request['intent']['slots']['Environment']['value'].lower()]\n        requested_version = '.'.join([\n            request['intent']['slots']['MajorVersion']['value'],\n            request['intent']['slots']['MinorVersion']['value'],\n            request['intent']['slots']['PatchVersion']['value']\n        ])\n    except KeyError:\n        response_string = \"Tell me a full version number and environment name, or I'm not doing anything.\"\n        return alexa.build_response(session=session, response_string=response_string)\n\n    logger = logging.getLogger()\n    logger.info(f'Pear website version requested: {requested_version}')\n\n    client = boto3.client('lambda')\n    lambda_response = client.invoke(\n        FunctionName=f'pear_deployer_{my_env}',\n        InvocationType='RequestResponse',\n        Payload=json.dumps({'request': 'deploy', 'version': requested_version, 'environment': requested_env})\n    )\n    logger.info('Response to deploy request: {}'.format(lambda_response))\n    if lambda_response['StatusCode'] == 200 and not lambda_response.get('FunctionError'):\n        response_string = 'Ok, fine.'\n        card_string = f'Started deploy of {requested_version} to {requested_env} at {get_timestamp()}.'\n        return alexa.build_response(session=session, response_string=response_string, card_string=card_string)\n    elif lambda_response['StatusCode'] == 200 and lambda_response.get('FunctionError'):\n        error_response = json.loads(lambda_response['Payload'].read())\n        logger.info(f'Error: {error_response}')\n        error = error_response['errorType']\n        error_message = error_response['errorMessage']\n        if error == 'VersionUnavailableError':\n            latest_version = error_message.split(':')[-1].strip()\n            response_string = f\"You blockhead. {requested_version} doesn't exist. Latest is {latest_version}.\"\n            return alexa.build_response(session=session, response_string=response_string)\n        elif error == 'ClientError':\n            if '(AccessDenied)' in error_message:\n                response_string = f\"I'm too classy to touch {requested_env}.\"\n                return alexa.build_response(session=session, response_string=response_string)\n            elif f'Stack [pear-{requested_env}] does not exist' in error_message:\n                response_string = f\"I couldn't find {requested_env}.\"\n                return alexa.build_response(session=session, response_string=response_string)\n            elif 'No updates are to be performed' in error_message:\n                response_string = \"That's already running. There's nothing to do.\"\n                return alexa.build_response(session=session, response_string=response_string)\n            elif 'state and can not be updated.' in error_message:\n                states = [s for s in CF_STATES if s in error_message]\n                assert len(states) == 1, 'Stacks can only have one state.'\n                state = states[0].replace('_', ' ')\n                response_string = f\"Pear's state is {state}, so I can't deploy right now.\"\n                return alexa.build_response(session=session, response_string=response_string)\n        else:\n            response_string = 'I started the deploy, but there was a problem.'\n            card_string = f'Error starting deploy of {requested_version} to {requested_env} at {get_timestamp()}.'\n            return alexa.build_response(session=session, response_string=response_string, card_string=card_string)\n    return alexa.build_response(session=session, response_string=\"I couldn't start the deploy.\")\n\n\ndef status(request, session, my_env):\n    try:\n        requested_env = ENV_NAMES[request['intent']['slots']['Environment']['value'].lower()]\n    except KeyError:\n        response_string = 'You have to tell me an environment name.'\n        return alexa.build_response(session=session, response_string=response_string)\n\n    client = boto3.client('lambda')\n    lambda_response = client.invoke(\n        FunctionName=f'pear_deployer_{my_env}',\n        InvocationType='RequestResponse',\n        Payload=json.dumps({'request': 'status', 'environment': requested_env})\n    )\n    response_data = json.loads(lambda_response['Payload'].read())\n    logger = logging.getLogger()\n    logger.info(f'Response to status request: {lambda_response}')\n    logger.info(f'Error: {response_data}')\n    if lambda_response['StatusCode'] == 200 and not lambda_response.get('FunctionError'):\n        state = response_data['stack_status'].replace('_', ' ')\n        response_string = f'Version is {response_data[\"version\"]}. State is {state}.'\n        return alexa.build_response(session=session, response_string=response_string)\n    elif lambda_response['StatusCode'] == 200 and lambda_response.get('FunctionError'):\n        error = response_data['errorType']\n        error_message = response_data['errorMessage']\n        if error == 'ClientError':\n            if '(AccessDenied)' in error_message:\n                response_string = f\"I'm too classy to touch {requested_env}.\"\n                return alexa.build_response(session=session, response_string=response_string)\n            elif f'Stack with id pear-{requested_env} does not exist' in error_message:\n                response_string = f\"I couldn't find {requested_env}.\"\n                return alexa.build_response(session=session, response_string=response_string)\n    return alexa.build_response(session=session, response_string=\"I couldn't get status.\")\n\n\ndef honeypot(request, session, my_env):\n    client = boto3.client('sns')\n    paginator = client.get_paginator('list_topics')\n    topics = list()\n    for page in paginator.paginate():\n        topics.extend([topic['TopicArn'] for topic in page['Topics']])\n    alerts_arns = [arn for arn in topics if arn.endswith(f'pear_alerts_{my_env}')]\n    assert len(alerts_arns) == 1, 'There should only be one alerts topic per Pear environment.'\n    alerts_arn = alerts_arns[0]\n    logger = logging.getLogger()\n    logger.info(f'Sending honeypot alert to this SNS topic: {alerts_arn}')\n    sns_response = client.publish(TopicArn=alerts_arn, Message='Pear honeypot triggered.')\n    logger.info(f'Response to SNS request: {sns_response}')\n    if sns_response['ResponseMetadata']['HTTPStatusCode'] == 200:\n        response_string = 'Access granted. Have fun.'\n        card_string = f'Honeypot triggered at {get_timestamp()}. Alert sent to admins.'\n    else:\n        response_string = 'Something went wrong.'\n        card_string = f'Honeypot triggered at {get_timestamp()}. Alert to admins failed.'\n    return alexa.build_response(session=session, response_string=response_string, card_string=card_string)\n","sub_path":"lambda_functions/deployer_alexa_skill/deployer_alexa_skill/pear.py","file_name":"pear.py","file_ext":"py","file_size_in_byte":7714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"621345311","text":"from r7insight import R7InsightHandler\nimport logging\nimport time\nimport bluetooth\nimport Adafruit_DHT\nimport datetime\n\nserver_sock=bluetooth.BluetoothSocket( bluetooth.RFCOMM )\n\nport = 25\nmacAddr = \"B8:27:EB:63:E4:8B\"\nserver_sock.bind((\"\",port))\nserver_sock.listen(1)\n\nlog = logging.getLogger('r7insight')\nlog.setLevel(logging.INFO)\ntest = R7InsightHandler(\"\", 'eu')\n\nlog.addHandler(test)\nsensorID= \"Varun\"\n\nclient_sock,address = server_sock.accept()\nprint (\"Accepted connection from \",address)\nwhile True:\n\tdata = client_sock.recv(1024)\n\tprint  (data)\n\t\n\thumidity, temperature = Adafruit_DHT.read_retry(11, 4)\n\tlog.info(data)\n\tnow =datetime.datetime.now()\n\tlog_text = \"{0}\\t  SensorID={1}, Temperature = {2}, Humidity = {3}\".format(now,sensorID,temperature,humidity)\n\tlog.info(log_text)\n\tprint(log_text)\n\nclient_sock.close()\nserver_sock.close()\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"138324894","text":"\"\"\"Implementation of pop function\"\"\"\n\nimport ctypes                                      # provides low-level arrays\nimport math\nclass DynamicArray:\n    \"\"\"A dynamic array class akin to a simplified Python list.\"\"\"\n\n    def __init__(self):\n        \"\"\"Create an empty array.\"\"\"\n        self._n = 0                                    # count actual elements\n        self._capacity = 1                             # default array capacity\n        self._A = self._make_array(self._capacity)     # low-level array\n\n    def __len__(self):\n        \"\"\"Return number of elements stored in the array.\"\"\"\n        return self._n\n\n    def __getitem__(self, k):\n        \"\"\"Return element at index k.\"\"\"\n        if 0 <= k < self._n:\n            return self._A[k]                          # retrieve from array\n        elif k == -1 and self._n != 0:\n            return self._A[-1]\n        else:\n            raise IndexError('invalid index')\n\n    def append(self, obj):\n        \"\"\"Add object to end of the array.\"\"\"\n        if self._n == self._capacity:                  # not enough room\n            self._resize(2 * self._capacity)           # so double capacity\n        self._A[self._n] = obj\n        self._n += 1\n\n    def _resize(self, c):                              # nonpublic utitity\n        \"\"\"Resize internal array to capacity c.\"\"\"\n        B = self._make_array(c)                        # new (bigger) array\n        for k in range(self._n):                       # for each existing value\n            B[k] = self._A[k]\n        self._A = B                                    # use the bigger array\n        self._capacity = c\n\n    def _make_array(self, c):                        # nonpublic utitity\n        \"\"\"Return new array with capacity c.\"\"\"\n        return (c * ctypes.py_object)()              # see ctypes documentation\n\n    def insert(self, k, value):\n        \"\"\"Insert value at index k, shifting subsequent values rightward.\"\"\"\n        # (for simplicity, we assume 0 <= k <= n in this verion)\n        if self._n == self._capacity:                  # not enough room\n            new_A = self._make_array(2 * self._capacity) \n            for i in range(k):\n                new_A[i] = self._A[i]\n            new_A[k] = value\n            for j in range(k,self._capacity):\n                new_A[j + 1] = self._A[j]\n            self._A = new_A\n            self._capacity = 2 * self._capacity\n        else:\n            for j in range(self._n, k, -1):                # shift rightmost first\n                self._A[j] = self._A[j-1]\n            self._A[k] = value                             # store newest element\n        self._n += 1\n\n    def remove(self, value):\n        \"\"\"Remove first occurrence of value (or raise ValueError).\"\"\"\n    # note: we do not consider shrinking the dynamic array in this version\n        for k in range(self._n):\n            if self._A[k] == value:              # found a match!\n                for j in range(k, self._n - 1):    # shift others to fill gap\n                    self._A[j] = self._A[j+1]\n                self._A[self._n - 1] = None        # help garbage collection\n                self._n -= 1                       # we have one less item\n                return                             # exit immediately\n        raise ValueError('value not found')    # only reached if no match\n    \"\"\"Needed in order to be able to print array (getitem too)\"\"\"\n    def __len__(self):\n        return self._n\n\n    def pop(self):\n        \"\"\"Pop is going to resize the array to N/2 any time the number of \n        elements goes below N/4, where N stands for capacity\"\"\"\n        self._n -= 1\n        if self._n <= math.floor(self._capacity/4):\n            self._resize(math.floor(self._capacity/2))\n            print(\"Capacity reduction took place\")\n\nif __name__ == '__main__':\n    array = DynamicArray()\n    array.append(3)\n    array.append(4)\n    \"\"\"Capacity is full\"\"\"\n    array.insert(1,10)\n    array.append(4)\n    array.append(4)\n    array.append(4)\n    array.append(4)\n    \n    \"\"\"Array class does not know how to print itself. This is why we iterate\"\"\"\n    print(\"Array after continuous pops  :\")\n    for i in range(len(array)):\n        array.pop()\n        for j in range(len(array)):\n            print(array[j])\n        print(\"=====\")\n    print()\n\n\n\n\n\n","sub_path":"chap5-Array-Based_Sequences/C-5.16.py","file_name":"C-5.16.py","file_ext":"py","file_size_in_byte":4285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"362482429","text":"'''\n市场深度单，即 GetDepth 返回数据中 Bids 、Asks 数组中的元素的数据结构。\n{\n    Price\t:价格\n    Amount\t:数量\n}\n'''\nimport copy\n\n\nclass MarketOrder:\n    \"\"\"\n    Abstract class of a market data\n    \"\"\"\n    class Side:\n        ORDER_TYPE_BUY = 0\n        ORDER_TYPE_SELL = 1\n        NONE = 2\n\n    class Status:\n        ORDER_STATE_PENDING = 0           #未完成\n        ORDER_STATE_CLOSED = 1            #已完成\n        ORDER_STATE_CANCELED = 2          #已取消\n\n    def __init__(self):\n        \"\"\"\n        Constructor\n        \"\"\"\n        pass\n\n    def __init__(self, price=0.0, amount=0.0):\n        \"\"\"\n        Constructor\n        \"\"\"\n        self.Price = price\n        self.Amount = amount\n\n    def copy(self):\n        return copy.deepcopy(self)\n\n    @staticmethod\n    def parse_side(value):\n        \"\"\"\n        Decode the value to Side (BUY/SELL)\n        :param value: Integer or string\n        :return: Side (NONE, BUY, SELL)\n        \"\"\"\n        if type(value) != int:\n            value = value.lower()\n            if value == 'buy' or value == 'bid' or value == 'b':\n                return MarketOrder.Side.ORDER_TYPE_BUY\n            elif value == 'sell' or value == 'ask' or value == 's':\n                return MarketOrder.Side.ORDER_TYPE_SELL\n            else:\n                return MarketOrder.Side.NONE\n\n        if value == 1:\n            return MarketOrder.Side.ORDER_TYPE_BUY\n        elif value == 2:\n            return MarketOrder.Side.ORDER_TYPE_SELL\n        else:\n            raise Exception(\"Cannot parse the side (%s)\" % value)\n\n\n\n","sub_path":"util/model/MarketOrder.py","file_name":"MarketOrder.py","file_ext":"py","file_size_in_byte":1584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"457709153","text":"import json\nimport unittest\n\nfrom .instances import frontik_test_app\n\n\nclass TestJsonResponse(unittest.TestCase):\n    def test_validation(self):\n        response = frontik_test_app.get_page('validate_arguments?list=1&list=2&string=safestring', notpl=True)\n\n        data = json.loads(response.content)\n        self.assertEqual([1, 2], data['list'])\n        self.assertEqual('safestring', data['string'])\n\n    def test_validation_failed(self):\n        response = frontik_test_app.get_page('validate_arguments?list=1&list=2&string=un/safe', notpl=True)\n\n        self.assertEqual(response.status_code, 400)\n\n    def test_validation_model(self):\n        response = frontik_test_app.get_page('validate_arguments?list=1&list=2&string=nword&model=true', notpl=True)\n\n        data = json.loads(response.content)\n        self.assertEqual([1, 2], data['list'])\n        self.assertEqual('customString', data['string'])\n","sub_path":"tests/test_arguments.py","file_name":"test_arguments.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"299226574","text":"# Copyright (c) 2016-present, Facebook, Inc.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport argparse\nimport atexit\nimport json\nimport logging\nimport os\nimport subprocess\nfrom logging import Logger\nfrom typing import List, Optional\n\nfrom .. import json_rpc\nfrom ..analysis_directory import AnalysisDirectory\nfrom ..configuration import Configuration\nfrom ..project_files_monitor import MonitorException, ProjectFilesMonitor\nfrom .command import (\n    ClientException,\n    ExitCode,\n    IncrementalStyle,\n    Result,\n    State,\n    typeshed_search_path,\n)\nfrom .reporting import Reporting\nfrom .start import Start\n\n\nLOG: Logger = logging.getLogger(__name__)\n\n\ndef _convert_to_result(response: json_rpc.Response) -> Result:\n    error_code = ExitCode.FAILURE if response.error else ExitCode.SUCCESS\n    return Result(output=json.dumps(response.result), code=error_code)\n\n\nclass Incremental(Reporting):\n    NAME = \"incremental\"\n\n    def __init__(\n        self,\n        arguments: argparse.Namespace,\n        original_directory: str,\n        configuration: Optional[Configuration] = None,\n        analysis_directory: Optional[AnalysisDirectory] = None,\n    ) -> None:\n        super(Incremental, self).__init__(\n            arguments, original_directory, configuration, analysis_directory\n        )\n        self._nonblocking: bool = arguments.nonblocking\n        self._incremental_style: bool = arguments.incremental_style\n        self._no_start_server: bool = arguments.no_start\n        self._no_watchman: bool = getattr(arguments, \"no_watchman\", False)\n\n    @classmethod\n    def add_subparser(cls, parser: argparse._SubParsersAction) -> None:\n        incremental_help = \"\"\"\n        Connects to a running Pyre server and returns the current type errors for your\n        project. If no server exists for your projects, starts a new one. Running `pyre`\n        implicitly runs `pyre incremental`.\n\n        By default, incremental checks ensure that all dependencies of changed files are\n        analyzed before returning results. If you'd like to get partial type checking\n        results eagerly, you can run `pyre incremental --nonblocking`.\n        \"\"\"\n        incremental = parser.add_parser(cls.NAME, epilog=incremental_help)\n        incremental.set_defaults(command=cls)\n        incremental.add_argument(\n            \"--nonblocking\",\n            action=\"store_true\",\n            help=(\n                \"Ask the server to return partial results immediately, \"\n                \"even if analysis is still in progress.\"\n            ),\n        )\n        incremental.add_argument(\n            \"--incremental-style\",\n            type=IncrementalStyle,\n            choices=list(IncrementalStyle),\n            default=IncrementalStyle.FINE_GRAINED,\n            help=\"How to approach doing incremental checks.\",\n        )\n        incremental.add_argument(\n            \"--no-start\", action=\"store_true\", help=argparse.SUPPRESS\n        )\n        # This is mostly to allow `restart` to pass on the flag to `start`.\n        incremental.add_argument(\n            \"--no-watchman\", action=\"store_true\", help=argparse.SUPPRESS\n        )\n\n    def _run(self) -> None:\n        if (not self._no_start_server) and self._state() == State.DEAD:\n            LOG.warning(\"Starting server at `%s`.\", self._analysis_directory.get_root())\n            arguments = self._arguments\n            arguments.no_watchman = self._no_watchman\n            arguments.terminal = False\n            arguments.store_type_check_resolution = False\n            exit_code = (\n                Start(\n                    arguments,\n                    self._original_directory,\n                    self._configuration,\n                    self._analysis_directory,\n                )\n                .run()\n                .exit_code()\n            )\n            if exit_code != ExitCode.SUCCESS:\n                self._exit_code = ExitCode.FAILURE\n                return\n        else:\n            self._refresh_file_monitor()\n\n        if self._state() != State.DEAD:\n            LOG.info(\"Waiting for server...\")\n\n        request = json_rpc.Request(method=\"displayTypeErrors\", parameters={\"files\": []})\n        self._send_and_handle_socket_request(request, self._version_hash)\n\n    def _socket_result_handler(self, result: Result) -> None:\n        errors = self._get_errors(result)\n        self._print(errors)\n        if errors:\n            self._exit_code = ExitCode.FOUND_ERRORS\n\n    def _flags(self) -> List[str]:\n        flags = super()._flags()\n        flags.extend([\"-expected-binary-version\", self._configuration.version_hash])\n\n        search_path = self._configuration.search_path + typeshed_search_path(\n            self._configuration.typeshed\n        )\n        if search_path:\n            flags.extend([\"-search-path\", \",\".join(search_path)])\n\n        excludes = self._configuration.excludes\n        for exclude in excludes:\n            flags.extend([\"-exclude\", exclude])\n\n        extensions = self._configuration.extensions\n        for extension in extensions:\n            flags.extend([\"-extension\", extension])\n\n        if self._nonblocking:\n            flags.append(\"-nonblocking\")\n\n        return flags\n\n    def _read_stderr(self, _stream) -> None:\n        stderr_file = os.path.join(self._log_directory, \"server/server.stdout\")\n        with subprocess.Popen(\n            [\"tail\", \"--follow\", \"--lines=0\", stderr_file],\n            stdout=subprocess.PIPE,\n            stderr=subprocess.DEVNULL,\n            text=True,\n        ) as stderr_tail:\n            atexit.register(stderr_tail.terminate)\n            super(Incremental, self)._read_stderr(stderr_tail.stdout)\n\n    def _refresh_file_monitor(self) -> None:\n        if self._no_watchman:\n            return\n        if not ProjectFilesMonitor.is_alive(self._configuration):\n            LOG.info(\"File monitor is not running.\")\n            try:\n                ProjectFilesMonitor(\n                    self._configuration,\n                    self._current_directory,\n                    self._analysis_directory,\n                ).daemonize()\n                LOG.info(\"Restarted file monitor.\")\n            except MonitorException as exception:\n                LOG.warning(\"Failed to restart file monitor: %s\", exception)\n","sub_path":"client/commands/incremental.py","file_name":"incremental.py","file_ext":"py","file_size_in_byte":6323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"508435114","text":"# MATSIKO BRUNO 2020/BSE/165/PS\n# DAVID NYAMIYALE 2020/BSE/057/PS\n# MAWANDA DENNIS 2020/BSE/155/PS\n# AKANDWANAHO NICKSON 2020/BSE/006/PS\n\n# while loop that starts at the last character in a string and works it way backwords to the start of string.\n# print each letter on seperate line except back words\n\nstringname = input('enter the string to be reversed')\nstringlength = len(stringname)\nwhile ( stringlength > 0 ):\n    print(stringname[stringlength - 1])\n    stringlength = stringlength - 1","sub_path":"src/chapter6/exercise1.py","file_name":"exercise1.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"35832299","text":"# -*- coding: utf-8 -*-\n# Copyright (C) 2004-2013 Mag. Christian Tanzer. All rights reserved\n# Glasauergasse 32, A--1130 Wien, Austria. tanzer@swing.co.at\n# ****************************************************************************\n#\n# This module is licensed under the terms of the BSD 3-Clause License\n# <http://www.c-tanzer.at/license/bsd_3c.html>.\n# ****************************************************************************\n#\n#++\n# Name\n#    TFL.Units.Liquid\n#\n# Purpose\n#    Liquid capacity units\n#\n# Revision Dates\n#     8-Aug-2004 (CT) Creation\n#    ««revision-date»»···\n#--\n\nfrom   _TFL import TFL\nimport _TFL._Meta.Object\nimport _TFL._Units.Kind\nimport _TFL._Units.Volume\n\nclass Liquid (TFL.Units.Kind) :\n    \"\"\"Units of liquid capacity\n\n       >>> Liquid (1)\n       1\n       >>> Liquid (1.0, \"oz\")\n       0.029573\n       >>> Liquid (1.0, \"dr\")\n       0.003696625\n       >>> Liquid (1.0, \"gal\")\n       3.785344\n       >>> Liquid (1.0, \"pt\")\n       0.473168\n    \"\"\"\n\n    Volume            = TFL.Units.Volume\n    Unit              = TFL.Units.Unit\n\n    base_unit         = Unit (\"liter\", 1.0, \"l\")\n    _ounce            = 0.029573\n    _units            = \\\n        (\n        # SI prefixes\n          Unit (\"nanoliter\",         TFL.Units.nano,  \"nl\")\n        , Unit (\"microliter\",        TFL.Units.micro, \"ul\")\n        , Unit (\"milliliter\",        TFL.Units.milli, \"ml\")\n        , Unit (\"centiliter\",        TFL.Units.centi, \"cl\")\n        , Unit (\"deciliter\",         TFL.Units.deci,  \"dl\")\n        , Unit (\"hectoliter\",        TFL.Units.hecto, \"hl\")\n        , Unit (\"kiloliter\",         TFL.Units.kilo,  \"kl\")\n        , Unit (\"cubic_decimeter\",      1.0)\n        , Unit (\"cubic_meter\",       1000.0)\n        # US customary units\n        , Unit (\"ounce\",             _ounce,          \"oz\")\n        , Unit (\"drop\",              _ounce    / 360)\n        , Unit (\"drams\",             _ounce    /   8, \"dr\")\n        , Unit (\"teaspoon\",          _ounce    /   6, \"tsp\")\n        , Unit (\"tablespoon\",        _ounce    /   2, \"tb\")\n        , Unit (\"cup\",               _ounce    *   8)\n        , Unit (\"pint\",              _ounce    *  16, \"pt\")\n        , Unit (\"quart\",             _ounce    *  32, \"qt\")\n        , Unit (\"gallon\",            _ounce    * 128, \"gal\")\n        )\n\n# end class Liquid\n\nif __name__ != \"__main__\" :\n    TFL.Units._Export (\"*\")\n### __END__ TFL.Units.Liquid\n","sub_path":"Functions/venv/lib/python3.6/site-packages/_TFL/_Units/Liquid.py","file_name":"Liquid.py","file_ext":"py","file_size_in_byte":2393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"228059963","text":"import sys, sqlite3, jdatetime\nfrom PyQt5 import QtWidgets, QtGui, QtCore\nfrom AddSamplingDetails import *\n\nclass Form_DialogAddSamplingDetails(QtWidgets.QDialog):\n    def __init__(self):\n        super().__init__()\n        self.statusmode = 0\n        self.project_id = 0\n        self.ui = Ui_DialogAddSamplingDetails()\n        self.ui.setupUi(self)\n        self.ui.comboBoxYear.currentIndexChanged.connect(self.TrigerMount)\n        self.ui.comboBoxMount.currentIndexChanged.connect(self.TrigerDay)\n        self.ui.pushButtonCancelSampling.clicked.connect(self.close)\n        self.ui.pushButtonCreateSampling.clicked.connect(self.CreateNewSampling)\n\n    def TrigerMount(self):\n        if self.ui.comboBoxYear.currentIndex() == 0:\n            self.ui.comboBoxMount.clear()\n            self.ui.comboBoxMount.addItem(\"ماه\")\n            self.ui.comboBoxMount.setEnabled(False)\n        else:\n            self.ui.comboBoxMount.setEnabled(True)\n            self.ui.comboBoxMount.clear()\n            self.ui.comboBoxMount.addItem(\"ماه\")\n            for i in range(1, 13):\n                self.ui.comboBoxMount.addItem(str(i).zfill(2))\n\n    def TrigerDay(self):\n        if self.ui.comboBoxMount.currentIndex() == 0:\n            self.ui.comboBoxDay.clear()\n            self.ui.comboBoxDay.addItem(\"روز\")\n            self.ui.comboBoxDay.setEnabled(False)\n        else:\n            self.ui.comboBoxDay.setEnabled(True)\n            self.ui.comboBoxDay.clear()\n            self.ui.comboBoxDay.addItem(\"روز\")\n            cond = self.ui.comboBoxYear.itemText(self.ui.comboBoxYear.currentIndex()) not in (\"1399\", \"1403\", \"1408\")\n            Mount = self.ui.comboBoxMount.currentIndex()\n            if cond:\n                if 0 < Mount < 7:\n                    for i in range(1,32):\n                        self.ui.comboBoxDay.addItem(str(i).zfill(2))\n                elif 6 < Mount < 12:\n                    for i in range(1,31):\n                        self.ui.comboBoxDay.addItem(str(i).zfill(2))\n                else:\n                    for i in range(1,30):\n                        self.ui.comboBoxDay.addItem(str(i).zfill(2))\n            else:\n                if 0 < Mount < 7:\n                    for i in range(1,32):\n                        self.ui.comboBoxDay.addItem(str(i).zfill(2))\n                elif 6 < Mount < 13:\n                    for i in range(1,31):\n                        self.ui.comboBoxDay.addItem(str(i).zfill(2))\n\n    def setProjectID(self, project_id):\n        self.project_id = project_id\n\n    def isValid_fc(self):\n        Valid_fc = (\n            self.ui.lineEdit_fc.text().isdigit()\n            or\n            self.ui.lineEdit_fc.text().replace('.','',1).isdigit())\n        if not Valid_fc:\n            return False\n        return True\n        \n    def CreateNewSampling(self):\n        fillCondition =(\n            self.ui.comboBoxElement.currentIndex() == 0,\n            len(self.ui.lineEditStory.text()) == 0,\n            len(self.ui.lineEditLevel.text()) == 0,\n            not self.ui.comboBoxDay.isEnabled(),\n            self.ui.comboBoxDay.currentIndex()== 0,\n            len(self.ui.lineEdit_fc.text()) == 0,\n            self.ui.comboBoxCementType.currentIndex() == 0\n        )\n        if any(fillCondition):\n            QMB = QtWidgets.QMessageBox.critical(self, \"خطا\", \"لطفاً همه فیلدها را تکمیل نمایید.\")\n            return\n        else:\n            if not self.isValid_fc():\n                QMB = QtWidgets.QMessageBox.critical(self, \"خطا\", \"مقاومت مشخصه وارد شده نامعتبر است.\")\n                return\n            else:\n                try:\n                    conn = sqlite3.connect('MainDB.db')\n                    cur = conn.cursor()\n                    sampling_date = jdatetime.datetime(int(self.ui.comboBoxYear.currentText()),int(self.ui.comboBoxMount.currentText()), int(self.ui.comboBoxDay.currentText()), locale='fa_IR')\n                    SamplingInput = (\n                        self.project_id,\n                        self.ui.comboBoxElement.currentText(),\n                        self.ui.lineEditStory.text(),\n                        self.ui.lineEditLevel.text(),\n                        sampling_date.strftime(\"%Y/%m/%d\"),\n                        self.ui.lineEdit_fc.text(),\n                        self.ui.comboBoxCementType.currentText()\n                    )\n                    sqlexpr = \"INSERT INTO Sampling (ProjectID, Element, Story, Level, SamplingDate, fc, CementType) VALUES (?, ?, ?, ?, ?, ?, ?)\"\n                    cur.execute(sqlexpr, SamplingInput)\n                    conn.commit()\n                    print(\"New Samplign Added\")\n                    self.statusmode = 1\n                except sqlite3.Error as err:\n                    print(err)\n                finally:\n                    conn.close()\n                    self.close()\n                    return self.statusmode\n\n# if __name__ ==\"__main__\":\n#     app = QtWidgets.QApplication(sys.argv)\n#     w = Form_DialogAddSamplingDetails()\n#     w.show()\n#     sys.exit(app.exec_())","sub_path":"AddSamplingDetailsCall.pyw","file_name":"AddSamplingDetailsCall.pyw","file_ext":"pyw","file_size_in_byte":5073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"147721595","text":"# :coding: utf-8\n# :copyright: Copyright (c) 2015 ftrack\n\nimport getpass\nimport sys\nimport pprint\nimport logging\n\nimport ftrack\nimport ftrack_connect.application\n\n\nclass LaunchApplicationAction(object):\n    '''Discover and launch action.'''\n\n    identifier = 'ftrack-connect-launch-apps'\n\n    def __init__(self, applicationStore, launcher):\n        '''Initialise action with *applicationStore* and *launcher*.\n\n        *applicationStore* should be an instance of\n        :class:`ftrack_connect.application.ApplicationStore`.\n\n        *launcher* should be an instance of\n        :class:`ftrack_connect.application.ApplicationLauncher`.\n\n        '''\n        super(LaunchApplicationAction, self).__init__()\n\n        self.logger = logging.getLogger(\n            __name__ + '.' + self.__class__.__name__\n        )\n\n        self.applicationStore = applicationStore\n        self.launcher = launcher\n\n    def register(self):\n        '''Register discover actions on logged in user.'''\n        ftrack.EVENT_HUB.subscribe(\n            'topic=ftrack.action.discover and source.user.username={0}'.format(\n                getpass.getuser()\n            ),\n            self.discover\n        )\n\n        ftrack.EVENT_HUB.subscribe(\n            'topic=ftrack.action.launch and source.user.username={0} '\n            'and data.actionIdentifier={1}'.format(\n                getpass.getuser(), self.identifier\n            ),\n            self.launch\n        )\n\n    def validateSelection(self, selection):\n        '''Return true if the selection is valid.\n        '''\n        self.logger.info(selection)\n\n        return True\n\n    def discover(self, event):\n        '''Return discovered applications.'''\n        items = []\n        data = event['data']\n        selection = data.get('selection', [])\n\n        if not self.validateSelection(selection):\n            return\n\n        applications = self.applicationStore.applications\n        applications = sorted(\n            applications, key=lambda application: application['label']\n        )\n\n        for application in applications:\n            applicationIdentifier = application['identifier']\n            label = application['label']\n            items.append({\n                'actionIdentifier': self.identifier,\n                'label': label,\n                'variant': application.get('variant', None),\n                'icon': application.get('icon', 'default'),\n                'applicationIdentifier': applicationIdentifier\n            })\n\n        return {\n            'items': items\n        }\n\n    def launch(self, event):\n        '''Handle *event*.\n\n        event['data'] should contain:\n\n            *applicationIdentifier* to identify which application to start.\n\n        '''\n        items = []\n        data = event['data']\n        selection = data.get('selection', [])\n\n        # if not self.validateArtist(selection):\n        #     return\n\n        applicationIdentifier = (\n            event['data']['applicationIdentifier']\n        )\n\n        context = event['data'].copy()\n\n        self.logger.info(\"YEAH\")\n\n        # Here you can modify the context setting extra data etc\n        # which can be retrieved by parsing the FTRACK_CONNECT_EVENT\n        # environment variable in the application.\n\n        return self.launcher.launch(\n            applicationIdentifier, context\n        )\n\n\nclass ApplicationStore(ftrack_connect.application.ApplicationStore):\n\n    def _discoverApplications(self):\n        '''Return a list of applications that can be launched from this host.\n\n        An application should be of the form:\n\n            dict(\n                'identifier': 'name_version',\n                'label': 'Name version',\n                'path': 'Absolute path to the file',\n                'version': 'Version of the application',\n                'icon': 'URL or name of predefined icon'\n            )\n\n        '''\n        applications = []\n\n        if sys.platform == 'darwin':\n            prefix = ['/', 'Applications']\n\n            applications.extend(self._searchFilesystem(\n                expression=prefix + [\n                    'Python*', 'Python.app'\n                ],\n                label='Python',\n                variant='{version}',\n                applicationIdentifier='python_{version}',\n                icon='http://www.unixstickers.com/image/data/stickers/python/python.sh.png'\n            ))\n\n        elif sys.platform == 'win32':\n            prefix = ['C:\\\\', 'Program Files.*']\n\n            applications.extend(self._searchFilesystem(\n                expression=[\n                    'C:\\\\', 'Python*', 'python.exe'\n                ],\n                label='Python',\n                variant='{version}',\n                applicationIdentifier='python_{version}',\n                icon='http://www.unixstickers.com/image/data/stickers/python/python.sh.png'\n            ))\n\n        self.logger.debug(\n            'Discovered applications:\\n{0}'.format(\n                pprint.pformat(applications)\n            )\n        )\n\n        return applications\n\n\ndef register(registry, **kw):\n    '''Register hooks.'''\n    if registry is not ftrack.EVENT_HANDLERS:\n        # Exit to avoid registering this plugin again.\n        return\n    # Create store containing applications.\n    applicationStore = ApplicationStore()\n\n    # Create a launcher with the store containing applications.\n    launcher = ftrack_connect.application.ApplicationLauncher(\n        applicationStore\n    )\n\n    # Create action and register to respond to discover and launch actions.\n    action = LaunchApplicationAction(applicationStore, launcher)\n    action.register()\n","sub_path":"actions/resource/hook/launch_python.py","file_name":"launch_python.py","file_ext":"py","file_size_in_byte":5596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"217934919","text":"# Copyright 2014-2016 by Akira Yoshiyama <akirayoshiyama@gmail.com>.\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\n\"\"\"\nResource class and its manager for volumes on Block Storage V1 API\n\"\"\"\n\nfrom osclient2 import base\nfrom osclient2 import mapper\n\n\nclass Snapshot(base.Resource):\n    \"\"\"resource class for volumes on Block Storage V1 API\"\"\"\n\n\nclass Manager(base.Manager):\n    \"\"\"manager class for roles on Block Storage V1 API\"\"\"\n\n    service_type = 'volume'\n    url_resource_path = '/snapshots'\n    url_resource_list_path = '/snapshots/detail'\n    json_resource_key = 'snapshot'\n    json_resources_key = 'snapshots'\n\n    resource_class = Snapshot\n    attr_mapping = [\n        ('id', 'id', mapper.Noop, 'Snapshot ID (string)'),\n        ('name', 'display_name', mapper.Noop, 'Snapshot name (string)'),\n        ('description', 'display_description', mapper.Noop,\n         'Description (string)'),\n        ('size', 'size', mapper.Noop, 'Volume size in GB (int)'),\n        ('status', 'status', mapper.Noop, 'Snapshot status (string)'),\n        ('volume', 'volume_id',\n         mapper.Resource('cinder.v1.volume'), 'Source Volume object'),\n        ('progress', 'os-extended-snapshot-attributes:progress', mapper.Noop,\n         'progress (string)'),\n        ('project', 'os-extended-snapshot-attributes:project_id',\n         mapper.Resource('project'), 'Project (object)'),\n        ('metadata', 'metadata', mapper.Noop, 'Snapshot metadata (dict)'),\n        ('created_at', 'created_at', mapper.DateTime,\n         'Created timestamp (datetime)'),\n        ('updated_at', 'updated_at', mapper.DateTime,\n         'Created timestamp (datetime)'),\n    ]\n\n    def create(self, name=None, description=None, volume=None,\n               force=False):\n        return super(Manager, self).create(name=name,\n                                           description=description,\n                                           volume=volume,\n                                           force=force)\n\n    def update(self, id):\n        raise NotImplementedError()\n","sub_path":"osclient2/cinder/v1/snapshot.py","file_name":"snapshot.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"469200827","text":"import torch\nimport torchvision\nfrom torchvision import transforms\nfrom torch import nn\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nfrom datetime import datetime\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom model import ResNet\nfrom model import ResidualBlock\nfrom torch.utils.tensorboard import SummaryWriter\n\n'''搭建ResNet18神经网络实现CIFAR-10数据集的图像分类'''\n# metrics = {\n#     'accuracy: ': accuracy_score,\n#     'precision: ': lambda x, y: precision_score(x, y, average='macro'),\n#     'recall: ': lambda x, y: recall_score(x, y, average='macro'),\n#     'confustion matrix: \\n': confusion_matrix\n# }\n\n# 检查GPU是否可用\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n# 设定超参数\nnum_epoch = 10\nbatch_size = 100\n\n# 数据下载与预处理\ntrain_transform = transforms.Compose([\n    transforms.RandomHorizontalFlip(),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n])\n\ntest_transform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n])\n\ntrain_set = torchvision.datasets.CIFAR10(root='./data', train=True,\n                                         download=True, transform=train_transform)\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size,\n                                           shuffle=True, num_workers=2)\n\ntest_set = torchvision.datasets.CIFAR10(root='./data', train=False,\n                                        download=True, transform=test_transform)\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size,\n                                          shuffle=False, num_workers=2)\n\n\ndef adjust_learning_rate(optimizer, epoch):\n    \"\"\"Sets the learning rate to the initial LR decayed by 10 every 30 epochs\"\"\"\n    lr = 0.1 * (0.1 ** (epoch // 3))\n    for param_group in optimizer.param_groups:\n        param_group['lr'] = lr\n\n\nif __name__ == '__main__':\n    # 用于实验的三种优化器\n    optims = ['Adam', 'Adagrad', 'SGD']\n    for optim in optims:\n        lr0 = 0.1\n        actfun = 'sigmoid'\n        # 计算实验耗时\n        start_time = datetime.now()\n        net = ResNet(ResidualBlock, actfun=actfun).to(device)\n        print(net)\n        # 创建tensorboard文件夹\n        writer_train_loss = SummaryWriter('./runs/train/' + actfun)\n        writer_test_acc = SummaryWriter('./runs/test/' + actfun)\n\n        # 损失函数使用交叉熵损失函数\n        loss_function = nn.CrossEntropyLoss()\n\n        # train_loss = []\n        # train_accu = []\n        # accuracy = []\n        if optim == 'Adam':\n            optimizer0 = torch.optim.Adam(net.parameters(), lr=lr0, betas=(0.9, 0.99))\n        elif optim == 'Adagrad':\n            optimizer0 = torch.optim.Adagrad(net.parameters(), lr=0.01, lr_decay=0, weight_decay=0, initial_accumulator_value=0)\n        elif optim == 'SGD':\n            optimizer0 = torch.optim.SGD(net.parameters(), lr=lr0)\n\n        for epoch in range(num_epoch):\n            print('\\nEpoch: %d' % (epoch + 1))\n            if epoch > 0 and epoch % 3 == 0:\n                lr0 *= 0.1\n                print('learning_rate=', lr0)\n            \n            adjust_learning_rate(optimizer0, epoch)\n            net.train()\n            sum_loss = 0.0\n            correct = 0.0\n            total = 0.0\n            # 每个i对应一个iteration\n            for i, data in enumerate(train_loader, 0):\n                length = len(train_loader)\n                inputs, labels = data\n                inputs, labels = inputs.to(device), labels.to(device)\n                # 将模型的参数梯度初始化为0\n                optimizer0.zero_grad()\n\n                outputs = net(inputs)\n                # 下三行代码用于输出网络结构，但是效果不是很好，隐去\n                # with SummaryWriter(comment='ResNet-18') as w:\n                #     w.add_graph(net, (inputs,))\n                # print(\"ResNet:\", outputs.shape)\n                # 求算损失\n                loss = loss_function(outputs, labels)\n                # 反向传播\n                loss.backward()\n                # 更新参数\n                optimizer0.step()\n\n                sum_loss += loss.item()\n                # 取得分最高的那个类 (outputs.data的索引号)\n                _, predicted = torch.max(outputs.data, 1)\n                total += labels.size(0)\n                correct += predicted.eq(labels.data).sum()\n                if i % 100 == 99:\n                #     print('[epoch:%d, iter:%d] Loss: %.03f | Acc: %.3f%% '\n                #           % (epoch + 1, (i + 1), sum_loss / (i + 1), 100. * correct / total))\n                # # train_loss.append(sum_loss / (i + 1))\n                # writer_train_loss_lr0.add_scalar('training loss',\n                #                                  sum_loss / (i + 1),\n                #                                  epoch * len(train_loader) + i)\n                    print('[epoch:%d, iter:%d] Loss: %.03f | Acc: %.3f%% '\n                      % (epoch + 1, (i + 1), loss.item(), 100. * correct / total))\n                # 也可以用如下的平均损失替代\n                # train_loss.append(sum_loss / (i + 1))\n                # 每个iteration向tensorboard日志中写入一次训练loss\n                writer_train_loss.add_scalar('training loss',\n                                             loss.item(),\n                                             epoch * len(train_loader) + i)\n            # train_accu.append(correct / total)\n            # 进行测试时不需要求算梯度，提高运算速度\n            with torch.no_grad():\n                correct = 0\n                total = 0\n                for data in test_loader:\n                    net.eval()\n                    images, labels = data\n                    images, labels = images.to(device), labels.to(device)\n                    outputs = net(images)\n                    # 取得分最高的那个类 (outputs.data的索引号)\n                    _, predicted = torch.max(outputs.data, 1)\n                    total += labels.size(0)\n                    correct += (predicted == labels).sum()\n            # 每个epoch向tensorboard中写入一次测试集准确率\n            writer_test_acc.add_scalar('test accuracy',\n                                        correct / total,\n                                        epoch + 1)\n            # accuracy.append(correct / total)\n\n            print('Test\\'s accuracy is: %.3f%%' % (100 * correct / total))\n        # 计算结束时间\n        end_time = datetime.now()\n        run_time = end_time - start_time\n\n        print('Train has finished, total epoch is %d' % num_epoch)\n        print(run_time)\n\n    #\n    # plt.figure(figsize=(10, 6))\n    # plt.subplot(1, 2, 1)\n    # plt.plot(range(1, len(train_loss) + 1), train_loss)\n    # matplotlib.rcParams['font.sans-serif'] = ['SimHei']\n    # matplotlib.rcParams['axes.unicode_minus'] = False\n    # plt.xlabel(\"迭代次数/次\")\n    # plt.ylabel(\"训练集损失\")\n    # plt.title(\"训练集损失-迭代次数\")\n    #\n    # plt.subplot(1, 2, 2)\n    # plt.plot(range(1, len(accuracy) + 1), accuracy,\n    #          marker='o', mec='r', mfc='w', label='test_accuracy')\n    # plt.plot(range(1, len(train_accu) + 1), train_accu,\n    #          marker='*', ms=10, label='train_accuracy')\n    # plt.legend()\n    # plt.xlabel(\"epoch\")\n    # plt.ylabel(\"准确率\")\n    # plt.title(\"训练集与测试集准确率-epoch\")\n    # plt.show()\n","sub_path":"cifar10_optimizer.py","file_name":"cifar10_optimizer.py","file_ext":"py","file_size_in_byte":7610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"514065530","text":"#https://linuxhint.com/google_search_api_python/\n\nG_API_KEY = 'AIzaSyAeVsQo5zymUz7DLj1aF_u8NYBbuhWrYqU'\nG_CSE_ID = '007428344889812528205:huvjasy6h0c'\n\nfrom googleapiclient.discovery import build\nmy_api_key = G_API_KEY\nmy_cse_id = G_CSE_ID\n\n\ndef google_search(search_term, api_key, cse_id, **kwargs):\n    service = build(\"customsearch\", \"v1\", developerKey=api_key)\n    res = service.cse().list(q=search_term, cx=cse_id, **kwargs).execute()\n    return res\n\n\nresult = google_search(\"Hoàng Tử Sơn Ca\", my_api_key, my_cse_id)\nprint(result)\n","sub_path":"gsearch.py","file_name":"gsearch.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"610470128","text":"\"\"\"\nDefines the serializers used in user REST api views.\n\"\"\"\n# pylint: disable=missing-class-docstring\n\nfrom django.db.utils import IntegrityError\nfrom rest_framework import serializers\n\nfrom user_auth.models import UserGroup\nfrom users.models import AppUser\n\n\nclass GroupSerializer(serializers.ModelSerializer):\n\n    class Meta:\n        model = UserGroup\n        fields = ['name', 'authority']\n\n\nclass AppUserListSerializer(serializers.ModelSerializer):\n    groups = GroupSerializer(many=True)\n\n    class Meta:\n        model = AppUser\n        fields = [\n            'id',\n            'username',\n            'email',\n            'is_superuser',\n            'groups']\n\n\nclass AppUserCreateSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = AppUser\n        fields = []\n\n    def create(self, validated_data):\n        try:\n            return super().create(validated_data)\n        except IntegrityError as ex:\n            raise serializers.ValidationError({\"detail\": ex.__cause__})\n\n\nclass GroupRetrieveSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = UserGroup\n        fields = ['name']\n\n\nclass AppUserRetrieveSerializer(serializers.ModelSerializer):\n    groups = GroupSerializer(many=True)\n    # groups = serializers.SerializerMethodField()\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n    class Meta:\n        model = AppUser\n        fields = [\n            'id',\n            'username',\n            'email',\n            'first_name',\n            'last_name',\n            'is_superuser',\n            'groups',\n            'avatar']\n\n    # def get_groups(self, instance):\n    #     return GroupRetrieveSerializer(\n    #         instance.groups.all(), many=True, context=self.context).data\n\n\nclass AppUserUpdateSerializer(serializers.ModelSerializer):\n    groups = GroupSerializer(many=True, read_only=True)\n\n    class Meta:\n        model = AppUser\n        fields = [\n            'id',\n            'username',\n            'email',\n            'is_superuser',\n            'first_name',\n            'last_name',\n            'groups',\n            'avatar']\n        extra_kwargs = {\n            'username': {'read_only': True},\n            'email': {'read_only': True},\n            'is_superuser': {'read_only': True},\n        }\n\n    def update(self, instance, validated_data):\n        try:\n            return super().update(instance, validated_data)\n        except IntegrityError as ex:\n            raise serializers.ValidationError({\"detail\": ex.__cause__})\n","sub_path":"users/api/app_user/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"535341998","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2016-2018\n# Author:  Marc van Dijk (marcvdijk@gmail.com)\n# file: io_dot_format.py\n\n\"\"\"\nFunctions for exporting and importing graphs to and from graph description\nlanguage (DOT) format\n\"\"\"\n\nimport logging\nimport json\nimport re\nimport shlex\n\nfrom graphit import __module__, version, Graph\nfrom graphit.graph_py2to3 import StringIO, PY_PRIMITIVES\nfrom graphit.graph_exceptions import GraphitException\nfrom graphit.graph_io.io_helpers import coarse_type, open_anything, StreamReader\n\ndirection_splitter = re.compile('(--|->)')\nattribute_splitter = re.compile('(?<=[^0-9]),(?=[^0-9]+)')\n\nlogger = logging.getLogger(__module__)\n\n__all__ = ['write_dot', 'read_dot']\n\n\ndef parse_graph_type(reader, graph):\n    \"\"\"\n    Parse the start of a DOT graph and set a new Graphit graph for it.\n\n    DOT syntax defines a graph as:\n\n        <'graph' or 'digraph'> <\"optional title\"> {\n\n    :param reader: StreamReader instance\n    :type reader:  StreamReader\n    :param graph:  Graph object to import DOT data in\n    :type graph:   :graphit:Graph\n\n    :return:       Graphit Graph\n    :rtype:        :graphit:Graph\n    \"\"\"\n\n    line, graph_type = reader.read_upto_block(['graph', 'digraph'], keep=True)\n    if graph_type:\n        line, char = reader.read_upto_char('{')\n        line = shlex.split(line)\n        if line:\n            graph.data['title'] = line[0]\n\n        graph.directed = True if graph_type == 'digraph' else False\n        graph.data['auto_nid'] = False\n\n        logging.info('Graph type \"{0}\" with title: {1}'.format(graph_type, graph.data.get('title', '')))\n        return graph\n\n    return None\n\n\ndef parse_edge(line, graph, attr=None):\n    \"\"\"\n    Add DOT edges to the graphit Graph\n\n    :param line:  line with edges to parse\n    :type line:   :py:str\n    :param graph: Graph to add edges to\n    :type graph:  :graphit:Graph\n    :param attr:  attributes to add to edges\n    :type attr:   :py:dict\n\n    :return:      added edges\n    :rtype:       :py:list\n    \"\"\"\n\n    added_edges = []\n    attr = attr or {}\n\n    edges = [e.strip() for e in direction_splitter.split(line)]\n    for n in [i for i, x in enumerate(edges) if x in ('--', '->')]:\n        start_edge, direction, target_edge = edges[n-1:n+2]\n        target_edge = target_edge.strip(' {} ').split()\n\n        directed = True if direction == '->' else False\n        for edge in target_edge:\n            node_from_edge = start_edge not in graph.nodes or edge not in graph.nodes\n            added_edges.append(graph.add_edge(start_edge, edge,\n                                              directed=directed,\n                                              node_from_edge=node_from_edge,\n                                              **attr))\n\n    return added_edges\n\n\ndef parse_attributes(line):\n    \"\"\"\n    Parse DOT keyword attributes\n\n    :param line:  line with attributes to parse\n    :type line:   :py:str\n\n    :return:      attributes\n    :rtype:       :py:dict\n    \"\"\"\n\n    part = ' '.join(shlex.split(line))\n    block = attribute_splitter.split(part)\n\n    attributes = {}\n    for attr in block:\n        attr = attr.split('=')\n        attributes[attr[0].strip()] = coarse_type(attr[1].strip())\n\n    return attributes\n\n\ndef parse_nodes(line, graph):\n    \"\"\"\n    Add DOT nodes to the graphit Graph\n\n    :param line:  line with nodes to parse\n    :type line:   :py:str\n    :param graph: Graph to add nodes to\n    :type graph:  :graphit:Graph\n\n    :return:      added nodes\n    :rtype:       :py:list\n    \"\"\"\n\n    nodes = [l.strip(',') for l in shlex.split(line)]\n\n    for node in nodes:\n        if node not in graph.nodes:\n            graph.add_node(node)\n\n    return nodes\n\n\ndef read_dot(dot, graph=None):\n    \"\"\"\n    Read graph in DOT format\n\n    :param dot:             DOT graph data.\n    :type dot:              File, string, stream or URL\n    :param graph:           Graph object to import DOT data in\n    :type graph:            :graphit:Graph\n\n    :return:                Graph object\n    :rtype:                 :graphit:Graph\n    \"\"\"\n\n    dot_stream = StreamReader(open_anything(dot))\n\n    # User defined or default Graph object\n    if graph is None:\n        graph = Graph()\n    elif not isinstance(graph, Graph):\n        raise GraphitException('Unsupported graph type {0}'.format(type(graph)))\n\n    block = None\n    node_attr = {}\n    edges = []\n    nodes = []\n\n    parse = True\n    init_graph = False\n    while parse or dot_stream.has_more:\n\n        # Parse start graph block\n        if not init_graph:\n            graph = parse_graph_type(dot_stream, graph)\n            if graph is not None:\n                init_graph = True\n\n        # Parse up to DOT reserved chars\n        line, char = dot_stream.read_upto_char(('\\n', ';', '[', ']', '}'))\n        line = line.strip() if line else ''\n\n        if line:\n\n            # Comment line\n            if line[0] in ('/', '#'):\n                logging.info('Skip DOT comment line: \"{0}\"'.format(line))\n\n                # Read till end of line\n                if char != '\\n':\n                    dot_stream.read_upto_char('\\n')\n\n            # Grouping not supported\n            elif line[0] == '{':\n                nxt_line, nxt_char = dot_stream.read_upto_char('}')\n                logging.info('Skip group: {0}{1}{2}{3}'.format(line, char, nxt_line, nxt_char))\n\n            # Subgraphs\n            elif 'subgraph' in line:\n                block = 'subgraph'\n                node_attr[block] = shlex.split(line)[1]\n\n            # Node attribute block\n            elif 'node' in line:\n                block = 'node'\n                node_attr = {}\n\n            # Parse edges\n            elif '--' in line or '->' in line:\n\n                attr = {}\n                if char == '[':\n                    attr = parse_attributes(dot_stream.read_upto_char(']')[0])\n                edges.extend(parse_edge(line, graph, attr=attr))\n\n            else:\n                if '=' in line:\n                    if block in ('subgraph', 'node'):\n                        node_attr.update(parse_attributes(line))\n                    else:\n                        graph.data.update(parse_attributes(line))\n                else:\n                    nodes.extend(parse_nodes(line, graph))\n\n        elif (char == '}' and block == 'subgraph') or block == 'node':\n            logging.info('Stop parsing {0} group at position: {1}'.format(block, dot_stream.block_pos[1]))\n\n            nodes.extend(list(set(sum(edges, ()))))\n            for node in nodes:\n                graph.nodes[node].update(node_attr)\n\n            node_attr = {}\n            edges = []\n            nodes = []\n            block = None\n\n        else:\n            parse = False\n\n    return graph\n\n\ndef write_dot(graph, graph_name=None):\n    \"\"\"\n    DOT graphs are either directional (digraph) or undirectional, mixed mode\n    is not supported.\n\n    Nodes and edges are all exported separably, short hand notations are not\n    supported. Grouping and supgraphs are not supported.\n    Graph attributes in graph.data, graph.edges and graph.nodes will be\n    exported as DOT directives regardless if they are official GraphVis DOT\n    graph directives as listed in the reference documentation:\n        https://www.graphviz.org/doc/info/attrs.html\n\n    Dot reserved rendering keywords part of the graphs global attributes in\n    graph.data or part of the node and edge attributes are exported as part\n    of the DOT graph.\n\n    :param graph:          Graph object to export\n    :type graph:           :graphit:Graph\n    :param graph_name:     name of the 'graph' or 'digraph'. Uses the 'title'\n                           attribute in graph.data by default, else graph_name\n    :type graph_name:      :py:str\n\n    :return:               DOT graph representation\n    :rtype:                :py:str\n    \"\"\"\n\n    indent = ' ' * 4\n    link = '->' if graph.directed else '--'\n\n    # Create empty file buffer\n    string_buffer = StringIO()\n\n    # Write header comment and graph container\n    graph_name = graph.data.get('title', graph_name or 'graph')\n    string_buffer.write('//Created by {0} version {1}\\n'.format(__module__, version()))\n    string_buffer.write('{0} \"{1}\" {2}\\n'.format('digraph' if graph.directed else 'graph', graph_name, '{'))\n\n    # Write global DOT directives\n    for dot_key, dot_value in graph.data.items():\n        if isinstance(dot_value, PY_PRIMITIVES) and not dot_key.startswith('$'):\n            string_buffer.write('{0}{1}={2}\\n'.format(indent, dot_key, json.dumps(dot_value)))\n\n    # Export nodes\n    string_buffer.write('{0}//nodes\\n'.format(indent))\n    for node in graph.iternodes():\n        attr = ['{0}={1}'.format(k, json.dumps(v)) for k, v in node.nodes[node.nid].items() if\n                isinstance(v, PY_PRIMITIVES) and not k.startswith('$')]\n        if attr:\n            string_buffer.write('{0}{1} [{2}];\\n'.format(indent, node.nid, ','.join(attr)))\n\n    # Export adjacency\n    string_buffer.write('{0}//edges\\n'.format(indent))\n    done = []\n    for edge in graph.edges:\n        if edge not in done:\n            edges = sorted([edge, edge[::-1]])\n\n            attr = []\n            for e in edges:\n                attr.extend(['{0}={1}'.format(k, json.dumps(v)) for k, v in graph.edges[e].items()\n                             if isinstance(v, PY_PRIMITIVES) and not k.startswith('$')])\n\n            start, end = edges[0]\n            if attr:\n                string_buffer.write('{0}{1} {2} {3} [{4}];\\n'.format(indent, start, link, end, ','.join(attr)))\n            else:\n                string_buffer.write('{0}{1} {2} {3};\\n'.format(indent, start, link, end))\n\n            done.extend(edges)\n\n    # Closing curly brace\n    string_buffer.write('}\\n')\n\n    # Reset buffer cursor\n    string_buffer.seek(0)\n    return string_buffer.read()\n","sub_path":"graphit/graph_io/io_dot_format.py","file_name":"io_dot_format.py","file_ext":"py","file_size_in_byte":9772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"553959676","text":"import json\n\n\nasync def test_cars_get(cli):\n    resp = await cli.get('/cars')\n    assert resp.status == 200\n    resp = await resp.json()\n    assert resp\n\n\nasync def test_cars_post(cli):\n    data = {\n        'year': 1997, 'mileage': 20000, 'price': 100500, 'body_type': 'Hatchback',\n        'transmission': 'Automatic', 'fuel_type': 'Diesel', 'exterior_color': 'White',\n    }\n    resp = await cli.post('/cars', data=json.dumps(data))\n    assert resp.status == 200\n    resp = await resp.json()\n    assert '_id' in resp\n    assert '$oid' in resp['_id']\n","sub_path":"tests/api/test_car.py","file_name":"test_car.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"155083476","text":"from google.appengine.api import search\nfrom google.appengine.ext import (\n  deferred,\n  ndb,\n)\nfrom protorpc import (\n  message_types,\n)\nimport endpoints\nimport tap\nimport tap.endpoints\n\nfrom api import api\nimport message\nimport model\n\nrate_limit = tap.endpoints.rate_limit(rate=50, size=50, key=tap.endpoints.get_user_id_or_ip, tag=\"timecard:api\")\n\nclass user(object):\n  @staticmethod\n  @ndb.tasklet\n  def store(user):\n    key = model.User.gen_key(user.user_id())\n    entity = yield key.get_async()\n    if entity is None:\n      entity = model.User(key=key)\n    entity.name = user.name\n    entity.language = user.language\n    entity.put_async()\n    UserSearchIndex.update(user)\n\n@api.api_class(resource_name=\"me\", path=\"me\")\nclass Me(tap.endpoints.CRUDService):\n\n  @endpoints.method(message_types.VoidMessage, message.UserResponse)\n  @ndb.toplevel\n  @rate_limit\n  def get(self, _request):\n    user_key = model.User.gen_key(tap.endpoints.get_user_id())\n    entity = yield user_key.get_async()\n    if entity is None:\n      raise endpoints.NotFoundException()\n\n    raise ndb.Return(message.UserResponse(\n      key       = entity.key.string_id(),\n      name      = entity.name,\n      language  = entity.language,\n    ))\n\n  @endpoints.method(message.UserRequestNew, message.UserResponse)\n  @ndb.toplevel\n  @rate_limit\n  def create(self, request):\n    user_key = model.User.gen_key(tap.endpoints.get_user_id())\n    entity = yield user_key.get_async()\n    if entity is not None:\n      raise endpoints.ForbiddenException()\n\n    entity = model.User(\n      key       = user_key,\n      name      = request.name,\n    )\n\n    language = request.language\n    if language is None:\n      accept_language = self.request_state.headers.get(\"Accept-Language\")\n      if accept_language:\n        for lang in [locale.split(\";\", 1)[0].replace(\"-\", \"_\") for locale in accept_language.split(\",\")]:\n          if lang in model.LANGUAGE_CHOICES_VALUES:\n            language = lang\n    if language is not None:\n      entity.language = language\n\n    try:\n      future = entity.put_async()\n      future.check_success()\n    except Exception as e:\n      raise endpoints.BadRequestException(e.message)\n    UserSearchIndex.update(entity)\n    raise ndb.Return(message.UserResponse(\n      key       = entity.key.string_id(),\n      name      = entity.name,\n      language  = entity.language,\n    ))\n\n  @endpoints.method(message.UserRequest, message.UserResponse)\n  @ndb.toplevel\n  @rate_limit\n  def update(self, request):\n    user_key = model.User.gen_key(tap.endpoints.get_user_id())\n    entity = yield user_key.get_async()\n    if entity is None:\n      raise endpoints.BadRequestException()\n\n    modified = False\n    for field in request.all_fields():\n      name = field.name\n      if name == \"key\":\n        continue\n      value = request.__getattribute__(name)\n      if value is None:\n        continue\n      entity.__setattr__(name, value)\n      modified = True\n    else:\n      if modified is False:\n        raise endpoints.BadRequestException()\n    try:\n      future = entity.put_async()\n      future.check_success()\n    except Exception as e:\n      raise endpoints.BadRequestException(e.message)\n    UserSearchIndex.update(entity)\n    raise ndb.Return(message.UserResponse(\n      key       = entity.key.string_id(),\n      name      = entity.name,\n      language  = entity.language,\n    ))\n\n  @endpoints.method(message.UserRequestDelete, message_types.VoidMessage)\n  @ndb.toplevel\n  @rate_limit\n  def delete(self, request):\n    user_id = tap.endpoints.get_user_id()\n    if request.key != user_id:\n      raise endpoints.BadRequestException()\n    user_key = model.User.gen_key(user_id)\n    entity = yield user_key.get_async()\n    if entity is None:\n      raise endpoints.NotFoundException()\n    if request.name != entity.name:\n      raise endpoints.BadRequestException()\n\n    query = model.Project.query(model.Project.admin == user_key)\n    result = yield query.get_async(keys_only=True)\n    if result:\n      raise endpoints.ForbiddenException()\n\n    future = entity.key.delete_async()\n    if future.check_success():\n      raise future.get_exception()\n    deferred.defer(UserSearchIndex.delete, user_id)\n    raise ndb.Return(message_types.VoidMessage())\n\n@api.api_class(resource_name=\"user\", path=\"user\")\nclass User(tap.endpoints.CRUDService):\n\n  @endpoints.method(message.UserRequestListCollection, message.UserResponseCollection)\n  @ndb.toplevel\n  @rate_limit\n  def list(self, request):\n    key_list = list()\n    for user_receive_list in request.items:\n      key_list.append(model.User.gen_key(user_receive_list.key))\n    if key_list:\n      entities = yield ndb.get_multi_async(key_list)\n    else:\n      raise endpoints.BadRequestException(\"Bad query\")\n    items = list()\n    for user in entities:\n      if user is None:\n        continue\n      items.append(message.UserResponse(key=user.user_id,\n                                    name=user.name,\n                                    language=user.language))\n    raise ndb.Return(message.UserResponseCollection(\n      items = items,\n    ))\n\n  @endpoints.method(message.UserRequestSearch, message.UserResponseCollection)\n  @ndb.toplevel\n  @rate_limit\n  def search(self, request):\n    if len(request.query.encode(\"utf-8\")) < 3:\n      raise endpoints.BadRequestException(\"Bad query\")\n    query = search.Query(\n      query_string = request.query,\n      options = search.QueryOptions(\n        limit  = 20,\n        cursor = search.Cursor(web_safe_string=request.pagination),\n        ids_only = True,\n      ),\n    )\n    key_list = list()\n    documents = UserSearchIndex.search_index.search(query)\n    for document in documents:\n      key_list.append(ndb.Key(model.User, document.doc_id))\n    items = list()\n    if key_list:\n      entities = yield ndb.get_multi_async(key_list)\n      for user in entities:\n        if user is None:\n          continue\n        items.append(message.UserResponse(key=user.user_id,\n                                      name=user.name,\n                                      language=user.language))\n    if documents.cursor:\n      cursor_string = documents.cursor.web_safe_string\n    else:\n      cursor_string = None\n    raise ndb.Return(message.UserResponseCollection(\n      items = items,\n      pagination = cursor_string,\n    ))\n\nclass UserSearchIndex(object):\n\n  search_index = search.Index(name=\"timecard:user\")\n\n  @classmethod\n  def update(cls, user):\n    deferred.defer(cls.put, user.user_id, user.name)\n\n  @classmethod\n  def put(cls, doc_id, name):\n    cls.search_index.put(search.Document(\n      doc_id = doc_id,\n      fields = [search.TextField(name=\"a\", value=name)],\n    ))\n\n  @classmethod\n  def delete(cls, doc_id):\n    cls.search_index.delete(doc_id)\n","sub_path":"gae/main_api/v1/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":6698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"134485303","text":"class BaseDedados:\r\n    def __init__(self):\r\n        self.__dados = {}\r\n    def inserir_cliente(self,id,nome):\r\n        if 'clientes' not in self.__dados:\r\n            self.__dados['clientes']={id: nome}\r\n        else:\r\n            self.__dados['clientes'].update({id:nome})\r\n    def lista_clientes(self):\r\n        for id,nome in self.__dados['clientes'].items():\r\n            print(id,nome)\r\n    def apaga_cliente(self,id):\r\n        del self.__dados['clientes'][id]\r\n\r\n\r\nbd = BaseDedados()\r\n\r\nbd.inserir_cliente(1,'Eduardo')\r\nbd.inserir_cliente(2,'Joao')\r\nbd.inserir_cliente(3,'Maria')\r\nbd.inserir_cliente(4,'Pedro')\r\n\r\nbd.lista_clientes()\r\nprint()\r\nbd.apaga_cliente(3)\r\nbd.lista_clientes()","sub_path":"encapsulamento.py","file_name":"encapsulamento.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"464970911","text":"# -*- coding:utf-8 -*-\n# @Script: main.py\n# @Author: Zhiwei.Yang\n# @Email: tencrance@gmail.com\n# @Create At: 2018-08-26 02:11:48\n# @Last Modified By: Zhiwei.Yang\n# @Last Modified At: 2018-08-26 02:17:16\n# @Description: This is description.\n\n\n\nimport requests\nfrom bs4 import Tag\nfrom bs4 import BeautifulSoup\n \n \ndef getHtml(url):\n    page = requests.get(url)\n    html =page.text\n    return html\n \ndef getText(html):\n    get_text = Tag.get_text\n    soup = BeautifulSoup(html, 'html.parser')\n    \n    author_info = soup.find_all('div', class_='info')\n    listauthor  = [x.get_text() for x in author_info]\n        \n    list_info = soup.find_all('div', class_='reply-div')\n    listtext  = [x.get_text() for x in list_info]\n \n    # global i\n    if i > 1:\n        listtext = [\"\"] + listtext\n    \n    for x in range(len(listauthor)):\n        if \"kkndme\" in listauthor[x]:\n            print (listtext[x].strip())\n            \nif __name__=='__main__':\n    for i in range(1,6):\n        url  = (\"http://bbs.tianya.cn/m/post-house-252774-%s.shtml\" % str(i))\n        html = getHtml(url)\n        getText(html)\n","sub_path":"web_crawl/tianya/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"291064654","text":"from collections import Counter\nfrom Bio import SeqIO\ndef determineGCCount(dnaString):\n\tcount = Counter(dnaString)\n\tcCount = count['C']\n\tgCount = count['G']\n\tpercentage = (gCount+cCount)/ len(dnaString)\n\treturn percentage * 100\n\n\nstring = []\nrecordID = []\ntestDict = {}\nwith open(\"rosalind_gc.txt\") as f:\n\tfor record in SeqIO.parse(f, \"fasta\"):\n\t\tstring.append(\"{}\".format(record.seq))\n\t\trecordID.append(record.id)\nf.close()\n\nfor x in range(0,len(string)):\n\ttest = string[x]\n\ttestDict.update({recordID[x]:determineGCCount(test)})\n\n#print(string)\n#print(recordID)\nprint(testDict)\n","sub_path":"Rosalind/GCContent.py","file_name":"GCContent.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"302059585","text":"import ai\r\nimport bi\r\nimport ci\r\nimport di\r\nimport ei\r\nimport csv\r\nfrom bs4 import BeautifulSoup\r\n\r\nprint('특허 정보 검색프로그램을 실행합니다.')\r\nfn = '개인과제data.txt'\r\n\r\nprint('원하시는 검색조건의 숫자를 입력하세요' , '\\n', '1) 출원인 입력하여 특허찾기 2) 발명가 입력하여 특허찾기 3) 키워드 입력하여 특허찾기 4) 특정 특허의 특허 설명 5) 본 데이터내의 특허 정보 정리')\r\n\r\na = input('1 or 2 or 3 or 4 or 5 : ')\r\n\r\nif a == '1' :\r\n    print('출원인을 검색하여 특허 찾기를 선택하셨습니다.')\r\n    language = input('출원인의 이름을 어떤 언어로 입력하시겠습니까? (\\'한글\\' 또는 \\'영어\\'로 입력해주세요) : ')\r\n\r\n    if language == '한글' :\r\n        print('출원인 - 한글이름으로 찾기를 선택하셨습니다. ')\r\n        name = input('찾고자하는 출원인의 이름을 입력해주세요. (출원인 목록을 확인하려면 1번을 눌러주세요.) : ')\r\n        print('잠시만 기다려 주세요..')\r\n        try :\r\n            if name == '1' :\r\n                ai.find_all_applicant(fn)\r\n                name = input('출원인의 이름을 입력해주세요. : ')\r\n                print('잠시만 기다려 주세요..')\r\n                ai.find_apllicant(fn,name)\r\n            else:\r\n                ai.find_apllicant(fn,name)\r\n            \r\n        except Exception :\r\n            print('이름을 확인해 주세요!')\r\n\r\n    elif language == '영어':\r\n        name = input('Please typing English name that you want to find. (If you want check the list of applicants, input number 1) : ')\r\n        print('Pleas waiting..')\r\n        try :\r\n            if name == '1' :\r\n                ai.find_all_applicant_eng(fn)\r\n                name = input('Please typing English name that you want to find. : ')\r\n                ai.find_apllicant_eng(fn,name)\r\n            else:\r\n                ai.find_apllicant_eng(fn,name)\r\n            \r\n        except Exception :\r\n            print('Please Check name! ')\r\n    else :\r\n        print('\\'한글\\' 또는 \\'영어\\'를 입력해주세요.')\r\n\r\n        \r\nelif a == '2' :\r\n    print('\\n','발명인을 검색하여 특허 찾기를 선택하셨습니다.')\r\n    language = input('발명인의 이름을 어떤 언어로 입력하시겠습니까? (\\'한글\\' 또는 \\'영어\\'로 입력해주세요) : ')\r\n\r\n    if language == '한글' :\r\n        name = input('찾고자하는 발명인의 이름을 입력해주세요. (발명인의 목록을 확인하려면 1번을 눌러주세요.) :')\r\n\r\n        if name == '1' :\r\n            bi.find_all_inventor(fn)\r\n            name = input('발명인의 이름을 입력해주세요. : ')\r\n            print('로딩 중입니다..')\r\n            bi.find_inventor(fn,name)\r\n        else:\r\n            print('로딩 중입니다..')\r\n            bi.find_inventor(fn,name)\r\n\r\n    elif language == '영어':\r\n        name = input('Please typing English name that you want to find. (If you want check the list of Inventors, input number 1) : ')\r\n        print('Pleas waiting..')\r\n        try :\r\n            if name == '1' :\r\n                \r\n                bi.find_all_inventor_eng(fn)\r\n                name = input('Please typing English name that you want to find. : ')\r\n                bi.find_inventor_eng(fn,name)\r\n                \r\n            else:\r\n                bi.find_inventor_eng(fn,name)\r\n        except Exception :\r\n            print('Please Check name! ')\r\n    else :\r\n        print('\\'한글\\' 또는 \\'영어\\'를 입력해주세요.')\r\n\r\nelif a == '3':\r\n    print('키워드을 검색하여 특허 찾기를 선택하셨습니다.')\r\n    print('로딩 중입니다..')    \r\n    ci.find_key_title(fn)\r\n\r\n\r\nelif a == '4':\r\n    print('\\n', '특정 특허의 특허 설명 찾기를 선택하셨습니다.')\r\n    name = input('찾고자하는 특허명을 입력해주세요. (특허 목록을 출력하려면 \\'특허-목록\\'을 입력해주세요.): ')\r\n    \r\n\r\n    if name == '특허-목록':\r\n        \r\n        di.find_titles(fn,name)\r\n        name = input('찾고자하는 특허명을 입력해주세요.:')\r\n        print('로딩 중입니다.. ')\r\n        di.find_claims(fn, name)\r\n\r\n        \r\n    else :\r\n        print('로딩 중입니다..')\r\n        di.find_claims(fn, name)\r\n    \r\nelif a == '5' :\r\n    print('본 데이터 내의 특허 명, 출원인, 발명가, 특허 초록, 특허 설명을 출력 및 저장하는 프로그램입니다. ')\r\n    print('로딩 중입니다..','\\n')\r\n    ei.write_Invent(fn)\r\n\r\n\r\nelse :\r\n    print('입력 번호를 확인 후 다시 시도해주세요.')\r\n","sub_path":"개인과제/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"609996598","text":"from setuptools import setup,find_packages\n\n\nimport os\nhere = os.path.abspath(os.path.dirname(__file__))\nreadme = open(os.path.join(here, 'README.md'), 'r').read()\nchangelog = open(os.path.join(here, 'README.md'), 'r').read()\n\nsetup(name='ppss_auth',\n  version='0.7.4.1',\n  description='simple auth scheme for pyramid, based on Mako template and sqlalchemy backend',\n  long_description=readme + \"\\n\\n\\n\" + changelog,\n  long_description_content_type=\"text/markdown\",\n  author='pdepmcp',\n  author_email='d.cariboni@pingpongstars.it',\n  license='MIT',\n  classifiers=[\n    'Development Status :: 4 - Beta',\n    'Intended Audience :: Developers',\n    'Operating System :: OS Independent',\n    'Framework :: Pyramid',\n    'Topic :: Internet :: WWW/HTTP :: Session',\n    'Programming Language :: Python :: 2',\n    'Programming Language :: Python :: 2.7',\n    'Programming Language :: Python :: 3.6',\n    'Programming Language :: Python :: 2.7',\n  ],\n  keywords=\"pyramid module authentication accelerator\",\n  python_requires='>=2.7',\n  url='http://www.pingpongstars.it',\n  install_requires=['pyramid_mako','pyramid_beaker' ],\n  #packages=['src/test1'],\n  packages=find_packages(),\n  include_package_data=True,\n\n)\n\n\n","sub_path":"pypi_install_script/ppss_auth-0.7.4.1.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"54655462","text":"#import packages.\nfrom skimage.filters import threshold_local\nimport numpy as np \nimport argparse\nimport cv2\nimport imutils\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--image\", required = True, help = \"path of image\")\nargs = vars(ap.parse_args())\n\nprint(args)\n\n#load image\n\n\nimage = cv2.imread(args[\"image\"])\nratio = image.shape[0] / 500.0\norig = image.copy()\nimage = imutils.resize(image, height = 500)\n\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\ngray = cv2.GaussianBlur(gray, (5, 5), 0)\nedged = cv2.Canny(gray, 75, 200)\n\nprint(\"Edge Detection yas\")\ncv2.imshow(\"Image\", image)\ncv2.imshow(\"Edged\", edged)\ncv2.waitKey(0)\n","sub_path":"misc/timestabler-scan.py","file_name":"timestabler-scan.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"413890508","text":"# https://www.youtube.com/watch?v=JcI5Vnw0b2c&index=2&list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v\n# Regression Intro - Practical Machine Learning Tutorial with Python p.2\nimport pandas as pd\nimport quandl\n\nstyle.use('ggplot')\n\n# quandl.ApiConfig.api_key = '9tnd3zJgSopfWdp5g7eT'\n# df = quandl.get(\"WIKI/GOOGL\")\n# df.to_csv('data/shift_google.csv')\n\ndf = pd.read_csv('data/shift_google.csv')\ndf = df[['Adj. Open', 'Adj. High', 'Adj. Low', 'Adj. Close', 'Adj. Volume']]\ndf['HL_PCT'] = (df['Adj. High'] - df['Adj. Close']) / df['Adj. Close'] * 100.0\ndf['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100.0\n\ndf = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]\nprint(df.head())\n","sub_path":"sentdex/machine-learning/p2.py","file_name":"p2.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"109575981","text":"import scrapy\n\nfrom scrapy.http import FormRequest\nfrom scrapy.spiders import Spider\nimport csv\nimport string\nimport re\nimport codecs\nimport json\nimport requests\n\n\n\n\n\n\nclass DmozSpider(Spider):\n\tname = \"delta\"\n\t\"allowed_domains = ['bancamarch.es']\"\n\t\"start_urls = ['https://telemarch.bancamarch.es/htmlVersion/index.jsp?idioma=es']\"\n\n\n\tdef start_requests(self):\n\t\t\"urls = ['file:///home/yago/tranz-scrapy/tutorial/tutorial/spiders/body.html']\"\n\t\turls = ['https://telemarch.bancamarch.es/htmlVersion/index.jsp?idioma=es']\n\t\tfor url in urls:\n\t\t\tyield scrapy.Request(url=url, callback=self.parse)\n\n\tdef parse(self, response):\n\t\tyield FormRequest.from_response(response,formname='basic',formdata={'usuario': '5078281','clave': '5709'},callback=self.parse1)\n\n\n\tdef parse1(self, response):\n\t\t\n\n\n\t\t\n\t\tnext_page = response.css('li.opid_163 a::attr(href)').extract_first()\n\t\t\n\t\tnext_page = response.urljoin(next_page)\n\t\t\n\n\t\t\n\t\tyield scrapy.Request(next_page, callback=self.parse2)\n\n\n\n\tdef parse2(self, response):\n\t\t\n\n\t\tyield FormRequest.from_response(response,formname='f3002',callback=self.parse3)\n\t\t\n\tdef parse3(self, response):\n\t\t\n\t\t\n\t\ttable1 = []\n\t\trow1 = []\n\t\trows = response.xpath('//table[@id=\"tablaMovimientos\"]/tbody/tr')\n\t\tfor row in rows:\n\t\t\t\n\t\t\t\n\t\t\ttd1 = row.xpath('td[1]/text()').extract()\n\t\t\t\n\t\t\ttd1 = ''.join(c for c in td1 if c not in '\\r\\t\\n')\n\t\t\t\n\t\t\ttd1 = re.sub('[\\t\\r\\n]','',td1)\n\t\t\t\n\t\t\trow1.append({'fecOp':td1})\n\t\t\ttd2 = row.xpath('td[2]/text()').extract()\n\t\t\ttd2 = ''.join(c for c in td2 if c not in '\\r\\t\\n')\n\t\t\ttd2 = re.sub('\\W+','', td2 )\n\n\n\t\t\trow1.append({'fecVal':td2})\n\t\t\ttd3 = row.xpath('td[3]/text()').extract()\n\t\t\ttd3 = ''.join(c for c in td3 if c not in '\\r\\t\\n')\n\t\t\t\"td3 = re.sub('\\W+','', td3 )\"\n\t\t\ttd3 = re.sub('[\\t\\r\\n]','',td3)\n\t\t\t\n\t\t\t\n\t\t\trow1.append({'oficina':td3})\n\t\t\ttd4 = row.xpath('td[4]/text()').extract()\n\t\t\ttd4 = ''.join(c for c in td4 if c not in '\\r\\t\\n')\n\t\t\ttd4 = re.sub('[\\t\\r\\n]','',td4)\n\t\t\trow1.append({'Concepto':td4})\n\t\t\ttd5 = row.xpath('td[5]/text()').extract()\n\t\t\ttd5 = ''.join(c for c in td5 if c not in '\\r\\t\\n')\n\t\t\ttd5 = re.sub('[\\t\\r\\n]','',td5)\n\t\t\t\n\t\t\ttd5 = td5.replace(u'\\u20ac','EUR')\n\t\t\trow1.append({'Importe':td5})\n\t\t\ttd6 = row.xpath('td[6]/text()').extract()\n\t\t\ttd6 = ''.join(c for c in td6 if c not in '\\r\\t\\n')\n\t\t\ttd6 = re.sub('[\\t\\r\\n]','',td6)\n\t\t\t\n\t\t\ttd6 = td6.replace(u'\\u20ac','EUR')\n\t\t\trow1.append({'Saldo':td6})\n\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\ttable1.append(row1)\n\t\t\trow1 = []\n\t\t\n\t\tpost_json(table1)\n\n\n\ndef post_json(tbl):\n\t\n\ttabla = json.dumps(tbl)\n\tr = requests.post('http://127.0.0.1:3000/bancamarch', data={'json': tabla})\n\t\n","sub_path":"tutorial/tutorial/spiders/delta.py","file_name":"delta.py","file_ext":"py","file_size_in_byte":2579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"288290369","text":"# /tests/unit_test.py\n#\n# Unit tests for various utilities.\n#\n# Disable no-self-use in tests as all test methods must be\n# instance methods and we don't necessarily have to use a matcher\n# with them.\n# pylint:  disable=no-self-use\n#\n# See LICENCE.md for Copyright information\n\"\"\"Unit tests for various utilities.\"\"\"\n\nimport os\n\nfrom psqtraviscontainer import architecture\nfrom psqtraviscontainer import directory\nfrom psqtraviscontainer import distro\n\nfrom testtools import ExpectedException\nfrom testtools import TestCase\n\nfrom testtools.matchers import AllMatch\nfrom testtools.matchers import DirExists\nfrom testtools.matchers import MatchesPredicate\n\n\nclass TestDirectoryNavigation(TestCase):\n\n    \"\"\"Tests for directory.py.\"\"\"\n\n    def test_enter_create_dir(self):\n        \"\"\"Check that we create a dir when entering a non-existent one.\"\"\"\n        does_not_exist = \"does_not_exist\"\n        with directory.Navigation(os.path.join(os.getcwd(),\n                                               does_not_exist)) as entered:\n            self.assertThat(entered, DirExists())\n\n    def test_enter_exist_dir(self):\n        \"\"\"Check that we can enter an existing dir.\"\"\"\n        existing_dir = os.path.join(os.getcwd(), \"existing\")\n        os.makedirs(existing_dir)\n        with directory.Navigation(existing_dir) as entered:\n            self.assertThat(entered, DirExists())\n\n\nclass TestArchitecture(TestCase):  # pylint:disable=R0903\n\n    \"\"\"Tests for architecture.py.\"\"\"\n\n    def test_unknown_architecture(self):\n        \"\"\"Check that creating a non-special architecture returns metadata.\"\"\"\n        check_methods = [\n            architecture.Alias.universal,\n            architecture.Alias.qemu,\n            architecture.Alias.debian\n        ]\n\n        def function_returns_input(function):\n            \"\"\"Return true if function returns input.\"\"\"\n            return function(\"input\") == \"input\"\n\n        self.assertThat(check_methods,\n                        AllMatch(MatchesPredicate(function_returns_input,\n                                                  \"% did not return same\")))\n\n\nclass TestDistroLookup(TestCase):  # pylint:disable=R0903\n\n    \"\"\"Tests for looking up the distro.\"\"\"\n\n    def test_error_lookup_bad_distro(self):\n        \"\"\"Check that looking up a non-existent distro throws.\"\"\"\n        with ExpectedException(RuntimeError):\n            distro.lookup(\"noexist\", \"noexist\", \"noexist\")\n","sub_path":"tests/unit_test.py","file_name":"unit_test.py","file_ext":"py","file_size_in_byte":2404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"21960335","text":"import sys, collections, heapq\n\ninput = sys.stdin.readline\nN = int(input())\nM = int(input())\narr = collections.defaultdict(list)\nfor i in range(M):\n    a, b, c = map(int, input().split())\n    arr[a].append((b, c))\nstart, end = map(int, input().split())\ndiscoverd = [False] * (N + 1)\nqueue = [(0, start,[start])]\nwhile queue:\n    dis, node, route= heapq.heappop(queue)\n    if node == end:\n        print(dis)\n        print(len(route))\n        print(' '.join(map(str,route)))\n        break\n    if not discoverd[node]:\n        discoverd[node] = True\n        for i, e in arr[node]:\n            heapq.heappush(queue, (dis + e, i, route+[i]))\n","sub_path":"백준/다익스트라/최소비용구하기2.py","file_name":"최소비용구하기2.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"43428025","text":"from rest_framework import serializers\nfrom back.models import Task, Volonteer, Post\n\n\nclass TaskSerializer(serializers.Serializer):\n    id = serializers.IntegerField(read_only=True)\n    status = serializers.IntegerField(default=1)\n    user_id = serializers.CharField(default=0)\n\n    def create(self, validated_data):\n        \"\"\"\n        Create and return a new `Task` instance, given the validated data.\n        \"\"\"\n        return Task.objects.create(**validated_data)\n\n    def update(self, instance, validated_data):\n        \"\"\"\n        Update and return an existing `Task` instance, given the validated data.\n        \"\"\"\n        instance.status = validated_data.get('status', instance.status)\n        instance.user_id = str(instance.user_id) + ',' + str(validated_data.get('user_id', instance.user_id))\n        instance.save()\n        return instance\n\nclass PostSerializer(serializers.Serializer):\n    id = serializers.IntegerField(read_only=True)\n    description = serializers.CharField(default='Не задано')\n    place = serializers.CharField(default='Не задано')\n    custom = serializers.CharField(default='Не задано')\n    lat = serializers.FloatField()\n    lon = serializers.FloatField()\n\n    def create(self, validated_data):\n        \"\"\"\n        Create and return a new `Task` instance, given the validated data.\n        \"\"\"\n        import vk_api\n        login, password = 'dostoyewski@yandex.ru', 'seleNa'\n        vk_session = vk_api.VkApi(login, password)\n        try:\n            vk_session.auth(token_only=True)\n        except vk_api.AuthError as error_msg:\n            print(error_msg)\n\n        vk = vk_session.get_api()\n        string = 'ALERT' + '\\n' + 'Описание проблемы: ' + validated_data.get('description') + '\\n' + 'Примерное местоположение: ' + validated_data.get('place') + '\\n' + 'Особые приметы: ' + validated_data.get('custom')\n        vk.wall.post(message=string, owner_id=-180054668, from_group=1, lat=validated_data.get('lat'), long=validated_data.get('lon'))\n        return Post.objects.create(**validated_data)\n\nclass VolonteerSerializer(serializers.Serializer):\n    id = serializers.IntegerField(read_only=True)\n    allergy = serializers.BooleanField(default=True)\n    karma = serializers.IntegerField(default=0)\n\n    def create(self, validated_data):\n        \"\"\"\n        Create and return a new `Volonteer` instance, given the validated data.\n        \"\"\"\n        return Volonteer.objects.create(**validated_data)\n\n    def update(self, instance, validated_data):\n        \"\"\"\n        Update and return an existing `Volonteer` instance, given the validated data.\n        \"\"\"\n        instance.allergy = validated_data.get('allergy', instance.allergy)\n        instance.karma = validated_data.get('karma', instance.karma)\n        instance.save()\n        return instance","sub_path":"back/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"109177614","text":"from django.urls import path\n\nfrom core.views import FetchExams, VerifyExamView, ExamListView, ExamDetailView, \\\n    ExamListItemUpdateView\n\n\n\nurlpatterns = [\n    path('', FetchExams.as_view({'post': 'post'}), name='fetch-exams'),\n    path('exams/', ExamListView.as_view(), name='exams-list'),\n    path('exams/<pk>/', ExamDetailView.as_view(), name='exam-detail'),\n    path('exam-list_item/<pk>/', ExamListItemUpdateView.as_view(), name='exam-item-update'),\n    path('<pk>/', VerifyExamView.as_view({'post': 'post'}), name='verify-exam'),\n\n]\n","sub_path":"core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"352374455","text":"import time\n\n\nt1 = time.time()\n\n\nBianaNetworkFile = open('subnetwork.sif.5', 'r')\ngeneIdNetworkFile = open('PPI_Network.txt', 'w')\nnodeGeneIdsFile = open('Network_Gene_Ids.txt', 'w')\nscoresFile = open('guild_scores.txt', 'r')\ngeneScores = scoresFile.readlines();\nscoresFile.close()\ngeneIds = set()\n\n\nfor edge in BianaNetworkFile:\n        node1 = 0\n        node2 = 0\n        found1 = False\n        found2 = False\n        splittedEdge = edge.split(' ')\n\n        for geneInfo in geneScores:\n                \n                splittedGeneInfo = geneInfo.split('\\t')\n                \n                if splittedGeneInfo[0] == splittedEdge[0]:\n                        node1 = splittedGeneInfo[5]\n                        found1 = True\n                        \n                elif splittedGeneInfo[0] == splittedEdge[2][:-1]:\n                        node2 = splittedGeneInfo[5]\n                        found2 = True\n                        \n                if found1 and found2:\n                        break\n\n        if node1.isdigit() and node2.isdigit():\n                geneIdNetworkFile.write(str(node1) + ' interaction ' + str(node2) + '\\n')\n                geneIds.add(node1)\n                geneIds.add(node2)\n\t\t\nfor geneId in geneIds:\n        nodeGeneIdsFile.write(geneId + '\\n')\n\t\t\n\nBianaNetworkFile.close()\ngeneIdNetworkFile.close()\nnodeGeneIdsFile.close()\nt2 = time.time()\nprint('Elapsed time = %f' %(t2-t1))\n","sub_path":"IdMapping.py","file_name":"IdMapping.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"419036719","text":"def NumberOfDigits(no):\r\n    counter=0\r\n    while no!=0:\r\n        no=no//10\r\n        print(no)\r\n        counter=counter+1\r\n\r\n    return counter\r\n\r\ndef main():\r\n    num = int(input(\"Enter a number: \"))\r\n    ans=NumberOfDigits(num)\r\n    print(\"Number of Digits in {} are {}\".format(num,ans))\r\n\r\nif __name__==\"__main__\":\r\n    main()\r\n              \r\n        \r\n","sub_path":"Assignment9_2.py","file_name":"Assignment9_2.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"50925197","text":"from django.shortcuts import render\nimport requests\nfrom bs4 import BeautifulSoup\n\n\ndef scrap():\n    url = 'https://www.gov.pl/web/koronawirus/wykaz-zarazen-koronawirusem-sars-cov-2'\n    page = requests.get(url)\n    soup = BeautifulSoup(page.content, 'html.parser')\n    table = soup.find(\"pre\").find(text=True)\n    x = table.split(\";\")\n    context = {'whole_amount': x[4], 'death_amount': x[5], 'dolnoslaskie': x[7], 'dolnoslaskie_d': x[8],\n               'kujawsko': x[10], 'kujawsko_d': x[11], 'lubelskie': x[13], 'lubelskie_d': x[14],\n               'lubuskie': x[16], 'lubuskie_d': x[17], 'lodzkie': x[19], 'lodzkie_d': x[20],\n               'malopolskie': x[22], 'malopolskie_d': x[23], 'mazowieckie': x[25], 'mazowieckie_d': x[26],\n               'opolskie': x[28], 'opolskie_d': x[29], 'podkarpackie': x[31], 'podkarpackie_d': x[32],\n               'podlaskie': x[34], 'podlaskie_d': x[35], 'pomorskie': x[37], 'pomorskie_d': x[38],\n               'slaskie': x[40], 'slaskie_d': x[41], 'swietokrzyskie': x[43], 'swietokrzyskie_d': x[44],\n               'warminsko_mazurskie': x[46], 'warminsko_mazurskie_d': x[47], 'wielkopolskie': x[49],\n               'wielkopolskie_d': x[50], 'zachodniopomorskie': x[52], 'zachodniopomorskie_d': x[53]\n    }\n    return context\n\n\ndef corona(request):\n    return render(request, 'corona/index.html', scrap())\n\n\ndef details(request):\n    return render(request, 'corona/detail.html', scrap())\n","sub_path":"corona/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"378971833","text":"from django.conf.urls import url\nfrom hotel.Notification import views\n\nurlpatterns = [\n   url(r'^notifications/$', views.index, name='notification'),\n   url(r'^getNotifications/$', views.getNotifications, name='getNotifications'),\n   url(r'^save_update/$', views.save_update, name='save_update'),\n   url(r'^save_update/(?P<val>[0-9]+)/$', views.save_update, name='save_update_main'),\n   url(r'^getNotificationsById/$', views.getNotificationsById, name='getNotificationsById'),\n   url(r'^removeNotifications/$', views.removeNotifications, name='removeNotifications'),\n   url(r'^updateStatus/$', views.updateStatus, name='updateStatus'),\n]","sub_path":"eCube_Hotel_2/eCube_UI_2/hotel/Notification/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"404199614","text":"'''\nfrom evelink import api\nfrom evelink import eve\nfrom evelink import char\n\ncharacter = 'Py Solette'\nkey = 4414604\nvcode = 'ThvGQXDMLClSSEyI6BfGZ431VetmE45oszUPQQNUQSW1ozUdu8WJQBwfyBjk4opo'\n\neve_wrap = eve.EVE()\nid_responce = eve_wrap.character_id_from_name(character)\nchar_id = id_responce.result\nchar_api = api.API(api_key=(key,vcode))\n\nchar = char.Char(char_id=char_id, api=char_api)\nkm = char.kills()\nprint(km)\n'''\n\nimport json\ndef log_to_json(data, file='log_to_json'):\n    '''Takes python list/dict and makes json file'''\n    with open(file + '.json', 'w') as outfile:\n        json.dump(data, outfile)\n\n\ncharacter = 'Py Solette'\nkey = 4414604\nvcode = 'ThvGQXDMLClSSEyI6BfGZ431VetmE45oszUPQQNUQSW1ozUdu8WJQBwfyBjk4opo'\n\n\nimport evelink.api  # Raw API access\nimport evelink.char # Wrapped API access for the /char/ API path\nimport evelink.eve  # Wrapped API access for the /eve/ API path\n\neve_wrap = evelink.eve.EVE()\n\n\n# Using authenticated calls\napi = evelink.api.API(api_key=(key, vcode))\nid_response = eve_wrap.character_id_from_name(character)\nchar = evelink.char.Char(char_id=id_response.result, api=api)\nbalance_response = char.kills()\nlog_to_json(balance_response.result)\n\n","sub_path":"for_eve/fetch_kills.py","file_name":"fetch_kills.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"129397745","text":"import numpy as np\nimport argparse\nimport pulp as pl\nclass MDP:\n    def __init__(self, states, actions):\n        self.S = states\n        self.A = actions\n        self.STR = [[[] for a in range(actions)] for s in range(states)]\n        self.TR = np.zeros((states, actions), dtype=float)\n        self.isNonTerminal = np.ones((states, actions), dtype=bool)\n        self.gamma = 1.0\n    \n    def computeTerminal(self):\n        for s in range(self.S):\n            for a in range(self.A):\n                if len(self.STR[s][a]) == 0:\n                    self.isNonTerminal[s, a] = False\n                    continue\n                if self.STR[s][a][0][0] == s and self.STR[s][a][0][1] == 1.0 and self.STR[s][a][0][2] == 0.0:\n                    self.isNonTerminal[s, a] = False\n\ndef constructMDP(path):\n    f = open(path, 'r')\n    S = int(f.readline().split()[1])        # number of states\n    A = int(f.readline().split()[1])        # number of actions\n    mdp = MDP(S, A)\n    f.readline()                            # end\n\n    line = f.readline().split()\n    while line[0] == 'transition':\n        a, b, c, d, e = int(line[1]), int(line[2]), int(line[3]), float(line[4]), float(line[5])\n        mdp.STR[a][b].append((c, e, d))\n        mdp.TR[a][b] += d * e\n        line = f.readline().split()\n    \n    for s in range(S):\n        for a in range(A):\n            mdp.STR[s][a].sort()\n    \n    mdp.computeTerminal()\n    \n    mdp.gamma = float(f.readline().split()[1])      # value of gamma\n\n    f.close()\n    return mdp\n\ndef value_eval(mdp, P):\n    P = P.astype(int)\n    isNonTerminal = mdp.isNonTerminal[np.arange(mdp.S), P]\n    k = np.sum(isNonTerminal)\n    \n    T1 = np.zeros((mdp.S,mdp.S))\n    for i in range(mdp.S):\n        if not isNonTerminal[i]:\n            continue\n        for t in mdp.STR[i][P[i]]:\n            s2 = t[0]\n            if not isNonTerminal[s2]:\n                continue\n            T1[i, s2] = t[1]\n    T1 = T1[:, isNonTerminal]\n    T1 = T1[isNonTerminal, :]\n\n    U = mdp.TR[np.arange(mdp.S), P]\n    U = U[isNonTerminal]\n\n    A = np.eye(k) - mdp.gamma * T1\n\n    V = np.zeros(mdp.S)\n    V[isNonTerminal] = np.linalg.solve(A, U)\n\n    return V\n\ndef calcQ(mdp, s, a, V):\n    Q = 0\n    for t in mdp.STR[s][a]:\n        Q += t[1] * V[t[0]]\n    Q = mdp.TR[s,a] + mdp.gamma * Q\n    return Q\n\ndef optPolicy(mdp, V):\n    P = np.zeros(mdp.S, dtype=int)\n    for s in range(mdp.S):\n        Qs = [calcQ(mdp, s, a, V) if len(mdp.STR[s][a]) > 0 else -np.inf for a in range(mdp.A)]\n        P[s] = np.argmax(Qs)\n    return P\n\ndef valueIter(mdp):\n    V = np.zeros(mdp.S)\n    V_old = np.zeros(mdp.S)\n    while True:\n        for s in range(mdp.S):\n            Qs = [calcQ(mdp, s, a, V_old) for a in range(mdp.A) if len(mdp.STR[s][a]) > 0]\n            V[s] = np.max(Qs) if len(Qs) > 0 else 0.0\n        if np.allclose(V, V_old, rtol=0, atol=1e-9):\n            break\n        V_old = V.copy()\n    return V, optPolicy(mdp, V)\n\ndef HowardPI(mdp):\n    P = np.zeros(mdp.S, dtype=int)\n    for s in range(mdp.S):\n        for a in range(mdp.A):\n            if len(mdp.STR[s][a]) == 0:\n                continue\n            P[s] = a\n            break\n    V = np.zeros(mdp.S)\n    optFound = False\n\n    while not optFound:\n        optFound = True\n        V = value_eval(mdp, P)\n\n        for s in range(mdp.S):\n            if not mdp.isNonTerminal[s, P[s]]:\n                continue\n            for a in range(mdp.A):\n                if a == P[s]:\n                    continue\n\n                if calcQ(mdp, s, a, V) > V[s]:\n                    P[s] = a\n                    optFound = False\n                    break\n    return V, P\n\ndef linearProgramming(mdp):\n    isNonTerminal = np.any(mdp.isNonTerminal, axis=1)\n\n    Vs = [pl.LpVariable('V' + str(i)) for i in range(mdp.S)]\n    prob = pl.LpProblem(\"MDPPlanning\", pl.LpMinimize)\n    for s in range(mdp.S):\n        for a in range(mdp.A):\n            prob += Vs[s] >= pl.lpSum([t[1] * (t[2] + mdp.gamma*Vs[t[0]]) for t in mdp.STR[s][a]])\n        if not isNonTerminal[s]:\n            prob += Vs[s] == 0\n    prob += pl.lpSum(Vs)\n\n    prob.solve(pl.PULP_CBC_CMD(msg=0))\n    return np.array([v.value() for v in Vs]), optPolicy(mdp, np.array([v.value() for v in Vs]))\n\ndef main_function(mdp_path, alg):\n    mdp = constructMDP(mdp_path)\n\n    V = P = np.zeros(mdp.S)\n\n    if alg == 'vi':\n        V, P = valueIter(mdp)\n    elif alg == 'hpi':\n        V, P = HowardPI(mdp)\n    elif alg == 'lp':\n        V, P = linearProgramming(mdp)\n    \n    return V, P\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--mdp', type=str, required=True)\n    parser.add_argument('--algorithm', type=str, default='lp')\n\n    args = parser.parse_args()\n    \n    V, P = main_function(args.mdp, args.algorithm)\n    print('\\n'.join(['%.6f %s'%(V[i], P[i]) for i in range(len(V))]))\n","sub_path":"Assignments/Assn2/submission/planner.py","file_name":"planner.py","file_ext":"py","file_size_in_byte":4821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"460802934","text":"import sys\nimport os\nimport pickle\nimport importlib\nfrom typing import Union, List\n\nfrom markov_pilot.environment.environment import JsbSimEnv_multi, NoFGJsbSimEnv_multi\nfrom markov_pilot.agents.agent_container import AgentContainer\nfrom markov_pilot.helper.lab_journal import LabJournal\n\n#these imports are needed to restore all the classes from the saved pickle-files\nfrom markov_pilot.tasks.tasks import SingleChannel_FlightTask, SingleChannel_MinimumProps_Task\nfrom markov_pilot.wrappers.episodePlotterWrapper import EpisodePlotterWrapper_multi\n\n\n\ndef save_test_run(env: JsbSimEnv_multi, agent_container: AgentContainer, lab_journal: LabJournal, arglist):\n    \"\"\"\n    - creates a suitable directory for the test run\n    - adds a sidecar file containing the meta information on the run (dict saved as pickle)\n    - adds a text file containing the meta information on the run\n    - add a line to the global csv-file for the test run\n    \"\"\"\n    # IMPORTANT to do this first,  to make the lab_journal aware of the start time of the run\n    lab_journal.set_run_start()\n\n    task_names = '_'.join([t.name for t in env.task_list])\n    date = lab_journal.run_start.strftime(\"%Y_%m_%d\")\n    time = lab_journal.run_start.strftime(\"%H-%M\")\n\n    #build the path name for the run protocol\n    save_path = os.path.join(lab_journal.journal_save_dir, env.aircraft.name, arglist.exp_name, task_names, date+'-'+time)\n\n    #create the base directory for this test_run\n    os.makedirs(os.path.dirname(save_path), exist_ok=True)\n    #create the directories for each agent_task\n    for a in agent_container.agents_m:\n        agent_path = os.path.join(save_path, a.name)\n        os.makedirs(os.path.join(save_path, a.name), exist_ok=True)\n        a.set_save_path(agent_path)\n\n    env.save_env_data(arglist, save_path)\n    agent_container.save_agent_container_data(save_path)\n    #eventually append the run data to the csv-file. \n    csv_line_nr = lab_journal.append_run_data(env, agent_container.agents_m, save_path)\n    env.set_meta_information(csv_line_nr = csv_line_nr)\n\ndef restore_env_from_journal(lab_journal, line_numbers: Union[int, List[int]], target_environment = None) -> NoFGJsbSimEnv_multi:\n    ENV_PICKLE = 'environment_init.pickle'  #these are hard default names for the files\n    TASKS_PICKLE ='task_agent.pickle'       #these are hard default names for the files\n\n    ln = line_numbers if isinstance(line_numbers, int) else line_numbers[0]\n\n    #get run protocol\n    try:\n        model_file = lab_journal.get_model_filename(ln)\n        run_protocol_path = lab_journal.find_associated_run_path(model_file)\n    except TypeError:\n        print(f\"there was no run protocol found that is associated with line_number {ln}\")\n        exit()\n\n    if run_protocol_path == None:\n        print(f\"there was no run protocol found that is associated with line_number {ln}\")\n        exit()\n\n    #load the TASKS_PICKLE and restore the task_list\n    with open(os.path.join(run_protocol_path, TASKS_PICKLE), 'rb') as infile:\n        task_agent_data = pickle.load(infile)\n    \n    task_agents = []\n    for idx in range(len(task_agent_data['task_list_class_names'])):\n        task_list_init = task_agent_data['task_list_init'][idx]\n        task_list_class_name = task_agent_data['task_list_class_names'][idx]\n        make_base_reward_components_file = task_agent_data['make_base_reward_components_file'][idx]\n        make_base_reward_components_fn = task_agent_data['make_base_reward_components_fn'][idx]\n\n        #load the make_base_reward_components function from a python-file\n        #load function from given filepath  https://stackoverflow.com/a/67692/2682209\n        spec = importlib.util.spec_from_file_location(\"make_base_rwd\", os.path.join(run_protocol_path, make_base_reward_components_file))\n        func_module = importlib.util.module_from_spec(spec)\n        spec.loader.exec_module(func_module)\n        make_base_reward_components = getattr(func_module, make_base_reward_components_fn)\n\n        #get the class for the task_agent https://stackoverflow.com/a/17960039/2682209\n        class_ = getattr(sys.modules[__name__], task_list_class_name)\n        #add the make_base_reward_components function to the parameter dict\n        task_list_init.update({'make_base_reward_components': make_base_reward_components})\n        #transform the setpoint_props and setpoint_values lists to setpoints dict\n        task_list_init.update({'setpoints': dict(zip(task_list_init['setpoint_props'], task_list_init['setpoint_values']))})\n        del task_list_init['setpoint_props']\n        del task_list_init['setpoint_values']\n        ta = class_(**task_list_init)\n        task_agents.append(ta)\n\n    #the task_agents are now ready, so now let's prepare the environment\n    #load the ENV_PICKLE and restore the task_list\n    with open(os.path.join(run_protocol_path, ENV_PICKLE), 'rb') as infile:\n        env_data = pickle.load(infile)\n\n    env_init_dicts = env_data['init_dicts']\n    env_classes = env_data['env_classes']\n\n    #create the innermost environment with the task_list added\n    #load the env class\n    env_class_ = getattr(sys.modules[__name__], env_classes[0])\n\n    if target_environment and (env_class_ == JsbSimEnv_multi or env_class_ == NoFGJsbSimEnv_multi):\n        #the user wants us to exchange the innermost environment\n        if target_environment == 'NoFG':\n            env_class_ = NoFGJsbSimEnv_multi\n        elif target_environment == 'FG':\n            env_class_ = JsbSimEnv_multi\n        else:\n            raise ValueError(\"parameter target_:environment must be either 'NoFG' or 'FG' or entirely omitted. Other values not allowed.\")\n\n    env_init = env_init_dicts[0]\n    env_init.update({'task_list':task_agents})\n\n    env = env_class_(**env_init)\n    \n    #apply wrappers if available\n    #TODO: what about other wrappers than EpisodePlotterWrapper? We don't save the VarySetpointWrapper to the wrappers list.\n    for idx in range(1, len(env_init_dicts)):\n        wrapper_class_ = getattr(sys.modules[__name__], env_classes[idx])\n        wrap_init = env_init_dicts[idx]\n        wrap_init.update({'env': env})\n        env = wrapper_class_(**wrap_init)\n    \n    env.set_meta_information(csv_line_nr = ln)  #set the line number, the environment was loaded from\n    return env\n\ndef restore_agent_container_from_journal(lab_journal, line_numbers: Union[int, List[int]], task_list_n = None, mapping_dict = None) -> 'AgentContainer':\n    CONTAINER_PICKLE = 'agent_container.pickle'\n\n    ln = [line_numbers] if isinstance(line_numbers, int) else line_numbers\n\n    #get run protocol\n\n    agent_pickle_files_m = [lab_journal.get_model_filename(line) for line in ln]\n    try:\n        model_file = lab_journal.get_model_filename(ln[0])\n        run_protocol_path = lab_journal.find_associated_run_path(model_file)\n    except TypeError:\n        print(f\"there was no run protocol found that is associated with line_number {ln}\")\n        exit()\n\n    agent_container = AgentContainer.init_from_save(os.path.join(run_protocol_path, CONTAINER_PICKLE), agent_pickle_files_m, task_list_n, mapping_dict)\n\n    return agent_container\n","sub_path":"markov_pilot/helper/load_store.py","file_name":"load_store.py","file_ext":"py","file_size_in_byte":7115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"395575760","text":"class Solution:\n    def reverseWords(self, s: str) -> str:\n        if len(s)<=0:\n            return s\n        items = s.split(\" \")\n        print(items)\n        res_items = []\n        for item in items:\n            print(type(item))\n            item = item[::-1]\n            res_items.append(item)\n        res = \"\"\n        for i,each in enumerate(res_items):\n            res +=each\n            if i!=len(res_items)-1:\n                res +=\" \"\n        return res\n","sub_path":"src/字符串操作/q557_反转字符串中的单词III/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"163508850","text":"import botbook_mcp3002 as mcp # for botbook_mcp3002 module import\n\t\t\t\t\t\t\t  # read gas sence\nimport time\t# for using time.sleep()\n\nimport RPi.GPIO as GPIO # for using GPIO port\nfrom sys import exit \t# for exception processing\nimport pygame.mixer     # for warning sound\n\nGPIO.setmode(GPIO.BCM)  # set GPIO port as GPIO.BCM\nGPIO.setup(17,GPIO.OUT) # moving motor\nGPIO.setup(21,GPIO.IN)  # button input\n\n# motor initialize\nmotor = GPIO.PWM(17,50) \nmotor.start(7.5) \n\n# sound initialize\npygame.mixer.init(48000,-16,1,1024) \nwarningSound = pygame.mixer.Sound(\"/home/pi/warning.wav\") \nsoundChannelA = pygame.mixer.Channel(1) \n\ntickSound = pygame.mixer.Sound(\"/home/pi/tick.wav\")\nsoundChannelB = pygame.mixer.Channel(2)\n\n# read Gas Function\ndef readPotent():\n\n\tglobal gasValue \n\tgasValue = mcp.readAnalog() # read gas information from mcp-3002 \n\n#m main Function\ndef main(): \n\n\tisGas = 0\n\tcount = 0\n\tflag = 0\n\ttickCount = 0\n\n\tprint(\" start program \")\n\n\ttry:\n\t\twhile True: # while Loop\n\t\t\treadPotent() # read Gas Funtion Call\n\t\t\tisButton = GPIO.input(21) # read button\n\t\t\t\n\t\t\t# burning the gas\n\t\t\t# for 10, call tickSound\n\t\t\tif (tickCount < 10): \n\t\t\t\ttickCount = tickCount + 1\n\t\t\t\tsoundChannelB.play(tickSound)\n\n\t\t\tif (gasValue > 600):\t# gas on\n\t\t\t\tisGas = 1\n\t\t\t\tprint(\" Gas !!! \")\n\t\t\t\tprint(\"The current potentiometer value is %i \" % gasValue) # \n\t\t\t\n\t\t\tif (isGas == 1): # gas count time per 0.5 sec\n\t\t\t\tcount = count + 1\n\t\t\t\tsoundChannelA.play(warningSound) # warning sound call\n\t\t\t\n\t\t\ttime.sleep(0.5) # program timer per 0.5 sec\n\t\t\n\t\t\tif( count == 10 ): # time 0.5 * 10 = 5sec , motor on.\n\t\t\t\tmotor.ChangeDutyCycle(12.5) \n\n\t\t\tif( isButton == 1 ): # button on\n\t\t\t\tisGas = 0\n\t\t\t\tcount = 0\n\t\t\t\ttickCount = 0\n\t\t\t\tmotor.ChangeDutyCycle(7.5) # motor off.\n\n\texcept KeyboardInterrupt:\n\t\tGPIO.cleanup()\nif (__name__ == \"__main__\"): #main Function Start\n\tmain()\n","sub_path":"project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":1847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"178278399","text":"from math import *\r\n\r\npeople = int(input(\"How many people are sharing the bill?\\n\"))\r\nbill = float(input(\"How much is the bill?\\n\"))\r\nprint(\"Kevin paid the bill first. But Kevin only has 100 dollar notes\")\r\nprint(\"So Kevin is going to paid $%d.00.\" %(100*ceil(bill/100.0)))  \r\nprint(\"The cafe is giving %.2f to Kevin.\" %(100-(bill%100))) \r\nprint(\"Each one should give %.2f to Kevin.\" %(bill/people))\r\n\r\nnumber = int(1)\r\nwhile (number <= 100):\r\n    if (number%7==0 or number%10==7):\r\n        print('X', end= ' ')\r\n    else:\r\n        print(number, end= ' ')    \r\n    number = number + 1\r\nprint(\"\\nGame Over.\")\r\n\r\n\r\nfrom random import randint\r\n\r\nnumber = randint(1,6)\r\nprint(\"I got a %d\" % number)\r\ncount = 1\r\nwhile number != 6 : \r\n    number=randint(1,6)\r\n    print(\"I got a %d\" % number)\r\n    count = count + 1\r\n\r\nprint(\"Oh, it takes me %d times to get a 6!!!\" % count)\r\n\r\ncount=0\r\nfor time in range(0,100):\r\n    number = randint(1,6)\r\n    count = count+1\r\n    while number != 6 : \r\n        number=randint(1,6)\r\n        count = count + 1\r\nprint('Average time taken is %.2f times.'%(count/100.0))\r\n\r\ninput()\r\n","sub_path":"L3/LawCheukHim_L3Q1.py","file_name":"LawCheukHim_L3Q1.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"492071247","text":"# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"). You\n# may not use this file except in compliance with the License. A copy of\n# the License is located at\n#\n# http://aws.amazon.com/apache2.0/\n#\n# or in the \"license\" file accompanying this file. This file is\n# distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF\n# ANY KIND, either express or implied. See the License for the specific\n# language governing permissions and limitations under the License.\n\nfrom ..objects.exceptions import ValidationError\nfrom ..core import fileoperations\n\n\ndef get_dockerrun(dockerrun_path):\n    \"\"\"\n    Return dict representation of Dockerrun.aws.json in dockerrun_path\n    Return None if Dockerrun doesn't exist at that path.\n    :param dockerrun_path: str: full path to Dockerrun.aws.json\n    :return: dict\n    \"\"\"\n\n    try:\n        return fileoperations.get_json_dict(dockerrun_path)\n    except ValueError:\n        err_msg = 'You provided an invalid Dockerrun.aws.json file. ' \\\n                  'Reason was: {}'\n        raise ValidationError(err_msg.format('Invalid JSON format.'))\n    except IOError:  # Dockerrun.aws.json doesn't exist\n        return None\n","sub_path":"lib/python2.7/site-packages/ebcli/docker/dockerrun.py","file_name":"dockerrun.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"252718944","text":"import re\nres = {}\ni =0\nwith open(\"..\\exam-schedule.csv\") as file:\n    for line in file:\n        if i==0:\n            i=1\n            continue\n        lArr = line.split(\",\")\n        cThing = lArr[len(lArr)-2] + \" | \" + lArr[3] + \" | \" + lArr[5]\n        if cThing in res:\n            res[cThing] +=1\n            # print(cThing)\n        else:\n            res[cThing] =1\n\n\n\nfor student in dict(sorted(res.items(),key=lambda item: item[1],reverse=True)):\n    if res[student] == 1:\n        continue\n    line = student.split(\"|\")\n    print(f\"{line[0]}has {res[student]} exams on{line[1]}({line[2]})\")\n    ","sub_path":"05-modules/data-formats/exam-schedule/overlapping-exams/overlap.py","file_name":"overlap.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"381789295","text":"from tkinter.scrolledtext import ScrolledText\nfrom threading import Thread\nimport tkinter as tk\nfrom src.constant import *\nfrom src.solution.sentiment import SentimentAnalysisTool, SentimentAnalysis\nfrom src.utils.browser import WebBrowser\n\n\nclass Page2(tk.Frame):\n    isInitialized = False\n\n    def __init__(self, parent, customers, couriers, controller, name):\n        tk.Frame.__init__(self, parent, bg=lightBlue)\n        print(f\"{name} Frame initialized\")\n\n        self.webBrowser = WebBrowser\n        self.customers = customers\n        self.couriers = couriers\n        self.controller = controller\n        self.name = name\n\n        self.wordFreqDisplay = ScrolledText\n        self.status = tk.Label\n        self.sentimentDescription = tk.Label\n        self.hasAnalyzed = False\n        self.scrapingText = tk.StringVar(self, \" Random Scraping\")\n\n        self.tool = SentimentAnalysisTool(self.couriers)\n\n        self.currentResult = SentimentAnalysis()\n        self.T1 = Thread\n        self.T2 = Thread\n\n        self.showArticle()\n        self.showStatistic()\n\n    def showArticle(self):\n        browserFrame = tk.Frame(self, bg='blue', width=810, height=500)\n        browserFrame.grid(row=0, column=0)\n\n        initialUrl = \"https://www.google.com/\"\n        browserSetting = {}\n        self.webBrowser = WebBrowser(browserFrame, browserSetting, initialUrl, \"Search For Article\")\n        self.webBrowser.start()\n\n        mining = getIconImage(\"mining\")\n        scraping = tk.Button(browserFrame, textvariable=self.scrapingText, image=mining, compound=\"left\",\n                             width=190, command=self.__randomScraping, **buttonConfig2)\n        scraping.image = mining\n        scraping.place(x=3, y=5)\n\n        frame = tk.Frame(self, bg=lightPurple, width=800, height=200)\n        frame.grid(row=1, column=0, sticky=\"w\")\n\n        linkSelected = tk.StringVar(frame, \"https://www.google.com/\")\n        dropDownMenu = tk.OptionMenu(frame, linkSelected, *(self.__getSampleLinks()))\n        dropDownMenu.config(width=80, anchor=\"w\", **dropDownConfig)\n        dropDownMenu.grid(row=0, column=0, sticky=\"w\")\n        linkSelected.trace(mode=\"w\", callback=lambda *args: self.__search(linkSelected.get()))\n\n        tk.Button(frame, text=\"Analyze Page\", command=self.__startSentiment, **buttonConfig3).grid(row=0, column=1)\n        tk.Label(frame, text=\"  STATUS  \", background=lightPurple, font=buttonFontN).grid(row=0, column=2, sticky='ns')\n\n        self.status = tk.Label(frame, text=\"Not yet analyze\", width=15, background=\"#BFC7E4\", font=buttonFontN)\n        self.status.grid(row=0, column=3, sticky='ns')\n\n    def showStatistic(self):\n        frame = tk.Frame(self, bg=lightBlue)\n        frame.grid(row=2, column=0, sticky=\"w\")\n\n        self.wordFreqDisplay = ScrolledText(frame, width=30, height=5, wrap=tk.WORD, bg=lightBlue)\n        self.wordFreqDisplay.grid(row=0, column=0, pady=7, padx=10, rowspan=2)\n        self.wordFreqDisplay.insert(tk.INSERT, \"No Word Frequency Found\")\n\n        wordFreqButton = tk.Button(frame, text=\"Word Frequency\", command=lambda: self.__showGraph(0))\n        wordFreqButton.config(width=17, height=2)\n        wordFreqButton.grid(row=0, column=1, padx=1, pady=7)\n\n        positveButton = tk.Button(frame, text=\"Positive Sentiment\", command=lambda: self.__showGraph(1))\n        positveButton.config(width=17, height=2)\n        positveButton.grid(row=0, column=2, padx=1, pady=7)\n\n        negativeButton = tk.Button(frame, text=\"Negative Sentiment\", command=lambda: self.__showGraph(2))\n        negativeButton.config(width=17, height=2)\n        negativeButton.grid(row=0, column=3, padx=1, pady=7)\n\n        neutralButton = tk.Button(frame, text=\"Neutral Sentiment\", command=lambda: self.__showGraph(3))\n        neutralButton.config(width=17, height=2)\n        neutralButton.grid(row=0, column=4, padx=1, pady=7)\n\n        self.sentimentDescription = tk.Label(frame, text=\"No sentiment analysis yet\", borderwidth=3, relief=\"groove\")\n        self.sentimentDescription.config(anchor=\"w\", padx=10, foreground=\"green\", font=subFontN)\n        self.sentimentDescription.grid(row=1, column=1, sticky=\"swe\", columnspan=4, padx=1, pady=7)\n\n    def __randomScraping(self):\n        if self.scrapingText.get().strip() == \"Analyzing\":\n            return\n\n        thread = Thread(target=self.__randomAnalysis)\n        thread.isDaemon = True\n        thread.start()\n\n    def __randomAnalysis(self):\n        self.scrapingText.set(\"{:^25}\".format(\"Analyzing\"))\n\n        queries = [\n            (\"city-link\", \"city-link express news\"),\n            (\"pos laju\", \"pos laju news\"),\n            (\"gdex\", \"gdex express news\"),\n            (\"j&t\", \"j&t express news\"),\n            (\"dhl\", \"dhl express news\"),\n        ]\n\n        for q in queries:\n            self.tool.randomSentiment(q, n=10)\n\n        print(\"DONE\")\n        self.scrapingText.set(\" Random Scraping\")\n\n    def __startSentiment(self):\n        status = self.status['text']\n        if status == \"Showing Graph\" or status == \"Analyzing Page\":\n            return\n        elif status == \"Has been analyzed\":\n            self.currentResult = self.tool.history[self.webBrowser.getUrl()]\n            self.__update()\n        else:\n            self.T1 = Thread(target=self.__getStatus)\n            self.T2 = Thread(target=self.__getSentiment)\n\n            self.T1.start()\n            self.T2.start()\n\n    def __getStatus(self):\n        self.status.config(text=\"Analyzing Page\")\n        self.T2.join()\n        self.status.config(text=\"Finished Analyzed\")\n        self.__update()\n\n    def __update(self):\n        self.wordFreqDisplay.delete(\"1.0\", \"end\")\n        self.wordFreqDisplay.insert(tk.INSERT, self.__printFreq())\n        self.sentimentDescription['text'] = self.currentResult.sentimentDescription\n\n    def __getSentiment(self):\n        url = self.webBrowser.getUrl()\n        self.currentResult = self.tool.getSentiment(url)\n\n    def __getSampleLinks(self):\n        with open(f\"{sentiment}/link.txt\", mode=\"r\") as file:\n            links = [link.strip() for link in file.readlines()]\n        return links\n\n    def __search(self, url):\n        if url in self.tool.getUrls():\n            self.status.config(text=\"Has been analyzed\")\n        elif url[:4] == \"file\":\n            self.status.config(text=\"Showing Graph\")\n        else:\n            self.status.config(text=\"Not yet analyze\")\n        self.webBrowser.loadUrl(url)\n\n    def __printFreq(self):\n        result = self.currentResult.sortedDict\n        output = \"\"\n        if result:\n            for word, freq in result.items():\n                output += f\"{word}, {freq}\\n\"\n        else:\n            output = \"No Word Frequency Found\"\n        return output\n\n    def __showGraph(self, mode=0):\n        if self.sentimentDescription['text'] == \"Current Page could not be analyzed\":\n            return None\n\n        # 0 = wordFreq, 1 = positive, 2 = negative, 3 = neutral\n        if mode == 0:\n            fname = \"wordFreq\"\n            self.currentResult.plotWordFrequency()\n        elif mode == 1:\n            fname = \"positive\"\n            self.currentResult.plotSentimentGraph(model=\"positive\")\n        elif mode == 2:\n            fname = \"negative\"\n            self.currentResult.plotSentimentGraph(model=\"negative\")\n        elif mode == 3:\n            fname = \"neutral\"\n            self.currentResult.plotSentimentGraph(model=\"neutral\")\n        else:\n            print(\"Invalid mode\")\n            return None\n\n        self.__search(url=f\"file:///{sentiment}/figures/{fname}_graph.html\")\n","sub_path":"src/ui_pages/page_2.py","file_name":"page_2.py","file_ext":"py","file_size_in_byte":7517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"56989129","text":"\nimport time\nimport sys\nimport pytz\nimport logging\nimport datetime\nimport itertools\nfrom urllib.parse import urlparse\nimport matplotlib.pyplot as plt\n\nfrom opcua import ua, Client\n\n\nclass OpcUaClient(object):\n    CONNECT_TIMEOUT = 15  # [sec]\n    RETRY_DELAY = 10  # [sec]\n    MAX_RETRIES = 3  # [-]\n\n    class Decorators(object):\n        @staticmethod\n        def autoConnectingClient(wrappedMethod):\n            def wrapper(obj, *args, **kwargs):\n                for retry in range(OpcUaClient.MAX_RETRIES):\n                    try:\n                        return wrappedMethod(obj, *args, **kwargs)\n                    except ua.uaerrors.BadNoMatch:\n                        raise\n                    except Exception:\n                        pass\n                    try:\n                        obj._logger.warn('(Re)connecting to OPC-UA service.')\n                        obj.reconnect()\n                    except ConnectionRefusedError:\n                        obj._logger.warn(\n                            'Connection refused. Retry in 10s.'.format(\n                                OpcUaClient.RETRY_DELAY\n                            )\n                        )\n                        time.sleep(OpcUaClient.RETRY_DELAY)\n                else:  # So the exception is exposed.\n                    obj.reconnect()\n                    return wrappedMethod(obj, *args, **kwargs)\n            return wrapper\n\n    def __init__(self, serverUrl):\n        self._logger = logging.getLogger(self.__class__.__name__)\n        self._client = Client(\n            serverUrl.geturl(),\n            timeout=self.CONNECT_TIMEOUT\n        )\n\n    def __enter__(self):\n        self.connect()\n        return self\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        self.disconnect()\n        self._client = None\n\n    @property\n    @Decorators.autoConnectingClient\n    def sensorList(self):\n        return self.objectsNode.get_children()\n\n    @property\n    @Decorators.autoConnectingClient\n    def objectsNode(self):\n        path = [ua.QualifiedName(name='Objects', namespaceidx=0)]\n        return self._client.get_root_node().get_child(path)\n\n    def connect(self):\n        self._client.connect()\n        self._client.load_type_definitions()\n\n    def disconnect(self):\n        try:\n            self._client.disconnect()\n        except Exception:\n            pass\n\n    def reconnect(self):\n        self.disconnect()\n        self.connect()\n\n    @Decorators.autoConnectingClient\n    def get_browse_name(self, uaNode):\n        return uaNode.get_browse_name()\n\n    @Decorators.autoConnectingClient\n    def get_node_class(self, uaNode):\n        return uaNode.get_node_class()\n\n    @Decorators.autoConnectingClient\n    def get_namespace_index(self, uri):\n        return self._client.get_namespace_index(uri)\n\n    @Decorators.autoConnectingClient\n    def get_child(self, uaNode, path):\n        return uaNode.get_child(path)\n\n    @Decorators.autoConnectingClient\n    def read_raw_history(self,\n                         uaNode,\n                         starttime=None,\n                         endtime=None,\n                         numvalues=0,\n                         cont=None):\n        details = ua.ReadRawModifiedDetails()\n        details.IsReadModified = False\n        details.StartTime = starttime or ua.get_win_epoch()\n        details.EndTime = endtime or ua.get_win_epoch()\n        details.NumValuesPerNode = numvalues\n        details.ReturnBounds = True\n        result = OpcUaClient._history_read(uaNode, details, cont)\n        assert(result.StatusCode.is_good())\n        return result.HistoryData.DataValues, result.ContinuationPoint\n\n    @staticmethod\n    def _history_read(uaNode, details, cont):\n        valueid = ua.HistoryReadValueId()\n        valueid.NodeId = uaNode.nodeid\n        valueid.IndexRange = ''\n        valueid.ContinuationPoint = cont\n\n        params = ua.HistoryReadParameters()\n        params.HistoryReadDetails = details\n        params.TimestampsToReturn = ua.TimestampsToReturn.Both\n        params.ReleaseContinuationPoints = False\n        params.NodesToRead.append(valueid)\n        result = uaNode.server.history_read(params)[0]\n        return result\n\n\nclass DataAcquisition(object):\n    LOGGER = logging.getLogger('DataAcquisition')\n    AXES = ('x', 'y', 'z')\n    ORDINATES = ('accel', 'veloc')\n    DOMAINS = ('time', 'freq')\n    MAX_VALUES_PER_ENDNODE = 100  # Num values per endnode\n    MAX_VALUES_PER_REQUEST = 2  # Num values per history request\n\n    @staticmethod\n    def selected_to_workbook(serverUrl,\n                             macIdsToCollect,\n                             starttime,\n                             endtime):\n        with OpcUaClient(serverUrl) as client:\n            for sensorNode in client.sensorList:\n                assert(client._client.uaclient._uasocket.timeout == 15)\n                macId = client.get_browse_name(sensorNode).Name\n                if macId not in macIdsToCollect:\n                    DataAcquisition.LOGGER.info(\n                        'Skipping sensor {:s}'.format(macId)\n                    )\n                    continue\n                tagPath = ua.QualifiedName(\n                    'deviceTag',\n                    sensorNode.nodeid.NamespaceIndex\n                )\n                DataAcquisition.LOGGER.info(\n                    'Processing sensor {:s} ({:s})'.format(\n                        macId,\n                        client.get_child(sensorNode, tagPath).get_value()\n                    )\n                )\n                DataAcquisition.get_sensor_data(\n                        client,\n                        sensorNode,\n                        starttime,\n                        endtime\n                )\n\n    @staticmethod\n    def get_sensor_data(serverUrl, macId, browseName, starttime, endtime):\n        allValues = []\n        allDates = []\n        with OpcUaClient(serverUrl) as client:\n            assert(client._client.uaclient._uasocket.timeout == 15)\n            sensorNode = DataAcquisition.get_sensor_node(\n                    client,\n                    macId,\n                    browseName\n            )\n            for path in DataAcquisition.endnodes_path_generator(sensorNode):\n                DataAcquisition.LOGGER.info(\n                        'Browsing {:s} -> {:s}'.format(\n                                macId,\n                                sensorNode.get_browse_name().Name\n                            )\n                )\n                endNode = client.get_child(sensorNode, path)\n                (values, dates) = DataAcquisition.get_endnode_data(\n                        client,\n                        endNode,\n                        starttime,\n                        endtime\n                )\n                allValues.extend(values)\n                allDates.extend(dates)\n        return (allValues, allDates)\n\n    @staticmethod\n    def endnodes_path_generator(sensorNode):\n        for (axis, ordinate, domain) in \\\n                itertools.product(DataAcquisition.AXES,\n                                  DataAcquisition.ORDINATES,\n                                  DataAcquisition.DOMAINS):\n            # browseName: e.g. xAccelTime\n            browseName = ''.join([\n                axis, ordinate.capitalize(), domain.capitalize()\n            ])\n            nsIdx = sensorNode.nodeid.NamespaceIndex  # iQunet namespace index\n            path = [\n                ua.QualifiedName(axis, nsIdx),        # e.g. 'x'\n                ua.QualifiedName(ordinate, nsIdx),    # e.g. 'accel'\n                ua.QualifiedName(browseName, nsIdx),  # e.g. 'xAccelTime'\n            ]\n            yield path\n\n    @staticmethod\n    def get_sensor_node(client, macId, browseName):\n        nsIdx = client.get_namespace_index(\n                'http://www.iqunet.com'\n        )  # iQunet namespace index\n        bpath = [\n                ua.QualifiedName(name=macId, namespaceidx=nsIdx),\n                ua.QualifiedName(name=browseName, namespaceidx=nsIdx)\n                ]\n        sensorNode = client.objectsNode.get_child(bpath)\n        return sensorNode\n\n    @staticmethod\n    def get_endnode_data(client, endNode, starttime, endtime):\n        dvList = DataAcquisition.download_endnode(\n                client,\n                endNode,\n                starttime,\n                endtime\n        )\n        dates, values = ([], [])\n        for dv in dvList:\n            dates.append(dv.SourceTimestamp.strftime('%Y-%m-%d %H:%M:%S'))\n            values.append(dv.Value.Value.y_ordinate)\n\n        # If no starttime is given, results of read_raw_history are reversed.\n        if starttime is None:\n            values.reverse()\n            dates.reverse()\n        return (values, dates)\n\n    @staticmethod\n    def download_endnode(client, endNode, starttime, endtime):\n        endNodeName = client.get_browse_name(endNode).Name\n        DataAcquisition.LOGGER.info(\n                'Downloading endnode {:s}'.format(\n                        endNodeName\n                    )\n        )\n        dvList, contId = [], None\n        while True:\n            remaining = DataAcquisition.MAX_VALUES_PER_ENDNODE - len(dvList)\n            assert(remaining >= 0)\n            numvalues = min(DataAcquisition.MAX_VALUES_PER_REQUEST, remaining)\n            partial, contId = client.read_raw_history(\n                uaNode=endNode,\n                starttime=starttime,\n                endtime=endtime,\n                numvalues=numvalues,\n                cont=contId\n            )\n            if not len(partial):\n                DataAcquisition.LOGGER.warn(\n                    'No data was returned for {:s}'.format(endNodeName)\n                )\n                break\n            dvList.extend(partial)\n            sys.stdout.write('\\r    Loaded {:d} values, {:s} -> {:s}'.format(\n                len(dvList),\n                str(dvList[0].ServerTimestamp.strftime(\"%Y-%m-%d %H:%M:%S\")),\n                str(dvList[-1].ServerTimestamp.strftime(\"%Y-%m-%d %H:%M:%S\"))\n            ))\n            sys.stdout.flush()\n            if contId is None:\n                break  # No more data.\n            if len(dvList) >= DataAcquisition.MAX_VALUES_PER_ENDNODE:\n                break  # Too much data.\n        sys.stdout.write('...OK.\\n')\n        return dvList\n\n\nif __name__ == \"__main__\":\n    logging.basicConfig(level=logging.INFO)\n    logging.getLogger(\"opcua\").setLevel(logging.WARNING)\n\n    # replace xx.xx.xx.xx with the IP address of your server\n    serverIP = \"xx.xx.xx.xx\"\n    serverUrl = urlparse('opc.tcp://{:s}:4840'.format(serverIP))\n\n    # replace xx:xx:xx:xx with your sensors macId\n    macId = 'xx:xx:xx:xx'\n\n    starttime = pytz.utc.localize(\n        datetime.datetime.strptime(\"2020-02-01 00:00:00\", '%Y-%m-%d %H:%M:%S')\n    )\n    endtime = pytz.utc.localize(\n        datetime.datetime.strptime(\"2020-02-24 00:00:00\", '%Y-%m-%d %H:%M:%S')\n    )\n\n    # acquire history data\n    (values, dates) = DataAcquisition.get_sensor_data(\n        serverUrl=serverUrl,\n        macId=macId,\n        browseName=\"vibration\",\n        starttime=starttime,\n        endtime=endtime\n    )\n\n    # plot data\n    for i in range(len(dates)):\n        plt.figure()\n        plt.plot(values[i])\n        plt.title(str(dates[i]))\n","sub_path":"examples/OPCuaGetVibrationDataStruct.py","file_name":"OPCuaGetVibrationDataStruct.py","file_ext":"py","file_size_in_byte":11201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"300875946","text":"#!/usr/bin/env python\n# encoding: utf-8\n\n\nclass Solution(object):\n    def trailingZeroes(self, n):\n        \"\"\"\n        :type n: int\n        :rtype: int\n        \"\"\"\n\n        result = 0\n\n        while n:\n            m = n / 5\n            result += m\n            n = m\n\n        return result\n","sub_path":"leetcode/172.py","file_name":"172.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"410781344","text":"#!/usr/bin/env python\n\"\"\"\nGiven a graph representing a schema and nodes to hit, this class does a poor\nman's travelling salesman solution.\n\"\"\"\n__author__ = \"Andrew J. Dolgert <ajd27@cornell.edu>\"\n__revision__ = \"$Revision: 1.1 $\"\n\n# system modules\nfrom logging import getLogger\n\n# local modules\nfrom pyquerybuilder.qb.DotGraph import DotGraph\nfrom pyquerybuilder.qb.Graph import Graph\n\n_LOGGER = getLogger(\"ConstructQuery\")\n\n\nclass ConstructQuery(object):\n    \"\"\"Given a graph representing a schema and nodes to hit, this class does \n    a poor man's travelling salesman solution.\"\"\"\n    def __init__(self, connectivity):\n        \"\"\"Connectivity describes which tables have foreign keys\n        into which other tables.\"\"\"\n        graph = Graph(connectivity)\n        undirected_graph = graph.get_undirected()\n        self._spanning = []\n        # traversing graph\n        #    get spanning path of BFS from this node\n        #    self._spannning stores those path\n        for node_index in range(0, len(undirected_graph)):\n            span = undirected_graph.breadth_first_search(node_index)\n            self._spanning.append(span)\n\n    def print_spans(self):\n        \"\"\"print every spans in this spanning collection\"\"\"\n        span_index = 0\n        for span in self._spanning:\n            dot = DotGraph(file(\"span%d.dot\" % (span_index),\"w\"))\n            span.write_graph(dot)\n            span_index = span_index+1\n\n    def get_smallest_subtree(self, query_elements):\n        '''Find the subtree containing the query elements.'''\n        query_set = set(query_elements)\n        min_len = len(self._spanning) + 1\n        smallest_span = None\n        span_index = 0\n        for span in self._spanning:\n            sub_span = span.subtree_including(query_set)\n            if sub_span is not None:\n                if sub_span.get_edges_number() < min_len:\n                    min_len = sub_span.get_edges_number()\n                    smallest_span = sub_span\n            span_index = span_index + 1\n\n        return smallest_span\n\n    def get_statement_joins(self, query_elements):\n        '''The argument is a list of which tables contain the desired\n        elements. The return value is a list of joins between those tables.'''\n        query_set = set(query_elements)\n\n        edge_sets = []\n        min_len = len(self._spanning)\n        min_index = -1\n        for node_index in range(0, len(self._spanning)):             \n            subs = self._spanning[node_index].get_edges_including(query_set)\n            if len(subs) < min_len:\n                min_len = len(subs)\n                min_index = node_index\n            edge_sets.append(subs)\n        return (min_index, edge_sets[min_index])\n\n","sub_path":"pyquerybuilder/qb/ConstructQuery.py","file_name":"ConstructQuery.py","file_ext":"py","file_size_in_byte":2696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"125059507","text":"# Default Imports\nfrom greyatomlib.pandas_project.q01_read_csv_data_to_df.build import read_csv_data_to_df\n\n# You have been give the dataset already in 'ipl_df'.\nipl_df = read_csv_data_to_df(\"./data/ipl_dataset.csv\")\n\n# Solution\nimport pandas as pd\n# Solution\ndef get_runs_counts_by_match():\n    new_df = pd.DataFrame(columns=['match_code','0','1','2','3','4','5','6'])\n    new_df['match_code']=ipl_df['match_code']\n    new_df['0']=ipl_df['runs']==0\n    new_df['1']=ipl_df['runs']==1\n    new_df['2']=ipl_df['runs']==2\n    new_df['3']=ipl_df['runs']==3\n    new_df['4']=ipl_df['runs']==4\n    new_df['5']=ipl_df['runs']==5\n    new_df['6']=ipl_df['runs']==6\n    pivot_df=new_df.pivot_table(['0','1','2','3','4','5','6'],index=['match_code'],aggfunc='sum')\n    return pivot_df\n","sub_path":"q07_get_run_counts_by_match/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"133888595","text":"import numpy as np\nimport tensorflow as tf\n\n\ndef placeholder(dim=None):\n    return tf.placeholder(dtype=tf.float32, shape=(None,dim) if dim else (None,))\n\ndef placeholders(*args):\n    return [placeholder(dim) for dim in args]\n\ndef mlp(x, hidden_sizes=(32,), activation=tf.tanh, output_activation=None):\n    for h in hidden_sizes[:-1]:\n        x = tf.layers.dense(x, units=h, activation=activation)\n    return tf.layers.dense(x, units=hidden_sizes[-1], activation=output_activation)\n\ndef q_mlp(x, hidden_sizes=(32,), activation=tf.tanh, output_activation=None):\n    for h in hidden_sizes[:-1]:\n        x = tf.layers.dense(x, units=h, activation=activation)\n    q_mu = tf.layers.dense(x, units=hidden_sizes[-1], activation=output_activation)\n    q_sigma = tf.layers.dense(x, units=hidden_sizes[-1], activation=tf.nn.relu)\n    q_mu = tf.squeeze(q_mu, axis=1)\n    q_sigma = tf.squeeze(q_sigma, axis=1)\n    q_dist = tf.distributions.Normal(loc=q_mu, scale=q_sigma)\n    q = q_dist.sample([1])\n    import pdb;\n    pdb.set_trace()\n    return q\n\ndef a_mlp(x, hidden_sizes=(32,), activation=tf.nn.relu, output_activation=tf.tanh):\n    for h in hidden_sizes[:-1]:\n        x = tf.layers.dense(x, units=h, activation=activation)\n    act_dim = hidden_sizes[-1]\n    a_mu = tf.layers.dense(x, units=act_dim, activation=output_activation)\n    a_sigma = tf.layers.dense(x, units=act_dim, activation=tf.sigmoid)\n    conc_factor = 1\n    a_dist = tf.distributions.Normal(a_mu, conc_factor*a_sigma)\n    a = a_dist.sample([1])[0]\n    a = tf.tanh(a)\n    return a,a_mu,a_sigma\n\ndef sample_action(a_mu, a_alpha, a_beta, concen_factor=1):\n    act_dim = len(a_mu)\n    # Shift a_alpha to range [-10,0] so that exp(a_alpha) in [4e-5,1]\n    a_alpha = (a_alpha-1)*5\n    a_cov = np.zeros((act_dim, act_dim))\n    beta_ind = 0\n    for i in range(act_dim):\n        a_cov[i,i] = np.exp(a_alpha[i])\n        if i+1 < act_dim:\n            for j in range(i+1, act_dim):\n                tmp = np.exp((a_alpha[i]+a_alpha[j])/2) * a_beta[beta_ind]\n                a_cov[i,j] = tmp\n                a_cov[j,i] = tmp\n    a_cov = concen_factor * a_cov\n    return np.random.multivariate_normal(a_mu, a_cov, 1)[0], a_cov\n\ndef sample_action_op(a_mu, a_para, act_dim):\n    # alpha and beta for information matrix: (act_dim * (act_dim+1)) / 2\n    # a_alpha[0:act_dim] = alpha\n    # a_alpha[act_dim:(act_dim * (act_dim+1)) / 2] = beta\n    a_alpha = a_para[:, :act_dim]\n    a_beta = a_para[:, act_dim:]\n    a_info_matrix = np.zeros((a_para.shape[0], act_dim, act_dim))\n    beta_ind = 0\n    for i in range(act_dim):\n        a_info_matrix[:, i, i] = tf.exp(a_alpha[:,i])\n        if i+1 < act_dim:\n            for j in range(i+1, act_dim):\n                a_info_matrix[:, i, j] = tf.exp((a_alpha[:,i]+a_alpha[:,j])/2) * tf.tanh(a_beta[:, beta_ind])\n                a_info_matrix[:, j, i] = tf.exp((a_alpha[:,i] + a_alpha[:,j]) / 2) * tf.tanh(a_beta[:, beta_ind])\n                beta_ind += 1\n    a_dist = tf.contrib.distributions.MultivariateNormalFullCovariance(a_mu, tf.linalg.inv(a_info_matrix))\n    a = a_dist.sample([1])\n    return a, a_info_matrix\n\ndef get_vars(scope):\n    return [x for x in tf.global_variables() if scope in x.name]\n\ndef count_vars(scope):\n    v = get_vars(scope)\n    return sum([np.prod(var.shape.as_list()) for var in v])\n\n\"\"\"\nActor-Critics\n\"\"\"\ndef mlp_actor_critic(x, a_ph, hidden_sizes=(400,300), activation=tf.nn.relu,\n                     output_activation=tf.tanh, action_space=None):\n    act_dim = a_ph.shape.as_list()[-1]\n    act_limit = action_space.high[0]\n    with tf.variable_scope('pi'):\n        # pi = act_limit * mlp(x, list(hidden_sizes)+[act_dim], activation, output_activation)\n        # import pdb; pdb.set_trace()\n        pi, pi_mu, pi_sigma = a_mlp(x, list(hidden_sizes) + [act_dim], activation, output_activation)\n        pi = act_limit * pi\n        pi_mu = act_limit * pi_mu\n    with tf.variable_scope('q'):\n        q = tf.squeeze(mlp(tf.concat([x, a_ph], axis=-1), list(hidden_sizes)+[1], activation, None), axis=1)\n        # q = q_mlp(tf.concat([x, a], axis=-1), list(hidden_sizes)+[1], activation, None)\n    with tf.variable_scope('q', reuse=True):\n        q_pi = tf.squeeze(mlp(tf.concat([x, pi], axis=-1), list(hidden_sizes)+[1], activation, None), axis=1)\n    with tf.variable_scope('q', reuse=True):\n        q_pi_mu = tf.squeeze(mlp(tf.concat([x, pi_mu], axis=-1), list(hidden_sizes)+[1], activation, None), axis=1)\n        # q_pi = q_mlp(tf.concat([x,pi], axis=-1), list(hidden_sizes)+[1], activation, None)\n    return pi, pi_mu, pi_sigma, q, q_pi, q_pi_mu\n","sub_path":"spinup/algos/sddpg_simple2/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":4558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"504154293","text":"class Solution(object):\n    def isAnagram(self, s, t):\n        \"\"\"\n        :type s: str\n        :type t: str\n        :rtype: bool\n        \"\"\"\n        dict1 = self.strToDict(s)\n        dict2 = self.strToDict(t)\n        if dict1 == dict2:\n            return True\n        else:\n            return False\n        \n    def strToDict(self, s):\n        dict = {}\n        for c in s:\n            if dict.has_key(c):\n                dict[c] = dict.get(c) + 1\n            else:\n                dict.setdefault(c, 1)\n        return dict\n\nclass Solution_With_Python_API(object):\n    def isAnagram(self, s, t):\n        \"\"\"\n        :type s: str\n        :type t: str\n        :rtype: bool\n        \"\"\"\n        d1 = collections.defaultdict(int)\n        d2 = collections.defaultdict(int)\n        for c in s:\n            d1[c] += 1\n        for c in t:\n            d2[c] += 1\n        if d1 == d2:\n            return True\n        else: \n            return False\n\nclass Solution_With_Less_Space(object):\n    def isAnagram(self, s, t):\n        \"\"\"\n        :type s: str\n        :type t: str\n        :rtype: bool\n        \"\"\"\n        temp = [0 for i in range(26)]\n        for c in s:\n            temp[ord(c) - ord('a')] += 1\n        for c in t:\n            temp[ord(c) - ord('a')] -= 1\n        for n in temp:\n            if n != 0:\n                return False\n        return True","sub_path":"valid_anagram.py","file_name":"valid_anagram.py","file_ext":"py","file_size_in_byte":1352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"425527093","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\nimport re\nimport time\nimport pickle\nimport os, subprocess, operator, argparse\nfrom sys import argv\n#import delims\nimport sys\nfrom Chinese_handler import *\nfrom Preprocessing import *\nimport gc\n\ndef countNgram(n,fp1,fp2):\n    fin = open(fp1, 'r')\n    fout = open(fp2, 'a+')\n    lines = fin.readlines()\n    #lines = [line.decode('utf-8') for line in fin.readlines()]\n    stop_words = load_stop_words(\"../data/fixed/stopWord.txt\")\n    for oline in lines:\n        line = oline.strip().split('\\t',2)\n        #print(line[0]) line[0] is the label!\n        #fout.write(line[0].encode('utf-8') + ' ')\n        uString = preProcessing(line[2])[0].decode('utf-8').split()  # the uString is the preprocessed line in lines\n        for word in uString:\n            if word in stop_words:\n                continue\n            L = len(word)\n            if L == 1:\n                fout.write(word.encode('utf-8', 'ignore') + ' ')\n                continue\n            for i in range(0, L - n + 1):\n                cur = word[i:i + n]\n                fout.write(cur.encode('utf-8', 'ignore') + ' ')\n                # cur = word[L-1:L]\n                # fout.write(cur.encode('utf-8', 'ignore') + ' ')\n        fout.write('\\n')\n    del lines\n    del stop_words\n    gc.collect()\n    fin.close()\n    fout.close()\n\n'''\n下面对ngram分词后得到的grams进行筛选\n'''\n'''\ndef load_dictionary():\n    bigTable = {}\n    fin = open('../data/derived/train_ngram_100w.txt', 'r')\n    lines = [line.decode('utf-8') for line in fin.readlines()]\n    for single_line in lines:\n        words = single_line.split()\n        length = len(words)\n        print words[0] # the class label\n        for i in range(1,length):\n            if words[i] in bigTable:\n                bigTable[words[i]] += 1\n            else:\n                bigTable.update({words[i]: 1})\n    return bigTable\n\n\ndef neighborFilter():\n    fin = open('../data/derived/train_ngram_100w.txt','r')\n    fout = open('../data/derived/train_ngram_100w_new.txt','a+')\n    lines = [line.decode('utf-8') for line in fin.readlines()]\n    dict = load_dictionary()\n    for line in lines:\n        new_line = line\n        words = line.split()\n        length = len(words)\n        for i in range(1,length-1):\n            word1 = words[i]\n            word2 = words[i+1]\n            if word1 in dict and word2 in dict:\n                freq1 = dict[word1]\n                freq2 = dict[word2]\n                if freq1 < 1.2 * freq2 and freq2 < 1.2 * freq1:\n                    dict.update({word1: 0})\n                    dict.update({word2: 0})\n                    new_line = new_line.replace(word1+\" \",\"\").replace(word2+\" \",\"\")\n                elif freq1 < 0.1 * freq2:\n                    dict.update({word1: 0})\n                    new_line = new_line.replace(word1 + \" \",\"\")\n                elif freq2 < 0.1 * freq1:\n                    dict.update({word2: 1})\n                    new_line = new_line.replace(word2 + \" \",\"\")\n        fout.write(new_line.encode('utf-8'))\n'''\n","sub_path":"SVM_new/src/Ngram.py","file_name":"Ngram.py","file_ext":"py","file_size_in_byte":3037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"353418201","text":"from typing import List, Dict\nimport sqlite3\nimport random\nfrom collections import defaultdict, OrderedDict, Counter\n\nfrom unidecode import unidecode\nfrom functional import seq\n\nfrom qanta.util.constants import MIN_APPEARANCES, PUNCTUATION\n\n\nclass Question:\n    def __init__(self, qnum, answer, category, naqt,\n                 tournaments, page, ans_type, fold, gender):\n        self.qnum = qnum\n        self.answer = answer\n        self.category = category\n        self.naqt = naqt\n        self.tournaments = tournaments\n        self.page = page\n        self.ans_type = ans_type\n        self.fold = fold\n        self.gender = gender\n        self.text = {}\n        self._last_query = None\n\n    def raw_words(self):\n        \"\"\"\n        Return a list of all words, removing all punctuation and normalizing\n        words\n        \"\"\"\n        for ii in sorted(self.text):\n            for jj in self.split_and_remove_punc(self.text[ii]):\n                yield jj\n\n    @staticmethod\n    def split_and_remove_punc(text):\n        for ii in text.split():\n            word = \"\".join(x for x in unidecode(ii.lower()) if x not in PUNCTUATION)\n            if word:\n                yield word\n\n    def partials(self, word_skip=-1):\n        assert(isinstance(word_skip, int)), \"Needs an integer %i\" % word_skip\n        for i in sorted(self.text):\n            previous = [self.text[x] for x in sorted(self.text) if x < i]\n\n            # TODO(jbg): Test to make sure this gives individual words\n            # correctly if word_skip > 0\n            if word_skip > 0:\n                words = self.text[i].split()\n                for j in range(word_skip, len(words), word_skip):\n                    yield i, j, previous + [\" \".join(words[:j])]\n\n            yield i + 1, 0, [self.text[x] for x in sorted(self.text) if x <= i]\n\n    def text_lines(self):\n        d = {}\n        d[\"id\"] = self.qnum\n        d[\"answer\"] = unidecode(self.page)\n        for ii in sorted(self.text):\n            d[\"sent\"] = ii\n            d[\"text\"] = unidecode(self.text[ii])\n            yield d\n\n    def get_text(self, sentence, token):\n        if self._last_query != (sentence, token):\n            self._last_query = (sentence, token)\n            previous = \"\"\n            for ii in range(sentence):\n                previous += self.text.get(ii, \"\")\n            if token > 0:\n                previous += \" \".join(self.text[sentence].split()[:token])\n            self._cached_query = previous\n        return self._cached_query\n\n    def add_text(self, sent, text):\n        self.text[sent] = text\n\n    def flatten_text(self):\n        return unidecode(\" \".join(self.text[x] for x in sorted(self.text)))\n\n\nclass QuestionDatabase:\n    def __init__(self, location):\n        self._conn = sqlite3.connect(location)\n\n    def query(self, command, arguments, text=True):\n        questions = {}\n        c = self._conn.cursor()\n        command = 'select id, page, category, answer, ' + \\\n            'tournament, type, naqt, fold, gender ' + command\n        c.execute(command, arguments)\n\n        for qq, pp, cc, aa, tt, kk, nn, ff, gg in c:\n            questions[qq] = Question(qq, aa, cc, nn, tt, pp, kk, ff, gg)\n\n        if text:\n            for ii in questions:\n                command = 'select sent, raw from text where question=? order by sent asc'\n                c.execute(command, (ii, ))\n                for ss, rr in c:\n                    questions[ii].add_text(ss, rr)\n\n        return questions\n\n    def all_questions(self):\n        return self.query('FROM questions where page != \"\"', ())\n\n    def guess_questions(self, appearance_filter=lambda pq: len(pq[1]) >= MIN_APPEARANCES):\n        question_pages = self.questions_with_pages()\n\n        dev_questions = seq(question_pages.values()) \\\n            .flatten() \\\n            .filter(lambda q: q.fold == 'train' or q.fold == 'dev') \\\n            .group_by(lambda q: q.page) \\\n            .filter(appearance_filter) \\\n            .flat_map(lambda pq: pq[1]) \\\n            .filter(lambda q: q.fold != 'train')\n\n        test_questions = seq(question_pages.values()) \\\n            .flatten() \\\n            .filter(lambda q: q.fold == 'test' or q.fold == 'devtest') \\\n            .filter(lambda q: q.page != '')\n\n        return (dev_questions + test_questions).list()\n\n    def answer_map(self, normalization=lambda x: x):\n        c = self._conn.cursor()\n        command = 'select answer, page from questions ' + \\\n            'where page != \"\"'\n        c.execute(command)\n\n        d = defaultdict(Counter)\n        for aa, pp in c:\n            d[normalization(aa)][pp] += 1\n\n        return d\n\n    def questions_with_pages(self) -> Dict[str, List[Question]]:\n        page_map = OrderedDict()\n\n        questions = self.query('from questions where page != \"\"', ()).values()\n\n        for row in sorted(questions, key=lambda x: x.answer):\n            if row.page not in page_map:\n                page_map[row.page] = []\n            page_map[row.page].append(row)\n        return page_map\n\n    def prune_text(self):\n        \"\"\"\n        Remove sentences that do not have an entry in the database\n        \"\"\"\n\n        c = self._conn.cursor()\n        command = 'select id from questions group by id'\n        c.execute(command)\n        questions = set(x for x in c)\n\n        c = self._conn.cursor()\n        command = 'select question from text group by question'\n        c.execute(command)\n        text = set(x for x in c)\n\n        orphans = text - questions\n\n        c = self._conn.cursor()\n        for ii in orphans:\n            command = 'delete from text where question=%i' % ii\n            c.execute(command)\n        print(\"Keeping %i Pruning %i\" % (len(questions - orphans),\n                                         len(orphans)))\n        self._conn.commit()\n\n    def page_by_count(self, min_count=1, exclude_test=False):\n        \"\"\"\n        Return all answers that appear at least the specified number\n        of times in a category.\n        \"\"\"\n        c = self._conn.cursor()\n        if exclude_test:\n            command = 'select page, count(*) as num from questions where ' + \\\n                      'page != \"\" and fold != \"test\" and fold != \"devtest\"' + \\\n                      'group by page order by num desc'\n        else:\n            command = 'select page, count(*) as num from questions where ' + \\\n                      'page != \"\" ' + \\\n                      'group by page order by num desc'\n        c.execute(command)\n\n        for aa, nn in c:\n            if nn < min_count:\n                continue\n            else:\n                yield aa\n\n    def get_all_pages(self, exclude_test=False):\n        c = self._conn.cursor()\n        if exclude_test:\n            c.execute('select distinct page from questions where page != \"\" and fold != \"devtest\" and fold != \"test\"')\n        else:\n            c.execute('select distinct page from questions where page != \"\"')\n        for result_tuple in c:\n            yield result_tuple[0]\n\n    def all_answers(self):\n        \"\"\"\n        Return a lookup from IDs to pages\n        \"\"\"\n        answers = {}\n        c = self._conn.cursor()\n        command = \"select id, page from questions where page != ''\"\n        c.execute(command)\n\n        for qid, page in c:\n            answers[int(qid)] = page\n        return answers\n","sub_path":"qanta/util/qdb.py","file_name":"qdb.py","file_ext":"py","file_size_in_byte":7273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"513864612","text":"import pandas as pd\nimport MeCab\nfrom pymongo import MongoClient\nimport re\nfrom statistics import mean\nimport time\nfrom timeMeasurement import time_measurement\n\nclient = MongoClient('localhost', 27017)\n\n# データベース指定\ndb = client.mydb\n\n# コレクション指定\ncount = db.kousienPN.find().limit(1000)\nsave = db.testMain\n\n# related_words読み込み\n# names=(関連語、関連値)\nrelated_df = pd.read_csv(\"related_words.txt\",\n                    sep=':',\n                    encoding='utf-8',\n                    names=('Word', 'Related')\n                    )\nword_list = list(related_df['Word'])\nrelated_list = list(related_df['Related'])  # 中身の型はnumpy.float64\nrelated_dict = dict(zip(word_list, related_list))\n\n# 各ツイートを形態素解析して、一単語ずつrelated_wordsと照合する\ndef related_tweets():\n    for record in count:\n        m = MeCab.Tagger('')\n        mecabPrint = m.parse(record['text'])\n        lines = mecabPrint.split('\\n')      # 解析結果を1行（1語）ごとに分けてリストにする\n        lines = lines[0:-2]     # 後ろ2行は不要なので削除\n        # 形態素解析されたツイートを入れる配列\n        diclist = []\n        for word in lines:\n            l = re.split('\\t|,',word)  # 各行はタブとカンマで区切られてるので\n            d = {'Surface':l[0], 'POS1':l[1], 'POS2':l[2], 'BaseForm':l[7]}\n            diclist.append(d)\n\n        # diclistの中身をpnTableと照合した結果を入れる配列\n        diclist_new = []\n        for word in diclist:\n            base = word['BaseForm']        # 個々の辞書から基本形を取得\n            if base in related_dict:\n                related = float(related_dict[base])  # 中身の型があれなので\n                word['Related'] = related\n                diclist_new.append(word)\n            else:\n                related = 'notfound'            # その語がrelationwordsになかった場合\n                word['Related'] = related\n                diclist_new.append(word)\n\n\n        related_list = []\n        # 各ツイートの類似度を算出\n        for word in diclist_new:\n            related = word['Related']\n            if related != 'notfound':\n                related_list.append(related)  # notfoundだった場合は追加もしない\n        if len(related_list) > 0:  # 「全部notfound」じゃなければ\n            relatedMean = mean(related_list)\n        else:\n            relatedMean = 0  # 全部notfoundならゼロにする\n\n        # 確認用\n        print('\\n' + str(diclist_new))\n        print(relatedMean)\n\n        # mongoDBに登録\n        tweetPN = {}\n        '''\n        id : ツイートID\n        text : テキスト本文\n        created_at : ツイート日時\n        user : ユーザ名\n        PN : PN平均値\n        related : 関連度\n        '''\n        tweetPN['id'] = record['id']\n        tweetPN['text'] = record['text']\n        tweetPN['created_at'] = record['created_at']\n        tweetPN['user'] = {'screen_name': record['user']['screen_name']}\n        tweetPN['PN'] = record['PN']\n        tweetPN['related'] = relatedMean\n\n        # コレクションに追加(コレクションもここで作成)\n        save.insert(tweetPN)\n\nstart = time.time()\nrelated_tweets()\nelapsed_time = time.time() - start\nprint(time_measurement(elapsed_time))\n","sub_path":"recommendation/related_tweets.py","file_name":"related_tweets.py","file_ext":"py","file_size_in_byte":3385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"30527926","text":"#-------------------------------------------------------------------------------\n# Name:        module1\n# Purpose:\n#\n# Author:      Wornes\n#\n# Created:     15/02/2014\n# Copyright:   (c) Wornes 2014\n# Licence:     <your licence>\n#-------------------------------------------------------------------------------\n#Necesario para iniciar pygame:\nimport sys, pygame, pygame.mixer\nfrom pygame.draw import *\nfrom pygame.surface import Surface\nfrom pygame.locals import *\n#from Game import Board,Player\nfrom Engine.Buttons.Button import *\npygame.init()\n\nexit = False\nstartGame = False\n\n#Crea una variable con dos atributos int\nsize = width, height = 1024, 768\nbuttonWidth = width/5\nbuttonHeight = height/10\n#color (rgb) negro usado para limpiar la pantalla\nblack = 0,0,0\npurple = 130,0,200\n#Crea un reloj. El reloj permite controlar los frames a los que se mueve el\n#programa, ya que si no se mueve a la velocidad que te permita el procesador.\nclock = pygame.time.Clock()\n#Una variable ventana\nscreen = pygame.display.set_mode(size)\nbackground =pygame.display.set_mode(size)\n#Buttons:\nstartGameButton = Button(width/2-width/10,height/4,buttonWidth,buttonHeight)\nexitButton = Button(width/2-width/10,height/2,buttonWidth,buttonHeight)\n#Text for the buttons\nmyfont = pygame.font.SysFont(\"monospace\", 15)\nlabelStartGame = myfont.render(\"Start Game\", 1, (255,255,0))\nlabelExit = myfont.render(\"Exit to desktop\", 1, (255,255,0))\n#Run\nwhile not exit:\n    ''' Leer boton pulsado'''\n    'Dos botones: Start Game y Exit.'\n    'Dos cuadros de texto: Jugador1 y Jugador2 para incluir el nombre'\n    for event in pygame.event.get():\n        if event.type == MOUSEBUTTONDOWN:\n            exit= exitButton.getRect().collidepoint(pygame.mouse.get_pos())\n            if startGameButton.getRect().collidepoint(pygame.mouse.get_pos()):\n                labelStartGame = myfont.render(\"GAME STARTED\", 1, (255,255,0))\n\n    #\"limpia\" la ventana, poniendola a negro para evitar distorsiones\n    # al moverse las imagenes\n    #screen.fill(black)\n##    screen.blit(background,(0,0))\n    #Draw Buttons\n    pygame.draw.rect(background,purple,startGameButton.getRect(),1)\n    pygame.draw.rect(background,purple,exitButton.getRect(),1)\n    #Draw text\n    screen.blit(labelStartGame, startGameButton.getRect().midleft)\n    screen.blit(labelExit, exitButton.getRect().midleft)\n    #Hacemos que funcione cada 60 frames o algo asi, para ralentizarlo\n    clock.tick(60)\n    #flip refresca la pantalla, si no se pone no se muestran los cambios\n    pygame.display.flip()\npygame.quit()\nsys.exit()","sub_path":"JuegoCartas/MainModule.py","file_name":"MainModule.py","file_ext":"py","file_size_in_byte":2550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"593642845","text":"\"\"\"Simple least squares classifier for face emotion recognition.\n\nAdditionally uses random Gaussian matrix as featurization\n\"\"\"\n\nimport numpy as np\n\n\ndef evaluate(A, Y, w):\n    Yhat = np.argmax(A.dot(w), axis=1)\n    return float(np.sum(Yhat == Y)) / Y.shape[0]\n\n\ndef main():\n    # load data\n    with np.load('data/fer2013_train.npz') as data:\n        X_train, X_test = data['X'], data['Y']\n\n    with np.load('data/fer2013_test.npz') as data:\n        Y_train, Y_test = data['X'], data['Y']\n\n    # one-hot labels\n    I = np.eye(3)\n    Y_oh_train, Y_oh_test = I[Y_train], I[Y_test]\n\n    # generate random Gaussian and featurize X\n    d = 100\n    W = np.random.normal(size=(X_train.shape[1], d))\n    A_train, A_test = X_train.dot(W), X_test.dot(W)\n\n    # train model\n    w = np.linalg.inv(A_train.T.dot(A_train)).dot(A_train.T).dot(Y_oh_train)\n\n    # evaluate model\n    ols_train_accuracy = evaluate(A_train, Y_train, w)\n    print('(ols) Train Accuracy:', ols_train_accuracy)\n    ols_test_accuracy = evaluate(A_test, Y_test, w)\n    print('(ols) Test Accuracy:', ols_test_accuracy)\n","sub_path":"src/step_6_ls_simple.py","file_name":"step_6_ls_simple.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"453986739","text":"__author__ = 'Chaddle'\n\nclass Armor(object):\n    import re\n    from bs4 import BeautifulSoup\n    import urllib.request\n\n    path = \"file:///.../equipment/armor.htm\"\n\n    page = urllib.request.urlopen(path)\n    soup = BeautifulSoup(page.read())\n    col_heads = ['Cost', 'Armor/Shield Bonus', 'Maximum Dex Bonus', 'Armor Check Penalty',\n                 'Arcane Spell Failure Chance', 'Speed30', 'Speed20', 'Weight']\n\n    def __init__(self, name):\n        self.name = str(name).strip()\n        self.armor_dict = {}\n\n    def get_stats(self):\n        equip_table = []\n        for line in self.soup.table.tbody.find_all('td'):\n            equip_table.append(str(line.text).strip())\n\n        armors_list = []\n        for i in range(len(equip_table)):\n            try:\n                if i % 9 == 0 and self.name == equip_table[i]:\n                    armors_list.append(equip_table[i])\n            except Exception as e:\n                print(e)\n\n        armor_dict = {}\n        for a in armors_list:\n            armor_dict[a] = {}\n            index = equip_table.index(a)\n            for h in self.col_heads:\n                armor_dict[a][h] = equip_table[index+1]\n                index += 1\n        self.armor_dict = armor_dict[self.name]\n\nbreastplate = Armor(\"Breastplate\")\nbreastplate.get_stats()\nprint(breastplate.armor_dict[\"Cost\"])\n","sub_path":"equipment.py","file_name":"equipment.py","file_ext":"py","file_size_in_byte":1333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"497544148","text":"from utils.control_bit import get_control_bit\nfrom utils.crc import get_crc_from_file\nfrom utils.modulo_sum import get_modulo_sum_from_file\nfrom utils.noise import set_noise_in_file\n\nif __name__ == '__main__':\n    file_path = \"files/files_to_test/test.jpg\"\n    crc_div = 78\n    mod_div = 102\n\n    file = open(file_path, \"rb\").read()\n\n    ctrl_bit = get_control_bit(file)\n    sum_mod = get_modulo_sum_from_file(file, mod_div)\n    crc = get_crc_from_file(file, crc_div)\n\n    print(\"Control bit: \", ctrl_bit)\n    print(\"Modulo sum: \", sum_mod)\n    print(\"CRC: \", crc)\n\n    print(\"\\nAdding noises...\")\n    file = set_noise_in_file(file, 0.0001)\n    print(\"Added noises...\\n\")\n\n    new_ctrl_bit = get_control_bit(file)\n    new_sum_mod = get_modulo_sum_from_file(file, mod_div)\n    new_crc = get_crc_from_file(file, crc_div)\n\n    print(\"New control bit: \", new_ctrl_bit, \", old:\", ctrl_bit)\n    print(\"New modulo sum: \", new_sum_mod, \", old: \", sum_mod)\n    print(\"New CRC: \", new_crc, \", old: \", crc, \". Checked with prev CRC: \", get_crc_from_file(file, crc_div, crc))\n\n\n\n","sub_path":"lab1/new_main.py","file_name":"new_main.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"406982854","text":"import primerSetFinder\n\n\n\ndef largeFileReader():\n    largeFile = 'nonRedundant.fasta'\n    lineCount = 0\n    coliPoint = 0\n    coliSeq = ''\n    file = open(largeFile,'r')\n    for line in file:\n        if '>' in line:\n            if coliPoint == 1:\n                primerSetFinder.fwdPrimerFinder(coliSeq,0,0)\n                file.close()\n                break\n            if 'Escherichia coli' in line:\n                coliPoint = 1\n        elif coliPoint == 1:\n            coliSeq = coliSeq + line\n            lineCount += 1 ","sub_path":"largeFileReader.py","file_name":"largeFileReader.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"249880007","text":"import os\nimport argparse\nimport collections\nfrom numpy.core.fromnumeric import argsort\nimport torch\nimport numpy as np\nimport data_loader.data_loaders as module_data\nimport model.loss as module_loss\nimport model.metric as module_metric\nimport model.model as module_arch\nfrom parse_config import ConfigParser\nfrom trainer import ConstraintTrainer, LossReweightConstraintTrainer\nfrom utils import prepare_device, deep_copy\nfrom data_loader.random_noise import label_noise, noisy_labeler\nimport time\n\ndef main(config, args):\n    logger = config.get_logger('train')\n\n    # setup data_loader instances\n    if config[\"data_loader\"][\"type\"] == \"CaltechDataLoader\":\n        train_data_loader = config.init_obj('data_loader', module_data, idx_start = 0, img_num = 30, phase = \"train\")\n        valid_data_loader = config.init_obj('data_loader', module_data, idx_start = 30, img_num = 20, phase = \"val\")\n        test_data_loader = config.init_obj('data_loader', module_data, idx_start = 50, img_num = 20, phase = \"test\")\n    elif config[\"data_loader\"][\"type\"] == \"AircraftsDataLoader\":\n        train_data_loader = config.init_obj('data_loader', module_data, phase = \"train\")\n        valid_data_loader = config.init_obj('data_loader', module_data, phase = \"val\")\n        test_data_loader = config.init_obj('data_loader', module_data, phase = \"test\")\n    elif config[\"data_loader\"][\"type\"] == \"BirdsDataLoader\" or \\\n        config[\"data_loader\"][\"type\"] == \"CarsDataLoader\" or \\\n        config[\"data_loader\"][\"type\"] == \"DogsDataLoader\" or \\\n        config[\"data_loader\"][\"type\"] == \"IndoorDataLoader\" or \\\n        config[\"data_loader\"][\"type\"] == \"Cifar10DataLoader\":\n        train_data_loader = config.init_obj('data_loader', module_data, valid_split = 0.1, phase = \"train\")\n        valid_data_loader = train_data_loader.split_validation()\n        test_data_loader = config.init_obj('data_loader', module_data, phase = \"test\")\n    elif config[\"data_loader\"][\"type\"] == \"FlowerDataLoader\":\n        train_data_loader = config.init_obj('data_loader', module_data)\n        valid_data_loader = train_data_loader.split_validation()\n        test_data_loader = train_data_loader.split_test()\n\n    device, device_ids = prepare_device(config['n_gpu'])\n    if args.noisy_labeler:\n        aux_model = module_arch.ResNet18(\n            n_classes=config[\"arch\"][\"args\"][\"n_classes\"]-1 if args.train_dac else config[\"arch\"][\"args\"][\"n_classes\"]\n        )\n        aux_model.load_state_dict(torch.load(args.noisy_labeler_dir)[\"state_dict\"])\n        wrong_indices, train_labels_old = noisy_labeler(\n            train_data_loader,\n            train_data_loader.dataset, \n            train_data_loader.sampler.indices,\n            aux_model,\n            device = device\n        )\n        del aux_model\n        logger.info(\"Randomizing {} number of labels\".format(wrong_indices.shape[0]))\n    elif args.noise_rate!=0:\n        wrong_indices, train_labels_old = label_noise(\n            train_data_loader.dataset, train_data_loader.sampler.indices, args.noise_rate, symmetric=not args.noise_nonuniform\n        )\n        logger.info(\"Randomizing {} number of labels\".format(wrong_indices.shape[0]))\n\n    # If small data, shrink training data size\n    logger.info(\"Train Size: {} Valid Size: {} Test Size: {}\".format(\n        len(train_data_loader.sampler), \n        len(valid_data_loader.sampler), \n        len(test_data_loader.sampler)))\n\n    # build model architecture, then print to console\n    model = config.init_obj('arch', module_arch)\n    logger.info(model)\n\n    # prepare for (multi-device) GPU training\n    model = model.to(device)\n    source_state_dict = deep_copy(model.state_dict())\n    if len(device_ids) > 1:\n        model = torch.nn.DataParallel(model, device_ids=device_ids)\n\n    # get function handles of loss and metrics\n    criterion = getattr(module_loss, config['loss'])\n    metrics = [getattr(module_metric, met) for met in config['metrics']]\n\n    accuracies = []\n    torch.manual_seed(int(time.time()))\n    for run in range(args.runs):\n        model.reset_parameters(source_state_dict)\n        # build optimizer, learning rate scheduler. delete every lines containing lr_scheduler for disabling scheduler\n        trainable_params = filter(lambda p: p.requires_grad, model.parameters())\n        optimizer = config.init_obj('optimizer', torch.optim, trainable_params)\n        lr_scheduler = config.init_obj('lr_scheduler', torch.optim.lr_scheduler, optimizer)\n\n        if args.train_correct_label:\n            checkpoint_dir = os.path.join(\n            \"./saved_label_noise\", \n            \"{}_{}_noise_rate_{}_correct_label\".format(config[\"arch\"][\"type\"], config[\"data_loader\"][\"type\"], args.noise_rate))\n            trainer = LossReweightConstraintTrainer(model, criterion, metrics, optimizer,\n                        config=config,\n                        device=device,\n                        train_data_loader=train_data_loader,\n                        valid_data_loader=valid_data_loader,\n                        test_data_loader=test_data_loader,\n                        lr_scheduler=lr_scheduler,\n                        checkpoint_dir=checkpoint_dir,\n                        adaptive_epoch=args.reweight_epoch,\n                        temp=args.reweight_temp,\n                        correct_epoch=args.correct_epoch,\n                        correct_thres=args.correct_thres,\n                        train_labels_old=train_labels_old)\n            lambda_extractor = config[\"reg_extractor\"]\n            lambda_pred_head = config[\"reg_predictor\"]\n            scale_factor = config[\"scale_factor\"]\n            if config[\"reg_method\"] == \"constraint\":\n                trainer.add_constraint(\n                    norm = config[\"reg_norm\"], lambda_extractor = lambda_extractor, lambda_pred_head=lambda_pred_head, \n                    state_dict = source_state_dict, scale_factor=scale_factor\n                )\n            if config[\"reg_method\"] == \"penalty\":\n                trainer.add_penalty(\n                    norm = config[\"reg_norm\"], lambda_extractor = lambda_extractor, lambda_pred_head=lambda_pred_head, \n                    state_dict = source_state_dict, scale_factor=scale_factor\n                )\n        else:\n            checkpoint_dir = os.path.join(\n            \"./saved_label_noise\", \n            \"{}_{}_{}_{}_{:.4f}_{:.4f}_noise_rate_{}\".format(config[\"arch\"][\"type\"], config[\"data_loader\"][\"type\"], \n                                    config[\"reg_method\"], config[\"reg_norm\"],\n                                    config[\"reg_extractor\"], config[\"reg_predictor\"], args.noise_rate))\n            trainer = ConstraintTrainer(model, criterion, metrics, optimizer,\n                            config=config,\n                            device=device,\n                            train_data_loader=train_data_loader,\n                            valid_data_loader=valid_data_loader,\n                            test_data_loader=test_data_loader,\n                            lr_scheduler=lr_scheduler,\n                            checkpoint_dir = checkpoint_dir)\n                        \n            lambda_extractor = config[\"reg_extractor\"]\n            lambda_pred_head = config[\"reg_predictor\"]\n            scale_factor = config[\"scale_factor\"]\n            if config[\"reg_method\"] == \"constraint\":\n                trainer.add_constraint(\n                    norm = config[\"reg_norm\"], lambda_extractor = lambda_extractor, lambda_pred_head=lambda_pred_head, \n                    state_dict = source_state_dict, scale_factor=scale_factor\n                )\n            if config[\"reg_method\"] == \"penalty\":\n                trainer.add_penalty(\n                    norm = config[\"reg_norm\"], lambda_extractor = lambda_extractor, lambda_pred_head=lambda_pred_head, \n                    state_dict = source_state_dict, scale_factor=scale_factor\n                )\n\n        trainer.train()\n        accuracies.append(trainer.test())\n    logger.info(\"Test Accuracy {:1.4f} +/- {:1.4f}\".format(np.mean(accuracies), np.std(accuracies)))\n\n\nif __name__ == '__main__':\n    args = argparse.ArgumentParser()\n    args.add_argument('-c', '--config', default=None, type=str,\n                      help='config file path (default: None)')\n    args.add_argument('-r', '--resume', default=None, type=str,\n                      help='path to latest checkpoint (default: None)')\n    args.add_argument('-d', '--device', default=None, type=str,\n                      help='indices of GPUs to enable (default: all)')\n    args.add_argument('--runs', type=int, default=3)\n    args.add_argument('--noise_rate', type=float, default=0)\n    args.add_argument('--noise_nonuniform', action=\"store_true\")\n    args.add_argument('--noisy_labeler', action=\"store_true\")\n    args.add_argument('--noisy_labeler_dir', type=str, \\\n        default=\"./saved_finetune_models/ResNet18_IndoorDataLoader_none_none_1.0000_1.0000/model_epoch_8.pth\")\n\n    args.add_argument('--train_correct_label', action=\"store_true\")\n    args.add_argument('--reweight_epoch', type=int, default=5)\n    args.add_argument('--reweight_temp', type=float, default=1)\n    args.add_argument('--correct_epoch', type=int, default=10)\n    args.add_argument('--correct_thres', type=float, default=0.9)\n\n    # custom cli options to modify configuration from default values given in json file.\n    CustomArgs = collections.namedtuple('CustomArgs', 'flags type target')\n    options = [\n        CustomArgs(['--lr', '--learning_rate'], type=float, target='optimizer;args;lr'),\n        CustomArgs(['--bs', '--batch_size'], type=int, target='data_loader;args;batch_size'),\n        CustomArgs(['--model'], type=str, target=\"arch;type\"),\n        CustomArgs(['--weight_decay'], type=float, target=\"optimizer;args;weight_decay\"),\n        CustomArgs(['--reg_method'], type=str, target='reg_method'),\n        CustomArgs(['--reg_norm'], type=str, target='reg_norm'),\n        CustomArgs(['--reg_extractor'], type=float, target='reg_extractor'),\n        CustomArgs(['--reg_predictor'], type=float, target='reg_predictor'),\n        CustomArgs(['--scale_factor'], type=int, target=\"scale_factor\")\n    ]\n    config, args = ConfigParser.from_args(args, options)\n    print(config)\n    main(config, args)\n","sub_path":"train_label_noise.py","file_name":"train_label_noise.py","file_ext":"py","file_size_in_byte":10213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"325812768","text":"import requests  \n\nclass BotHandler:\n\n    def __init__(self, token):\n        self.token = token\n        self.api_url = \"https://api.telegram.org/bot{}/\".format(token)\n        self.offset = None\n        self.timeout = 0\n\n    def get_updates(self):\n        method = 'getUpdates'\n        params = {'timeout': self.timeout, 'offset': self.offset}\n        resp = requests.get(self.api_url + method, params)\n        result_json = resp.json()['result']\n        if len(result_json) > 0:\n            self.offset = result_json[0]['update_id'] + 1\n        else:\n            self.offset = None\n        return result_json\n\n    def send_message(self, chat_id, text):\n        params = {'chat_id': chat_id, 'text': text}\n        method = 'sendMessage'\n        resp = requests.post(self.api_url + method, params)\n        return resp\n\n    def reply_to_message(self, chat_id, text, reply_to_message_id):\n        params = {'chat_id': chat_id, 'text': text, 'reply_to_message_id': reply_to_message_id}\n        method = 'sendMessage'\n        resp = requests.post(self.api_url + method, params)\n        return resp\n\nmybot = BotHandler('649982709:AAEH6u7pmW6deVuJWfYDNOj5I5u-dXaN2rI');\n\nclass TranslaThor:\n\n    def __init__(self, api_key):\n        self.api_key = api_key\n        self.api_url = 'https://translate.yandex.net/api/v1.5/tr.json/'\n\n    def detect(self, text_for_detection):\n        method = 'detect'\n        params = {'key': self.api_key, 'text': text_for_detection}\n        resp = requests.get(self.api_url + method, params) #prox\n        detected = resp.json()\n        return detected\n\n    def translate(self, text_for_translation, lang = 'en'):\n        method = 'translate'\n        params = {'key': self.api_key, 'text': text_for_translation, 'lang': lang}\n        resp = requests.get(self.api_url + method, params) #prox\n        translated = resp.json()['text']\n        return translated\n    \ntranslator = TranslaThor('trnsl.1.1.20181209T145918Z.7dd52327550b6c7e.ed590e7965dad56809bb01fe77f376e97aa7619b')\n\ndef main():\n    update = mybot.get_updates()\n    if len(update) == 0:\n        mybot.timeout = 100\n    else:\n        mybot.timeout = 100\n        mybot.offset = update[-1]['update_id'] + 1\n    while 1:\n        update = mybot.get_updates()\n        if len(update) > 0:\n            mybot.send_message(666892256, str(update[0]))\n            if('message' in update[0]):\n                if ('text' in update[0]['message']):\n                    text_for_translation = update[0]['message']['text']\n                    chat_id = update[0]['message']['chat']['id']\n                    reply_to_message_id = update[0]['message']['message_id']\n                    lang = translator.detect(text_for_translation)['lang']\n                    if (lang != 'en' and lang != ''):\n                        translated_text = translator.translate(text_for_translation)\n                        mybot.reply_to_message(chat_id, translated_text, reply_to_message_id)\n\nif __name__ == '__main__':  \n    try:\n        main()\n    except KeyboardInterrupt:\n        exit()\n","sub_path":"tran.py","file_name":"tran.py","file_ext":"py","file_size_in_byte":3036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"557399433","text":"import json\nfrom pprint import pprint\nimport PythonParse\nimport random\nimport sys\n\nbaseName = sys.argv[1]\noriginal =  PythonParse.ParsePlayerLogs(baseName+\"orig\")\nplayerLogs = PythonParse.ParsePlayerLogs(baseName)\nngramPlayerLogs = PythonParse.ParsePlayerLogs(baseName+\"ngram\")\nghostPlayerLogs = PythonParse.ParsePlayerLogs(baseName+\"ghost\")\nAIPlayerLogs = PythonParse.ParsePlayerLogs(baseName+\"AI\")\n\n\ndef getSimilarity(h1_hist, h2_hist):\n    similarity = 1\n    difference = 0\n\n    keys = set(h1_hist.keys()).union(set(h2_hist.keys()))\n    normalize = 1.0/len(keys)\n\n    for key in keys:\n        h1_count = h1_hist[key] if key in h1_hist else 0\n        h2_count = h2_hist[key] if key in h2_hist else 0\n        difference += float(abs(h1_count - h2_count)) / float((h1_count + h2_count))\n\n    similarity -= normalize * difference\n    print(similarity)\n\n    return similarity\n\ndef evaluate(original, retry, ngram, Ghost, AI):\n    original_hist = dict()\n    retry_hist = dict()\n    ngram_hist = dict()\n    Ghost_hist = dict()\n    AI_hist = dict()\n    Other_hist = dict()\n\n    #Comparator is a compilation of 1 to 3 grams\n    for i in range(3):\n        n = i+1\n        for round in original:\n            history = []\n            for entry in round:\n                if(entry['initiatedPlayer'] == 0):\n                    history.append(entry['p1Action'])\n                    if(len(history) > n):\n                        history.pop(0)\n                    if(len(history) == n):\n                        if(str(history) not in original_hist):\n                            original_hist[str(history)] = 0\n                        original_hist[str(history)] += 1\n\n        for round in retry:\n            history = []\n            for entry in round:\n                if(entry['initiatedPlayer'] == 0):\n                    history.append(entry['p1Action'])\n                    if(len(history) > n):\n                        history.pop(0)\n                    if(len(history) == n):\n                        if(str(history) not in retry_hist):\n                            retry_hist[str(history)] = 0\n                        retry_hist[str(history)] += 1\n        for round in ngram:\n            history = []\n            for entry in round:\n                if(entry['initiatedPlayer'] == 0):\n                    history.append(entry['p1Action'])\n                    if(len(history) > n):\n                        history.pop(0)\n                    if(len(history) == n):\n                        if(str(history) not in ngram_hist):\n                            ngram_hist[str(history)] = 0\n                        ngram_hist[str(history)] += 1\n        for round in Ghost:\n            history = []\n            for entry in round:\n                if(entry['initiatedPlayer'] == 0):\n                    history.append(entry['p1Action'])\n                    if(len(history) > n):\n                        history.pop(0)\n                    if(len(history) == n):\n                        if(str(history) not in Ghost_hist):\n                            Ghost_hist[str(history)] = 0\n                        Ghost_hist[str(history)] += 1\n        for round in AI:\n            history = []\n            for entry in round:\n                if(entry['initiatedPlayer'] == 0):\n                    history.append(entry['p1Action'])\n                    if(len(history) > n):\n                        history.pop(0)\n                    if(len(history) == n):\n                        if(str(history) not in AI_hist):\n                            AI_hist[str(history)] = 0\n                        AI_hist[str(history)] += 1\n    #print(original_hist)\n    #print(retry_hist)\n    #print(ngram_hist)\n    #print(Ghost_hist)\n    #print(AI_hist)\n    #print(Other_hist)\n\n    print(\"~~~~~~~~~~~~~~~~~~~~~\")\n    print(\"retry\")\n    retry_sim = getSimilarity(retry_hist, original_hist)\n    print(\"ngram\")\n    ngram_sim = getSimilarity(retry_hist, ngram_hist)\n    print(\"Ghost\")\n    ghost_sim = getSimilarity(retry_hist, Ghost_hist)\n    print(\"AI\")\n    AI_sim = getSimilarity(retry_hist, AI_hist)\n    print(\"~~~~~~~~~~~~~~~~~~~~~\")\n\n    hists = [retry_sim, ngram_sim, ghost_sim, AI_sim]\n    \n    return hists\n\nretry_avg = []\nngram_avg = []\nghost_avg = []\nAI_avg = []\nother_avg = []\n\nfor i in range(5):\n    if(i == int(sys.argv[3])):\n        print(\"skip\")\n        pass\n    else:\n        m_retry = playerLogs[i:i+1]\n        m_ngram = ngramPlayerLogs[i:i+1]\n        m_Ghost = ghostPlayerLogs[i:i+1] \n        m_AI = AIPlayerLogs[i:i+1] \n        res = evaluate(original, m_retry, m_ngram, m_Ghost, m_AI)\n        if(res[0] != 1.0):\n            retry_avg.append(res[0])\n        ngram_avg.append(res[1])\n        ghost_avg.append(res[2])\n        AI_avg.append(res[3])\n\nimport numpy as np\nprint(\"Mean\")\nprint(np.mean(retry_avg))\nprint(np.mean(ngram_avg))\nprint(np.mean(ghost_avg))\nprint(np.mean(AI_avg))\nprint(\"Standard Deviation\")\nprint(np.std(retry_avg))\nprint(np.std(ngram_avg))\nprint(np.std(ghost_avg))\nprint(np.std(AI_avg))","sub_path":"AnalysisScripts-Train vs Test/SequenceSimilarityAnalysis.py","file_name":"SequenceSimilarityAnalysis.py","file_ext":"py","file_size_in_byte":4964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"495334903","text":"import sys\n\nis_frozen = getattr(sys, 'frozen', False)\nfrozen_temp_path = getattr(sys, '_MEIPASS', '')\n\nimport os\n\n# This is needed to find resources when using pyinstaller\nif is_frozen:\n    basedir = frozen_temp_path\nelse:\n    basedir = os.path.dirname(os.path.abspath(__file__))\nresource_dir = os.path.join(basedir, 'resources')\n\n\n\n\nimport gi\ngi.require_version(\"Gtk\", \"3.0\")\nfrom gi.repository import Gtk, Gdk\n\nfrom matplotlib import pyplot\nfrom matplotlib.figure import Figure\nfrom matplotlib.backends.backend_gtk3agg import FigureCanvasGTK3Agg as FigureCanvas\n# from matplotlib.backends.backend_gtk3cairo import FigureCanvasGTK3Cairo as FigureCanvas\nfrom matplotlib.backends.backend_gtk3 import NavigationToolbar2GTK3 as NavigationToolbar\n\nfrom PlotWindow import PlotBox\nfrom About import AboutWindow\nfrom ErrorMessage import ErrorMessage\n\nclass ZoomWindow():\n    def __init__(self, parent, simulation, colormap, zoom_value=0):\n        self.zoom = 100.0\n\n        self.parent = parent\n        self.simulation = simulation\n        self.colormap = colormap\n        self.zoom_value = zoom_value\n\n        \n        self.builder = Gtk.Builder()\n        \n        self.builder.add_from_file(resource_dir + \"/zoom.glade\")\n        self.window = self.builder.get_object(\"wndZoom\")\n        self.statBar = self.builder.get_object(\"statBar\")\n        self.btnApplyZoom = self.builder.get_object(\"btnApplyZoom\")\n        self.txtZoomValue = self.builder.get_object(\"txtZoomValue\")\n        self.boxPlot = self.builder.get_object(\"boxPlot\")\n        self.menuColorMap = self.builder.get_object(\"menuColorMap\")\n        self.btnQuit = self.builder.get_object(\"btnQuit\")\n        self.btnAbout = self.builder.get_object(\"btnAbout\")\n\n        self.window.set_transient_for(self.parent.window)\n\n        self.btnApplyZoom.connect(\"clicked\", self.on_apply_zoom)\n        self.btnQuit.connect(\"activate\", lambda _: self.window.close())\n\n        self.txtZoomValue.connect(\"key-press-event\", self.on_key_press_event)\n\n        self.plot = PlotBox(self, self.simulation, self.colormap, self.statBar)\n        self.boxPlot.pack_start(self.plot.boxPlot, True, True, 0)\n\n        self.btnAbout.connect(\"activate\", lambda _: AboutWindow(self.window))\n\n        # Get a list of the colormaps in matplotlib.  Ignore the ones that end with\n        # '_r' because these are simply reversed versions of ones that don't end\n        # with '_r'\n        maps = sorted(m for m in pyplot.cm.datad if not m.endswith(\"_r\"))\n\n        firstitem = Gtk.RadioMenuItem(self.colormap)\n        firstitem.set_active(True)\n        firstitem.connect('activate', self.on_color_bar_menu, self.colormap)\n        self.menuColorMap.append(firstitem)\n        for name in maps:\n            if name != self.colormap:\n                item = Gtk.RadioMenuItem.new_with_label([firstitem], name)\n                item.set_active(False)\n                item.connect('activate', self.on_color_bar_menu, name)\n                self.menuColorMap.append(item)\n\n\n        self.window.show_all()\n\n        self.btnApplyZoom.emit(\"clicked\")\n\n    def on_key_press_event(self, widget, event):\n\n        # print(\"Key press on widget: \", widget)\n        # print(\"          Modifiers: \", event.state)\n        # print(\"      Key val, name: \", event.keyval, Gdk.keyval_name(event.keyval))\n\n        # check the event modifiers (can also use SHIFTMASK, etc)\n        ctrl = (event.state & Gdk.ModifierType.CONTROL_MASK)\n\n        # see if we recognise a keypress\n        if Gdk.keyval_name(event.keyval) == 'Return':\n            # print(\"Enter\")\n            self.on_apply_zoom(None)\n\n\n    def isNumeric(self, val, func=float):\n        try:\n            func(val)\n            return True\n        except Exception as e:\n            return False\n\n    def on_apply_zoom(self, widget):\n        value = self.txtZoomValue.get_text().replace(\"%\", \"\")\n        self.zoom = float(value) if self.isNumeric(value) else False\n\n        if not (self.zoom and self.zoom > 0):\n            ErrorMessage(self.window, \"Invalid input parameters\", \"Zoom value must be a positive real.\")\n            return\n\n        zmin, zmax, ymin, ymax = self.plot.compute_zoom(self.zoom)\n        \n        self.parent.plot.draw_rectangle(zmin, zmax, ymin, ymax)\n        self.txtZoomValue.set_text(\"{}%\".format(self.zoom))\n\n\n\n    def on_color_bar_menu(self, widget, name):\n        self.colormap = name\n        self.plot.update_plot(name)","sub_path":"src/ZoomWindow.py","file_name":"ZoomWindow.py","file_ext":"py","file_size_in_byte":4399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"309717197","text":"import matplotlib.pyplot as plt\r\n\r\ndata = open(\"populacao_brasileira.csv\").readlines()\r\n\r\nx = [] #Anos\r\ny = [] #População\r\n\r\nfor i in range(len(data)):\r\n\tif i != 0:\r\n\t\tlign = data[i].split(\";\")\r\n\t\tx.append(int(lign[0]))\r\n\t\ty.append(int(lign[1]))\r\n\r\nplt.plot(x, y, color=\"k\")\r\nplt.bar(x, y, color=\"#a9a9a9\")\r\nplt.title(\"Crescimento da população brasileira 1980-2016\")\r\nplt.xlabel(\"Ano\")\r\nplt.ylabel(\"População x 100.000.000\")\r\n#plt.show()\r\nplt.savefig(\"populacao_brasileira.png\", dpi=300)","sub_path":"populacao_brasileira.py","file_name":"populacao_brasileira.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"268891053","text":"# #!venv/bin/python\nimport logging\nimport os\n\nfrom aiogram import Bot, Dispatcher, executor, types\nfrom aiogram.dispatcher import FSMContext\nfrom aiogram.contrib.fsm_storage.memory import MemoryStorage\nfrom aiogram.dispatcher.filters import Text\n\nfrom profile import profile_start, register_profile_handlers\nfrom girths import girth_start, register_girth_handlers, show_girths\n# from entries import entries_start, register_entries_handlers\n\nfrom models.database import DB_NAME\nimport db\nfrom config import menu, text_welcome\nfrom dotenv import load_dotenv\n\nlogging.basicConfig(level=logging.INFO)\n\nload_dotenv()\ntoken = os.getenv(\"TELEGRAM_TOKEN\")\n\nbot = Bot(token)\ndp = Dispatcher(bot, storage=MemoryStorage())\n\n\n@dp.message_handler(commands=['start'], state=\"*\")\nasync def start_handler(message: types.Message, state: FSMContext):\n    \"\"\"Обработка команды start. Вывод текста и меню\n    Поиск пользователя в базе. Если не найден - предложить заполнить анкету.\n    Если найден - показываем меню\"\"\"\n    await state.finish()\n\n    await message.answer(text_welcome)\n    result = db.get_user(message.from_user.id)\n    if not bool(result):\n        await state.update_data(telegram_id=message.from_user.id)\n        await profile_start(message)\n    else:\n        await message.answer(menu)\n\n\n@dp.message_handler(commands=['add_girths'], state=\"*\")\nasync def start_girths(message: types.Message, state: FSMContext):\n    await state.finish()\n\n    result = db.get_user(message.from_user.id)\n    if bool(result):\n        await state.update_data(user_id=result[0].id)\n        await girth_start(message)\n    else:\n        await message.answer(\"Что-то пошло не так.\")\n\n\n@dp.message_handler(commands=['add_entries'], state=\"*\")\nasync def start_girths(message: types.Message, state: FSMContext):\n    await state.finish()\n\n    result = db.get_user(message.from_user.id)\n    if bool(result):\n        await state.update_data(user_id=result[0].id)\n        # await entries_start(message)\n    else:\n        await message.answer(\"Что-то пошло не так.\")\n\n\n@dp.message_handler(commands=['get_girths'])\nasync def start_girths(message: types.Message):\n    await show_girths(message)\n\n\nregister_profile_handlers(dp)\n# register_entries_handlers(dp)\nregister_girth_handlers(dp)\n\n# @dp.message_handler(commands=['help'])\n# async def help_handler(message: types.Message):\n#     '''Обработка команды help. Вывод текста и меню'''\n\n#     await message.answer(f'Привет, {message.from_user.first_name}!') \n\n\n# @dp.message_handler(lambda c: c.data == 'main_window')\n# async def show_main_window(calback_query: types.CallbackQuery):\n#     '''Главный экран'''\n\n\n# @dp.message_handler()\n# async def unknown_message(message: types.Message):\n#     \"\"\"Ответ на любое неожидаемое сообщение\"\"\"\n\n@dp.message_handler(commands=\"cancel\", state=\"*\")\n@dp.message_handler(Text(equals=\"отмена\", ignore_case=True), state=\"*\")\nasync def cancel(message: types.Message, state: FSMContext):\n    await state.finish()\n    await message.answer(\"Действие отменено\")\n\nif __name__ == '__main__':\n    if not os.path.exists(DB_NAME):\n        db.create()\n\n    executor.start_polling(dp, skip_updates=True)\n\n\n","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":3390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"22127458","text":"import sys\nimport requests\nimport csv\n\ndef fetchArtistId(name):\n    \"\"\"Using the Spotify API search method, take a string that is the artist's name, \n    and return a Spotify artist ID.\n    \"\"\"\n    namespace = name.replace(\" \",\"%20\")\n    url = 'https://api.spotify.com/v1/search?q=' + namespace + '&type=artist'\n    req = requests.get(url)\n    artistInfo = req.json()\n    artists = artistInfo['artists']['items']\n    \n    if not artists: #If the artistInfo['artists']['items'] is empty, then it returns the error code\n    \tsearchresultid = \"Search does not match an artist name\"\n    else:\n    \tsearchresultid = artists[0]['id']\n\n    return searchresultid\n\n\ndef fetchArtistInfo(artist_id):\n    \"\"\"Using the Spotify API, takes a string representing the id and\n    returns a dictionary including the keys 'followers', 'genres', \n    'id', 'name', and 'popularity'.\n    \"\"\"\n    url = 'https://api.spotify.com/v1/artists/' + artist_id\n    req = requests.get(url)\n    artistInfo = req.json()\n\n    artist_dict = {}\n\n    artist_dict['followers'] = artistInfo['followers']['total']\n    artist_dict['genres'] = artistInfo['genres']\n    artist_dict['id'] = artistInfo['id']\n    artist_dict['name'] = artistInfo['name']\n    artist_dict['popularity'] = artistInfo['popularity']\n\n    return artist_dict\n\n\n\n\n\n","sub_path":"Assignment7/fetchArtist.py","file_name":"fetchArtist.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"55918676","text":"import cv2\nimport numpy as np  \n\nfrom matplotlib import pyplot as plt\nimport matplotlib.image as mpimg\n\n# abrindo imagem de nome \"imgteste\"\n\nimg = cv2.imread('imgteste.jpg', 0)\ncv2.imshow('Imagem', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\nimg = cv2.imread(\"imgteste.jpg\", 0)\nplt.hist(img.ravel(), 256, [0, 256])\ncv2.imshow(\"Histograma\", img)\nplt.show()\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n# examinando os parâmetros do histograma\n#cv2.calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]])\n#hist = cv2.calcHist([gray_img],[0],None,[256],[0,256])\n#\n#segmentando a imagem\n#img = cv2.imread('imgteste.jpg',0)\n#ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)\n#ret,thresh2 = cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV)\n#ret,thresh3 = cv2.threshold(img,127,255,cv2.THRESH_TRUNC)\n#ret,thresh4 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO)\n#ret,thresh5 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO_INV)\n#titles = ['Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']\n#images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]\n#for i in xrange(6):\n#    plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')\n#    plt.title(titles[i])\n#    plt.xticks([]),plt.yticks([])\n#plt.show()\n#\n#cv2.waitKey(0)\n#cv2.destroyAllWindows()\n\nimg = cv2.imread('imgteste.jpg', 0)\ncv2.imshow('Imagem', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\nhist = cv2.calcHist([img],[0],None,[256],[0,256])\nhist,bins = np.histogram(img,256,[0,256])\n\nplt.hist(img.ravel(),256,[0,256])\nplt.title('Histograma')\nplt.show()\n\nwhile True:\n    k = cv2.waitKey(0) & 0xFF     \n    if k == 27: break             # ESC key to exit\ncv2.destroyAllWindows()","sub_path":"cg.py","file_name":"cg.py","file_ext":"py","file_size_in_byte":1654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"429948235","text":"x = 5\n\nprint(x)\nprint(x)\n# print(Have a nice day!)\nprint(\"Have a nice day!\")\n\ntemperature_in_celsius = 5\ncar_speed = 55\ntemperatureInCelsius = 55\n# 4runner = 5\nrunner4 = 5\n\nPI = 3.14159\nSCREEN_WIDTH = 800\nm = 294 / 10.5\nprint(m)\n\nm = 294\ng = 10.5\nm2 = m / g\nprint(m2)\n\n#Shift + F6 to change variable names\n\nx = 5 + 10\nprint(x)\n\nimport arcade\n\nSCREEN_WIDTH = 800\n\n# arcade.draw_circle_filled(400, 50, 10, arcade.color.RED)\n","sub_path":"Lab 02 - Draw a Picture/Test.py","file_name":"Test.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"560333287","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time     : 2019/12/29 13:44\n# @Author   : StephenZ\n# @Site     : \n# @File     : Case27.py\n# @Purpose  :\n# @Software : PyCharm\n# @Copyright:   (c) StephenZ 2019\n# @Licence  :     <@2019>\nfrom typing import List\n\n\nclass Solution:\n    def removeElement(self, nums: List[int], val: int) -> int:\n        count = nums.count(val)\n        for i in range(count):\n            nums.remove(val)\n        return len(nums)\n\n\n\n\ndef test_sulotion():\n    nums = [0, 1, 2, 2, 3, 0, 4, 2]\n    val = 2\n\n    s = Solution()\n    s.removeElement(nums, val)\n\n\nif __name__ == '__main__':\n    test_sulotion()\n","sub_path":"Case/Case27.py","file_name":"Case27.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"351135059","text":"import discord\nfrom discord.ext import commands, menus\n\n\nclass HelpSource(menus.ListPageSource):\n    \"\"\"\n    This class is used to manage pagination of the help command.\n    \"\"\"\n    def __init__(self, ctx, fields, per_page=3):\n        self.ctx = ctx\n        self.num_fields = int(len(fields) / per_page)\n        if len(fields) % per_page:\n            self.num_fields += 1\n        super().__init__(fields, per_page=per_page)\n\n    async def format_page(self, menu, entries):\n        offset = menu.current_page * self.per_page\n\n        embed = discord.Embed(\n            title=f\"\\N{NEWSPAPER} Help Menu [{menu.current_page + 1}/{self.num_fields}]\",\n            description=f\"A listing of all available commands sorted by grouping.\\n\"\n                        f\"To learn more about specific commands, use `{self.ctx.bot.config['prefix']}help <command>`\"\n        )\n        for field in [field for i, field in enumerate(entries, start=offset)]:\n            embed.add_field(\n                name=field['name'],\n                value=field['value'],\n                inline=field['inline']\n            )\n        embed.set_footer(\n            text=self.ctx.bot.config['footer']['text'],\n            icon_url=self.ctx.bot.config['footer']['icon_url']\n        )\n        return embed\n\n\nclass HelpCommand(commands.MinimalHelpCommand):\n    \"\"\"\n    Contains all of the features for a custom help message depending on certain\n    values set when defining a command in the first place.\n\n    NOTE: Users who use the help command can only see commands that they are actually allowed to use in permissions.\n    Similarly, any commands that have `hidden=True` in their decorator are hidden.\n    \"\"\"\n    async def send_bot_help(self, mapping):\n        \"\"\"\n        Send a help list for all of the bot commands.\n        \"\"\"\n        fields = []\n        # Parsing through all cogs and all commands contained within each command.\n        for cog in mapping.keys():\n            if cog:\n                command_list = await self.filter_commands(mapping[cog], sort=True)\n                if len(command_list) > 0:\n                    # If a cog contains visible commands, add the to an embed field.\n                    fields.append({\n                        \"name\": cog.qualified_name,\n                        \"value\": f\"{cog.description}\\nCommands:\\n\" +\n                                 \", \".join(f\"`{command}`\" for command in command_list),\n                        \"inline\": False\n                    })\n\n        # Create the paginated help menu\n        pages = menus.MenuPages(source=HelpSource(self.context, fields), delete_message_after=True)\n        await pages.start(self.context)\n\n    async def send_cog_help(self, cog):\n        \"\"\"\n        Sends help for all commands contained within a cog, by cog name.\n        \"\"\"\n        embed = discord.Embed(\n            title=f\"{cog.qualified_name} Help\",\n            description=f\"{cog.description}\\nTo learn more about specific commands, \"\n                        f\"use `{self.clean_prefix}help <command>`\"\n        )\n        embed.set_author(\n            name=self.context.message.author,\n            icon_url=self.context.message.author.avatar_url\n        )\n        embed.add_field(\n            name=\"Commands\",\n            value=\"\\n\".join(\n                \"`{1.qualified_name}`\".format(self, command)\n                for command in cog.walk_commands()\n                if not command.hidden\n            )\n        )\n        embed.set_footer(\n            text=self.context.bot.config['footer']['text'],\n            icon_url=self.context.bot.config['footer']['icon_url']\n        )\n        await self.get_destination().send(embed=embed)\n\n    async def send_group_help(self, group):\n        \"\"\"\n        Sends help message for all commands grouped in a parent command.\n        \"\"\"\n        command_list = group.walk_commands()\n        command_activation = []\n        command_example = []\n        for command in command_list:\n            if f'`{command.qualified_name} {command.signature}` - {command.help}' not in command_activation:\n                if not command.hidden:\n                    command_activation.append(f'`{command.qualified_name} {command.signature}` - {command.help}')\n                    if command.brief not in [None, \"\"]:\n                        command_example.append(f'`{self.clean_prefix}{command.qualified_name} {command.brief}`')\n                    else:\n                        command_example.append(f'`{self.clean_prefix}{command.qualified_name}`')\n\n        fields = []\n        if group.aliases:\n            fields.append({\n                \"name\": \"Aliases\",\n                \"value\": \", \".join('`{}`'.format(alias) for alias in group.aliases),\n                \"inline\": False\n            })\n        fields.append({\n            \"name\": \"Commands\",\n            \"value\": \"\\n\".join(command_activation),\n            \"inline\": False\n        })\n        fields.append({\n            \"name\": \"Examples\",\n            \"value\": \"\\n\".join(command_example),\n            \"inline\": False\n        })\n\n        embed = discord.Embed(\n            title=f\"'{group.qualified_name.capitalize()}' Help\",\n            description=f\"{group.help}\\n\\n\"\n                        f\"For more information on each command, use `{self.clean_prefix}help [command]`.\"\n        )\n        for field in fields:\n            embed.add_field(\n                name=field['name'],\n                value=field['value'],\n                inline=field['inline']\n            )\n        embed.set_author(\n            name=self.context.message.author,\n            icon_url=self.context.message.author.avatar_url\n        )\n        embed.set_footer(\n            text=self.context.bot.config['footer']['text'],\n            icon_url=self.context.bot.config['footer']['icon_url']\n        )\n        await self.get_destination().send(embed=embed)\n\n    async def send_command_help(self, command):\n        \"\"\"\n        Send help for a specific given single command.\n        \"\"\"\n        fields = []\n        if command.aliases:\n            fields.append({\n                \"name\": \"Aliases\",\n                \"value\": \", \".join('`{}`'.format(alias) for alias in command.aliases),\n                \"inline\": False\n            })\n        fields.append({\n            \"name\": \"Usage\",\n            \"value\": f\"`{self.clean_prefix}{command.qualified_name}\"\n                     f\"{' ' + command.signature if command.signature else ''}`\",\n            \"inline\": False\n        })\n        fields.append({\n            \"name\": \"Example\",\n            \"value\": f\"`{self.clean_prefix}{command.qualified_name}\"\n                     f\"{' ' + command.brief if command.brief else ''}`\",\n            \"inline\": False\n        })\n\n        embed = discord.Embed(\n            title=f\"'{command.name.capitalize()}' Help\",\n            description=f\"{command.help}\"\n        )\n        for field in fields:\n            embed.add_field(\n                name=field['name'],\n                value=field['value'],\n                inline=field['inline']\n            )\n        embed.set_author(\n            name=self.context.message.author,\n            icon_url=self.context.message.author.avatar_url\n        )\n        embed.set_footer(\n            text=self.context.bot.config['footer']['text'],\n            icon_url=self.context.bot.config['footer']['icon_url']\n        )\n        await self.get_destination().send(embed=embed)\n\n\nclass LoadHelp(commands.Cog, name=\"Help\"):\n    \"\"\"\n    The actual discord cog that is loaded when this file is added, simply wrapping the help command.\n    \"\"\"\n    def __init__(self, bot):\n        self._original_help_command = bot.help_command\n        self.bot = bot\n        bot.help_command = HelpCommand()\n        bot.help_command.cog = self\n        print(f\"Loaded Help Cog.\")\n\n    \"\"\"\n    Called when the cog is unloaded from the system.\n    \"\"\"\n    def cog_unload(self):\n        print(f\"Unloaded Help Cog.\")\n\n\ndef setup(bot):\n    \"\"\"\n    The function called by Discord.py when adding a file extension in a multi-file project.\n    \"\"\"\n    bot.add_cog(LoadHelp(bot))\n","sub_path":"cogs/help.py","file_name":"help.py","file_ext":"py","file_size_in_byte":8035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"215529153","text":"# Databricks notebook source\n# MAGIC %sh\n# MAGIC curl -O https://projecttextsummarization.s3.amazonaws.com/Apple.txt\n\n# COMMAND ----------\n\n# MAGIC %sh\n# MAGIC curl -O https://projecttextsummarization.s3.amazonaws.com/cosine_similarity_input_final_excel.txt\n\n# COMMAND ----------\n\ndir = '/tmp'\n\n# COMMAND ----------\n\ndbutils.fs.cp('file:/databricks/driver/Apple.txt', dir, recurse=True)\ndbutils.fs.cp('file:/databricks/driver/cosine_similarity_input_final_excel.txt', dir, recurse=True)\n\n# COMMAND ----------\n\n# apple store\n#  /FileStore/tables/Apple.txt\n# terry collins\n# /FileStore/tables/OneArticle.txt\n\n# COMMAND ----------\n\nimport sys\nimport numpy\n\nnumpy.set_printoptions(threshold=sys.maxsize)\n\n# COMMAND ----------\n\nimport nltk\nfrom nltk import tokenize\nfrom nltk.cluster.util import cosine_distance\nimport numpy\nimport operator\n\n# COMMAND ----------\n\nnltk.download('all')\n\n# COMMAND ----------\n\nfile = open('/dbfs/tmp/Apple.txt')\narticle = file.read()\nprint(article)\narticle_sentences = tokenize.sent_tokenize(article)\n\n# COMMAND ----------\n\n# stop words in english -  /FileStore/tables/english_stopwords.txt\nfrom nltk.corpus import stopwords\n\nstop_words = set(stopwords.words('english'))\n\n\n# COMMAND ----------\n\n# method to check if the word is a stop word\ndef is_stop_word(word):\n    # print(\"found\")\n    if word in stop_words:\n        return True\n\n\n# COMMAND ----------\n\ndef normalize(matrix):\n    for idx in range(len(matrix)):\n        matrix[idx] /= matrix[idx].sum()\n\n    return matrix\n\n\n# COMMAND ----------\n\ndef cosine_similarity_matrix(sentences):\n    sentences_count = len(article_sentences)\n    print(\"Sentence count\", sentences_count)\n    # Create an empty similarity matrix\n    cosine_similarity_matrix = numpy.zeros((sentences_count, sentences_count))\n\n    for i in range(sentences_count):\n        for j in range(sentences_count):\n            if i == j:\n                continue\n\n            sentence_one = [word.lower() for word in article_sentences[i]]\n            sentence_two = [word.lower() for word in article_sentences[j]]\n\n            all_words = list(set(sentence_one + sentence_two))\n            count_of_words = len(all_words)\n\n            vector_for_sent1 = [0] * count_of_words\n            vector_for_sent2 = [0] * count_of_words\n\n            # build the vector for the first sentence\n            for word in sentence_one:\n                if (is_stop_word(word)): continue\n                vector_for_sent1[all_words.index(word)] = vector_for_sent1[all_words.index(word)] + 1\n\n            # build the vector for the second sentence\n            for word in sentence_two:\n                if (is_stop_word(word)): continue\n                vector_for_sent2[all_words.index(word)] = vector_for_sent1[all_words.index(word)] + 1\n\n            cosine_similarity_matrix[i][j] = 1 - cosine_distance(vector_for_sent1, vector_for_sent2)\n\n            # print the similarity matrix\n    # print(cosine_similarity_matrix)\n\n    # return the cosine similarity matrix\n    return normalize(cosine_similarity_matrix)\n\n\n# COMMAND ----------\n\ndef page_rank(cosine_similarity_matrix):\n    jump_factor = 0.85\n    error = 0.0001\n    initial_page_rank = numpy.ones(len(cosine_similarity_matrix)) / len(cosine_similarity_matrix)\n\n    while True:\n        length_cosine_similarity = len(cosine_similarity_matrix)\n        dot_product = cosine_similarity_matrix.T.dot(initial_page_rank)\n        page_rank = numpy.ones(length_cosine_similarity) * (1 - jump_factor) / len(\n            cosine_similarity_matrix) + jump_factor * dot_product\n\n        if (abs(page_rank - initial_page_rank).sum()) <= error:\n            return page_rank\n        initial_page_rank = page_rank\n\n\n# COMMAND ----------\n\ndef get_summary(sentences, top_n=5):\n    sentence_ranks = page_rank(cosine_similarity_matrix(sentences))\n\n    # printing page rank\n    # print(sentence_ranks)\n\n    # Sort the sentence ranks\n    ranked_sentence_indexes = [item[0] for item in sorted(enumerate(sentence_ranks), key=lambda item: -item[1])]\n    top_n_sentences = ranked_sentence_indexes[:top_n]\n    summary = operator.itemgetter(*sorted(top_n_sentences))(sentences)\n    return summary\n\n\n# COMMAND ----------\n\n# DECISION TREE\n\n# COMMAND ----------\n\nimport numpy as np\nfrom math import log\nimport random\n\n\n# COMMAND ----------\n\n# Partition the output label based on the value\ndef partition(x):\n    partition_split = {}\n    counter = 0\n\n    for i in x:\n        if i in partition_split:\n            partition_split[i].append(counter)\n        else:\n            partition_split[i] = [counter]\n        counter += 1\n\n    return partition_split\n\n\n# COMMAND ----------\n\ndef predict(x, tree):\n    for split_criterion in tree:\n        sub_tree = tree[split_criterion]\n        attribute_index = split_criterion[0]\n        attribute_value = split_criterion[1]\n        split_decision = split_criterion[2]\n\n        if split_decision == (x[attribute_index] == attribute_value):\n            if type(sub_tree) is dict:\n                prediction = predict(x, sub_tree)\n            else:\n                prediction = sub_tree\n\n    return prediction\n\n\n# COMMAND ----------\n\n# Calculates the entropy value of the output label\ndef entropy(y, w=None):\n    if w is None:\n        w = np.ones((len(y), 1), dtype=int)\n\n    total_weight = np.sum(w)\n    partition_dict = partition(y)\n    curr_entropy = 0.\n\n    for key in partition_dict.keys():\n        split_w = []\n        for indices in partition_dict[key]:\n            split_w.append(w[indices])\n        y_val = np.sum(split_w) / total_weight\n        curr_entropy -= y_val * log(y_val, 2)\n\n    return curr_entropy\n\n\n# COMMAND ----------\n\n# Calculates the mutual information factor for the given input and the respective output label\ndef mutual_information(x, y, w=None):\n    if w is None:\n        w = np.ones((len(y), 1), dtype=int)\n\n    y_entropy = entropy(y, w)\n\n    conditional_entropy = 0\n    total_weight = np.sum(w)\n    partition_split = partition(x)\n\n    for j in partition_split.keys():\n        split_y = []\n        split_w = []\n        for x in partition_split[j]:\n            split_y.append(y[x])\n            split_w.append(w[x])\n        conditional_entropy += np.sum(split_w) * entropy(split_y, split_w)\n    curr_info_gain = y_entropy - (conditional_entropy / total_weight)\n\n    return curr_info_gain\n\n\n# COMMAND ----------\n\n# Decision tree implementation\ndef id3(x, y, attribute_value_pairs=None, depth=0, max_depth=5, w=None):\n    local_attribute_val_pairs = []\n    if depth == 0:\n        column_counter = 0\n        for column in x.T:\n            unique_values = np.unique(column)\n            for i in unique_values:\n                local_attribute_val_pairs.append(tuple((column_counter, i)))\n            column_counter += 1\n    else:\n        local_attribute_val_pairs = attribute_value_pairs\n\n    values, counts = np.unique(y, return_counts=True)\n\n    if (depth == max_depth) or (len(values) == 1) or (len(local_attribute_val_pairs) == 0):\n        return values[counts.argmax()]\n\n    best_attribute = 0\n    best_value = 0\n    prev_best_info_gain = 0\n\n    for attribute, value in local_attribute_val_pairs:\n        x_part = (np.array(x)[:, attribute] == value).astype(int)\n        curr_best_info_gain = mutual_information(x_part, y)\n        if curr_best_info_gain > prev_best_info_gain:\n            prev_best_info_gain = curr_best_info_gain\n            best_attribute = attribute\n            best_value = value\n\n    partition_split = partition((np.array(x)[:, best_attribute] == best_value).astype(int))\n    build_tree = {}\n\n    for value, indices in partition_split.items():\n        decision = bool(value)\n\n        build_tree[(best_attribute, best_value, decision)] = id3(x.take(indices, axis=0)\n                                                                 , y.take(indices, axis=0)\n                                                                 , attribute_value_pairs=local_attribute_val_pairs\n                                                                 , depth=depth + 1\n                                                                 , max_depth=max_depth\n                                                                 , w=w)\n    return build_tree\n\n\n# COMMAND ----------\n\n# Calculates the error percentage for the predicted output label\ndef compute_error(y_true, y_pred, w=None):\n    if w is None:\n        w = np.ones((len(y_true), 1), dtype=int)\n    error = []\n    for i in range(len(y_true)):\n        error.append(w[i] * (y_true[i] != y_pred[i]))\n\n    return np.sum(error) / np.sum(w)\n\n\n# COMMAND ----------\n\n# Decision tree - Bagging implementation\ndef bagging(x, y, max_depth, num_trees):\n    indices = []\n    h_i = {}\n    alpha_i = 1\n    for t in range(num_trees):\n        for k in range(len(y)):\n            indices.append(random.randrange(len(y)))\n        decision_tree = id3(x[indices], y[indices], max_depth=max_depth)\n        h_i[t] = (alpha_i, decision_tree)\n\n    return h_i\n\n\n# COMMAND ----------\n\n# Predicts the output label for the given X feature vector and the ensemble\ndef predict_example(x, h_ens):\n    y_pred = []\n    for row in x:\n        pred = []\n        n = 0\n        for bag in h_ens.keys():\n            alpha, tree = h_ens[bag]\n            pred.append(alpha * predict(row, tree))\n            n += alpha\n\n        avg_value = np.sum(pred) / n\n        if avg_value >= 0.5:\n            y_pred.append(1)\n        else:\n            y_pred.append(0)\n\n    return y_pred\n\n\n# COMMAND ----------\n\n# Applies the decision tree bagging implementation on the cosine similarity matrix of the input text\nM = np.genfromtxt('/dbfs/tmp/cosine_similarity_input_final_excel.txt', missing_values=0, skip_header=0,\n                  delimiter='\t', dtype=float)\nytrn = M[:, 0]\nXtrn = M[:, 1:]\n\n# Load the test data\nM = np.genfromtxt('/dbfs/tmp/cosine_similarity_input_final_excel.txt', missing_values=0, skip_header=0,\n                  delimiter='\t', dtype=float)\nytst = M[:, 0]\nXtst = M[:, 1:]\n\nprint('Bagging parameters and test data error')\n\nfor num_trees in (5, 10):\n    for max_depth in (15, 17):\n        h_ens = bagging(Xtrn, ytrn, max_depth, num_trees)\n        y_pred = predict_example(Xtst, h_ens)\n        test_error = compute_error(ytst, y_pred)\n        print('Number of trees: ', num_trees)\n        print('Maximum depth of the tree: ', max_depth)\n        print('Test Error = {0:4.2f}%'.format(test_error * 100))\n        print('----------')\n\n# COMMAND ----------\n\n# display the result\nfor idx, summary in enumerate(get_summary(article_sentences)):\n    print(summary)\n\n# COMMAND ----------\n\n\n","sub_path":"Document Summarization.py","file_name":"Document Summarization.py","file_ext":"py","file_size_in_byte":10512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"225436624","text":"from argparse import ArgumentParser\nfrom dataclasses import dataclass\nfrom datetime import datetime\nfrom typing import List\n\nfrom rota.adapters.driven.formatter import MonthTableFormatter\nfrom rota.adapters.driven.generator import MonthGenerator\nfrom rota.adapters.driven.storage import Spreadsheet\nfrom rota.domain.rota import Rota\n\n\n@dataclass(frozen=True)\nclass AppConfig:\n    file_name: str\n    year: int\n    month_name: str\n    day_names: List[str]\n\n    @classmethod\n    def read_environment(cls):\n        parser = ArgumentParser()\n\n        parser.add_argument('-f', '--file_name', help=\"file name to store rota output\",\n                            default='rota.xlsx')\n        parser.add_argument('-y', '--year', help=\"Desired year for rota\", type=int,\n                            default=datetime.now().year)\n        parser.add_argument('-m', '--month_name', help=\"Name of the month desired for rota\",\n                            required=True)\n        parser.add_argument('-d', '--day_names', help=\"Names of days desired for rota\",\n                            required=True, nargs='+')\n\n        args = parser.parse_args()\n\n        return cls(\n            file_name=args.file_name,\n            year=args.year,\n            month_name=args.month_name,\n            day_names=args.day_names,\n        )\n\n\nif __name__ == '__main__':\n    config = AppConfig.read_environment()\n\n    Rota(\n        date_generator=MonthGenerator(),\n        formatter=MonthTableFormatter(),\n        storage=Spreadsheet()\n    ).generate(file_name=config.file_name, year=config.year, month_name=config.month_name,\n               day_names=config.day_names)\n","sub_path":"rota/adapters/driver/cmd.py","file_name":"cmd.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"624350978","text":"STOP = 0\nFORWARD = 1\nRIGHT = 2\nBACKWARD = 3\nLEFT = 4\n\nANGULAR_SPEED = 1.0\nLINEAR_SPEED = 0.3\n\nFRAMING_ANGULAR_SPEED = 0.1\nFRAMING_LINEAR_SPEED = 0.3\n\nANGULAR_VELOCITY = 1\nLINEAR_VELOCITY = 1\n\nDIRECTION_SOURCE_FRAMING = 2\nDIRECTION_SOURCE_OBSTACLE_AVOIDANCE = 1\nDIRECTION_SOURCE_NONE = 0\n\n\nOBSTACLE_NONE = 0\nOBSTACLE_AT_LEFT = 1\nOBSTACLE_AT_RIGHT = 2\nOBSTACLE_AT_LEFT_RIGHT = 3\nOBSTACLE_AT_RIGHT_LEFT = 4\nOBSTACLE_STUCK = 5\n\nFRAMING_NONE = 0\nFRAMING_LEFT = 1\nFRAMING_RIGHT = 2\nFRAMING_KEEP_FRAME = 3\nFRAMING_KEEP_FRAME_BUT_FAR = 4\n","sub_path":"ROS/constants/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"277753404","text":"import re\nimport sys\n\n\ndef solve(data):\n    m = re.sub(r'!.', '', data)\n    depth = 0\n    total = 0\n    garbage = False\n    for c in m:\n        if garbage and c != '>':\n            pass\n        elif c == '>':\n            garbage = False\n        elif c == '<':\n            garbage = True\n        elif c == '{':\n            depth += 1\n        elif c == '}':\n            total += depth\n            depth = depth - 1 if depth > 0 else 0\n    return total\n\n\nprint(solve(input()))\n","sub_path":"advent/2017/day09/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"345972666","text":"#!usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport sys\nimport getopt\nfrom urllib import request\nfrom bs4 import BeautifulSoup\n\n#html下载器\nclass HtmlDownloader(object):\n\tdef download(self,url):\n\t\treq=request.Request(url)\n\t\treq.add_header('User-Agent', 'Mozilla/6.0 (iPhone; CPU iPhone OS 8_0 like Mac OS X) \\\n\t\t\tAppleWebKit/536.26 (KHTML, like Gecko) Version/8.0 Mobile/10A5376e Safari/8536.25')\n\t\tresponse = request.urlopen(req)\n\t\tif response.getcode() != 200:\n\t\t\treturn None\n\t\treturn response.read()\n\n#html解析器\nclass HtmlParser(object):\n\tdef _get_new_urls(self,soup):\n\t\tnew_urls=set() #用来存放url\n\t\tlinks=soup.find_all('a')\n\t\tfor link in links:\n\t\t\tnew_url=link['href']\n\t\t\tnew_urls.add(new_url)\n\t\treturn new_urls\n\tdef parse(self,url,html_cont):\n\t\tsoup=BeautifulSoup(html_cont,'html.parser',from_encoding='utf-8') #解析数据\n\t\tnew_urls=self._get_new_urls(soup) #获取新的url\n\t\treturn new_urls\n\n#主函数\ndef mainfunc(urls,deep):\n\twhile deep > 0: #判断深度\n\t\tfor url in urls: #遍历每层深度的url\n\t\t\tnew_all_urls=set() #设置一个set()放置下一层的url\n\t\t\thtml_cont = HtmlDownloader().download(url) #下载页面\n\t\t\tnew_urls=HtmlParser().parse(url,html_cont) #解析url\n\t\t\tfor new_url in new_urls:\n\t\t\t\tnew_all_urls.add(new_url)\n\t\tdeep = deep - 1 \n\t\turls = new_all_urls #取得下一层深度的所有url\n\t\tprint(urls)\n\n#使用提示和帮助\ndef usage():\n\tprint('''\n\t\t用法：python spider.py -u url -d deep -f logfile -l loglevel(1-5) --help --thread threadnum --dbfile filepath --key keyword\n\t\t-u 指定爬虫开始地址\n\t\t-d 指定爬虫深度,默认为1\n\t\t-f 日志记录文件，默认spider.log\n\t\t-l 日志记录文件记录详细程度，数字越大记录越详细，可选参数，1-5取值，默认为1\n\t\t--help 输出帮助\n\t\t--thread 指定线程池大小，多线程爬取页面，可选参数，默认10\n\t\t--dafile 存放结果数据到指定的数据库（sqlite）文件中,默认为spide.db\n\t\t--key 页面内的关键词，获取满足该关键词的网页，可选参数，默认为所有页面\n\t\t''')\nif __name__ == \"__main__\":\n\turl = None\n\tdeep = 1\n\tlogfile = 'spider.log'\n\tloglevel = 5\n\tthreadnum = 1\n\tfilepath = 'spider.db'\n\tkeyword = None\n\ttry:\n\t\topts,args = getopt.getopt(sys.argv[1:],\"u:d:f:l:\",[\"help\",\"thread=\",\"dbfile=\",\"key=\"])\n\t\tfor name, value in opts:\n\t\t\tif name == '-u':\n\t\t\t\turl = value\n\t\t\telif name == '-d':\n\t\t\t\tdeep = value\n\t\t\telif name == '-f':\n\t\t\t\tlogfile = value\n\t\t\telif name == '-l':\n\t\t\t\tloglevel=value\n\t\t\telif name == '--thread':\n\t\t\t\tthreadnum = value\n\t\t\telif name == '--dafile':\n\t\t\t\tfilepath = value\n\t\t\telif name == '--key':\n\t\t\t\tkeyword = value\n\t\t\telif name == '--help':\n\t\t\t\tusage()\n\texcept:\n\t\tusage()\n\tdeep = int(deep)\n\tloglevel = int(loglevel)\n\tthreadnum = int(threadnum)\n\turls = set() #防止抓取重复URL\n\turls.add(url) #加入初始url\n\tif deep < 1 or loglevel < 1 or loglevel >5 or threadnum < 1 or not url:\n\t\tusage()\n\t\texit()\n\tmainfunc(urls,deep)\n\n","sub_path":"spider-u-d.py","file_name":"spider-u-d.py","file_ext":"py","file_size_in_byte":2937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"137922495","text":"from sequana.iotools import YamlDocParser\nfrom sequana import sequana_data\nfrom sequana import snaketools\n\n\ndef test_yamlreader():\n    filename = sequana_data(\"test_gui_generic_config.yaml\")\n    r = YamlDocParser(filename)\n    assert r.sections['N'] == '# example of docstring\\n'\n\n    # check docstring is parsed with #### removed\n    r = YamlDocParser(snaketools.Module('quality_control').config)\n    docstring = r._block2docstring(\"fastqc\")\n    assert docstring.startswith(\"FastQC\")\n\n    assert r._block2docstring(\"dummy\") is None\n\n    # check that pipelines can be parsed\n    for pipeline in snaketools.pipeline_names:\n        filename = snaketools.Module(pipeline).config\n        r = YamlDocParser(filename)\n","sub_path":"test/test_iotools.py","file_name":"test_iotools.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"535001151","text":"import re\n\n\ndef match(t1, t2):\n    result = False\n    try:\n        if re.match(t1 + '$', t2):\n            result = True\n    except:\n        pass\n    if t1 == t2:\n        result = True\n    return result\n\n\nclass Config:\n\n    def __init__(self):\n        self.name = \"\"\n        self.set_map = {}\n        self.unset_list = []\n        self.sub_config_list = []\n        self.edit_list = []\n\n    def set_name(self, name):\n        self.name = name\n\n    def get_name(self):\n        return self.name\n\n    def set_set_map(self, set_map):\n        self.set_map = set_map\n\n    def get_set_map(self):\n        return self.set_map\n\n    def set_unset_list(self, unset_list):\n        self.unset_list = unset_list\n\n    def get_unset_list(self):\n        return self.unset_list\n\n    def set_sub_config_list(self, sub_config_list):\n        self.sub_config_list = sub_config_list\n\n    def get_sub_config_list(self):\n        return self.sub_config_list\n\n    def set_edit_list(self, edit_list):\n        self.edit_list = edit_list\n\n    def get_edit_list(self):\n        return self.edit_list\n\n    def is_same(self, config):\n        if not isinstance(config, Config):\n            return False\n        if self.name == config.name:\n            return True\n        else:\n            return False\n\n    def is_empty(self):\n        ret = False\n        if self.name == \"\" and len(self.set_map.keys()) == 0 and len(self.unset_list) == 0 \\\n                and len(self.sub_config_list) == 0 and len(self.edit_list) == 0:\n            ret = True\n        return ret\n\n    def get_sub_config_name_list(self):\n        ret = []\n        for config in self.sub_config_list:\n            ret.append(config.get_name())\n        return ret\n\n    def get_sub_config_by_name(self, name):\n        ret = None\n        for config in self.sub_config_list:\n            if name == config.get_name():\n                ret = config\n        return ret\n\n    def get_edit_name_list(self):\n        ret = []\n        for edit in self.edit_list:\n            ret.append(edit.get_name())\n        return ret\n\n    def get_edit_by_name(self, name):\n        ret = None\n        for edit in self.edit_list:\n            if name == edit.get_name():\n                ret = edit\n        return ret\n\n    def compare(self, config):\n        if not self.is_same(config):\n            return self, config\n\n        more = Config()\n        less = Config()\n\n        # compare set\n        self_set_keys = self.set_map.keys()\n        other_set_keys = config.get_set_map().keys()\n        for key in [i for i in self_set_keys if i not in other_set_keys]:\n            more.get_set_map()[key] = self.set_map[key]\n\n        for key in [i for i in other_set_keys if i not in self_set_keys]:\n            less.get_set_map()[key] = config.get_set_map()[key]\n\n        for key in [i for i in self_set_keys if i in other_set_keys]:\n            if not match(self.set_map[key], config.get_set_map()[key]):\n                more.get_set_map()[key] = self.set_map[key]\n                less.get_set_map()[key] = config.get_set_map()[key]\n\n        # compare unset\n        other_unset_list = config.get_unset_list()\n        for key in [i for i in self.unset_list if i not in other_unset_list]:\n            more.get_unset_list().append(key)\n\n        for key in [i for i in other_unset_list if i not in self.unset_list]:\n            less.get_unset_list().append(key)\n\n        # compare sub config\n        self_sub_conf_name_list = self.get_sub_config_name_list()\n        other_sub_conf_name_list = config.get_sub_config_name_list()\n        for name in [i for i in self_sub_conf_name_list if i not in other_sub_conf_name_list]:\n            more.get_sub_config_list().append(self.get_sub_config_by_name(name))\n\n        for name in [i for i in other_sub_conf_name_list if i not in self_sub_conf_name_list]:\n            less.get_sub_config_list().append(config.get_sub_config_by_name(name))\n\n        for name in [i for i in self_sub_conf_name_list if i in other_sub_conf_name_list]:\n            sub_config_more, sub_config_less = \\\n                self.get_sub_config_by_name(name).compare(config.get_sub_config_by_name(name))\n            if not sub_config_more.is_empty():\n                more.get_sub_config_list().append(sub_config_more)\n            if not sub_config_less.is_empty():\n                less.get_sub_config_list().append(sub_config_less)\n\n        # compare edit\n        self_edit_name_list = self.get_edit_name_list()\n        other_edit_name_list = config.get_edit_name_list()\n        for name in [i for i in self_edit_name_list if i not in other_edit_name_list]:\n            more.get_edit_list().append(self.get_edit_by_name(name))\n\n        for name in [i for i in other_edit_name_list if i not in self_edit_name_list]:\n            less.get_edit_list().append(config.get_edit_by_name(name))\n\n        for name in [i for i in self_edit_name_list if i in other_edit_name_list]:\n            edit_more, edit_less = self.get_edit_by_name(name).compare(config.get_edit_by_name(name))\n            if not edit_more.is_empty():\n                more.get_edit_list().append(edit_more)\n            if not edit_less.is_empty():\n                less.get_edit_list().append(edit_less)\n\n        #set name\n        if not more.is_empty():\n            more.set_name(self.name)\n        if not less.is_empty():\n            less.set_name(self.name)\n\n        return more, less\n\n    def equals(self, config):\n        ret = False\n        if self.is_same(config):\n            more, less = self.compare(config)\n            if more.is_empty() and less.is_empty():\n                ret = True\n        return ret\n\n    def to_string(self, blank=\"\"):\n        ret = \"\"\n        indentation = \"    \"\n        # name\n        ret = blank + \"config \" + self.name\n        # set\n        for key, value in self.set_map.items():\n            ret += \"\\n\" + blank + indentation + \"set \" + key + \" \" + value\n        # unset\n        for key in self.unset_list:\n            ret += \"\\n\" + blank + indentation + \"unset \" + key\n        # sub config\n        for config in self.sub_config_list:\n            ret += \"\\n\" + config.to_string(blank + indentation)\n        # edit\n        for edit in self.edit_list:\n            ret += \"\\n\" + edit.to_string(blank + indentation)\n        #end\n        ret += \"\\n\" + blank + \"end\"\n\n        return ret\n\n    def to_diff_string(self, config, blank=\"\"):\n        ret = \"\"\n        indentation = \"    \"\n\n        if self.name != config.name:\n            ret += self.diff_str(blank, \"-\")\n            ret += \"\\n\" + config.diff_str(blank, \"+\")\n            return\n\n        diff = False\n        self_set_keys = self.set_map.keys()\n        other_set_keys = config.get_set_map().keys()\n        del_set_list = []\n        add_set_list = []\n        for key in [i for i in self_set_keys if i not in other_set_keys]:\n            del_set_list.append([key, self.set_map[key]])\n            diff = True\n\n        for key in [i for i in other_set_keys if i not in self_set_keys]:\n            add_set_list.append([key, config.set_map[key]])\n            diff = True\n\n        for key in [i for i in self_set_keys if i in other_set_keys]:\n            if self.set_map[key] != config.get_set_map()[key]:\n                del_set_list.append([key, self.set_map[key]])\n                add_set_list.append([key, config.set_map[key]])\n                diff = True\n\n        other_unset_list = config.get_unset_list()\n        del_unset_list = []\n        add_unset_list = []\n        for key in [i for i in self.unset_list if i not in other_unset_list]:\n            del_unset_list.append(key)\n            diff = True\n\n        for key in [i for i in other_unset_list if i not in self.unset_list]:\n            add_unset_list.append(key)\n            diff = True\n\n        # compare sub config\n        self_sub_conf_name_list = self.get_sub_config_name_list()\n        other_sub_conf_name_list = config.get_sub_config_name_list()\n        del_subconfig_list = []\n        add_subconfig_list = []\n        upd_subconfig_list = []\n        for name in [i for i in self_sub_conf_name_list if i not in other_sub_conf_name_list]:\n            del_subconfig_list.append(self.get_sub_config_by_name(name))\n            diff = True\n\n        for name in [i for i in other_sub_conf_name_list if i not in self_sub_conf_name_list]:\n            add_subconfig_list.append(config.get_sub_config_by_name(name))\n            diff = True\n\n        for name in [i for i in self_sub_conf_name_list if i in other_sub_conf_name_list]:\n            sub_config_more, sub_config_less = \\\n                self.get_sub_config_by_name(name).compare(config.get_sub_config_by_name(name))\n            if not sub_config_more.is_empty() or not sub_config_less.is_empty():\n                upd_subconfig_list.append([self.get_sub_config_by_name(name), config.get_sub_config_by_name(name)])\n                diff = True\n\n        # compare edit\n        self_edit_name_list = self.get_edit_name_list()\n        other_edit_name_list = config.get_edit_name_list()\n        del_edit_list = []\n        add_edit_list = []\n        upd_edit_list = []\n        for name in [i for i in self_edit_name_list if i not in other_edit_name_list]:\n            del_edit_list.append(self.get_edit_by_name(name))\n            diff = True\n\n        for name in [i for i in other_edit_name_list if i not in self_edit_name_list]:\n            add_edit_list.append(config.get_edit_by_name(name))\n            diff = True\n\n        for name in [i for i in self_edit_name_list if i in other_edit_name_list]:\n            edit_more, edit_less = self.get_edit_by_name(name).compare(config.get_edit_by_name(name))\n            if not edit_more.is_empty() or not edit_less.is_empty():\n                upd_edit_list.append([self.get_edit_by_name(name), config.get_edit_by_name(name)])\n                diff = True\n\n        if diff:\n            ret += \" \" + blank + \"config \" + self.name\n\n            for set in del_set_list:\n                ret += \"\\n\" + \"-\" + blank + indentation + \"set \" + set[0] + \" \" + set[1]\n            for key in del_unset_list:\n                ret += \"\\n\" + \"-\" + blank + indentation + \"unset \" + key\n            for sub_config in del_subconfig_list:\n                ret += \"\\n\" + sub_config.diff_str(blank + indentation, \"-\")\n            for edit in del_edit_list:\n                ret += \"\\n\" + edit.diff_str(blank + indentation, \"-\")\n\n            for set in add_set_list:\n                ret += \"\\n\" + \"+\" + blank + indentation + \"set \" + set[0] + \" \" + set[1]\n            for key in add_unset_list:\n                ret += \"\\n\" + \"+\" + blank + indentation + \"unset \" + key\n            for sub_config in add_subconfig_list:\n                ret += \"\\n\" + sub_config.diff_str(blank + indentation, \"+\")\n            for edit in add_edit_list:\n                ret += \"\\n\" + edit.diff_str(blank + indentation, \"+\")\n\n            for cofig_pair in upd_subconfig_list:\n                ret += \"\\n\" + cofig_pair[0].to_diff_string(cofig_pair[1], blank + indentation)\n            for edit_pair in upd_edit_list:\n                ret += \"\\n\" + edit_pair[0].to_diff_string(edit_pair[1], blank + indentation)\n            ret += \"\\n\" + \" \" + blank + \"end\"\n\n        return ret\n\n    def diff_str(self, blank, prefix):\n        ret = \"\"\n        indentation = \"    \"\n        # name\n        ret = prefix + blank + \"config \" + self.name\n        # set\n        for key, value in self.set_map.items():\n            ret += \"\\n\" + prefix + blank + indentation + \"set \" + key + \" \" + value\n        # unset\n        for key in self.unset_list:\n            ret += \"\\n\" + prefix + blank + indentation + \"unset \" + key\n        # sub config\n        for config in self.sub_config_list:\n            ret += \"\\n\" + config.diff_str(blank + indentation, prefix)\n        # edit\n        for edit in self.edit_list:\n            ret += \"\\n\" + edit.diff_str(blank + indentation, prefix)\n        # end\n        ret += \"\\n\" + prefix + blank + \"end\"\n\n        return ret\n","sub_path":"lib/fg_config_compare/lib/unit/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":11884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"521672480","text":"####################\n###### CLASS #######\n####################\nclass Machine:\n  def __init__(self, nom, ip,cpu,ram,hdd,os):\n    self.nom= nom\n    self.ip = ip\n    self.cpu= cpu\n    self.ram= ram\n    self.hdd= hdd\n    self.os = os\n\n####################################################\n#########       CRUD     ###########################\n####################################################\n    \ndef readAll():\n    with open('machines.txt') as csv_file:\n        csv_reader = csv.reader(csv_file, delimiter=',')\n        line_count = 0\n        for row in csv_reader:\n            if line_count == 0:\n                print(f'Column names are {\", \".join(row)}')\n                line_count += 1\n            else:\n                print(f'\\t hostname={row[0]}, ip={row[1]}, nombre CPU={row[2]}, RAM={row[3]}, HardDisk={row[4]}, OS={row[5]} ')\n                line_count += 1\n    print(f'Processed {line_count} lines.')\n    return line_count\n\n\ndef readAllv2():\n    \n    with open('machines.txt') as csv_file:\n        csv_reader = csv.reader(csv_file, delimiter=',')\n        line_count = 0\n        str_machine =\"\"\n        for row in csv_reader:\n            #if line_count != 0:\n            m1 = Machine(row[0],row[1],row[2],row[3],row[4],row[5])\n            str_machine =  str_machine + json.dumps(m1.__dict__)\n            line_count += 1\n    return str_machine\n\ndef readByName(hostname):\n    with open('machines.txt') as csv_file:\n        csv_reader = csv.reader(csv_file, delimiter=',')\n        line_count = 0\n        strReturn=\"\"\n        for row in csv_reader:\n            if row[0] == hostname:\n                print(f'\\t hostname={row[0]}, ip={row[1]}, nombre CPU={row[2]}, RAM={row[3]}, HardDisk={row[4]}, OS={row[5]} ')\n                m1 = Machine(row[0],row[1],row[2],row[3],row[4],row[5])\n                print('the row found is '+strReturn)\n                line_count += 1\n    if line_count == 0:\n        print (\"Hostname NOT FOUND\")      \n    return m1\n\n\ndef create(machine):\n    with open('machines.txt', mode='a',newline='') as employee_file:\n            employee_writer = csv.writer(employee_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n            employee_writer.writerow([machine.nom, machine.ip, machine.cpu,machine.ram,machine.hdd,machine.os])\n    return ''\n    \n\ndef createOrUpdate(machine):\n\n    if find(machine.nom) >= 0:\n        delete(machine.nom)\n\n    create(machine)\n    return ''\n\n\ndef find(host):\n    with open('machines.txt', 'rt') as f:\n         reader = csv.reader(f, delimiter=',')\n         line_count = -1\n         line_found = -1\n         for row in reader:\n                line_count +=1\n                if host == row[0]: # if the username shall be on column 3 (-> index 2)\n                  print ('existe dans la colon N°'+str(line_count))\n                  line_found = line_count\n    print ('ligne trouver :'+str(line_found))\n    return line_found\n\n\n\n\ndef delete(host):\n    a_file = open(\"machines.txt\", \"r\")\n    lines = a_file.readlines()\n    a_file.close()\n    #supprimer ligne\n    del lines[find(host)]\n    #réecrir fichier apres suppression ligne\n    new_file = open(\"machines.txt\", \"w+\")\n    for line in lines:\n        new_file.write(line)\n    new_file.close()\n    return ''\n\n\"\"\"\n\n#################################\n######## test unitair ###########\n#################################\nclass TestStringMethods(unittest.TestCase):\n\n    def test_readAll(self):\n\n        file = open(\"machines.txt\",\"r\")\n        Counter = 0\n        # Reading from file\n        Content = file.read()\n        CoList = Content.split(\"\\n\")\n        \n        for i in CoList:\n            if i:\n                Counter += 1\n        file.close()\n        self.assertEqual(readAll(), Counter)\n        \n\nif __name__ == '__main__':\n    unittest.main()\n    \n\"\"\"\n\n\n#############################\n########### API #############\n#############################\napp = Flask(__name__)\n\n@app.route(\"/machine\",methods=[\"GET\"])\ndef getAllAPI():\n  return readAllv2()\n\n@app.route(\"/machine/<hostname>\", methods=[\"GET\"])\ndef getByIdAPI(hostname):\n    print('hostname ======' +hostname)\n    m1 =readByName(hostname)\n    return json.dumps(m1.__dict__)\n\n@app.route('/machine/addorupdate', methods=['POST'])\ndef createAPI():\n   data = request.get_json()\n   nom= data.get('nom', '')\n   ip= data.get('ip', '')\n   cpu= data.get('cpu', '')\n   ram= data.get('ram', '')\n   hdd= data.get('hdd', '')\n   os= data.get('os', '')\n   m1 = Machine(nom,ip,cpu,ram,hdd,os)\n   createOrUpdate(m1)\n   return \"Creation de l'hote \"+nom+\" est enregistrer avec succée !!\"\n\n@app.route('/machine/del/<host>', methods=['DELETE'])\ndef deleteApi(host):\n  delete(host)\n  return \"le host \"+host+\" est supprimer avec succée !!\"\n\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0')\n\n\n\"\"\"\n#########################\n#### INTERACTIVE ########\n#########################\n\nprint (\"Ce program permet de gérer les hosts\")\nprint (\"FORMAT : nom, ip, cpu, ram, hdd, os\")\nprint (\"Tapez votre choix\")\nprint (\"1:afficher , 2:ajouter , 3:supprimer, 4:modifier\")\naction = input(\"\")\n\nif action == \"1\":\n    hostname = input(\"Merci de saisir le hostname a afficher:\")\n    readByName(hostname)\nelif action==\"2\":\n    print(\"Saisir les information de la machine a ajouter\")\n    hostname = input(\"Saisir nom du hote:\")\n    ip = input(\"Saisir l'adresse IP du hote:\")\n    cpu = input(\"Saisir le nombre de CPU du hote:\")\n    ram = input(\"Saisir la taille RAM du hote:\")\n    hdd = input(\"Saisir la taille disque dur du hote:\")\n    os = input(\"Saisir l'OS du hote:\")\n    machine = Machine(hostname, cpu,ip,ram,hdd,os)\n    createOrUpdate(machine)\n\n    \nelif action==\"3\":\n    hostname = input(\"Merci de saisir le hostname a supprimer:\")\n    delete(hostname)\nelse:\n    print(\"Saisir les information de la machine a modifier, s'il n'existe pas il va êtres créer\")\n    hostname = input(\"Saisir nom du hote:\")\n    readByName(hostname)\n    print(\"****\")\n    ip = input(\"Saisir l'adresse IP du hote:\")\n    cpu = input(\"Saisir le nombre de CPU du hote:\")\n    ram = input(\"Saisir la taille RAM du hote:\")\n    hdd = input(\"Saisir la taille disque dur du hote:\")\n    os = input(\"Saisir l'OS du hote:\")\n    machine = Machine(hostname, cpu,ip,ram,hdd,os)\n    createOrUpdate(machine)\n\"\"\"\n####### tester CRUD #######\n#m1 = Machine(\"host1\", \"ababababa\",\"8\",\"256\",\"500\",\"Ubuntu\")\n#createOrUpdate(m1)\n#create(m1)\n#delete(\"host2\")\n#find(\"host2\")\n\n\n","sub_path":"Data/host.py","file_name":"host.py","file_ext":"py","file_size_in_byte":6376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"568572146","text":"import json\n\nimport requests\nimport typing\nfrom flask import current_app, url_for, request, redirect\nfrom rauth import OAuth2Service\n\nfrom anyway.dataclasses.user_data import UserData\n\n\nclass OAuthSignIn(object):\n    providers = {}\n\n    def __init__(self, provider_name):\n        self.provider_name = provider_name\n        credentials = current_app.config[\"OAUTH_CREDENTIALS\"][provider_name]\n        self.consumer_id = credentials[\"id\"]\n        self.consumer_secret = credentials[\"secret\"]\n\n    def authorize(self):\n        pass\n\n    def callback(self) -> typing.Optional[UserData]:\n        pass\n\n    def get_callback_url(self):\n        return url_for(\"oauth_callback\", provider=self.provider_name, _external=True)\n\n    @classmethod\n    def get_provider(self, provider_name):\n        # This is quick fix for the DEMO - to make sure that anyway.co.il is fully compatible with https://anyway-infographics-staging.web.app/\n        if provider_name != \"google\":\n            return None\n\n        if not self.providers:\n            self.providers = {}\n            for provider_class in self.__subclasses__():\n                provider = provider_class()\n                self.providers[provider.provider_name] = provider\n        return self.providers[provider_name]\n\n\nclass FacebookSignIn(OAuthSignIn):\n    def __init__(self):\n        super(FacebookSignIn, self).__init__(\"facebook\")\n        self.service = OAuth2Service(\n            name=\"facebook\",\n            client_id=self.consumer_id,\n            client_secret=self.consumer_secret,\n            authorize_url=\"https://graph.facebook.com/oauth/authorize\",\n            access_token_url=\"https://graph.facebook.com/oauth/access_token\",\n            base_url=\"https://graph.facebook.com/\",\n        )\n\n    def authorize(self):\n        return redirect(\n            self.service.get_authorize_url(\n                scope=\"email\", response_type=\"code\", redirect_uri=self.get_callback_url()\n            )\n        )\n\n    def callback(self) -> typing.Optional[UserData]:\n        if \"code\" not in request.args:\n            return None\n        oauth_session = self.service.get_auth_session(\n            data={\n                \"code\": request.args[\"code\"],\n                \"grant_type\": \"authorization_code\",\n                \"redirect_uri\": self.get_callback_url(),\n            },\n            decoder=json.loads,\n        )\n        # TODO: enrich facebook data collection\n        me = oauth_session.get(\"me?fields=id,email\").json()\n        name = me.get(\"email\").split(\"@\")[0],  # Facebook does not provide username, so the email's user is used instead\n        data_of_user = UserData(name=name, email=me.get(\"email\"), service_user_id=\"facebook$\" + me[\"id\"])\n\n\n        return data_of_user\n\n\nclass GoogleSignIn(OAuthSignIn):\n    def __init__(self):\n        super(GoogleSignIn, self).__init__(\"google\")\n        googleinfo = requests.get(\"https://accounts.google.com/.well-known/openid-configuration\")\n        googleinfo.raise_for_status()\n        google_params = googleinfo.json()\n        self.service = OAuth2Service(\n            name=\"google\",\n            client_id=self.consumer_id,\n            client_secret=self.consumer_secret,\n            authorize_url=google_params.get(\"authorization_endpoint\"),\n            base_url=google_params.get(\"userinfo_endpoint\"),\n            access_token_url=google_params.get(\"token_endpoint\"),\n        )\n\n    def authorize(self):\n        return redirect(\n            self.service.get_authorize_url(\n                scope=\"email\", response_type=\"code\", redirect_uri=self.get_callback_url()\n            )\n        )\n\n    def callback(self) -> typing.Optional[UserData]:\n        if \"code\" not in request.args:\n            return None\n        oauth_session = self.service.get_auth_session(\n            data={\n                \"code\": request.args[\"code\"],\n                \"grant_type\": \"authorization_code\",\n                \"redirect_uri\": self.get_callback_url(),\n            },\n            decoder=json.loads,\n        )\n        me = oauth_session.get(\"\").json()\n        # The data in me dict includes information about the user, as described in\n        # OpenID Connect Standard Claims(https://openid.net/specs/openid-connect-core-1_0.html#StandardClaims) and the\n        # claims_supported metadata value of the Discovery document(https://accounts.google.com/.well-known/openid-configuration)\n        # So in the best case we have \"aud\", \"email\", \"email_verified\", \"exp\", \"family_name\", \"given_name\", \"iat\",\n        # \"iss\", \"locale\", \"name\", \"picture\", \"sub\"\n        # and in the worst case we only have \"sub\".\n        # Note - in some places in the docs there is an indication that more fields exists.\n        name = me.get(\"name\")\n        if not name:\n            if me.get(\"given_name\"):\n                name = me.get(\"given_name\")\n            if me.get(\"family_name\"):\n                name = f\"{name} {me.get('family_name')}\" if name else me.get(\"family_name\")\n\n        data_of_user = UserData(name=name, email=me.get(\"email\"), service_user_id=me[\"sub\"],\n                                service_user_domain=me.get(\"hd\"), service_user_locale=me.get(\"locale\"),\n                                picture_url=me.get(\"picture\"), user_profile_url=me.get(\"profile\"))\n\n        return data_of_user\n\n","sub_path":"anyway/oauth.py","file_name":"oauth.py","file_ext":"py","file_size_in_byte":5257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"134432929","text":"#!/usr/bin/python3\n\n# A script to replicate the function of DNA polymerase,\n# that is to reads a straind of DNA or RNA and return its complementary\n# strand.\n\ndef readfile(file_location):\n    file_string=''\n    with open(file_location,'r') as file_object:\n        file_string += file_object.readline().strip().upper()\n    \n    return file_string\n\n# This function translates the nucleic acid into its complementary strand.\n# In the function, the argument nucleic_acid specifies what you're translating. 'dna' or 'rna'.\n\ndef polymerase(na_string, nucleic_acid='dna'):\n    dna_translation_table=str.maketrans('ACGT','TGCA')\n    rna_translation_table=str.maketrans('ACGU','UGCA')\n    \n    if nucleic_acid == 'dna':\n        reversed_complementary_strand=na_string.translate(dna_translation_table)\n    elif nucleic_acid == 'rna':\n        reversed_complementary_strand=na_string.translate(rna_translation_table)\n    \n    complementary_strand=reversed_complementary_strand[::-1]      # Extended slice operator to reverse the string\n    return complementary_strand\n    \nif __name__=='__main__':\n    from sys import argv\n    print (polymerase(readfile(argv[-1]),argv[-2]))        \n","sub_path":"Rosalind/polymerase.py","file_name":"polymerase.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"319526105","text":"\"\"\"changing case riders to JSON settings column\n\nRevision ID: 2eef59adf24d\nRevises: 11f6fc11b5eb\nCreate Date: 2015-12-10 21:32:36.167955\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '2eef59adf24d'\ndown_revision = '11f6fc11b5eb'\n\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\ndef upgrade():\n    ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('cases', sa.Column('case_rider_settings', postgresql.JSON(), nullable=True))\n    op.drop_column('cases', 'case_riders')\n    ### end Alembic commands ###\n\n\ndef downgrade():\n    ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('cases', sa.Column('case_riders', sa.VARCHAR(length=64), autoincrement=False, nullable=True))\n    op.drop_column('cases', 'case_rider_settings')\n    ### end Alembic commands ###\n","sub_path":"alembic/versions/2eef59adf24d_changing_case_riders_to_json_settings_.py","file_name":"2eef59adf24d_changing_case_riders_to_json_settings_.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"48162391","text":"'''\nFind the sum of all left leaves in a given binary tree.\n\nExample:\n\n    3\n   / \\\n  9  20\n    /  \\\n   15   7\n\nThere are two left leaves in the binary tree, with values 9 and 15 respectively. Return 24.\n'''\n# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution(object):\n    def sumOfLeftLeaves(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: int\n        \"\"\"\n        if not root:\n            return 0\n        sum = 0\n        stack = [root]\n        cur = root\n        while cur.left:\n            stack.append(cur.left)\n            cur = cur.left\n        if cur != root and not cur.right:\n            sum += cur.val\n        while stack:\n            node = stack.pop()\n            if node.right:\n                stack.append(node.right)\n                cur = node.right\n                while cur.left:\n                    stack.append(cur.left)\n                    cur = cur.left\n                if cur != node.right and not cur.right:\n                    sum += cur.val\n        return sum\n\n# easy solution as we don't care the order of the leaves\nclass Solution(object):\n    def sumOfLeftLeaves(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: int\n        \"\"\"\n        if not root:\n            return 0\n        sum = 0\n        stack = [root]\n        while len(stack):\n            cur = stack.pop()\n            if cur.left != None:\n                if cur.left.left == None and cur.left.right == None:\n                    sum += cur.left.val\n                else:\n                    stack.append(cur.left)\n            if cur.right != None:\n                if cur.right.left != None or cur.right.right != None:\n                    stack.append(cur.right)\n        return sum\n","sub_path":"tree/sum_0f_left_leaves.py","file_name":"sum_0f_left_leaves.py","file_ext":"py","file_size_in_byte":1844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"228856251","text":"from sklearn.neighbors import NearestNeighbors\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nX = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\r\nnbrs = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(X)\r\ndistances, indices = nbrs.kneighbors(X)\r\nplt.scatter(X[:,0],X[:,1])\r\nplt.show()\r\nprint(indices)\r\nprint(distances)\r\n","sub_path":"sample2.py","file_name":"sample2.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"540495912","text":"from django.urls import path\nfrom events import views\n#from django.conf import settings\n#from django.conf.url import static\nfrom .views import Login, Logout, Signup, home\n\nurlpatterns = [\n\tpath('', home, name='home'),\n    path('event/', views.event, name='event'),\n    path('add/', views.add_event, name='add_event'),\n    path('dashboard/', views.dashboard, name='dashboard'),\n    path('booked/', views.booked, name='booked'),\n    path('<int:event_id>/booking/', views.booking, name='booking'),\n    path('<int:event_id>/', views.event_detail, name='event-detail'),\n    path('<int:event_id>/delete/', views.delete_event, name='delete_event'),\n    path('<int:event_id>/update/', views.update_event, name='update_event'),\n    path('signup/', Signup.as_view(), name='signup'),\n    path('login/', Login.as_view(), name='login'),\n    path('logout/', Logout.as_view(), name='logout'),\n    #path('events_search/', views.events_search, name='events_search'),\n]\n\n\n\n#if settings.DEBUG:\n    #urlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)\n    #urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)","sub_path":"events/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"273831504","text":"import json\nfrom .dynamics import Dynamics\nfrom .bbug import Bbug\nfrom boto3.dynamodb.conditions import Key, Attr\nfrom decimal import *\n\nclass Accounts(Dynamics):\n\n    def __init__(self,settings):\n        Dynamics.__init__(self,settings)\n        self.table=self.dynamodb.Table('dynamics_accounts')\n\n    def base_uri(self):\n        return '/accounts'\n\n    def save(self,account):\n        self.table.put_item(Item=account)\n        self.messages.append(\"Account \" + account['name'] +\n                             \" was saved in dynamodb.\")\n\n    def get_from_dynamics(self, param_modifiedon=''):\n        \"\"\"Get all the accounts with modifiedon greather than the latest\n        modifiedon updated account. To change the defuault filter can use the\n        param_modfiedon.\n\n        Args:\n            self (Accounts): Instance of Accounts.\n            param_modifiedon (str): By defualt is an empty str, only used to\n            force a modifiedon date.\n        \"\"\"\n        # get in dynamo the date of latest update\n        table_modifiedon = self.dynamodb.Table('dynamics_accounts_greather_modifiedon')\n\n        try:\n            last_update=table_modifiedon.get_item( Key={ 'bbug_company_id':\n                                                        self.bbug_company_id })\n            modifiedon=last_update['Item']['modifiedon']\n        except:\n            modifiedon='2000-01-01'\n\n        # update all accounts greather than a param_modifiedon\n        if param_modifiedon!='':\n            modifiedon=param_modifiedon\n\n        # get all accounts modifiedon after the latest update\n        self.query({'$filter': 'modifiedon gt ' + modifiedon })\n        self.data=json.loads(self.response.read())\n\n\n        for account in self.data['value']:\n            account['bbug_company_id']=self.bbug_company_id\n            for k in account.keys():\n                if isinstance(account[k] , float):\n                    account[k]=Decimal(str(account[k]))\n            self.save(account)\n        return self.get_messages()\n\n    def get_messages(self):\n        \"\"\"Get messages and clean the messages instance variable\n        \"\"\"\n        messages = self.messages\n        self.messages=[]\n        return messages\n\n    def get_from_dynamo(self, limit = 100 ):\n        response=self.table.query( KeyConditionExpression= Key('bbug_company_id').eq( self.bbug_company_id), Limit=limit)\n        return response['Items']\n\n    def to_update(self):\n        response=self.table.query( KeyConditionExpression= Key('bbug_company_id').eq( self.bbug_company_id),\n                                 FilterExpression='attribute_not_exists(bbug_updated_at)')\n        return response['Items']\n","sub_path":"bbug_dynamics/accounts.py","file_name":"accounts.py","file_ext":"py","file_size_in_byte":2655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"26085690","text":"import json\r\nimport time\r\nimport requests\r\nimport re\r\n\r\nfrom dateutil.parser import parse\r\nfrom .utils import get_soup\r\nfrom .parser import parse_legislate\r\n\r\nsearch_url = 'http://watch.peoplepower21.org/index.php?mid=Euian&show=&page={}&title={}&rec_num=1000&lname={}&sangim={}&bill_result={}'\r\nrequest_url = 'http://watch.peoplepower21.org/opages/Lawinfo/_vote_table.php'\r\nurl_base = 'http://watch.peoplepower21.org/?mid=LawInfo&bill_no={}'\r\n\r\n\r\ndef yield_law(bill_result, max_page =10, end_date = '2018-12-01', begin_date= '2017-12-01', title = '', lname = '', sangim ='', sleep=1.0):\r\n    \"\"\"\r\n    Artuments\r\n    ---------\r\n    law_status : str\r\n        eg. 2018-01-01\r\n    max_num : int\r\n        Maximum number of news to be scraped\r\n    sleep : float\r\n        Sleep time. Default 1.0 sec\r\n\r\n    It yields\r\n    ---------\r\n    news : json object\r\n    \"\"\"\r\n\r\n    # prepare parameters\r\n\r\n    d_begin = parse(begin_date)\r\n    d_end = parse(end_date)\r\n    end_page = 30\r\n    n_news = 0\r\n    outdate = False\r\n\r\n    for page in range(1, max_page+1):\r\n\r\n        # check number of scraped news\r\n        if outdate:\r\n            break\r\n\r\n        links_all =[]\r\n        url = search_url.format(page, title, lname, sangim, bill_result)\r\n        soup = get_soup(url)\r\n        sub_links = soup.find_all('tr')[1:]\r\n        links = [i.find('a')['href'] for i in sub_links]\r\n        links_all = ['http://watch.peoplepower21.org/?mid=LawInfo&'+url[29:] for url in links]\r\n\r\n        dates = soup.find_all('tr')[1:]\r\n        day = [d.find('td').text for d in dates]\r\n\r\n        for i, url in enumerate(links_all):\r\n            d_news = parse(day[i])\r\n            if d_end < d_news:\r\n                print('ignore scrapping. {} legistlation was scrapped'.format(d_news))\r\n                print('The newest legistlation has been created after {}'.format(end_date))\r\n                continue\r\n            else:\r\n                new_json = parse_legislate(url)\r\n                new_json['date'] = day[i]\r\n                # check date\r\n                if new_json is not None:\r\n\r\n                    if d_begin > d_news:\r\n                        outdate = True\r\n                        print('Stop scrapping. {} / {} legistlation was scrapped'.format(n_news, max_page*100))\r\n                        print('The oldest legistlation has been created after {}'.format(begin_date))\r\n                        break\r\n                    else:\r\n                        yield new_json\r\n\r\n                else:\r\n                    continue\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef gather_url(bill_result, max_page =10,end_date = '2019-12-01',begin_date= '2016-04-01', title = '', lname = '', sangim ='', sleep=1.0):\r\n    \"\"\"\r\n    Artuments\r\n    ---------\r\n    law_status : str\r\n        eg. 2018-01-01\r\n    max_num : int\r\n        Maximum number of news to be scraped\r\n    sleep : float\r\n        Sleep time. Default 1.0 sec\r\n\r\n    It yields\r\n    ---------\r\n    news : json object\r\n    \"\"\"\r\n\r\n    # prepare parameters\r\n\r\n    d_begin = parse(begin_date)\r\n    d_end = parse(end_date)\r\n    end_page = 30\r\n    n_news = 0\r\n    outdate = False\r\n\r\n    for page in range(0, max_page+1):\r\n\r\n        # check number of scraped news\r\n        if outdate:\r\n            break\r\n\r\n        links_all =[]\r\n        url = search_url.format(page, title, lname, sangim, bill_result)\r\n        soup = get_soup(url)\r\n        sub_links = soup.find_all('tr')[1:]\r\n        links = [i.find('a')['href'] for i in sub_links]\r\n        links_all = ['http://watch.peoplepower21.org/?mid=LawInfo&'+url[29:] for url in links if is_matched(url)]\r\n    return links_all\r\n","sub_path":"scraper/scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":3575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"127545737","text":"# Default imports\nfrom greyatomlib.python_intermediate.q05_read_csv_data.build import read_ipl_data_csv\npath = 'data/ipl_matches_small.csv'\nimport numpy as np\n# Enter Code Here\n\ndef get_unique_matches_count():\n    ipl_matches = np.genfromtxt(path,delimiter=\",\",dtype='|S50',skip_header=1)\n    ipl_matches1 = ipl_matches[:,0]\n    ipl_matches2 = np.unique(ipl_matches1)\n    return ipl_matches2.size\n","sub_path":"q06_get_unique_matches_count/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"147820836","text":"import hazelcast\n\nfrom hazelcast import ClientConfig\nfrom hazelcast.serialization.api import Portable\n\n\nclass Customer(Portable):\n    FACTORY_ID = 1\n    CLASS_ID = 1\n\n    def __init__(self, id=None, name=None, last_order=None):\n        self.id = id\n        self.name = name\n        self.last_order = last_order\n\n    def read_portable(self, object_data_input):\n        self.id = object_data_input.read_int(\"id\")\n        self.name = object_data_input.read_utf(\"name\")\n        self.last_order = object_data_input.read_long(\"last_order\")\n\n    def write_portable(self, object_data_output):\n        object_data_output.write_int(\"id\", self.id)\n        object_data_output.write_utf(\"name\", self.name)\n        object_data_output.write_long(\"last_order\", self.last_order)\n\n    def get_factory_id(self):\n        return self.FACTORY_ID\n\n    def get_class_id(self):\n        return self.CLASS_ID\n\n\nif __name__ == \"__main__\":\n    config = ClientConfig()\n    my_factory = {Customer.CLASS_ID: Customer}\n    config.serialization_config.add_portable_factory(Customer.FACTORY_ID, my_factory)\n    # Start the Hazelcast Client and connect to an already running Hazelcast Cluster on 127.0.0.1\n    hz = hazelcast.HazelcastClient(config)\n    # Customer can be used here\n    hz.shutdown()\n","sub_path":"examples/org-website/portable_serializable_sample.py","file_name":"portable_serializable_sample.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"542996601","text":"import dramatiq\nimport requests\nimport json\nimport uuid\nimport os\nimport shutil\nfrom compiler import Compiler\nfrom JudgeClient import JudgeClient\nfrom judger_config import TEST_CASE_DIR, JUDGER_WORKSPACE_BASE\n\nrunner_id = '00001'\ncompiler_config = {\n        \"src_name\": \"main.c\",\n        \"exe_name\": \"main\",\n        \"max_cpu_time\": 3000,\n        \"max_memory\": 512 * 1024 * 1024,\n        \"compile_command\": \"/usr/bin/gcc -DONLINE_JUDGE -O2 -w -fmax-errors=3 -std=c99 {src_path} -lm -o {exe_path}\",\n    }\ncode = r\"\"\"\n    #include <stdio.h>\n    int main(){\n        int a, b;\n        scanf(\"%d%d\", &a, &b);\n        printf(\"%d\\n\", a+b);\n        return 0;\n    }\n    \"\"\"\n\n\ndef clean_up(path):\n    print(\"CLEANING UP \", path)\n    shutil.rmtree(path)\n\n\nif __name__ == '__main__':\n    print(code)\n    runner_dir = os.path.join(JUDGER_WORKSPACE_BASE, runner_id)\n    if os.path.exists(runner_dir):\n        clean_up(runner_dir)\n    os.makedirs(runner_dir)\n    src_path = os.path.join(runner_dir, compiler_config['src_name'])\n    with open(src_path, 'w', encoding='utf-8') as code_file:\n        code_file.write(code)\n    exe_path = Compiler.compile(compiler_config, src_path, runner_dir)\n    print(exe_path)\n","sub_path":"compile_test.py","file_name":"compile_test.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"414277815","text":"import os\n\nos.environ[\"APP_SETTINGS\"] = \"config.DevelopmentConfig\"\n# v3 database\nos.environ[\"DATABASE_URL\"] = \"postgres://ddrxtxgazwxidu:1b200c6e68a289654b65d64542d36da338105c7b40a2dd335f4a42420584ecf0@ec2-52-21-153-207.compute-1.amazonaws.com:5432/d1hdtre41fscdn\"\n# v2 database\n# os.environ[\"DATABASE_URL\"] = \"postgres://bivxusanexnbjp:12f8e000800ba66e4f98d0df4795edc962bdfd5318cbd9f19c7eaf16ef244f54@ec2-52-23-45-36.compute-1.amazonaws.com:5432/dd46p10a83f3m0\"\n# os.environ[\"DATABASE_URL\"] = \"postgresql://changwei:w45039w45039@localhost/app_trial\"\nimport json\nfrom app.irsystem.models.eigenvector import eigenvector\nfrom app.irsystem.models.game import Game\nfrom app.irsystem.models.movie import Movie\nfrom app import db\n\nDATA_DIR = os.path.abspath(os.path.join(__file__, \"..\", \"..\", \"..\", \"data\"))\n\n# TODO\nLOCAL_DATA_DIR = os.path.abspath(os.path.join(\n    __file__, \"..\", \"..\", \"..\", \"data\", 'pca_svd'))\n\nEIGENVECTORS_PCA_COLUMNS = \"game_movie_eigenvectors_column.json\"\nEIGENVECTORS_PCA_COLUMNS_RESHAPED = \"game_movie_eigenvectors_column_reshaped.json\"\nTOKEN_LST_BEFORE_PCA = \"token_list_before_pca.json\"\nDICT_TOKEN_TO_IDX_BEFORE_PCA = \"dict_token_to_id_before_pca.json\"\nMOVIE_VECTORS_PCA = \"dict_movieid_to_vector_pca.json\"\nGAME_VECTORS_PCA = \"dict_gameid_to_vector_pca.json\"\nGAME_INFO_FILENAME = 'game_info.json'\nMOVIE_INFO_FILENAME = \"movie_info.json\"\nGAME_INFO_FILENAME = \"game_info.json\"\nTOP3000_MOVIE_GAME_TITLE_SIMILARITY_FILENAME = \"top3000movie_game_title_similarity.json\"\nGAMES_TITLE_SIMILARITY_FILENAME = \"games_title_similarity.json\"\nMOVIE_NAME_FILENAME = \"movie_id_to_title.json\"\n\n\ndef init_db():\n    # Create tables\n    print(\"Create db tables...\")\n    db.create_all()\n\n    # Dump data\n\n    with open(os.path.join(DATA_DIR, MOVIE_INFO_FILENAME), \"r\") as in_json_file:\n        movie_info = json.load(in_json_file)\n    with open(os.path.join(DATA_DIR, GAME_INFO_FILENAME), \"r\") as in_json_file:\n        game_info = json.load(in_json_file)\n    with open(os.path.join(LOCAL_DATA_DIR, EIGENVECTORS_PCA_COLUMNS),\n              \"r\") as in_json_file:\n        eigenvectors_pca = json.load(in_json_file)\n\n    # eigenvectors_pca_reshaped reshapes eigenvectors_pca vector to fit into 1388 rows\n    # Original vector dimension: 9716 x 2576\n    # Reshaped dimension: 1388 x 7 x 2576\n    # with open(os.path.join(LOCAL_DATA_DIR, EIGENVECTORS_PCA_COLUMNS_RESHAPED), \"r\") as in_json_file:\n    #     eigenvectors_pca_reshaped = json.load(in_json_file)\n    with open(os.path.join(LOCAL_DATA_DIR, MOVIE_VECTORS_PCA),\n              \"r\") as in_json_file:\n        movie_vectors = json.load(in_json_file)\n    with open(os.path.join(LOCAL_DATA_DIR, GAME_VECTORS_PCA),\n              \"r\") as in_json_file:\n        game_vectors = json.load(in_json_file)\n    with open(os.path.join(DATA_DIR, TOP3000_MOVIE_GAME_TITLE_SIMILARITY_FILENAME),\n              \"r\") as in_json_file:\n        movie_game_title_sim = json.load(in_json_file)\n    with open(os.path.join(DATA_DIR, GAMES_TITLE_SIMILARITY_FILENAME), 'r' ) as in_json_file:\n        game_title_sim = json.load(in_json_file)\n    with open(os.path.join(DATA_DIR, MOVIE_NAME_FILENAME), \"r\") as in_json_file:\n        movie_titles = json.load(in_json_file)\n\n    print(\"Dump movie data...\")\n    for (link, info) in movie_info.items():\n        db.session.add(Movie(\n            link_id=str(link),\n            name=str(movie_titles[link]),\n            games_review_match=json.dumps(info['games']),\n            games_title_match=json.dumps(movie_game_title_sim.get(link, [])),\n            genre=json.dumps(info['genre']),\n            desc_keywords=json.dumps(info['desc_keywords']),\n            vector_pca=json.dumps(movie_vectors[link])\n        ))\n\n    print(\"Dump game data...\")\n    for (appid, info) in game_info.items():\n        if info['mature_content'] == 'true':\n            mature_content = True\n        else:\n            mature_content = False\n        single_player = False\n        multi_player = False\n        if 'single-player' in info['num_players']:\n            single_player = True\n        if 'multi-player' in info['num_players']:\n            multi_player = True\n        db.session.add(Game(\n            app_id=str(appid),\n            name=info['name'],\n            developer=json.dumps(info['developer']),\n            publisher=json.dumps(info['publisher']),\n            tags=json.dumps(info['tags']),\n            genre=json.dumps(info['genre']),\n            single_player=single_player,\n            multi_player=multi_player,\n            rating=str(info['rating']),\n            mature_content=mature_content,\n            url=str(info['url']),\n            desc_keywords=json.dumps(info['desc_keywords']),\n            vector_pca=json.dumps(game_vectors[appid]),\n            games_title_match=json.dumps(game_title_sim.get(appid, []))\n        ))\n\n    print(\"Dump basis eigenvectors...\")\n    # for row, vectors in eigenvectors_pca_reshaped.items():\n    #     db.session.add(eigenvector(rn=row, vec=json.dumps(vectors)))\n    db.session.add(eigenvector(rn=\"1\", vec=json.dumps(eigenvectors_pca)))\n\n    print(\"Dump basis eigenvectors complete...\")\n\n    db.session.commit()\n\n    print(\"init_db done\")\n\n\ndef drop_db():\n    print(\"Drop all db tables...\")\n    db.session.remove()\n    db.drop_all()\n\n\ndef modify_db():\n    with open(os.path.join(DATA_DIR, MOVIE_INFO_FILENAME), \"r\") as in_json_file:\n        movie_info = json.load(in_json_file)\n    with open(os.path.join(DATA_DIR, GAME_INFO_FILENAME), \"r\") as in_json_file:\n        game_info = json.load(in_json_file)\n    with open(os.path.join(LOCAL_DATA_DIR, EIGENVECTORS_PCA_COLUMNS),\n              \"r\") as in_json_file:\n        eigenvectors_pca = json.load(in_json_file)\n    with open(os.path.join(LOCAL_DATA_DIR, MOVIE_VECTORS_PCA),\n              \"r\") as in_json_file:\n        movie_vectors = json.load(in_json_file)\n    with open(os.path.join(LOCAL_DATA_DIR, GAME_VECTORS_PCA),\n              \"r\") as in_json_file:\n        game_vectors = json.load(in_json_file)\n\n    print(\"adding in movie vector...\")\n    for key in movie_info.keys():\n        thisMovie = Movie.query.filter_by(link_id=key)\n        thisMovie.vector_pca = json.dumps(movie_vectors[key])\n    db.session.commit()\n    print(\"adding in movie vector complete...\")\n\n    print(\"adding in game vector...\")\n    for key in game_info.keys():\n        thisGame = Game.query.filter_by(app_id=key)\n        thisGame.vector_pca = json.dumps(game_vectors[key])\n    db.session.commit()\n    print(\"adding in game vector complete...\")\n\n    print(\"adding in basis eigenvectors...\")\n    db.session.add(eigenvector(rn=\"1\", vec=json.dumps(eigenvectors_pca)))\n    db.session.commit()\n    print(\"adding in movie vector complete...\")\n\n\ndef modify_title_similarity():\n    print(\"updating similarity scores ...\")\n    with open(os.path.join(DATA_DIR,\n                           TOP3000_MOVIE_GAME_TITLE_SIMILARITY_FILENAME),\n              \"r\") as in_json_file:\n        movie_game_sim = json.load(in_json_file)\n\n    for link_id in movie_game_sim.keys():\n        movie_obj: Movie = Movie.query.filter_by(link_id=link_id).first()\n        movie_obj.games_title_match = json.dumps(movie_game_sim[link_id])\n    db.session.commit()\n    print(\"finish updating similarity scores ...\")\n\n\nif __name__ == '__main__':\n    # drop_db()\n    init_db()\n    # modify_db()\n    # modify_title_similarity()\n","sub_path":"app/irsystem/models/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":7309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"490359256","text":"import random\n\nfrom datetime import *\nfrom django.utils import timezone\n\nfrom login.models import *\n\n\nclass CrimeModel:\n    name = \"\"\n    succes = .0\n    nothing = .0\n    waitMinutes = 0\n    timesJail = 1\n\n    pointsBySucces = 0\n    pointsByJail = -0\n\n    def _getResultOnChange(self):\n        rf = random.random()\n        if rf < self.succes:\n            return \"succes\"\n        elif rf < self.succes + self.nothing:\n            return \"nothing\"\n        else:\n            return \"jail\"\n\n    def Execute(self, user):\n        result = self._getResultOnChange()\n        newdate = timezone.now() + timedelta(minutes=self.waitMinutes) if result in [\"succes\", \"nothing\"] else timezone.now() + timedelta(minutes=self.waitMinutes*self.timesJail)\n        message = \"for the crime \" + self.name + \" the action result in: \" + result + \" you have to wait to: \" + newdate.strftime(\"%H:%M:%S\")\n        WaitAction(message=(message), user=user, waitUntil=newdate).save()\n\n        if result == \"succes\":\n            ScoreTransaction.objects.create(user=user, score=self.pointsBySucces)\n        elif result == \"jail\":\n            ScoreTransaction.objects.create(user=user, score=self.pointsByJail)\n\n        return message\n\n\nclass StealCar(CrimeModel):\n    def __init__(self):\n        self.name = \"steal a car\"\n        self.succes = 0.3\n        self.nothing = 0.4\n        self.waitMinutes = 15\n        self.timesJail = 4\n\n        self.pointsBySucces = 50\n        self.pointsByJail = -30\n\n\nclass RobBank(CrimeModel):\n    def __init__(self):\n        self.name = \"rob a bank\"\n        self.succes = 0.2\n        self.nothing = 0.2\n        self.waitMinutes = 25\n        self.timesJail = 6\n\n        self.pointsBySucces = 5000\n        self.pointsByJail = -500\n\nclass SmugglingDrugs(CrimeModel):\n    def __init__(self):\n        self.name = \"smuggling drugs\"\n        self.succes = .4\n        self.nothing = .4\n        self.waitMinutes = 10\n        self.timesJail = 6\n\n        self.pointsBySucces = 3000\n        self.pointsByJail = -300\n\n\nclass CrimesHelper:\n    @staticmethod\n    def getDefaultCrimes():\n        return [StealCar(), RobBank(), SmugglingDrugs()]\n\n    @staticmethod\n    def getByName(name):\n        return [x for x in CrimesHelper.getDefaultCrimes() if x.name == name][0]\n","sub_path":"MaffiaGame/Crimes/crimeModels.py","file_name":"crimeModels.py","file_ext":"py","file_size_in_byte":2258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"509587063","text":"# /DP\n\nimport sys,time\nimport numpy as np\n\nsys.path.insert(0, '../lib/')\n\nfrom load import load_funcs_2\nfrom DP_lib import cost_itemnum, percentile, DP_click, DP, DP_sse, create_emptycount, wvec2prob, gs_prob, full, isexistclick\n\ndatapath1 = '../../data/'\ndatapath2 = '../../python_preprocess/code/data/data_range/aggregate/'\n\nloads = load_funcs_2(datapath1, datapath2)\n\nsuffix = sys.argv[1]\n\nbin_size = 5\nbin_num = 4000\nthisK = 100\n\ncount_vec = np.array([0.0] * bin_num)\n\nfin = open(datapath1 + 'count/clickcount_' + suffix + '.txt', 'r')\nfout = open(datapath1 + 'count/partitions_' + suffix + '.txt', 'w')\n\nfor i in range(0, bin_num):\n\tline = fin.readline()\n\ttokens = line.strip('\\r\\n').split('\\t')\n\tidx = int(tokens[0])\n\tcount = float(tokens[1])\n\tcount_vec[idx] = count\n\ncum_sqr_sum = np.array([0.0] * (bin_num + 1))\ncum_sum = np.array([0.0] * (bin_num + 1))\n\ncum_sqr_sum[0] = 0\ncum_sum[0] = 0\n\nfor i in range(1, (bin_num + 1)):\n\tcum_sqr_sum[i] = cum_sqr_sum[i-1] + count_vec[i - 1] * count_vec[i - 1]\n\tcum_sum[i] = cum_sum[i-1] + count_vec[i - 1]\n\npartitions = DP_sse(bin_num, thisK, cum_sqr_sum, cum_sum)\nfor i in range(0, len(partitions)):\n\tfout.write(str(partitions[i] * bin_size) + '\\r\\n')\n\t\nfout.close()","sub_path":"DP/DP_bucket.py","file_name":"DP_bucket.py","file_ext":"py","file_size_in_byte":1212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"632906049","text":"x = input()\nt_dict = {s:i for i, s in enumerate(x)}\nn = int(input())\ns = [input() for _ in range(n)]\n\ndef sort_key(t_dict, string):\n    i = 10\n    ans = 0\n    for s in string:\n        ans += (t_dict[s]+1) * (27 ** i)\n        i -= 1\n    return ans\n\ns.sort(key=lambda x: sort_key(t_dict, x))\n[print(i) for i in s]\n","sub_path":"contest/abc219/c.py","file_name":"c.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"131080267","text":"import pytest\nfrom datetime import timedelta\n\nfrom django.utils.timezone import now as tz_now\nfrom django.test.utils import override_settings\n\nfrom awx.main.models import AuthToken, User\n\n\n@override_settings(AUTH_TOKEN_PER_USER=3)\n@pytest.mark.django_db\ndef test_get_tokens_over_limit():\n    now = tz_now()\n    # Times are relative to now\n    # (key, created on in seconds , expiration in seconds)\n    test_data = [\n        # a is implicitly expired\n        (\"a\", -1000, -10),\n        # b's are invalid due to session limit of 3\n        (\"b\", -100, 60),\n        (\"bb\", -100, 60),\n        (\"c\", -90, 70),\n        (\"d\", -80, 80),\n        (\"e\", -70, 90),\n    ]\n    user = User.objects.create_superuser('admin', 'foo@bar.com', 'password')\n    for key, t_create, t_expire in test_data:\n        AuthToken.objects.create(\n            user=user,\n            key=key,\n            request_hash='this_is_a_hash',\n            created=now + timedelta(seconds=t_create),\n            expires=now + timedelta(seconds=t_expire),\n        )\n    invalid_tokens = AuthToken.get_tokens_over_limit(user, now=now)\n    invalid_keys = [x.key for x in invalid_tokens]\n    assert len(invalid_keys) == 2\n    assert 'b' in invalid_keys\n    assert 'bb' in invalid_keys\n","sub_path":"awx/main/tests/functional/test_auth_token_limit.py","file_name":"test_auth_token_limit.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"339036437","text":"import numpy as np\nfrom . import containers, httping, utils, wrk\nfrom copy import deepcopy\nfrom docker import DockerClient\nfrom loguru import logger\nfrom pandas import DataFrame, ExcelWriter\nfrom yaml import load, Loader\nfrom click import command, argument, File\n\n\ndef expand_setup(setup, name):\n    \"\"\"\n    \n    \"\"\"\n    for section in setup.keys():\n        for node in ['X', 'Y', 'Z']:\n            if node not in setup[section]:\n                logger.warning(f\"Node '{node}' missing in section '{section}'.\")\n\n    # Only expand when multiple delays are specified for X.\n    network = setup['network']\n    x_delay = network['X']['delay']\n    if type(network['X']['delay']) is not list:\n        return [setup]\n\n    length = len(x_delay)\n    y_delay = utils.as_list_with_len(network['Y']['delay'], length)\n    z_delay = utils.as_list_with_len(network['Z']['delay'], length)\n    \n    setups = []\n    for delays in zip(x_delay, y_delay, z_delay):\n        s = deepcopy(setup)\n        s['network']['X']['delay'] = delays[0]\n        s['network']['Y']['delay'] = delays[1]\n        s['network']['Z']['delay'] = delays[2]\n\n        setups.append(s)\n\n    return setups\n        \n\n@command()\n@argument(\"file\", type=File('rb'))\ndef main(file: File):\n    utils.configure_loguru()\n    dc = DockerClient.from_env()\n\n    # Load benchmark matrix\n    matrix = load(file, Loader=Loader)\n    client = matrix['client']\n    output = matrix['output']\n    runs_per_setup = matrix['runs-per-setup']\n    defaults = matrix['defaults']\n\n    # Setup the Docker network.\n    tc = containers.create_tc_container(dc)\n    net = containers.create_network(dc)\n\n    # Results are written at the end.\n    xlsx_sheets = {}\n\n    for setup in matrix['setups']:\n        setup_name = list(setup.keys())[0]\n        logger.info(f\"===== {setup_name} =====\")\n\n        setup = utils.merge(defaults, setup[setup_name])\n        setups = expand_setup(setup, setup_name)\n\n        for setup in setups:\n            variant_times = DataFrame()\n            variant_name = utils.add_delay_postfix(setup_name, setup)\n            runs = range(1, runs_per_setup+1)\n\n            for r in runs:\n                try:\n                    logger.info(f\"===== {variant_name} ({r}/{runs_per_setup}) =====\")\n                    y, z = (None, None)\n\n                    if 'Y' in setup['containers']:\n                        y = containers.create_container('Y', setup, net, dc)\n\n                    if 'Z' in setup['containers']:\n                        z = containers.create_container('Z', setup, net, dc)\n\n                    # An X node is mandatory.\n                    x = containers.create_container('X', setup, net, dc)\n                    \n                    # Capture stdout until X completes.\n                    logger.info(f\"Waiting for container '{x.name}' (X) to finish.\")\n                    stdout = []\n                    for line in x.attach(stream=True):\n                        stdout.append(line)\n\n                    # Process results\n                    logger.info(f\"Container '{x.name}' (X) finished. Processing results.\")\n                    \n                    if client == \"httping\":\n                        (times, stats) = httping.extract_httping_run_result(stdout)\n                    elif client == \"wrk\":\n                        (times, stats) = wrk.extract_wrk_run_result(stdout)\n                    \n                    variant_times[f'run{r}'] = times\n                    variant_times[f'run{r}-stats'] = stats\n                \n                # Always perform cleanup\n                finally:\n                    if y is not None:\n                        y.kill()\n\n                    if z is not None:\n                        z.kill()\n\n            # Calculate variant-wide stats\n            variant_stats = np.zeros(min(len(variant_times['run1']), 4))\n            combined = np.array([])\n            means = np.zeros(runs_per_setup)\n            for (i, r) in enumerate(runs):\n                means[i] = variant_times[f'run{r}-stats'][1]\n                combined = np.append(combined, variant_times[f'run{r}'])\n            \n            variant_stats[0] = means.mean()\n            variant_stats[1] = means.std()\n            variant_stats[2] = combined.mean()\n            variant_stats[3] = combined.std()\n\n            # Add variant-wide stats to variant sheet\n            variant_times['stats'] = variant_stats\n            xlsx_sheets[variant_name] = variant_times\n\n    # Write output to file\n    logger.info(\"Writing output\")\n    with ExcelWriter(output) as w:\n        for (name, df) in xlsx_sheets.items():\n            df.to_excel(w, sheet_name=name)\n\n    # Cleanup\n    net.remove()\n    tc.kill()\n\n\nif __name__ == '__main__':\n    main()","sub_path":"proxy_bench/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":4706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"46137158","text":"from .base import *\n\nSECRET_KEY = 'wzq(el+esq1ywilp&(u^gki183#a$i*c!u92(sx6l8rp-b1l54'\nDEBUG = True\nTEMPLATE_DEBUG = True\nDATABASES = {\n    'default':{\n        'ENGINE':'django.db.backends.sqlite3',\n        'NAME':os.path.join(os.path.dirname(BASE_DIR), 'db.sqlite3'),\n    }\n}\n","sub_path":"lunch/settings/local.py","file_name":"local.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"606816597","text":"import os\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as mtick\nimport pickle\n\nprob1 = False\n\nif __name__ == '__main__':\n    if prob1:\n        with open(\"saved_rewards/batch32.pkl\", \"rb\") as handle32:\n            batch32 = pickle.load(handle32)\n        time_step, mean_batch32, best_batch32 = batch32[\"time\"], batch32[\"mean\"], batch32[\"best\"]\n        fig = plt.figure()\n        ax = fig.add_subplot(111)\n        mean32, = ax.plot(time_step, mean_batch32, label=\"mean rew for batch size 32\")\n        best32, = ax.plot(time_step, best_batch32, label=\"best rew for batch size 32\")\n        ax.set_title(\"Performance with Default Setting\")\n        ax.set_xlabel(\"time step\")\n        ax.set_ylabel(\"episode reward\")\n        ax.xaxis.set_major_formatter(mtick.FormatStrFormatter('%.2g'))\n        ax.legend(handles=[mean32, best32], loc=\"lower right\", prop={'size': 11})\n        plt.show()\n    else:\n        # batch size = 8, 16, 32, 64\n        with open(\"saved_rewards/batch8.pkl\", \"rb\") as handle8:\n            batch8 = pickle.load(handle8)\n        with open(\"saved_rewards/batch16.pkl\", \"rb\") as handle16:\n            batch16 = pickle.load(handle16)\n        with open(\"saved_rewards/batch32.pkl\", \"rb\") as handle32:\n            batch32 = pickle.load(handle32)\n        with open(\"saved_rewards/batch64.pkl\", \"rb\") as handle64:\n            batch64 = pickle.load(handle64)\n\n        time_step = batch32[\"time\"]\n        mean_batch8, best_batch8 = batch8[\"mean\"], batch8[\"best\"]\n        mean_batch16, best_batch16 = batch16[\"mean\"], batch16[\"best\"]\n        mean_batch32, best_batch32 = batch32[\"mean\"], batch32[\"best\"]\n        mean_batch64, best_batch64 = batch64[\"mean\"], batch64[\"best\"]\n\n        fig = plt.figure()\n        ax = fig.add_subplot(111)\n        mean8, = ax.plot(time_step, mean_batch8, label=\"mean rew for batch size 8\")\n        best8, = ax.plot(time_step, best_batch8, label=\"best rew for batch size 8\")\n        mean16, = ax.plot(time_step, mean_batch16, label=\"mean rew for batch size 16\")\n        best16, = ax.plot(time_step, best_batch16, label=\"best rew for batch size 16\")\n        mean32, = ax.plot(time_step, mean_batch32, label=\"mean rew for batch size 32\")\n        best32, = ax.plot(time_step, best_batch32, label=\"best rew for batch size 32\")\n        mean64, = ax.plot(time_step, mean_batch64, label=\"mean rew for batch size 64\")\n        best64, = ax.plot(time_step, best_batch64, label=\"best rew for batch size 64\")\n        ax.set_title(\"Performance with Different Batch Size\")\n        ax.set_xlabel(\"time step\")\n        ax.set_ylabel(\"episode reward\")\n        ax.xaxis.set_major_formatter(mtick.FormatStrFormatter('%.2g'))\n        lst = [mean8, best8, mean16, best16, mean32, best32, mean64, best64]\n        ax.legend(handles=lst, loc=\"best\", prop={'size': 11})\n        plt.show()\n\n","sub_path":"hw3/plot_rewards.py","file_name":"plot_rewards.py","file_ext":"py","file_size_in_byte":2809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"623784376","text":"import sys\nimport logging\nimport re\n\nfrom django.http import HttpResponse, StreamingHttpResponse\n\nfrom .filesystem import FileWrapper\n\n\nCHUNK_SIZE = 2**15\n\nlogger = logging.getLogger(__name__)\n\n\ndef stream_file(filelike, filename, request, content_type):\n    start, end = 0, None\n    file_size = filelike.seek(0,2)\n\n    if \"HTTP_RANGE\" in request.META:\n        try:\n            start, end = re.findall(r\"/d+\", request.META[\"HTTP_RANGE\"])\n        except TypeError:\n            logger.exception(\n                \"Malformed HTTP_RANGE in download request: {}\"\n                .format(request.META[\"HTTP_RANGE\"])\n            )\n            return HttpResponse(\n                status=416\n            )\n\n        if start > end or end > file_size:\n            return HttpResponse(\n                status=416\n            )\n\n    fwrapper = FileWrapper(\n        filelike,\n        blksize=CHUNK_SIZE,\n        start=start,\n        end=end\n    )\n    response = StreamingHttpResponse(\n        fwrapper,\n        content_type=content_type,\n    )\n    response[\"Content-Disposition\"] = \\\n        'attachment; filename=\"' + filename + '\"'\n    response[\"Content-Length\"] = file_size\n    response[\"Accept-Ranges\"] = 'bytes'\n\n    if \"HTTP_RANGE\" in request.META:\n        response[\"status\"] = 206\n        response[\"Content-Range\"] = \"bytes {}-{}/{}\".format(start,\n                                                            end,\n                                                            file_size)\n\n    return response\n","sub_path":"snodas/utils/http.py","file_name":"http.py","file_ext":"py","file_size_in_byte":1498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"611739569","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Feb  7 18:15:22 2018\n\n@author: js-wxyu\n\"\"\"\n\ndef isprime(n):\n    for k in range(2,int(n**0.5)+1):\n        if n%k==0:\n            return False\n    return True\nn=3\nnum=1\nwhile num<=10000:\n    if isprime(n):\n        num+=1\n    n+=2\nprint(num,n-2)","sub_path":"Q7.py","file_name":"Q7.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"188365387","text":"\"\"\"\nUtils\n\"\"\"\nfrom typing import List, Callable\n\nimport time\nfrom tqdm import tqdm\n\n# pylint: disable=too-many-arguments,too-many-locals\n\n\ndef row_generator_from_paginated_calls(\n    skip: int,\n    first: int,\n    count_method: Callable[..., int],\n    count_kwargs: dict,\n    paged_call_method: Callable[..., List[dict]],\n    paged_call_payload: dict,\n    fields: List[str],\n    disable_tqdm: bool,\n):\n    \"\"\"\n    Builds a row generator from paginated calls.\n\n    Parameters\n    ----------\n    - skip : int\n        Number of assets to skip (they are ordered by their date of creation, first to last).\n    - first : int\n        Maximum number of assets to return.\n    - count_method: ... -> int\n        Callable returning the number of available assets given `count_args`.\n    - count_kwargs: dict\n        Keyword arguments passed to the `count_method`.\n    - paged_call_method: ... -> List[dict]\n        Callable returning the list of samples.\n    - paged_call_payload: dict\n        Payload for the GraphQL query.\n    - fields: List[str]\n        The list of strings to retrieved.\n    - disable_tqdm: bool\n        If `True`, disables tqdm.\n    \"\"\"\n    count_rows_retrieved = 0\n    if not disable_tqdm:\n        count_rows_available = count_method(**count_kwargs)\n        count_rows_queried_total = min(count_rows_available,\n                                       first) if first is not None else count_rows_available\n    else:\n        # dummy value that won't have any impact since tqdm is disabled\n        count_rows_queried_total = 1 if first != 0 else 0\n    count_rows_query_default = min(100, first or 100)\n    throttling_delay = 60 / 250\n\n    if count_rows_queried_total == 0:\n        yield from ()\n    else:\n        with tqdm(total=count_rows_queried_total, disable=disable_tqdm) as pbar:\n            while True:\n                query_start = time.time()\n                rows = paged_call_method(\n                    count_rows_retrieved + skip,\n                    count_rows_query_default,\n                    paged_call_payload,\n                    fields,\n                )\n                query_time = time.time() - query_start\n\n                if query_time < throttling_delay:\n                    time.sleep(throttling_delay - query_time)\n\n                if rows is None or len(rows) == 0:\n                    break\n\n                for row in rows:\n                    yield row\n\n                count_rows_retrieved += len(rows)\n                pbar.update(len(rows))\n                if first is not None and count_rows_retrieved >= first:\n                    break\n","sub_path":"kili/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"517065885","text":"from setuptools import setup, find_packages\n\n\nwith open(\"README.md\", \"r\") as f:\n    long_description = f.read()\n\nsetup(\n    name=\"vvvvid-downloader\",\n    version=\"1.1.0\",\n    author=\"Nearata\",\n    author_email=\"williamdicicco@protonmail.com\",\n    description=\"Uno script in Python che permette di scaricare facilmente i video da VVVVID.\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/nearata/vvvvid-downloader\",\n    packages=find_packages(),\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"Programming Language :: Python :: 3.8\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n        \"Intended Audience :: End Users/Desktop\",\n        \"Natural Language :: Italian\",\n        \"Development Status :: 5 - Production/Stable\",\n        \"Environment :: Console\"\n    ],\n    python_requires=\">=3.8\",\n    install_requires=[\n        \"requests==2.24.0\",\n        \"inquirer==2.7.0\",\n        \"colorama==0.4.3\"\n    ]\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"505606403","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n#\nimport sys\nimport os\nimport MeCab\nimport numpy as np\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\nfrom sklearn.cluster import KMeans\n\nDIRS = ['dokujo-tsushin', 'movie-enter', 'it-life-hack']\ntagger = MeCab.Tagger(\"-Ochasen\")\n\n\ndef mecab_tokenizer(text):\n    word_list = list()\n    node = tagger.parseToNode(text)\n    while node:\n        if node.surface != \"\":\n            res = node.feature.split(\",\")\n            if node.surface not in ['(', ')', '-', '+', '\"'] and \\\n                    res[0] in [\"名詞\", \"動詞\", \"形容詞\", \"副詞\"] and \\\n                    res[1] not in [\"数\", '記号', '非自立', '接尾']:\n                basic_word = res[6] if res[6] != \"*\" else node.surface\n                word_list.append(basic_word)\n\n        node = node.next\n    return word_list\n\n\ndef main():\n    # 単なる単語頻度によるベクトル化\n    vectorizer = CountVectorizer(tokenizer=mecab_tokenizer)\n\n    # tf-idfベクトル化変数(正規化はL2, tfにlogを用いる)\n    # vectorizer = TfidfVectorizer(norm='l2', sublinear_tf=True,\n    #                              tokenizer=mecab_tokenizer)\n\n    text_list = []\n    id_list = []\n    for d in DIRS:\n        # ファイルリストから'LICENSE.txt'を除く\n        files = [f for f in os.listdir(\"text/\" + d) if f != 'LICENSE.txt']\n        # カテゴリごとに10ファイルずつ\n        for fi in files[:10]:\n            with open('text/{}/{}'.format(d, fi)) as f:\n                lines = f.readlines()\n                text_list.append(\"\".join(lines[3:]))\n                id_list.append(fi.split('.')[0])\n\n    # テキストデータのベクトル化\n    weighted_matrix = vectorizer.fit_transform(text_list)\n\n    # ベクトル情報を確認\n    print(\"テキスト数:%d,単語の種類数:%d\" % weighted_matrix.shape)\n\n    # k-meansの実行\n    cluster = KMeans(n_clusters=3).fit_predict(weighted_matrix)\n\n    ## 出力用のデータ編集\n    index = weighted_matrix.toarray().argsort(axis=1)[:, ::-1]\n    features = np.array(vectorizer.get_feature_names())\n    feature_words = features[index]\n\n    # 出力単語の数\n    num_words = 5\n    print(\"クラスタ\\tファイル名\\ttf-idf値の高���単語\")\n    for cl, name, fwords in zip(cluster, id_list, feature_words[:, :num_words]):\n        print(\"{}\\t{}\\t{}\".format(cl, name, fwords))\n\n\nif __name__ == '__main__':\n    main()","sub_path":"practice_5/k-means.py","file_name":"k-means.py","file_ext":"py","file_size_in_byte":2449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"363581598","text":"import requests\nfrom pyquery import PyQuery as pq\nfrom urllib.parse import urlparse\nimport multiprocessing\nimport csv\nimport time\n\n\nhearders = {\n\t'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',\n\t}\na_url = ['baidu', 'news', 'sike', 'qq', 'hao123', 'sport', 'sina', 'worldbank', \n'tuniu', 'liepin', '163', 'org', 'gov', 'net', '666c', 'eastmoney', 'fashion', \n'hotel', 'bbs', 'job', 'people', 'money', 'unionpay', 'ticket', 'ali', 'dujia', 'miss', 'voc', 'sohu', 'pingan', '.cn',\n'jmw', 'home', 'panjk', 'admaimai', 'zxart', 'gongjiao', 'jiancai', 'blog', '.tw', 'liebiao', '51sole', '591hx', '17house', 'space',\n'site', '.ltd', 'dream', 'java', 'sonhoo', 'zhaoshang100', 'chn0769', 'taobao', 'live', '360', 'gx211', 'huangye88', '554757', 'china', 'city', 'chat',\n'agent', 'zhuangyi', 'b2b', '99cfw', 'cnjy', 'game', 'ci123', 'house', 'bao315', 'xyj321', 'fenlei', 'mgd', 'kugou', 'bizhi', 'e2say', '54086', 'qy39', 'xyj321', '7999',\n'jixiexinxi', '.xyz', 'info', 'car', 'uc', 'shop', 'lin', 'xg557', 'xg67', 'club', '.st']\n\nb_url = ['.jpg', '.html', '.png', '.htm', '.php'\n]\n\n\n\ndef get_data(url):\n\ttry:\n\t\tres = requests.get(url, headers=hearders, timeout=5)\n\texcept:\n\t\tpass\n\telse:\n\t\tif res.status_code == 200:\n\t\t\tres.encoding = res.apparent_encoding\n\t\t\treturn res.text\n\n\ndef parse_data(url, html, l, d, n):\n\tdoc = pq(html)\n\taes = doc('a').items()\n\tlist_link = []\n\tfor a in aes:\n\t\tif '<img' in a.__str__():\n\t\t\ts = a.attr(\"href\")\n\t\t\tif s:\n\t\t\t\tif s.startswith('http') and is_contain_chinese(s):\n\t\t\t\t\tres=urlparse(s)\n\t\t\t\t\tif res.scheme and res.hostname:\n\t\t\t\t\t\ts56 = f'{res.scheme}://{res.hostname}'\n\t\t\t\t\tif res.port:\n\t\t\t\t\t\ts56 = f'{s56}:{res.port}'\n\t\t\t\t\tif s56 not in list_link:\n\t\t\t\t\t\tpwq = True\n\t\t\t\t\t\tfor shai_url in b_url:\n\t\t\t\t\t\t\tif shai_url in s:\n\t\t\t\t\t\t\t\tpwq = False\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\tif pwq:\n\t\t\t\t\t\t\tlist_link.append(s56)\n\tl.acquire()\n\tfor item in list_link:\n\t\tppww = True\n\t\tfor uuu1 in a_url:\n\t\t\tif uuu1 in item:\n\t\t\t\tppww = False\n\t\t\t\tbreak\n\t\tif ppww:\n\t\t\tif item in d:\n\t\t\t\td[item] += 1\n\t\t\telse:\n\t\t\t\td[item] = 1\n\tl.release()\n\tif n == 0:\n\t\treturn\n\twwww = 5\n\tiframe = doc('iframe').items()\n\tfor i in iframe:\n\t\tsrc = i.attr('src')\n\t\tif src.startswith('/'):\n\t\t\tsrc = url + src\n\t\telif src.startswith('http'):\n\t\t\tpass\n\t\telse:\n\t\t\tsrc = url + '/' + src\n\t\tdata = get_data(src)\n\t\tif data:\n\t\t\ts = parse_data(url, data, l, d, n-1)\n\t\twwww -= 1\n\t\tif wwww == 0:\n\t\t\tbreak\n\n\ndef get_url(file):\n\tl = []\n\twith open(file, 'rb') as f:\n\t\tfor url in f:\n\t\t\tp = True\n\t\t\ttry:\n\t\t\t\ts_url = url.decode()\n\t\t\texcept Exception as e:\n\t\t\t\tprint(e)\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tfor i in a_url:\n\t\t\t\t\tif i in s_url:\n\t\t\t\t\t\tp = False\n\t\t\t\t\t\tbreak\n\t\t\t\tif p:\n\t\t\t\t\tif s_url.startswith('http'):\n\t\t\t\t\t\tl.append(s_url.strip('/\\r\\n'))\n\t\t\t\t\telse:\n\t\t\t\t\t\tl.append('http://' + s_url.strip('/\\r\\n'))\n\treturn l\n\n\ndef is_contain_chinese(check_str):\n    for ch in check_str:\n        if u'\\u4e00' <= ch <= u'\\u9fff':\n            return False\n    return True\n\n\ndef open_file(data):\n\tfile = \"./result.csv\"\n\tcsvFile = open(file, \"w\")\n\twriter = csv.writer(csvFile)\n\tfor i in data:\n\t\twriter.writerow(i)\n\tcsvFile.close()\n\n\ndef run(i, l, d, LEN):\n\tdata = get_data(i)\n\tif data:\n\t\tdic = parse_data(i, data, l, d, 1)\n\tif LEN % 5000 == 0:\n\t\tif d:\n\t\t\td = sorted(d.items(), key = lambda item:item[1], reverse = True)\n\t\t\topen_file(d)\n\t\telse:\n\t\t\topen_file([('nodata', '2222')])\n\t\tsend_file('./result.csv', pppp=False)\n\n\ndef send_file(file, pppp=True):\n\turl = 'https://api.telegram.org/bot711166180:AAErNuMGY5LU72YP7ZeOBwH53jRKSp5NeXY/sendDocument'\n\tfiles = {\"document\" : open(file)}\n\tif pppp:\n\t\tdata = {'chat_id': -398945112}\n\telse:\n\t\tdata = {'chat_id': -285548732}\n\tres = requests.post(url, data=data, files=files)\n\tprint(res.text)\n\n\ndef main():\n\tpool = multiprocessing.Pool(processes=8)\n\tmanager = multiprocessing.Manager()\n\tl = manager.Lock()\n\td = manager.dict()\n\tfile = r'./yuan.txt'\n\turl_l = get_url(file)\n\trrrr = 1\n\tfor i in url_l:\n\t\tpool.apply_async(run, (i, l, d, rrrr))\n\t\trrrr += 1\n\tdel url_l\n\tpool.close()\n\tpool.join()\n\td = sorted(d.items(), key = lambda item:item[1], reverse = True)\n\topen_file(d)\n\tsend_file('./result.csv')\n\nif __name__ == '__main__':\n\tprint(time.time())\n\tmain()\n\tprint(time.time())\n\n\n\n\n\n","sub_path":"test9.py","file_name":"test9.py","file_ext":"py","file_size_in_byte":4233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"493949279","text":"import matplotlib.pyplot as plt\n\ninput_values = [1, 2, 3, 4, 5]\nsquares = [1, 4, 9, 16, 25]\nplt.plot(input_values, squares, linewidth = 5)\n\n#Set chart title and label axes\nplt.title(\"Square Numbers\", fontsize=24)\nplt.xlabel(\"Value\", fontsize = 14)\nplt.ylabel(\"Square of Value\", fontsize = 14)\n\n#Set size of tick label\nplt.tick_params(axis=\"both\", which=\"major\", labelsize = 14)\nplt.scatter(2, 4, s=200)\n\n# x_values = [1, 2, 3, 4, 5]\n# y_values = [1, 4, 9, 16, 25]\n\nx_values = list(range(1, 1001))\ny_values = [x**2 for x in x_values]\n\nplt.scatter(x_values, y_values, c=y_values, cmap=plt.cm.Blues, edgecolor=\"none\", s = 40)\n\n#\nplt.axis([0, 1100, 0, 1100000])\n\nplt.show()","sub_path":"Data/mpl_squares.py","file_name":"mpl_squares.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"146325349","text":"\"\"\"边角检测\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport skimage.data as data\n\n\ndef susan_corner_detection(img):\n    # 将数据类型改为浮点型\n    img = img.astype(np.float64)\n    g = 10  # 阈值\n    output = np.zeros(img.shape)  # 将要输出的数组\n    # 从三开始，因为模板是7元素，不能溢出\n    for i in range(3, img.shape[0]):\n        for j in range(3, img.shape[1]):\n            r0 = img[i, j]\n            r = img[i - 3:i + 4, j - 3:j + 4]\n            nr = np.sum(np.exp(-((r0 - r) / 6) ** 6))\n            if nr < g:\n                output[i, j] = g-nr\n\n    # 非最大抑制\n    for i in range(1, img.shape[0]):\n        for j in range(1, img.shape[1]):\n            arr = img[i-1:i+1, j-1:j+1]\n            if img[i, j] < np.max(arr):\n                img[i, j] = 0\n\n    return output\n\n\nimage = data.camera()\nimage_out = susan_corner_detection(image)\nplt.imshow(image_out, cmap='gray')\nplt.show()\n\n","sub_path":"1/5/5.5.1.1.py","file_name":"5.5.1.1.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"371969785","text":"'''Trains a single convolutional nerual network for specified epochs\nnumber with a specified random seed\n'''\nfrom keras.layers import Dense, Activation, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.models import Sequential\nfrom keras.datasets import cifar10\nfrom keras import backend as K\nimport tensorflow as tf\nfrom common import helper_classes\nimport random as rn\nimport numpy as np\nimport pickle\nimport keras\nimport os\n\n\ndef set_random_seed(seed):\n    os.environ['PYTHONHASHSEED'] = '0'\n    np.random.seed(seed)\n    rn.seed(seed)\n    # turn off parallel computing for reproductivity\n    session_conf = tf.ConfigProto(intra_op_parallelism_threads=1,\n                                  inter_op_parallelism_threads=1)\n    tf.set_random_seed(seed)\n    sess = tf.Session(graph=tf.get_default_graph(),\n                      config=session_conf)\n    K.set_session(sess)\n\n\ndef eval_network(individual, epochs, seed):\n    assert(isinstance(individual, helper_classes.Individual))\n    set_random_seed(seed)\n    batch_size = 32\n    num_classes = 10\n\n    print(\"Layers : \" + str(individual.phenotype))\n    # The data, split between train and test sets:\n    (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n\n    # Convert class vectors to binary class matrices.\n    y_train = keras.utils.to_categorical(y_train, num_classes)\n    y_test = keras.utils.to_categorical(y_test, num_classes)\n\n    layers = individual.phenotype\n    model = Sequential()\n    for idx, layer in enumerate(layers):\n        if idx == 0:\n            model.add(Conv2D(layer, (3, 3), padding='same',\n                             input_shape=x_train.shape[1:]))\n            model.add(Activation('relu'))\n        else:\n            model.add(Conv2D(layer, (3, 3)))\n            model.add(Activation('relu'))\n            # add 1 pooling layer in the middle\n            if (idx + 1) == (len(layers) // 2):\n                model.add(MaxPooling2D(pool_size=(2, 2)))\n    model.add(Flatten())\n    model.add(Dense(128))\n    model.add(Dense(num_classes))\n    model.add(Activation('softmax'))\n\n    # initiate RMSprop optimizer\n    opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)\n\n    # Let's train the model using RMSprop\n    model.compile(loss='categorical_crossentropy',\n                  optimizer=opt,\n                  metrics=['accuracy'])\n\n    x_train = x_train.astype('float32')\n    x_test = x_test.astype('float32')\n    x_train /= 255\n    x_test /= 255\n\n    history = model.fit(x_train, y_train,\n                        batch_size=batch_size,\n                        epochs=epochs,\n                        validation_data=(x_test, y_test),\n                        shuffle=False)\n\n    # Score trained model.\n    scores = model.evaluate(x_test, y_test, verbose=1)\n\n    print('Test loss:', scores[0])\n    print('Test accuracy:', scores[1])\n    individual.loss = scores[0]\n    individual.score = scores[1]\n    individual.training_history = str(history.history)\n    with open(\"result\",'wb') as f:\n        pickle.dump(individual, f)\n","sub_path":"mgr/code/eval_cnn.py","file_name":"eval_cnn.py","file_ext":"py","file_size_in_byte":3018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"64918579","text":"\"\"\"\nDescription\n===========\nDate                          :  2018-02-06\nPurpose                       :  GPP project: Identify missing trades in FA\nDepartment and Desk           :  FO Prime Services\nRequester                     :  Eveshnee Naidoo\nDeveloper                     :  Ondrej Bahounek\n\nDetails:\n========\nA user has to download trades from GPP Portal in csv files.\nThere are 3 different files - for Equity, Equity Swaps and Futures & Options.\nThis module can be given all 3 on its input.\nResult will be a report with all trades that are in input files, but not in FA.\n\"\"\"\n\nimport csv\nfrom collections import defaultdict\nfrom os.path import basename, join\nimport string\nfrom datetime import datetime\nimport acm\nimport FRunScriptGUI\nfrom at_logging import getLogger\nfrom at_ael_variables import AelVariableHandler\n\n\nLOGGER = getLogger(__name__)\nael_variables = AelVariableHandler()\nDEFAULT_OUT_FILENAME = '${DATE}_gpp_recon.csv'\n\n\nclass ColumnIndexer(object):\n\n    def __init__(self, file_path):\n    \n        self.file_path = file_path    \n        self.trade_ref = None\n        self.account = None\n        self.trade_date = None\n        self.ins_descr = None\n        self.ins_code = None\n        self.ins_type = None\n        self.trans_desc_col = None\n        self.trans_desc_excl = None\n        \n    def line_to_row(self, line):\n        return [\n            line[self.ins_type] if isinstance(self.ins_type, int) else self.ins_type,\n            line[self.trade_ref],\n            line[self.account],\n            line[self.trade_date],\n            line[self.ins_descr],\n            \n            basename(self.file_path)\n            ]\n            \n    @staticmethod\n    def get_headers():\n        return [\"Ins Type\", \"Trade Ref\", \"Account\", \"Trade Date\", \"Instr Description\",\n            \"Filename\"]\n\n\nfileFilter = \"CSV Files (*.csv)|*.csv|\"\ninputFile = FRunScriptGUI.InputFileSelection(FileFilter=fileFilter)\ndirectorySelection = acm.FFileSelection()\ndirectorySelection.PickDirectory(True)\ndirectorySelection.SelectedDirectory('')\n\n\nael_variables.add(\n    'file_equities',\n    label = 'File Equities',\n    cls = inputFile,\n    default = inputFile,\n    #default=r'c:\\DEV\\Perforce\\FA\\features\\ABITFA-5097 - SBL uploader.br\\input\\sl_upload_file_excel_test.xlsx',\n    mandatory = False,\n    multiple = True,\n    alt = 'Input file equities.'\n    )\nael_variables.add(\n    'file_equity_swaps',\n    label = 'File Equity Swaps',\n    cls = inputFile,\n    default = inputFile,\n    #default=r'c:\\DEV\\Perforce\\FA\\features\\ABITFA-5097 - SBL uploader.br\\input\\sl_upload_file_excel_test.xlsx',\n    mandatory = False,\n    multiple = True,\n    alt = 'Input file equity swaps (CFDs).'\n    )\nael_variables.add(\n    'file_futures',\n    label = 'File Futs Opts',\n    cls = inputFile,\n    default = inputFile,\n    #default=r'c:\\DEV\\Perforce\\FA\\features\\ABITFA-5097 - SBL uploader.br\\input\\sl_upload_file_excel_test.xlsx',\n    mandatory = False,\n    multiple = True,\n    alt = 'Input file futures and options.'\n    )\nael_variables.add(\n    'out_filename',\n    label = 'Output File',\n    cls = 'string',\n    default = DEFAULT_OUT_FILENAME,\n    mandatory = False,\n    multiple = False,\n    alt = 'Name of output file'\n    )\nael_variables.add(\n    'directory',\n    label = 'Directory',\n    cls = directorySelection,\n    collection =  None,\n    default = directorySelection,\n    mandatory = False,\n    multiple = True,\n    alt = 'Output path'\n    )\nael_variables.add_bool(\n    'show_all',\n    label = 'Show All Trades?',\n    default = False,\n    alt = 'Show all trades or just missing trades?'\n    )\n\n\ndef porcess_trdref(trd_ref):\n    if trd_ref.startswith('0'):\n        trd_ref = trd_ref[1:]\n        \n    if trd_ref[-3:-1] == 'T0':\n        trd_ref = trd_ref[:-3]\n        \n    return trd_ref\n\n\ndef get_output_fn(directory, file_name):\n    fname_template = string.Template(file_name)\n    now = datetime.now()\n    today = now.strftime(\"%Y-%m-%d\")\n    file_name = fname_template.substitute(DATE=today)\n\n    return join(directory, file_name)\n    \ndef ael_main(ael_dict):\n    LOGGER.msg_tracker.reset()\n    out_file = None\n    if ael_dict['out_filename']:\n        out_file = get_output_fn(str(ael_dict['directory']), str(ael_dict['out_filename']))\n    show_all = ael_dict['show_all']\n    missing_trades = defaultdict(list)\n    \n    eq_col_obj = ColumnIndexer(str(ael_dict['file_equities']))\n    eq_col_obj.trade_ref = 0\n    eq_col_obj.account = 2\n    eq_col_obj.trade_date = 15\n    eq_col_obj.ins_descr = 4\n    eq_col_obj.ins_type = 1\n    \n    fut_col_obj = ColumnIndexer(str(ael_dict['file_futures']))\n    fut_col_obj.trade_ref = 1\n    fut_col_obj.account = 0\n    fut_col_obj.trade_date = 2\n    fut_col_obj.ins_descr = 11\n    fut_col_obj.ins_type = \"F&O\"\n    fut_col_obj.trans_desc_col = 3\n    fut_col_obj.trans_desc_excl = ('Future Settlement',)\n    \n    swap_col_obj = ColumnIndexer(str(ael_dict['file_equity_swaps']))\n    swap_col_obj.trade_ref = 3\n    swap_col_obj.account = 0\n    swap_col_obj.trade_date = 11\n    swap_col_obj.ins_descr = 9\n    swap_col_obj.ins_type = 2\n    \n    file_objects = (eq_col_obj, fut_col_obj, swap_col_obj)\n    \n    for file_obj in file_objects:\n        if not file_obj.file_path:\n            continue\n        \n        base_name = basename(file_obj.file_path)\n        col_index = file_obj.trade_ref  # GPP trade ref column number\n        LOGGER.info('Reading: %s', file_obj.file_path)\n        \n        with open(file_obj.file_path, \"rb\") as csv_file:\n            \n            reader = csv.reader(csv_file, delimiter=\",\")\n            reader.next()\n            for line in reader:\n                if file_obj.trans_desc_col is not None and \\\n                        line[file_obj.trans_desc_col] in file_obj.trans_desc_excl:\n                    continue\n                orig_trd_ref = line[col_index]\n                trd_ref = porcess_trdref(orig_trd_ref)\n                gpp_trade = orig_trd_ref\n                fa_trade = acm.FTrade.Select('optionalKey=\"%s\"' %trd_ref)\n                if show_all:\n                    print(\"%s --> %s\" %(gpp_trade, fa_trade))\n                if not fa_trade:\n                    missing_trades[base_name].append(file_obj.line_to_row(line))\n            \n    for fn, lines in missing_trades.items():\n        LOGGER.error(\"Missing GPP trades from: '%s'\", fn)\n        for row in lines:\n            print(row[1])\n    \n    if out_file:  # output report is not mandatory\n        with open(out_file, \"wb\") as csv_out_file:\n            writer = csv.writer(csv_out_file)\n            writer.writerow(ColumnIndexer.get_headers())\n            for fn, lines in missing_trades.items():\n                for row in lines:\n                    writer.writerow(row)\n        LOGGER.info(\"Wrote output to: '%s'\", out_file)\n        \n    if LOGGER.msg_tracker.errors_counter:\n        raise RuntimeError(\"ERRORS occurred. Please check the log.\")\n        \n    LOGGER.info(\"Completed successfully.\")\n        \n","sub_path":"Python modules/pb_gpp_recon_trades.py","file_name":"pb_gpp_recon_trades.py","file_ext":"py","file_size_in_byte":6923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"521659381","text":"from .models import Message, Chat\nfrom rest_framework import serializers\nfrom users.serializers import UserSerializer\n\n\nclass MessageSerializer(serializers.ModelSerializer):\n    user = UserSerializer(fields=('id', 'first_name'), read_only=True)\n\n    class Meta:\n        model = Message\n        fields = ('id', 'user', 'date', 'message')\n\n\nclass ChatAbstractSerializer(serializers.ModelSerializer):\n    users = UserSerializer(fields=('id', 'first_name'), read_only=True, many=True)\n    last_message = MessageSerializer(read_only=True)\n    title = serializers.SerializerMethodField(read_only=True)\n    is_unread = serializers.SerializerMethodField(read_only=True)\n\n    class Meta:\n        model = Chat\n        fields = ('id', 'title', 'date', 'users', 'last_message', 'is_unread')\n\n    def get_title(self, obj):\n        user_request_id = self.context['request'].user.id\n        chat_user = obj.users.exclude(pk=user_request_id).first()\n        if chat_user:\n            return chat_user.first_name\n        else:\n            return None\n\n    def get_is_unread(self, obj):\n        return obj.unread.filter(user=self.context['request'].user).exists()\n\n\nclass ChatSerializer(ChatAbstractSerializer):\n\n    class Meta:\n        model = Chat\n        fields = ('id', 'title', 'users')\n\n\nclass ChatListSerializer(ChatAbstractSerializer):\n\n    class Meta:\n        model = Chat\n        fields = ('id', 'title', 'users', 'last_message', 'is_unread')\n","sub_path":"react_chat/apps/chats/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"522508123","text":"#!/usr/bin/python\n\nfrom random import randint\n\nglobal counter\n\ndef swap(x,i,j):\n    x[i],x[j]=x[j],x[i]\n\ndef pivotFirst(x,lmark,rmark):\n    pivot_val=x[lmark]\n    pivot_idx=lmark\n    while lmark<=rmark:\n        while lmark <= rmark and x[lmark]<=pivot_val:\n            lmark+=1\n        while lmark <=rmark and x[rmark] >=pivot_val:\n            rmark -=1\n        if lmark <= rmark:\n            swap(x,lmark,rmark)\n            lmark+=1\n            rmark-=1\n    swap(x,pivot_idx,rmark)\n    return rmark\n\ndef quickSort(x,pivotMethod=pivotFirst):\n    def _qsort(x,first,last): #왼쪽 인덱스:first 오른쪽 인덱스 :last\n        if first < last:\n            splitpoint=pivotMethod(x,first,last)\n            _qsort(x,first,splitpoint-1)\n            _qsort(x,splitpoint+1,last)\n        _qsort(x,0,len(x)-1)\n\ndef binary_search(a_list,wanted_data):\n    global counter\n    first=0\n    las=len(a_list)-1\n\n    while first <= last:\n        idx=(first+last) //2\n        counter+=1\n        if a_list[idx] ==wanted_data:\n            print('{item} found at position {i}'.format(item=wanted_data,i=idx))\n            return True\n        elif a_list[idx]>wanted_data:\n            last=idx -1\n        elif a_list[idx]>wanted_data:\n            first =idx+1\n        else:\n            print('{item} not found in the list'.format(item=wanted_data))\n            return False\ndef binary_search_recursive(a_list,wanted_data):\n    global counter\n    first=0\n    last=len(a_list)-1\n\n    if len(a_list)==0:\n        print('{item} not found in the list'.format(item=wanted_data))\n        return False\n    else:\n        idx=(first+last)//2\n        counter+=1\n        if wanted_data==a_list[idx]:\n            print('{item} found at position {i}'.format(item=wanted_data, i=idx))\n            return True\n        else:\n            if a_list[idx]< wanted_data:\n                return binary_search_recursive(a_list[idx+1:],wanted_data)\n            else:\n                return binary_search_recursive(a_list[:idx], wanted_data)\n\nif __name__=='__main__':\n    data=[]\n    counter=0\n    input_n = input(\"The number of data :\")\n    data =[randint(1,100) for x in range (int(input_n))]\n\n    print(\"<Before the arrangement>\")\n    print(data)\n\n    quickSort(data)\n\n    print(\"<After the arrangement>\")\n    print(data)\n\n    msg=binary_search_recursive(data,50)\n    if msg==True:\n        print(\"Compare {} times to search 50\".format(counter))\n\n","sub_path":"concepts/BinarySearch.py","file_name":"BinarySearch.py","file_ext":"py","file_size_in_byte":2403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"343709335","text":"# Author:Zhang Yuan\nimport MyPackage\n\n\n__mypath__ = MyPackage.MyClass_Path.MyClass_Path()  #路径类\nmyfile = MyPackage.MyClass_File.MyClass_File()  #文件操作类\n\n# MyPackage_PathList = __mypath__.GetMyPackagePath()\nMyPackage_PathList = [\"C:\\\\Users\\\\i2011\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python37\\\\lib\\\\site-packages\\\\MyPackage\"]\n\n# ---备份到桌面\nmyfile.ZipDir(MyPackage_PathList[0], zipPath=\"Desktop\" , zipName=None, autoName=True)\n\n# ---备份到OneDrive的Work-Python备份文件夹\nZipPath = __mypath__.GetOneDrivePath() + \"\\\\Work-Python备份\"\nmyfile.ZipDir(MyPackage_PathList[0], zipPath=ZipPath , zipName=None, autoName=True)\n\n\n\n","sub_path":"My_Python_Items/AutoMyPackageZip.py","file_name":"AutoMyPackageZip.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"556598621","text":"\n\"\"\"\nERROR DEFINITION:\n\n1000-1999 Request Errors\n2000-2999 Account Errors\n\"\"\"\n\n\nclass ApplicationException(Exception):\n    \"\"\"Base error class for application.\n       code - A numeric code identifying the error for external use\n       internal_code - A numeric code identifying the error for internal use\n       message - A human-friendly description of the error suitable to display to users.\"\"\"\n\n    def __init__(self, code, status_code, msg):\n        self.code = code\n        self.msg = msg\n        self.status_code = status_code\n\n    def __str__(self):\n        return \"[PROFILE-%s] STATUS=%s MESSAGE=%s\" % (self.code, self.status_code, self.msg)\n\n\nclass GeneralException(ApplicationException):\n    CODE = 1000\n    STATUS_CODE = 500\n    TEXT = 'General Exception: %s'\n\n    def __init__(self, error='unknown'):\n        new_msg = self.TEXT % (type(error).__name__)\n        new_msg = \"%s %s\" % (new_msg, str(error)) \n        ApplicationException.__init__(self, self.CODE, self.STATUS_CODE, new_msg)\n\n\nclass InvalidRequestException(GeneralException):\n    CODE = 1001\n    STATUS_CODE = 400\n    TEXT = 'Invalid request: %s'\n\n\n","sub_path":"project/infection/common/errors.py","file_name":"errors.py","file_ext":"py","file_size_in_byte":1123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"184722803","text":"# Determine if one string is permutation of the other\n\ndef check_permutation(str1, str2):\n  if len(str1) != len(str2):\n    return False\n  \n  occur = dict()\n\n  for c in str1:\n    occur[c] = occur.get(c,0) + 1\n  \n  for c in str2:\n    if occur.get(c, 0) == 0:\n      return False\n    else:\n      occur[c] = occur.get(c) - 1\n  \n  return True\n\nprint(check_permutation(\"ameen\", \"neema\")) # true\nprint(check_permutation(\"ameen\", \"notme\")) # false","sub_path":"1. Strings/1.2 check_permutations.py","file_name":"1.2 check_permutations.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"298577697","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# author: kcc time:2020/1/16\n\nimport socket,time\n\n# TCP服务器\n\n# 1.创建套接字\nserver = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n\n# 2.绑定端口号\nip = \"0.0.0.0\"\nport = 7330\nserver.bind((ip,port))\n\n# 监听端口,最大连接数128\nserver.listen(128)\n\n# 阻塞等待连接着发送数据,收到一个请求时\n# 返回一个新的套接字,和请求客户端的地址\nprint(\"-----等待客户端连接------\")\nclient_socket,clientAddr = server.accept()\nprint(\"已有客户端连接,连接地址是:{}\".format(clientAddr))\n\n# 阻塞等待客户端发送数据\nrecvdata = client_socket.recv(1024)\nprint(recvdata.decode(\"gbk\"))\n\nclient_socket.send(\"nihao\".encode(\"utf8\"))\n\n","sub_path":"自研究/网络编程/tcp服务器.py","file_name":"tcp服务器.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"565449732","text":"#!/usr/bin/env python3\n\nimport sys\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_pdf import PdfPages\n\ndef generator(file_name):\n    for line in open(file_name, 'r'):\n        yield tuple(map(float, line.split()))\n\ndef to_list(file_name):\n    return list(map(list, zip(*generator(file_name))))\n\n\nlabel = ['IBM-M1', 'IBM-M2', 'IBM-M2-Rand(1)', 'IBM-M2-Rand(2)', 'IBM-M2-Rand(3)', 'IBM-M2-Uniform', 'IBM-M1-AddN', 'IBM-M1-SmoothHeavyNull', 'IBM-M1-AllImprove']\ncolor = ['red', 'green', 'blue', 'black', 'yellow', 'orange', 'pink', 'grey', 'cyan']\nperplexity = to_list(sys.argv[1])\nlikelihood = to_list(sys.argv[2])\nrecall = to_list(sys.argv[3])\nprecision = to_list(sys.argv[4])\naer = to_list(sys.argv[5])\nprint(len(aer), len(precision), len(recall), len(likelihood), len(perplexity))\n\niterations = [i for i in range(1, len(perplexity[0]) + 1)]\n    \n\nwith PdfPages('perplexity_' + sys.argv[-1]) as pdf:\n    fig, ax = plt.subplots()\n    for perplexity_config, color_config, label_config in zip(perplexity, color, label):\n        ax.plot(iterations, perplexity_config, color=color_config, label=label_config)\n    plt.xlabel('Iteration')\n    plt.ylabel('Perplexity')\n\n    legend = ax.legend(loc='upper right', ncol=2, shadow=True, prop={'size':9})\n\n    plt.grid(True)\n    plt.figure(figsize=(8, 6))\n    pdf.savefig(fig)\n    plt.close()\n\nwith PdfPages('likelihood_' + sys.argv[-1]) as pdf:\n    fig, ax = plt.subplots()\n    for likelihood_config, color_config, label_config in zip(likelihood, color, label):\n        ax.plot(iterations, likelihood_config, color=color_config, label=label_config)\n    plt.xlabel('Iteration')\n    plt.ylabel('Log-Likelihood')\n\n    legend = ax.legend(loc='lower right', ncol=2, shadow=True, prop={'size':9})\n\n    plt.grid(True)\n    plt.figure(figsize=(8, 6))\n    pdf.savefig(fig)\n    plt.close()\n\nwith PdfPages('recall_' + sys.argv[-1]) as pdf:\n    fig, ax = plt.subplots()\n    for recall_config, color_config, label_config in zip(recall, color, label):\n        ax.plot(iterations, recall_config, color=color_config, label=label_config)\n    plt.xlabel('Iteration')\n    plt.ylabel('Recall')\n\n    legend = ax.legend(loc='lower right', ncol=2, shadow=True, prop={'size':9})\n\n    plt.grid(True)\n    plt.figure(figsize=(8, 6))\n    pdf.savefig(fig)\n    plt.close()\n\nwith PdfPages('precision_' + sys.argv[-1]) as pdf:\n    fig, ax = plt.subplots()\n    for precision_config, color_config, label_config in zip(precision, color, label):\n        ax.plot(iterations, precision_config, color=color_config, label=label_config)\n    plt.xlabel('Iteration')\n    plt.ylabel('Precision')\n\n    legend = ax.legend(loc='lower right', ncol=2, shadow=True, prop={'size':9})\n\n\n    plt.grid(True)\n    plt.figure(figsize=(8, 6))\n    pdf.savefig(fig)\n    plt.close()\n\nwith PdfPages('aer_' + sys.argv[-1]) as pdf:\n    fig, ax = plt.subplots()\n    for aer_config, color_config, label_config in zip(aer, color, label):\n        ax.plot(iterations, aer_config, color=color_config, label=label_config)\n    plt.xlabel('Iteration')\n    plt.ylabel('AER')\n\n    legend = ax.legend(loc='center right', ncol=2, shadow=True, prop={'size':9})\n\n    plt.grid(True)\n    plt.figure(figsize=(8, 6))\n    pdf.savefig(fig)\n    plt.close()\n","sub_path":"Assignment1/logs_25000/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":3225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"585340661","text":"import operator\nimport numpy as np\n\nfrom .utils import Base, ppm_error, range\n\n\nclass FittedPeak(Base):\n\n    __slots__ = [\n        \"mz\", \"intensity\", \"signal_to_noise\", \"peak_count\",\n        \"index\", \"full_width_at_half_max\", \"area\",\n        \"left_width\", \"right_width\"\n    ]\n\n    def __init__(self, mz, intensity, signal_to_noise, peak_count, index, full_width_at_half_max, area,\n                 left_width=0, right_width=0):\n        self.mz = mz\n        self.intensity = intensity\n        self.signal_to_noise = signal_to_noise\n        self.peak_count = peak_count\n        self.index = index\n        self.full_width_at_half_max = full_width_at_half_max\n        self.area = area\n        self.left_width = left_width\n        self.right_width = right_width\n\n    def clone(self):\n        return FittedPeak(self.mz, self.intensity, self.signal_to_noise,\n                          self.peak_count, self.index, self.full_width_at_half_max,\n                          self.area, self.left_width, self.right_width)\n\n    def __reduce__(self):\n        return self.__class__, (self.mz, self.intensity, self.signal_to_noise,\n                                self.peak_count, self.index, self.full_width_at_half_max,\n                                self.area, self.left_width, self.right_width)\n\n    def __hash__(self):\n        return hash((self.mz, self.intensity, self.signal_to_noise, self.full_width_at_half_max))\n\n    def __eq__(self, other):\n        if other is None:\n            return False\n        return (abs(self.mz - other.mz) < 1e-5) and (\n            abs(self.intensity - other.intensity) < 1e-5) and (\n            abs(self.signal_to_noise - other.signal_to_noise) < 1e-5) and (\n            abs(self.full_width_at_half_max - other.full_width_at_half_max) < 1e-5)\n\n    def __ne__(self, other):\n        return not (self == other)\n\n\ndef _get_nearest_peak(peaklist, mz):\n    lo = 0\n    hi = len(peaklist)\n\n    tol = 1\n\n    def sweep(lo, hi):\n        best_error = float('inf')\n        best_index = None\n        for i in range(hi - lo):\n            i += lo\n            v = peaklist[i].mz\n            err = abs(v - mz)\n            if err < best_error:\n                best_error = err\n                best_index = i\n        return peaklist[best_index], best_error\n\n    def binsearch(lo, hi):\n        if (hi - lo) < 5:\n            return sweep(lo, hi)\n        else:\n            mid = (hi + lo) / 2\n            v = peaklist[mid].mz\n            if abs(v - mz) < tol:\n                return sweep(lo, hi)\n            elif v > mz:\n                return binsearch(lo, mid)\n            else:\n                return binsearch(mid, hi)\n    return binsearch(lo, hi)\n\n\nclass PeakSet(Base):\n    def __init__(self, peaks):\n        self.peaks = tuple(peaks)\n\n    def __len__(self):\n        return len(self.peaks)\n\n    def reindex(self):\n        self._index()\n\n    def _index(self):\n        self.peaks = sorted(self.peaks, key=operator.attrgetter('mz'))\n        i = 0\n        for i, peak in enumerate(self.peaks):\n            peak.peak_count = i\n            if peak.index == -1:\n                peak.index = i\n        return i\n\n    def has_peak(self, mz, tolerance=1e-5):\n        return binary_search(self.peaks, mz, tolerance)\n\n    get_nearest_peak = _get_nearest_peak\n\n    def __repr__(self):\n        return \"<PeakSet %d Peaks>\" % (len(self))\n\n    def __getitem__(self, item):\n        if isinstance(item, slice):\n            return PeakSet(self.peaks[item])\n        return self.peaks[item]\n\n    def clone(self):\n        return PeakSet(p.clone() for p in self)\n\n    def between(self, m1, m2, tolerance=1e-5):\n        p1, _ = self.get_nearest_peak(m1)\n        p2, _ = self.get_nearest_peak(m2)\n\n        return self[p1.peak_count - 1:p2.peak_count + 1]\n\n\ndef _sweep_solution(array, value, lo, hi, tolerance, verbose=False):\n    best_index = -1\n    best_error = float('inf')\n    best_intensity = 0\n    for i in range(hi - lo):\n        target = array[lo + i]\n        error = ppm_error(value, target.mz)\n        abs_error = abs(error)\n        if abs_error < tolerance and (abs_error < best_error * 1.1) and (target.intensity > best_intensity):\n            best_index = lo + i\n            best_error = abs_error\n            best_intensity = target.intensity\n    if best_index == -1:\n        return None\n    else:\n        return array[best_index]\n\n\ndef _binary_search(array, value, lo, hi, tolerance, verbose=False):\n    if (hi - lo) < 5:\n        return _sweep_solution(array, value, lo, hi, tolerance, verbose)\n    else:\n        mid = (hi + lo) // 2\n        target = array[mid]\n        target_value = target.mz\n        error = ppm_error(value, target_value)\n\n        if abs(error) <= tolerance:\n            return _sweep_solution(array, value, max(mid - (mid if mid < 5 else 5), lo), min(\n                mid + 5, hi), tolerance, verbose)\n        elif target_value > value:\n            return _binary_search(array, value, lo, mid, tolerance, verbose)\n        elif target_value < value:\n            return _binary_search(array, value, mid, hi, tolerance, verbose)\n    raise Exception(\"No recursion found!\")\n\n\ndef binary_search(array, value, tolerance=2e-5, verbose=False):\n    size = len(array)\n    if size == 0:\n        return None\n    return _binary_search(array, value, 0, size, tolerance, verbose)\n\n\ntry:\n    _FittedPeak = FittedPeak\n    _PeakSet = PeakSet\n    _p_binary_search = binary_search\n    from ._c.peak_set import FittedPeak, PeakSet\nexcept ImportError:\n    pass\n\n\ndef to_array(peak_set):\n    array = np.zeros((len(peak_set), 6))\n    array[:, 0] = [p.mz for p in peak_set]\n    array[:, 1] = [p.intensity for p in peak_set]\n    array[:, 2] = [p.signal_to_noise for p in peak_set]\n    array[:, 3] = [p.full_width_at_half_max for p in peak_set]\n    array[:, 4] = [p.index for p in peak_set]\n    array[:, 5] = [p.peak_count for p in peak_set]\n    return array\n","sub_path":"ms_peak_picker/peak_set.py","file_name":"peak_set.py","file_ext":"py","file_size_in_byte":5843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"184942245","text":"from PyQt5 import QtWidgets\nfrom PyQt5.QtWidgets import QMainWindow\nfrom ApiHandler import ApiHandler\n\n\nclass GuiWindow(QMainWindow):\n    def __init__(self):\n        super(GuiWindow, self).__init__()\n        self.setGeometry(200, 200, 500, 500)\n        self.setWindowTitle(\"Weather Check\")\n        self.initUI()\n\n    def initUI(self):\n        self.labelTemp = QtWidgets.QLabel(self)\n        self.labelTemp.setText(\"Temp: \")\n        self.labelTemp.move(50, 150)\n\n        self.labelTempShow = QtWidgets.QLabel(self)\n        self.labelTempShow.move(100, 150)\n\n        self.labelTempMin = QtWidgets.QLabel(self)\n        self.labelTempMin.setText(\"Temp min: \")\n        self.labelTempMin.move(50, 200)\n\n        self.labelTempMinShow = QtWidgets.QLabel(self)\n        self.labelTempMinShow.move(100, 200)\n\n        self.labelTempMax = QtWidgets.QLabel(self)\n        self.labelTempMax.setText(\"Temp max: \")\n        self.labelTempMax.move(50, 250)\n\n        self.labelTempMaxShow = QtWidgets.QLabel(self)\n        self.labelTempMaxShow.move(100, 250)\n\n        self.labelWeatherDescription = QtWidgets.QLabel(self)\n        self.labelWeatherDescription.setText(\"Warunki pogodowe: \")\n        self.labelWeatherDescription.move(150, 150)\n\n        self.labelWeatherDescriptionShow = QtWidgets.QLabel(self)\n        self.labelWeatherDescriptionShow.move(250, 150)\n\n        self.button = QtWidgets.QPushButton(self)\n        self.button.setText(\"Sprawdz\")\n        self.button.move(200, 100)\n        self.button.clicked.connect(self.clicked)\n\n        self.inputCity = QtWidgets.QLineEdit(self)\n        self.inputCity.move(50, 100)\n\n    def clicked(self):\n        cityInput = self.inputCity.text()\n        apiHandler = ApiHandler(str(cityInput))\n        apiHandler.request()\n        self.labelTempShow.setText(str(round(apiHandler.temp - 273.15)))\n        self.labelTempMinShow.setText(str(round(apiHandler.tempMin - 273.15)))\n        self.labelTempMaxShow.setText(str(round(apiHandler.tempMax - 273.15)))\n        self.labelWeatherDescriptionShow.setText(str(apiHandler.weatherDescription))\n\n","sub_path":"GuiWindow.py","file_name":"GuiWindow.py","file_ext":"py","file_size_in_byte":2067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"230662121","text":"from xml.etree import ElementTree\n\net = ElementTree.parse('stations.xml') # or parse(filename_or_file)\nfor lng in et.iter('lng'):\n    newLNG = float(lng.text)/1000000\n    lng.text = str(newLNG)\n\nfor lat in et.iter('lat'):\n    newLAT = float(lat.text)/1000000\n    lat.text = str(newLAT)\n\net.write('stations_updated.xml')\n","sub_path":"scripts_parsing/parseXMLLAT.py","file_name":"parseXMLLAT.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"193492883","text":"# -*- coding: utf-8 -*-\n'''\nBuilds a Vega grammar specification from vincent.Area()\n'''\n\nimport vincent\nimport random\n\nvis = vincent.Area()\nvis.tabular_data([random.randint(10, 100) for x in range(0, 16, 1)])\nvis.axis_label(x_label='X Axis Label', y_label='Y Axis Label', title='Title')\n\n#Generate both the Vega JSON and a data JSON. \npath = r'vega.json'\nvis.to_json(path, split_data=True, html=True)\n\n#Lets add a data interpolation parameter and resave the JSON\nvis += ({'value': 'monotone'}, 'marks', 0, 'properties', 'enter', 'interpolate')\nvis.to_json(path, split_data=True, html=True)","sub_path":"examples/vincent_area.py","file_name":"vincent_area.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"299783085","text":"#Create a 3x3 matrix with values ranging from 0 to 8.\nimport numpy as np\nnp.arange(9).reshape(3,3)\n\n#Create a random array of size 10 and sort it\nx=np.random.random(10)\nnp.sort(x)\n\n#Remove from one array those items that exist in another For example, a1 = [1, 2, 3], a2 = [1, 3] --> result = [2]\na1=np.array([1, 2, 3])\na2=np.array([1,3])\nnp.setdiff1d(a1,a2)\n\n#Get the positions where elements of two arrays match For example, a1 = [1, 2, 3, 10], a2 = [5, 4, 3, 10] --> result = [2, 3]\na1=np.array([1,2,3,10])\na2=np.array([5,4,3,10])\nnp.intersect1d(a1,a2)\n\n#Replace \"Michael\" with \"Mike\" in x x = np.array(['Hello name is Michael'], dtype=np.str)\nx = np.array(['Hello name is Michael'], dtype=np.str)\nx[0].replace(\"Michael\",\"Mike\")\n\n#Lex x be an array [[ 1, 2],[ 3, 4]] Rotate x 90 degrees counter-clockwise. expected result = [[3, 1], [4, 2]]\nx=np.array([[1,2],\n         [3,4]])\nnp.rot90(x,-1)\n","sub_path":"summer-bootcamp/week1-intro/homework/{%github-liuwei}/week2-homework-Numpy.py","file_name":"week2-homework-Numpy.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"387843271","text":"import pyaudio\nimport numpy as np\nfrom Tkinter import *\n\np = pyaudio.PyAudio()\n\n\nclass Soundgenerator(object):\n\tdef __init__(self, master):\n\t\tself.master = master\n\t\tmaster.title(\"VINAYA TECHNOLOGIES\")\n\t\tself.initialising(master)\n\t\n\tdef initialising(self, master):\n\t\tself.entryVar_freq_box = DoubleVar()\n\t\tself.entryVar_samp_freq = IntVar()\n\t\tself.entryVar_duration = DoubleVar()\n\t\tself.entryVar_volume = DoubleVar()\n\t\t\n\t\tself.label_title = Label(master, text=\"FREQUENCY GENERATOR\", font = \"-weight bold\").grid(row=0, column=0, columnspan =2)\n\t\tself.label_freq_box = Label(master, text=\"FREQUENCY OF THE WAVE (Hz)\").grid(row=1, column=0)\n\t\tself.entry_freq_box = Entry(master, textvariable=self.entryVar_freq_box).grid(row=1, column=1)\n\t\tself.label_samp_freq = Label(master, text=\"SAMPLING FREQUENCY (Hz)\").grid(row=2, column=0)\n\t\tself.entry_samp_freq = Entry(master,textvariable=self.entryVar_samp_freq).grid(row=2, column=1)\n\t\tself.label_duration = Label(master, text = \"DURATION (s)\").grid(row=3, column=0)\n\t\tself.entry_duration = Entry(master,textvariable=self.entryVar_duration).grid(row=3, column=1)\n\t\tself.label_volumne = Label(master, text=\"VOLUME [0.1 - 1.0]\").grid(row=4, column=0)\n\t\tself.entry_volume = Entry(master,textvariable=self.entryVar_volume).grid(row=4, column=1)\n\t\tself.button = Button(master, text = \"GENERATE\", command=self.cal_data).grid(row=5, column=0, columnspan=2)\n\n\t\tself.entryVar_freq_box.set('25')\n\t\tself.entryVar_samp_freq.set('44100')\n\t\tself.entryVar_duration.set('1')\n\t\tself.entryVar_volume.set('0.3')\n\n\tdef cal_data(self):\n\t\tself.f = (self.entryVar_freq_box.get())     # range [0.0, 1.0]\n\t\tself.fs = (self.entryVar_samp_freq.get())       # sampling rate, Hz, must be integer\n\t\tself.duration = (self.entryVar_duration.get())  # in seconds, may be float\n\t\tself.volume = (self.entryVar_volume.get())        # sine frequency, Hz, may be float\n\t\t\t\t\n\t\t# generate samples, note conversion to float32 array\n\t\tsamples = (np.sin(2*np.pi*np.arange(self.fs*self.duration)*self.f/self.fs)).astype(np.float32)\n\n\t\t# for paFloat32 saample values must be in range [-1.0, 1.0]\n\t\tstream = p.open(format=pyaudio.paFloat32,\n\t\t\t\t\t\tchannels=1,\n\t\t\t\t\t\trate=self.fs,\n\t\t\t\t\t\toutput=True)\n\n\t\t# play. May repeat with different volume values (if done interactively) \n\n\t\tstream.write(self.volume*samples)\n\n\t\tstream.stop_stream()\n\t\tstream.close()\n\n\t\t#~ p.terminate()\n\nroot = Tk()\nSoundgenerator(root)\nroot.mainloop()\n","sub_path":"frequency_generator.py","file_name":"frequency_generator.py","file_ext":"py","file_size_in_byte":2417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"21125928","text":"from proteus import *\nfrom dissipation_p import *\n\ntimeIntegration = BackwardEuler_cfl\nstepController  = Min_dt_cfl_controller\n\nfemSpaces = {0:basis}\n\nmassLumping       = False\nconservativeFlux  = None\nnumericalFluxType = Dissipation.NumericalFlux\nsubgridError      = Dissipation.SubgridError(coefficients=coefficients,nd=nd)\nshockCapturing    = Dissipation.ShockCapturing(coefficients,nd,shockCapturingFactor=dissipation_shockCapturingFactor,\n                                         lag=True)\n\nfullNewtonFlag  = True\nmultilevelNonlinearSolver = Newton\nlevelNonlinearSolver      = Newton\n\nnonlinearSmoother = None\nlinearSmoother    = None\n#printNonlinearSolverInfo = True\nmatrix = SparseMatrix\nif use_petsc4py:\n    multilevelLinearSolver = KSP_petsc4py\n    levelLinearSolver      = KSP_petsc4py\nelse:\n    multilevelLinearSolver = LU\n    levelLinearSolver      = LU\n\nlinear_solver_options_prefix = 'dissipation_'\nlevelNonlinearSolverConvergenceTest = 'r'#'rits'\nlinearSolverConvergenceTest         = 'r'#'rits'\n\ntolFac = 0.0\n\nnl_atol_res = 1.0e-6\nnl_rtol_res = 0.0\n\nmaxNonlinearIts = 10\nmaxLineSearches = 0\n\n","sub_path":"benchmarks/windtunnel/dissipation_n.py","file_name":"dissipation_n.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"524466256","text":"from mcpi.minecraft import Minecraft\nmc = Minecraft.create()\n\nimport random\n\n\nglobal level\nlevel = 0\n\t\nmc.setting(\"world_immutable\", False)\t\t### cannot break blocks\n\nx,y,z = mc.player.getTilePos()\n\n# mc.postToChat(str(x) + \"  \" + str(y) + \"  \" + str(z))\n\n\n\n### sets basic plain landscape\n'''\nmc.setBlocks(x-100, \ty-10,  \tz-100, \tx+100, \ty-1,   \tz+100, \t\t2)\nmc.setBlocks(x-100, \ty, \t\tz-100, \tx+100, \ty+20,\tz+100, \t\t0)\n'''\n\t\n############################################################ variables - 25 room\n\n\nblock = 48\nfill = 0\nwidth = 5\t\t#room is 5x5 block area \nrooms = 5  \t\t#5 x 5 rooms\n\nbx = x+5\nby = y\nbz = z+5\n\n\n############################################################ build solid building\n\nmc.setBlocks(bx, by, bz, bx+((width+1)*rooms), by+5, bz+((width+1)*rooms), block)\n\n\n############################################################ insert rooms\n\n\nbuildx = list(range(1,6))\nbuildz = list(range(1,6))\n\nfor x1 in buildx:\n\tfor z1 in buildz:\n\t\tmc.setBlocks(bx+x1+(width*(x1-1)), by+1, bz+z1+(width*(z1-1)), bx+(x1-1)+width*x1,\tby+4, bz+(width*z1)+(z1-1), fill)\n\n\t\t\n############################################################ doors (add step)\n\n# front door\n\nmc.setBlocks(bx+15,by+1,bz,bx+15,by+2,bz,0)\nmc.setBlock(bx+15,by,bz,67,2)\n\n# internal doors\n\ndoorx = [6,12,18,24]\ndoorz = [3,9,15,21,27]\n\nfor dx1 in doorx:\n\tfor dz1 in doorz:\n\t\tmc.setBlocks(bx+dx1, by+1, bz+dz1, bx+dx1, by+2, bz+dz1, 48)\n\t\tmc.setBlock (bx+dx1, by+4, bz+dz1, 89)\n\t\t\nfor dx1 in doorx:\n\tfor dz1 in doorz:\n\t\tmc.setBlocks(bx+dz1, by+1, bz+dx1, bx+dz1, by+2, bz+dx1, 48)\n\t\tmc.setBlock (bx+dz1, by+4, bz+dx1, 89)\n\t\t\n############################################################ door group per level\n\ndef redlevel():\n\t\n\tred \t= [(6,3), (12,3), (6,3), (3,6), (9,6), (6,9)]\n\t\n\tfor rx,rz in red:\n\t\tmc.setBlock (bx+rx, by+1, bz+rz, 0)\n\t\tmc.setBlock (bx+rx, by+2, bz+rz, 0)\n\t\ndef orangelevel():\n\n\torange\t= []\n\ndef yellowlevel():\n\n\tyellow\t= []\t\n\ndef greenlevel():\n\n\tgreen\t= []\n\t\ndef bluelevel():\n\n\tblue\t= []\n\t\ndef indigolevel():\n\n\tindigo\t= []\n\ndef violetlevel():\n\n\tviolet\t= []\n\n\nredlevel()\n\t\n############################################################ random location\n\nred1 = list(range(1,6))\nred2 = list(range(7,12))\n\nrandomred = []\nredspot = []\n\n\n\nfor r1x in red1:\n\tfor r1z in red1:\n\t\trandomred.append([r1x, r1z])\n\t\t\nfor r2x in red1:\n\tfor r2z in red2:\n\t\trandomred.append([r2x, r2z])\n\t\t\nfor r3x in red2:\n\tfor r3z in red1:\n\t\trandomred.append([r3x, r3z])\n\t\t\nfor r4x in red2:\n\tfor r4z in red2:\n\t\trandomred.append([r4x, r4z])\n\nredspot = random.choice(randomred)\n\na,c = redspot\n\nmc.setBlock(bx+a, by+1, bz+c, 46)\n\n\n","sub_path":"minecraft-pi/projects/pc/game005.py","file_name":"game005.py","file_ext":"py","file_size_in_byte":2578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"549067937","text":"import numpy as np\r\nimport pandas as pd\r\nfrom sklearn.metrics import accuracy_score\r\nfrom numpy import linalg\r\nfrom numpy.linalg import norm\r\nimport xgboost\r\nimport tensorflow as tf\r\n\r\nimport sklearn\r\nfrom sklearn.manifold import TSNE\r\nfrom sklearn.datasets import load_digits\r\nfrom sklearn.preprocessing import scale\r\n\r\nX = pd.read_csv(\"MLB/X_sort.csv\", encoding='latin-1')\r\ny = pd.read_csv(\"MLB/y_sort.csv\", encoding='latin-1', names=['Score'])\r\n\r\nfrom constant_variables import features_top_list\r\nX2 = X[features_top_list]\r\nX3 = X2.drop('Expected_Runs', axis = 1)\r\n\r\n#X_train, X_validate, X_test = np.split(X.sample(frac=1), [int(.6*len(X)), int(.8*len(X))])\r\n#y_train, y_validate, y_test = np.split(y.sample(frac=1), [int(.6*len(y)), int(.8*len(y))])\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nX_train, X_test, y_train, y_test = train_test_split(X3, y, test_size = 0.20, random_state = 0)\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nX_train, X_train1, y_train, y_train1 = train_test_split(X_train, y_train, test_size = 0.50, random_state = 0)\r\n\r\n# Feature Scaling (Important for high intensity computations)\r\nfrom sklearn.preprocessing import StandardScaler\r\nsc = StandardScaler()\r\nX_train = sc.fit_transform(X_train)\r\nX_test = sc.transform(X_test)\r\nX_train1 = sc.fit_transform(X_train1)\r\n#X_test1 = sc.transform(X_test1)\r\n\r\n# Fitting XGBoost to the Training set\r\nfrom xgboost import XGBClassifier\r\nclassifier = XGBClassifier()\r\nclassifier.fit(X_train, y_train)\r\n\r\n# Fitting XGBoost to the Training set\r\nclassifier1 = XGBClassifier()\r\nclassifier1.fit(X_train1, y_train1)\r\n\r\n# Predicting the Test set results\r\ny_pred_X = classifier.predict(X_train)\r\ny_pred1_X1 = classifier.predict(X_train1)\r\ny_pred_X_test = classifier.predict(X_test)\r\n\r\nmae_X_test = np.round(abs(y_test.values - y_pred_X_test).mean(),decimals=2)\r\nrmse_X_test = (((y_test.values - y_pred_X_test)**2).mean())**(1/2)\r\nr_X_test = (((y_test.values-y_test.values.mean())*(y_pred_X_test - y_pred_X_test.mean())).sum()) / ((1-len(y_test))*(y_test.values.std())*(y_pred_X_test.std()))\r\n\r\ny_test_total = ((pd.DataFrame(y_pred_test_rd) + pd.DataFrame(y_pred_X_test.ravel()))/2).astype(int)\r\nmae_test_total = np.round(abs(y_test.values - y_test_total[0]).mean(),decimals=2)\r\nrmse_test_total = (((y_test.values - y_test_total[0])**2).mean())**(1/2)\r\nr_test_total = (((y_test.values-y_test.values.mean())*(y_test_total[0] - y_test_total[0].mean())).sum()) / ((1-len(y_test))*(y_test.values.std())*(y_test_total[0].std()))\r\n\r\n\r\ny_pred_error = y_train.values - pd.DataFrame(y_pred_X.ravel())\r\n\r\n# Fitting XGBoost to the Training set\r\nclassifier1 = XGBClassifier()\r\nclassifier1.fit(X_train, y_pred_error)\r\n\r\ny_pred_Xr = classifier1.predict(X_train)\r\ny_pred_error_r = y_pred_error - pd.DataFrame(y_pred_Xr.ravel())\r\ny_predicted = y_pred_X + y_pred_Xr\r\n\r\nmae_Xr = np.round(abs(y_test.values - y_predicted).mean(),decimals=2)\r\nmae_pred = abs(y_test.values - y_pred_test_rd).mean()\r\n\r\nrmse_Xr = (((y_test.values - y_predicted)**2).mean())**(1/2)\r\nrmse_pred = (((y_test.values - y_pred_test_rd)**2).mean())**(1/2)\r\n\r\nr_Xr = (((y_test.values-y_test.values.mean())*(y_pred_X - y_predicted.mean())).sum()) / ((1-len(y_test))*(y_test.values.std())*(y_predicted.std()))\r\nr_pred = (((y_test.values-y_test.values.mean())*(y_pred_test_rd - y_pred_test_rd.mean())).sum()) / ((1-len(y_test))*(y_test.values.std())*(y_pred_test_rd.std()))\r\n\r\ny_mean = y.mean().astype(int)\r\ny_res = y-y_mean\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Predicting the Test set results\r\n#y_pred1 = classifier1.predict(X_test1)\r\n\r\n# Making the Confusion Matrix\r\n#from sklearn.metrics import confusion_matrix\r\n#cm = confusion_matrix(y_test, y_pred)\r\n\r\n# Applying k-Fold Cross Validation\r\nfrom sklearn.model_selection import cross_val_score\r\naccuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)\r\nmean_acc = accuracies.mean()\r\ntrain_std = accuracies.std()\r\n\r\n# Applying k-Fold Cross Validation\r\naccuracies1 = cross_val_score(estimator = classifier1, X = X_train1, y = y_train1, cv = 10)\r\nmean_acc1 = accuracies1.mean()\r\ntrain1_std = accuracies1.std()\r\n'''\r\n# Applying k-Fold Cross Validation\r\naccuracies2 = cross_val_score(estimator = classifier2, X = X_test, y = y_test, cv = 10)\r\nmean_acc1 = accuracies1.mean()\r\naccuracies1.mean()\r\naccuracies1.std()'''\r\n\r\nfeature_importance = classifier.feature_importances_\r\nfeatures = pd.Series(feature_importance, index = X3.columns)\r\nfeatures_sorted = pd.Series(features.sort_values())\r\n\r\nfeature_importance1 = classifier1.feature_importances_\r\nfeatures1 = pd.Series(feature_importance1, index = X3.columns)\r\nfeatures_sorted1 = pd.Series(features1.sort_values())\r\n\r\n'''\r\nfeatures.sort_values()\r\nfeatures_index_sort = features.index()\r\n\r\nfeatures1.sort_values()\r\nfeatures_index_sort1 = features1.index()\r\n'''\r\n\r\nX_train_weighted = X_train*feature_importance\r\nX_train1_weighted = X_train1*feature_importance1\r\nX_train_weighted_total = np.concatenate([X_train_weighted, X_train1_weighted], axis=0)\r\nX_train_weighted_total_norm = (X_train_weighted_total - X_train_weighted_total.mean()) / (X_train_weighted_total.max() - X_train_weighted_total.min())\r\ny_train_weighted = np.concatenate([y_train, y_train1], axis=0)\r\n#X_test_total = np.concatenate([X_test, X_test1], axis=0)\r\n#y_test_total = pd.DataFrame(np.concatenate([y_test, y_test1], axis=0), index = list(np.concatenate([y_test.index,y_test1.index], axis=0)))\r\n'''\r\nfeatures_avg = (features + features1)/2\r\nX_test_weighted = X_test*features_avg.values\r\nX_test_weighted_norm = (X_test_weighted - X_test_weighted.mean()) / (X_test_weighted.max() - X_test_weighted.min())\r\n'''\r\n\r\nX4 = X_train_weighted_total_norm\r\ny1 = y_train_weighted\r\n\r\nimport keras\r\nfrom keras import optimizers\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Activation\r\nfrom keras.layers import Dropout\r\nfrom keras import objectives\r\nfrom keras import backend as K\r\n\r\n# Initialising the ANN\r\nregressor1 = Sequential()\r\n\r\n# Adding the input layer and the first hidden layer with dropout (start 0.1 (10%))\r\nregressor1.add(Dense(output_dim = 64, init = 'uniform', activation = 'relu', input_dim = len(X3.columns)))\r\nregressor1.add(Dropout(p=0.1))\r\n\r\n# Adding the second hidden layer\r\nregressor1.add(Dense(output_dim = 50, init = 'uniform', activation = 'relu'))\r\nregressor1.add(Dropout(p=0.1))\r\n\r\n# Adding the second hidden layer\r\nregressor1.add(Dense(output_dim = 35, init = 'uniform', activation = 'relu'))\r\nregressor1.add(Dropout(p=0.1))\r\n\r\n# Adding the third hidden layer\r\nregressor1.add(Dense(output_dim = 20, init = 'uniform', activation = 'relu'))\r\nregressor1.add(Dropout(p=0.1))\r\n\r\n# Adding the output layer\r\nregressor1.add(Dense(output_dim = 1, init = 'uniform'))\r\n\r\n# Compiling the ANN (adam[SGD] - optimizer function to find optimal weights)\r\n# Binary Dept Var(Binary_CrossEntropy) Dependent Var > 2 Outcomes (Categorical_CrossEntropy)\r\n# [Accuracy] in brackets because list expected\r\nregressor1.compile(optimizer = 'adam', loss = 'mse', metrics=['binary_crossentropy','acc'])\r\n\r\n# Fitting the ANN to the Training set\r\nregressor1.fit(X_train, y_train, batch_size = 35, epochs = 500)\r\n'''\r\n# Predicting the Test set results\r\ny_pred_test = regressor1.predict(X_test)\r\ny_pred_test_rd = np.round(y_pred_test)\r\ny_test_index = [x for x in y_test.index]\r\n\r\n# Predicting a new result\r\ny_pred_train = regressor1.predict(X4)\r\ny_pred_train_rd = np.round(y_pred_train)\r\ny_train_index = [x for x in y.index]'''\r\n\r\nfeatures_avg = (features + features1)/2\r\nX_test_weighted = X_test*features_avg.values\r\nX_test_weighted_norm = (X_test_weighted - X_test_weighted.mean()) / (X_test_weighted.max() - X_test_weighted.min())\r\n\r\n# Predicting the Test set results\r\ny_pred_test = regressor1.predict(X_test_weighted_norm)\r\ny_pred_test_rd = np.round(y_pred_test)\r\ny_test_total_index = [x for x in pd.Series(y_test.index)]\r\ny_pred_test1 = regressor1.predict(X_test)\r\ny_pred_test1_rd = np.round(y_pred_test1)\r\n\r\n\r\nexpected = X2['Expected_Runs']\r\ny_expected_test = expected[y_test_total_index]\r\ny_expected_test_round = np.round(y_expected_test)\r\n\r\nmae = np.round(abs(y_test.values - y_expected_test_round.values).mean(),decimals=2)\r\nmae_pred = abs(y_test.values - y_pred_test_rd).mean()\r\nmae_test = np.round(abs(y_test.values - y_pred_test1_rd).mean(),decimals=2)\r\n\r\nrmse = (((y_test.values - y_expected_test_round.values)**2).mean())**(1/2)\r\nrmse_pred = (((y_test.values - y_pred_test_rd)**2).mean())**(1/2)\r\nrmse_test = (((y_test.values - y_pred_test1_rd)**2).mean())**(1/2)\r\n\r\nr = (((y_test.values-y_test.values.mean())*(y_expected_test_round.values - y_expected_test_round.values.mean())).sum()) / ((1-len(y_test))*(y_test.values.std())*(y_expected_test_round.values.std()))\r\nr_pred = (((y_test.values-y_test.values.mean())*(y_pred_test_rd - y_pred_test_rd.mean())).sum()) / ((1-len(y_test))*(y_test.values.std())*(y_pred_test_rd.std()))\r\nr_test = (((y_test.values-y_test.values.mean())*(y_pred_test1_rd - y_pred_test1_rd.mean())).sum()) / ((1-len(y_test))*(y_test.values.std())*(y_pred_test1_rd.std()))\r\n\r\ny_test_total = ((pd.DataFrame(y_pred_test1_rd) + pd.DataFrame(y_pred_X_test.ravel()))/2).astype(int)\r\nmae_test_total = np.round(abs(y_test.values - y_test_total).mean(),decimals=2)\r\nrmse_test_total = (((y_test.values - y_test_total)**2).mean())**(1/2)\r\nr_test_total = (((y_test.values-y_test.values.mean())*(y_test_total - y_test_total.mean())).sum()) / ((1-len(y_test))*(y_test.values.std())*(y_test_total.std()))\r\n\r\n\r\nimport thinkstats2\r\nfrom thinkstats2 import *\r\nfrom code import *\r\nimport thinkplot\r\n\r\ny2 = y1.flatten()\r\n\r\npmf_scores = thinkstats2.Pmf(y2)\r\nthinkplot.Hist(pmf_scores)\r\nthinkplot.Config(xlabel='Runs Scored', ylabel='probability', axis=[0, 20, 0, 0.3])\r\n\r\ncdf_scores = thinkstats2.Cdf(y2, label='Runs Scored')\r\ncdf_ld = thinkstats2.Cdf(X3['bat_LD%'], label='Line Drives')\r\ncdf_pop = thinkstats2.Cdf(X3['bat_POP%'], label='Pop Ups')\r\ncdf_gb = thinkstats2.Cdf(X3['bat_GB%'], label='Ground Balls')\r\n\r\nthinkplot.PrePlot(4)\r\nthinkplot.Cdfs([cdf_scores, cdf_ld, cdf_pop, cdf_gb])\r\nthinkplot.Show(xlabel='balls in play (%)', ylabel='CDF')\r\n\r\n# Visualizing data in One Dimension (1-D)\r\nimport matplotlib.pyplot as plt\r\ny.hist(bins=15, color='steelblue', edgecolor='black', linewidth=1.0,\r\n           xlabelsize=8, ylabelsize=8, grid=False)    \r\nplt.tight_layout(rect=(0, 15, 0, 15)) \r\n\r\n# visualizing one of the continuous, numeric attributes\r\n# Histogram\r\nfig = plt.figure(figsize = (10,4))\r\ntitle = fig.suptitle(\"Runs\", fontsize=14)\r\nfig.subplots_adjust(top=0.85, wspace=0.1)\r\n\r\nax = fig.add_subplot(1,1, 1)\r\nax.set_xlabel(\"SDTHB_BAT\")\r\nax.set_ylabel(\"Frequency\") \r\nax.set_xlim([0.35, 0.55])\r\n#ax.text(0.8, 300, '%='+str(round(census_data['IncomePerCap'].dropna(),1)), fontsize=10)\r\nfreq, bins, patches = ax.hist(X3['SDTHB_BAT'].dropna(), color='steelblue', bins=15,\r\n                                    edgecolor='black', linewidth=1)\r\n\r\n\r\n# Density Plot\r\nimport seaborn as sns\r\nfig = plt.figure(figsize = (6, 4))\r\ntitle = fig.suptitle(\"SDTHB_BAT\", fontsize=14)\r\nfig.subplots_adjust(top=0.85, wspace=0.3)\r\n\r\nax1 = fig.add_subplot(1,1, 1)\r\nax1.set_xlabel(\"SDTHB_BAT\")\r\nax1.set_ylabel(\"Frequency\") \r\nsns.kdeplot(X3['SDTHB_BAT'].dropna(), ax=ax1, shade=True, color='steelblue')\r\n\r\n# Multivariate Analysis\r\n# Visualizing data in Two Dimensions (2-D)\r\n# Correlation Matrix Heatmap\r\nf, ax = plt.subplots(figsize=(10, 6))\r\ncorr = X3.corr()\r\nhm = sns.heatmap(round(corr,2), annot=False, ax=ax, cmap=\"coolwarm\",fmt='.2f',\r\n                 linewidths=.05)\r\nf.subplots_adjust(top=0.93)\r\nt= f.suptitle('Game Correlation Heatmap', fontsize=14)\r\n\r\n\r\n# Pair-wise Scatter Plots\r\ncols = list(X3.columns)\r\npp = sns.pairplot(X3[cols[-15:]], size=1.8, aspect=1.8,\r\n                  plot_kws=dict(edgecolor=\"k\", linewidth=0.5),\r\n                  diag_kind=\"kde\", diag_kws=dict(shade=True))\r\n\r\nfig = pp.fig \r\nfig.subplots_adjust(top=0.93, wspace=0.3)\r\nt = fig.suptitle('Game Attributes Pairwise Plots', fontsize=14)\r\n\r\n#  parallel coordinates\r\n# Scaling attribute values to avoid few outiers\r\ncols = list(X3.columns)\r\nsubset_df = X3[cols[-15:]]\r\n\r\nfrom sklearn.preprocessing import StandardScaler\r\nss = StandardScaler()\r\n\r\nscaled_df = ss.fit_transform(subset_df)\r\nscaled_df = pd.DataFrame(scaled_df, columns=cols[-15:])\r\nfinal_df = pd.concat([scaled_df, X3['SDTHB_BAT']], axis=1)\r\nfinal_df.head()\r\n\r\n\r\n# plot parallel coordinates\r\nfrom pandas.plotting import parallel_coordinates\r\npc = parallel_coordinates(final_df, 'SDTHB_BAT', color=('#FFE888', '#FF9999', 'DarkGreen'))\r\n\r\n# visualize two continuous, numeric attributes. Scatter plots and joint plots\r\n# Scatter Plot\r\nplt.scatter(X3['SDTHB_BAT'], y,\r\n            alpha=0.4, edgecolors='w')\r\n\r\nplt.xlabel('SDTHB_BAT')\r\nplt.ylabel('bat_GB%')\r\nplt.title('SDTHB_BAT - bat_GB%',y=1.05)\r\n\r\n# Joint Plot\r\njp = sns.jointplot(x='SDTHB_BAT', y='bat_GB%', data=X3,\r\n                   kind='reg', space=0, size=5, ratio=4)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# visualizing two discrete, categorical attributes\r\n# Using subplots or facets along with Bar Plots\r\nfig = plt.figure(figsize = (10, 4))\r\ntitle = fig.suptitle(\"TB_batter - Era\", fontsize=14)\r\nfig.subplots_adjust(top=0.85, wspace=0.3)\r\n# red wine - wine quality\r\nax1 = fig.add_subplot(1,2, 1)\r\nax1.set_title(\"TB_batter for pitchers with ERA <= 3.5\")\r\nax1.set_xlabel(\"TB_batter\")\r\nax1.set_ylabel(\"Era\") \r\nrw_q = X3['TB_batter'][X3['pitcher_ERA']<=3.5].value_counts()\r\nrw_q = (list(rw_q.index), list(rw_q.values))\r\nax1.set_ylim([0, 2500])\r\nax1.tick_params(axis='both', which='major', labelsize=8.5)\r\nbar1 = ax1.bar(rw_q[0], rw_q[1], color='red', \r\n               edgecolor='black', linewidth=1)\r\n\r\n# white wine - wine quality\r\nax2 = fig.add_subplot(1,2, 2)\r\nax2.set_title(\"New York\")\r\nax2.set_xlabel(\"Unemployment\")\r\nax2.set_ylabel(\"Frequency\") \r\nww_q = census_drop['Unemployment_Rate'][census_drop['State']=='New York'].value_counts()\r\nww_q = (list(ww_q.index), list(ww_q.values))\r\nax2.set_ylim([0, 2500])\r\nax2.tick_params(axis='both', which='major', labelsize=8.5)\r\nbar2 = ax2.bar(ww_q[0], ww_q[1], color='white', \r\n               edgecolor='black', linewidth=1)\r\n\r\n\r\nxint = census_drop[\"Unemployment\"].astype(int)\r\n# stacked bars or multiple bars\r\n# Multi-bar Plot\r\ncp = sns.countplot(x=xint, hue=\"Unemployment_Rate\", data=census_drop, \r\n                   palette={\"high\": \"#FF9999\", \"medium\": \"#FFE888\", \"low\": '#001C7F'})\r\n\r\n\r\n# visualizing mixed attributes in two-dimensions\r\n#  faceting\\subplots along with generic histograms or density plots.\r\n# facets with histograms\r\nfig = plt.figure(figsize = (10,4))\r\ntitle = fig.suptitle(\"pitcher_ERA vs runners that touched base\", fontsize=14)\r\nfig.subplots_adjust(top=0.85, wspace=0.3)\r\n\r\nax1 = fig.add_subplot(1,3, 1)\r\nax1.set_title(\"Low TB's\")\r\nax1.set_xlabel(\"pitcher_ERA\")\r\nax1.set_ylabel(\"Frequency\") \r\nax1.set_ylim([0, 200])\r\nax1.text(1.2, 800, r'$mu$='+str(round(X3['pitcher_ERA'][X3['TB_batter']<25].mean(),2)), \r\n         fontsize=12)\r\nr_freq, r_bins, r_patches = ax1.hist(X3['pitcher_ERA'][X3['TB_batter']<25], color='red', bins=15,\r\n                                     edgecolor='Black', linewidth=1)\r\n\r\nax2 = fig.add_subplot(1,3, 2)\r\nax2.set_title(\"Medium TB's\")\r\nax2.set_xlabel(\"pitcher_ERA\")\r\nax2.set_ylabel(\"Frequency\")\r\nax2.set_ylim([0, 200])\r\nax2.text(0.8, 800, r'$mu$='+str(round(X3['pitcher_ERA'][X3['TB_batter']<45].mean(),2)), \r\n         fontsize=12)\r\nw_freq, w_bins, w_patches = ax2.hist(X3['pitcher_ERA'][X3['TB_batter']<45], color='white', bins=15,\r\n                                     edgecolor='Black', linewidth=1)\r\n\r\n\r\nax3 = fig.add_subplot(1,3, 3)\r\nax3.set_title(\"High TB's\")\r\nax3.set_xlabel(\"pitcher_ERA\")\r\nax3.set_ylabel(\"Frequency\")\r\nax3.set_ylim([0, 200])\r\nax3.text(0.8, 800, r'$mu$='+str(round(X3['pitcher_ERA'][X3['TB_batter']>=45].mean(),2)), \r\n         fontsize=12)\r\nw_freq, w_bins, w_patches = ax3.hist(X3['pitcher_ERA'][X3['TB_batter']>=45], color='green', bins=15,\r\n                                     edgecolor='Black', linewidth=1)\r\n\r\n\r\n# facets with density plots\r\nfig = plt.figure(figsize = (10, 4))\r\ntitle = fig.suptitle(\"Unemployment Content in USA\", fontsize=14)\r\nfig.subplots_adjust(top=0.85, wspace=0.5)\r\n\r\nax1 = fig.add_subplot(1,3, 1)\r\nax1.set_title(\"Low Unemployment\")\r\nax1.set_xlabel(\"Black\")\r\nax1.set_ylabel(\"Density\") \r\nsns.kdeplot(X3['Black'][X3['Unemployment_Rate']=='low'], ax=ax1, shade=True, color='r')\r\n\r\nax2 = fig.add_subplot(1,3, 2)\r\nax2.set_title(\"Medium Unemployment\")\r\nax2.set_xlabel(\"Black\")\r\nax2.set_ylabel(\"Density\") \r\nsns.kdeplot(X3['Black'][X3['Unemployment_Rate']=='medium'], ax=ax2, shade=True, color='g')\r\n\r\nax3 = fig.add_subplot(1,3, 3)\r\nax3.set_title(\"High Unemployment\")\r\nax3.set_xlabel(\"Black\")\r\nax3.set_ylabel(\"Density\") \r\nsns.kdeplot(X3['Black'][X3['Unemployment_Rate']=='high'], ax=ax3, shade=True, color='y')\r\n\r\n\r\n# Using multiple Histograms \r\nfig = plt.figure(figsize = (6, 4))\r\ntitle = fig.suptitle(\"Unemployment Content in USA\", fontsize=14)\r\nfig.subplots_adjust(top=0.85, wspace=0.3)\r\nax = fig.add_subplot(1,1, 1)\r\nax.set_xlabel(\"Black\")\r\nax.set_ylabel(\"Frequency\") \r\n\r\ng = sns.FacetGrid(X3, hue='Unemployment_Rate', palette={\"high\": \"r\", \"medium\": \"y\", 'low': 'g'})\r\ng.map(sns.distplot, 'Black', kde=False, bins=15, ax=ax)\r\nax.legend(title='Unemployment')\r\nplt.close(3)\r\n\r\n\r\n# Box Plots\r\nf, (ax) = plt.subplots(1, 1, figsize=(12, 4))\r\nf.suptitle('Unemployment - Black', fontsize=14)\r\n\r\nsns.boxplot(x=\"Unemployment_Rate\", y=\"Black\", data=X3,  ax=ax)\r\nax.set_xlabel(\"Unemployment\",size = 12,alpha=0.8)\r\nax.set_ylabel(\"Black %\",size = 12,alpha=0.8)\r\n\r\n\r\n# Violin Plots\r\nf, (ax) = plt.subplots(1, 1, figsize=(12, 4))\r\nf.suptitle('Unemployment - Black', fontsize=14)\r\n\r\nsns.violinplot(x=\"Unemployment_Rate\", y=\"Black\", data=X3,  ax=ax)\r\nax.set_xlabel(\"Unemployment_Rate\",size = 12,alpha=0.8)\r\nax.set_ylabel(\"Black\",size = 12,alpha=0.8)\r\n\r\n\r\n# Visualizing data in Three Dimensions (3-D)\r\n# pair-wise scatter plot \r\n# Scatter Plot with Hue for visualizing data in 3-D\r\ncols = ['CensusTract', 'TotalPop', 'Men', 'Women', 'Hispanic', 'White', 'Black', 'Native', 'Asian', 'Pacific', 'Citizen',\r\n       'Income', 'IncomeErr', 'IncomePerCap', 'IncomePerCapErr', 'Poverty', 'ChildPoverty', 'Professional', 'Service', 'Office', 'Construction',\r\n       'Production', 'Drive', 'Carpool', 'Transit', 'Walk', 'OtherTransp', 'WorkAtHome', 'MeanCommute', 'Employed', 'PrivateWork', 'PublicWork',\r\n       'SelfEmployed', 'FamilyWork', 'Unemployment', 'Unempoyment_Rate']\r\npp = sns.pairplot(X3[cols], hue='Unemployment_Rate', size=1.8, aspect=1.8, \r\n                  palette={\"high\": \"#FF9999\", \"medium\": \"#FFE888\", \"low\": '#001C7F'},\r\n                  plot_kws=dict(edgecolor=\"black\", linewidth=0.5))\r\nfig = pp.fig \r\nfig.subplots_adjust(top=0.93, wspace=0.3)\r\nt = fig.suptitle('Unemployment_Rate Pairwise Plots', fontsize=14)\r\n\r\n\r\n# visualizing three continuous, numeric attributes\r\n# Visualizing 3-D numeric data with Scatter Plots\r\n# length, breadth and depth\r\nfig = plt.figure(figsize=(8, 6))\r\nax = fig.add_subplot(111, projection='3d')\r\n\r\nxs = X3['Black']\r\nys = X3['Professional']\r\nzs = X3['Unemployment']\r\nax.scatter(xs, ys, zs, s=50, alpha=0.6, edgecolors='w')\r\n\r\nax.set_xlabel('Black')\r\nax.set_ylabel('Professional')\r\nax.set_zlabel('Unemployment Rate')\r\n\r\n\r\n# # Visualizing 3-D numeric data with a bubble chart\r\n# length, breadth and size\r\nplt.scatter(X3['Black'], X3['Professional'], s=X3['Unemployment']*25, \r\n            alpha=0.4, edgecolors='w')\r\n\r\nplt.xlabel('Professional')\r\nplt.ylabel('Black')\r\nplt.title('Black - Professional - Unemployment',y=1.05)\r\n\r\n\r\n# visualizing three discrete, categorical attributes\r\n# Visualizing 3-D categorical data using bar plots\r\n# leveraging the concepts of hue and facets\r\nfc = sns.factorplot(x=\"Black\", hue=\"Unemployment_Rate\", col=\"Unemployment_Rate\", \r\n                    data=X3, kind=\"count\",\r\n                    palette={\"high\": \"#FF9999\", \"medium\": \"#FFE888\", \"low\": '#001C7F'})\r\n\r\n\r\n\r\n# three mixed attributes\r\n# Visualizing 3-D mix data using scatter plots\r\n# leveraging the concepts of hue for categorical dimension\r\njp = sns.pairplot(X3, x_vars=[\"Black\"], y_vars=[\"Professional\"], size=4.5,\r\n                  hue=\"Unemployment_Rate\", palette={\"high\": \"#FF9999\", \"medium\": \"#FFE888\", \"low\": '#001C7F'},\r\n                  plot_kws=dict(edgecolor=\"k\", linewidth=0.5))\r\n                  \r\n# we can also view relationshipscorrelations as needed                  \r\nlp = sns.lmplot(x='Black', y='Professional', hue='Unemployment_Rate', \r\n                palette={\"high\": \"#FF9999\", \"medium\": \"#FFE888\", \"low\": '#001C7F'},\r\n                data=X3, fit_reg=True, legend=True,\r\n                scatter_kws=dict(edgecolor=\"k\", linewidth=0.5))    \r\n\r\n\r\n    \r\n# Visualizing 3-D mix data using kernel density plots\r\n# leveraging the concepts of hue for categorical dimension\r\nax = sns.kdeplot(X3['Black'][X3['Unemployment_Rate']=='high'], X3['Professional'][X3['Unemployment_Rate']=='high'],\r\n                  cmap=\"YlOrBr\", shade=True, shade_lowest=False)\r\nax = sns.kdeplot(X3['Black'][X3['Unemployment_Rate']=='low'], X3['Professional'][X3['Unemployment_Rate']=='low'],\r\n                  cmap=\"Reds\", shade=True, shade_lowest=False)\r\n    \r\n    \r\n\r\n# Predicting the Full Results\r\ny_pred_full = regressor.predict(X3)\r\ny_pred_full_rd = np.round(y_pred_full)\r\n\r\n# Predicting a new result\r\ny_pred_train = regressor.predict(X_train)\r\ny_pred_train_rd = np.round(y_pred_train)\r\ny_score_train = regressor.evaluate(X_train, y_train, batch_size = 10)\r\ny_score_test = regressor.evaluate(X_test, y_test, batch_size = 10)\r\n\r\n\r\n\r\n\r\nfeature_importance = classifier.feature_importances_\r\nfeatures = pd.Series(feature_importance, index=list(X.columns))\r\n\r\nfeatures.sort_values()\r\nfeatures_index_sort = features.sort_values()\r\nfeatures_index_sort1 = features_index_sort.ravel()\r\n\r\nfeatures_top = [x for x in features_index_sort1 if x>0.005]\r\nfeatures_top_name = features_index_sort[-65:]\r\nfeatures_top_list = [str(x) for x in features_top_name.index]\r\n\r\n\r\n\r\n\r\n","sub_path":"pretraining.py","file_name":"pretraining.py","file_ext":"py","file_size_in_byte":21919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"144375504","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nimport errno\nimport shutil\nimport logging\nimport sys\nimport imghdr\nimport ConfigParser\nfrom optparse import OptionParser\nfrom discogstagger.ext.mediafile import MediaFile\nfrom discogstagger.taggerutils import (\n    TaggerUtils,\n    create_nfo,\n    create_m3u,\n    get_images)\n\nlogger = logging.getLogger(__name__)\n\np = OptionParser()\np.add_option(\"-r\", \"--releaseid\", action=\"store\", dest=\"releaseid\",\n             help=\"The discogs.com release id of the target album\")\np.add_option(\"-s\", \"--source\", action=\"store\", dest=\"sdir\",\n             help=\"The directory that you wish to tag\")\np.add_option(\"-d\", \"--destination\", action=\"store\", dest=\"destdir\",\n             help=\"The (base) directory to copy the tagged files to\")\np.add_option(\"-c\", \"--conf\", action=\"store\", dest=\"conffile\",\n             help=\"The discogstagger configuration file.\")\n\np.set_defaults(conffile=\"/etc/discogstagger/discogs_tagger.conf\")\n(options, args) = p.parse_args()\n\nif not options.releaseid:\n    p.error(\"Please specify the discogs.com releaseid ('-r')\")\n\nif not options.sdir or not os.path.exists(options.sdir):\n    p.error(\"Please specify a valid source directory ('-s')\")\n\nif not options.destdir or not os.path.exists(options.destdir):\n    destdir = options.sdir\nelse:\n    destdir = options.destdir\n\nconfig = ConfigParser.ConfigParser()\nconfig.read(options.conffile)\n\nlogging.basicConfig(level=config.getint(\"logging\", \"level\"))\n\nkeep_original = config.getboolean(\"details\", \"keep_original\")\nembed_coverart = config.getboolean(\"details\", \"embed_coverart\")\nuse_style = config.getboolean(\"details\", \"use_style\")\n\nrelease = TaggerUtils(options.sdir, destdir, options.releaseid)\nrelease.nfo_format = config.get(\"file-formatting\", \"nfo\")\nrelease.m3u_format = config.get(\"file-formatting\", \"m3u\")\nrelease.dir_format = config.get(\"file-formatting\", \"dir\")\nrelease.song_format = config.get(\"file-formatting\", \"song\")\nrelease.group_name = config.get(\"details\", \"group\")\n\n# ensure we were able to map the release appropriately.\nif not release.tag_map:\n    logging.error(\"Unable to match file list to discogs release '%s'\" %\n                  options.releaseid)\n    sys.exit()\n\n#\n# start tagging actions.\n#\nlogging.info(\"Tagging album '%s - %s'\" % (release.album.artist,\n             release.album.title))\n\nif os.path.exists(release.dest_dir_name):\n    logging.error(\"Destination already exists %s\" % release.dest_dir_name)\n    sys.exit(\"%s directory already exists, aborting.\" % release.dest_dir_name)\nelse:\n    logging.info(\"Creating destination directory '%s'\" %\n                 release.dest_dir_name)\n    os.mkdir(release.dest_dir_name)\n\nlogging.info(\"Downloading and storing images\")\nget_images(release.album.images, release.dest_dir_name)\n\nfor track in release.tag_map:\n    logger.info(\"Writing file %s\" % os.path.join(release.dest_dir_name,\n                track.new_file))\n    logger.debug(\"metadata -> %.2d %s - %s\" % (track.position, track.artist,\n                 track.title))\n\n    # copy old file into new location\n    shutil.copyfile(os.path.join(options.sdir, track.orig_file),\n                    os.path.join(release.dest_dir_name, track.new_file))\n\n    # load metadata information\n    metadata = MediaFile(os.path.join(\n                         release.dest_dir_name, track.new_file))\n    # remove current metadata\n    metadata.delete()\n    metadata.title = track.title\n    metadata.artist = track.artist\n    metadata.album = release.album.title\n    metadata.composer = release.album.artist\n    metadata.albumartist = release.album.artist\n    metadata.label = release.album.label\n    metadata.year = release.album.year\n    # adding two are there is no standard. discogstagger pre v1\n    # used (TXXX desc=\"Catalog #\")\n    # mediafile uses TXXX desc=\"CATALOGNUMBER\"\n    metadata.catalognum = release.album.catno\n    metadata.catalognumber = release.album.catno\n\n    genre = release.album.genre\n    if use_style:\n        genre = release.album.styles[0]\n\n    metadata.genre = genre\n    metadata.track = track.position\n    metadata.tracktotal = len(release.tag_map)\n\n    if embed_coverart and os.path.exists(os.path.join(release.dest_dir_name,\n                                         \"00-image-01.jpg\")):\n        imgdata = open(os.path.join(release.dest_dir_name,\n                       \"00-image-01.jpg\")).read()\n        imgtype = imghdr.what(None, imgdata)\n\n        if imgtype in (\"jpeg\", \"png\"):\n            logger.info(\"Embedding album art.\")\n            metadata.art = imgdata\n\n    metadata.save()\n\n#\n# start supplementary actions\n#\nlogging.info(\"Generating .nfo file\")\ncreate_nfo(release.album.album_info, release.dest_dir_name,\n           release.nfo_filename)\n\nlogging.info(\"Generating .m3u file\")\ncreate_m3u(release.tag_map, release.dest_dir_name, release.m3u_filename)\n\n# remove source directory, if configured as such.\nif not keep_original:\n    logging.info(\"Deleting source directory '%s'\" % options.sdir)\n    shutil.rmtree(options.sdir)\n\nlogging.info(\"Tagging complete.\")\n","sub_path":"scripts/discogs_tagger.py","file_name":"discogs_tagger.py","file_ext":"py","file_size_in_byte":5029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"622126044","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n\nimport subprocess\n\n\ndef mem():\n\n\n    raw_data = subprocess.Popen(\"sudo dmidecode -t memory\", stdout=subprocess.PIPE, shell=True)\n    raw_list = raw_data.stdout.read().decode().split(\"\\n\")\n    raw_ram_list = []\n    item_list = []\n    for line in raw_list:\n        if line.startswith(\"Memory Device\"):\n            raw_ram_list.append(item_list)\n            item_list = []\n        else:\n            item_list.append(line.strip())\n\n    ram_list = []\n    for item in raw_ram_list:\n        item_ram_size = 0\n        ram_item_to_dic = {}\n        for i in item:\n            data = i.split(\":\")\n            if len(data) == 2:\n                key, v = data\n                if key == 'Size':\n                    if v.strip() != \"No Module Installed\":\n                        ram_item_to_dic['capacity'] = v.split()[0].strip()\n                        item_ram_size = round(float(v.split()[0]))\n                    else:\n                        ram_item_to_dic['capacity'] = 0\n\n                if key == 'Type':\n                    ram_item_to_dic['model'] = v.strip()\n                if key == 'Manufacturer':\n                    ram_item_to_dic['manufacturer'] = v.strip()\n                if key == 'Serial Number':\n                    ram_item_to_dic['sn'] = v.strip()\n                if key == 'Asset Tag':\n                    ram_item_to_dic['asset_tag'] = v.strip()\n                if key == 'Locator':\n                    ram_item_to_dic['slot'] = v.strip()\n\n        if item_ram_size == 0:\n            pass\n        else:\n            ram_list.append(ram_item_to_dic)\n\n    raw_total_size = subprocess.Popen(\"cat /proc/meminfo|grep MemTotal \", stdout=subprocess.PIPE, shell=True)\n    raw_total_size = raw_total_size.stdout.read().decode().split(\":\")\n    ram_data = {'ram': ram_list}\n    if len(raw_total_size) == 2:\n        total_gb_size = int(raw_total_size[1].split()[0]) / 1024**2\n        ram_data['ram_size'] = total_gb_size\n\n\n    print(ram_data)\n\nmem()","sub_path":"python/Hardware/mem.py","file_name":"mem.py","file_ext":"py","file_size_in_byte":1995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"243502072","text":"# -*- coding: utf-8 -*-\n# flake8: noqa\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('millionmilestogether', '0004_moment'),\n    ]\n\n    operations = [\n        migrations.AlterModelOptions(\n            name='moment',\n            options={'ordering': ('-occurred',)},\n        ),\n        migrations.RemoveField(\n            model_name='photo',\n            name='is_album_cover',\n        ),\n    ]\n","sub_path":"millionmilestogether/migrations/0005_no_album_cover.py","file_name":"0005_no_album_cover.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"358025862","text":"from machine import Pin\n\nclass Config:\n        \n    def __init__(self, box_id, pinList, patternList):\n        self.state = 0\n        self.box_id = box_id\n        self.pinList = pinList\n        self.numSwitch = len(pinList)\n        self.patternList = patternList\n        self.numPattern = len(patternList)\n        self.switch_0 = Pin(pinList[0], Pin.OUT, value=0)\n        if self.numSwitch > 1:\n            self.switch_1 = Pin(pinList[1], Pin.OUT, value=0)\n        if self.numSwitch > 2:\n            self.switch_2 = Pin(pinList[2], Pin.OUT, value=0)\n        if self.numSwitch > 3:\n            self.switch_3 = Pin(pinList[3], Pin.OUT, value=0)\n        #...\n        #...\n        \n    def turn(self, pattern_num):\n        p = self.patternList[pattern_num]\n        self.switch_0.value(int(p[0]))\n        if self.numSwitch > 1:\n            self.switch_1.value(int(p[1]))\n        if self.numSwitch > 2:\n            self.switch_2.value(int(p[2]))\n        if self.numSwitch > 3:\n            self.switch_3.value(int(p[3]))\n        #...\n        #...\n\n    def push(self):\n        self.state += 1\n        if self.state >= self.numPattern:\n            self.state = 0\n        return self.state\n","sub_path":"onebutton.py","file_name":"onebutton.py","file_ext":"py","file_size_in_byte":1179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"352618281","text":"\nfrom dataset.kgforest import *\nfrom dataset.tool import *\n\nfrom net.rates import *\nfrom net.util import *\n\nfrom net.model.inceptionv3 import inception_v3 as Net\n\nSIZE = 256\nSRC = 'tif'\nCH = 'NIR'\nNCH = 1  # 4\nSEED = 123\n\nuse_gpu = True\n\n# CH = 'irrgb'\n# SRC = 'jpg' #channel\n#CH = 'rgb'\n#CH = 'irrg'\n\n\ndef loss_func(logits, labels):\n    loss = nn.MultiLabelSoftMarginLoss()(logits, Variable(labels))\n    return loss\n\n\ndef multi_f_measure(probs, labels, threshold=0.235, beta=2):\n\n    SMALL = 1e-6  # 0  #1e-12\n    batch_size = probs.size()[0]\n\n    # weather\n    l = labels\n    p = (probs > threshold).float()\n\n    num_pos = torch.sum(p,  1)\n    num_pos_hat = torch.sum(l,  1)\n    tp = torch.sum(l * p, 1)\n    precise = tp / (num_pos + SMALL)\n    recall = tp / (num_pos_hat + SMALL)\n\n    fs = (1 + beta * beta) * precise * recall / \\\n        (beta * beta * precise + recall + SMALL)\n    f = fs.sum() / batch_size\n    return f\n\n\ndef f2_score(y_pred, y_true, thres=0.235):\n    y_true, y_pred, = np.array(y_true), np.array(y_pred)\n    y_pred = y_pred > thres\n    return fbeta_score(y_true, y_pred, beta=2, average='samples')\n\n\ndef augment(x, u=0.75):\n    if random.random() < u:\n        if random.random() > 0.5:\n            x = randomDistort1(x, distort_limit=0.35, shift_limit=0.25, u=1)\n        else:\n            x = randomDistort2(x, num_steps=10, distort_limit=0.2, u=1)\n        x = randomShiftScaleRotate(\n            x, shift_limit=0.0625, scale_limit=0.10, rotate_limit=45, u=1)\n\n    x = randomFlip(x, u=0.5)\n    x = randomTranspose(x, u=0.5)\n    #x = randomContrast(x, limit=0.2, u=0.5)\n    #x = randomSaturation(x, limit=0.2, u=0.5),\n    x = randomFilter(x, limit=0.5, u=0.2)\n    return x\n\n\ndef do_thresholds(probs, labels):\n    print('\\n--- [START %s] %s\\n\\n' %\n          (datetime.now().strftime('%Y-%m-%d %H:%M:%S'), '-' * 64))\n    print('\\n')\n\n    nClass = len(CLASS_NAMES)\n    tryVals = np.arange(0, 1, 0.005)\n    scores = np.zeros(len(tryVals))\n\n    # single threshold\n    for i, t in enumerate(tryVals):\n        scores[i] = fbeta_score(labels, probs > t, beta=2, average='samples')\n\n    best_single_thres = tryVals[scores.argmax()]\n    best_single_thres_score = scores.max()\n\n    print('*best_threshold (fixed)*\\n')\n    print(\"%0.4f\" % best_single_thres)\n    print('\\n')\n    print('*best_score*\\n')\n    print('%0.4f\\n' % best_single_thres_score)\n\n    # per class threshold\n    best_thresholds = np.ones(nClass) * best_single_thres\n    best_multi_thres_score = 0\n    noChange = 0\n    for iter in range(nClass * 10):\n        print(\"thres scan iter %i\" % iter)\n        trial_thresholds = best_thresholds.copy()\n        targetClass = iter % nClass\n        for i, t in enumerate(tryVals):\n            trial_thresholds[targetClass] = t\n            scores[i] = fbeta_score(\n                labels, probs > trial_thresholds, beta=2, average='samples')\n\n        best_threshold = tryVals[scores.argmax()]\n        best_multi_thres_score = scores.max()\n        if best_threshold == best_thresholds[targetClass]:\n            noChange += 1\n        else:\n            noChange = 0\n            best_thresholds[targetClass] = best_threshold\n\n        if noChange == nClass:\n            break\n\n    print('*best_threshold (per class)*\\n')\n    print(np.array2string(best_thresholds, formatter={\n          'float_kind': lambda x: ' %.3f' % x}, separator=','))\n    print('\\n')\n    print('*best_score*\\n')\n    print('%0.4f\\n' % best_multi_thres_score)\n\n    return best_single_thres, best_thresholds\n\n\ndef do_predict(net, dataset, batch_size=20, silent=True):\n    if use_gpu:\n        net.cuda().eval()\n    else:\n        net.eval()\n\n    num_classes = len(CLASS_NAMES)\n    logits = np.zeros((len(dataset), num_classes), np.float32)\n    probs = np.zeros((len(dataset), num_classes), np.float32)\n\n    tot_samples = 0\n\n    loader = DataLoader(\n        dataset,\n        sampler=SequentialSampler(dataset),  # None,\n        batch_size=batch_size,\n        # drop_last   = False,\n        num_workers=0,\n        pin_memory=True)\n\n    for it, batch in enumerate(loader, 0):\n        if not silent:\n            print(\"predict batch %i / %i\" % (it, len(loader)))\n        # images = batch['tif'][:,1:,:,:] #IR R G B to R G B\n        images = batch[SRC]\n        if SRC == 'tif':\n            if CH == 'rgb':\n                images = images[:, 1:, :, :]  # IR R G B to  R G B\n            if CH == 'irrg':\n                images = images[:, :3, :, :]  # IR R G B to IR R G\n            if CH == 'NIR':\n                images = images[:, 0:1, :, :]  # NIR\n            if CH == 'irrgb':\n                pass\n\n        batch_size = len(images)\n        tot_samples += batch_size\n        start = tot_samples - batch_size\n        end = tot_samples\n\n        # forward\n        if use_gpu:\n            ls, ps = net(Variable(images.cuda(), volatile=True))\n        else:\n            ls, ps = net(Variable(images, volatile=True))\n        logits[start:end] = ls.data.cpu().numpy().reshape(-1, num_classes)\n        probs[start:end] = ps.data.cpu().numpy().reshape(-1, num_classes)\n\n    assert(len(dataset) == tot_samples)\n\n    return logits, probs\n\n\ndef do_submit(prob, thres, imgKeys, outfile=\"submit.csv\"):\n    tagsVec = probs > thres\n    tagsCol = []\n    for arow in tagsVec:\n        tags = [CLASS_NAMES[i] for i in np.where(arow)[0]]\n        tags = \" \".join(tags)\n        tagsCol.append(tags)\n    output = pd.DataFrame()\n    output['image_name'] = imgKeys\n    output['tags'] = tagsCol\n    output.to_csv(outfile, index=False)\n\n\ndef get_model(init_file=None):\n    print('** net setting **\\n')\n    num_classes = len(CLASS_NAMES)\n    net = Net(in_shape=(NCH, SIZE, SIZE), num_classes=num_classes)\n    print('%s\\n\\n' % (type(net)))\n    print('\\n')\n\n    # for param in net.parameters():\n    #        param.requires_grad = False\n    # untrainable_layers = [\n    #    net.conv1,\n    #    #net.bn1,\n    #    net.relu,\n    #    net.maxpool,\n    #    net.layer1,\n    #    net.layer2,\n    #    net.layer3\n    #]\n    # for aLayer in untrainable_layers:\n    #    for param in aLayer.parameters():\n    #        param.requires_grad = False\n\n    #trainable_layers = [net.layer4, net.fc]\n    #trainable_paras = []\n    # for aLayer in trainable_layers:\n    #    for param in aLayer.parameters():\n    #        param.requires_grad = True\n    #        trainable_paras.append(param)\n\n    optimizer = optim.Adam(net.parameters())\n    # optimizer = optim.SGD(net.parameters(), lr=0.1,\n    #                      momentum=0.9, weight_decay=0.0005)  # 0.0005\n    # optimizer = optim.SGD(trainable_paras, lr=0.1, momentum=0.9,\n    # weight_decay=0.0005)  ###0.0005\n\n    # resume from previous ----------------------------------\n    start_epoch = 0\n    if init_file is not None:\n        init_content = torch.load(init_file)\n        if isinstance(init_content, dict):\n            if 'epoch' in init_content:\n                # checkpoint\n                start_epoch = init_content['epoch']\n                optimizer.load_state_dict(init_content['optimizer_state'])\n                load_model_weight(net, init_content[\n                                  'model_state'], skip_list=[])\n            else:\n                # pretrained weights\n                if CH == 'irrgb':\n                    skip_list = ['conv1.weight', 'fc.weight', 'fc.bias']\n                    load_model_weight(net, init_content, skip_list=skip_list)\n                    tmp = init_content['conv1.weight']\n                    if isinstance(tmp, torch.nn.Parameter):\n                        tmp = tmp.data\n                    net.state_dict()['conv1.weight'][:, 1:, :, :] = tmp\n                elif CH == 'NIR':\n                    # pretrained weights for inception model\n                    skip_list = ['Conv2d_1a_3x3.conv.weight',\n                                 'fc.weight', 'fc.bias']\n                    load_model_weight(\n                        net, init_content, skip_list=skip_list)\n                    tmp = torch.mean(init_content[\n                                     'Conv2d_1a_3x3.conv.weight'], 1)\n                    if isinstance(tmp, torch.nn.Parameter):\n                        tmp = tmp.data\n                    net.state_dict()['Conv2d_1a_3x3.conv.weight'][\n                        :, 0, :, :] = tmp\n                else:\n                    skip_list = ['fc.weight', 'fc.bias']\n                    load_model_weight(net, init_content, skip_list=skip_list)\n        else:\n            # full model\n            net = init_content\n\n    return net, optimizer, start_epoch\n\n\ndef do_training(out_dir='../../output/inception_tif_NIR_out'):\n\n    init_file = '../../input/inception_v3_google-1a9a5a14.pth'\n\n    # ------------------------------------\n    os.makedirs(out_dir + '/snap', exist_ok=True)\n    # os.makedirs(out_dir + '/checkpoint', exist_ok=True)\n\n    print('\\n--- [START %s] %s\\n\\n' %\n          (datetime.now().strftime('%Y-%m-%d %H:%M:%S'), '-' * 64))\n    print('** some experiment setting **\\n')\n    print('\\tSEED    = %u\\n' % SEED)\n    print('\\tfile    = %s\\n' % __file__)\n    print('\\tout_dir = %s\\n' % out_dir)\n    print('\\n')\n\n    # dataset ----------------------------------------\n    print('** dataset setting **\\n')\n    num_classes = len(CLASS_NAMES)\n    batch_size = 20  # 48  #96 #96  #80 #96 #96   #96 #32  #96 #128 #\n\n    train_dataset = KgForestDataset('train_35479.txt',\n                                    # train_dataset =\n                                    # KgForestDataset('train_320.txt',\n                                    transform=[\n                                        # tif_color_corr,\n                                        augment,\n                                        img_to_tensor,\n                                    ],\n                                    outfields=[SRC, 'label'],\n                                    height=SIZE, width=SIZE,\n                                    )\n\n    train_loader = DataLoader(\n        train_dataset,\n        sampler=RandomSampler(train_dataset),\n        batch_size=batch_size,\n        # drop_last   = True,\n        num_workers=2,\n        pin_memory=True)\n\n    test_dataset = KgForestDataset('val_5000.txt',\n                                   # test_dataset =\n                                   # KgForestDataset('val_320.txt',\n                                   height=SIZE, width=SIZE,\n                                   transform=[\n                                       # tif_color_corr,\n                                       img_to_tensor,\n                                   ],\n                                   outfields=[SRC, 'label'],\n                                   cacheGB=6,\n                                   )\n\n    # num worker = 0 is important\n    test_loader = DataLoader(\n        test_dataset,\n        sampler=SequentialSampler(test_dataset),  # None,\n        batch_size=batch_size,\n        # drop_last   = False,\n        num_workers=0,\n        pin_memory=True)\n\n    print('\\tbatch_size           = %d\\n' % batch_size)\n    print('\\ttrain_loader.sampler = %s\\n' % (str(train_loader.sampler)))\n    print('\\ttest_loader.sampler  = %s\\n' % (str(test_loader.sampler)))\n    print('\\n')\n\n    net, optimizer, start_epoch = get_model(init_file)\n\n    if use_gpu:\n        net.cuda()\n\n    # optimiser ----------------------------------\n    # LR = StepLR([ (0,0.1),  (10,0.01),  (25,0.005),  (35,0.001), (40,0.0001), (43,-1)])\n    # fine tunning\n    # LR = StepLR([(0, 0.01),  (10, 0.005),\n    #             (23, 0.001),  (35, 0.0001), (38, -1)])\n    # LR = CyclicLR(base_lr=0.001, max_lr=0.01, step=5., mode='triangular', gamma=1., scale_fn=None, scale_mode='cycle')\n\n    num_epoches = 50  # 100\n    it_print = 20  # 20\n    epoch_test = 1\n    epoch_save = 5\n\n    # start training here! ##############################################3\n    print('** start training here! **\\n')\n\n    print(' optimizer=%s\\n' % str(optimizer))\n    # print(' LR=%s\\n\\n' % str(LR))\n    print(' epoch   iter  |  smooth_loss   |  train_loss  (acc)  |  valid_loss  (acc)  | min\\n')\n    print('----------------------------------------------------------------------------------------\\n')\n\n    smooth_loss = 0.0\n    train_loss = np.nan\n    train_acc = np.nan\n    test_loss = np.nan\n    test_acc = np.nan\n    best_acc = 0\n    time = 0\n\n    start0 = timer()\n    for epoch in range(start_epoch, num_epoches):  # loop over the dataset multiple times\n        start = timer()\n\n        #---learning rate schduler ------------------------------\n        # lr = LR.get_rate(epoch, num_epoches)\n        # if lr < 0:\n        #    break\n\n        # adjust_learning_rate(optimizer, lr)\n        #--------------------------------------------------------\n\n        smooth_loss_sum = 0.0\n        smooth_loss_n = 0\n        if use_gpu:\n            net.cuda().train()\n        else:\n            net.train()\n        num_its = len(train_loader)\n        for it, batch in enumerate(train_loader, 0):\n            # images = batch['tif'][:,1:,:,:] #IR R G B to R G B\n            images = batch[SRC]\n            if SRC == 'tif':\n                if CH == 'rgb':\n                    images = images[:, 1:, :, :]  # IR R G B to  R G B\n                if CH == 'irrg':\n                    images = images[:, :3, :, :]  # IR R G B to IR R G\n                if CH == 'NIR':\n                    images = images[:, 0:1, :, :]  # NIR\n                if CH == 'irrgb':\n                    pass\n            labels = batch['label'].float()\n            if use_gpu:\n                logits, probs = net(Variable(images.cuda()))\n                loss = loss_func(logits, labels.cuda())\n            else:\n                logits, probs = net(Variable(images))\n                loss = loss_func(logits, labels)\n\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n\n            # additional metrics\n            smooth_loss_sum += loss.data[0]\n            smooth_loss_n += 1\n\n            # print statistics\n            if it % it_print == it_print - 1:\n                smooth_loss = smooth_loss_sum / smooth_loss_n\n                smooth_loss_sum = 0.0\n                smooth_loss_n = 0\n\n                if use_gpu:\n                    train_acc = multi_f_measure(probs.data, labels.cuda())\n                else:\n                    train_acc = multi_f_measure(probs.data, labels)\n                train_loss = loss.data[0]\n\n                print('\\r%5.1f   %5d   |  %0.4f  | %0.4f  %6.4f | ... ' %\n                      (epoch + it / num_its, it + 1,\n                       smooth_loss, train_loss, train_acc),\n                      end='', flush=True)\n\n        #---- end of one epoch -----\n        end = timer()\n        time = (end - start) / 60\n\n        if epoch % epoch_test == epoch_test - 1 or epoch == num_epoches - 1:\n            if use_gpu:\n                net.cuda().eval()\n            else:\n                net.eval()\n            test_logits, test_probs = do_predict(net, test_dataset)\n            test_labels = torch.from_numpy(\n                test_dataset.df[CLASS_NAMES].values.astype(np.float32))\n            test_acc = f2_score(test_probs, test_labels.numpy())\n            test_loss = loss_func(torch.autograd.Variable(\n                torch.from_numpy(test_logits)), test_labels).data[0]\n\n            print('\\r', end='', flush=True)\n            print('%5.1f   %5d   |  %0.4f  | %0.4f  %6.4f | %0.4f  %6.4f  |  %3.1f min \\n' %\n                  (epoch + 1, it + 1, smooth_loss, train_loss, train_acc, test_loss, test_acc, time))\n\n            if test_acc > best_acc:\n                best_acc = test_acc\n                torch.save(net, out_dir + '/snap/best_acc_inception_%s_%03d.torch' %\n                           ((\"%.4f\" % best_acc).replace('.', 'd'), epoch + 1))\n                # torch.save(net, out_dir + '/snap/best_acc_inception.torch')\n\n        # if epoch % epoch_save == epoch_save - 1 or epoch == num_epoches - 1:\n            #torch.save(net, out_dir +'/snap/%03d.torch'%(epoch+1))\n            # torch.save({\n            #     'state_dict': net.state_dict(),\n            #     'optimizer': optimizer.state_dict(),\n            #     'epoch': epoch,\n            # }, out_dir + '/checkpoint/%03d.pth' % (epoch + 1))\n            # https://github.com/pytorch/examples/blob/master/imagenet/main.py\n\n            #---- end of all epoches -----\n    end0 = timer()\n    time0 = (end0 - start0) / 60\n\n    # check : load model and re-test\n    torch.save(net, out_dir + '/snap/final.torch')\n\n    net = torch.load(out_dir + '/snap/final.torch')\n    if use_gpu:\n        net.cuda().eval()\n    else:\n        net.eval()\n    test_logits, test_probs = do_predict(net, test_dataset)\n    test_labels = torch.from_numpy(\n        test_dataset.df[CLASS_NAMES].values.astype(np.float32))\n    test_acc = f2_score(test_probs, test_labels.numpy())\n    test_loss = loss_func(torch.autograd.Variable(\n        torch.from_numpy(test_logits)), test_labels).data[0]\n\n    print('\\n')\n    print('%s:\\n' % (out_dir + '/snap/final.torch'))\n    print('\\tall time to train=%0.1f min\\n' % (time0))\n    print('\\ttest_loss=%f, test_acc=%f\\n' % (test_loss, test_acc))\n\n    return net\n\nif __name__ == '__main__':\n    print('%s: calling main function ... ' % os.path.basename(__file__))\n\n    do_training(out_dir='../../output/inception_tif_NIR_out')\n\n    # find thres\n    #net,_,_ = get_model(\"../../output/resnet34_tif_irrgb_nocorr_out/snap/best_acc_0d9221_026.torch\")\n    # train_dataset = KgForestDataset('labeled.txt',\n    #                                transform=[\n    #                                    #tif_color_corr,\n    #                                    img_to_tensor,\n    #                                    ],\n    #                                outfields = [SRC, 'label'],\n    #                                height=SIZE, width=SIZE,\n    #                               )\n    #logits, probs = do_predict(net, train_dataset, silent=False)\n    #labels = train_dataset.df[CLASS_NAMES].values.astype(np.float32)\n    #do_thresholds(probs, labels)\n\n    # do submit\n    # net, _, _ = get_model(\n    #     \"../../output/resnet34_tif_irrgb_nocorr_out/snap/best_acc_0d9221_026.torch\")\n    test_dataset = KgForestDataset('unlabeled.txt',\n                                   transform=[\n                                       # tif_color_corr,\n                                       img_to_tensor,\n                                   ],\n                                   outfields=[SRC],\n                                   height=SIZE, width=SIZE,\n                                   )\n    logits, probs = do_predict(net, test_dataset, silent=False)\n\n    # from resnet34_tif_rgb\n    ##best_threshold = np.ones(len(CLASS_NAMES))* 0.2200\n    # best_thresholds = np.array( [ 0.170, 0.245, 0.150, 0.195, 0.145, 0.230, 0.225, 0.245, 0.190, 0.240,\n    # 0.095, 0.305, 0.255, 0.135, 0.145, 0.240, 0.060] )\n\n    # from resnet34_tif_irgb\n    ##best_threshold = np.ones(len(CLASS_NAMES))* 0.2250\n    # best_thresholds = np.array([ 0.190, 0.250, 0.225, 0.125, 0.270, 0.235, 0.200, 0.240, 0.240, 0.250,\n    # 0.120, 0.100, 0.240, 0.150, 0.210, 0.190, 0.050])\n\n    # from resnet34_tif_irgb\n    #best_threshold = np.ones(len(CLASS_NAMES))* 0.2200\n    # best_thresholds = np.array(\n    #        [ 0.195, 0.235, 0.230, 0.095, 0.295, 0.215, 0.175, 0.250, 0.220, 0.250,\n    #          0.130, 0.345, 0.145, 0.170, 0.215, 0.260, 0.090]\n    #                             )\n\n    # from resnet34_tif_irrgb\n    best_threshold = np.ones(len(CLASS_NAMES)) * 0.2100\n    best_thresholds = np.array(\n        [0.155, 0.260, 0.225, 0.110, 0.280, 0.240, 0.225, 0.205, 0.205, 0.250,\n         0.080, 0.145, 0.180, 0.075, 0.130, 0.160, 0.075]\n    )\n    do_submit(probs, best_thresholds, test_dataset.df.index,\n              \"submit_resnet34_tif_irrgb.csv\")\n","sub_path":"DVS_Inception/nnPyTorch/train-forest-inception_NIR.py","file_name":"train-forest-inception_NIR.py","file_ext":"py","file_size_in_byte":19671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"336351578","text":"import argparse\n\nimport numpy as np\nimport pandas as pd\nfrom sentence_transformers import SentenceTransformer\n\n\nclass BertEncoder:\n    def __init__(self, model_name='bert-base-nli-stsb-mean-tokens'):\n        self.model = SentenceTransformer(model_name)\n\n    def encode(self, sentences):\n        return np.stack(self.model.encode(sentences, show_progress_bar=True))\n\n\ndef main():\n    argument_parser = argparse.ArgumentParser()\n    argument_parser.add_argument(\"--data_src\", type=str,\n                                 help='Input CSV file containing input data', required=False, default=None)\n    args = argument_parser.parse_args()\n    encoder = BertEncoder()\n    sentences = pd.read_csv(args.corpus_file, index_col=0)['sentence'].values\n    embs = encoder.encode(sentences)\n    print(embs)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"src/encoders/bert_encoder.py","file_name":"bert_encoder.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"223077530","text":"# -*- coding : utf-8 -*-\r\n\r\nimport os, sys, subprocess\r\nimport urllib.request\r\nimport threading\r\nfrom PyQt5 import QtCore, QtWidgets\r\nfrom UI.updateUI import *\r\n\r\n#下载路径\r\nDOWNLOAD_PATH = \"https://raw.githubusercontent.com/q381528589/Publisher/master/Addwork/\"\r\nAPP_NAME = \"AddWork.exe\"\r\n\r\nclass CDownload(threading.Thread):\r\n    Percent = 0\r\n    bDownload = False\r\n    \r\n    def __init__(self):\r\n        super(CDownload, self).__init__()\r\n        \r\n    def run(self):\r\n        self.__HandleAddwork()\r\n        return\r\n\r\n    def Schedule(self, a,b,c):\r\n        '''''\r\n        a:已经下载的数据块\r\n        b:数据块的大小\r\n        c:远程文件的大小\r\n       '''\r\n        self.Percent = 100.0 * a * b / c\r\n        if self.Percent > 100 :\r\n            self.Percent = 100\r\n        #print ('%.1f%%' % self.Percent)\r\n        \r\n    def __HandleAddwork(self):\r\n        #获取网络上最新版本\r\n        try:\r\n            urllib.request.urlretrieve(DOWNLOAD_PATH+APP_NAME, \r\n                                       \"./%s.download\" % (APP_NAME), self.Schedule)\r\n        except:\r\n            print (\"获取更新程序失败\")\r\n            return\r\n        \r\n        #关闭程序\r\n        try:\r\n            subprocess.check_call(\"taskkill /F /IM %s\" % (APP_NAME))\r\n        except:\r\n            #TODO：用户手动关闭程序\r\n            pass\r\n        \r\n        #下载更新完成\r\n        self.bDownload = True\r\n        return\r\n    \r\nclass CUpdate(QtWidgets.QDialog, Ui_Dialog):\r\n    __translate = QtCore.QCoreApplication.translate\r\n    __cDownload = CDownload()\r\n    __bShow = False\r\n    \r\n    def __init__(self):\r\n        super(CUpdate, self).__init__()\r\n        self.setupUi(self)\r\n        self.Btn_OK.clicked.connect(self.__StartDownload)\r\n    \r\n    def closeEvent(self, event):\r\n        #如果没有更新成功，在直接退出\r\n        if (False == self.__cDownload.bDownload):\r\n            event.accept()\r\n            return\r\n        \r\n        #重命名文件\r\n        if (os.path.exists(\"./%s\" % (APP_NAME))):\r\n            os.remove(\"./%s\" % (APP_NAME))\r\n        os.rename(\"./%s.download\" % (APP_NAME), \"./%s\" % (APP_NAME))\r\n        #写入版本文件\r\n        self.__WriteLocalVersion()\r\n        \r\n        #关闭窗口\r\n        event.accept()\r\n        return\r\n    \r\n    def Update(self):\r\n        #是否需要更新\r\n        __bUpdate = False\r\n        #update程序版本\r\n        __UpdateVersion=\"0.0.0.0\"\r\n        #Addwork程序版本\r\n        __AddWorkVersion=\"0.0.0.0\"\r\n        \r\n        #获取自身版本号和其它版本号\r\n        if (0 != self.__ReadLocalVersion()):\r\n            #TODO：后期版本更新：文件不存在时校验MD5，两者均相同下载version.txt，否则执行后续更新步骤\r\n            print (\"读取本地文件发生错误\")\r\n            sys.exit()\r\n        #获取网络上最新版本号\r\n        try:\r\n            f = urllib.request.urlopen(DOWNLOAD_PATH+\"version.txt\")\r\n            data = f.read().decode(\"utf-8\")\r\n            f.close()\r\n        except:\r\n            print (\"获取版本信息失败\")\r\n            sys.exit()\r\n        VersionList = data.split(\"\\r\\n\")\r\n        for VersionInfo in VersionList:\r\n            #更新update\r\n            if (-1 != VersionInfo.find(\"update\")):\r\n                Version = VersionInfo.split('=')\r\n                if (2 > len(Version)):\r\n                    continue\r\n                self.__bUpdate = self.__CheckVersion(self.__UpdateVersion, Version[1])\r\n                if (False == self.__bUpdate):\r\n                    continue\r\n                self.__UpdateSelf()\r\n                #写入版本文件\r\n                self.__UpdateVersion = Version[1]\r\n                self.__WriteLocalVersion()\r\n            #更新AddWork\r\n            elif (-1 != VersionInfo.find(\"AddWork\")):\r\n                Version = VersionInfo.split('=')\r\n                if (2 > len(Version)):\r\n                    continue\r\n                self.__bUpdate = self.__CheckVersion(self.__AddWorkVersion, Version[1])\r\n                if (False == self.__bUpdate):\r\n                    continue\r\n                self.__UpdateAddwork(Version[1])\r\n                #写入版本文件\r\n                self.__AddWorkVersion = Version[1]\r\n        \r\n        #退出更新程序\r\n        if (False == self.__bShow):\r\n            sys.exit()\r\n        return\r\n    \r\n    def __ReadLocalVersion(self):\r\n        try:\r\n            File = open(\"./version.txt\", 'r')\r\n            Text = File.read()\r\n            List = Text.split(\"\\n\")\r\n            File.close()\r\n        except:\r\n            print (\"打开文件错误\")\r\n            return -1\r\n        \r\n        for VersionInfo in List:\r\n            #update版本\r\n            if (-1 != VersionInfo.find(\"update\")):\r\n                Version = VersionInfo.split('=')\r\n                if (2 > len(Version)):\r\n                    continue\r\n                self.__UpdateVersion = Version[1]\r\n            #AddWork版本\r\n            elif (-1 != VersionInfo.find(\"AddWork\")):\r\n                Version = VersionInfo.split('=')\r\n                if (2 > len(Version)):\r\n                    continue\r\n                self.__AddWorkVersion = Version[1]\r\n        \r\n        return 0\r\n    \r\n    def __WriteLocalVersion(self):\r\n        szTemp = \"update=%s\\n\" % (self.__UpdateVersion)\r\n        szTemp += \"AddWork=%s\" % (self.__AddWorkVersion)\r\n        try:\r\n            File = open(\"./version.txt\", 'w')\r\n            File.write(szTemp)\r\n            File.close()\r\n        except IOError as err:\r\n            print (\"写入文件错误：%s\" % (str(err)))\r\n            \r\n        return\r\n        \r\n    def __CheckVersion(self, OldVersion, NewVersion):\r\n        OldList = OldVersion.split('.')\r\n        NewList = NewVersion.split('.')\r\n        \r\n        if (4!=len(NewList) or 4!=len(OldList)):\r\n            return False\r\n        \r\n        #检查版本\r\n        if (int(NewList[0]) > int(OldList[0])):\r\n            #大版本更新\r\n            return True\r\n        elif (int(NewList[0]) < int(OldList[0])):\r\n            return False\r\n        \r\n        if (int(NewList[1]) > int (OldList[1])):\r\n            return True\r\n        elif (int(NewList[1]) < int (OldList[1])):\r\n            return False\r\n        \r\n        if (int(NewList[2]) > int (OldList[2])):\r\n            return True\r\n        elif (int(NewList[2]) < int (OldList[2])):\r\n            return False\r\n        \r\n        #svn版本号更新的不算在列\r\n        return False\r\n                \r\n    def __UpdateSelf(self):#TODO：多线程\r\n        #静默更新\r\n        #获取网络上最新版本\r\n        try:\r\n            f = urllib.request.urlopen(DOWNLOAD_PATH+\"update.exe\")\r\n            data = f.read()\r\n            f.close()\r\n        except:\r\n            print (\"获取更新程序失败\")\r\n            sys.exit()\r\n        #写至缓存文件\r\n        try:\r\n            File = open(\"./update.exe.download\", \"wb\")\r\n            File.write(data)\r\n            File.close()\r\n        except:\r\n            print (\"写入更新程序失败\")\r\n            sys.exit()\r\n        #重命名文件\r\n        if (os.path.exists(\"./update.exe.tmp\")):\r\n            os.remove(\"./update.exe.tmp\")\r\n        os.rename(\"./update.exe.download\", \"./update.exe.tmp\")\r\n        \r\n        return\r\n    \r\n    def __UpdateProcessBar(self):\r\n        while (True == self.__cDownload.isAlive()):  \r\n            self.progressBar.setValue(self.__cDownload.Percent)  \r\n            QtCore.QThread.msleep(100)\r\n            QtWidgets.QApplication.processEvents()\r\n        return\r\n    \r\n    def __UpdateAddwork(self, CurVersion):\r\n        self.label.setText(self.__translate(\"Dialog\", \"发现新版本：%s，是否更新？\") % (CurVersion))\r\n        self.progressBar.hide()\r\n        self.__bShow = True\r\n        self.show()\r\n        return\r\n    \r\n    def __StartDownload(self):\r\n        self.label.hide()\r\n        self.Btn_OK.setText(self.__translate(\"Dialog\", \"下载中……\"))\r\n        self.Btn_Cancel.setText(self.__translate(\"Dialog\", \"取消\"))\r\n        self.Btn_OK.setEnabled(False)\r\n        self.progressBar.show()\r\n        self.__cDownload.setDaemon(True)\r\n        self.__cDownload.start()\r\n        self.__UpdateProcessBar()\r\n        self.Btn_OK.hide()\r\n        self.Btn_Cancel.setText(self.__translate(\"Dialog\", \"完成\"))\r\n        return\r\n              \r\nif __name__ == \"__main__\":\r\n    #加载QT窗口\r\n    app = QtWidgets.QApplication(sys.argv)\r\n    cUpdate = CUpdate()\r\n    cUpdate.Update()\r\n    sys.exit(app.exec_())","sub_path":"Codes/update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":8479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"101446402","text":"#!/usr/bin/env python\n\nfrom __future__ import division\n\nimport argparse\nimport datetime\nimport functools\nimport os.path as osp\nimport PIL.Image\nimport random\nimport socket\n\nimport chainer\nfrom chainer import training\nfrom chainer.training import extensions\nimport chainer_mask_rcnn as cmr\nfrom chainercv import transforms\nfrom chainercv.utils.mask.mask_to_bbox import mask_to_bbox\nimport fcn\nimport numpy as np\n\nfrom grasp_data_generator.datasets import OIDualarmGraspDatasetV1\nfrom grasp_data_generator.extensions import ManualScheduler\nfrom grasp_data_generator.models import OccludedMaskRCNNResNet101\nfrom grasp_data_generator.models import OccludedMaskRCNNTrainChain\n\n\nthisdir = osp.dirname(osp.abspath(__file__))\n\n\nclass Transform(object):\n\n    def __init__(self, occluded_mask_rcnn):\n        self.occluded_mask_rcnn = occluded_mask_rcnn\n\n    def __call__(self, in_data):\n        img, ins_label, label, _, _ = in_data\n        bbox = mask_to_bbox(ins_label != 0)\n        _, orig_H, orig_W = img.shape\n        img = self.occluded_mask_rcnn.prepare(img)\n        _, H, W = img.shape\n        scale = H / orig_H\n        ins_label = transforms.resize(ins_label, (H, W), PIL.Image.NEAREST)\n        bbox = transforms.resize_bbox(bbox, (orig_H, orig_W), (H, W))\n\n        return img, ins_label, label, bbox, scale\n\n\ndef main():\n    parser = argparse.ArgumentParser(\n        formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n    parser.add_argument(\n        '--dataset', choices=['v1'], default='v1', help='Dataset version')\n    parser.add_argument('--gpu', '-g', type=int, help='GPU id.')\n    parser.add_argument('--multi-node', action='store_true',\n                        help='use multi node')\n    parser.add_argument('--max-epoch', type=float,\n                        default=12, help='epoch')\n    parser.add_argument('--seed', '-s', type=int, default=1234)\n    args = parser.parse_args()\n\n    if args.multi_node:\n        import chainermn\n        comm = chainermn.create_communicator('hierarchical')\n        device = comm.intra_rank\n\n        args.n_node = comm.inter_size\n        args.n_gpu = comm.size\n        chainer.cuda.get_device_from_id(device).use()\n    else:\n        args.n_node = 1\n        args.n_gpu = 1\n        chainer.cuda.get_device_from_id(args.gpu).use()\n        device = args.gpu\n\n    now = datetime.datetime.now()\n    random.seed(args.seed)\n    np.random.seed(args.seed)\n\n    # Default Config\n    # args.min_size = 800\n    # args.max_size = 1333\n    # args.anchor_scales = (2, 4, 8, 16, 32)\n    args.min_size = 600\n    args.max_size = 1000\n    args.anchor_scales = (4, 8, 16, 32)\n    args.rpn_dim = 512\n\n    if not args.multi_node or comm.rank == 0:\n        out = osp.join(thisdir, 'logs', now.strftime('%Y%m%d_%H%M%S.%f'))\n    else:\n        out = None\n\n    if args.multi_node:\n        args.out = comm.bcast_obj(out)\n    else:\n        args.out = out\n    del out\n\n    # 0.00125 * 8 = 0.01  in original\n    args.batch_size = 1 * args.n_gpu\n    args.lr = 0.00125 * args.batch_size\n    args.weight_decay = 0.0001\n\n    args.step_size = [2 / 3 * args.max_epoch, 8 / 9 * args.max_epoch]\n\n    # -------------------------------------------------------------------------\n    # Dataset\n    if args.dataset == 'v1':\n        train_data = OIDualarmGraspDatasetV1(split='train')\n    else:\n        raise ValueError(\n            'Given dataset is not supported: {}'.format(args.dataset))\n    # test_data = OIDualarmGraspDatasetV1(split='test', imgaug=False)\n    label_names = train_data.label_names\n\n    # -------------------------------------------------------------------------\n    # Model + Optimizer.\n    occluded_mask_rcnn = OccludedMaskRCNNResNet101(\n        n_fg_class=len(label_names),\n        anchor_scales=args.anchor_scales,\n        min_size=args.min_size,\n        max_size=args.max_size,\n        rpn_dim=args.rpn_dim)\n    occluded_mask_rcnn.nms_thresh = 0.3\n    occluded_mask_rcnn.score_thresh = 0.05\n\n    model = OccludedMaskRCNNTrainChain(occluded_mask_rcnn)\n    if args.multi_node or args.gpu >= 0:\n        model.to_gpu()\n\n    optimizer = chainer.optimizers.MomentumSGD(lr=args.lr, momentum=0.9)\n    if args.multi_node:\n        optimizer = chainermn.create_multi_node_optimizer(optimizer, comm)\n    optimizer.setup(model)\n    optimizer.add_hook(chainer.optimizer.WeightDecay(rate=args.weight_decay))\n\n    occluded_mask_rcnn.extractor.conv1.disable_update()\n    occluded_mask_rcnn.extractor.bn1.disable_update()\n    occluded_mask_rcnn.extractor.res2.disable_update()\n    for link in occluded_mask_rcnn.links():\n        if isinstance(link, cmr.links.AffineChannel2D):\n            link.disable_update()\n\n    # -------------------------------------------------------------------------\n    # Transform dataset.\n    train_data = chainer.datasets.TransformDataset(\n        train_data, Transform(occluded_mask_rcnn))\n\n    # -------------------------------------------------------------------------\n    # Iterator.\n\n    if args.multi_node:\n        if comm.rank != 0:\n            train_data = None\n        train_data = chainermn.scatter_dataset(train_data, comm, shuffle=True)\n\n    # for training\n    train_iter = chainer.iterators.SerialIterator(train_data, batch_size=1)\n    # test_iter = chainer.iterators.SerialIterator(\n    #     test_data, batch_size=1, repeat=False, shuffle=False)\n\n    # -------------------------------------------------------------------------\n    converter = functools.partial(\n        cmr.datasets.concat_examples,\n        padding=0,\n        # img, ins_labels, labels, bboxes, scales\n        indices_concat=[0, 1, 2, 4],  # img, ins_labels, labels, _, scales\n        indices_to_device=[0, 3],  # img, bbox\n    )\n    updater = chainer.training.updater.StandardUpdater(\n        train_iter, optimizer, device=device,\n        converter=converter)\n\n    trainer = training.Trainer(\n        updater, (args.max_epoch, 'epoch'), out=args.out)\n\n    def lr_schedule(updater):\n        base_lr = 0.0005 * 1.25 * args.batch_size\n        if args.multi_node:\n            base_lr = base_lr * comm.size\n        warm_up_duration = 500\n        warm_up_rate = 1 / 3\n\n        iteration = updater.iteration\n        if iteration < warm_up_duration:\n            rate = warm_up_rate \\\n                + (1 - warm_up_rate) * iteration / warm_up_duration\n        elif iteration < (args.step_size[0] * len(train_data)):\n            rate = 1\n        elif iteration < (args.step_size[1] * len(train_data)):\n            rate = 0.1\n        else:\n            rate = 0.01\n        return base_lr * rate\n\n    trainer.extend(ManualScheduler('lr', lr_schedule))\n\n    # eval_interval = 1, 'epoch'\n    log_interval = 20, 'iteration'\n    plot_interval = 0.1, 'epoch'\n    print_interval = 20, 'iteration'\n\n    if not args.multi_node or comm.rank == 0:\n        # evaluator = InstanceSegmentationVOCEvaluator(\n        #     test_iter, model.occluded_mask_rcnn, device=device,\n        #     use_07_metric=False, label_names=label_names)\n        # trainer.extend(evaluator, trigger=eval_interval)\n        # trainer.extend(\n        #     extensions.snapshot_object(\n        #         model.occluded_mask_rcnn, 'snapshot_model.npz'),\n        #     trigger=training.triggers.MaxValueTrigger(\n        #         'validation/main/mpq', eval_interval))\n        model_name = model.occluded_mask_rcnn.__class__.__name__\n        trainer.extend(\n            chainer.training.extensions.snapshot_object(\n                model.occluded_mask_rcnn,\n                savefun=chainer.serializers.save_npz,\n                filename='%s_model_iter_{.updater.iteration}.npz'\n                         % model_name),\n            trigger=(1, 'epoch'))\n        args.git_hash = cmr.utils.git_hash()\n        args.hostname = socket.gethostname()\n        trainer.extend(fcn.extensions.ParamsReport(args.__dict__))\n        # trainer.extend(\n        #     InstanceSegmentationVisReport(\n        #         test_iter, model.occluded_mask_rcnn,\n        #         label_names=label_names),\n        #     trigger=eval_interval)\n        trainer.extend(chainer.training.extensions.observe_lr(),\n                       trigger=log_interval)\n        trainer.extend(extensions.LogReport(trigger=log_interval))\n        trainer.extend(extensions.PrintReport(\n            ['iteration', 'epoch', 'elapsed_time', 'lr',\n             'main/loss',\n             'main/roi_loc_loss',\n             'main/roi_cls_loss',\n             'main/roi_mask_loss',\n             'main/rpn_loc_loss',\n             'main/rpn_cls_loss',\n             'validation/main/mpq']),\n            trigger=print_interval,\n        )\n        trainer.extend(extensions.ProgressBar(update_interval=10))\n\n        # plot\n        assert extensions.PlotReport.available()\n        trainer.extend(\n            extensions.PlotReport(\n                ['main/loss',\n                 'main/roi_loc_loss',\n                 'main/roi_cls_loss',\n                 'main/roi_mask_loss',\n                 'main/rpn_loc_loss',\n                 'main/rpn_cls_loss'],\n                file_name='loss.png', trigger=plot_interval,\n            ),\n            trigger=plot_interval,\n        )\n        # trainer.extend(\n        #     extensions.PlotReport(\n        #         ['validation/main/map',\n        #          'validation/main/msq',\n        #          'validation/main/mdq',\n        #          'validation/main/mpq'],\n        #         file_name='accuracy.png', trigger=plot_interval\n        #     ),\n        #     trigger=eval_interval,\n        # )\n\n        trainer.extend(extensions.dump_graph('main/loss'))\n\n    trainer.run()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"demos/grasp_data_generator/experiments/occluded_mask_rcnn/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":9561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"29991085","text":"import sys\nimport os\nsys.path.append(os.path.dirname(os.path.abspath(__file__)) + \"/..\")\n\nimport numpy as np\nimport logging\nimport pandas as pd\nimport pickle as pkl\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nimport torch\nimport torch.nn as nn\nimport torch.utils.data as utils\nfrom models.eigan import DiscriminatorFCN\nfrom common.torchsummary import summary\nfrom common.utility import log_shapes, log_time, torch_device,\\\n    time_stp, logger, sep, weights_init, load_processed_data\nfrom common.argparser import comparison_argparse\n\n\ndef main(\n        model,\n        time_stamp,\n        device,\n        encoding_dim,\n        hidden_dim,\n        leaky,\n        test_size,\n        batch_size,\n        n_epochs,\n        shuffle,\n        lr,\n        expt,\n        pca_ckpt,\n        autoencoder_ckpt,\n        encoder_ckpt,\n):\n    device = torch_device(device=device)\n\n    X, targets = load_processed_data(\n        expt, 'processed_data_X_targets.pkl')\n    log_shapes(\n        [X] + [targets[i] for i in targets],\n        locals(),\n        'Dataset loaded'\n    )\n\n    targets = {i: elem.reshape(-1, 1) for i, elem in targets.items()}\n\n    X_train, X_valid, \\\n        y_adt_train, y_adt_valid = train_test_split(\n            X,\n            targets['admission_type'],\n            test_size=test_size,\n            stratify=pd.DataFrame(np.concatenate(\n                (\n                    targets['admission_type'],\n                ), axis=1)\n            )\n        )\n\n    log_shapes(\n        [\n            X_train, X_valid,\n            y_adt_train, y_adt_valid,\n        ],\n        locals(),\n        'Data size after train test split'\n    )\n\n    y_train = y_adt_train\n    y_valid = y_adt_valid\n\n    scaler = StandardScaler()\n    X_normalized_train = scaler.fit_transform(X_train)\n    X_normalized_valid = scaler.transform(X_valid)\n\n    log_shapes([X_normalized_train, X_normalized_valid], locals())\n\n    ckpts = {\n        # 12A: checkpoints/mimic/n_advr_ind_gan_training_history_02_12_2020_19_30_38.pkl\n        0: 'checkpoints/mimic/n_advr_eigan_torch_model_02_12_2020_00_10_16_A_n_1_device_cuda_dim_256_hidden_512_batch_32768_epochs_1001_advr_0_encd_-0.1220_advr_0.4970.pkl',\n        # 12A: checkpoints/mimic/n_advr_ind_gan_training_history_02_12_2020_19_30_38.pkl\n        1: 'checkpoints/mimic/n_advr_eigan_torch_model_02_12_2020_02_02_15_A_n_2_device_cuda_dim_256_hidden_512_batch_32768_epochs_1001_advr_1_encd_-0.1086_advr_0.4969.pkl',\n        # 34A: checkpoints/mimic/n_advr_ind_gan_training_history_02_12_2020_20_01_01.pkl\n        2: 'checkpoints/mimic/n_advr_eigan_torch_model_02_12_2020_15_03_17_A_n_3_device_cuda_dim_256_hidden_512_batch_32768_epochs_1001_advr_2_encd_-0.0949_advr_0.4908.pkl',\n        # 34A: checkpoints/mimic/n_advr_ind_gan_training_history_02_12_2020_20_01_01.pkl\n        3: 'checkpoints/mimic/n_advr_eigan_torch_model_02_12_2020_15_02_44_A_n_4_device_cuda_dim_256_hidden_512_batch_32768_epochs_1001_advr_3_encd_-0.1082_advr_0.4981.pkl',\n        # 56A: checkpoints/mimic/n_advr_ind_gan_training_history_02_12_2020_23_07_22.pkl\n        4: 'checkpoints/mimic/n_advr_eigan_torch_model_02_12_2020_20_01_18_A_n_5_device_cuda_dim_256_hidden_512_batch_32768_epochs_1001_advr_4_encd_-0.1145_advr_0.4966.pkl',\n        # 56A: checkpoints/mimic/n_advr_ind_gan_training_history_02_12_2020_23_07_22.pkl\n        5: 'checkpoints/mimic/n_advr_eigan_torch_model_02_12_2020_21_34_32_A_n_6_device_cuda_dim_256_hidden_512_batch_32768_epochs_1001_advr_5_encd_-0.1172_advr_0.4960.pkl',\n        # 789A: checkpoints/mimic/n_advr_ind_gan_training_history_02_13_2020_13_54_53.pkl\n        6: 'checkpoints/mimic/n_advr_eigan_torch_model_02_12_2020_23_56_04_A_n_7_device_cuda_dim_256_hidden_512_batch_32768_epochs_1001_advr_6_encd_-0.0955_advr_0.4904.pkl',\n        # 789A: checkpoints/mimic/n_advr_ind_gan_training_history_02_13_2020_13_54_53.pkl\n        7: 'checkpoints/mimic/n_advr_eigan_torch_model_02_12_2020_23_59_23_A_n_8_device_cuda_dim_256_hidden_512_batch_16384_epochs_1001_advr_7_encd_-0.1173_advr_0.4964.pkl',\n        # 789A: checkpoints/mimic/n_advr_ind_gan_training_history_02_13_2020_13_54_53.pkl\n        8: 'checkpoints/mimic/n_advr_eigan_torch_model_02_13_2020_04_35_19_A_n_9_device_cuda_dim_256_hidden_512_batch_32768_epochs_1001_advr_8_encd_-0.1105_advr_0.4947.pkl',\n        # nA: checkpoints/mimic/n_advr_ind_gan_training_history_02_13_2020_18_31_15.pkl\n        9: 'checkpoints/mimic/n_advr_eigan_torch_model_02_13_2020_13_32_21_A_n_10_device_cuda_dim_256_hidden_512_batch_32768_epochs_1001_advr_9_encd_-0.1104_advr_0.4955.pkl',\n    }\n\n    h = {}\n\n    for idx, ckpt in ckpts.items():\n        encoder = torch.load(ckpt, map_location=device)\n        encoder.eval()\n\n        optim = torch.optim.Adam\n        criterionBCEWithLogits = nn.BCEWithLogitsLoss()\n\n        h[idx] = {\n            'epoch_train': [],\n            'epoch_valid': [],\n            'advr_train': [],\n            'advr_valid': [],\n        }\n\n        dataset_train = utils.TensorDataset(\n            torch.Tensor(X_normalized_train),\n            torch.Tensor(y_train),\n        )\n\n        dataset_valid = utils.TensorDataset(\n            torch.Tensor(X_normalized_valid),\n            torch.Tensor(y_valid),\n        )\n\n        def transform(input_arg):\n            return encoder(input_arg)\n\n        dataloader_train = torch.utils.data.DataLoader(\n            dataset_train,\n            batch_size=batch_size,\n            shuffle=shuffle,\n            num_workers=1\n        )\n\n        dataloader_valid = torch.utils.data.DataLoader(\n            dataset_valid,\n            batch_size=batch_size,\n            shuffle=shuffle,\n            num_workers=1\n        )\n\n        clf = DiscriminatorFCN(\n            encoding_dim, hidden_dim, 1,\n            leaky).to(device)\n\n        clf.apply(weights_init)\n\n        sep('{} {}'.format(idx+1, 'ally'))\n        summary(clf, input_size=(1, encoding_dim))\n\n        optimizer = optim(clf.parameters(), lr=lr)\n\n        # adversary 1\n        sep(\"adversary with {} ally encoder\".format(idx+1))\n        logging.info('{} \\t {} \\t {}'.format(\n            'Epoch',\n            'Advr Train',\n            'Advr Valid',\n        ))\n\n        for epoch in range(n_epochs):\n            clf.train()\n\n            nsamples = 0\n            iloss_advr = 0\n            for i, data in enumerate(dataloader_train, 0):\n                X_train_torch = transform(data[0].to(device))\n                y_advr_train_torch = data[1].to(device)\n\n                optimizer.zero_grad()\n                y_advr_train_hat_torch = clf(X_train_torch)\n\n                loss_advr = criterionBCEWithLogits(\n                    y_advr_train_hat_torch, y_advr_train_torch)\n                loss_advr.backward()\n                optimizer.step()\n\n                nsamples += 1\n                iloss_advr += loss_advr.item()\n\n            h[idx]['advr_train'].append(iloss_advr/nsamples)\n            h[idx]['epoch_train'].append(epoch)\n\n            if epoch % int(n_epochs/10) != 0:\n                continue\n\n            clf.eval()\n\n            nsamples = 0\n            iloss_advr = 0\n            correct = 0\n            total = 0\n\n            for i, data in enumerate(dataloader_valid, 0):\n                X_valid_torch = transform(data[0].to(device))\n                y_advr_valid_torch = data[1].to(device)\n                y_advr_valid_hat_torch = clf(X_valid_torch)\n\n                valid_loss_advr = criterionBCEWithLogits(\n                    y_advr_valid_hat_torch, y_advr_valid_torch,)\n\n                predicted = y_advr_valid_hat_torch > 0.5\n\n                nsamples += 1\n                iloss_advr += valid_loss_advr.item()\n                total += y_advr_valid_torch.size(0)\n                correct += (predicted == y_advr_valid_torch).sum().item()\n\n            h[idx]['advr_valid'].append(iloss_advr/nsamples)\n            h[idx]['epoch_valid'].append(epoch)\n\n            logging.info(\n                '{} \\t {:.8f} \\t {:.8f} \\t {:.8f}'.\n                format(\n                    epoch,\n                    h[idx]['advr_train'][-1],\n                    h[idx]['advr_valid'][-1],\n                    correct/total\n                ))\n\n    checkpoint_location = \\\n        'checkpoints/{}/{}_training_history_{}.pkl'.format(\n            expt, model, time_stamp)\n    sep()\n    logging.info('Saving: {}'.format(checkpoint_location))\n    pkl.dump(h, open(checkpoint_location, 'wb'))\n\n\nif __name__ == \"__main__\":\n    expt = 'mimic'\n    model = 'n_advr_ind_gan'\n    marker = 'A'\n    pr_time, fl_time = time_stp()\n\n    logger(expt, model, fl_time, marker)\n\n    log_time('Start', pr_time)\n    args = comparison_argparse()\n    main(\n        model=model,\n        time_stamp=fl_time,\n        device=args['device'],\n        encoding_dim=args['dim'],\n        hidden_dim=args['hidden_dim'],\n        leaky=args['leaky'],\n        test_size=args['test_size'],\n        batch_size=args['batch_size'],\n        n_epochs=args['n_epochs'],\n        shuffle=args['shuffle'] == 1,\n        lr=args['lr'],\n        expt=args['expt'],\n        pca_ckpt=args['pca_ckpt'],\n        autoencoder_ckpt=args['autoencoder_ckpt'],\n        encoder_ckpt=args['encoder_ckpt']\n    )\n    log_time('End', time_stp()[0])\n    sep()\n","sub_path":"expt_mimic/training_on_gan_n_advr.py","file_name":"training_on_gan_n_advr.py","file_ext":"py","file_size_in_byte":9211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"227800902","text":"from queue import Queue\n\n\n# 定义 Queue 对象，并初始化容量为 10\nq = Queue(10)\n\n# 向队列中添加元素\nq.put('yang')\nq.put(4)\nq.put(['yan', 'xing'])\n\n# 从队列中取出元素，默认的队列是先进先出的\nq.get()\n# 'yang'\nq.get()\n# 4\nq.get()\n# ['yan', 'xing']\n","sub_path":"Basement/code/section_18/pqueue.py","file_name":"pqueue.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"58941251","text":"is_increasing = lambda pwd: not True in [pwd[i] > pwd[i + 1] for i in range(len(pwd) - 1)]\n\n# If we know that each succeeding value is greater,\n# then multiple occurrences of the same number must be \n# adjacent (not 6 unique elements)\nhas_adjacent = lambda pwd: len(set(pwd)) != 6\n\ndef is_larger_group(pwd):\n\tgroups = dict([(char, pwd.count(char)) for char in pwd])\n\treturn not 2 in groups.values()\n\ndef is_valid(pwd, part_2=False):\n\ts = str(pwd)\n\tcriteria = (part_2 and is_larger_group(s)) or (not has_adjacent(s) or not is_increasing(s))\n\treturn False if criteria else s\t\n\nif __name__ == '__main__':\n\twith open('input.txt', 'r') as f:\n\t\tFROM, TO = map(int, f.read().split(','))\n\n\t\t# Part 1\n\t\ta = [is_valid(p) for p in range(FROM, TO + 1)]\n\t\ta = list(filter(lambda x: x, a))\n\t\tprint(len(a))\n\n\t\t# Part 2\n\t\tv = [is_valid(p, part_2=True) for p in a]\n\t\tv = list(filter(lambda x: x, v))\n\t\tprint(len(v))","sub_path":"04/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"366814182","text":"# NOTE: Consider adding logging to capture the year, month, day of the geoIP Database file\n# Import modules needed for script to work\n# StringIO: This module implements a file-like class, StringIO, that reads and writes a \n#   string buffer (also known as memory files).\n# ZipFile: The ZIP file format is a common archive and compression standard. This module \n#   provides tools to create, read, write, append, and list a ZIP file\n# urlopen: This module provides a high-level interface for fetching data across the World \n#   Wide Web. In particular, the urlopen() function is similar to the built-in function \n#   open(), but accepts Universal Resource Locators (URLs) instead of filenames.\n# os: This module provides a portable way of using operating system dependent \n#   functionality.\nfrom StringIO import StringIO\nfrom zipfile import ZipFile\nfrom urllib import urlopen\nfrom os.path import lexists\nfrom os import makedirs\n\n# Establish global variable used by the Python script\nipsetRulesFile = \"ipset.us.rules\"\nipsetRulesDir = \"/etc/ipset/US\"\nipsetName = \"usnetworks\"\nipsetType = \"hash:net\"\nipsetFamily = \"inet\"\nipsetHashSize = \"131072\"\nipsetmaxelem = \"100000\"\nzipMember = \"GeoLite2-Country-Blocks-IPv4.csv\"\ngeoipDBurl = \"http://geolite.maxmind.com/download/geoip/database/GeoLite2-Country-CSV.zip\"\nusCountryID = \"6252001\"\n\n# Check to see if ipset rules directory exists, if it does not then create it.\nif not lexists(ipsetRulesDir):\n  #print (\"INFO: \" + ipsetRulesDir + \" directory does not exist.  Creating directory.\")\n  makedirs(ipsetRulesDir)\n#else:\n#  print (\"INFO: \" + ipsetRulesDir + \" already exists.\")\n  \n# Create ipset file for writing out networks that are part of the United States\n#print (\"INFO: Creating file \" + ipsetRulesDir + \"/\" + ipsetRulesFile + \" for ipset rules.\")\nipsetfile = open(ipsetRulesDir + \"/\" + ipsetRulesFile, \"w\")\n\n# Write first row of file establishing maxelem\nipsetfile.write(\"%s\\n\" % (\"create \" + ipsetName + \" \" + ipsetType + \" family \" +  ipsetFamily + \" hashsize \" + ipsetHashSize + \" maxelem \" +  ipsetmaxelem))\n\n# Download the GeoLite2 Country Files\nurl = urlopen(geoipDBurl)\n\n# Read the zipfile\nzipfile = ZipFile(StringIO(url.read()))\n\n# Store the list of zipfile members to the namelist array\nnamelist = zipfile.namelist()\n\n# Step through each zipfile member that is part of the namelist value\nfor fileName in namelist:\n  # Check to see if the file name ends with the TargetFile value.\n  #print (\"INFO: Checking \" + fileName)\n  \n  if fileName.endswith(zipMember):\n    #print (\"INFO:  We found the file we are looking for: \" + zipMember)\n    # Step through each line\n    with zipfile.open(fileName) as geoList:\n     for geoEntry in geoList:\n        currentLine = geoEntry.split(\",\")\n        countryGeoID = currentLine[1]\n        if countryGeoID == usCountryID:\n          networkAddress = currentLine[0]\n          #print (\"Network Address: \" + networkAddress)\n          ipsetfile.write(\"%s\\n\" % (\"add \" + ipsetName + \" \" + networkAddress))\n","sub_path":"getGeo.py","file_name":"getGeo.py","file_ext":"py","file_size_in_byte":2988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"333452995","text":"import sys, pygame, math\n\n# Compute the area by triangle fan\ndef area(coords):\n    area = 0\n    for i in range(len(coords)-2):\n        v1,v2,v3 = 0,i+1,i+2\n        area += math.fabs(0.5 * (\n            coords[v1].x*(coords[v2].y - coords[v3].y) +\n            coords[v2].x*(coords[v3].y - coords[v1].y) +\n            coords[v3].x*(coords[v1].y - coords[v2].y)\n        ))\n    if( area == 0.0):\n        print(\"zero\")\n        return 1.0\n\n    return area\n\ndef squeeze(ref, mobile, proportion):\n    mobile.x = ref.x + (mobile.x - ref.x) * proportion\n    mobile.y = ref.y + (mobile.y - ref.y) * proportion\n\n# keep this puppy on screen\ndef boxin(pt):\n    a = pt.x\n    b = pt.y\n    pt.x, pt.y = min(max(0.0,pt.x), 800.0), min(max(0.0,pt.y), 600.0)\n    if( a != pt.x or b != pt.y):\n        print(\"changed\")\n\ndef circumference(poly):\n    circ = 0.0\n    for i in range(1,len(poly)):\n        circ += poly[i].distance_to(poly[i-1])\n    return circ\n\n# one frame's total blob move\ndef moveblob(blob):\n    for pt in blob:\n        pt.y += 3 # Aristotelian gravity\n\n    for iter in range(10):\n        prop = math.sqrt(6000.0 / area(blob))\n        \n        squeeze(blob[-1], blob[0], prop)\n        boxin(blob[0])\n        for i in range(1,len(blob)):\n            squeeze(blob[i-1], blob[i], prop)\n            boxin(blob[i])\n\n\n\n\npygame.init()\n\nsize = width, height = 800, 600\nspeed = [2, 2]\nblack = 0, 0, 0\ngreen = 0, 255, 0\nred = 255, 0, 0\n\nscreen = pygame.display.set_mode(size)\n\nball = pygame.image.load(\"intro_ball.gif\")\nballrect = ball.get_rect()\npoints = [\n    pygame.Vector2(50,50), \n    pygame.Vector2(50, 100), \n    pygame.Vector2(150, 75)]\nmorepoints = [\n    pygame.Vector2(60,50), \n    pygame.Vector2(60, 100), \n    pygame.Vector2(110, 75)]\n\nprint(area(points))\n\nblob = []\nfor i in range(31):\n    blob.append(\n        pygame.Vector2(\n            400 + 100 * math.sin(math.pi * 2.0 * i / 31.0),\n            300 + 100 * math.cos(math.pi * 2.0 * i / 31.0)\n    )   )\n\n\n\nwhile 1:\n    pygame.time.wait(30)\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT: sys.exit()\n\n    ballrect = ballrect.move(speed)\n    if ballrect.left < 0 or ballrect.right > width:\n        speed[0] = -speed[0]\n    if ballrect.top < 0 or ballrect.bottom > height:\n        speed[1] = -speed[1]\n    moveblob(blob)\n\n    screen.fill(black)\n    pygame.draw.polygon(screen, green, points)\n    pygame.draw.polygon(screen, red, blob)\n    screen.blit(ball, ballrect)\n    pygame.display.flip()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"63002986","text":"\n\nfrom xai.brain.wordbase.verbs._delegate import _DELEGATE\n\n#calss header\nclass _DELEGATED(_DELEGATE, ):\n\tdef __init__(self,): \n\t\t_DELEGATE.__init__(self)\n\t\tself.name = \"DELEGATED\"\n\t\tself.specie = 'verbs'\n\t\tself.basic = \"delegate\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/verbs/_delegated.py","file_name":"_delegated.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"456281455","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 2018/11/18 下午8:57\n# @Author  : zhaoyu\n# @Site    : \n# @File    : test1.py\n# @Software: PyCharm\n\nimport sys, os\nfrom PIL import Image\nfrom functools import reduce\nimport numpy as np\nimport operator\nimport math\nimport xlrd\nfrom xlwt import *\nfrom common_requests import get_request\n\n\ndef compose(*func):\n    return reduce(lambda f, g: lambda *a, **b: g(f(*a, **b)), func)\n\n\ndef comp1(a, b):\n    c = a + b\n    print(c)\n    return c\n\n\ndef comp2(a):\n    c = a * a\n    print(c)\n    return c\n\n\ndef comp3(a):\n    c = a / (a + a)\n    print(c)\n    return c\n\n\ndef comp4(a):\n    c = a * (a + a)\n    print(c)\n    return c\n\n\ndef comp5(a, b):\n    c = a * a * b * b\n    print(c)\n    return c\n\n\ndef testcomp():\n    temp = compose(comp1, comp2, comp3, comp4, comp5(1))(3, 4)\n    a = temp\n    print(a)\n\n\ndef test():\n    \"\"\"\n    a = os.system('./test1.sh')\n    a = a >> 8\n    print(a)\n    \"\"\"\n    h = 200\n    w = 400\n    num_anchors = 9\n    num_classes = 20\n    a = [(h // {0: 32, 1: 16}[l], w // {0: 32, 1: 16}[l], num_anchors // 2, num_classes + 5) for l in range(2)]\n    test1(*a)\n    test2(a)\n    c = lambda a, b: b\n    print(c)\n\n\ndef test1(*a):\n    b = a\n    print(b)\n\n\ndef test2(a):\n    b = a\n    print(b)\n\n\ndef DarknetConv2D_BN_Leaky(*args, **kwargs):\n    print(args)\n    print(kwargs)\n\n\ndef tiny_yolo_body(inputs, num_anchors, num_classes):\n    a = DarknetConv2D_BN_Leaky(16, (3, 3))\n    print(a)\n\n\ndef test_lstm1():\n    length = 5\n    seq = np.array([i / float(length) for i in range(length)])\n    print(seq)\n\n\ndef fei(years, ratio, per_num):\n    sum_num = 0\n    for i in range(years):\n        if i > 13:\n            sum_num = sum_num * (1 + ratio)\n        else:\n            sum_num = (sum_num + per_num) * (1 + ratio)\n    print(sum_num)\n\n\ndef comp():\n    w1 = np.load('emb_weights.npy')\n    w2 = np.load('emb_weights.npy.1')\n    dif = w1 - w2\n    print(dif)\n\n\ndef sales(count):\n    ini = 6\n    rate = 0.1\n    all_sale = ini\n    for i in range(count - 1):\n        ini = ini * (1 + rate)\n        # print('{} {}'.format(i, ini))\n        all_sale += ini\n        print('{} {}'.format(i + 1, all_sale))\n\n\ndef temp():\n    feed = ['feed_id','notice_type','state','fantuan_id','typ','my_sort_time','notice_type','fantuan_id','state','typ','weight','fantuan_id','state','notice_type','my_sort_time','fantuan_id','uuid','state','typ','weight','hot_count','comment_count','praise_count','id','fantuan_id','uuid','state','typ','weight','id','hot_count','comment_count','praise_count','feed_id','uuid','notice_type','state','typ','source','id','data_key','data_key','typ','typ','data_key','state','fantuan_id','notice_type','typ','create_date','uuid','fantuan_id','id','state','fantuan_id','id','state','create_time','uuid','fantuan_id','fantuan_id','state','state','fantuan_id','id','state','fantuan_id','uuid','notice_type','id','state','fantuan_id','uuid','typ','notice_type','id','state','fantuan_id','uuid','typ','notice_type','feed_id','id','fantuan_id','state','notice_type','typ','id','source','link_key','typ','source','data_key']\n    feed_bell = ['state','expire_time','feed_id','bell_type','fnatuan_id','topic_id','hot_search_id','update_time','feed_id','bell_type','state','expire_time','feed_id','state','expire_time','bell_type','bell_type','feed_id','fnatuan_id','topic_id','hot_search_id','update_time','id']\n    feed_expand = ['feed_id','id','feed_id','feed_id','id']\n    feed_rec = ['feed_id','state','update_time','id','feed_id','id','feed_id','state','feed_id','state','fantuan_id','uuid','create_time']\n\n    dic_f = {}\n    for f in feed:\n        if f in dic_f.keys():\n            dic_f[f] = dic_f[f] + 1\n        else:\n            dic_f[f] = 1\n    sorted_list_a = sorted(dic_f.items(), key=operator.itemgetter(1))\n    print('feed')\n    print(sorted_list_a)\n\n    dic_f = {}\n    for f in feed_bell:\n        if f in dic_f.keys():\n            dic_f[f] = dic_f[f] + 1\n        else:\n            dic_f[f] = 1\n    sorted_list_a = sorted(dic_f.items(), key=operator.itemgetter(1))\n    print('feed_bell')\n    print(sorted_list_a)\n\n    dic_f = {}\n    for f in feed_expand:\n        if f in dic_f.keys():\n            dic_f[f] = dic_f[f] + 1\n        else:\n            dic_f[f] = 1\n    sorted_list_a = sorted(dic_f.items(), key=operator.itemgetter(1))\n    print('feed_expand')\n    print(sorted_list_a)\n\n    dic_f = {}\n    for f in feed_rec:\n        if f in dic_f.keys():\n            dic_f[f] = dic_f[f] + 1\n        else:\n            dic_f[f] = 1\n    sorted_list_a = sorted(dic_f.items(), key=operator.itemgetter(1))\n    print('feed_rec')\n    print(sorted_list_a)\n\n    s = 'abc'\n    per = int(len(s)/2)\n    print(per)\n    s1 = 'abcd'\n    per1 = int(len(s1)/2)\n    print(per1)\n\n\n    x = s[math.ceil(len(s)/2):]+s1[math.ceil(len(s1)/2):]+s[:math.ceil(len(s)/2)]+s1[:math.ceil(len(s1)/2)]\n    print(x)\n\n\ndef fill_user_mobile():\n    # ------------------读数据---------------------------------\n    fileName = \"/Users/xiangzy/Desktop/py.xls\"\n    bk = xlrd.open_workbook(fileName)\n    shxrange = range(bk.nsheets)\n    try:\n        sh = bk.sheet_by_name(\"Sheet1\")\n    except Exception as e:\n        print('read exception {}'.format(e))\n    nrows = sh.nrows  # 获取行数\n    book = Workbook(encoding='utf-8')\n    sheet = book.add_sheet('Sheet1')  # 创建一个sheet\n    for i in range(1, nrows):\n        row_data = sh.row_values(i)\n        # 获取第i行第3列数据\n        # sh.cell_value(i,3)\n        # ---------写出文件到excel--------\n        print(\"-----正在写入 \" + str(i) + \" 行\")\n        try:\n            uuid = sh.cell_value(i, 4)\n            data = get_request('http://idp.hifuntv.com/in/GetUserInfoByUuid?invoker=itvsdk&sign=d3cafca88773700f411999fec9e159a9f38daf4f&data=%7B%22uuid%22%3A%22{}%22%2C%22return%22%3A%22relate_mobile%22%2C%22_support%22%3A%2210000000%22%2C%22uip%22%3A%22127.0.0.1%22%2C%22seqid%22%3A%2220b9bc2644476e5f2dff094bc7d8b3c5%22%2C%22is_sign%22%3A1%7D'.format(uuid))\n            print('uuid:{} result:{}'.format(uuid, data))\n            if data is not None:\n                mobile = data.get('msg', {}).get('relate_mobile', '')\n                if len(mobile) < 1:\n                    continue\n                sheet.write(i, 38, label=mobile)  # 向第1行第1列写入获取到的值\n        except Exception as e:\n            print('{} have exception {}'.format(i, e))\n    book.save(\"/Users/xiangzy/Desktop/py-mobile.xls\")\n\n\n# test()\n# tiny_yolo_body(1, 3, 20)\n# testcomp()\n# test_lstm1()\n# fei(3, 0.02, 1000)\n# comp()\n# sales(40)\n# temp()\nfill_user_mobile()","sub_path":"test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":6546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"548189634","text":"\"\"\"video_project URL Configuration\r\n\r\nThe `urlpatterns` list routes URLs to views. For more information please see:\r\n    https://docs.djangoproject.com/en/3.2/topics/http/urls/\r\nExamples:\r\nFunction views\r\n    1. Add an import:  from my_app import views\r\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\r\nClass-based views\r\n    1. Add an import:  from other_app.views import Home\r\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\r\nIncluding another URLconf\r\n    1. Import the include() function: from django.urls import include, path\r\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\r\n\"\"\"\r\nfrom django.contrib import admin\r\nfrom django.urls import path, re_path\r\nfrom django.conf import settings\r\nfrom django.conf.urls.static import static\r\nfrom app_main import views\r\n\r\nurlpatterns = [\r\n    path('', views.index, name='index'),\r\n    path('index2/', views.index2, name='index2'),\r\n    path('rank/', views.rank, name='rank'),\r\n    path('rank2/', views.rank2, name='rank2'),\r\n    path('radom/', views.radom, name='radom'),\r\n    path('upload/', views.upload, name='upload'),\r\n    path('rank_update/', views.rank_update, name='rank_update'),\r\n    path('del_update/', views.del_update, name='del_update'),\r\n    path('favorite/', views.favorite, name='favorite'),\r\n    path('admin/', admin.site.urls),\r\n]\r\n\r\nif settings.DEBUG:\r\n    urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)  # noqa\r\n","sub_path":"video_project/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"115340357","text":"class Address:\n    def __init__(self, address):\n        self.address = address\n\n    def __repr__(self):\n        return str(self.address)\n\n\nclass Contact:\n    def __init__(self, name, addresses=[]):\n        self.name = name\n        self.addresses = addresses\n\n\njose = Contact(name='Jose Jimenez')\njsc = Address('2101 E NASA Pkwy, Houston, TX')\njose.addresses.append(jsc)\nprint(jose.addresses)\n# [2101 E NASA Pkwy, Houston, TX]\n\nivan = Contact(name='Ivan Ivanovich')\nprint(ivan.addresses)\n# [2101 E NASA Pkwy, Houston, TX]\n","sub_path":"object-oriented-programming/src/oop-init-shared-state.py","file_name":"oop-init-shared-state.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"85274808","text":"# -*- coding: utf-8 -*-\n\"\"\"\nTencentBlueKing is pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available.\nCopyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved.\nLicensed under the MIT License (the \"License\"); you may not use this file except in compliance with the License.\nYou may obtain a copy of the License at http://opensource.org/licenses/MIT\nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on\nan \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the\nspecific language governing permissions and limitations under the License.\n\"\"\"\n\nfrom django.utils.deprecation import MiddlewareMixin\n\nfrom .http import force_response_ee_format, force_response_raw_format, should_use_raw_response\n\n\nclass MethodOverrideMiddleware(MiddlewareMixin):\n    METHOD_OVERRIDE_HEADER = \"HTTP_X_HTTP_METHOD_OVERRIDE\"\n\n    def process_request(self, request):\n        \"\"\"因为 GET 方法 URL 长度有限制，可以使用 POST 实现 GET 效果\"\"\"\n        if request.method == \"POST\" and self.METHOD_OVERRIDE_HEADER in request.META:\n            request.method = request.META[self.METHOD_OVERRIDE_HEADER]\n\n\nclass DynamicResponseFormatMiddleware:\n    \"\"\"根据动态修改返回值格式\n    - 原生格式\n    - 企业版包装格式\n    \"\"\"\n\n    def __init__(self, get_response):\n        self.get_response = get_response\n\n    def __call__(self, request):\n        response = self.get_response(request)\n\n        # exception handler 将添加标记, 避免重复处理\n        if getattr(response, \"from_exception\", None):\n            return response\n\n        if should_use_raw_response(request, response):\n            return force_response_raw_format(response)\n        else:\n            return force_response_ee_format(response)\n","sub_path":"src/api/bkuser_core/common/middlewares.py","file_name":"middlewares.py","file_ext":"py","file_size_in_byte":1897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"586154179","text":"import argparse\nimport os\nfrom time import sleep\n\nimport requests\n\nfrom .client import Client, HttpError\n\ndef get_auth_token():\n    parser = argparse.ArgumentParser(description='Obtain an authentication token')\n    add_auth_arguments(parser)\n    _args, client = parse_args(parser)\n\n    print(\"Authentication token: {}\".format(client.token))\n\ndef upload_video():\n    parser = argparse.ArgumentParser(description='Upload a video file')\n    parser.add_argument('--playlist', help='Playlist ID to which the video should be added')\n    parser.add_argument('video', help='Path to video file')\n    add_auth_arguments(parser)\n    args, client = parse_args(parser)\n    video_path = args.video\n\n    # Create upload url\n    data = {'playlist': args.playlist} if args.playlist else {}\n    upload_url = client.post('videouploadurls/', data=data).json()\n\n    # Upload the file\n    video_id = upload_url['id']\n    requests.post(\n        client.endpoint(\"videos/{}/upload/\".format(video_id)),\n        files={'file': open(video_path)},\n    )\n\n    # Monitor transcoding progress\n    video = client.get('videos/' + video_id).json()\n    title = video['title']\n    video_id = video['id']\n    print(u\"Video uploaded: id={} title='{}'\".format(video_id, title))\n    message = None\n    status = None\n    while status is None or status in ['processing', 'pending']:\n        sleep(1) # don't flood the server\n        video = client.get('videos/' + video_id).json()\n        status = video['processing']['status']\n        new_message = \"    {} {:.2f}%\".format(status, video['processing']['progress'])\n        if message != new_message:\n            message = new_message\n            print(message)\n\n    for video_format in video['formats']:\n        print(\"    {}: {}\".format(video_format['name'], video_format['url']))\n\n\ndef delete_videos():\n    parser = argparse.ArgumentParser(description='Delete a video')\n    add_auth_arguments(parser)\n    parser.add_argument('video_ids', nargs='+', help='Video IDs')\n    args, client = parse_args(parser)\n\n    for video_id in args.video_ids:\n        try:\n            response = client.delete('videos/' + video_id)\n        except HttpError as e:\n            print(u\"Failed to delete video: id={} error={}\".format(video_id, e.message))\n        else:\n            print(u\"Video deleted: id={} status code={}\".format(video_id, response.status_code))\n\ndef search_playlists():\n    parser = argparse.ArgumentParser(description='Search playlists that have a given name')\n    add_auth_arguments(parser)\n    parser.add_argument('name', help='Playlist name')\n    args, client = parse_args(parser)\n\n    response = client.get('playlists', params={'name': args.name})\n    playlists = response.json()\n    print(\"{} results found\".format(len(playlists)))\n    for playlist in playlists:\n        print(\"    id={} name='{}'\".format(playlist['id'], playlist['name']))\n\ndef create_playlist():\n    parser = argparse.ArgumentParser(description='Create a playlist')\n    add_auth_arguments(parser)\n    parser.add_argument('name', help='Playlist name')\n    args, client = parse_args(parser)\n\n    response = client.post('playlists/', data={'name': args.name})\n    playlist = response.json()\n    print(playlist)\n\ndef delete_playlists():\n    parser = argparse.ArgumentParser(description='Create a playlist')\n    add_auth_arguments(parser)\n    parser.add_argument('playlist_ids', nargs='+', help='Playlist ID')\n    args, client = parse_args(parser)\n\n    for playlist_id in args.playlist_ids:\n        client.delete('playlists/{}'.format(playlist_id))\n        print(\"Deleted {}\".format(playlist_id))\n\ndef upload_subtitle():\n    parser = argparse.ArgumentParser(description='Upload a subtitle file')\n    add_auth_arguments(parser)\n    parser.add_argument('video_id', help='Video ID')\n    parser.add_argument('language', help='Language code. E.g: \"fr\"')\n    parser.add_argument('path', help='Path to subtitle file')\n    args, client = parse_args(parser)\n\n    response = client.post(\n        'videos/{}/subtitles/'.format(args.video_id),\n        data={'language': args.language},\n        files={'file': open(args.path, 'rb')},\n    )\n    subtitle = response.json()\n    print(subtitle)\n\ndef add_auth_arguments(parser):\n    \"\"\"\n    Add arguments for authenticating with the remote host.\n    \"\"\"\n    parser.add_argument('--host', default=os.environ.get('VIDEOFRONT_HOST', 'http://127.0.0.1:8000'),\n                        help='Videofront host. You may define the VIDEOFRONT_HOST environment variable instead.')\n    parser.add_argument('-t', '--token',\n                        help='Authentication token. You may define the VIDEOFRONT_TOKEN environment variable instead.')\n    parser.add_argument('-u', '--username', help='Authentication username')\n    parser.add_argument('-p', '--password', help='Authentication password')\n\ndef parse_args(parser):\n    \"\"\"\n    Parse CLI arguments with authentication credentials.\n    \"\"\"\n    args = parser.parse_args()\n\n    try:\n        client = Client(args.host, token=args.token, username=args.username, password=args.password)\n    except ValueError as e:\n        raise argparse.ArgumentError(None, e.message)\n\n    return args, client\n","sub_path":"videofront/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":5141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"263274818","text":"import tweepy\nimport os\nimport logging\n\nfrom flask import Flask, render_template\n\napp = Flask(__name__)\n\nfrom bots.config import create_api\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger()\n\n\ndef get_friends_not_followers(api):\n\n    fnf = []\n    logger.info(\"Retrieving my friends not followers (fnf)...\")\n    followers = tweepy.Cursor(api.followers).items()\n    followers_id = [follower.id for follower in followers]\n\n    for friend in tweepy.Cursor(api.friends).items():\n        if friend.id not in followers_id: \n            fnf.append(friend)\n    return fnf\n\n\n@app.route(\"/\")\ndef index():\n\n    logger.info(\"Started logging ...\")\n    api = create_api()\n    fnf = get_friends_not_followers(api)\n\n    return render_template(\"index.html\", fnf=fnf, len_fnf=len(fnf))\n\n\nif __name__ == \"__main__\":\n    pass\n","sub_path":"Stats-Bot.py","file_name":"Stats-Bot.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"590397118","text":"from pathlib import Path\n\nfrom aiohttp import web\nimport aiohttp_jinja2\nimport jinja2\nimport magic\n\n@aiohttp_jinja2.template('gallery.html.jinja2')\nasync def handle(request):\n    path = request.match_info.get('path', '')\n    is_root = path == ''\n    my_path = (request.app['root'] / path).resolve()\n    try:\n        my_path.relative_to(request.app['root'])\n    except ValueError as e:\n        return web.Response(text='not allowed', status=403)\n    gallery_name = my_path.name\n    try:\n        dir_items = my_path.iterdir()\n        dir_items = [item for item in dir_items]\n        dir_items.sort(key=lambda i: str(i).casefold())\n    except FileNotFoundError as e:\n        return web.Response(text='no such directory', status=404)\n    dirs = []\n    files = []\n    if not is_root:\n        dirs.append((my_path / '..').relative_to(request.app['root']))\n    for item in dir_items:\n        rel_item = item.relative_to(request.app['root'])\n        if item.is_dir():\n            dirs.append(rel_item)\n        #else:\n        #    print(magic.from_file(str(item), mime=True))\n        elif magic.from_file(str(item), mime=True) in request.app['imagetypes']:\n            files.append({'file': rel_item, 'thumb': rel_item, 'type': 'Image'})\n        elif magic.from_file(str(item), mime=True) in request.app['videotypes']:\n            files.append({'file': rel_item, 'thumb': None, 'type': 'Video'})\n    return {'dirs': dirs, 'files': files, 'gallery_name': gallery_name}\n\nasync def create_app(gallery_root=None):\n    app = web.Application()\n    app['root'] = Path(gallery_root).absolute() if gallery_root else Path().absolute()\n    aiohttp_jinja2.setup(app, loader=jinja2.PackageLoader('spg', 'templates'))\n\n    # https://developer.mozilla.org/en-US/docs/Web/Media/Formats/Image_types\n    app['imagetypes'] =  ['image/apng', 'image/bmp', 'image/gif', 'image/x-icon', 'image/jpeg', 'image/png', 'image/svg+xml', 'image/webp']\n\n    app['videotypes'] = ['video/mp4']\n\n    app.add_routes([\n        web.static('/pics', app['root']),\n    ])\n    app.add_routes([web.get('/{path:.*}', handle)])\n    return app","sub_path":"spg/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"74144885","text":"from telegram.ext import Updater, CommandHandler, MessageHandler, Filters\r\nimport pymysql\r\nimport apiai, json\r\n\r\nupdater = Updater(token='787296993:AAH3m0mVTrcLzMFu5wjaZrn5-LTTUoxrI7U')  # Токен API к Telegram\r\ndispatcher = updater.dispatcher\r\ncnx = pymysql.connect(user='wkfencjQr7',\r\n                      password='RYEA7WmsRb',\r\n                      host='remotemysql.com',\r\n                      database='wkfencjQr7')\r\ncursor = cnx.cursor()\r\nquery = \"SELECT code_login FROM student\"\r\ncursor.execute(query)\r\nresult = cursor.fetchall()\r\n# Запис у список всіх користувачів\r\nfinal_result = [list(i) for i in result]\r\nfinal_user = []\r\nfor i in range(len(final_result)):\r\n            final_user.append(final_result[i][0])\r\n\r\ndef startCommand(bot, update):\r\n    bot.send_message(chat_id=update.message.chat_id,\r\n                     text='Hello, I`m  \\n'\r\n                          'Post bot\\n'\r\n                          'Drop_of_care Org.\\n'\r\n                          'If you dont know how to make\\n'\r\n                          ' a post you can use /help')\r\n    bot.send_message(chat_id=update.message.chat_id, text='But if you dont need help,\\n'\r\n                                                          'Make your profile more cool,\\n'\r\n                                                          'Make a post, dont be like everyone else,\\n'\r\n                                                          ' be yourself')\r\n    bot.send_message(chat_id=update.message.chat_id, text='Make a post:')\r\n\r\ndef textMessage(bot, update):\r\n    login = str(update.message.text)\r\n    res1_new = []\r\n    res1 = login.split(\"}\")\r\n    for val in res1:\r\n        val = val.strip()\r\n        res1_new.append(val)\r\n\r\n\r\n    if res1_new[0] in final_user:\r\n        cursor = cnx.cursor()\r\n        query = \"SELECT email FROM student where code_login ='%s'\" % res1_new[0]\r\n\r\n        cursor.execute(query)\r\n        result2 = cursor.fetchall()\r\n        s1 = str(result2)\r\n        s2 = s1.replace(\"'\", \"\")\r\n        s2 = s2.replace(\"(\", \"\")\r\n        s2 = s2.replace(\")\", \"\")\r\n        s2 = s2.replace(\",\", \"\")\r\n        email = s2\r\n        cursor = cnx.cursor()\r\n        query = \"SELECT name FROM student where code_login ='%s'\" % res1_new[0]\r\n        cursor.execute(query)\r\n        result2 = cursor.fetchall()\r\n        s1 = str(result2)\r\n        s3 = s1.replace(\"'\", \"\")\r\n        s3 = s3.replace(\"(\", \"\")\r\n        s3 = s3.replace(\")\", \"\")\r\n        s3 = s3.replace(\",\", \"\")\r\n        name = s3\r\n        title = res1_new[1]\r\n        bot.send_message(chat_id=update.message.chat_id, text=' your post is added to profile ')\r\n        text = res1_new[2]\r\n        tags = res1_new[3]\r\n\r\n        cursor = cnx.cursor()\r\n        query = \"INSERT INTO post (id, email, name, title, tags, text) VALUES (NULL,'%s','%s','%s','%s','%s')\" % (\r\n        email, name, title, tags, text)\r\n        cursor.execute(query)\r\n        bot.send_message(chat_id=update.message.chat_id, text='Dear '+name + ' your post is added to profile\\n '\r\n                                                              'Do the same if you want to post smth again😊😊😊 ')\r\n\r\n\r\n    else:\r\n        bot.send_message(chat_id=update.message.chat_id,\r\n                         text = 'Invalid')\r\n\r\n#Обработка команд\r\n\r\ndef helpCommand(bot, update):\r\n    bot.send_message(chat_id=update.message.chat_id,\r\n                     text='So, you need help\\n'\r\n                          'in writing message \\n'\r\n                          'to make a post\\n'\r\n                          'This link will help \\n'\r\n                          'you to solve this problem\\n'\r\n                          'https://telegra.ph/Example-of-sorrectly-written-message-to-Drop-of-Care-bot-04-08')\r\n\r\n\r\n# Хендлеры\r\nstart_command_handler = CommandHandler('start', startCommand)\r\nhelp_command_handler = CommandHandler('help', helpCommand)\r\ntext_message_handler = MessageHandler(Filters.text, textMessage)\r\nWrite_message_handler = MessageHandler(Filters.text, textMessage)\r\n# Добавляем хендлеры в диспетчер\r\ndispatcher.add_handler(start_command_handler)\r\ndispatcher.add_handler(help_command_handler)\r\ndispatcher.add_handler(text_message_handler)\r\ndispatcher.add_handler(Write_message_handler)\r\n\r\n# Начинаем поиск обновлений\r\nupdater.start_polling(clean=True)\r\n# Останавливаем бота, если были нажаты Ctrl + C\r\nupdater.idle()\r\n","sub_path":"DOOOCcC.py","file_name":"DOOOCcC.py","file_ext":"py","file_size_in_byte":4454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"447121442","text":"#Jesse Bruce\n#cpts355 HW5\n#ScopedSimplePostScript\n#closer to working model\n#static scoping actually implemented\n\n# The operand stack\nopstack = []\n\ndef opPop():\n    return opstack.pop()\ndef opPush(value):\n    if type(value) is str:\n        if value[0] is '(':\n            opstack.append(value[1:-1])\n            return\n    opstack.append(value)\n\n# The dictionary stack\ndictstack = []\n\nstatic=1\nnumscopes=0\n\ndef dictPop():\n    return dictstack.pop()\ndef dictPush(linkv, dictionary):\n    dictstack.append((linkv, dictionary))\ndef define(name, value):\n    (link, ddict)=dictPop()\n    ddict[name]=value\n    dictPush(link, ddict)\ndef getlink(name):\n    #find the index of name for link value\n    i=0\n    dictstack.reverse()\n    for (link, gdict) in dictstack:\n        if name in gdict:\n            dictstack.reverse()\n            print(len(dictstack)-i-1)\n            return len(dictstack)-i-1\n        i=i+1\ndef lookup(name):\n    global static\n    if static:\n        #check current scope\n        for curlink in range (-1, len(dictstack)-1):\n            (link, ldict) = dictstack[curlink]\n            if name in ldict.keys():\n                return ldict[name]\n            else:\n                if curlink == link:\n                    #inf loop breaker\n                    break\n                else:\n                    #else access static link\n                    curlink = link\n    else:\n        #dynamic scope\n        curlink =getlink(name)\n        (link, ldict)=dictstack[curlink]\n        return ldict[name]\n    global numscopes\n    numscopes+=1\n    return numscopes\n\n# arithmetic operators-- add, sub, mul, div, mod, eq, lt\ndef add():\n    var2 = opPop()\n    var1 = opPop()\n    opPush(var1+var2)\ndef sub():\n    var2 = opPop()\n    var1 = opPop()\n    opPush(var1-var2)\ndef mul():\n    var2 = opPop()\n    var1 = opPop()\n    opPush(var1*var2)\ndef div():\n    var2 = opPop()\n    var1 = opPop()\n    opPush(var1/var2)\ndef mod():\n    var2 = opPop()\n    var1 = opPop()\n    opPush(var1%var2)\ndef eq():\n    var2 = opPop()\n    var1 = opPop()\n    if var1 == var2: return True\n    else: return False\ndef lt():\n    var2 = opPop()\n    var1 = opPop()\n\n# string operators-- length, get, put, getinterval\ndef length():\n    var1 = opPop()\n    opPush(len(var1))\ndef get():\n    index = opPop()\n    string = opPop()\n    opPush(ord(string[index]))\ndef put():\n    char = opPop()\n    index = opPop()\n    key = opPop()\n    if key in dictstack[-1].keys():\n        dictstack[-1][key] = dictstack[-1][key][:index]+chr(char)+dictstack[-1][key][index+1:]\ndef getinterval():\n    count = opPop()\n    index = opPop()\n    string = opPop()\n    opPush(string[index:index+count])\n\n# stack manipulation and print operators-- dup, exch, pop, roll, copy, clear, stack, count, psDef, psFor\ndef dup():\n    var1 = opPop()\n    opPush(var1)\n    opPush(var1)\ndef exch():\n    top = opPop()\n    sec = opPop()\n    opPush(top)\n    opPush(sec)\ndef pop():\n    return opPop()\ndef roll():\n    rollstack = []\n    extrastack = []\n    nvals = opPop()\n    pos = opPop()\n    for i in range(pos):\n        rollstack.append(opPop())\n    for i in range(pos-nvals):\n        extrastack.append(opPop())\n    for item in rollstack:\n        opPush(item)\n    for item in extrastack:\n        opPush(item)\ndef copy():\n    vals=[]\n    nvals = opPop()\n    for i in range(nvals):\n        vals.append(opPop())\n    for i in range(2):\n        for val in vals:\n            opPush(val)\ndef clear():\n    del opstack[:]\ndef stack():\n    print(\"==============\")\n    for val in opstack[::-1]:\n        print(val)\n    print(\"==============\")\n    if len(dictstack) == 1:\n        (link, sdict)=dictstack[0]\n        print(\"----\"+str(0)+\"----\"+str(link)+\"----\")\n        for (key, val) in sdict.items():\n            print(str(key)+\" \"+str(val))\n    else:\n        for i in range(len(dictstack)-1,-1,-1):\n            (link, sdict)=dictstack[i]\n            print(\"----\"+str(i)+\"----\"+str(link)+\"----\")\n            for (key, val) in sdict.items():\n                print(str(key)+\" \"+str(val))\n\n    print(\"==============\")\ndef count():\n    opPush(len(opstack))\ndef psDef():\n    value = opPop()\n    name = opPop()\n    (link, ddict)=dictPop()\n    ddict[name]=value\n    dictPush(link, ddict)\ndef psFor():\n    code = opPop()\n    limit = opPop()\n    incre = opPop()\n    i = opPop()\n    for x in range(i, limit ,incre):\n        interpret(code)\n\n# string to code array functions\nimport re\ndef tokenize(s):\n    return re.findall(\"/?[a-zA-Z][a-zA-Z0-9_]*|[(][a-zA-Z0-9_\\s!][a-zA-Z0-9_\\s!]*[)]|[-]?[0-9]+|[}{]+|%.*|[^ \\t\\n]\", s)\ndef groupMatching(it):\n    res = []\n    for c in it:\n        if c==')':\n            return res\n        else:\n            # note how we use a recursive call to group the inner\n            # matching parenthesis string and append it as a whole\n            # to the list we are constructing.\n            res.append(groupMatching(it))\n    return False\n# function to turn a string of properly nested parentheses\n# into a list of properly nested lists.\ndef group(s):\n    if s[0]=='(':\n        return groupMatching(iter(s[1:]))\n    else: return False\ndef groupCode(lpart):\n    if lpart[0] is '{':\n        return codeMatching(lpart[1:])\ndef codeMatching(it):\n    i = 0\n    n = 0\n    x = 0\n    l=[]\n    for i, token in enumerate(it):\n        if i > 0 and token == '{':\n            n=findMatchCurly(it[i:])\n            l.append(codeMatching(it[i+1:n+i]))\n            x = i + n + 1\n        elif i is 0 or i >= x:\n            try:\n                l.append(int(token))\n            except:\n                l.append(token)\n    print(\"L: \"+str(l))\n    return l\ndef findMatchCurly(l):\n    lcurl = 0\n    for i, token in enumerate(l):\n        if token is '{':\n            lcurl+=1\n        elif token is '}':\n            lcurl-=1\n        if lcurl is 0:\n            return i\ndef parse(tokens):#returns a code array for interpet\n    n = 0\n    i=0\n    x=0\n    code = []\n    for i, token in enumerate(tokens):\n        if token == '{' and i > x:\n            n = findMatchCurly(tokens[i:])\n            print(n)\n            code.append(groupCode(tokens[i:n+i]))\n            x = i+n+1\n        elif i == 0 or i >= x:\n            try:\n                code.append(int(token))\n            except:\n                code.append(token)\n    return code\n\ncodeOptions = {0:'lookup', 1:'add', 2:'sub', 3:'mul', 4:'div', 5:'mod', 6:'len', 7:'get', 8:'put', 9:'getinterval', 10:'dup', 11:'exch', 12:'pop', 13:'roll', 14:'copy', 15:'clear', 16:'stack', 17:'count', 18:'dict', 19:'begin', 20:'end', 21:'def', 22:'for'}\nnonos=[\"'\",',','[',']']\ndef interpret(code): # code is a code array\n    for i in range(0,len(code)):\n        if code[i] in nonos:\n            continue\n        if code[i] in codeOptions.values():\n            #if code is command, call it\n            if code[i] == 'lookup':\n                lookup()\n            elif code[i] == 'add':\n                add()\n            elif code[i] == 'sub':\n                sub()\n            elif code[i] == 'mul':\n                mul()\n            elif code[i] == 'div':\n                div()\n            elif code[i] == 'mod':\n                mod()\n            elif code[i] == 'len':\n                length()\n            elif code[i] == 'get':\n                get()\n            elif code[i] == 'put':\n                put()\n            elif code[i] == 'getinterval':\n                getinterval()\n            elif code[i] == 'dup':\n                dup()\n            elif code[i] == 'exch':\n                exch()\n            elif code[i] == 'pop':\n                pop()\n            elif code[i] == 'roll':\n                roll()\n            elif code[i] == 'copy':\n                copy()\n            elif code[i] == 'clear':\n                clear()\n            elif code[i] == 'stack':\n                stack()\n            elif code[i] == 'count':\n                count()\n            elif code[i] == 'dict':\n                psDict()\n            elif code[i] == 'begin':\n                begin()\n            elif code[i] == 'end':\n                end()\n            elif code[i] == 'def':\n                psDef()\n            elif code[i] == 'for':\n                psFor()\n        else:\n            #else either push to opstack or lookup\n            if type(code[i]) is int:\n                opPush(code[i])\n            elif code[i][0] == '/':\n                opPush(code[i][1::])\n            elif code[i][0] == '[' or type(code[i]) is list:\n                opPush(code[i])\n            elif code[i] == \"None\":\n                continue\n            else:\n                lup = lookup(code[i])\n                link=99\n                if static:\n                    link = getlink(code[i])\n                dictPush(link, {})\n                interpreter(str(lup))\n                dictPop()\ndef interpreter(s): # s is a string\n    interpret(parse(tokenize(s)))\ndef main():\n    global static\n    static =1\n    dictPush(0, {})\n    st=input(\"static or dynamic? \")\n    if st[0] == \"s\":\n        static=1\n    else:\n        static=0\n    interpreter(\"\"\"/x 4 def\n    /g { x stack } def\n    /f { /x 7 def g } def\n    f\"\"\")\n    # interpreter(\"\"\"/m 50 def\n    # /n 100 def\n    # /egg1 {/m 25 def n} def\n    # /chic\n    # {/n 1 def\n    # /egg2 {n (n in egg2) } def\n    # m\n    # n\n    # egg1\n    # egg2\n    # stack} def\n    # n\n    # chic\"\"\")\n\n    #\n    # interpreter(\"\"\"/m 50 def\n    # /n 100 def\n    # /egg1 {/m 25 def n (n in egg1) } def\n    # /chic\n    # {/n 1 def\n    # /egg2 {n (n in egg2) } def\n    # m (m in chic) n (n in chic)\n    # egg1\n    # egg2\n    # stack} def\n    # n  (n in main)\n    # chic\"\"\")\n\n    inp=input(\"SSPS: \")\n    interpreter(inp)\n    while(inp != \"exit\" and inp != \"quit\" and inp != \"terminate\"):\n        inp=input(\"SSPS: \")\n        interpreter(inp)\nmain()\n","sub_path":"HW5/HW5.py","file_name":"HW5.py","file_ext":"py","file_size_in_byte":9683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"584202105","text":"# -*- coding:utf-8 -*-\n\n\"\"\"\nJR-100 FSK Loader\n\"\"\"\n\nimport wave\nimport numpy as np\nimport struct\nimport sys\nimport math\nimport re\n\n_verbosity = 0\n\n_config_file = 'fskloader.ini'\n_mark_frequency = 2400.0\n_space_frequency = 1200.0\n_fsk_thresholds = None\n_cp_threshold = 0.15\n_wave_param = {}\n\ndef _read_wave(path):\n    \"\"\"\n    waveファイルからサンプリングデータを読み込む。\n\n    Parameters\n    ----------\n    path: str\n        waveファイルへのパス名\n\n    Returns\n    -------\n    data: numpy.ndarray\n        読み込んだサンプリングデータ\n    \"\"\"\n    global _fsk_thresholds\n\n    if _verbosity >= 1:\n        print('Reading WAV file: {}'.format(path))\n    \n    with wave.open(path) as f:\n        param = f.getparams()\n        _wave_param['nchannels'] = f.getnchannels()\n        _wave_param['sample_width'] = f.getsampwidth()\n        _wave_param['sampling_rate'] = f.getframerate()\n        data = f.readframes(f.getnframes())\n\n    # 不完全な音声ファイルの場合があるので余りを切り捨てる。\n    r = len(data) % _wave_param['sample_width']\n    data = data[:len(data)-r]\n\n    # 各サンプルを-1.0から1.0に正規化する。\n    if _wave_param['sample_width'] == 1:\n        data = np.frombuffer(data, dtype = 'uint8')\n        data = (data.astype('float') - 128) / 128\n    elif _wave_param['sample_width'] == 2:\n        data = np.frombuffer(data, dtype = 'int16')\n        data = data.astype('float') / 32768\n    else:\n        raise NotImplementedError(\n            \"Sample width = {} is not supported)\"\n            .format(_wave_param['sample_width']))\n\n    # ステレオの場合は左チャンネルを使う。\n    if _wave_param['nchannels'] == 2:\n        data = data[::2]\n    elif _wave_param['nchannels'] != 1:\n        raise NotImplementedError(\n            \"The number of channels = {} is not supported)\".format(ch))\n\n    if not _fsk_thresholds:\n        _fsk_thresholds = _decide_thresholds(_wave_param['sampling_rate'],\n                                             _mark_frequency,\n                                             _space_frequency)\n\n    if _verbosity >= 2:\n        print('  nchannels = {}, sample_width = {}, frame_rate = {}, nframes = {}'\n              .format(_wave_param['nchannels'],\n                      _wave_param['sample_width'],\n                      _wave_param['sampling_rate'],\n                      len(data)))\n        print('  FSK thresholds = {}'.format(_fsk_thresholds))\n        \n    return data\n\ndef _comparator(x0, x1, th_h, th_l):\n    \"\"\"\n    ヒステリシス付きのコンパレータの動作を模擬する。\n\n    Parameters\n    ----------\n    th_h: float\n        立ち上がりエッジのしきい値\n    th_l: float\n        立ち下がりエッジのしきい値\n\n    Returns\n    -------\n    value: float\n        コンパレータの出力値\n    \"\"\"\n    if x0 < th_h and x1 >= th_h:\n        return 1.0\n    elif x0 > th_l and x1 <= th_l:\n        return -1.0\n    else:\n        return x0\n\ndef _write_wave(name, x, rate):\n    '''\n    サンプリング データをファイルに出力する。\n\n    Parameters\n    ----------\n    name: str\n        ファイル名\n    x: numpy.ndarray\n        サンプリング データ\n    rate:\n        サンプリング周波数\n    '''\n    import scipy.io.wavfile as siw\n    siw.write(name, rate, x)\n    \ndef _shape_wave(x):\n    \"\"\"\n    サンプルデータを2値化する。\n\n    Parameters\n    ----------\n    x: numpy.ndarray\n        正規化されたサンプリングデータ\n\n    Returns\n    -------\n    x: numpy.ndarray\n        2値化されたサンプリングデータ\n    \"\"\"\n    global _cp_threshold\n    \n    if _verbosity >= 1:\n        print('Shaping')\n        \n    # 直流成分を除去する。\n    offset = np.mean(x)\n    if _verbosity >= 1:\n        print('  Elminating DC component: offset = {}'.format(offset))\n    x = x - offset\n\n    # 波形の絶対値の最大値が1になるよう増幅する。\n    a = 1 / max(np.max(x), abs(np.min(x)))\n    if _verbosity >= 1:\n        print('  Amplifying: a = {}'.format(a))\n    x = a * x\n\n    # 先頭と末尾の無音部分を除去する。\n    pos_head = np.where(abs(x) >= 0.5)\n    if _verbosity >= 1:\n        print('  Removing silient part: {} - {}'\n              .format(pos_head[0][0] / _wave_param['sampling_rate'],\n                      pos_head[0][-1] / _wave_param['sampling_rate']))\n    # x = x[pos_head[0][0]:pos_head[0][-1]+1]\n\n    if _verbosity >= 4:\n        _write_wave('shaped1.wav', x, _wave_param['sampling_rate'])\n\n    # データを+1.0と-1.0に2値化する。\n    if _verbosity >= 1:\n        print('  Binarizing')\n    x = np.append(-1, x) # 先頭にダミー初期値を追加\n    for i in range(len(x)-1):\n        x[i+1] = _comparator(x[i], x[i+1], _cp_threshold, -_cp_threshold)\n\n    if _verbosity >= 4:\n        _write_wave('shaped2.wav', x, _wave_param['sampling_rate'])\n\n    return x[1:] # ダミーを削除して返す。\n\ndef _decide_thresholds(fs, f1, f0):\n    \"\"\"\n    FSKで0と1の判定のためのしきい値を決定する。\n\n    Parameters\n    ----------\n    fs: float\n        音声データのサンプリング周波数\n    f1: float\n        FSKにおけるマーク(1)周波数\n    f0: float\n        FSKにおけるスペース(0)周波数\n    \"\"\"\n    pos_f0 = math.floor(fs / f0)\n    pos_f1 = math.floor(fs / f1)\n    pos_center = (pos_f0 + pos_f1) / 2\n    distance = math.floor(pos_center - pos_f1)\n    return ((int(math.floor(pos_f1 - distance)),\n             int(math.floor(pos_f1 + distance))),\n            (int(math.floor(pos_f1 + distance) + 1),\n             int(math.floor(pos_f0 + distance))))\n\ndef set_fsk_thresholds(min_1, max_1, min_0, max_0):\n    \"\"\"\n    FSKで0と1の判定のためのしきい値を設定する。\n\n    Parameters\n    ----------\n    min_1: int\n        '1'と判定するための最小幅\n    max_1:\n        '1'と判定するための最大幅\n    min_0: int\n        '0'と判定するための最小幅\n    max_0:\n        '0'と判定するための最大幅\n    \"\"\"\n    global _fsk_thresholds\n    _fsk_thresholds = ((min_1, max_1), (min_0, max_0))\n\ndef set_cp_threshold(value):\n    \"\"\"\n    コンパレータで立ち上がりと立ち下がりを判定す��ためのしきい値を設定する。\n\n    Parameters\n    ----------\n    value: float\n        しきい値\n    \"\"\"\n    global _cp_thresholds\n    _cp_thresholds = value\n    \ndef _detector(v):\n    \"\"\"\n    波形のフェーズを検出する。\n\n    Parameters\n    ----------\n    v: float\n        正規化されたサンプルデータ\n\n    Returns\n    -------\n    phase: str\n        フェーズ\n\n    Notes\n    -----\n    フェーズは以下のいずれかである。\n    * 0: 値'0'に対するフェーズ\n    * 1: 値'1'に対するフェーズ\n    * unknown: それ以外の未知のフェーズ\n    \"\"\"\n    if _fsk_thresholds[1][0] * 2 <= v <= _fsk_thresholds[1][1] * 2:\n        return 0\n    elif  _fsk_thresholds[0][0] * 2 <= v <= _fsk_thresholds[0][1] * 2:\n        return 1\n    else:\n        return -1\n\ndef _fsk_value(x, i):\n    \"\"\"\n    FSKで符号化された音声フェーズ列から1つの値を求める。\n\n    Parameters\n    ----------\n    x: bytearray\n        フェーズ列\n    i: int\n        オフセット\n\n    Returns\n    -------\n    value: int\n        復号化された1つの値\n    offset: int\n        次のオフセット    \n\n    Notes\n    -----\n    valueとoffsetはタプルで返す。\n    \"\"\"\n    try:\n        if _detector(x[i]+x[i+1]) == 0:\n            return (0, i + 2)\n        elif (_detector(x[i]+x[i+1]) == 1\n              and _detector(x[i+2]+x[i+3]) == 1):\n            return (1, i + 4)\n        else:\n            return (255, i + 1)\n    except IndexError:\n        return (255, i + 1)\n\ndef _dump_histogram(d):\n    '''\n    ヒストグラムを出力する。\n\n    Parameters\n    ----------\n    d: numpy.ndarray\n        立ち下がりエッジ間の距離のリスト\n    '''\n    hist, bins = np.histogram(\n        abs(d),\n        bins=np.arange(abs(d).min(), min(60, abs(d).max()+2), step=1))\n    print('  --------------------')\n    if _fsk_thresholds[0][0] < abs(d).min():\n        print('  ---- start of H ----')\n    for i, n in zip(bins, hist):\n        if i == _fsk_thresholds[0][0]:\n            print('  ---- start of H ----')\n        elif i == _fsk_thresholds[1][0]:\n            print('  ---- start of L ----')\n            \n        print('  {:3d}: {}'.format(i, n))\n            \n        if i == _fsk_thresholds[0][1]:\n            print('  ---- end of H ----')\n        elif i == _fsk_thresholds[1][1]:\n            print('  ---- end of L ----')\n    if _fsk_thresholds[1][1] > abs(d).max():\n        print('  ---- end of L ----')\n    print('  --------------------')\n        \ndef _serialize(x):\n    \"\"\"\n    ビット列を取り出す。\n\n    Parameters\n    ----------\n    x: numpy.ndarray\n        2値化されたサンプリングデータ\n\n    Returns\n    -------\n    bits: bytearray\n        ビット列\n    \"\"\"\n    if _verbosity >= 1:\n        print('Serializing')\n\n    # 立ち下がりエッジの位置を検出する。\n    falling_edge_pos = []\n    previous = x[0]\n    for i in range(1, len(x)):\n        if previous > 0 and x[i] < 0:\n            falling_edge_pos.append(i)\n        previous = x[i]\n\n    # 立ち下がりエッジ間の距離を求める。\n    f = np.array(falling_edge_pos)\n    d = np.diff(f)\n\n    # FSK値1相当のパルス幅が10個連続で現れたらセーブデータの開始とみなす。\n    start_of_signal_found = False\n    i = 0\n    count = 0\n    while i < len(d) and not start_of_signal_found:\n        if _fsk_thresholds[0][0] <= d[i] <= _fsk_thresholds[0][1]:\n            count += 1\n        else:\n            count = 0\n        if count == 10:\n            start_of_signal_found = True\n            break\n        i += 1\n    if not start_of_signal_found:\n        print('  signal head not found')\n    else:\n        if _verbosity >= 2:\n            print('  signal starts from sample #{}'.format(i))\n\n    # 最初のゼロを探す。\n    # このゼロの位置を起点に、2個('0'の場合)または4個('1'の場合)の単位で\n    # FSKの復号を行う。\n    first_zero_found = False\n    while i < len(d) and not first_zero_found:\n        if _fsk_thresholds[1][0] <= d[i] <= _fsk_thresholds[1][1]:\n            first_zero_found = True\n            break\n        i += 1\n    if not first_zero_found:\n        print('  first zero not found')\n    else:\n        if _verbosity >= 2:\n            print('  first zero found at sample #{}'.format(i))\n\n    if _verbosity >= 2:\n        print('  Histogram of the interval betwen falling edges')\n        _dump_histogram(d)\n        \n    bits = bytearray()\n    while i < len(d):\n        value, new_i = _fsk_value(d, i)\n        if value == 0 or value == 1:\n            bits.append(value)\n        i = new_i\n\n    if _verbosity >= 3:\n        print('  Bit sequence length = {}'.format(len(bits)))\n        print('  Bit sequence contents:')\n        print('------------------------')\n        for k in range(0, len(bits), 1000):\n            print('{} - {}'.format(k, k + 1000 - 1))\n            box = bits[k:k+1000]\n            print('\\n'.join([''.join([str(box[i])\n                                      if box[i] == 0 or box[i] == 1\n                                      else '.'\n                                      for i in range(len(box))][j:j+100])\n                             for j in range(0, len(box), 100)]))\n        print('----------------------')\n\n    return bits\n\ndef _bits_to_byte(b):\n    '''\n    リトルエンディアンのビット列を整数値に変換する。\n\n    Parameters\n    ----------\n    b: bytearray\n        ビット列\n\n    Returns\n    -------\n    value: int\n        ビット列に対するバイト値\n    '''\n    a = [e if e == 0 or e == 1 else 0 for e in b]\n    return sum([a[i] * 2**i for i in range(len(a))])\n    \ndef _get_byte(b, i):\n    \"\"\"\n    音声データのフェーズ列からビット列を取り出す。\n\n    Parameters\n    ----------\n    b: bytearray\n        ビット列\n    i: int\n        オフセット\n\n    Returns\n    -------\n    byte: int\n        バイト値\n    offset: int\n        次のオフセット\n    skip: int\n        スキップしたビット数\n\n    Notes\n    -----\n    返値はbyteとoffsetのタプルである。\n    \"\"\"\n    if i+10 < len(b):\n        start = b[i]\n        value = _bits_to_byte(b[i+1:i+9])\n        stop1, stop2 = b[i+9], b[i+10]\n        if (start, stop1, stop2) == (0, 1, 1):\n            return (value, i+11, 0)\n        else:\n            if _verbosity >= 1:\n                print('  invalid separator bits: {} at {}'.format(b[i:i+11], i))\n            found = False\n            current_i = i\n            i += 1\n            while not found and i+10 < len(b):\n                if (b[i], b[i+9], b[i+10]) == (0, 1, 1):\n                    found = True\n                    break\n                i += 1\n            if found:\n                value = _bits_to_byte(b[i+1:i+9])\n                return (value, i+11, i - current_i)\n            else:\n                return (value, i+1, 0)\n    else:\n        return (255, i+1, 0)\n    \ndef _decode(bits):\n    \"\"\"\n    ビット列を復号化する。\n\n    Parameters\n    ----------\n    bits: bytearray\n        FSKデータから得られたビット列\n\n    Returns\n    -------\n    byte_sequence: bytearray\n        ビット列から取り出したバイト列    \n    \"\"\"\n    if _verbosity >= 1:\n        print('Decoding')\n\n    bit_sequence = np.array(bits)\n    header_index = -1\n\n    # データ末尾のストップビットが欠落する場合があるため明示的に追加しておく。\n    bit_sequence = np.append(bit_sequence, 1)\n\n    header_index = -1\n    count = 0\n    pattern = np.array([0, 1, 1, 1, 1, 1, 1, 1, 1])\n    for i in range(len(bit_sequence)):\n        if np.all(bit_sequence[i:i+9] == pattern):\n            header_index = i\n            break\n\n    if _verbosity >= 2:\n        print('  header start from {}'.format(header_index))\n    \n    if header_index >= 0:\n        count = 0\n        i = header_index\n        while i < len(bit_sequence) and np.all(bit_sequence[i:i+9] == pattern):\n            count += 1\n            i += 9\n\n        if _verbosity >= 2:\n            print('  pattern count = {}'.format(count))\n\n    # 0b011111111(9bit)のパターンが10個以上出現したらJR-100が生成するCMTデータとみなす。\n    if header_index < 0 or count < 10:\n        if _verbosity >= 1:\n            print('  JR-100 Header not found')\n        return None\n    else:\n        if _verbosity >= 2:\n            print('  JR-100 Header found')\n\n    # 最初の0(最初のバイトのスタートビット)の位置を求める。\n    i += np.where(bit_sequence[i:] == 0)[0][0]\n        \n    byte_sequence = bytearray()\n\n    # ヘッダ分の読み取り\n    if _verbosity >= 1:\n        print('  Reading Header from {}'.format(i))\n    for _ in range(33):\n        v, i, skip = _get_byte(bit_sequence, i)\n        if skip > 0:\n            print('  skip {} bits, tentative value = 0x{:02x}'.format(skip, v))\n            print('  ...{}'.format(byte_sequence[-30:]))\n        byte_sequence.append(v)\n\n    # プログラムデータ分の読み取り\n    if _verbosity >= 1:\n        print('  Reading Body from {}'.format(i))\n    i = np.where(bit_sequence[i:] == 0)[0][0] + i\n    length = (byte_sequence[18] << 8) + byte_sequence[19]\n    for _ in range(length + 1):\n        v, i, skip = _get_byte(bit_sequence, i)\n        if skip > 0:\n            print('  skip {} bits, tentative value = 0x{:02x}'.format(skip, v))\n            print('  ...{}'.format(byte_sequence[-30:]))\n        byte_sequence.append(v)\n\n    # チェックサムの検査\n    if _verbosity >= 1:\n        print('  Checking checksum')\n    if np.sum(byte_sequence[0:31]) % 256 != byte_sequence[32]:\n        print('Check sum error (header)')\n    else:\n        if _verbosity >= 2:\n            print('  Chedk sum (header) OK')\n\n    if np.sum(byte_sequence[33:33+length]) % 256 != byte_sequence[33+length]:\n        print('Check sum error (body)')\n    else:\n        if _verbosity >= 2:\n            print('  Chedk sum (body) OK')\n\n    return byte_sequence\n\ndef get_param(program):\n    \"\"\"\n    プログラムのパラメータを取り出す。\n\n    Parameter\n    ---------\n    program: bytearray\n        プログラムデータ\n\n    Returns\n    -------\n    param: dict\n        プログラムのパラメータ\n\n    Notes\n    -----\n    以下のパラメータを返す。\n    * name: プログラム名\n    * start_addr: プログラムの開始アドレス\n    * flag: BASICプログラムの場合は0、マシン語の場合は77(0x4d)\n    \"\"\"\n    name, addr, len, flag = struct.unpack('>16sHHB11x', program[0:32])\n    name = name.replace(b'\\00', b'')\n    return {'name': name, 'start_addr': addr, 'length': len, 'flag': flag}\n\ndef get_body(program):\n    \"\"\"\n    プログラム本体を取り出す。\n\n    Parameter\n    ---------\n    program: bytearray\n        プログラムデータ\n\n    Returns\n    -------\n    param: bytearray\n        プログラム本体\n    \"\"\"\n    length = get_param(program)['length']\n    return program[33:33+length]\n\ndef write_file(program, path):\n    \"\"\"\n    プログラムをPROG形式のファイルとして書き込む。\n\n    Parameters\n    ----------\n    program: bytearray\n        プログラムデータ\n    path: str\n        出力するファイルのパス名\n    \"\"\"\n    param = get_param(program)\n    name = param['name']\n    addr = int(param['start_addr'])\n    flag = 1 if param['flag'] == 0x4d else 0\n    body = get_body(program)\n\n    data = struct.pack('<4sII{}sIII{}s'.format(len(name), len(body)),\n                       b'PROG',      # 'PROG' (4s)\n                       0x0000_0001,  # フォーマットバージョン (I)\n                       len(name),    # プログラム名長 (I)\n                       name,         # プログラム名 ({}s)\n                       addr,         # 先頭アドレス (I)\n                       len(body),    # プログラム長 (I)\n                       flag,         # フラグ (I)\n                       body)         # プログラム ({}s)\n\n    with open(path, 'wb') as f:\n        f.write(data)\n\ndef decode_file(infile):\n    \"\"\"\n    FSKで符号化されたJR-100プログラムの音声データを復号化する。\n\n    Parameter\n    ---------\n    infile: str\n        waveファイルへのパス名\n\n    Returns\n    -------\n    program: bytearray\n        復号化されたプログラム\n    \"\"\"\n    return _decode(_serialize(_shape_wave(_read_wave(infile))))\n\ndef dump_basic(program):\n    \"\"\"\n    読み込んだプログラムをBASICとしてダンプする。\n\n    Parameter\n    ---------\n    program: bytes\n        復号化されたプログラム\n    \"\"\"\n    body = get_body(program)\n    for m in re.finditer(\n            b'(?P<num>[^\\xdf][^\\xdf])(?P<line>[^\\x00]*?)\\x00',\n            body, flags=re.DOTALL):\n        n = struct.unpack('>H', m.group('num'))[0]\n        line = ''.join([r'\\x{:02x}'.format(x) if x >= 128 else chr(x)\n                        for x in m.group('line')])\n        print('{} {}'.format(n, line))\n\ndef dump_binary(program):\n    \"\"\"\n    読み込んだプログラムをバイナリデータとしてダンプする。\n\n    Parameter\n    ---------\n    program: bytes\n        復号化されたプログラム\n    \"\"\"\n    param = get_param(program)\n    body = get_body(program)\n    \n    if param['start_addr'] % 16 != 0:\n        print('{:04x}: '.format(param['start_addr'] & 0xfff0), end='')\n        print(' ' * ((param['start_addr'] & 0xf) * 3), end='')\n\n    for offset in range(param['length']):\n        addr = param['start_addr'] + offset\n        if addr % 16 == 0:\n            print('{:04x}: '.format(addr), end='')\n        print('{:02x} '.format(body[offset]), end='')\n        if addr % 16 == 15:\n            print()\n    print()\n\ndef dump_program(program):\n    \"\"\"\n    読み込んだプログラムのフラグに応じてダンプする。\n\n    Parameter\n    ---------\n    program: bytes\n        復号化されたプログラム\n    \"\"\"\n    if get_param(program)['flag'] == 0x4d:\n        dump_binary(program)\n    else:\n        dump_basic(program)\n\ndef set_verbosity(level):\n    global _verbosity\n    _verbosity = level\n\ndef main():\n    import argparse\n    import os\n    import traceback\n    import configparser\n    from collections import OrderedDict\n    \n    parser = argparse.ArgumentParser()\n    parser.add_argument('input_file',\n                        help=\"specify an input wave file\")\n    parser.add_argument('-o', '--output_file',\n                        help=\"write to a PROG format file\",\n                        metavar=\"file\")\n    parser.add_argument('-s', '--config_section',\n                        help=\"configuration section\",\n                        metavar=\"name\")\n    parser.add_argument('--overwrite',\n                        help=\"overwrite to the existing file\",\n                        action='store_true')\n    parser.add_argument('--fsk_thresholds',\n                        help=\"specify threshold values for FSK detector\",\n                        metavar=('min_1', 'max_1', 'min_0', 'max_0'),\n                        nargs=4, type=int)\n    parser.add_argument('--cp_threshold',\n                        help=\"specify a threshold value for comparator\",\n                        metavar=('value'),\n                        type=float)\n    parser.add_argument('--dump_auto',\n                        help=\"Dump this program depending on the type of program\",\n                        action='store_true')\n    parser.add_argument('--dump_basic',\n                        help=\"Dump this program as a BASIC text\",\n                        action='store_true')\n    parser.add_argument('--dump_binary',\n                        help=\"Dump this program as a binary data\",\n                        action='store_true')\n    parser.add_argument('-v', '--verbose',\n                        help=\"enable verbose output\",\n                        action=\"count\", default=0)\n    \n    args = parser.parse_args()\n    if args.verbose:\n        set_verbosity(args.verbose)\n\n    config = configparser.ConfigParser()\n    sec = OrderedDict()\n    if os.path.exists(_config_file):\n        config.read('fskloader.ini')\n        sec.update(config.defaults())\n        if config.has_section(args.config_section):\n            sec.update(config[args.config_section])\n\n    if args.fsk_thresholds:\n        set_fsk_thresholds(\n            args.fsk_thresholds[0],\n            args.fsk_thresholds[1],\n            args.fsk_thresholds[2],\n            args.fsk_thresholds[3],\n        )\n    elif sec.get('fsk_thresholds'):\n        s = sec.get('fsk_thresholds')\n        set_fsk_thresholds(*[int(x) for x in s.split()])\n\n    if args.cp_threshold:\n        set_cp_threshold(args.cp_threshold)\n    elif sec.get('cp_threshold'):\n        set_cp_threshold(float(sec.get('cp_threshold')))\n\n    _do_dump = (args.dump_auto\n                or args.dump_basic\n                or args.dump_binary)\n\n    try:\n        # プログラム読み込み前に上書きチェックを行う。\n        if (args.output_file\n            and os.path.isfile(args.output_file)\n            and not args.overwrite):\n                raise FileExistsError(\"File exists: {}\".format(args.output_file))\n        program = decode_file(args.input_file)\n        if not program:\n            raise Exception(\"Invalid JR-100 FSK format\")\n        if args.verbose >= 1:\n            print('Decoding Successed: {}'.format(get_param(program)['name']))\n                \n        if args.output_file:\n            write_file(program, args.output_file)\n    \n        if _do_dump:\n            param = get_param(program)\n            if args.dump_basic:\n                dump_basic(program)\n            elif args.dump_binary:\n                dump_binary(program)\n            else:\n                dump_program(program)\n    except Exception as e:\n        if _verbosity >= 2:\n            print(traceback.format_exc())\n        else:\n            print(e)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"jr100emulib/fskloader.py","file_name":"fskloader.py","file_ext":"py","file_size_in_byte":24132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"428192175","text":"import unittest\nfrom test.TestHelper import TestHelper\nfrom src.parser.web_shop.amazon.AmazonParser import AmazonParser\n\nhelper = TestHelper()\nhtml = helper.read_file('../../../../other_files/amazon_goods.html' )\nparser = AmazonParser(html)\n\nclass TestAmazonParser(unittest.TestCase):\n    def test_get_start_page(self):\n        all_links_with_word = parser.get_start_page(html, 'all')\n\n        self.assertEquals(['http://site.com/1/', 'http://site.com/', 'http://site.com/'], all_links_with_word)\n        self.assertNotEquals([], all_links_with_word)\n\n    def test_get_goods(self):\n        all_links_with_item = parser.get_goods(html, 'all')\n\n        self.assertEquals(['http://site.com/1/', 'http://site.com/2/', 'http://site.com/3/'], all_links_with_item)\n        self.assertNotEquals([], all_links_with_item)\n\n    def test_get_users(self):\n        all_links_with_users = parser.get_users(html, 'all')\n\n        self.assertEquals(['http://site.com/1/', 'http://site.com/2/', 'http://site.com/3/'], all_links_with_users)\n        self.assertNotEquals([], all_links_with_users)\n","sub_path":"test/parser/web_shop/amazon/TestAmazonParser.py","file_name":"TestAmazonParser.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"19125439","text":"import readFile\nimport random\n\ndef topPop(bag, pop, percent=100): #выбор наиболее приспособленных особей\n    percent = percent % 100\n    sort = {}\n    oldpop = sorted(sort, key=sort.__getitem__)\n    oldpop.reverse()\n    top = int(len(oldpop)*percent/100)\n    if top % 2 == 1:\n        top -= 1\n    h = []\n    t = []\n    for i in range(0, top):\n            h.append(pop[oldpop[i]])\n    for i in range(top, len(oldpop)):\n            t.append(pop[oldpop[i]])\n    return {\"h\" : h, \"t\" : t}\n\ndef fitness(): #приспособленность\n    for i in range(0,len(oldpop)):\n        val = 0\n        vol=0\n        w=0\n        for j in range(0,len(oldpop[i][0])):\n            if oldpop[i][0][j]==1:\n                val+=things[j][2]\n                w+=things[j][0]\n                vol+=things[j][1]\n                if w>readFile.weight or vol>readFile.volume:\n                    val=0\n                    break\n        oldpop[i].append([val])\n\t\t\ndef mutation(): #мутация путем инвентирования всех битов 1 особи\n    ind = random.randint(0, len(oldpop)-1)\t\t\t\t\n    for x in range(0,len(oldpop[ind])-1):\n        if oldpop[ind][x]==0 :\n            oldpop[ind][x] = 1\n        else:\n            oldpop[ind][x] = 0\t\t\n\t\t\ndef selection(): #отбор особей для скрещивания\n    sum=0\n    par=oldpop.copy()\n    for k in range(0,len(oldpop)):\n        sum+=oldpop[k][1][0]\n    while len(par)>0:\n        i = 0\n        random.seed()\n        r = random.randint(0, round(sum))\n        csum=0\n        while csum<r and i<len(par):\n            csum+=par[i][1][0]\n            i+=1\n        sum-=par[i-1][1][0]\n        m=par[i-1][0]\n        par.remove(par[i-1])\n        i=0\n        random.seed()\n        r = random.randint(0, round(sum))\n        csum=0\n        while csum<r and i<len(par):\n            csum+=par[i][1][0]\n            i+=1\n        sum -= par[i - 1][1][0]\n        f = par[i - 1][0]\n        par.remove(par[i - 1])\n        cr(m, f)\n\t\t\n\ndef startPop(): #запуск популяции\n    it=sorted(readFile.items,key=lambda x: x[2])\n    global things\n    things=it[::-1]\n    for i in range (0,pcount):\n        random.seed()\n        ind=random.randint( 0, len(things))\n        cw=0\n        cv=0\n        oldpop.append([])\n        oldpop[i].append([])\n        for j in range(0,ind):\n            oldpop[i][0].append(0)\n        for j in range(ind,len(things)):\n            if cw+things[j][0]<readFile.weight and cv+things[j][1]<readFile.volume:\n                oldpop[i][0].append(1)\n                cw+=things[j][0]\n                cv+=things[j][1]\n            else:\n                oldpop[i][0].append(0)\n        for k in range(0,len(oldpop[i][0])):\n            if oldpop[i][0][k]==0:\n                if cw + things[k][0] < readFile.weight and cv + things[k][1] < readFile.volume:\n                    oldpop[i][0][k]=1\n                    cw += things[k][0]\n                    cv += things[k][1]\n            else:\n                break\n\ndef selh(): #вспомогательная функция\n    sum=0\n    for p in oldpop:\n        sum += p[1]\n    sp = random.randint(0, sum)\n    sum=0\n    i=0\n    while sum<sp:\n        sum+=oldpop[i][1]\n        i+=1\n    item=oldpop[i-1]\n    old.append(item)\n    del oldpop[i-1]\n    return item\t  \n\n\ndef SelectPar():#скрещивание 2-х пар\n    item1=Selection()\n    item2 = Selection()\n    child=Cross(item1[0],item2[0])\n    for c in child:\n        new.append([c,FitnessFunc(c)])\n\ndef Cross(x,y):#многоточечное скрещивание с 3мя точками  \n    c1= []\n    c2= []\n    leng=len(y)\n    d1=random.randint(0, leng//2)\n    d2=random.randint(d1, leng-1)\n    d3=random.randint(d2, leng-1)\n    for i in range(0,d1):\n        c1.append(x[i])\n        c2.append(y[i])\n    for i in range(d1,d2):\n        c1.append(y[i])\n        c2.append(x[i])\n    for i in range(d2, d3):\n        c1.append(x[i])\n        c2.append(y[i])\n    for i in range(d3,leng):\n        c1.append(y[i])\n        c2.append(x[i])\n    return [c1,c2]\t\n       \n\ndef gF(child): \n    val=0\n    vol = 0\n    w = 0\n    for j in range(0, len(child)):\n        if child[j] == 1:\n            val += things[j][2]\n            w += things[j][0]\n            vol += things[j][1]\n            if w > readFile.weight or vol > readFile.volume:\n                val = 0\n                break\n    return val\t\n\t\ndef cr(x, y):\n    child=[]\n    for i in range(0,len(x)):\n        random.seed()\n        ind = random.random()\n        if (ind<0.5):\n            child.append(x[i])\n        else:\n            child.append(y[i])\n    ch=[]\n    ch.append(child)\n    ch.append([gF(child)])\n    newpop.append(ch)\n\n\n\ndef newPop(): #формирование новой популяции\n    global oldpop\n    spop=sorted(oldpop+newpop,key=lambda x: x[1])\n    spop=spop[::-1]\n    oldpop=spop[0:200]\n\ndef start(x): #объединяем функции\n    global oldpop\n    oldpop = []\n    global newpop\n    newpop = []\n    global things\n    things = []\n    startPop()\n    fitness()\n    for k in range(0,x):\n        selection()\n        newPop()\n    return oldpop[0]\n\t\t\noldpop=[]\nnewpop=[]\npcount=200\nthings=[]\t\n\t\n\t\nreadFile.read()\nres=start(100)\nprint(res[1],res[0])\n","sub_path":"task2.py","file_name":"task2.py","file_ext":"py","file_size_in_byte":5237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"146199739","text":"# -*- coding: utf-8 -*-\r\nimport os\r\nimport re\r\nimport datetime\r\nfrom app import app\r\nimport telegram\r\nfrom flask import request, Blueprint\r\nimport requests\r\nenv = os.getenv('SERVER_SOFTWARE')\r\nif env and env.startswith('Google App Engine/'):\r\n    import requests_toolbelt.adapters.appengine\r\n    requests_toolbelt.adapters.appengine.monkeypatch()\r\n\r\n\r\nLK_BOT = telegram.Bot(token=app.config['LK_BOT_TOKEN'])\r\n\r\n\r\nlk = Blueprint('lk', __name__)\r\n\r\n\r\n\r\n@lk.route('/hook', methods=['POST'])\r\ndef lk_webhook_handler():\r\n    # retrieve the message in JSON and then transform it to Telegram object\r\n    update = telegram.Update.de_json(request.get_json(force=True), LK_BOT)\r\n\r\n    chat_id = update.message.chat.id\r\n\r\n    # Telegram understands utf-8, so encode text for unicode compatibility\r\n    text = update.message.text.encode('utf-8')\r\n\r\n    LK_BOT.sendMessage(chat_id=chat_id,\r\n                       text=\"Привет, твой chat_id: '{}'\".format(chat_id))\r\n    return 'ok'\r\n\r\n\r\n@lk.route('/set_webhook', methods=['GET', 'POST'])\r\ndef lk_set_webhook():\r\n    s = LK_BOT.setWebhook('{}/lk/hook'.format(app.config['HOOK_SITE']))\r\n    if s:\r\n        return \"webhook setup ok\"\r\n    else:\r\n        return \"webhook setup failed\"\r\n\r\n","sub_path":"FlaskWebProject1/lk_bot.py","file_name":"lk_bot.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"34731989","text":"import numpy as np\nfrom collections import defaultdict\n\nclass Bayes:\n    def __init__(self):\n        self._feats_count = defaultdict(int)\n        self._feats_total = defaultdict(int)\n\n        self._goals_count = defaultdict(int)\n        self._goals_total = 0\n        self._goals = None\n\n        self._category_count = 0\n\n    def train(self, train_data, train_goals):\n        if train_data.ndim != 2:\n            raise ValueError(\"Expected data to be two-dimensional\")\n\n        if train_goals.ndim != 1:\n            raise ValueError(\"Expected goals to be one-dimensional\")\n\n        if train_data.shape[0] != train_goals.shape[0]:\n            raise ValueError(\"Data row and goal counts do not match\")\n\n        if self._category_count == 0:\n            self._category_count = train_data.shape[1]\n        else:\n            if self._category_count != train_data.shape[1]:\n                raise ValueError(\"Number of categories in given data does not match existing number\")\n\n        for data_row, goal in zip(train_data, train_goals):\n            self._goals_count[goal] += 1\n\n            for category, data in enumerate(data_row):\n                self._feats_count[(data, category, goal)] += 1\n                self._feats_total[(category, goal)] += 1\n\n        self._goals_total += train_goals.shape[0]\n\n        if self._goals is not None:\n            self._goals = np.concatenate((self._goals, train_goals))\n        else:\n            self._goals = train_goals\n\n        self._goals = np.unique(self._goals)\n            \n    def predict(self, samples):\n        if self._category_count == 0:\n            raise UnboundLocalError(\"No training data to estimate on\")\n\n        if samples.ndim != 2:\n            raise ValueError(\"Data should be two-dimensional\")\n\n        if samples.shape[1] != self._category_count:\n            raise ValueError(f\"Feature count in data should be {self._category_count}\")\n\n        categories = [0] * (samples.shape[0])\n\n        for sample_no, sample_row in enumerate(samples):\n            probability_array = np.zeros(self._goals.shape[0])\n\n            for index, goal in enumerate(self._goals):\n                goal_prob = self._calc_goal_chance(goal)\n                \n                feats_prob = 1\n                for category, sample_val in enumerate(sample_row):\n                    feats_prob *= self._calc_feat_chance(sample_val, category, goal)\n\n                probability_array[index] = goal_prob * feats_prob\n\n            max_chance_index = np.argmax(probability_array)\n            categories[sample_no] = self._goals[max_chance_index]\n\n        return categories\n\n\n\n    def _calc_goal_chance(self, goal):\n        return self._goals_count[goal] / self._goals_total\n\n    def _calc_feat_chance(self, data, category, goal):\n        return self._feats_count[(data, category, goal)] / self._feats_total[(category, goal)]\n","sub_path":"classifiers/bayes.py","file_name":"bayes.py","file_ext":"py","file_size_in_byte":2846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"16343038","text":"'''\nGiven a List of words, return the words that can be \ntyped using letters of alphabet on only one row's of American keyboard like the image below.\n\n\nExample:\n\nInput: [\"Hello\", \"Alaska\", \"Dad\", \"Peace\"]\nOutput: [\"Alaska\", \"Dad\"]\n \n\nNote:\n\nYou may use one character in the keyboard more than once.\nYou may assume the input string will only contain letters of alphabet.\n'''\n\ndef find_word_rows(words):\n    levels = ['qwertyuiopQWERTYUIOP',\n              'asdfghjklASDFGHJKL',\n              'zxcvbnmZXCVBNM']\n\n    result = []\n    for word in words:\n        for level in levels:\n            found = True\n            for char in word:\n                if char not in level:\n                    found = False\n                    break\n            if found: \n                result.append(word)\n                break\n    \n\n    return result\n\ndef find_words_v2(words):\n    line1, line2, line3 = set('qwertyuiop'), set('asdfghjkl'), set('zxcvbnm')\n    ret = []\n    for word in words:\n        w = set(word.lower())\n        if w <= line1 or w <= line2 or w <= line3:\n            ret.append(word)\n    return ret","sub_path":"algorithms/hashtables/keyboard_row.py","file_name":"keyboard_row.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"90202011","text":"import requests\n\nSHARDS = ['ea', 'na', 'sg', 'eu', 'sa', 'cn']\nMODE_JA = {\n    'casual':                           'カジュアル',\n    'ranked':                           'ランク',\n    'casual_aral':                      '大乱闘',\n    'blitz_pvp_ranked':                 '電撃',\n    'blitz_rounds_pvp_casual':          'ガチンコ',\n    'private':                          'プラベカジュ',\n    'private_party_draft_match':        'プラベドラフト',\n    'private_party_aral_match':         'プラベ大乱闘',\n    'private_party_blitz_match':        'プラベ電撃',\n    'private_party_blitz_rounds_match': 'プラベガチンコ',\n    '5v5_pvp_casual':                   '5V5カジュ',\n}\n\n\nclass VainAPI:\n    ''' vainglory api '''\n\n    def __init__(self):\n        with open('secrets/vain-api', 'r') as f:\n            self.apikey = f.read().rstrip()\n\n    def request(self, url, params):\n        headers = {\n            'Authorization': self.apikey,\n            'X-TITLE-ID': 'semc-vainglory',\n            'Accept': 'application/vnd.api+json',\n            # 'Accept-Encoding': 'gzip',\n        }\n        return requests.get(url, headers=headers, params=params).json()\n\n    def single_player(self, reg, ign):\n        url = f'https://api.dc01.gamelockerapp.com/shards/{reg}/players'\n        params = {\n            'filter[playerNames]': [ign],\n        }\n        res = self.request(url, params)\n        if res.get('errors', ''):\n            wrapped = res\n        else:\n            _attributes = res['data'][0]['attributes']\n            wrapped = {\n                'id': res['data'][0]['id'],\n                'time': _attributes['createdAt'],\n                'shrd': _attributes['shardId'],\n                'name': _attributes['name'],\n                'elo8': _attributes['stats']['elo_earned_season_8'],\n                'wstr': _attributes['stats']['winStreak'],\n                'lstr': _attributes['stats']['lossStreak'],\n                'wins': _attributes['stats']['wins'],\n                'tier': _attributes['stats']['skillTier'],\n                'rnkp': _attributes['stats']['rankPoints']['ranked'],\n                'blzp': _attributes['stats']['rankPoints']['blitz'],\n            }\n        return wrapped\n\n    def matches(self, reg, ign):\n        url = f'https://api.dc01.gamelockerapp.com/shards/{reg}/matches'\n        params = {\n            'filter[playerNames]': [ign],\n            'sort': '-createdAt',\n        }\n        res = self.request(url, params)\n\n        # ================\n        # Reshape the data\n        # ================\n\n        if res.get('errors', ''):\n            matches = res\n        else:\n\n            # matches is one time only\n            _matches = {i['id']: [i['attributes']['duration'],\n                                  i['attributes']['gameMode'],\n                                  i['attributes']['patchVersion'],\n                                  i['relationships']['rosters']['data']]\n                        for i in res['data']}\n            _rosters = {i['id']: [i['attributes']['stats']['heroKills'],\n                                  i['attributes']['stats']['side'],\n                                  i['attributes']['stats']['turretKills'],\n                                  i['attributes']['stats']['turretsRemaining'],\n                                  i['relationships']['participants']['data']]\n                        for i in res['included'] if i['type'] == 'roster'}\n            # participants is to be saved\n            _participants = {i['id']: [i['attributes']['actor'],\n                                       i['attributes']['shardId'],\n                                       i['attributes']['stats']['kills'],\n                                       i['attributes']['stats']['deaths'],\n                                       i['attributes']['stats']['assists'],\n                                       i['attributes']['stats']['gold'],\n                                       i['attributes']['stats']['farm'],\n                                       i['attributes']['stats']['items'],\n                                       i['attributes']['stats']['skillTier'],\n                                       i['attributes']['stats']['winner'],\n                                       i['relationships']['player']['data']['id']]\n                             for i in res['included'] if i['type'] == 'participant'}\n            participants = {k: {'actor': v[0],\n                                'shard': v[1],\n                                'kills': v[2],\n                                'deaths': v[3],\n                                'assists': v[4],\n                                # KDA\n                                'kda': (v[2] + v[4]) / (v[3] + 1),\n                                'gold': int(v[5]),\n                                'farm': int(v[6]),\n                                'items': v[7],\n                                'tier': v[8],\n                                'won': v[9],\n                                'player_id': v[10]}\n                            for k, v in _participants.items()}\n            rosters = {k: {'team_kill_score': v[0],\n                           'side': v[1].replace('/', '-'),\n                           'turret_kill': v[2],\n                           'turret_remain': v[3],\n                           'participants': [participants[data['id']] for data in v[4]]}\n                       for k, v in _rosters.items()}\n            matches = [{'duration': v[0],\n                        'mode': v[1],\n                        'version': v[2],\n                        'rosters': [rosters[r['id']] for r in v[3]],\n                        }\n                       for v in _matches.values()]\n            # for m in matches:\n            #     m['participants'] = {k: v for k, v in participants.items()\n            #                          if k in [i for r in m['rosters']\n            #                                   for i in r['participants']]}\n        return matches\n\n    def player_matches(self, reg, ign):\n        url = f'https://api.dc01.gamelockerapp.com/shards/{reg}/matches'\n        params = {\n            'filter[playerNames]': [ign],\n            'sort': '-createdAt',\n        }\n        res = self.request(url, params)\n\n        # ================\n        # Reshape the data\n        # ================\n\n        if res.get('errors', ''):\n            result = res\n        else:\n\n            # matches is one time only\n            _matches = {i['id']: [i['attributes']['duration'],\n                                  i['attributes']['gameMode'],\n                                  i['attributes']['patchVersion'],\n                                  i['relationships']['rosters']['data']]\n                        for i in res['data']}\n            _rosters = {i['id']: [i['attributes']['stats']['heroKills'],\n                                  i['attributes']['stats']['side'],\n                                  i['attributes']['stats']['turretKills'],\n                                  i['attributes']['stats']['turretsRemaining'],\n                                  i['relationships']['participants']['data']]\n                        for i in res['included'] if i['type'] == 'roster'}\n            # participants is to be saved\n            _participants = {i['id']: [i['attributes']['actor'],\n                                       i['attributes']['shardId'],\n                                       i['attributes']['stats']['kills'],\n                                       i['attributes']['stats']['deaths'],\n                                       i['attributes']['stats']['assists'],\n                                       i['attributes']['stats']['gold'],\n                                       i['attributes']['stats']['farm'],\n                                       i['attributes']['stats']['items'],\n                                       i['attributes']['stats']['skillTier'],\n                                       i['attributes']['stats']['winner'],\n                                       i['relationships']['player']['data']['id']]\n                             for i in res['included'] if i['type'] == 'participant'}\n            players = {\n                i['id']: {\n                    'name':     i['attributes']['name'],\n                    'shard':    i['attributes']['shardId'],\n                    'guild':    i['attributes']['stats']['guildTag'],\n                    'elo':      i['attributes']['stats']['rankPoints']['ranked'],\n                    'tier':     i['attributes']['stats']['skillTier'],\n                    'wins':     i['attributes']['stats']['wins'],\n                }\n                for i in res['included'] if i['type'] == 'player'}\n            participants = {k: {'actor': v[0],\n                                'shard': v[1],\n                                'kills': v[2],\n                                'deaths': v[3],\n                                'assists': v[4],\n                                # KDA\n                                'kda': (v[2] + v[4]) / (v[3] + 1),\n                                'gold': int(v[5]),\n                                'farm': int(v[6]),\n                                'items': v[7][::-1][:6][::-1],\n                                'tier': v[8],\n                                'won': v[9],\n                                'player_id': v[10]}\n                            for k, v in _participants.items()}\n            rosters = {k: {'team_kill_score': v[0],\n                           'side': v[1].replace('/', '-'),\n                           'turret_kill': v[2],\n                           'turret_remain': v[3],\n                           'participants': [participants[data['id']] for data in v[4]]}\n                       for k, v in _rosters.items()}\n            matches = [{'duration': v[0],\n                        'mode': MODE_JA.get(v[1], v[1]),\n                        'version': v[2],\n                        'rosters': [rosters[r['id']] for r in v[3]],\n                        }\n                       for v in _matches.values()]\n            result = {'matches': matches,\n                      'players': players}\n        return result\n\n    def _request_without_region(self, ign, method):\n        for r in SHARDS:\n            res = method(r, ign)\n            if res is not dict:\n                break\n            elif res.get('errors', ''):\n                # もしエラーがあれば。\n                continue\n            else:\n                break\n        return res\n\n    def single_player_without_region(self, ign):\n        return self._request_without_region(ign, self.single_player)\n\n    def matches_without_region(self, ign):\n        return self._request_without_region(ign, self.matches)\n\n    def player_matches_wo_region(self, ign):\n        return self._request_without_region(ign, self.player_matches)\n\n\n# ================\n# Utilities\n# ================\ndef itemname_to_cssreadable(name):\n    return name.replace(' ', '-').replace(\"'\", \"\").lower()\n\n\ndef particularplayer_from_singlematch(match, player_id):\n    for r in match['rosters']:\n        for p in r['participants']:\n            if p['player_id'] == player_id:\n                return p\n","sub_path":"mymetrics/vain_api.py","file_name":"vain_api.py","file_ext":"py","file_size_in_byte":11156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"246170489","text":"import logging\nimport os\nfrom multiprocessing.pool import ThreadPool\n\nimport cv2\n\nfrom lib.aug.dataset_augmenter import IDataAugmenter\nfrom lib.dataset.image import Image\nfrom lib.dataset.pascal_voc.pascal_voc_file import PascalVOCFile\nfrom lib.dataset.sample import Sample\nfrom lib.list_utils import flatten\nfrom lib.util import os_utils\n\nlogger = logging.getLogger()\n\n\nclass DataAugmenter(IDataAugmenter):\n    def __init__(self, dataset, output_path, max_parallel_augs=50):\n        self.__output_path = output_path\n        os_utils.create_path(output_path)\n        self.__dataset = dataset\n        self.max_parallel_augs = max_parallel_augs\n\n    def augment(self, samples, aug_strategy, aug_count=10):\n        with ThreadPool(self.max_parallel_augs) as pool:\n            return flatten(\n                pool.starmap_async(\n                    self.sample_augment,\n                    [(aug_strategy, s, s_num, aug_count) for s_num, s in enumerate(samples, start=1) if\n                     s.bounding_boxes is not None and len(s.bounding_boxes) > 0]\n                ).get()\n            )\n\n    def sample_augment(self, aug_strategy, sample, sample_num, aug_count):\n        max_pool = self.max_parallel_augs if aug_count > self.max_parallel_augs else aug_count\n\n        logger.info(f'>>> {sample_num} sample <<< - File: {sample.image.filename()}...')\n\n        with ThreadPool(max_pool) as pool:\n            return pool.starmap_async(\n                self.sample_augment_time,\n                [(aug_strategy, sample, index) for index in range(0, aug_count)]\n            ).get()\n\n    def sample_augment_time(self, aug_strategy, sample, index):\n        input_image_path = f'{self.__dataset.path}/{sample.image.filename()}'\n\n        if not os.path.exists(input_image_path):\n            raise Exception(f'Not found {input_image_path} image path!')\n\n        aug_image_path = self.destiny_path(sample.image, index)\n        aug_xml_path = os.path.splitext(aug_image_path)[0] + '.xml'\n        if os.path.exists(aug_image_path) and os.path.exists(aug_xml_path):\n            logger.info(f'Already exist {os.path.basename(aug_image_path)} augmented sample...')\n            return PascalVOCFile.load(aug_xml_path)\n\n        original_raw_image = cv2.imread(input_image_path)\n\n        aug_raw_image, aug_bboxes = aug_strategy.augment(\n            original_raw_image,\n            sample.bounding_boxes\n        )\n\n        aug_sample = self.__create_sample(sample.image, aug_raw_image, aug_bboxes, index)\n\n        cv2.imwrite(aug_sample.image.path, aug_raw_image)\n        PascalVOCFile.write(aug_sample)\n\n        return aug_sample\n\n    def __create_sample(self, img, aug_raw_image, aug_bboxes, index):\n        image = Image(\n            self.destiny_path(img, index),\n            height=aug_raw_image.shape[0],\n            width=aug_raw_image.shape[1],\n            depth=img.depth\n        )\n        return Sample(image, aug_bboxes)\n\n    def destiny_path(self, img, index):\n        parts = img.filename().split('.')\n        return f'{self.__output_path}/{parts[0]}{index}.{parts[1]}'\n","sub_path":"lib/aug/impl/data_augmenter.py","file_name":"data_augmenter.py","file_ext":"py","file_size_in_byte":3063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"522591183","text":"import numpy as np\nimport math\nfrom helpers_conv import *\nfrom helpers_fc import *\nfrom helpers1 import solve_fc, solve_conv\n\ndef random_mini_batches(X, Y, mini_batch_size = 64, seed = 0):\n    \"\"\"\n    Creates a list of random minibatches from (X, Y)\n    \n    Arguments:\n    X -- input data, of shape (input size, number of examples) (m, Hi, Wi, Ci)\n    Y -- true \"label\" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples) (m, n_y)\n    mini_batch_size - size of the mini-batches, integer\n    seed -- this is only for the purpose of grading, so that you're \"random minibatches are the same as ours.\n    \n    Returns:\n    mini_batches -- list of synchronous (mini_batch_X, mini_batch_Y)\n    \"\"\"\n    \n    m = X.shape[0]                  # number of training examples\n    mini_batches = []\n    np.random.seed(seed)\n    \n    # Step 1: Shuffle (X, Y)\n    permutation = list(np.random.permutation(m))\n    shuffled_X = X[permutation,:,:,:]\n    shuffled_Y = Y[permutation,:]\n\n    # Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.\n    num_complete_minibatches = math.floor(m/mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning\n    for k in range(0, num_complete_minibatches):\n        mini_batch_X = shuffled_X[k * mini_batch_size : k * mini_batch_size + mini_batch_size,:,:,:]\n        mini_batch_Y = shuffled_Y[k * mini_batch_size : k * mini_batch_size + mini_batch_size,:]\n        mini_batch = (mini_batch_X, mini_batch_Y)\n        mini_batches.append(mini_batch)\n    \n    # Handling the end case (last mini-batch < mini_batch_size)\n    if m % mini_batch_size != 0:\n        mini_batch_X = shuffled_X[num_complete_minibatches * mini_batch_size : m,:,:,:]\n        mini_batch_Y = shuffled_Y[num_complete_minibatches * mini_batch_size : m,:]\n        mini_batch = (mini_batch_X, mini_batch_Y)\n        mini_batches.append(mini_batch)\n    \n    return mini_batches\n\ndef cross_entropy_loss(AL, Y):\n    \n    \"\"\"\n    Implement the cost function\n    \n    Arguments:\n    a3 -- post-activation, output of forward propagation size of [num_of_atributes x num_of_examples ]\n    Y -- \"true\" labels vector, same shape as a3\n    \n    Returns:\n    cost - value of the cost function\n    \"\"\"\n    m = Y.shape[1]\n    #mnozenje = np.multiply(Y, np.log(AL))\n    #negsum = -np.sum(mnozenje)\n    #final = negsum / m\n    return 1./m * -1 * np.sum(Y * (AL + (-np.max(AL) - np.log(np.sum(np.exp(AL-np.max(AL)))))))\n\ndef predict(X, y, parameters, layers_dims_conv, channels, nc):\n    \"\"\"   \n    X -- train/test data of shape (#examples x dim(image))\n    y -- train/test data of shape (#examples x 1)\n    parameters -- parameters of the trained model    \n    Returns:\n    p -- predictions for the given dataset X of shape (#examples x 1)    \n    \"\"\"    \n    m = X.shape[0]\n    hparameters1 = {\"pad\" : 2, \"stride\": 3}\n    hparameters2 = {\"stride\" : 2, \"f\": 2}\n    hparameters3 = {\"pad\" : 1, \"stride\": 3}\n    hparameters4 = {\"stride\" : 2, \"f\": 2}    \n    \n    parameters_conv = solve_conv(parameters, layers_dims_conv, channels, nc)    \n    # Forward propagation conv \n    P2, cache_pool2, cache_pool1, cache_conv1, cache_conv2, c1, c2 = Convolutional_Forward(X, parameters_conv, hparameters1, hparameters2, hparameters3, hparameters4)\n    \n    # Forward propagation FC\n    FC1 = P2.reshape(P2.shape[0], -1).T\n    \n    # Get parameters for FC network, parameters_fc\n    parameters_fc = solve_fc(parameters, layers_dims_conv)    \n    # Forward propagation FC\n    A3_fc, cache_fc = L_model_forward(FC1, parameters_fc)\n    \n    a, b = A3_fc.shape\n    p = np.zeros((a, b))    \n    # convert probas to 0/1 predictions\n    tmp = np.argmax(A3_fc, axis=0)\n    for i in range(0, b):\n        p[tmp[i],i] = 1\n    p = p.T\n    print(\"Accuracy: \"  + str(np.mean((p[:,:] == y[:,:]))))    \n    return p","sub_path":"jupyterNotebook_additionalFiles_pictures/helpers2.py","file_name":"helpers2.py","file_ext":"py","file_size_in_byte":3817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"421611432","text":"# Parentheses Trees (geez)\n# by Roy Gustafson\n\n# from https://www.reddit.com/r/dailyprogrammer/comments/5l42dx/20161230_challenge_297_hard_parentheses_trees/\n\n# data structure\nclass Tree(object):\n    def __init__(self):\n        self.parent = None\n        self.left = None\n        self.right = None\n        self.data = None\n\ndef addLeft(self):\n    self.left = newL = Tree()\n    newL.parent = self\n    return newL\ndef addRight(self):\n    self.right = newR = Tree()\n    newR.parent = self\n    return newR\n\n# iterators\nopened = 0\nclosed = 0\n\nroot = Tree()\ncurr = addLeft(root)\n\n# input\ndata = input('Enter data: ')\nbuffer = ''\n\nfor c in data:\n    if c == '(':\n        print('found: (')   # edge case, first level\n        curr.data = buffer\n        if curr.left == None:\n            print('adding left')\n            curr = addLeft(curr)\n        else:\n            print('left full. adding right')\n            curr = addRight(curr)\n    elif c == ')':\n        print('found: )')\n        curr.data = buffer\n        curr = curr.parent\n    else:\n        print('found: char')\n        buffer += c\n        \n\n# output\nx = 0\n\ndef preorder( self, level ):\n    if self != None:\n        print( level * '\\t' + self.data )\n        level += 1\n        preorder( self.left, level )\n        preorder( self.right, level )\n        level -= 1\n\npreorder( root, x ) # not running\n","sub_path":"ParentheTrees.py","file_name":"ParentheTrees.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"133307519","text":"#import os\nfrom flask import Flask\n\napp = Flask(__name__)\n\n@app.route('/')\ndef hello_world():\n    import pdb; pdb.set_trace()\n    i = 0\n    i = i + 4\n    visited = i\n    return 'Hello World!'+str(visited)\n\nif __name__ == '__main__':\n    #host = os.getenv('IP', '0.0.0.0')\n    #port = int9os.getenv('PORT', 5000))\n    app.debug = True\n    app.run()","sub_path":"hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"383752752","text":"from keras.models import load_model\nfrom src.data.types import ShapeType\nimport cv2\nimport numpy as np\n\n\nclass NetworkClassifier:\n    \"\"\"\n    Neural network classifier.\n    \"\"\"\n\n    prediction_threshold = 0.95\n\n    def __init__(self, model_dir, flatten):\n        \"\"\"\n        Classifier constructor.\n\n        :param model_dir: Neural network model directory.\n        :type model_dir: str\n        :param flatten: Specifies whether input image should be flattened (for a model that recognizes 1D vectors)\n                        or kept as a 2D image (for a model that recognizes 2D images).\n        :type flatten: bool\n        \"\"\"\n        self.model = load_model(model_dir)\n        self.flatten = flatten\n\n    def classify(self, region, verbose=False):\n        \"\"\"\n        Performs classification on a single input image using a neural network.\n        The input region image should be thresholded (only 1 bit per pixel allowed).\n        The input region image should contain one black shape on a white background.\n\n        :param region: The region being classified.\n        :type region: src.data.types.Region\n        :param verbose: Specifies whether the classified image should be shown in a separate window.\n        :type verbose: bool\n        :return: Type of shape classified on the input image.\n        :rtype: ShapeType\n        \"\"\"\n        image = region.image\n        if verbose:\n            cv2.imshow(\"\", image * 255)\n\n        # flatten the image\n        if self.flatten:\n            image = image.reshape(1, np.prod(image.shape))\n        else:\n            image = image.reshape(1, image.shape[0], image.shape[1], 1)\n\n        # feed image into model\n        prediction = self.model.predict(image)[0].tolist()\n\n        # verify the prediction\n        if max(prediction) > self.prediction_threshold:\n            if prediction[0] == max(prediction):\n                return ShapeType.CIRCLE\n            if prediction[1] == max(prediction):\n                return ShapeType.SQUARE\n            if prediction[2] == max(prediction):\n                return ShapeType.STAR\n            if prediction[3] == max(prediction):\n                return ShapeType.TRIANGLE\n        else:\n            return None\n","sub_path":"shapes/src/classifiers/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":2202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"229506076","text":"# -*- coding: utf-8 -*-\nfrom picamera.array import PiRGBArray\nfrom picamera import PiCamera\nimport matplotlib.pyplot as plt\nimport time\nimport cv2\nimport io\nimport numpy as np\n\nclass Camera:\n    def __init__(self):\n        self.camera = PiCamera()\n        self.camera.resolution = (640, 480)\n        self.camera.framerate = 32\n        self.camera.vflip = True\n        time.sleep(2)\n\n    def getFrameArray(self):\n        stream = io.BytesIO()\n        self.camera.capture(stream, format=\"jpeg\", use_video_port=True)\n        frameArray = np.array(stream.getvalue())\n        #~ frameArray = np.fromstring(stream.getvalue(), dtype = np.uint8)\n        return frameArray\n        \n    def getFrame(self):\n        frameArray = self.getFrameArray()\n        frame = cv2.imdecode(frameArray, 1)\n        return frame\n        \nif __name__ == \"__main__\":\n    camera = Camera()\n    frame = camera.getFrame()\n    plt.imsave(\"frame.jpeg\", frame)\n\n","sub_path":"control/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"503929978","text":"from sys import stdin as I\n\ndef check_text(_str):\n    # print(f\"_str : {_str}\")\n    sl = len(_str)\n    # print(f\"len(_str) : {len(_str)}\")\n\n    if sl <= 1:\n        return True\n\n    if _str[0].lower() != _str[-1].lower():\n        return False\n\n    return check_text(_str[1:(sl - 1)])\n\nif __name__ == '__main__':\n    I = open(\"./prac_3.txt\")\n    text = I.readline()\n    text.lower()\n    # print(type(text))\n    if check_text(text):\n        print(f\"{text}는 회문 입니다.\")\n    else:\n        print(f\"{text}는 회문이 아닙니다.\")","sub_path":"20171483_한태규_Puzzlie10/prac_3.py","file_name":"prac_3.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"46487835","text":"import ListNode\n#主要是要考虑\ndef findKthToTail(listHead,k):\n    # 为空时的情况，求倒数第k个的情况\n    if listHead == None or k <= 0:\n        return None\n    head = listHead\n    for i in range(0,k-1):\n        # 当k大于链表长度的情况\n        if head.next:\n            head = head.next\n        else:\n            return None\n\n    kthTail = listHead\n    while head.next:\n        head = head.next\n        kthTail = kthTail.next\n\n    return kthTail\n\n\n","sub_path":"code-offer/no22findKthToTail.py","file_name":"no22findKthToTail.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"540572582","text":"\"\"\"Count prime numbers in a range between 1 and a given number.\n\nCount prime numbers with an iterative algorithm optimized by storing found\nprimes in a dictionary.\n\nUsage\n-----\n\n    $ python3 primes_memo.py num\n\nnum - an upper limit for a range to look for prime numbers in.\n\"\"\"\n\nfrom math import sqrt\nimport sys\n\n\nprimes = {}  # a global dict to store prime numbers\n\n\ndef is_prime(num):\n    \"\"\"Checks if the number is prime.\"\"\"\n    if num == 1:\n        return False\n\n    if num == 2:\n        return True\n\n    if num % 2 == 0:\n        return False\n\n    limit = int(sqrt(num)) + 1\n    for i in range(1, limit):\n        try:\n            if primes[i] > limit:\n                break\n            if num % primes[i] == 0:\n                return False\n        except KeyError:\n            break\n    return True\n\n\ndef get_primes_count(num):\n    \"\"\"Iteratively counts prime numbers in a range between 1 and num.\"\"\"\n    if num == 1:\n        return 0\n\n    count = 0\n    primes[0] = 2\n\n    for i in range(3, num + 1, 2):\n        if is_prime(i):\n            count += 1\n            primes[count] = i\n    return count + 1\n\n\ndef main():\n    if len(sys.argv) > 1:\n        num = int(sys.argv[1])\n    else:\n        sys.exit()\n\n    output = get_primes_count(num)\n    print(output)\n\n\nif __name__ == '__main__':\n    main()\n\n    # unit tests for is_prime\n    # assert not is_prime(1)\n    # assert is_prime(3)\n    # assert not is_prime(8)\n\n    # unit tests for get_primes_count\n    # assert get_primes_count(1) == 0\n    # assert get_primes_count(10) == 4\n    # assert get_primes_count(100) == 25\n","sub_path":"number_theoretic_algorithms/primes/primes_memo.py","file_name":"primes_memo.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"63581843","text":"from twisted.conch.telnet import TelnetTransport, TelnetProtocol\nfrom twisted.internet import protocol, task, reactor, endpoints\nfrom twisted.internet.protocol import ServerFactory\nfrom twisted.application.internet import TCPServer\nfrom twisted.application.service import Application\n\nimport json\nimport sys\nimport configparser\nimport os\nimport argparse\n\nimport twisted.scripts.twistd as t\nfrom pytradfri import Gateway\nfrom pytradfri.api.libcoap_api import APIFactory\n\nversion = \"0.8.1\"\nverbose = False\ndryRun = False\n\nhostConfig = {}\n\nINIFILE = \"{0}/devices.ini\".format(os.path.dirname(os.path.realpath(__file__)))\ndeviceDefaults = {\"Dimmable\": True, \"HasWB\": True, \"HasRGB\": False}\n\ndeviceConfig = configparser.ConfigParser()\n\nif os.path.exists(INIFILE):\n    deviceConfig.read(INIFILE)\n\ncurrentError = False\n\nCONFIGFILE = \"{0}/config.json\".format(os.path.dirname(os.path.realpath(__file__)))\n\nif os.path.isfile(CONFIGFILE):\n\n    with open(CONFIGFILE) as json_data_file:\n        hostConfig = json.load(json_data_file)\nelse:\n    print (\"Fatal: No config.json found\")\n    exit()\n\ndef error(f):\n    global currentError\n    print (f.getErrorMessage())\n    currentError = True\n\ndef verbosePrint(txt):\n    if verbose:\n        print(txt)\n\ndef stringToBool(boolString):\n    if boolString == \"True\":\n        return True\n    elif boolString == \"False\":\n        return False\n    else:\n        return None\n\nclass CoapAdapter(TelnetProtocol):\n    api = \"\"\n    gateway = \"\"\n\n\n    def __init__(self, factory):\n        self.factory = factory\n\n    def connectionMade(self):\n        answer = {}\n        print(\"Connected from \" + str(self.transport.getPeer()))\n        self.factory.clients.append(self)\n\n        answer[\"action\"] = \"clientConnect\"\n        answer[\"status\"] = \"Ok\"\n        answer[\"version\"] = version\n\n        self.transport.write(json.dumps(answer).encode(encoding='utf_8'))\n\n    def dataReceived(self, data):\n        verbosePrint(\"Data received: \" + str(data))\n\n        decoded = data.decode(\"utf-8\")\n        decoded = '[' + decoded.replace('}{', '},{') + ']'\n        \n        commands = json.loads(decoded)\n\n        for command in commands:\n            if command['action']==\"initGateway\":\n                # print(\"Setting config\")\n                self.factory.initGateway(self, command)\n                #self.factory.initGateway(self, command['gateway'], command['key'], command['observe'], command['pollinterval'], command['groups'])\n\n            if command['action']==\"getLights\":\n                self.factory.sendDeviceList(self)\n\n            if command['action']==\"setLevel\":\n                self.factory.setLevel(self, command[\"deviceID\"], command[\"level\"])\n\n            if command['action']==\"setState\":\n                self.factory.setState(self, command[\"deviceID\"], command[\"state\"])\n\n            if command['action']==\"setHex\":\n                self.factory.setHex(self, command[\"deviceID\"], command['hex'])\n\n        # except:\n        #    print(\"Error: Failed to parse JSON\")\n        #    print(\"Unexpected error:\", sys.exc_info()[0])\n\n    def connectionLost(self, reason):\n        print(\"Disconnected\")\n        self.factory.clients.remove(self)\n\nclass ikeaGroup():\n    deviceID = None\n    deviceName = None\n    lastState = None\n    lastLevel = None\n    factory = None\n\n    def __init__(self, factory, group):\n        self.deviceID = group.id\n        self.deviceName = group.name\n        self.lastState = group.state\n        self.lastLevel = group.dimmer\n        self.factory = factory\n\n    def hasChanged(self, group):\n        # targetGroup = self.factory.api(self.factory.gateway.get_group(int(self.deviceID)))\n\n        curState = group.state\n        curLevel = group.dimmer\n\n        if (curState == self.lastState) and (curLevel == self.lastLevel):\n            return False\n        else:\n            self.lastState = curState\n            self.lastLevel = curLevel\n            return True\n        \n    def sendState(self, client):\n        devices = []\n        answer = {}\n        \n        devices.append({\"DeviceID\": self.deviceID, \"Name\": self.deviceName, \"State\": self.lastState, \"Level\": self.lastLevel})\n\n        answer[\"action\"] = \"deviceUpdate\"\n        answer[\"status\"] = \"Ok\"\n        answer[\"result\"] =  devices\n\n        verbosePrint(answer)\n        try:\n            client.transport.write(json.dumps(answer).encode(encoding='utf_8'))\n        except Exception as e:\n            print(\"Error sending group state\")\n\nclass ikeaLight():\n\n    whiteTemps = {\"cold\":\"f5faf6\", \"normal\":\"f1e0b5\", \"warm\":\"efd275\"}\n\n    deviceID = None\n    deviceName = None\n    lastState = None\n    lastLevel = None\n    lastWB = None\n    modelNumber = None\n\n    device = None\n    factory = None\n\n    def __init__(self, factory, device):\n        self.device = device\n        self.deviceID = device.id\n        self.deviceName = device.name\n        self.modelNumber = device.device_info.model_number\n        self.lastState = device.light_control.lights[0].state\n        self.lastLevel = device.light_control.lights[0].dimmer\n        self.lastWB = device.light_control.lights[0].hex_color\n        self.factory = factory\n \n\n    def hasChanged(self, device):\n        curState = device.light_control.lights[0].state\n        curLevel = device.light_control.lights[0].dimmer\n        curWB = device.light_control.lights[0].hex_color\n\n        if (curState == self.lastState) and (curLevel == self.lastLevel) and (curWB == self.lastWB):\n            return False\n        else:\n            self.lastState = curState\n            self.lastLevel = curLevel\n            self.lastWB = curWB\n            return True\n\n    def sendState(self, client):\n        devices = []\n        answer = {}\n       \n        targetLevel = self.lastLevel\n        if targetLevel == None:\n            targetLevel = 0\n\n        devices.append({\"DeviceID\": self.deviceID, \"Name\": self.deviceName, \"State\": self.lastState, \"Level\": targetLevel, \"Hex\": self.lastWB})\n\n        answer[\"action\"] = \"deviceUpdate\"\n        answer[\"status\"] = \"Ok\"\n        answer[\"result\"] =  devices\n\n        verbosePrint(answer)\n        try:\n            client.transport.write(json.dumps(answer).encode(encoding='utf_8'))\n        except Exception as e:\n            print(\"Error sending light state\")\n\nclass ikeaSocket():\n    deviceID = None\n    deviceName = None\n    lastState = None\n    modelNumber = None\n\n    device = None\n    factory = None\n\n    def __init__(self, factory, device):\n        self.deviceID = device.id\n        self.deviceName = device.name\n        self.modelNumber = device.device_info.model_number\n        self.lastState = device.socket_control.sockets[0].state\n        self.device = device\n        self.factory = factory\n\n    def setState(self, client, state):\n        answer = {}\n        answer[\"action\"] = \"setState\"\n        answer[\"status\"] = \"Ok\"\n\n        self.lastState = state\n\n        targetDevice = self.factory.api(self.factory.gateway.get_device(int(self.deviceID)))\n        setStateCommand = targetDevice.socket_control.set_state(state)\n        \n        try:\n            self.factory.api(setStateCommand)\n        except Exception as e:\n            verbosePrint(\"Failed to set state for socked with ID: {0}\".format(self.deviceID))\n            answer[\"status\"]=\"Failed\"\n\n        if client != None:\n            client.transport.write(json.dumps(answer).encode(encoding='utf_8'))\n            self.sendState(client)\n\n    def sendState(self, client):\n        devices = []\n        answer = {}\n\n        devices.append({\"DeviceID\": self.deviceID, \"Name\": self.deviceName, \"State\": self.lastState})\n\n        answer[\"action\"] = \"deviceUpdate\"\n        answer[\"status\"] = \"Ok\"\n        answer[\"result\"] =  devices\n\n        verbosePrint(answer)\n\n        if client != None:\n            try:\n                client.transport.write(json.dumps(answer).encode(encoding='utf_8'))\n            except Exception as e:\n                verbosePrint(\"Error sending socket state\")\n\n    def hasChanged(self, device):\n        # NOTE: Device is a pytradfri-device-object\n        curState = device.socket_control.sockets[0].state\n\n        if curState != self.lastState:\n            self.lastState = curState\n            return True\n        else:\n            return False\n\nclass AdaptorFactory(ServerFactory):\n\n    ikeaLights = {}\n    ikeaGroups = {}\n    ikeaSockets = {}\n\n    devices = None\n    groups = None\n\n    observe = False\n    groups = False\n\n    def __init__(self):\n        self.clients = []\n        self.gateway = None\n        self.api = None\n        self.devices = None\n        self.lights = None\n        self.groups = None\n\n        self.lighStatus = {}\n        self.lc = task.LoopingCall(self.announce)\n\n        if dryRun:\n            self.initGateway(None, None)\n            self.sendDeviceList(None)\n            self.setState(None, \"65548\", False)\n\n    def logout(self):\n        # print(\"Logout\")\n        reactor.stop()\n\n    def buildProtocol(self, addr):\n        return CoapAdapter(self)\n\n    def announce(self):\n        try:\n            self.devices = self.api(self.api(self.gateway.get_devices()))\n            if self.groups:\n                self.groups = self.api(self.api(self.gateway.get_groups()))\n\n            for dev in self.devices:\n                if dev.has_light_control:\n                    if self.ikeaLights[dev.id].hasChanged(dev):\n                        for client in self.clients:\n                            self.ikeaLights[dev.id].sendState(client)\n\n                if dev.has_socket_control:\n                    if self.ikeaSockets[dev.id].hasChanged(dev):\n                        for client in self.clients:\n                            self.ikeaSockets[dev.id].sendState(client)\n\n            if self.groups:\n                for group in self.groups:\n                    if self.ikeaGroups[group.id].hasChanged(group):\n                        for client in self.clients:\n                            self.ikeaGroups[group.id].sendState(client)\n        except Exception as e: \n            print(\"Error in annouce: {0}:{1}\".format(e, e.message))\n\n    #def initGateway(self, client, ip, key, observe, interval, groups):\n    def initGateway(self, client, command):\n        verbosePrint(\"Initializing gateway\")\n        connectedToGW = False\n\n        if command != None:\n            if command['observe']==\"True\":\n                self.observe = True\n            else:\n                self.observe = False\n\n            if command['groups']==\"True\":\n                self.groups = True\n            else:\n                self.groups = False\n\n        #try:\n        api_factory = APIFactory(hostConfig[\"Gateway\"], hostConfig['Identity'], hostConfig['Passkey'])\n \n        self.api = api_factory.request\n        self.gateway = Gateway()\n        \n        connectedToGW = True\n        #except:\n        #    connectedToGW = False\n\n        if connectedToGW:\n            self.devices = self.api(self.api(self.gateway.get_devices()))\n            #self.devices = self.api(self.api(self.gateway.get_devices()))\n            if self.groups:\n                self.groups = self.api(self.api(self.gateway.get_groups()))\n        \n            try:\n                for dev in self.devices:\n                    if dev.has_light_control:\n                        verbosePrint(\"Adding light with ID: {0}\".format(dev.id))\n                        self.ikeaLights[dev.id] = ikeaLight(factory=self, device=dev)\n                    if dev.has_socket_control:\n                        verbosePrint(\"Adding socket with ID: {0}\".format(dev.id))\n                        self.ikeaSockets[dev.id] = ikeaSocket(factory=self, device=dev)\n            except Exception as e:\n                print(\"Unable to iterate devices\")\n\n            if self.groups:\n                try:\n                    for group in self.groups:\n                        self.ikeaGroups[group.id] = ikeaGroup(factory=self, group=group)\n                except Exception as e:\n                    print(\"Unable to iterate groups\")\n\n            if self.observe:\n                if not self.lc.running:\n                    self.lc.start(int(command['pollinterval']))\n            else:\n                if self.lc.running:\n                    self.lc.stop()\n                self.announce()\n\n            if client != None:\n                client.transport.write(json.dumps({\"action\":\"initGateway\", \"status\": \"Ok\"}).encode(encoding='utf_8'))\n        else:\n            if client != None:\n                client.transport.write(json.dumps({\"action\":\"initGateway\", \"status\": \"Failed\", \"error\": \"Connection timed out\"}).encode(encoding='utf_8'))\n\n    def sendDeviceList(self, client):\n        devices = []\n        answer = {}\n        configChanged = False\n\n        # Lights\n        for key, aDevice in self.ikeaLights.items():\n            # print (aDevice.modelNumber)\n            if not aDevice.modelNumber in deviceConfig:\n                verbosePrint(\"Device settings not found for {0}. Creating defaults!\".format(aDevice.modelNumber))\n                deviceConfig[aDevice.modelNumber] = deviceDefaults\n                configChanged = True\n\n            \n            devices.append({\"DeviceID\": aDevice.deviceID, \"Name\": aDevice.deviceName, \"Type\": \"Light\", \"Dimmable\": stringToBool(deviceConfig[aDevice.modelNumber]['dimmable']), \"HasWB\": stringToBool(deviceConfig[aDevice.modelNumber]['haswb']), \"HasRGB\": stringToBool(deviceConfig[aDevice.modelNumber]['hasrgb'])})\n\n        # Outlets\n        for key, aDevice in self.ikeaSockets.items():\n            devices.append({\"DeviceID\": aDevice.deviceID, \"Name\": aDevice.deviceName, \"Type\": \"Outlet\"})\n\n        if self.groups:\n            for key, aGroup in self.ikeaGroups.items():\n                #print (aGroup)\n                devices.append({\"DeviceID\": aGroup.deviceID, \"Name\": \"Group - \"+aGroup.deviceName, \"Type\": \"Group\", \"Dimmable\": True, \"HasWB\": False})\n\n        if configChanged:\n            with open(INIFILE, \"w\") as configfile:\n                deviceConfig.write(configfile)\n\n        answer[\"action\"] = \"getLights\"\n        answer[\"status\"] = \"Ok\"\n        answer[\"result\"] =  devices\n\n        verbosePrint(json.dumps(answer))\n        \n        if client != None:\n            client.transport.write(json.dumps(answer).encode(encoding='utf_8'))\n\n        for aDev in self.ikeaLights:\n            self.ikeaLights[aDev].sendState(client)\n\n        for aSocket in self.ikeaSockets:\n            self.ikeaSockets[aSocket].sendState(client)\n\n        if self.groups:\n            for aGroup in self.ikeaGroups:\n                self.ikeaGroups[aGroup].sendState(client)\n\n    def setLevel(self, client, deviceID, level):\n        answer = {}\n        answer[\"action\"] = \"setLevel\"\n        answer[\"status\"] = \"Ok\"\n\n        setLevelCommand = None\n        targetDevice = None\n\n        deviceID=int(deviceID)\n        target = None\n\n        if deviceID in self.ikeaLights.keys():\n            targetDeviceCommand = self.gateway.get_device(deviceID)\n            targetDevice = self.api(targetDeviceCommand)\n            setLevelCommand = targetDevice.light_control.set_dimmer(level)\n            target = self.ikeaLights[deviceID]\n            # Set\n            self.api(setLevelCommand)\n\n        if self.groups:\n            if deviceID in self.ikeaGroups.keys():\n                # First set level\n                targetDevice=self.api(self.gateway.get_group(int(deviceID)))\n                setLevelCommand = targetDevice.set_dimmer(level)\n                target = self.ikeaGroups[deviceID]\n                self.api(setLevelCommand)\n\n                # Then switch the group on\n             \n                setStateCommand = targetDevice.set_state(True)\n                target = self.ikeaGroups[deviceID]\n                self.api(setStateCommand)\n                \n        client.transport.write(json.dumps(answer).encode(encoding='utf_8'))\n  \n        self.announce()\n\n    def setState(self, client, deviceID, state):\n        answer = {}\n        answer[\"action\"] = \"setState\"\n        answer[\"status\"] = \"Ok\"\n\n        setStateCommand = None\n        target = None\n        targetDevice = None\n        deviceID = int(deviceID)\n\n        if state == \"On\":\n            state = True\n\n        if state == \"Off\":\n            state = False\n\n        # Device is a light\n        if deviceID in self.ikeaLights.keys():\n            targetDevice = self.api(self.gateway.get_device(int(deviceID)))\n            setStateCommand = targetDevice.light_control.set_state(state)\n            target = self.ikeaLights[deviceID]\n\n        # Device is as outled\n        if deviceID in self.ikeaSockets.keys():\n            self.ikeaSockets[deviceID].setState(client, state)\n            return\n\n        # Device is a group\n        if self.groups:\n            if deviceID in self.ikeaGroups.keys():\n                targetDevice = self.api(self.gateway.get_group(int(deviceID)))\n                setStateCommand = targetDevice.set_state(state)\n                target = self.ikeaGroups[deviceID]\n\n        try:\n            self.api(setStateCommand)\n        except Exception as e:\n            print(\"Failed to set state\")\n            answer[\"status\"]=\"Failed\"\n        \n        client.transport.write(json.dumps(answer).encode(encoding='utf_8'))\n\n        self.announce()\n\n    def setHex(self, client, deviceID, hex):\n        answer = {}\n        answer[\"action\"] = \"setHex\"\n        answer[\"status\"] = \"Ok\"\n\n        deviceID = int(deviceID)\n\n        if deviceID in self.ikeaLights.keys():\n            targetDevice = self.api(self.gateway.get_device(int(deviceID)))\n            setStateCommand = targetDevice.light_control.set_hex_color(hex)\n\n        self.api(setStateCommand)\n        client.transport.write(json.dumps(answer).encode(encoding='utf_8'))\n\n        self.announce()\n\n    \nif __name__ == \"__main__\":\n    print (\"IKEA-tradfri COAP-adaptor version {0} started (command line)!\".format(version))\n    verbose = True\n    \n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--dryrun\", action=\"store_true\")\n\n    args = parser.parse_args()\n\n    if args.dryrun:\n        dryRun = True\n    \n    endpoints.serverFromString(reactor, \"tcp:1234\").listen(AdaptorFactory()).addErrback(error)\n    \n    if not currentError:\n        reactor.run()\n    \nelse:\n    factory = AdaptorFactory()\n    service = TCPServer(1234, factory)\n    application = Application(\"IKEA Tradfri Adaptor\")\n    service.setServiceParent(application)\n","sub_path":"tradfri.tac","file_name":"tradfri.tac","file_ext":"tac","file_size_in_byte":18294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"285431426","text":"# FOR EACH HDA IN FOLDER PLACE THEM IN HOUDINI FOR QA of lib\n# THIS CREATES THE PYTHON CODE TO THEN BE PASTED INTO SHELL\nOBJ_start = '''OBJ_start = hou.node(\"/obj/ALL_HDA\")'''\nprint(OBJ_start)\nPYquotes = \"\"\"'\"\"\"\nHDA_path = \"H:/MODELS/HOUDINI/00_OTL_DIGITALASSET/Z/otls\"\n\nNODE_create_start = \"\"\" = OBJ_start.createNode('\"\"\"\nNODE_create_name = \"EXAMPLE\"\nNODE_create_mid = \"\"\"', \"\"\"\nNODE_create_end = ''')'''\n\n#NODE_param_start = '''.setParms({'sopoutput':'''\n#NODE_param_end = '''})'''\n\nimport os\n\ndirectory = HDA_path\nfor filename in os.listdir(directory):\n  if filename.endswith(\".hda\") :\n    filenameonly = filename.replace('.hda', '')\n\n    NODE_create_name = filenameonly\n    NODE_name = filenameonly\n    #PARAM_name = NODE_EXPORT_start + PATH_name + NODE_EXPORT_end\n\n    PYNODE_create_final = NODE_name + NODE_create_start + NODE_create_name + NODE_create_mid + PYquotes + NODE_name + PYquotes + NODE_create_end\n    #PYNODE_param_final = NODE_name + PYNODE_param_start + PYquotes + PARAM_name + PYquotes + NODE_param_end\n    print(PYNODE_create_final)\n    print(\"time.sleep(1)\")","sub_path":"otls/00_ALL_HDA_PLACE.py","file_name":"00_ALL_HDA_PLACE.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"268680923","text":"import numpy as np\nfrom .distance import latlon2distance\nfrom .transform import uv2vrvt_rt, uv2vrvt_xy\nfrom .fourier import interp_xy_closure\nfrom . import pseudo_coord\n\n\n__all__ = [\n    'inertial_stability_xy',\n    'inertial_stability_rt'\n]\n\n\ndef inertial_stability_xy(u, v, f, lon, lat, clon, clat, radius=None, thetas=None, dxdy=None):\n    \"\"\"\n    Calculate (cyclinic) inertial stability at x-y (longtitude-latitude) coordinate.\n    \n    Inertial stability is defined as\n        I^2 = (f + 2*Vt/r) * (f + 1/r * d(r*Vt)/dr)\n    where `f` is coriolis parameter, `Vt` is tangential wind speed, `r` is radius.\n    The returned variable is sqrt(I^2).\n    \n    Parameter:\n    ---------\n    u, v : array, shape = (nz, ny, nx)\n        Zonal wind and meridional wind at x-y coordinate.\n    f : scalar or array, shape = (ny, nx)\n        Coriolis parameter\n    lon, lat : array, shape = (ny, nx)\n        Longtitude and latitude\n    clon, clat : scalar\n        TC center coordinate\n    radius : 1d array, shape = (nradius,). Optional\n        Radial coordinate (used to calculate the radial gradient)\n    thetas : 1d array, shape = (ntheta,). Optional\n        The angles (radians) of each sampled points on the circle.\n        See `circular.interp_circle`\n        Default is np.arange(*np.deg2rad([0, 360, 1])), the whole circle.\n    dxdy: 2-elements tuple, (dx, dy). Optional\n        Spatial resolution. \n        Default is None, and it would automatically derive dx and dy besed on `lon`\n        and `lat`.\n        \n    Return:\n    ------\n    I : array, shape = (nz, ny, nx)\n        Inertial stability.\n    \"\"\"\n    if thetas is None:\n        thetas = np.arange(*np.deg2rad([0, 360, 1]))\n        \n    if radius is None:\n        if dxdy is None:\n            dx = latlon2distance(lon[:,1:], lat[:,1:], lon[:,:-1], lat[:,:-1]).mean()\n            dy = latlon2distance(lon[1:,:], lat[1:,:], lon[:-1,:], lat[:-1,:]).mean()\n            dr = max(dx, dy)\n        else:\n            dr = max(dxdy)\n        \n        n = 5\n        slc = (slice(n, -n), slice(n, -n))\n        dist_x = latlon2distance(clon, lat[slc], lon[slc], lat[slc])\n        dist_y = latlon2distance(lon[slc], clat, lon[slc], lat[slc])\n        maxdist = min(dist_x.max(), dist_y.max())\n        radius = np.arange(0, maxdist, dr)\n        \n    nz, ny, nx = u.shape\n    nradius = radius.size\n    ntheta = thetas.size\n\n    # calculate vt at radius-theta coordinate, shape = (nz, nradius, ntheta)\n    _, vt = uv2vrvt_rt(u, v, lon, lat, clon, clat, radius, thetas, dxdy)\n        \n    # radial gradient: central difference for interior points, and one-side difference for edge points\n    drvt_dr = np.empty((nz, nradius, ntheta))\n    rvt = radius[None,:,None] * vt\n    drvt_dr[:,1:-1,:] = (rvt[:,2:,:] - rvt[:,:-2,:]) / (radius[2:] - radius[:-2])[None,:,None]\n    drvt_dr[:,0,:] = (rvt[:,1,:] - rvt[:,0,:]) / (radius[1] - radius[0])\n    drvt_dr[:,-1,:] = (rvt[:,-1,:] - rvt[:,-2,:]) / (radius[-1] - radius[-2])\n    \n    # interpolate to x-y coordinate\n    res = np.empty((nz, ny, nx))\n    X, Y = pseudo_coord.lonlat2xy(lon, lat, clon, clat)   # (ny, nx)\n    func = interp_xy_closure(radius, thetas, X, Y, center=(0, 0))\n    for ilev in range(nz):\n        res[ilev,:,:] = func(drvt_dr[ilev,:,:])\n    drvt_dr = res\n    \n    # vt at x-y coordinate\n    _, vt = uv2vrvt_xy(u, v, lon, lat, clon, clat)   # (nz, ny, nx)\n        \n    # finally\n    dist_lon = latlon2distance(clon, lat, lon, lat)    # (ny, nx)\n    dist_lat = latlon2distance(lon, clat, lon, lat)\n    r = 1000 * np.sqrt(X**2 + Y**2)    # km -> m\n    r[np.isclose(r, 0)] = np.nan\n\n    I2 = (f + 2*vt/r) * (f + drvt_dr/r)\n    I = np.sqrt(np.abs(I2))\n    return I\n\n\ndef inertial_stability_rt(vt, f, radius, thetas):\n    \"\"\"\n    Calculate (cyclinic) inertial stability at cylindrical (radius-theta) coordinate.\n    \n    Inertial stability is defined as\n        I^2 = (f + 2*Vt/r) * (f + 1/r * d(r*Vt)/dr)\n    where `f` is coriolis parameter, `Vt` is tangential wind speed, `r` is radius.\n    \n    Parameter:\n    ---------\n    vt : array, shape = (nz, nradius, ntheta)\n        Tangential wind speed at cylindrical coordinate\n    f : scalar or array, shape = (nradius, ntheta)\n        Coriolis parameter\n    radius : 1d array, shape = (nradius,)\n        Radial coordinate of `vt` and `f`\n    thetas : 1d array, shape = (ntheta,)\n        Azimuth coordinate of `vt` and f`\n        \n    Return:\n    ------\n    I : array, shape = (nz, nradius, ntheta)\n        Inertial stability\n    \"\"\"\n    nz, nradius, ntheta = vt.shape\n        \n    # radial gradient: central difference for interior points, and one-side difference for edge points\n    drvt_dr = np.empty((nz, nradius, ntheta))\n    rvt = radius[None,:,None] * vt\n    drvt_dr[:,1:-1,:] = (rvt[:,2:,:] - rvt[:,:-2,:]) / (radius[2:] - radius[:-2])[None,:,None]\n    drvt_dr[:,0,:] = (rvt[:,1,:] - rvt[:,0,:]) / (radius[1] - radius[0])\n    drvt_dr[:,-1,:] = (rvt[:,-1,:] - rvt[:,-2,:]) / (radius[-1] - radius[-2])\n    \n    r = radius[None,:,None].astype(float)\n    r[np.isclose(r, 0)] = np.nan\n    I2 = (f + 2*vt/r) * (f + drvt_dr/r)\n    I = np.sqrt(np.abs(I2))\n    return I","sub_path":"hurricane_tools/specialvar.py","file_name":"specialvar.py","file_ext":"py","file_size_in_byte":5107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"517996033","text":"from flask import Flask,render_template,request\n#from flask import session\n#from datetime import timedelta\nimport sqlite3\nbank = Flask(__name__)\n#bank.secret_key = \"#oihriefjipejfie09802832hefhijfijjouhoi\"\n\"\"\"@bank.before_request\ndef before_request():\n    session.permanent = True\n    bank.permanent_session_lifetime=timedelta(minutes=1)\"\"\"\n'''db = sqlite3.connect('bank.db')\nc = db.cursor()\nc.execute('create table user(name varchar(150), email varchar(150) not null primary key, password varchar(150), cpassword varchar(150))')\nc.execute('create table contact(name varchar(150), email varchar(150) not null primary key, msg varchar(150))')\nc.execute('create table account(id integer primary key autoincrement, name varchar(150), dob date, adhar varchar(15), phone varchar(150), uname varchar(150), password varchar(150), amount double)')\ndb.commit() '''  \n@bank.route('/')\ndef index():\n    return render_template('home.html')\n@bank.route('/login1/')\ndef login():\n    return render_template('login1.html')\n@bank.route('/form/')\ndef signin():\n    return render_template('form.html')\n@bank.route('/afterbanking/', methods=['GET','POST'])\ndef afterbanking():\n    if request.method=='POST':\n        email=request.form.get('email')\n        pswd=request.form.get('pswd')\n       \n\n        try:\n            db = sqlite3.connect('bank.db')\n        except Exception as e:\n            return f'{e}'\n        else:\n            c = db.cursor()\n            c.execute('select * from user where email=\"{}\"'.format(email))\n            data = c.fetchone()\n            if data:\n                if pswd==data[2]:\n                    #session['email']=email\n                    msg = email\n                    return render_template('banking.html',error=msg)\n                else:\n                    msg='Password is wrong'\n                    return render_template('login1.html',error=msg)\n            else:\n                msg = 'Email does not exsist'\n                return render_template('login1.html',error=msg)\n@bank.route('/contact/')\ndef contact():\n    return render_template('contact.html')\n@bank.route('/withdrawl/')\ndef withdrawl():\n    return render_template('withdrawl.html')\n@bank.route('/deposit/')\ndef deposit():\n    return render_template('deposit.html')\n@bank.route('/enquiry/')\ndef enquiry():\n    return render_template('enquiry.html')\n@bank.route('/account/')\ndef account():\n    return render_template('account.html')\n@bank.route('/detail/')\ndef detail():\n    return render_template('detail.html')\n@bank.route('/aftersign/',methods=['GET','POST'])\ndef aftersign():\n    if request.method=='POST':\n        name=request.form.get('name')\n        email=request.form.get('email')\n        pswd=request.form.get('pswd')\n        cpswd=request.form.get('cpswd')\n        #return ' {} {} {} {}'.format(name,email,pswd,cpswd)\n        try:\n           db = sqlite3.connect('bank.db')\n        except Exception as e:\n           return f'{e}'\n        else:\n            c=db.cursor()\n            c.execute('select email from user where email=\"{}\"'.format(email))\n            data=c.fetchone()\n            if data:\n                msg = 'This email is already exist'\n                return render_template('form.html',error=msg)\n            else:\n                if pswd==cpswd:\n                    cmd=\"insert into user values('{}','{}','{}','{}')\".format(name,email,pswd,cpswd)\n                    c.execute(cmd)\n                    db.commit()\n                    msg='you can login'\n                    return render_template('login1.html',error=msg)\n                else:\n                    msg = 'Please confirm your password'\n                    return render_template('form.html',error=msg)\n    else:\n        msg = 'Invalid method'\n        return render_template('form.html',error=msg)\n@bank.route('/aftercontact/',methods=['GET','POST'])\ndef aftercontact():\n    if request.method=='POST':\n        name=request.form.get('name')\n        email=request.form.get('email')\n        msg=request.form.get('msg')\n        \n        try:\n            db = sqlite3.connect('bank.db')\n        except Exception as e:\n            return f'{e}'\n        else:\n            c=db.cursor()\n            c.execute('insert into contact values(\"{}\",\"{}\",\"{}\")'.format(name,email,msg))\n            db.commit()\n            msg = 'Your message has been sent'\n            #session['name'] = name\n            return render_template('see.html',error=msg,errors=name)\n@bank.route('/contact/')\ndef hey():\n    return render_template('contact.html')\n\n@bank.route('/afteropen/',methods=['GET','POST'])\ndef afteropen():\n    if request.method=='POST':\n        name=request.form.get('name')\n        dob=request.form.get('dob')\n        adhar=request.form.get('adhar')\n        phone=request.form.get('phone')\n        uname=request.form.get('uname')\n        password=request.form.get('password')\n        #return ' {} {} {} {} {} {}'.format(name,dob,adhar,phone,uname,password)\n        try:\n            db = sqlite3.connect('bank.db')\n        except Exception as e:\n            return f'{e}'\n        else:\n            c = db.cursor()\n            c.execute('insert into account(name,dob,adhar,phone,uname,password) values(\"{}\",\"{}\",\"{}\",\"{}\",\"{}\",\"{}\")'.format(name,dob,adhar,phone,uname,password))\n            db.commit()\n            msg = 'done'\n            return render_template('account.html',error=msg)\n            \n    else:\n        msg = 'Invalid method'\n        return render_template('account.html',error=msg)\n@bank.route('/afterdeposit/',methods=['GET','POST'])\ndef afterdeposit():\n    if request.method=='POST':\n        acnumber = request.form.get('acnumber')\n        uname = request.form.get('uname')\n        amount=request.form.get('amount')\n        try:\n            db = sqlite3.connect('bank.db')\n        except Exception as e:\n            return f'{e}'\n        else:\n            c=db.cursor()\n            c.execute('select * from account where uname=\"{}\"'.format(uname))\n            data = c.fetchone()\n            if data:\n                c.execute('update account set amount=\"{}\" where id=\"{}\"'.format(amount,acnumber))\n                db.commit()\n                return 'done'\n            else:\n                return \"data not found\"\n    else:\n        msg='Invalid method'\n        return render_template('deposit.html',error=msg)\n@bank.route('/afterenquiry/',methods=['GET','POST'])\ndef afterenquiry():\n    if request.method=='POST':\n        uname=request.form.get('uname')\n        pswd=request.form.get('pswd')\n        try:\n            db = sqlite3.connect('bank.db')\n        except Exception as e:\n            return f'{e}'\n        else:\n            c=db.cursor()\n            c.execute('select * from account where uname=\"{}\"'.format(uname))\n            data = c.fetchone()\n            return render_template('page.html',error=data)\n    else:\n        msg='Invalid method'\n        return render_template('enquiry.html',error=msg)\n@bank.route('/afterdetail/',methods=['GET','POST'])\ndef afterdetail():\n    if request.method=='POST':\n        uname = request.form.get('uname')\n        pswd = request.form.get('pswd')\n        newadhar=request.form.get('newadhar')\n        newphone=request.form.get('newphone')\n        newuname=request.form.get('newuname')\n        newpswd=request.form.get('newpswd')\n        try:\n            db = sqlite3.connect('bank.db')\n        except Exception as e:\n            return f'{e}'\n        else:\n            c=db.cursor()\n            c.execute('select * from account where uname=\"{}\"'.format(uname))\n            data = c.fetchone()\n            if data:\n                c.execute('update account set uname=\"{}\",phone=\"{}\",adhar=\"{}\",password=\"{}\" where uname=\"{}\"'.format(newuname,newphone,newadhar,newpswd,uname))\n                db.commit()\n                return 'done'\n            else:\n                return \"data not found\"\n    else:\n        msg='Invalid method'\n        return render_template('detail.html',error=msg)\n@bank.route('/afterwithdraw/',methods=['GET','POST'])\ndef afterwithdraw():\n    if request.method=='POST':\n        username=request.form.get('username')\n        pswd = request.form.get('pswd')\n        amount = request.form.get('amount')\n        try:\n            db = sqlite3.connect('bank.db')\n        except Exception as e:\n            return f'{e}'\n        else:\n            c=db.cursor()\n            c.execute('select * from account where uname = \"{}\"'.format(username))\n            data = c.fetchone()\n            if data:\n                balance = data[7]\n                balance = balance-float(amount)\n                #return f'{balance}'\n                c.execute('update account set amount = \"{}\" where uname=\"{}\"'.format(balance,username))\n                db.commit()\n                msg = 'done'\n                return msg\n            else:\n                return f'data not found'\n    else:\n        msg = 'Invalid method'\n        return render_template('withdrawl.html',error=msg)\nbank.run(host='localhost',port=80,debug=True)","sub_path":"flaskbank/bank.py","file_name":"bank.py","file_ext":"py","file_size_in_byte":8958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"94927508","text":"import torch\nimport torch.optim\nimport math\nfrom torch.optim.optimizer import Optimizer\nfrom fairseq.optim import LegacyFairseqOptimizer, register_optimizer\n\n@register_optimizer(\"acmo\")\nclass FairseqACMo(LegacyFairseqOptimizer):\n    def __init__(self, args, params):\n        super().__init__(args)\n        self._optimizer = Acutum_Original(params, **self.optimizer_config)\n\n    @staticmethod\n    def add_args(parser):\n        \"\"\"Add optimizer-specific arguments to the parser.\"\"\"\n        # fmt: off\n        parser.add_argument('--acmo-betas', default='(0.9, 0.999)', metavar='B',\n                            help='betas for ACMo optimizer')\n        parser.add_argument('--acmo-eps', type=float, default=1e-8, metavar='D',\n                            help='epsilon for ACMo optimizer')\n        parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',\n                            help='weight decay')\n        # fmt: on\n\n    @property\n    def optimizer_config(self):\n        \"\"\"\n        Return a kwarg dictionary that will be used to override optimizer\n        args stored in checkpoints. This allows us to load a checkpoint and\n        resume training using a different set of optimizer args, e.g., with a\n        different learning rate.\n        \"\"\"\n        return {\n            \"lr\": self.args.lr[0],\n            \"betas\": eval(self.args.acmo_betas),\n            \"eps\": self.args.acmo_eps,\n            \"weight_decay\": self.args.weight_decay,\n        }\n\n\nclass Acutum_Original(Optimizer):\n\n    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,\n                 weight_decay=0):\n        if not 0.0 <= lr:\n            raise ValueError(\"Invalid learning rate: {}\".format(lr))\n        if not 0.0 <= eps:\n            raise ValueError(\"Invalid epsilon value: {}\".format(eps))\n        if not 0.0 <= betas[0] < 1.0:\n            raise ValueError(\"Invalid beta parameter at index 0: {}\".format(betas[0]))\n        if not 0.0 <= betas[1] < 1.0:\n            raise ValueError(\"Invalid beta parameter at index 1: {}\".format(betas[1]))\n        defaults = dict(lr=lr, betas=betas, eps=eps,\n                        weight_decay=weight_decay)\n        super(Acutum_Original, self).__init__(params, defaults)\n\n    def __setstate__(self, state):\n        super(Acutum_Original, self).__setstate__(state)\n\n    def step(self, closure=None):\n        \"\"\"Performs a single optimization step.\n        Arguments:\n            closure (callable, optional): A closure that reevaluates the model\n                and returns the loss.\n        \"\"\"\n        loss = None\n        if closure is not None:\n            loss = closure()\n\n        grad_host = None\n        moment_host = None\n\n        for group in self.param_groups:\n            for p in group['params']:\n                if p.grad is None:\n                    continue\n                grad = p.grad.data\n                if grad.is_sparse:\n                    raise RuntimeError('Autum does not support sparse gradients, please consider SparseAdam instead')\n                state = self.state[p]\n                # State initialization\n                if len(state) == 0:\n                    state['step'] = 0\n                    state['exp_avg'] = torch.zeros_like(p.data)\n                exp_avg= state['exp_avg']\n\n                if grad_host is None:\n                    grad_host = grad.view(-1)\n                else:\n                    grad_host = torch.cat((grad_host, grad.view(-1)), dim=-1)\n\n                \n                if moment_host is None:\n                    moment_host = exp_avg.view(-1)\n                else:\n                    moment_host = torch.cat((moment_host, exp_avg.view(-1)), dim=-1)\n        \n        grad_norm = torch.norm(grad_host)\n        moment_norm = torch.norm(moment_host)\n\n\n        for group in self.param_groups:\n            for p in group['params']:\n                if p.grad is None:\n                    continue\n                grad = p.grad.data\n                if grad.is_sparse:\n                    raise RuntimeError('Autum does not support sparse gradients, please consider SparseAdam instead')\n\n                state = self.state[p]\n\n                # State initialization\n                if len(state) == 0:\n                    state['step'] = 0\n                    # Exponential moving average of gradient values\n                    state['exp_avg'] = torch.zeros_like(p.data)\n\n                exp_avg= state['exp_avg']\n                beta1, beta2 = group['betas']\n                state['step'] += 1\n\n                if group['weight_decay'] != 0:\n                    grad.add_(group['weight_decay'], p.data)\n\n                # make gradient and momentum sharp\n                # grad_norm = torch.norm(grad.reshape(-1, ))\n                # moment_norm = torch.norm(exp_avg.reshape(-1, ))\n\n                exp_avg.mul_(grad_norm.div(moment_norm.add(group['eps']))).add_(grad).mul_(0.9)\n                step_size = group['lr']\n\n                p.data.add_(exp_avg.mul(-step_size))\n\n        return loss","sub_path":"pytorch_nmt/acmo/acmo.py","file_name":"acmo.py","file_ext":"py","file_size_in_byte":5014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"98717410","text":"import os, sys\n\n\nwith open('법정동코드 전체자료.txt', 'r') as read_file:\n    lines = read_file.readlines()\n\n\n\nresult = dict()\nfor line in lines:\n    words = line.split()\n    result[words[0][0:5]] = 'true'\n\nwith open('output.txt', 'w') as w_file:\n    for key in result.keys():\n        w_file.write(key+'\\n')","sub_path":"tool/filter.py","file_name":"filter.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"303357264","text":"#!/usr/bin/env python\n\nimport rospy as ros;\nfrom std_msgs.msg import Int8;\nfrom geometry_msgs.msg import Twist;\nimport time;\nimport sys;\n\nros.init_node('ZumoMoveForward', anonymous=True);\npub = ros.Publisher('/zumo/1/cmd_vel', Twist, queue_size=10);\n\ndef moveForward(msg):\n\tvelMsg = Twist();\n\tvelMsg.linear.x = 1;\n\tvelMsg.linear.y = 0;\n\tvelMsg.linear.z = 0;\n\tvelMsg.angular.x = 0;\n\tvelMsg.angular.y = 0;\n\tvelMsg.angular.z = 0;\n\tpub.publish(velMsg);\n\ttime.sleep(0.1);\n\nros.Subscriber('/zumo/prox_front_left', Int8, moveForward);\nros.spin();\n\t\n","sub_path":"src/sit310_lab6/scripts/ZumoMoveForward.py","file_name":"ZumoMoveForward.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"334740993","text":"from typing import List\n\n\nclass Solution:\n    def countBits(self, num: int) -> List[int]:\n        res = []\n        for n in range(num + 1):\n            res.append(self.countOnes(n))\n        return res\n\n    def countOnes(self, n):\n        if n == 1:\n            return 1\n        ones = 0\n        while n > 1:\n            if n % 2 == 1:\n                ones += 1\n            n = n // 2\n        ones = ones + 1 if n == 1 else ones\n        return ones\n\n    def countBitsV2(self, num: int) -> List[int]:\n        res = [0] * (num + 1)\n        offset = 1\n        res[0] = 0\n        for i in range(1, num + 1):\n            if offset * 2 == i:\n                offset *= 2\n            res[i] = res[i - offset] + 1\n\n        return res\n\n\ns = Solution()\ntests = [\n    (2, [0, 1, 1]),\n    (5, [0, 1, 1, 2, 1, 2]),\n    (0, [0]),\n]\n\nfor i, (x, y) in enumerate(tests):\n    out = s.countBits(x)\n    assert y == out, 'Test {}: Expect: {}, got {}'.format(i, y, out)\n\nprint('Example cases passed!')\n","sub_path":"0300-0399/0338/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"255371711","text":"#!/usr/bin/env python\n#\n# Copyright 2007 Google Inc.\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 os\nimport re\nimport random\nimport hashlib\nimport hmac\nimport string\nimport json\nimport logging\nimport time\n\n\nimport webapp2\nimport jinja2\n\nfrom google.appengine.ext import db\nfrom google.appengine.api import urlfetch\n\n\n# Commands to access the directory where the files html, js are.\n\ntemplate_dir = os.path.join(os.path.dirname(__file__))\njinja_env = jinja2.Environment(loader = jinja2.FileSystemLoader(template_dir),\n                              autoescape = True)\n\n\ndef render_str(template, **params):\n    t = jinja_env.get_template(template)\n    return t.render(params)\n\nclass BaseHandler(webapp2.RequestHandler):\n    def render(self, template, **kw):\n        self.response.out.write(render_str(template, **kw))\n\n    def write(self, *a, **kw):\n        self.response.out.write(*a, **kw)\n\n    def set_cookie(self, name, val):\n        self.response.headers.add_header('Set-Cookie',\n                                         '%s=%s; Path=/' % (name, val))\n\n\n#### Funciones \n\ndef data_payload(url, obj,  offset, count):\n    return str(str(url) + '/' + str(obj) + '?'+ 'offset='+ str(offset) + '&count=' + str(count))\n\ndef connexion_obj_data(url):\n\turl1 = data_payload(url, 'adi/data.json', 0, 16)\n\tres  = urlfetch.fetch(url1)\n\tadi_list = json.loads(res.content) \n\treturn {x: adi_list[x] for x in range(0, 16)}\n\ndef metadata_request(url, offset, count):\n\turl = data_payload(url, 'adi/metadata.json', offset, count)\n\tres = urlfetch.fetch(url)\n\treturn json.loads(res.content)\n\ndef info_request(url):\n    ip_OK = False\n    obj = 'module/info.json'\n    url = str(url + '/' + obj)\n    res = urlfetch.fetch(url)\n    if res:\n        ip_OK = True\n    return json.loads(res.content) # no olvidar anadir una verificacion !\n\n\n#### Update section \n\ndef data_payload_update(url, a, b):\n\treturn str(url + '?' + 'inst='+ a + '&value='  + b)\n\ndef request_update_mod(target, instance, value):\n    if len(value) == 1:\n        val = '0' + value + '000000'\n    if len(value) == 2:\n        val =  value + '000000'\n    if len(value) == 7:\n        val = '0' + value\n    if len(value) == 8:\n        val = value\n    url = data_payload_update(target + '/adi/update.json', instance, val)\n    res = urlfetch.fetch(url)\n    return json.loads(res.content)\n\n\n\n######  \n\n\nclass MainDisplay(BaseHandler):\n    the_url = ''\n    def get(self):\n        self.the_url = self.request.get('url')\n        k = info_request(self.the_url)\n        self.render('index2.html', over_outcome = k, dirr = self.the_url )\n\n    def post(self):\n        inst = self.request.get('inst')\n        val = self.request.get('val')\n        result_error = request_update_mod(self.request.get('url'), inst, val) # send update request to device, get error\n        k = info_request(self.request.get('url'))\n       \n        # display\n        self.render('index2.html', \n                     up_outcome = result_error['result'], last_request = 'Instance:' + inst + \" \" + 'Value:' + val, \n                     dirr = self.request.get('url'), over_outcome = k)\n\n    \n\nclass FirstDisplay(BaseHandler):\n    URL = ''\n    def get(self):\n        self.render('index1.html')\n\n    def post(self):\n        IP = self.request.get('ip')\n        port = self.request.get('port')\n        self.URL = str('http://' + IP + ':' + port)\n        self.set_cookie('connex_url', str(self.URL)) #create the cookie\n        self.redirect('/update?url=' + self.URL)\n\n\n\nclass Result(BaseHandler):\n    def get(self):\n        cookieValue = self.request.cookies.get('connex_url')\n        list_name = metadata_request(cookieValue, 0, 16)\n        list_value = connexion_obj_data(cookieValue)\n        k = {x: [list_name[x][\"instance\"], list_name[x][\"name\"], list_name[x][\"datatype\"],list_name[x][\"access\"], list_value[x]] for x in range (0, 16)}\n        self.render('resultspage.html', ADI_dict = k)\n\n\n\n\n\napp = webapp2.WSGIApplication([\n    ('/', FirstDisplay),\n    ('/result', Result),\n    ('/update', MainDisplay)], \n    debug=True)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"275661566","text":"from sig import Yimg, width_img, heigth_img, nb_channels\nimport csv\nimport pickle\nimport re\nfrom datetime import timezone, datetime\nfrom dateutil.parser import parse\nfrom calendar import timegm\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\nimport numpy as np\nimport pandas as pd\n\nfrom os import listdir\nfrom os.path import isfile, join\n\n\ndef loadData(filename):\n\n    data_original = pd.read_csv(filename)\n\n    openp = data_original.ix[:, 'Open'].tolist()\n    highp = data_original.ix[:, 'High'].tolist()\n    lowp = data_original.ix[:, 'Low'].tolist()\n    closep = data_original.ix[:, 'Close'].tolist()\n    volumep = data_original.ix[:, 'Volume'].tolist()\n    dates = data_original.ix[:, 'Local time'].tolist()\n\n    data = np.column_stack((openp, highp, lowp, closep, volumep))\n\n    return np.transpose(data)\n\n\ndef stringSplitByNumbers(x):\n    r = re.compile('(\\d+)')\n    l = r.split(x)\n    return [int(y) if y.isdigit() else y for y in l]\n\n\ndef load_data_from_imgs(dir ,y_file):\n    files = [f for f in listdir(dir) if isfile(join(dir, f)) and '.pkl' not in f]\n    if len(files) < 2:\n        print('Run create_dataset.py first!')\n        exit(1)\n    files.sort(key=stringSplitByNumbers)\n    X = np.empty((len(files), heigth_img, width_img, nb_channels))\n    counter = 0\n\n    with tqdm(total=len(files)) as pbar:\n        for f in files:\n            pbar.update(1)\n            img = mpimg.imread(dir+f)\n            img = 1.0*(img > 0)\n            X[counter] = img[:, :, 0:3]\n            counter+=1\n\n    with open(Yimg, 'rb') as fid:\n            Y = pickle.load(fid)\n\n    return X, Y\n\n\ndef load_data_from_pkl(dir, y_file, x_file):\n    with open(y_file, 'rb') as fid:\n            Y = pickle.load(fid)\n    with open(x_file, 'rb') as fid:\n            X = pickle.load(fid)\n\n    return X, Y\n","sub_path":"loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":1825,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"2237335","text":"from flask import Flask, render_template, request\r\nfrom flask_wtf import FlaskForm\r\nfrom wtforms import TextField\r\nfrom wtforms.validators import InputRequired\r\nfrom alchemy import db\r\nfrom alchemy import Package\r\nimport time\r\napp = Flask(__name__)\r\napp.secret_key = 'random string'\r\n\r\nclass FieldForm(FlaskForm):#All the form in this class with FlaskForm as constructor\r\n   fnms = TextField('fnms', validators=[InputRequired('Provide Sender Name')])\r\n   fnmr = TextField('fnmr', validators=[InputRequired('Provide Receiver Name')])\r\n   address = TextField('address', validators=[InputRequired('Address is Required')])\r\n   city = TextField('city', validators=[InputRequired('City Required')])\r\n   pc = TextField('pc', validators=[InputRequired('Postal code Required')])\r\nclass idForm(FlaskForm):\r\n   id = TextField('id', validators=[InputRequired('Id Required')])\r\nclass RadioForm(FlaskForm):\r\n   radioform = TextField('radioform', validators=[InputRequired('Field Is Required')])\r\n\r\n\r\n@app.route('/',methods = ['POST', 'GET'])\r\ndef home():\t\r\n\treturn render_template('Home.html')\r\n   \r\n@app.route('/NewPackage',methods = ['POST', 'GET'])\r\ndef newpackage():\r\n\tform = FieldForm()#form variable \r\n\tmsg=None\r\n\r\n\tif request.method=='POST':\r\n\t\tif form.validate_on_submit():\r\n\t\t\tnew_package=Package(sender_name=request.form['fnms'],rec_name=request.form['fnmr'],city=request.form['city'],address=request.form['address'],tk=request.form['pc'],rdate=time.strftime('%d/%m/%Y'))\r\n\t\t\tdb.session.add(new_package)\r\n\t\t\tdb.session.commit()\r\n\t\t\tmsg='You successfully added a new entry'\r\n\treturn render_template('NewPackage.html',msg=msg, form=form)#define form\r\n\t\r\n@app.route('/PackageSend',methods = ['POST', 'GET'])\r\ndef packagesend():\r\n\tform = idForm()#form variable \r\n\tmsg=None\r\n\tif request.method=='POST':\r\n\t\tif form.validate_on_submit():\r\n\t\t\tupdate_this=Package.query.filter_by(id=request.form['id']).first()\r\n\t\t\tif update_this:\r\n\t\t\t\tif update_this.sdate==None:\r\n\t\t\t\t\tupdate_this.sdate=time.strftime('%d/%m/%Y')\r\n\t\t\t\t\tdb.session.commit()\r\n\t\t\t\t\tmsg='Sent Date added to entry'\r\n\t\t\t\telse:\r\n\t\t\t\t\tmsg='Package already sent'\r\n\t\t\telse:\r\n\t\t\t\tmsg='Entry does not exist'\r\n\treturn render_template('PackageSend.html',msg=msg, form=form)#define form\r\n\r\n@app.route('/Inventory',methods = ['POST', 'GET'])\r\ndef inventory():\r\n\tform = RadioForm()#form variable \r\n\tlist=None\r\n\tif request.method=='POST':\r\n\t\tif request.form[\"action\"]==\"Submit\":\r\n\t\t\tif request.form['radio']==\"0\":\r\n\t\t\t\tlist=Package.query.all()\r\n\t\t\telif request.form['radio']==\"1\":\r\n\t\t\t\tif form.validate_on_submit():\r\n\t\t\t\t\tlist=Package.query.filter_by(sender_name=request.form['radioform'])\r\n\t\t\telif request.form['radio']==\"2\":\r\n\t\t\t\tif form.validate_on_submit():\r\n\t\t\t\t\tlist=Package.query.filter_by(rec_name=request.form['radioform'])\r\n\t\t\telif request.form['radio']==\"3\":\r\n\t\t\t\tif form.validate_on_submit():\r\n\t\t\t\t\tlist=Package.query.filter_by(id=request.form['radioform'])\r\n\t\telse:\r\n\t\t\tfor f in request.form.getlist('row'):\r\n\t\t\t\tPackage.query.filter_by(id=f).delete()\r\n\t\t\t\tdb.session.commit()\r\n\t\t\t\tlist=Package.query.all()\r\n\treturn render_template('Inventory.html',list=list,form=form)#define form\t\r\n\r\n\t\r\nif __name__ == '__main__':\r\n   app.run(debug = True)\r\n \r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"176375738","text":"import requests\nfrom datetime import datetime\n\nBASE_URL = 'http://localhost:8080/'\n\nMENU_OPTIONS = [\n    'NEW GAME',\n    'RANKING',\n    'EXIT'\n]\n\ndef get_list_of_the_best_players():\n    return requests.get(url = BASE_URL + '/rank/sorted').json()\n\ndef save_game__to_database(login: str, points: int):\n    payload = {'login': login,\n        'date': datetime.now(),\n        'points': points }\n\n    return requests.post(url = BASE_URL + '/rank', data = payload)\n\ndef print_menu(current_position_index: int):\n    options = MENU_OPTIONS[:]\n    options[current_position_index] = MENU_OPTIONS[current_position_index] + ' <--'\n\n    [print(x) for x in options]\n\n\n\nif __name__ == \"__main__\":\n    print_menu(0)","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"487432448","text":"#!/usr/bin/env python\nimport random\nimport torch\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nimport models\nimport envs\n\n\ndef set_random_seeds(train_env, test_env, seed=0xc0ffee):\n    \"\"\"\n    Set random seeds for reproducibility.\n    \"\"\"\n    train_env.seed(seed)\n    test_env.seed(seed)\n    random.seed(seed)\n    torch.backends.cudnn.deterministic = True\n    torch.manual_seed(seed)\n\ndef get_best_action(net, obs):\n    \"\"\"\n    Return best action for given observation according to the neural network.\n    Best action has lower \"Q\" value since it estimates cumulative cost.\n    \"\"\"\n    obs_left = torch.cat([torch.FloatTensor(obs), torch.FloatTensor([0])], 0)\n    obs_right = torch.cat([torch.FloatTensor(obs), torch.FloatTensor([1])], 0)\n    q_left = net(obs_left)\n    q_right = net(obs_right)\n    action = 1 if q_left >= q_right else 0\n\n    return action\n\ndef generate_rollout(env, net=None):\n    \"\"\"\n    Generate rollout using given neural network. If a network is not given,\n    generate random rollout instead.\n    \"\"\"\n    rollout = []\n    obs = env.reset()\n    done = False\n    while not done:\n        action = get_best_action(net, obs) if net else env.action_space.sample()\n        next_obs, cost, done, _ = env.step(action)\n        rollout.append((obs, action, cost, next_obs, done))\n        obs = next_obs\n\n    return rollout\n\ndef train(net, optimizer, rollout, gamma=0.95):\n    \"\"\"\n    Train neural network with a given rollout.\n    \"\"\"\n    state_batch, action_batch, cost_batch, next_state_batch, done_batch = zip(*rollout)\n    state_batch = torch.FloatTensor(state_batch)\n    action_batch = torch.FloatTensor(action_batch)\n    cost_batch = torch.FloatTensor(cost_batch)\n    next_state_batch = torch.FloatTensor(next_state_batch)\n    done_batch = torch.FloatTensor(done_batch)\n\n    state_action_batch = torch.cat([state_batch, action_batch.unsqueeze(1)], 1)\n    predicted_q_values = net(state_action_batch).squeeze()\n\n    # Compute min_a Q(s', a)\n    q_next_state_left_batch = net(torch.cat([next_state_batch, torch.zeros(len(rollout), 1)], 1)).squeeze()\n    q_next_state_right_batch = net(torch.cat([next_state_batch, torch.ones(len(rollout), 1)], 1)).squeeze()\n    q_next_state_batch = torch.min(q_next_state_left_batch, q_next_state_right_batch)\n\n    with torch.no_grad():\n        target_q_values = cost_batch + gamma * q_next_state_batch * (torch.FloatTensor(1) - done_batch)\n\n    loss = F.mse_loss(predicted_q_values, target_q_values)\n    \n    optimizer.zero_grad()\n    loss.backward()\n    optimizer.step()\n\ndef hint_to_goal(net, optimizer, factor=100):\n    \"\"\"\n    Use hint-to-goal heuristic to clamp network output.\n    \"\"\"\n    for _ in range(factor):\n        state_action_pair = torch.FloatTensor([[random.random() * 0.1 - 0.05,\n                                                random.random() - 0.5,\n                                                random.random() * 0.6 - 0.3,\n                                                random.random() - 0.5,\n                                                random.randint(0, 1)]])\n        predicted_q_value = net(state_action_pair).squeeze()\n        loss = F.mse_loss(predicted_q_value, torch.zeros(1))\n\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\ndef test(env, net, episodes=1000):\n    steps = 0\n    nb_success = 0\n    for _ in range(episodes):\n        obs = env.reset()\n        done = False\n\n        while not done:\n            action = get_best_action(net, obs)\n            obs, _, done, info = env.step(action)\n            steps += 1\n\n        nb_success += 1 if info['success'] else 0\n\n    print('Average Number of Steps: ', float(steps) / episodes)\n    print('Success rate: ', float(nb_success) / episodes)\n\ndef main():\n    train_env = envs.make_cartpole(100)\n    test_env = envs.make_cartpole(3000)\n    set_random_seeds(train_env, test_env)\n\n    net = models.Net()\n    optimizer = optim.Rprop(net.parameters())\n    # TODO Initialize weights randomly within [-0.5, 0.5]\n\n    for epoch in range(500):\n        rollout = generate_rollout(train_env, net)\n        if epoch % 10 == 9:\n            print('Epoch {:4d} | Steps: {:3d}'.format(epoch + 1, len(rollout)))\n        train(net, optimizer, rollout)\n        hint_to_goal(net, optimizer)\n        # test(test_env, net)\n\n    train_env.close()\n    test_env.close()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"nfq.py","file_name":"nfq.py","file_ext":"py","file_size_in_byte":4362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"505407906","text":"import os\nimport cv2\nimport heapq\nimport operator\nfrom bitarray import bitarray\n\n\ndef read_file(file_name):\n    file = open(file_name, 'r')\n    return file.read()\n\n\ndef read_binary_file(file_name):\n    content = ''\n    with open(file_name, 'rb') as file:\n        temp = list(file.read())\n        content = ''.join([chr(x) for x in temp])\n    return content\n\n\ndef analyze_content(content):\n    dictionary = {}\n    for char in content:\n        cardinality = dictionary.get(char, 0)\n        dictionary.update({char: cardinality + 1})\n\n    return dictionary\n\n\ndef sort_dictionary_items(dictionary):\n    return dict(sorted(dictionary.items(), key=operator.itemgetter(1), reverse=True))\n\n\ndef int2bits(i, fill=8):\n    return bin(i)[2:].zfill(fill)\n\n\nclass BasicLZW:\n    def __init__(self, content, characters):\n        self.content = content\n        self.characters_dictionary = characters\n\n    def create(self):\n        dictionary_with_codes = {}\n        for index, key in enumerate(self.characters_dictionary.keys()):\n            dictionary_with_codes.update({key: index + 1})\n\n        return dictionary_with_codes\n\n    @staticmethod\n    def codes_to_bits(codes):\n        dict_with_bits_codes = {}\n        total_items = len(codes)\n        fill = total_items.bit_length()\n        for (char, code) in codes.items():\n            bits = int2bits(code, fill=fill)\n            dict_with_bits_codes.update({char: bits})\n\n        return dict_with_bits_codes\n\n    def encode(self, codes):\n        code = []\n        current_key = ''\n        basic_key = ''\n        last_element_num = len(codes)\n        current_bit_length = last_element_num.bit_length()\n\n        for letter in self.content:\n            current_key += letter\n            if current_key in codes:\n                basic_key += letter\n            else:\n                code.append(codes.get(basic_key))\n                last_element_num += 1\n\n                # Bump bits array - max length used\n                if last_element_num.bit_length() > current_bit_length:\n                    current_bit_length += 1\n                    for key, value in codes.items():\n                        codes.update({key: '0' + value})\n\n                # Update dictionary\n                codes.update({current_key: str(int2bits(last_element_num, fill=current_bit_length))})\n                basic_key = letter\n                current_key = letter\n\n        code.append(codes.get(basic_key))\n        return code\n\n    @staticmethod\n    def decode(codes, encoded_content):\n        content = []\n        last_key = ''\n        index = 0\n        last_element_num = len(codes)\n        current_bit_length = last_element_num.bit_length()\n        max_index = len(encoded_content)\n\n        while True:\n            bits_code = encoded_content[index:index + current_bit_length].to01()\n            index += current_bit_length\n            char = codes.get(bits_code)\n\n            if char is not None:\n                last_element_num += 1\n                codes.update({int2bits(last_element_num, fill=current_bit_length): char})\n\n                if last_key != '':\n                    last_key += char[0]\n                    codes.update({int2bits((last_element_num - 1), fill=current_bit_length): last_key})\n\n                    if last_element_num.bit_length() > current_bit_length:\n                        current_bit_length += 1\n                        temp = {}\n                        for key, value in codes.items():\n                            temp.update({'0' + key: value})\n                        codes = temp\n\n                last_key = char\n                content.append(char)\n\n                if index >= max_index:\n                    break\n            else:\n                break\n\n        return ''.join(content)\n\n\nclass LZWHuffman:\n    def __init__(self, content, characters):\n        self.content = content\n        self.characters_dictionary = characters\n\n    def create(self):\n        dictionary_with_codes = {}\n        for index, key in enumerate(self.characters_dictionary.keys()):\n            dictionary_with_codes.update({key: index + 1})\n\n        return dictionary_with_codes\n\n    @staticmethod\n    def append_counters(codes):\n        for key, value in codes.items():\n            codes.update({key: [value, 0]})\n        return codes\n\n    @staticmethod\n    def codes_to_bits(codes):\n        dict_with_bits_codes = {}\n        total_items = len(codes)\n        fill = total_items.bit_length()\n        for (char, code) in codes.items():\n            bits = int2bits(code, fill=fill)\n            dict_with_bits_codes.update({char: bits})\n\n        return dict_with_bits_codes\n\n    def pre_encode(self, codes):\n        current_key = ''\n        basic_key = ''\n        counter = 0\n        last_element_num = len(codes)\n        current_bit_length = last_element_num.bit_length()\n\n        for letter in self.content:\n            current_key += letter\n            if current_key in codes:\n                basic_key += letter\n            else:\n                [ind, card] = codes.get(basic_key)\n                codes.update({basic_key: [ind, card + 1]})\n                last_element_num += 1\n                counter += 1\n\n                # Bump bits array - max length used\n                if last_element_num.bit_length() > current_bit_length:\n                    current_bit_length += 1\n                    for key, value in codes.items():\n                        [ind, card] = value\n                        codes.update({key: ['0' + ind, card]})\n\n                # Update dictionary\n                codes.update({current_key: [str(int2bits(last_element_num, fill=current_bit_length)), 0]})\n                basic_key = letter\n                current_key = letter\n\n        [ind, card] = codes.get(basic_key)\n        codes.update({basic_key: [ind, card+1]})\n        counter += 1\n        return codes, counter\n\n    @staticmethod\n    def remove_n_cardinality_items(codes, n=0):\n        temp = {}\n        for key, item in codes.items():\n            [code, cardinality] = item\n            if cardinality > n:\n                temp.update({key: [code, cardinality]})\n        return temp\n\n    @staticmethod\n    def make_nodes_heap(dictionary):\n        heap = []\n        for key, value in dictionary.items():\n            [_bits, cardinality] = value\n            node = Node(key, cardinality)\n            heapq.heappush(heap, node)\n        return heap\n\n    @staticmethod\n    def merge_nodes(heap):\n        while len(heap) > 1:\n            node1 = heapq.heappop(heap)\n            node2 = heapq.heappop(heap)\n\n            merged = Node(None, node1.freq + node2.freq)\n            merged.left = node1\n            merged.right = node2\n\n            heapq.heappush(heap, merged)\n        return heap\n\n    def code_helper(self, root, current_code, codes):\n        # Return when None\n        if not root:\n            return\n\n        # Required assign character to node (only leaf has)\n        if root.char:\n            codes[root.char] = current_code\n            return\n\n        # Recursion\n        self.code_helper(root.left, current_code + '0', codes)\n        self.code_helper(root.right, current_code + '1', codes)\n\n    def make_codes(self, heap):\n        current_code = ''\n        codes = {}\n        root = heapq.heappop(heap)\n\n        self.code_helper(root, current_code, codes)\n        return codes\n\n    def huffman_codes(self, dictionary):\n        nodes = self.make_nodes_heap(dictionary)\n        heap = self.merge_nodes(nodes)\n        codes = self.make_codes(heap)\n        return codes\n\n    def encode(self, dictionary):\n        code = []\n        current_key = ''\n        basic_key = ''\n\n        for letter in self.content:\n            current_key += letter\n            if current_key in dictionary:\n                basic_key += letter\n            else:\n                code.append(dictionary.get(basic_key))\n                basic_key = letter\n                current_key = letter\n\n        code.append(dictionary.get(basic_key))\n        return code\n\n    @staticmethod\n    def decode(dictionary, content):\n        END_CONTENT_CHAR = '^'\n        decoded = ''\n        current_code = ''\n\n        for bit in content.to01():\n            current_code += str(bit)\n            if current_code in dictionary:\n                char = dictionary.get(current_code)\n                decoded += char\n                current_code = ''\n\n        return decoded\n\n\nclass Node:\n    def __init__(self, char, card):\n        self.char = char\n        self.freq = card\n        self.left = None\n        self.right = None\n\n    def __eq__(self, other):\n        return self.freq == other.freq\n\n    def __lt__(self, other):\n        return self.freq < other.freq\n\n    def __gt__(self, other):\n        return self.freq > other.freq\n\n\ndef load_dictionary(file_name):\n    dictionary = {}\n    with open(file_name, 'r') as file:\n        content = file.read()\n        fill = len(content).bit_length()\n        for index, char in enumerate(content):\n            bits = int2bits(index + 1, fill=fill)\n            dictionary.update({bits: char})\n\n    return dictionary\n\n\ndef load_huffman_dictionary(file_name):\n    dictionary = {}\n    with open(file_name, 'r') as file:\n        content = file.read()\n        slited = content.split('^')\n        for index, val in enumerate(slited):\n            if index % 2 == 1:\n                dictionary.update({val: slited[index - 1]})\n\n    return dictionary\n\n\ndef load_content(file_name):\n    content = bitarray()\n    with open(file_name, 'rb') as file:\n        content.fromfile(file)\n    return content\n\n\ndef write_list(encoded_content_list, file_name):\n    with open(file_name, 'wb') as file:\n        bitarray.tofile(bitarray(''.join(encoded_content_list)), file)\n\n\ndef write_dictionary(dictionary, file_name):\n    with open(file_name, 'w') as file:\n        for key in dictionary.keys():\n            file.write(key)\n\n\ndef write_huffman_dictionary(dictionary, file_name):\n    with open(file_name, 'w') as file:\n        for key, value in dictionary.items():\n            file.write(key)\n            file.write('^')\n            file.write(value)\n            file.write('^')\n\n\ndef calculate_size(file_name):\n    return os.stat(file_name).st_size\n\n\ndef main():\n    # file_name = '../Exercise_3/short_sample.txt'\n    file_name = 'wiki_sample.txt'\n    # content = read_binary_file('lena.bmp')\n\n    ################################\n    ####### Lempel–Ziv–Welch #######\n    ################################\n    content = read_file(file_name)\n    characters_dictionary = analyze_content(content)\n    sorted_characters_dictionary = sort_dictionary_items(characters_dictionary)\n    basic_lzw = BasicLZW(content, sorted_characters_dictionary)\n    lzw_basic_code = basic_lzw.create()\n    bits_codes = basic_lzw.codes_to_bits(lzw_basic_code)\n    write_dictionary(lzw_basic_code, 'lzw_dictionary.txt')\n\n    encoded_content = basic_lzw.encode(bits_codes)\n    write_list(encoded_content, 'lzw_content.bin')\n    encoded = load_content('lzw_content.bin')\n    decode_codes = load_dictionary('lzw_dictionary.txt')\n    decoded = basic_lzw.decode(decode_codes, encoded)\n\n    print('LZW basic format')\n    print('\\tBefore =>', calculate_size(file_name), '[bytes]')\n    print('\\tAfter  =>', calculate_size('lzw_content.bin'), '[bytes]')\n\n    #############################\n    ####### LZW + Huffman #######\n    #############################\n    content = read_file(file_name)\n    characters_dictionary = analyze_content(content)\n    sorted_characters_dictionary = sort_dictionary_items(characters_dictionary)\n    lzw_huffman = LZWHuffman(content, sorted_characters_dictionary)\n    lzw_basic_code = lzw_huffman.create()\n    bits_codes = lzw_huffman.codes_to_bits(lzw_basic_code)\n    codes_with_counters = lzw_huffman.append_counters(bits_codes)\n    codes_with_cardinality, total_counter = lzw_huffman.pre_encode(codes_with_counters)\n    print(codes_with_cardinality)\n    codes_clear_cardinality = lzw_huffman.remove_n_cardinality_items(codes_with_cardinality, n=2)\n    huffman_codes = lzw_huffman.huffman_codes(codes_with_cardinality)\n    encoded = lzw_huffman.encode(huffman_codes)\n    write_list(encoded, 'lzw_huffman_content.bin')\n    write_huffman_dictionary(huffman_codes, 'lzw_huffman_keys.txt')\n    loaded_dictionary = load_huffman_dictionary('lzw_huffman_keys.txt')\n    loaded_content = load_content('lzw_huffman_content.bin')\n    decoded = lzw_huffman.decode(loaded_dictionary, loaded_content)\n\n    print('LZW + Huffman format')\n    print('\\tBefore =>', calculate_size(file_name), '[bytes]')\n    print('\\tAfter  =>', calculate_size('lzw_huffman_content.bin'), '[bytes]')\n    print('\\tKeys   =>', calculate_size('lzw_huffman_keys.txt'), '[bytes]')\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"semestr_6_metody_kompresji_danych/Exercise_6/Exercise_6.py","file_name":"Exercise_6.py","file_ext":"py","file_size_in_byte":12679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"618354756","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport json\nimport os\nimport csv \nimport getopt\nimport sys\nimport argparse\n\n######################################################################\n# Parse all JSON files to construct syndrome to omim ID mapping        \n#######################################################################\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Generate summary file')\n    parser.add_argument('-c', '--case', help='path to convert file')\n    parser.add_argument('-o', '--output', help='path to output file')\n\n    args = parser.parse_args()\n    # syn_dict = {'name':[omim_id]}\n    syn_dict = {}\n    case_path = args.case\n    for case in os.listdir(case_path):\n        print(case)\n        file_name = os.path.join(case_path, case)\n        case_file = open(file_name)\n        case_data = json.load(case_file)\n        if 'detected_syndromes' not in case_data:\n            continue\n        data = case_data['detected_syndromes']\n        for syn_data in data:\n            name = syn_data['syndrome_name']\n            omim = syn_data['omim_id']\n            if type(name) == list:\n                continue\n            if syn_data['has_mask'] == 0:\n                continue\n            if name in syn_dict and omim == None:\n                continue\n            omim_out = []\n            if type(omim) == int:\n                omim_out.append(omim)\n            else:\n                omim_out = omim\n            syn_dict.update({name:omim_out})\n    print(len(syn_dict))\n    print(syn_dict)\n\n    syn_file = open(os.path.join(args.output,'gestalt_syn_to_omim.csv'), 'w')\n    syn_writer = csv.writer(syn_file, delimiter='\\t')\n    for syn in syn_dict:\n        print(syn)\n        print(syn_dict[syn])\n        syn_writer.writerow([syn, syn_dict[syn]])\n    syn_file.close()\n","sub_path":"helper/syndrome_to_gene_dict.py","file_name":"syndrome_to_gene_dict.py","file_ext":"py","file_size_in_byte":1826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"166159599","text":"import csv \n\noutput = \"D:\\\\MyDownloads\\\\fakenews.csv\"\nout = open(output, 'w', newline='')\nwriter = csv.writer(out)\ntry:\n\twith open('D:\\\\MyDownloads\\\\fake.csv', encoding='utf8') as csvfile:\n\t\treadCSV = csv.reader(csvfile, delimiter=',')\n\t\tfor row in readCSV:\n\t\t\twriter.writerow((row[4],row[5],'fake'))\nexcept:\n\tprint(\"Error\")\n\nout.close()","sub_path":"crawler/cleaner.py","file_name":"cleaner.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"297521651","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug  4 15:41:01 2017\n\n@author: sabrina.yue.wang\n\"\"\"\nimport sys\nimport os\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom sf import Ui_Dialog\nfrom PyQt5.QtCore import QThread, pyqtSignal, pyqtSlot, QObject\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QInputDialog, QFileDialog, QStatusBar\nimport matplotlib\nmatplotlib.use('Qt5Agg')\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom matplotlib.figure import Figure\nimport numpy\nimport pickle\nimport math\nimport importlib\nimport os.path\n\ncurrentdir=os.getcwd()\nsys.path.insert(1, '{}/../'.format(currentdir))\nsys.path.insert(1, '{}/../utils/'.format(currentdir))\nsys.path.insert(1, '{}/../plots'.format(currentdir))\nsys.path.insert(1, '{}/../objects'.format(currentdir))\n\nfrom analysis_dust import BEffectsAnalysis\nfrom utils import generate_particle_equilibrium_positions, prepare_modified_b_field\nfrom plots import dustplots\n\nfrom IPython import get_ipython\n\n\nipython = get_ipython()\nipython.magic('load_ext autoreload')\nipython.magic('autoreload 2')\n\n\nclass SpectrumCanvas(FigureCanvas):\n\t'''\n\tProvides ability for displaying figures and animations on gui\n\t'''\n\tdef __init__(self, parent=None, width=7.31, height=4.21, dpi=100):\n\t\tself.fig = Figure(figsize=(width, height), dpi=dpi)\n\t\t\n\t\tFigureCanvas.__init__(self, self.fig)\n\t\tself.setParent(parent)\n\t\tFigureCanvas.setSizePolicy(self, QtWidgets.QSizePolicy.Expanding,\n\t\t\t\t\t\t\t\t   QtWidgets.QSizePolicy.Expanding)\n\t\tFigureCanvas.updateGeometry(self)\n\t\n\t\n\n\nclass SFGui(Ui_Dialog): #Setting up/Connecting the gui buttons and connecting them to their associated functions\n\tdef __init__(self, dialog):\n\t\tUi_Dialog.__init__(self)\n\t\tself.setupUi(dialog)\n\t\tself.graph = SpectrumCanvas(self.graphicsView)\n\t\tself.graph.setObjectName(\"graph\")\n\t\tself.error=QtWidgets.QGraphicsScene()\n\t\tself.pushButton.clicked.connect(self.simtime)\n\t\tself.pushButton_2.clicked.connect(self.particlenumber)\n\t\tself.pushButton_2.clicked.connect(self.run)\n\t\tself.time = 0;\n\t\tself.particlenumber = 0;\n \n\tdef addInputTextToListbox(self): #Add user input\n\t\ttxt = self.myTextInput.text()\n\n\tdef simtime(self):\n\t\ttext2,ok2 = QInputDialog.getText(dialog, 'User Input','Enter simulation time in seconds')\n\t\tself.time=float(text2.split()[0])\n\n\tdef particlenumber(self):\n\t\ttext, ok = QInputDialog.getText(dialog, 'User Input','Enter particle number')\n\t\tself.particlenumber = float(text.split()[0])\n\tdef run(self):\n\t\tbeffect1 = BEffectsAnalysis()\n\t\tbeffect1.create_particles(\n\t\t\tnumparticles=10,\n\t\t\tinitpositions=generate_particle_equilibrium_positions()\n\t\t)\n\t\tbeffect1.create_pairs()\n\t\tbeffect1.interact_and_iterate(\n\t\t\titerationsB=100,\n\t\t\tinit_iterations=100,\n\t\t\tmethod='NoGibs',\n\t\t\tmodified_b_field=prepare_modified_b_field()\n\t\t)\n\t\tbeffect1.sort_positions_of_particles()\n\n\t\n\n\n\t\t\n \nif __name__ == '__main__':\n\tapp = QtWidgets.QApplication(sys.argv)\n\tdialog = QtWidgets.QDialog()\n \n\tprog = SFGui(dialog)\n \n\tdialog.show()\n\tsys.exit(app.exec_())\n\t\n\n\t","sub_path":"gui/sfrun.py","file_name":"sfrun.py","file_ext":"py","file_size_in_byte":2994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"384070341","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#     \n#     launcher.py\n#     \n#     This file is part of the RoboEarth Cloud Engine framework.\n#     \n#     This file was originally created for RoboEearth\n#     http://www.roboearth.org/\n#     \n#     The research leading to these results has received funding from\n#     the European Union Seventh Framework Programme FP7/2007-2013 under\n#     grant agreement no248942 RoboEarth.\n#     \n#     Copyright 2012 RoboEarth\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#     \\author/s: Dominique Hunziker \n#     \n#     \n\n# ROS specific imports\nimport rospy\n\n# twisted specific imports\nfrom twisted.python import log\n\n# Custom imports\nfrom core.types import cmd as types\nfrom core.manager import NodeManager\nfrom core.command import ControlDistributor, NodeCommand\nfrom comm import definition\nfrom comm import types as msgTypes\nfrom comm.manager import CommManager\nfrom comm.protocol import RCEServerFactory\nfrom remote.message import CommandSerializer, TagSerializer, \\\n    CommandProcessor, TagProcessor\n\nfrom settings import ROS_NODE_PORT\n\n\nclass User(object):\n    def __init__(self):\n        self.nodes = {}\n\nNodeManager._USER_CLS = User\n\n\ndef main(reactor, commID, port):\n    f = open('/home/ros/launcher.log', 'w')\n    log.startLogging(f)\n    \n    rospy.init_node('RCE_Launcher')\n    \n    manager = NodeManager(reactor)\n    commManager = CommManager(reactor, commID)\n    cmdSerializer = CommandSerializer()\n    cmdSerializer.registerCommand([NodeCommand])\n    commManager.registerContentSerializers([cmdSerializer,\n                                            TagSerializer()])\n    \n    distributor = ControlDistributor()\n    distributor.addHandler(types.NODE, manager.addNode)\n    distributor.addHandler(types.RM_NODE, manager.removeNode)\n    commManager.registerMessageProcessors([CommandProcessor(distributor),\n                                           TagProcessor(distributor)])\n    \n    factory = RCEServerFactory(commManager)\n    factory.addApprovedMessageTypes([msgTypes.COMMAND, msgTypes.TAG])\n    reactor.listenTCP(port, factory)\n    \n    def terminate():\n        reactor.callFromThread(manager.shutdown)\n        reactor.callFromThread(commManager.shutdown)\n        reactor.callFromThread(reactor.stop)\n\n    rospy.on_shutdown(terminate)\n    \n    reactor.run(installSignalHandlers=False)\n    \n    f.close()\n\n\nif __name__ == '__main__':\n    from twisted.internet import reactor\n    \n    main(reactor, definition.NEIGHBOR_ADDR, ROS_NODE_PORT)\n","sub_path":"framework/launcher.py","file_name":"launcher.py","file_ext":"py","file_size_in_byte":3052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"163321686","text":"import logging\nimport sys\nimport argparse,textwrap\n\ndef crisprseq_parseargs():\n    \"\"\"\n    Parsing mageck arguments.\n    \"\"\"\n    parser=argparse.ArgumentParser(description='MAGeCK-NEST: performs sgRNA, gene and pathway analysis on CRISPR-Cas9 screening data.')\n    # definition of sub commands\n    parser.add_argument('-v', '--version',action='version',version='%(prog)s 0.0.1')\n    subparser=parser.add_subparsers(help='commands to run mageck',dest='subcmd')\n    subm_nest=subparser.add_parser('nest',help='Perform estimation of gene essentiality.')\n\n    reqgroup=subm_nest.add_argument_group(title='Required arguments',\n    description='')\n    reqgroup.add_argument('-k','--count_table',required=True,help='Provide a tab-separated count table. Each line in the table should include sgRNA name (1st column), target gene (2nd column) and read counts in each sample.')\n    reqgroup.add_argument('-d','--design_matrix',required=True,help='Provide a design matrix, either a quoted string of the design matrix or a file name. If using quoted string, for instance, \"1,0;1,1\" can be used for 2-sample conditions like 0_day and 4_weeks, and --include-samples should be specified as \"0_day,4_weeks\". If a file is given, the row of the design matrix must match the order of the samples in the count table, or the order of the samples by the --include-samples option.')\n\n    iogroup=subm_nest.add_argument_group(title='Optional arguments for input and output',\n    description='')\n    iogroup.add_argument('-n','--output-prefix',default=None,help='The prefix of the output file(s).')\n    iogroup.add_argument('-i', '--include-samples', help='Specify the sample labels if the design matrix is not given by file in the --design-matrix option. Sample labels are separated by \",\", and must match the labels in the count table.')\n    iogroup.add_argument('-b', '--beta-labels', help='Specify the labels of the variables (i.e., beta), if the design matrix is not given by file in the --design-matrix option. Should be separated by \",\", and the number of labels must equal to (# columns of design matrix), including baseline labels. Default value: \"bata_0,beta_1,beta_2,...\".')\n\n    genegroup=subm_nest.add_argument_group(title='Optional arguments for normalization.',\n    description='\"--norm-method control -e non_essential_genes_control\" is recommended, which you have to specify negative control names, such as AAVS1')\n    genegroup.add_argument(\"--norm-method\",choices=['none','median','total','control'],default='median',help='Method for normalization, including \"none\" (nonormalization), \"median\" (median normalization, default), \"total\" (normalization by total read counts), \"control\" (normalization by control sgRNAs specified by the --control-sgrna option). Defautl is median.')\n    genegroup.add_argument(\"-e\",\"--negative_control\",help=\"The name of negative controls, such as AAVS1.\",default=[])\n    genegroup.add_argument('--genes-varmodeling',default=\"2000\",help='The number of genes for mean-variance modeling. Default is 2000.')\n    genegroup.add_argument('--adjust-method',choices=['fdr','holm','pounds'],default='fdr',help='Method for sgrna-level p-value adjustment, including false discovery rate (fdr), holm\\'s method (holm), or pounds\\'s method (pounds). Defautl is FDR.')\n\n    advancegroup=subm_nest.add_argument_group(title='Optional arguments for PPI incorporation and outliers removal',\n    description='')\n    advancegroup.add_argument(\"-o\",\"--outliers_removal\",action='store_true',help=\"Speicify whehter you want to remove outliers and recalculate..\")\n    advancegroup.add_argument(\"-p\",\"--PPI_prior\",action='store_true',help=\"Specify whether you want to incorporate PPI as prior\")\n    advancegroup.add_argument(\"-q\",\"--QC_metric\",action='store_true',help=\"Specify whether you want to derive quality control metrics\")\n\n    advancegroup=subm_nest.add_argument_group(title='Example',\n    description='mageck_nest nest -n mageck_nest_cell_line_A -i day_0,week_4 -k readcount_table.txt --norm-method control -e AAVS1 -d \"1,0;1,1\" -q')\n    #--------------------------------------------------------------------------------\n    args=parser.parse_args()\n\n    if args.subcmd == None:\n        parser.print_help()\n        sys.exit(0)\n    if args.output_prefix==None:\n        args.output_prefix=\"mageck_nest_{}\".format(args.count_table)\n    if args.negative_control!=[]:\n        args.negative_control=args.negative_control.split(',')\n        args.negative_control=[i.upper() for i in args.negative_control]\n\n    return args\n\ndef postargs(args):\n    '''\n    post-processing of argument parsing\n    '''\n    # configure logging information\n    logging.basicConfig(level=10,\n        format='%(levelname)-5s @ %(asctime)s.%(msecs)03d: %(message)s ',\n        datefmt='%a, %d %b %Y %H:%M:%S',\n        # stream=sys.stderr,\n        filename=args.output_prefix+'.log',\n        filemode='w'\n    )\n    console = logging.StreamHandler()\n    console.setLevel(logging.INFO)\n    # set a format which is simpler for console use\n    formatter = logging.Formatter('%(levelname)-5s @ %(asctime)s.%(msecs)03d: %(message)s ','%a, %d %b %Y %H:%M:%S')\n    console.setFormatter(formatter)\n    # add the handler to the root logger\n    logging.getLogger('').addHandler(console)\n    logging.info('Parameters: '+' '.join(sys.argv))\n\n    from mageck_nest.mledesignmat import parse_designmat\n\n    try:\n        import scipy\n        from scipy.stats import nbinom\n    except ImportError:\n        logging.error('Cannot find scipy (required for mle approach). Please check your scipy installation.')\n        sys.exit(-1)\n    try:\n        import numpy as np\n        import numpy.linalg as linalg\n    except ImportError:\n        logging.error('Cannot find numpy (required for mle approach). Please check your numpy installation.')\n        sys.exit(-1)\n    # parsing design matrix\n    (desmat,sampleid,betalabel)=parse_designmat(args.design_matrix)\n    args.design_matrix=desmat\n\n    # parsing sample label\n    if sampleid ==None:\n        # design matrix is provided as a string\n        if args.include_samples !=None:\n            args.include_samples=args.include_samples.split(',')\n            if len(args.include_samples) != desmat.shape[0]:\n                logging.error('The number of samples in the --include-samples option do not match rows in design matrix.')\n                sys.exit(-1)\n        if args.beta_labels!=None:\n            args.beta_labels=args.beta_labels.split(',')\n            if len(args.beta_labels) != desmat.shape[1]:\n                logging.error('The number of labels in the --beta-labels option do not match columns in design matrix.')\n                sys.exit(-1)\n    else:\n        # design matrix is provided as file\n        if args.include_samples !=None:\n            logging.error('Sample labels are included in the design matrix file '+args.design_matrix+'. The --include-samples option should not be used.')\n            sys.exit(0)\n        if args.beta_labels!=None:\n            logging.error('Beta labels are included in the design matrix file '+args.design_matrix+'. The --beta-labels option should not be used.')\n            sys.exit(0)\n        args.include_samples=sampleid\n        args.beta_labels=betalabel\n        if len(args.include_samples) != desmat.shape[0]:\n            logging.error('The number of samples in the --include-samples option do not match rows in design matrix.')\n            sys.exit(-1)\n        if len(args.beta_labels) != desmat.shape[1]:\n            logging.error('The number of labels in the --beta-labels option do not match columns in design matrix.')\n            sys.exit(-1)\n    # log design matrix and column, row labels\n    logging.info('Design matrix:')\n    for desmat_1line in str(desmat).split('\\n'):\n        logging.info(desmat_1line)\n    if args.beta_labels != None:\n        logging.info('Beta labels:'+','.join(args.beta_labels))\n    if args.include_samples != None:\n        logging.info('Included samples:'+','.join(args.include_samples))\n\n    return args\n","sub_path":"argParser.py","file_name":"argParser.py","file_ext":"py","file_size_in_byte":7995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"372217939","text":"import torch\r\nfrom torch.nn import functional as F\r\nfrom torch import Tensor\r\n\r\n__all__ = [\"Lovasz_softmax\"]\r\n\r\ndef dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon=1e-6):\r\n    # Average of Dice coefficient for all batches, or for a single mask\r\n    assert input.size() == target.size()\r\n    if input.dim() == 2 and reduce_batch_first:\r\n        raise ValueError(f'Dice: asked to reduce batch but got tensor without batch dimension (shape {input.shape})')\r\n\r\n    if input.dim() == 2 or reduce_batch_first:\r\n        inter = torch.dot(input.contiguous().view(-1), target.contiguous().view(-1))\r\n        sets_sum = torch.sum(input) + torch.sum(target)\r\n        if sets_sum.item() == 0:\r\n            sets_sum = 2 * inter\r\n\r\n        return (2 * inter + epsilon) / (sets_sum + epsilon)\r\n    else:\r\n        # compute and average metric for each batch element\r\n        dice = 0\r\n        for i in range(input.shape[0]):\r\n            dice += dice_coeff(input[i, ...], target[i, ...])\r\n        return dice / input.shape[0]\r\n\r\ndef multiclass_dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon=1e-6):\r\n    # Average of Dice coefficient for all classes\r\n    assert input.size() == target.size()\r\n    dice = 0\r\n    for channel in range(input.shape[1]):\r\n        dice += dice_coeff(input[:, channel, ...], target[:, channel, ...], reduce_batch_first, epsilon)\r\n\r\n    return dice / input.shape[1]\r\n\r\n\r\ndef dice_loss(input: Tensor, target: Tensor, multiclass: bool = False):\r\n    # Dice loss (objective to minimize) between 0 and 1\r\n    assert input.size() == target.size()\r\n    fn = multiclass_dice_coeff if multiclass else dice_coeff\r\n    return 1 - fn(input, target, reduce_batch_first=True)\r\n\r\ndef lovasz_grad(gt_sorted):\r\n    \"\"\"\r\n    Computes gradient of the Lovasz extension w.r.t sorted errors\r\n    See Alg. 1 in paper\r\n    \"\"\"\r\n    p = len(gt_sorted)\r\n    gts = gt_sorted.sum()\r\n    intersection = gts - gt_sorted.float().cumsum(0)\r\n    union = gts + (1 - gt_sorted).float().cumsum(0)\r\n    jaccard = 1. - intersection / union\r\n    if p > 1:  # cover 1-pixel case\r\n        jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]\r\n    return jaccard\r\n\r\nclass Lovasz_softmax(torch.nn.Module):\r\n    def __init__(self, reduction='mean'):\r\n        super(Lovasz_softmax, self).__init__()\r\n        self.reduction = reduction\r\n\r\n    def prob_flatten(self, input, target):\r\n        assert input.dim() in [4, 5]\r\n        num_class = input.size(1)\r\n        if input.dim() == 4:\r\n            input = input.permute(0, 2, 3, 1).contiguous()\r\n            input_flatten = input.view(-1, num_class)\r\n        elif input.dim() == 5:\r\n            input = input.permute(0, 2, 3, 4, 1).contiguous()\r\n            input_flatten = input.view(-1, num_class)\r\n        target_flatten = target.view(-1)\r\n        return input_flatten, target_flatten\r\n\r\n    def lovasz_softmax_flat(self, inputs, targets):\r\n        num_classes = inputs.size(1)\r\n        losses = []\r\n        for c in range(num_classes):\r\n            target_c = (targets == c).float()\r\n            if num_classes == 1:\r\n                input_c = inputs[:, 0]\r\n            else:\r\n                input_c = inputs[:, c]\r\n            loss_c = (torch.autograd.Variable(target_c) - input_c).abs()\r\n            loss_c_sorted, loss_index = torch.sort(loss_c, 0, descending=True)\r\n            target_c_sorted = target_c[loss_index]\r\n            losses.append(torch.dot(loss_c_sorted, torch.autograd.Variable(lovasz_grad(target_c_sorted))))\r\n        losses = torch.stack(losses)\r\n\r\n        if self.reduction == 'none':\r\n            loss = losses\r\n        elif self.reduction == 'sum':\r\n            loss = losses.sum()\r\n        else:\r\n            loss = losses.mean()\r\n        return loss\r\n\r\n    def forward(self, inputs, targets):\r\n        inputs_ls, targets_ls = self.prob_flatten(inputs, targets)\r\n        losses = self.lovasz_softmax_flat(inputs_ls, targets_ls)\r\n        losses_combine= losses + dice_loss(F.softmax(inputs,dim=1).float(),\r\n                                                  F.one_hot(targets,2).permute(0,3,1,2).float(),\r\n                                                  multiclass=True)\r\n        return losses_combine","sub_path":"network/loss_function.py","file_name":"loss_function.py","file_ext":"py","file_size_in_byte":4208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"487003918","text":"from tkinter import * \n\n#dummy function to execute on menu or button click \ndef function1():\n    print(\"menu item clicked\")\n\n#create window called Tk\nroot = Tk()\n\n#create menu object from Menu class and add it to root \nmymenu = Menu(root)\n#configure root to mymenu object \nroot.config(menu=mymenu)\n\n#create filemenu \nfilemenu = Menu(mymenu)\nmymenu.add_cascade(label=\"File\", menu=filemenu)\n#add submenus project and save\nfilemenu.add_command(label=\"Project\", command=function1)\nfilemenu.add_command(label=\"save\", command=function1)\n#add seperator\nfilemenu.add_separator()\n# add submenu exit\nfilemenu.add_command(label=\"Exit\", command=function1)\n#add edit main menu \neditmenu = Menu(mymenu)\nmymenu.add_cascade(label=\"edit\", menu=editmenu)\n# add undo sub menu under edit \neditmenu.add_command(label=\"undo\",command=function1)\n\n#create toolbar using frame class and add it to root \ntoolbar = Frame(root, bg=\"light grey\")\n\n#create inser and print button to add to tool bar\ninsertbutton = Button(toolbar, text=\"insert files\", command = function1)\ninsertbutton.pack(side = LEFT,padx=2, pady=3)\n\nprintbutton = Button(toolbar, text=\"print\", command=function1)\nprintbutton.pack(side=LEFT, padx = 2, pady =3)\n\n#add toolbar to window\ntoolbar.pack(side=TOP, fill=X)\n\nstatus = Label(root, text = \"This is the status\", bd = 1, relief=SUNKEN, anchor=W)\n\nstatus.pack(side=BOTTOM, fill=X)\nroot.mainloop()","sub_path":"Sections/section10/statusbar.py","file_name":"statusbar.py","file_ext":"py","file_size_in_byte":1385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"27056053","text":"from cardData import *\n\n\nclass DataBases:\n\n    DOUBLE_QUOTE = \"\\\"\"\n    SEMI_COLON = \";\"\n    SEPARATOR = \", \"\n    SINGLE_QUOTE = r\"'\"\n\n    @staticmethod\n    def enclose(string, enclose_char):\n        return enclose_char + string + enclose_char\n\n    def value_data_to_insert_sql(self, table, columns, data_sets):\n        columns = self.SEPARATOR.join(columns)\n        sql = f\"INSERT INTO {table}({columns})\\n VALUES\"\n        for data_set in data_sets:\n            for data in data_set:\n                if isinstance(data, str):\n                    index = data_set.index(data)\n                    data = self.enclose(data, self.DOUBLE_QUOTE)\n                    data_set[index] = data\n            data_set = self.SEPARATOR.join(data_set)\n            sql += f\"({data_set})\"\n            sql += self.SEPARATOR\n        sql = sql[:-len(self.SEPARATOR)]\n        return sql + self.SEMI_COLON  # DON'T FORGET A SEMICOLON!\n\n\nsql_code = DataBases().value_data_to_insert_sql(\"cards\",\n                                                [\"cardName\", \"cardRarity\",\n                                                 \"cardCondition\", \"cardGame\",\n                                                 \"cardLocation\"],\n                                                vanilla_box)\ntry:\n    f = open(\"sqlCode.sql\", \"x\")\n    f.close()\nexcept IOError:\n    pass\nfinally:\n    with open(\"sqlCode.sql\", \"r+\") as f:\n        f.write(sql_code)\n\n\nquit()\n","sub_path":"cardDB.py","file_name":"cardDB.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"516635425","text":"\n\nbudget = int(input())\n\nitem = input()\ntotalValue = 0\nwhile item != \"Stop\":\n    itemValue = 0\n    for letter in item:\n        itemValue += ord(letter)\n    if itemValue <= budget:\n        budget -= itemValue\n        print(\"Item successfully purchased!\")\n    else:\n        print(\"Not enough money!\")\n        break\n    item = input()\n\nif item == \"Stop\":\n    print(f\"Money left: {budget}\")","sub_path":"General/Learning/SoftUni Test/ProgrammingBasics/ChristmasDecoration.py","file_name":"ChristmasDecoration.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"419570525","text":"# Code for caluclating the Henry's Constants for a variety of gas mixutres.\n\nimport ast\nimport copy\nimport csv\nimport glob\nimport itertools\nimport matplotlib\nimport numpy as np\nimport os\nimport re\nimport scipy.stats as ss\nimport yaml\n\nfrom collections import OrderedDict\nimport matplotlib as mpl\nfrom matplotlib import pyplot as plt\n\nfrom generate_compositions import create_uniform_comp_list\n\nimport time\nimport tensorflow as tf\nimport tensorflow_probability as tfp\n\n\n\n\n# ----- General Use -----\ndef yaml_loader(filepath):\n    with open(filepath, 'r') as yaml_file:\n        data = yaml.load(yaml_file)\n    return data\n\n\ndef import_simulated_data(sim_results, sort_by_gas=False, gas_for_sorting=None):\n    with open(sim_results) as file:\n        reader = csv.DictReader(file, delimiter='\\t')\n        reader_list = list(reader)\n        keys = reader.fieldnames\n\n        for row in reader_list:\n            # Isolate Mass Data since currently being assigned to single key\n            mass_data_temp = [float(val) for val in row[keys[2]].split(' ')]\n            num_gases = len(row)-len(mass_data_temp)-2\n            # Reassign Compositions\n            for i in range(num_gases):\n                row[keys[-num_gases+i]] = row[keys[i+3]]\n            # Reassign Masses\n            for i in range(num_gases*2+2):\n                row[keys[i+2]] = mass_data_temp[i]\n\n        if sort_by_gas == True:\n            reader_list = sorted(reader_list, key=lambda k: k[gas_for_sorting+'_comp'], reverse=False)\n\n        return keys, reader_list\n\n\n\n\n# ----- Calculating Herny's Coefficients -----\ndef plot_adsorbed_masses(all_comps, all_masses, all_errors, gases, figname=None, gas_name=None, mof_name=None):\n    plt.clf()\n    plt.figure(figsize=(4,3), dpi=600)\n\n    for i in range(len(gases)):\n        comps = all_comps[i]\n        mass = all_masses[i]\n        plt.plot(comps, mass, 'o', markersize=2, alpha=0.7)\n\n    if mof_name != None:\n        plt.title(mof_name)\n    plt.xlabel(gases[0]+' Mole Fractions')\n    plt.ylabel('Adsorbed Mass')\n    plt.legend(gases)\n    plt.tight_layout()\n    if figname != None:\n        plt.savefig(figname)\n    plt.close()\n\n\ndef plot_kH_single_gas(comps, mass, error, max_comp, kH, intercept, R2, figname='None', gas_name='None', mof_name='None'):\n\n    # Initialize Figure\n    plt.clf()\n    plt.figure(figsize=(4,3), dpi=600)\n    if kH != None:\n        fit = np.poly1d([kH, intercept])\n        plt.plot(comps, fit(comps), 'r-')\n    plt.errorbar(comps, mass, error, marker='o', markersize=3, elinewidth=1, linewidth=0)\n    if mof_name != 'None':\n        plt.title(mof_name, fontsize=12)\n    if gas_name != 'None':\n        plt.xlabel(gas+' Mole Fraction', fontsize=10)\n    else:\n        plt.xlabel('Mole Fraction', fontsize=10)\n    plt.xticks(np.linspace(0,0.05,6), fontsize=8)\n    plt.ylabel('Adsorbed Mass\\n[mg/g Framework]', fontsize=10)\n    plt.yticks(fontsize=8)\n    plt.ylim([0.75*(min(mass)-max(error)),1.25*(max(mass)+max(error))])\n    plt.tight_layout()\n    textstr='\\n'.join(['K_H = '+str(np.round(kH, 2)),'Max. Comp. = '+str(np.round(max_comp, 3))])\n    props = dict(boxstyle='round', facecolor='white', alpha=1)\n    plt.text(0.3*max(comps), 1.08*max(mass)+max(error), textstr, fontsize=10, bbox=props)\n    plt.savefig(figname)\n    plt.close()\n\n\ndef plot_kH_air(comps, mass, error, max_comp, kH, intercept, R2, figname='None', gas_name='None', mof_name='None'):\n    # Redefine the Fit\n    fit = np.poly1d([kH, intercept])\n\n    # Initialize Figure\n    plt.clf()\n    plt.figure(figsize=(4,3), dpi=600)\n    plt.plot(comps, fit(comps), 'r-')\n    plt.errorbar(comps, mass, error, marker='o', markersize=3, elinewidth=1, linewidth=0)\n    if mof_name != 'None':\n        plt.title(mof_name, fontsize=12)\n    if gas_name != 'None':\n        plt.xlabel(gas+' Mole Fraction', fontsize=10)\n    else:\n        plt.xlabel('Mole Fraction', fontsize=10)\n    # plt.xticks(np.linspace(0,0.05,6), fontsize=8)\n    plt.ylabel('Adsorbed Mass\\n[mg/g Framework]', fontsize=10)\n    plt.yticks(fontsize=8)\n    plt.ylim([0,1.5*(max(mass)+max(error))])\n    plt.tight_layout()\n    textstr='\\n'.join(['K_H = '+str(np.round(kH, 2)),'Max. Comp. = '+str(np.round(max_comp, 3))])\n    props = dict(boxstyle='round', facecolor='white', alpha=1)\n    plt.text(0.01, 1.4*(max(mass)+max(error)), textstr, fontsize=10, verticalalignment='top', bbox=props)\n    plt.savefig(figname)\n    plt.close()\n\n\ndef plot_kH_multiple_gases(all_comps, all_masses, all_errors, kH, intercept, figname='None', gas_name='None', mof_name='None'):\n    # Define Colors\n    cmap_vals = np.linspace(0,1,2)\n    colors = [mpl.cm.bwr(x) for x in cmap_vals]\n\n    # Initialize Figure\n    plt.clf()\n    plt.figure(figsize=(4,3), dpi=600)\n\n    for i in range(all_comps):\n        kH = all_kHs[i]\n        comps = all_comps[i]\n        mass = all_masses[i]\n        error = all_errors[i]\n        intercepts = all_intercepts[i]\n\n        fit = np.poly1d([kH, intercept])\n        plt.plot(comps, fit(comps), 'r-')\n        plt.errorbar(comps, mass, error, marker='o', markersize=3, elinewidth=1, linewidth=0, color=colors[0])\n\n    # Define labels with respect to Henry's Gas (positionally first).\n    if mof_name != 'None':\n        plt.title(mof_name, fontsize=12)\n    if gas_name != 'None':\n        plt.xlabel(gas+' Mole Fraction', fontsize=10)\n    else:\n        plt.xlabel('Mole Fraction', fontsize=10)\n    plt.xticks(np.linspace(0,0.05,6), fontsize=8)\n    plt.ylabel('Absolute Loading\\n[mg/g Framework]', fontsize=10)\n    plt.yticks(fontsize=8)\n    plt.tight_layout()\n    plt.savefig(figname)\n    plt.close()\n\n\ndef calculate_kH(comps, mass, error, eval_type='R2', r2_min=0.99, rmse_min=0.10, weight='error', flipped=False, i_offset=0, counter=1):\n\n    # Make sure data is properly sorted\n    indicies = np.argsort(comps)\n    comps_sorted = [comps[i] for i in indicies]\n    mass_sorted = [mass[i] for i in indicies]\n    error_sorted = [error[i] for i in indicies]\n    comps = comps_sorted\n    mass = mass_sorted\n    error = error_sorted\n\n    if flipped == True:\n        print('Flipped!')\n        comps = np.flipud(comps)\n        mass = np.flipud(mass)\n        error = np.flipud(error)\n\n    for i in range(len(comps)-i_offset,-counter,-1):\n\n        # Check if enough points for a proper fit\n        if i <= 2:\n            print('Could not fit to given data!')\n            return None, 0, 0, 0, 0, 0\n\n        # Create a weighting matrix\n        if weight == 'None':\n            weights=np.ones(len(error[0:i]))\n        elif weight == 'error':\n            if 0 not in error[0:i]:\n                weights=np.divide(1,error[0:i])\n            else:\n                weights_temp = [1/val for val in error[0:i] if val != 0]\n                if len(weights_temp) != 0:\n                    weights = np.array([1/val if val != 0 else max(weights_temp) for val in error[0:i]])\n                else:\n                    weights = np.ones(len(error[0:i]))\n        elif weight == 'error_squared':\n            error_squared = []\n            for j in error[0:i]:\n                error_squared.extend([j**2])\n            if 0 not in error_sqaured:\n                weights = np.divide(1,error_squared)\n            else:\n                weights_temp = [1/val for val in error_squared if val != 0]\n                if len(weights_temp) != 0:\n                    weights = np.array([1/val if val != 0 else max(weights_temp) for val in error_squared])\n                else:\n                    weights = np.ones(len(error_sqaured[0:i]))\n        else:\n            raise NameError('Invalid Error Type!')\n\n        # Fit a Line\n        # p, V = np.polyfit(comps[0:i], mass[0:i], 1, cov=True, w=weights)\n        p = np.polyfit(comps[0:i], mass[0:i], 1, cov=False, w=weights)\n\n        # Calculate R_Squared and RMSE\n        fit = np.poly1d(p)\n        predicted_vals = fit(comps[0:i])\n        residuals = predicted_vals-mass[0:i]\n        ybar = np.sum(mass[0:i])/len(mass[0:i])\n        ss_res = np.sum(residuals**2)\n        ss_tot = np.sum((mass[0:i]-ybar)**2)\n        R2 = 1-(ss_res/ss_tot)\n        MSE = ss_res/len(mass[0:i])\n        RMSE = MSE**(0.5)\n\n        max_comp = comps[i-1]\n\n        # Check R_Squared and RMSE\n        if eval_type == None:\n            break\n        elif eval_type == 'R2':\n            if R2 >= r2_min:\n                break\n        elif eval_type == 'RMSE':\n            if RMSE <= rmse_min*ybar:\n                break\n        elif eval_type == 'Either':\n            if R2 >= r2_min or RMSE <= rmse_min*ybar:\n                break\n        elif eval_type == 'Both':\n            if R2 >= r2_min and RMSE <= rmse_min*ybar:\n                break\n        else:\n            raise NameError('Invalid eval_type!')\n\n    return p[0], p[1], max_comp, R2, RMSE, len(comps)-i\n\n\ndef calculate_kH_alt(comps, mass, error, r2_min=0.99, weight='error', flipped=False):\n\n    # Make sure data is properly sorted\n    indicies = np.argsort(comps)\n    comps_sorted = [comps[i] for i in indicies]\n    mass_sorted = [mass[i] for i in indicies]\n    error_sorted = [error[i] for i in indicies]\n    comps = comps_sorted\n    mass = mass_sorted\n    error = error_sorted\n\n    if flipped == True:\n        print('Flipped!')\n        comps = np.flipud(comps)\n        mass = np.flipud(mass)\n        error = np.flipud(error)\n\n    for i in range(len(comps),-1,-1):\n\n        # Check if enough points for a proper fit\n        if i <= 2:\n            print('Could not fit to given data!')\n            return None, 0, 0, 0\n\n        # Linear Fit\n        if weight == 'None':\n            weights=np.ones(len(error[0:i]))\n        elif weight == 'error':\n            if 0 not in error[0:i]:\n                weights=np.divide(1,error[0:i])\n            else:\n                weights_temp = [1/val for val in error[0:i] if val != 0]\n                if len(weights_temp) != 0:\n                    weights = np.array([1/val if val != 0 else max(weights_temp) for val in error[0:i]])\n                else:\n                    weights = np.ones(len(error[0:i]))\n        elif weight == 'error_squared':\n            error_squared = []\n            for j in error[0:i]:\n                error_squared.extend([j**2])\n            if 0 not in error_sqaured:\n                weights = np.divide(1,error_squared)\n            else:\n                weights_temp = [1/val for val in error_squared if val != 0]\n                if len(weights_temp) != 0:\n                    weights = np.array([1/val if val != 0 else max(weights_temp) for val in error_squared])\n                else:\n                    weights = np.ones(len(error_sqaured[0:i]))\n        else:\n            raise NameError('Invalid Error Type!')\n\n        x = np.array(comps[0:i])\n        x = np.vstack([x, np.ones(len(x))]).T\n        y = np.array(mass[0:i])\n\n        xw = x*np.sqrt(weights[:,np.newaxis])\n        yw = y*np.sqrt(weights)\n        a, b, c, d = np.linalg.lstsq(xw, yw, rcond=None)\n\n        # Calculate R_Squared\n        fit = np.poly1d(a)\n        predicted_vals = fit(comps[0:i])\n        residuals = predicted_vals-mass[0:i]\n        ybar = np.sum(mass[0:i])/len(mass[0:i])\n        ss_res = np.sum(residuals**2)\n        ss_tot = np.sum((mass[0:i]-ybar)**2)\n        R2 = 1-(ss_res/ss_tot)\n\n        max_comp = comps[i-1]\n\n        # Check R_Squared\n        # print('i = '+str(i)+'\\t R^2 = '+str(R2))\n        if R2 >= r2_min:\n            # print('Number of Points Used: ', len(comps[0:i]))\n            # print('R^2 = ', R2)\n            # print('Henry Coeff. = ', p[0])\n            # print('Intercept (should be 0) = ',p[1])\n            max_comp = comps[i-1]\n            break\n\n    return a[0], a[1], R2, max_comp\n\n\ndef calculate_kH_fixed_intercept(comps, mass, error, eval_type='R2', r2_min=0.99, rmse_min=0.10, weight='error', counter=1):\n\n    # Make sure data is properly sorted\n    indicies = np.argsort(comps)\n    comps_sorted = [comps[i] for i in indicies]\n    mass_sorted = [mass[i] for i in indicies]\n    error_sorted = [error[i] for i in indicies]\n    comps = comps_sorted\n    mass = mass_sorted\n    error = error_sorted\n\n    for i in range(len(comps),-counter,-1):\n\n        # Check if out of points\n        if i <= 2:\n            print('Could not fit to given data!')\n            return None, 0, 0, 0, 0\n\n        # Linear Fit\n        if weight == 'None':\n            weights=np.ones(len(error[0:i]))\n        elif weight == 'error':\n            if 0 not in error[0:i]:\n                weights=np.divide(1,error[0:i])\n            else:\n                weights_temp = [1/val for val in error[0:i] if val != 0]\n                if len(weights_temp) != 0:\n                    weights = np.array([1/val if val != 0 else max(weights_temp) for val in error[0:i]])\n                else:\n                    weights = np.ones(len(error[0:i]))\n        elif weight == 'error_squared':\n            error_squared = []\n            for j in error[0:i]:\n                error_squared.extend([j**2])\n            if 0 not in error_sqaured:\n                weights = np.divide(1,error_squared)\n            else:\n                weights_temp = [1/val for val in error_squared if val != 0]\n                if len(weights_temp) != 0:\n                    weights = np.array([1/val if val != 0 else max(weights_temp) for val in error_squared])\n                else:\n                    weights = np.ones(len(error_sqaured[0:i]))\n        else:\n            raise NameError('Invalid Error Type!')\n\n        x = np.array(comps[0:i])\n        x = x[:,np.newaxis]\n        y = np.array(mass[0:i])\n\n        xw = x*np.sqrt(weights[:,np.newaxis])\n        yw = y*np.sqrt(weights)\n        a, b, c, d = np.linalg.lstsq(xw, yw, rcond=None)\n\n        # Calculate R_Squared\n        fit = np.poly1d([a[0], 0])\n        predicted_vals = fit(comps[0:i])\n        residuals = predicted_vals-mass[0:i]\n        ybar = np.sum(mass[0:i])/len(mass[0:i])\n        ss_res = np.sum(residuals**2)\n        ss_tot = np.sum((mass[0:i]-ybar)**2)\n        R2 = 1-(ss_res/ss_tot)\n        MSE = ss_res/len(mass[0:i])\n        RMSE = MSE**(0.5)\n\n        max_comp = comps[i-1]\n\n        # Check R_Squared and RMSE\n        if eval_type == 'R2':\n            if R2 >= r2_min:\n                break\n        elif eval_type == 'RMSE':\n            if RMSE <= rmse_min*ybar:\n                break\n        elif eval_type == 'Either':\n            if R2 >= r2_min or RMSE <= rmse_min*ybar:\n                break\n        elif eval_type == 'Both':\n            if R2 >= r2_min and RMSE <= rmse_min*ybar:\n                break\n        else:\n            raise NameError('Invalid eval_type!')\n\n    return a[0], max_comp, R2, RMSE, len(comps)-i\n\n\ndef analyze_single_ratio(gas, air_ratio, sim_results_path, analysis_results_path, force_intercept_hg=True, weight='error', r2_min=0.99):\n    \"\"\"\n    Acutally Calculating Henry's Coefficients with Error\n    Each air ratio analyzed/plotted separately\n    \"\"\"\n\n    kH_results = []\n    for file in list(glob.glob(sim_results_path+'*/*.csv')):\n        # Prepare Dict\n        temp_dict = {}\n        temp_dict['Gas'] = gas\n        temp_dict['Background'] = air_ratio\n\n        # Load Results / Extract MOF Name\n        keys, dict_data = import_simulated_data(file, gas)\n        mof_name = dict_data[0]['MOF']\n        temp_dict['MOF'] = mof_name\n\n        # Isolate Data for Fitting\n        comps = [float(row[gas+'_comp']) for row in dict_data]\n        mass = [float(row[gas+'_mass']) for row in dict_data]\n        error = [float(row[gas+'_error']) for row in dict_data]\n        total_mass = [float(row['total_mass']) for row in dict_data]\n        total_error = [float(row['total_mass_error']) for row in dict_data]\n\n        # Calculate kH (uncomment desired method)\n        figname = analysis_results_path+mof_name+'_kH.png'\n        if force_intercept_hg == True:\n            kH, R2, max_comp = calculate_kH_fixed_intercept(comps, mass, error, r2_min=r2_min, weight=weight, figname=figname, gas_name=gas, mof_name=mof_name)\n        elif force_intercept_hg == False:\n            kH, intercept, R2, max_comp = calculate_kH(comps, mass, error, r2_min=r2_min, weight=weight, figname=figname, gas_name=gas, mof_name=mof_name)\n        else:\n            print('Invalid Intercept Option!')\n\n\n        temp_dict['k_H'] = kH[0]\n        temp_dict['R^2'] = R2\n        temp_dict['Maximum Composition'] = max_comp\n        kH_results.extend([temp_dict])\n\n    filename = '/Users/brian_day/Desktop/HC_Work/HenrysConstants_Analysis_Results/'+folder+'_henrys_coefficients.csv'\n    with open(filename, 'w', newline='') as csvfile:\n        writer = csv.writer(csvfile, delimiter='\\t', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n        for row in kH_results:\n            writer.writerow([row])\n\n\ndef analyze_all_ratios(gas, sim_results_path, analysis_results_path, force_intercept_hg=True, weight='error', hg_eval_type='R2', air_eval_type='Either', r2_min_hg=0.99, r2_min_air=0.99, rmse_min_hg=0.10, rmse_min_air=0.10, consolidate_data=False):\n    \"\"\"\n    Acutally Calculating Henry's Coefficients with Error\n    All Air Ratios Combined into Single Data Set\n    \"\"\"\n\n    all_files = list(glob.glob(sim_results_path+str(gas)+'*/*/*.csv'))\n    # all_files = list(glob.glob(sim_results_path+'*/*.csv'))\n    all_files.sort()\n\n    kH_results_hg = []\n    kH_results_air = []\n    for file in all_files[0:50]:\n        # Establish counter to use when eliminating datapoints\n        counter = 1\n\n        # Extract Filename\n        filename = file.split('/')[-1]\n\n        # Prepare Dict\n        temp_dict_hg = {}\n        temp_dict_air = {}\n        temp_dict_hg['Gas'] = gas\n        temp_dict_air['Gas'] = gas\n\n        # Load Results / Extract MOF Name\n        keys, dict_data = import_simulated_data(file, gas)\n        mof_name = dict_data[0]['MOF']\n        temp_dict_hg['MOF'] = mof_name\n        temp_dict_air['MOF'] = mof_name\n\n        # Isolate Data for Fitting\n        # Henry's Gas\n        comps = [float(row[gas+'_comp']) for row in dict_data]\n        mass = [float(row[gas+'_mass']) for row in dict_data]\n        error = [float(row[gas+'_error']) for row in dict_data]\n        # Nitrogen\n        comps_N2 = [float(row['N2_comp']) for row in dict_data]\n        mass_N2 = [float(row['N2_mass']) for row in dict_data]\n        error_N2 = [float(row['N2_error']) for row in dict_data]\n        # Oxygen\n        comps_O2 = [float(row['O2_comp']) for row in dict_data]\n        mass_O2 = [float(row['O2_mass']) for row in dict_data]\n        error_O2 = [float(row['O2_error']) for row in dict_data]\n        # Air: Nitrogen + Oxygen\n        comps_air = [sum(x) for x in zip(comps_N2, comps_O2)]\n        mass_air = [sum(x) for x in zip(mass_N2, mass_O2)]\n        error_air = [sum(x) for x in zip(error_N2, error_O2)]\n        # Total Mass\n        total_mass = [float(row['total_mass']) for row in dict_data]\n        total_error = [float(row['total_mass_error']) for row in dict_data]\n\n        for file2 in all_files[50::]:\n\n            # Check if same MOF\n            filename2 = file2.split('/')[-1]\n            if filename2 == filename:\n                # Update counter\n                counter += 1\n\n                # Load Results\n                keys, dict_data = import_simulated_data(file2, gas)\n\n                # Isolate Data for Fitting\n                comps_temp = [float(row[gas+'_comp']) for row in dict_data]\n                mass_temp = [float(row[gas+'_mass']) for row in dict_data]\n                error_temp = [float(row[gas+'_error']) for row in dict_data]\n                # Nitrogen\n                comps_N2_temp = [float(row['N2_comp']) for row in dict_data]\n                mass_N2_temp = [float(row['N2_mass']) for row in dict_data]\n                error_N2_temp = [float(row['N2_error']) for row in dict_data]\n                # Oxygen\n                comps_O2_temp = [float(row['O2_comp']) for row in dict_data]\n                mass_O2_temp = [float(row['O2_mass']) for row in dict_data]\n                error_O2_temp = [float(row['O2_error']) for row in dict_data]\n                # Air: Nitrogen + Oxygen\n                comps_air_temp = [sum(x) for x in zip(comps_N2_temp, comps_O2_temp)]\n                mass_air_temp = [sum(x) for x in zip(mass_N2_temp, mass_O2_temp)]\n                error_air_temp = [sum(x) for x in zip(error_N2_temp, error_O2_temp)]\n                # Total Mass\n                total_mass_temp = [float(row['total_mass']) for row in dict_data]\n                total_error_temp = [float(row['total_mass_error']) for row in dict_data]\n\n                # Add Data to Original Data Set\n                # Henry's Gas\n                comps.extend(comps_temp)\n                mass.extend(mass_temp)\n                error.extend(error_temp)\n                # Nitrogen\n                comps_N2.extend(comps_N2_temp)\n                mass_N2.extend(mass_N2_temp)\n                error_N2.extend(error_N2_temp)\n                # Oxygen\n                comps_O2.extend(comps_O2_temp)\n                mass_O2.extend(mass_O2_temp)\n                error_O2.extend(error_O2_temp)\n                # Air: Nitrogen + Oxygen\n                comps_air.extend(comps_air_temp)\n                mass_air.extend(mass_air_temp)\n                error_air.extend(error_air_temp)\n                # Total Mass\n                total_mass.extend(total_mass_temp)\n                total_error.extend(total_error_temp)\n\n        # Write Raw Air Data (sorted) to a new file for inspection\n        if consolidate_data == True:\n            indicies = np.argsort(comps)\n            comps_sorted = [comps_air[i] for i in indicies]\n            mass_sorted = [mass_air[i] for i in indicies]\n            error_sorted = [error_air[i] for i in indicies]\n            all_data = np.vstack([comps_sorted, mass_sorted, error_sorted]).T\n            header = ['Comps', 'Mass', 'Error']\n\n            filepath = analysis_results_path+'rawdata/'\n            filename = filepath+mof_name+'_airdata.csv'\n\n            if os.path.isdir(filepath) != True:\n                os.mkdir(filepath)\n\n            with open(filename, 'w', newline='') as csvfile:\n                writer = csv.writer(csvfile, delimiter='\\t', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n                # writer.writerows([comps_sorted])\n                # writer.writerows([mass_sorted])\n                # writer.writerows([error_sorted])\n                writer.writerow(header)\n                for row in all_data:\n                    writer.writerow(row)\n\n        # Plot Adsorption Data\n        figname = analysis_results_path+mof_name+'_ads_data.png'\n        all_comps = [comps, comps, comps]\n        all_masses = [mass, mass_air, total_mass]\n        all_errors = [error, error_air, total_error]\n        gases = [gas, 'Air', 'Total']\n        plot_adsorbed_masses(all_comps, all_masses, all_errors, gases, figname=figname, gas_name=gas, mof_name=mof_name)\n\n        # Calculate kH - Henry's Gas\n        if force_intercept_hg == True:\n            kH, max_comp, R2, RMSE, i_offset = calculate_kH_fixed_intercept(comps, mass, error, eval_type=hg_eval_type, r2_min=r2_min_hg, rmse_min=rmse_min_hg, weight=weight, counter=counter)\n            intercept = 0\n        elif force_intercept_hg == False:\n            kH, intercept, max_comp, R2, RMSE, i_offset = calculate_kH(comps, mass, error, eval_type=hg_eval_type, r2_min=r2_min_hg, rmse_min=rmse_min_hg, weight=weight, counter=counter)\n        else:\n            print('Invalid Intercept Option!')\n\n        if kH != None:\n            figname = analysis_results_path+mof_name+'_kH.png'\n            plot_kH_single_gas(comps, mass, error, max_comp, float(kH), intercept, R2, figname=figname, gas_name=gas, mof_name=mof_name)\n\n        temp_dict_hg['k_H'] = kH\n        # temp_dict_hg['Intercept'] = intercept\n        temp_dict_hg['R^2'] = R2\n        temp_dict_hg['RMSE'] = RMSE\n        temp_dict_hg['Maximum Composition'] = max_comp\n        kH_results_hg.extend([temp_dict_hg])\n\n        # Calculate kH - Air Mixture\n        if air_eval_type == 'With hg':\n            if kH != None:\n                kH, intercept, max_comp, R2, RMSE, _ = calculate_kH(comps_air, mass_air, error_air, eval_type=None, r2_min=r2_min_air, rmse_min=rmse_min_air, weight=weight, flipped=True, i_offset=i_offset, counter=counter)\n            else:\n                kH, intercept, max_comp, R2, RMSE, _ = (None, 0, 0, 0, 0, 0)\n        else:\n            kH, intercept, max_comp, R2, RMSE, _ = calculate_kH(comps_air, mass_air, error_air, eval_type=air_eval_type, r2_min=r2_min_air, rmse_min=rmse_min_air, weight=weight, flipped=True, counter=counter)\n\n        if kH != None:\n            # figname = analysis_results_path+mof_name+'_kH_air.png'\n            # plot_kH_air(comps_air, mass_air, error_air, max_comp, float(kH), intercept, R2, figname=figname, gas_name=gas, mof_name=mof_name)\n            figname = analysis_results_path+mof_name+'_kH_air_on_henry.png'\n            plot_kH_single_gas(comps, mass_air, error_air, max_comp, -float(kH), intercept+kH, R2, figname=figname, gas_name=gas, mof_name=mof_name)\n\n        temp_dict_air['k_H'] = kH\n        if kH != None:\n            temp_dict_air['Pure Air Mass'] = kH+intercept\n        else:\n            temp_dict_air['Pure Air Mass'] = None\n        temp_dict_air['R^2'] = R2\n        temp_dict_air['RMSE'] = RMSE\n        temp_dict_air['Maximum Composition'] = max_comp\n        kH_results_air.extend([temp_dict_air])\n\n    # Write Henry's Gas csv\n    filename = '/Users/brian_day/Desktop/HC_Work/HenrysConstants_Analysis_Results/'+str(gas)+'_AllRatios/_henrys_coefficients_hg.csv'\n    with open(filename, 'w', newline='') as csvfile:\n        writer = csv.writer(csvfile, delimiter='\\t', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n        for row in kH_results_hg:\n            writer.writerow([row])\n\n    # Write Air csv\n    filename = '/Users/brian_day/Desktop/HC_Work/HenrysConstants_Analysis_Results/'+str(gas)+'_AllRatios/_henrys_coefficients_air.csv'\n    with open(filename, 'w', newline='') as csvfile:\n        writer = csv.writer(csvfile, delimiter='\\t', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n        for row in kH_results_air:\n            writer.writerow([row])\n\n\ndef read_kH_results(filename):\n    with open(filename, newline='') as csvfile:\n        output_data = csv.reader(csvfile, delimiter=\"\\t\")\n        output_data = list(output_data)\n        full_array = []\n        for i in range(len(output_data)):\n            row = output_data[i][0]\n            row = row.replace('nan', '\\'nan\\'')\n            row = row.replace('inf', '\\'inf\\'')\n            row = row.replace('-\\'inf\\'', '\\'-inf\\'')\n            temp_array = []\n            temp_row = ast.literal_eval(row)\n            # if type(temp_row['R^2']) == str or temp_row['R^2'] < 0:\n            #     continue\n            temp_array.append(temp_row)\n            full_array.extend(temp_array)\n        return full_array\n\n\ndef plot_all_kH(gas, data, figname):\n    colors = mpl.cm.get_cmap('RdBu')\n    color0 = colors(0.80)\n    color1 = colors(0.20)\n\n    comps = []\n    khs = []\n    for row in data:\n        comps.extend([row['Maximum Composition']])\n        khs.extend([row['k_H']])\n\n    if gas != 'None':\n        if gas == 'CO2':\n            gas_for_title = '$CO_2$'\n        else:\n            gas_for_title = '$' + gas[0].upper() + gas[1::] +'$'\n\n    plt.clf()\n    plt.figure(figsize=(4.5,4.5), dpi=600)\n    plt.semilogy(comps,khs,'o', alpha=0.7, color=color0)\n    plt.ylim([1e-2,1e8])\n    plt.xlim([-0.001,0.051])\n    plt.title(gas_for_title+' in Air', fontsize=16)\n    plt.xlabel('Maximum Mole Fraction\\n(End of Henry\\'s Regime)', fontsize=16)\n    plt.ylabel('Henry\\'s Coefficient\\n[mg/g/mole fraction]',fontsize=16)\n    plt.xticks(fontsize=12)\n    plt.yticks(fontsize=12)\n    plt.tight_layout()\n    plt.savefig(figname)\n    plt.close()\n","sub_path":"sensor_array/helper/henrys_coefficient_analysis.py","file_name":"henrys_coefficient_analysis.py","file_ext":"py","file_size_in_byte":27576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"506718974","text":"import time\nimport sys\nfrom urllib.request import urlretrieve\nfrom urllib.error import URLError, HTTPError\n\ndef progress(downloaded, block_size, total_size):\n    global download_size\n    download_size = total_size\n    completed = int(downloaded / (total_size//block_size) * 100)\n    sys.stdout.write(f\"\\r|{'█' * completed}{' ' * (100-completed)}{completed}%\")\n\ndef download_bar(url,filename):\n    start = time.time()\n    try:\n        urlretrieve(url,filename,progress)\n    except URLError:\n        sys.stderr.write(\"The URL is invalid.\\n\")\n        exit(0)\n    except HTTPError as e:\n        sys.stderr.write(f\"Error: {e.code} {e.reason}\\n\")\n        exit(0)\n    except Exception as e:\n        sys.stderr.write(\"An error occured\\n\")\n        sys.stderr.write(type(e),e)\n        exit(0)\n    end = time.time()\n    sys.stdout.write(\"\\n\")\n    time_taken = end - start\n    download_speed = round(download_size/(time_taken*1024),2)\n    if download_speed >= 1024:\n        sys.stdout.write(f\"Downloaded in {(round(time_taken, 2))} s ({download_speed/1024}  MB/s)\\n\")\n    if download_speed < 1024:\n        sys.stdout.write(f\"Downloaded in {(round(time_taken, 2))} s ({download_speed}  kB/s)\\n\")\n    if download_speed < 1:\n        sys.stdout.write(f\"Downloaded in {(round(time_taken, 2))} s ({download_speed*1024}  B/s)\\n\")\n","sub_path":"progress.py","file_name":"progress.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"560933367","text":"import os\nimport neat\nimport numpy as np\nimport pandas as pd\n\nn_test = 20\nLOCAL_PATH = os.path.dirname(__file__)\nDATA_PATH  = os.path.join(LOCAL_PATH,\"data.csv\")\n\n# read data from csv file\ndata = pd.read_csv(DATA_PATH)\ndata = data.drop(columns=[data.columns[-1]])\nwidht,height = data.shape\nndata = widht\ndims  = int(height/2)\n\n# split data in input and output\ninput_columns  = data.columns[:dims]\noutput_columns = data.columns[dims:]\n\nX = data[input_columns]\ny = data[output_columns]\n\n# split in train and test data\nX_train = X.head(ndata-n_test).values\ny_train = y.head(ndata-n_test).values\n\nX_test = X.tail(n_test).values\ny_test = y.tail(n_test).values\n\n# neat configuration\nconfig_file = os.path.join(LOCAL_PATH, 'config-feedforward.txt')\nconfig      = neat.config.Config(neat.DefaultGenome, neat.DefaultReproduction,\n                         neat.DefaultSpeciesSet, neat.DefaultStagnation,\n                         config_file)\n\n# create the population\np = neat.Population(config)\n\n# reporters\np.add_reporter(neat.StdOutReporter(True))\nstats = neat.StatisticsReporter()\np.add_reporter(stats)\n\ndef error_measure(outp,expt):\n    if outp.shape != expt.shape:\n        print(\"Dimensions don't match: \",outp.shape,expt.shape)\n        exit()\n    else:\n        dist = outp - expt\n        err  = np.dot(dist,dist.T)\n        \n        return err\n\ndef fitness_function(genomes, config):\n\n    len_data,dims = X_train.shape\n\n    for genome_id, genome in genomes:\n        # create nn\n        net = neat.nn.FeedForwardNetwork.create(genome,config)\n        genome.fitness = dims*len_data\n    \n        for d in range(len_data):\n            output = np.array(net.activate(tuple(X_train[d])))\n            genome.fitness -= error_measure(output,y_train[d])\n\nwinner = p.run(fitness_function, 25)\nprint('\\nBest genome:\\n{!s}'.format(winner))\n\nwinner_net = neat.nn.FeedForwardNetwork.create(winner,config)\n\nfor d in range(n_test):\n    output = np.array(winner_net.activate(tuple(X_test[d])))\n    score  = dims - error_measure(output,y_test[d])\n    print(\"input:           \",X_test[d])\n    print(\"expected output: \",y_test[d])\n    print(\"output:          \", [round(i,2) for i in output])\n    print(\"score:           \",round(score,2))\n    print()\n\n","sub_path":"neat-python/NEAT-NoGate/neat_model.py","file_name":"neat_model.py","file_ext":"py","file_size_in_byte":2226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"128211296","text":"import numpy as np \nimport configparser\n\nimport gym \nfrom gym import wrappers, logger, spaces\nfrom stable_baselines.common.policies import MlpPolicy\nfrom stable_baselines.common import make_vec_env\nfrom stable_baselines import PPO2\n\n# Custom flight controller environment\nfrom gymfc.envs.fc_env import FlightControlEnv\n\n# Gazebo model config file\nAIRCRAFT_CONFIG = \"/data/gymfc-digitaltwin-solo/models/nf1/model.sdf\"\n\n# This is where you set the reward parameters\n# Check compute_reward() for more details\nREWARD_CONFIG = \"gymfc/reward_params.config\"\n\n\n## Attitude Flight Controller Environment ##\n#  This environment is for training a drone to maintain a desired \n#  angular velocity by directly controlling the 4 motor values. By \n#  teaching the drone to reach desired angular velocities, a pilot \n#  can then fly the drone in \"Acro\" mode (direct control over roll pitch yaw\n#  angular velocities). \n#\n#  Inherits from a base Gym environment.\nclass AttitudeFlightControlEnv(FlightControlEnv, gym.Env):\n    def __init__(self, max_sim_time=4.5):\n\n        # Initialize the base GymFC environment and Gym env\n        super(AttitudeFlightControlEnv, self).__init__(aircraft_config=AIRCRAFT_CONFIG)\n\n        # Total episode time\n        self.max_sim_time = max_sim_time\n\n        # Define the action space\n        # This is currently defined for DShot ESCs that can receive a \n        # continous range of motor signals between 0 and 1\n        # The dimension of the action space is dependent on the motor count\n        action_space_low = np.array([0] * self.motor_count)\n        action_space_high = np.array([1] * self.motor_count)\n        self.action_space = spaces.Box(low=action_space_low,\n                                       high=action_space_high, \n                                       dtype=np.float64)\n\n        # Define the observation space\n        # The observation space is a continuous range between -infinity and +infinity.\n        self.observation_space = spaces.Box(low=np.array([-np.inf] * 6),\n                                            high=np.array([np.inf] * 6),\n                                            dtype=np.float64)\n\n        # The observation and state will be initially set when reset() is called.\n        # The state is a flattened array with sensor measurements in the same order\n        # as defined in the model.sdf file. \n        self.state = None\n\n        # The observation is the angular velocity error and the change in angular velocity error\n        # since the last time step\n        #\n        # error = omega_target - omega_actual\n        # delta_error = error - error_prev\n        self.observation = None\n\n        # The current angular velocity of the UAV (roll, pitch, yaw) in rad/s.\n        # This will be used to calculate the observation\n        self.omega_actual = None\n\n        # The desired angular velocity of the flight controller\n        self.omega_target = [0, 0, 0]\n\n        # Load reward parameters\n        cfg = configparser.ConfigParser()\n        cfg.read(REWARD_CONFIG)\n        params = cfg[\"PARAMETERS\"]\n\n        self.beta = int(params[\"Beta\"])               # Scaling factor for penalizing large changes in control signals\n        self.epsilon = float(params[\"Epsilon\"])       # Scaling factor for the desired error gap around omega_target\n        self.alpha = int(params[\"Alpha\"])             # Scaling factor for rewarding smaller control signal values\n        self.max_penalty = int(params[\"Max_penalty\"]) # To heavily penalize for certain actions and make sure they never happen again\n\n        # Debug stuff\n        self.episode_count = 0\n        self.episode_reward = 0\n        self.hit_max_1 = 0\n        self.hit_max_2 = 0\n        self.max_penalty_count_2 = 0\n        self.hit_max_3 = 0\n        self.max_penalty_count_3 = 0\n\n    # Action: Output from the neural net. \n    # The current RL algorithm implementation is from stable_baselines(https://github.com/hill-a/stable-baselines),\n    # Which clips and scales the NN output to the action space defined in environment.\n    # Therefore, the action can be directly passed to the environment simulation, assuming that\n    # the action space has been defined to mirror the expected range of motor control signals (DShot: 0 to 1).\n    def step(self, action):\n\n        # transform_output() goes here for PPO1 (Will Koch's implementation)\n        # action is Gaussian between -1 and 1, clip to [-1,1] and then scale to [0, 1]\n\n        # Perform a step in the simulation and receive an updated observation\n        self.state = self.step_sim(action)\n        self.omega_actual = self.state[0:3]\n\n        # Compute the reward for the current time step\n        self.reward = self.compute_reward(action)\n\n        self.episode_reward += self.reward\n\n        self.generate_command()\n\n        # Update the observation that is fed into the neural net\n        self.update_observation()\n\n        # Check if the simulation has completed\n        self.dones = self.sim_time >= self.max_sim_time\n        \n        # Record some info on the simulation step\n        self.info = {\"sim_time\": self.sim_time, \"sp\": self.omega_target, \"current_rpy\": self.omega_actual}\n\n        self.prev_action = action \n\n        return self.observation, self.reward, self.dones, self.info\n        \n\n    # Reset the environment.\n    # Obtains the initial state and calculates the initial observation.\n    # Generates the target angular velocity command (omega_target)\n    def reset(self):\n\n        self.error = 0 # Error between desired omega and actual omega\n        self.error_prev = 0 # Omega error at last time step\n        self.delta_error = 0 # Change in error since last time step\n        self.pulse_command = False # True if pulsing a desired angular velocity \n        self.prev_action = 0 # Store the previous agent action\n\n        # Get the initial state\n        self.state = super().reset()\n        self.omega_actual = self.state[0:3]\n\n        # Reset reward counts\n        self.reward = 0 # Episodic reward\n        self.error_reward = 0\n        self.signal_flux_reward = 0\n        self.small_signal_reward = 0\n        self.saturated_output_count = 0\n        self.saturated_max_penalty = 0\n        self.inactive_output_count = 0\n        self.inactive_max_penalty = 0\n\n        # Generate a target angular velocity command\n        self.generate_command()\n\n        # Calculate the initial observation\n        self.update_observation()\n\n        return self.observation\n        \n\n    # Update the observation based on the new state.\n    def update_observation(self):\n        \n        # Calculate the error between the desired angular velocity and the current angular velocity\n        self.error = self.omega_target - self.omega_actual\n\n        # Calculate the change in error since the last time step\n        self.delta_error = self.error - self.error_prev\n\n        # Update the previous error as the simulation steps forward\n        self.error_prev = self.error\n\n        # The observation is an array of the error and the change in error\n        self.observation = np.array([self.error, self.delta_error]).flatten()\n\n    # Generate a desired angular velocity command \n    # This is used to calculate the angular velocity error which is sent to the agent.\n    def generate_command(self):\n        \n        # The first 0.5 seconds are set to 0 so agent can learn its idle/hover state\n        # With a 1ms sim step, this is equivalent to 500 iterations\n        if self.sim_time < 0.5:\n            self.omega_target = np.array([0, 0, 0])\n\n        # Pulse ON for 2 seconds to teach maintaining a set angular velocity\n        elif self.sim_time < 2.5 and not self.pulse_command:\n            self.omega_target = self.sample_target_command(mean=np.array([0, 0, 0]), stdev=100)\n            self.pulse_command = True\n\n        # Keep set angular velocity for duration of pulse\n        elif self.sim_time < 2.5:\n            pass\n\n        # Pulse OFF for 2 seconds to teach deceleration back to idle/hover\n        else:\n            self.omega_target = np.array([0, 0, 0])\n\n    \n    # Sample a target command from a normal distribution.\n    # The distribution has a mean of 0 deg/s and a standard deviation of\n    # 100 deg/s so that aggressive maneuvers are included in the training.\n    def sample_target_command(self, mean, stdev):\n        return np.random.normal(loc=mean, scale=stdev)\n                        \n\n    # Calculate the reward function.\n    def compute_reward(self, action):\n\n        reward = 0 # Store the cumulative reward per step\n\n        # Calculate the updated error \n        error = self.omega_target - self.omega_actual\n        \n        # Sum of squared error for the current and previous time step\n        sum_squared_error = np.sum(np.square(error))\n        sum_squared_error_prev = np.sum(np.square(self.error_prev))\n        reward += -(sum_squared_error - sum_squared_error_prev)\n        self.error_reward += -(sum_squared_error - sum_squared_error_prev)\n\n        # Penalize for large control signal fluctuations\n        reward -= self.beta * np.max(np.abs(action - self.prev_action))\n        self.signal_flux_reward -= self.beta * np.max(np.abs(action - self.prev_action))\n\n        # Reward smaller control signal values\n        # Only apply this reward if the agent is already able to achieve an error\n        # within the specified error band gap. This gap can be scaled with epsilon.\n        if np.all(np.abs(error) < self.epsilon * np.abs(self.omega_target)):\n            reward += self.alpha * (1 - np.mean(action))\n            self.small_signal_reward += self.alpha * (1 - np.mean(action))\n\n        ## Right now, the next two don't do anything\n        # PPO2 output is already clipped, so it can never be oversaturated\n\n        # Penalize for oversaturating any element in the control signal\n        # NOTE: This has been modified from the original work to penalize any action being equal to 1.\n        # May not be a good reward.\n        # if np.sum(np.maximum(action - 1, np.zeros(shape=action.shape))) != 0:\n            # reward -= self.max_penalty * np.sum(np.maximum(action - 1, np.zeros(shape=action.shape)))\n            # self.hit_max_1 += 1\n        \n        # if np.sum(np.maximum(action - 1, np.zeros(shape=action.shape))) != 0:\n            # reward -= self.max_penalty * np.sum(np.maximum(action - 1, np.zeros(shape=action.shape)))\n            # self.hit_max_1 += 1\n\n        # Penalize if all control signals have been saturated\n        if np.all(action >= 1):\n            self.saturated_max_penalty -= self.max_penalty\n            reward -= self.max_penalty\n            self.saturated_output_count += 1\n\n        \n        # Penalize if agent does nothing (action = 0) and there is an omega_target other than 0. \n        if np.count_nonzero(action) < 2 and np.any(self.omega_target):\n            self.inactive_max_penalty -= self.max_penalty\n            reward -= self.max_penalty\n            self.inactive_output_count += 1\n        \n\n        return reward\n","sub_path":"gymfc/envs/gym_env.py","file_name":"gym_env.py","file_ext":"py","file_size_in_byte":10966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"634756962","text":"import os\nimport sys\nimport getopt\nimport operations\nimport shutil\nfrom db import *\nimport ig_template\nimport os\nimport subprocess\nimport string\n\nclass operation(operations.operation):\n    \"Build a navigation dataset\"\n    def __init__(self):\n        operations.operation.__init__(self,\"generate_navigate\",False)\n        self.add_argument(\"help\",None,None,\"show help message\")\n        self.add_argument(\"root\",\"str\",None,\"root path\")\n        self.add_argument(\"template\",\"str\",None,\"template name\")\n        self.add_argument(\"size\",\"int\",10000,\"verbose level\")\n        self.add_argument(\"source\",\"str\",\".\",\"source path\")\n        self.add_argument(\"target\",\"str\",\"query\",\"target path\")\n        self.add_argument(\"vertex\",\"str\",None,\"vertex name\")\n        self.add_argument(\"dist\",\"str\",\"uniform\",\"query distribution: uniform , gauss,mu,sigma\")\n        self.add_argument(\"verbose\",\"int\",0,\"verbose level\")\n        self.add_argument(\"dfs\",None,None,\"Use Depth first search\")\n        self.add_argument(\"depth\",\"int\",\"5\",\"Navigation Depth\")\n        self.add_argument(\"results\",\"int\",\"100\",\"Max number of results.\")\n        pass\n\n    def __run__(self,template,source,target,size,verbose,dist,vertex):\n        ig_template.Data.Options.source = source\n        ig_template.Data.Options.target_file = target\n        parser = ig_template.Schema.Parser(template,None)\n        if dist == \"uniform\":\n            parser.setUniformDistribution()\n        elif dist == \"gauss\":\n            if len(dist) != 3:\n                self.error(\"When using gauss distribution, you must specify gauss,mu,sigma\")\n                return False\n            if (float(dist[1]) < 0) or (float(dist[1]) > 1) or (float(dist[2]) < 0) or (float(dist[2]) > 1):\n                self.error(\"When using gauss distribution, values of mu,sigma must be between 0 and 1.\")\n                return False\n            parser.setGaussDistribution(float(dist[1]),float(dist[2]))\n            pass\n        parser.setVerboseLevel(verbose)\n        parser.generateQuery(vertex,size)\n        ig_template.Data.Options.CloseAllFiles()\n        return True\n        \n    def operate(self):\n        if operations.operation.operate(self):\n            rootPath = self.getSingleOption(\"root\")\n            template = self.getSingleOption(\"template\")\n            size = self.getSingleOption(\"size\")\n            source = self.getSingleOption(\"source\")\n            target = self.getSingleOption(\"target\")\n            dist = self.getSingleOption(\"dist\")\n            vertex = self.getOption(\"vertex\")\n            if rootPath == None:\n                self.error(\"Root path is not given.\")\n                return False\n            if template == None:\n                self.error(\"Template is not given.\")\n                return False\n            if not template.endswith(\".xml\"):\n                template = template + \".xml\"\n                pass\n            if (not vertex) or len(vertex) == 0:\n                self.error(\"Vertex is not given.\")\n                return False\n            template = os.path.join(rootPath,\"templates\",template)\n            if not os.path.exists(template):\n                self.error(\"Unable to find template file {0}.\".format(template))\n                return False\n            return self.__run__(template,source,target,size,self.verbose,dist,vertex)\n        return True\n","sub_path":"src/generate_navigation_operation.py","file_name":"generate_navigation_operation.py","file_ext":"py","file_size_in_byte":3327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"604008028","text":"import ujson\nfrom app.crawler.utils.ext.country import isoCountries\nfrom app.crawler.world.csse import CSSEApi\n\n\nasync def globalStatus(cache, loop):\n    if not await cache.exists('global'):\n        ads = CSSEApi()\n        data = await ads.apiProcess()\n        global_ = {\n            \"global\": data\n        }\n        _global = await loop.run_in_threadpool(\n            lambda: ujson.dumps(global_, ensure_ascii=False, escape_forward_slashes=False, sort_keys=True).encode('utf-8'))\n        await cache.set('global', _global, expire=3600)\n        return global_[\"global\"]\n    else:\n        global_ = await cache.get('global')\n        _global = await loop.run_in_threadpool(lambda: ujson.loads(global_))\n        return _global[\"global\"]\n\n\nasync def CountryCodeConverter(source):\n    for index in isoCountries:\n        if index[\"ccode\"].lower() == source.lower():\n            return index[\"cname\"].lower()\n\n\nasync def CountrySelect(country: str):\n    data = CSSEApi()\n    source = await data.apiProcess()\n    for index in source:\n        if index['country'].lower() == await CountryCodeConverter(source=country):\n            return index\n\n\nasync def GlobalCoronaSearch(cache, loop, country):\n    if not await cache.exists(country.lower()):\n        data = await CountrySelect(country=country)\n        jsonObject = {\n            country.lower(): data\n        }\n        _result = await loop.run_in_threadpool(lambda: ujson.dumps(jsonObject, ensure_ascii=False, escape_forward_slashes=False, sort_keys=True).encode('utf-8'))\n        await cache.set(country.lower(), _result, expire=3600)\n        return jsonObject[country.lower()]\n    else:\n        result_ = await cache.get(country.lower())\n        _result = await loop.run_in_threadpool(lambda: ujson.loads(result_))\n        return _result[country.lower()]\n\n\n\n","sub_path":"app/ext/route/world/globalStatus.py","file_name":"globalStatus.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"341471324","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on \n\n@author: Bottini Gianfausto @Pi school\n\"\"\"\n\nimport pandas as pd\nimport gensim\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom sklearn.decomposition import PCA\nimport glob\nfrom gensim import models\nimport string\nimport numpy as np\nimport json\nfrom stop_words import get_stop_words\nimport re\nfrom sklearn import preprocessing\nfrom sklearn.cluster import DBSCAN\nfrom scipy.stats import mode\nimport plotly.plotly as py\nimport plotly.graph_objs as go\nimport plotly\n#plotly.tools.set_credentials_file(username='faffids', api_key='fk1d3sbNzj81AECxuWlO')\nimport math\nimport plotly.offline as offline\noffline.init_notebook_mode(connected=True)\nfrom gensim.models import translation_matrix\nfrom gensim.models import KeyedVectors\n\n\npd.set_option('display.max_colwidth', 100)\n#matrix is the vector space created during the previous script\n#\"matrix_user_vector_facounnier_200\" is the name of the csv file loaded into pandas dataframe\nmatrix = pd.read_csv('i18n_matrix_user_vector.csv',sep='\\t',index_col=0)\n#it is the word2vec dictionary , the .bin file can be downloaded (as for the following file) or trained with gensim\nfile_where_dictionary_is_stored = \"/home/ubuntu/mynotebooks/fr_w2v_keyed_senzaht\"\n#french dictionary as KeyedVectors (check gensim library)\nfr_dictionary = KeyedVectors.load(file_where_dictionary_is_stored)\n#en_dictionary = KeyedVectors.load('/home/ubuntu/mynotebooks/en_w2v_keyed')\n#loaded_transm = translation_matrix.TranslationMatrix.load('/home/ubuntu/mynotebooks/translation_matrix_2')\n\ndimensions = len(fr_dictionary['ok'])\ndimensions_string_1 = str(dimensions-1)\n\n\n#function input: the matrix vector space of users \n#function output: the same matrix adding a last row , the marketing topic plus its vector\n\n\ndef addwords(matrix,word):\n    newmatrix = matrix.copy()\n    newmatrix.loc[word] = fr_dictionary[word]\n    \n    return newmatrix\n\n\n#function input: the marketing campaign topic in a single french word\n#function output: a column sorted by the similarity of users to that topic\n\ndef results(topic_fr):\n    txt_analysis = addwords(matrix,topic_fr )\n    txt_similarity = pd.DataFrame(cosine_similarity(txt_analysis) , columns= txt_analysis.index , index = txt_analysis.index)[topic_fr]\n    return txt_similarity.sort_values(ascending=False).iloc[1:]\n\n\n\n#function input: 1 column dataframe with ids as index and topic closeness measure as unique feature\n#function output: a dataframe completed with number other kind of features taking back jsons informations (total likes , likes per day , followers)\n\n\ndef finaldataframe(df_similarity):\n    likes = []\n    followers = []\n    delta = []\n    for index, row in df_similarity.iterrows():\n        \n        index = str(index)\n        #change path with respect to where user jsons are stored\n        file = \"/home/ubuntu/mynotebooks/dati_utenti/dati_utenti/\"+index+\".json\"\n        \n        try:\n            data = json.load(open(file))\n            \n            nfollowers = len(data[index][\"followers\"])\n            followers.append(nfollowers)\n            delta_max = data[index][\"items\"][\"0\"][1]\n            delta_min = np.inf\n            total_likes = 0\n            for post in data[index][\"items\"]:\n                nlikes = data[index][\"items\"][post][2]\n                \n                try:\n                    total_likes += nlikes\n                except:\n                    total_likes += 0\n                timestamp = data[index][\"items\"][post][1]\n                \n                try:\n                    if timestamp <= delta_min:\n                        delta_min = timestamp\n                except:\n                    pass\n            likes.append(total_likes)\n            try:  \n                delta.append(delta_max-delta_min)\n            except:\n                delta.append(None)\n        except:\n            likes.append(None)\n            followers.append(None)\n            delta.append(None)\n\n    \n    df_similarity['likes'] = likes\n    df_similarity['followers'] = followers\n    df_similarity['delta'] = delta\n    df_similarity['deltadays'] = df_similarity['delta']/86400.0\n    df_similarity['likesperday'] = df_similarity['likes'] / df_similarity['deltadays']\n    \n    return df_similarity\n\n\n\n#function input: the previous dataframe having ids as index and closeness measure , likes , followers , likes per day as features and 4 kind of quantiles \n#q1 : topic closeness\n#q2 : likes\n#q3 : followers\n#q4 : likes per day\n#function output: skimming process , rows having a value less than the fixed quantile over the entire list are deleted , even if just one of the 4 feature does not pass the exam\n\n#obviously quantiles are not fixed\n\ndef select (df_,q1 , q2 , q3 ,q4):\n    \n    a_ = df_[['final_measure','likes','followers','likesperday']]\n    lower_quantile1, upper_quantile1 = a_.final_measure.quantile([q1, .99])\n    lower_quantile2, upper_quantile2 = a_.likes.quantile([q2, .99])\n    lower_quantile3, upper_quantile3 = a_.followers.quantile([q3, .99])\n    lower_quantile4, upper_quantile4 = a_.likesperday.quantile([q4, .99])\n\n    a_ = a_.loc[(a_.final_measure > lower_quantile1)& (a_.likes > lower_quantile2) & (a_.followers > lower_quantile3) & (a_.likesperday > lower_quantile4)]\n    \n    return a_\n\n\n#function input: vector space matrix (it is needed to have the users 200 dimensions vectors) , the dataframe of selected users \n#function output: a dataframe with standard features plus the X and Y coming from the projection from 200 dimensions to 2\n\ndef pcaxy(dfpca,df_plot):\n    pca = PCA(n_components=2)\n    pca.fit(dfpca)\n    PCA(copy=True, n_components=2, whiten=False)\n    X = pd.DataFrame(pca.transform(dfpca) ,index = dfpca.index , columns = ['x','y'])\n    users = df_plot.index\n    pd.concat([df_plot,X.loc[users]], axis=1)\n    \n\n    return pd.concat([df_plot,X.loc[users]], axis=1)\n\n\n\n#function input: dataframe with features like : URL , topic closeness , lokes , followers , likes per day , x , y\n#function output: plotly interactive map\n\ndef finalplot(df_plot):\n\n    hover_text = []\n    bubble_size = []\n    \n    for index, row in df_plot.iterrows():\n\n        hover_text.append(('instagram URL: {url}<br>'+\n                           'instagram ID: {id}<br>'+\n                          'final measure: {measure}<br>'+\n                          'likes: {likes}<br>'+\n                          'followers: {flw}<br>'+\n                          'likesperday: {lpd}<br>').format(url = row['url'],\n                                                id=index,\n                                                measure=row['final_measure'],\n                                                likes=row['likes'],\n                                                flw=row['followers'],\n                                                lpd=row['likesperday']\n                                                ))\n        bubble_size.append(math.sqrt(row['followers']*row['likes']*row['likesperday'])/10000)\n\n    df_plot['text'] = hover_text\n    df_plot['size'] = bubble_size\n\n    data = go.Scatter(\n        x = df_plot['x'],\n        y = df_plot['y'],\n        text = df_plot['text'],\n        mode = 'markers',\n        marker =  {\n\n                 \"color\" :  df_plot['final_measure'],\n                \"size\" : df_plot['size'],\n                \"showscale\" :  True\n        }\n\n\n\n    )\n    \n\n    data = ([data])\n    fig = go.Figure(data=data)\n\n    \n    offline.iplot(fig)\n    return\n\n    \ndef make_clickable(val):\n    return '<a href=\"{}\">{}</a>'.format(val,val)\n\n\n\n#function input: any dataframe having IDs as index\n#function output: a dataframe with users pictaram website (pictarame is instagram with ID instead of screennames)\ndef addurl(dataframe):\n    dataframe['url'] = f'http://instagram.com/web/friendships/'+dataframe.index.astype(str) + '/follow/'\n    return dataframe\n\n\nimport seaborn as sns\ncm = sns.light_palette(\"green\", as_cmap=True)\n\ndef beautifuldf(df):\n    df = df.style.background_gradient(cmap=cm)\\\n        .format({'url': make_clickable})\\\n        .set_properties(**{'text-align': 'left'})\n    return df\n\n#function input: marketing campaign topic in a single word\n#function output: final map plus final dataframe of candidates\n#you can increase or decrease quantiles with respect to the search purposes\ndef launch_campaign_about(word):\n    M = addwords(matrix , word)\n    \n    df_similarity = pd.DataFrame(results(topic_fr=word))\n    a = finaldataframe(df_similarity)\n    a.columns.values[0] = 'final_measure'\n    q = select(a , 0.9, 0.3 , 0.3 ,0.3 ).sort_values('final_measure' , ascending=False).head(100).round({'final_measure': 2, 'likesperday': 0})\n    finalplot(addurl(pcaxy(M.loc[:, '1':dimensions_string_1],q)))\n    return beautifuldf(addurl(q).head(10))","sub_path":"modules_for_demo_i18n.py","file_name":"modules_for_demo_i18n.py","file_ext":"py","file_size_in_byte":8738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"22411953","text":"from bitcasa import BitcasaClient, BitcasaFolder, BitcasaFile\nfrom bitcasa.exception import BitcasaException\nimport time, os, sys, traceback, threading\nimport utils\nfrom logger import logger as log\nfrom threads import RunThreaded\nfrom unidecode import unidecode\nfrom collections import deque\n\nclass FileDownload(object):\n    def __init__(self, filename, filepath, filesize, fullpath, filedir):\n        self.filename = filename\n        self.filepath = filepath\n        self.filesize = filesize\n        self.fullpath = fullpath\n        self.filedir = filedir\n        self.thread = None\n\n    def start(self, thread_num, parent):\n        item = {\n            \"filename\": self.filename,\n            \"filepath\": self.filepath,\n            \"filesize\": self.filesize,\n            \"fullpath\": self.fullpath,\n            \"filedir\": self.filedir\n        }\n        log.debug(\"Starting file download %s\", self.filename)\n        thread = RunThreaded(item, thread_num, parent)\n        thread.start()\n        self.thread = thread\n\n    def pause(self):\n        thread.pause()\n\n    def resume(self):\n        thread.resume()\n\nclass DownloadThreaded(threading.Thread):\n    def __init__(self, parent):\n        threading.Thread.__init__(self, name=\"Downloader\")\n        self.parent = parent\n\n    def run(self):\n        log.debug(\"Starting Downloader\")\n        while not self.parent.done and not self.parent.end:\n            if len(self.parent.todownload) > 0:\n                next_thread = self.parent.getNextThread()\n                while next_thread is None and not self.parent.end:\n                    log.debug(\"Downloader waiting for thread space. %s left to process\", len(self.parent.todownload))\n                    time.sleep(5)\n                    next_thread = self.parent.getNextThread()\n                if not self.parent.end:\n                    download = self.parent.todownload.popleft()\n                    log.debug(\"Downloader processing %s\", download.filename)\n                    download.start(next_thread, self.parent)\n                    self.parent.threads[next_thread] = download\n                else:\n                    log.debug(\"Got exit signal. Not creating any more threads\")\n                    break\n            else:\n                time.sleep(10)\n\n        if self.parent.end:\n            log.info(\"Stopping Downloader\")\n        else:\n            log.info(\"Downloader shutting down\")\n\nclass QueueThreaded(threading.Thread):\n    def __init__(self, qid, parent):\n        threading.Thread.__init__(self, name=\"Queuer \"+qid)\n        self.parent = parent\n        self.qid = qid\n\n    def run(self):\n        log.debug(\"Starting Queuer\")\n        while not self.parent.done and not self.parent.end and self.parent.moreToQueue():\n            if len(self.parent.folders_queue) and self.parent.folders_lock is None:\n                self.parent.queuers[int(self.qid)-1][\"processing\"] = True\n                self.parent.folder_lock = self.qid\n                folder = self.parent.folders_queue.popleft()\n                log.debug(\"Grabbing folder %s\", folder[\"folder\"].name)\n                self.parent.folder_lock = None\n                try:\n                    self.parent.folder_list(folder[\"folder\"], folder[\"path\"], folder[\"depth\"])\n                except KeyboardInterrupt:\n                    log.warn(\"Received end signal\")\n                    self.parent.end = True\n                    break\n                else:\n                    time.sleep(1)\n\n                self.parent.queuers[int(self.qid)-1][\"processing\"] = False\n                time.sleep(5)\n            else:\n                if len(self.parent.folders_queue) > 1:\n                    time.sleep(1)\n                else:\n                    time.sleep(int(self.qid))\n\n        if self.parent.end:\n            self.parent.queuers[int(self.qid)-1][\"processing\"] = False\n            log.info(\"Stopping Queuer\")\n        elif self.parent.done:\n            log.info(\"Queuer shutting down\")\n        else:\n            log.info(\"Queuer shutting down. All folders processed\")\n\n\nclass BitcasaDownload(object):\n    def getNextThread(self):\n        for thread_num in xrange(len(self.threads)):\n            if self.threads[thread_num] is None:\n                return thread_num\n        return None\n    def getActiveThreads(self):\n        activeThreads = []\n        for thread in self.threads:\n            if thread and thread.thread:\n                activeThreads.append(thread.thread)\n        for queue_thread in self.queuers:\n            if queue_thread[\"processing\"]:\n                activeThreads.append(queue_thread[\"thread\"])\n        return activeThreads\n\n    def moreToQueue(self):\n        if len(self.folders_queue) > 0:\n            return True\n        for queue_thread in self.queuers:\n            if queue_thread[\"processing\"]:\n                return True\n\n        return False\n\n    def folder_list(self, fold, path, depth):\n        if self.end:\n            log.debug(\"Received end signal. Stopping folder list\")\n            return\n        if path:\n            log.info(path)\n        fulldest = os.path.join(self.dest, path)\n        remainingtries = 3\n         #Create temp dir and dest dir if needed\n        while remainingtries > 0:\n            try:\n                if fulldest:\n                    try:\n                        os.makedirs(fulldest)\n                    except (OSError, IOError):\n                        if not os.path.isdir(fulldest):\n                            raise\n            except:\n                remainingtries -=  1\n                log.exception(\"Couldn't create folder %s\",fulldest)\n                if remainingtries > 0:\n                    log.error(\"Will retry to create folder %s more times\", remainingtries)\n                    time.sleep(2)\n                else:\n                    self.writeErrorDir(fold.name, fulldest, fold.path, traceback.format_exc())\n                    return\n            else:\n                remainingtries = 0\n\n        log.debug(fulldest)\n        remainingtries = 3\n        apiratecount = 0\n        while remainingtries > 0 and not self.end:\n            if self.end:\n                 log.info(\"Program received exit signal\")\n                 return\n            try:\n                folderitems = fold.items\n            except BitcasaException as e:\n                if self.end:\n                 log.info(\"Program received exit signal\")\n                 return\n                remainingtries -= 1\n                if e.code in [9006, 503, 429]:\n                    apiratecount += 1\n                    remainingtries += 1\n                    log.warn(\"API rate limit reached. Will retry\")\n                else:\n                    log.warn(\"Failed to get folder contents %s. Will retry %s more times\", e.code, remainingtries)\n\n                if remainingtries > 0:\n                    try:\n                        time.sleep(10 * apiratecount)\n                    except KeyboardInterrupt:\n                        self.end = True\n                        log.info(\"Program received exit signal\")\n                        return\n                else:\n                    log.error(\"Error downloading at folder %s\", path)\n                    return\n            except KeyboardInterrupt:\n                self.end = True\n                log.info(\"Program received exit signal\")\n                return\n            else:\n                remainingtries = 0\n\n        for item in folderitems:\n            if self.end:\n                 log.info(\"Program received exit signal\")\n                 return\n            remainingtries = 3\n            while remainingtries > 0:\n                try:\n                    if self.end:\n                        return\n                    nm = item.name\n                    try:\n                        nm = unidecode(nm)\n                        nm = nm.encode('utf-8')\n                        nm = \"\".join(i for i in nm if i not in \"\\/:*?<>|%\\\"\")\n                        nm = nm.strip()\n                    except:\n                        log.warn(\"Error encoding to utf-8. Will parse anyway\")\n\n                    pt = item.path\n                    filesize = None\n                    tfd = os.path.join(fulldest, nm)\n                    fexists = False\n                    if isinstance(item, BitcasaFile):\n                        filesize = item.size\n                        try:\n                            # it is possible that an error here could tell us\n                            # downloading later won't be possible but there are \n                            # too many errno's to try and catch that right now\n                            fexists = os.path.getsize(tfd) >= filesize\n                        except OSError:\n                            pass\n\n                    if filesize is not None and not fexists:\n                        filedownload = FileDownload(nm, pt, filesize, tfd, fulldest)\n                        log.debug(\"Queuing file download for %s\", nm)\n                        self.todownload.append(filedownload)\n                    elif isinstance(item, BitcasaFolder):\n                        if (self.depth == None or self.depth > depth) and self.rec:\n                            folder = {\n                                \"folder\": item,\n                                \"path\": os.path.join(path, nm),\n                                \"depth\": (depth+1)\n                            }\n                            log.debug(\"Queuing folder listing for %s\", nm)\n                            self.folders_queue.append(folder)\n                    elif fexists:\n                        self.writeSkipped(tfd, pt, nm)\n                    elif self.end:\n                        log.debug(\"Got exit signal. Discontinue recurse\")\n                        return\n                except KeyboardInterrupt:\n                    self.end = True\n                    log.info(\"Program received exit signal\")\n                except: #Hopefully this won't get called\n                    self.writeError(nm, tfd, pt, traceback.format_exc())\n                else:\n                    remainingtries = 0\n\n    def __init__(self, args):\n        log.debug(\"src: %s\", args.src)\n        log.debug(\"dst: %s\", args.dst)\n        log.debug(\"at: %s\", args.token)\n        log.debug(\"tmp: %s\", args.temp)\n        log.debug(\"logdir: %s\", args.logdir)\n        log.debug(\"rec: %s\", args.rec)\n        log.debug(\"depth: %s\", args.depth)\n        log.debug(\"mt: %s\", args.threads)\n        log.debug(\"p: %s\", args.progress)\n        #destination directory\n        self.dest = args.dst\n        #temp directory\n        self.tmp = args.temp\n        self.successfiles = os.path.join(args.logdir, \"successfiles.txt\")\n        self.errorfiles = os.path.join(args.logdir, \"errorfiles.txt\")\n        self.skippedfiles = os.path.join(args.logdir, \"skippedfiles.txt\")\n        #bittcasa base64 encdoded path\n        self.basefolder = args.src\n        #Access token\n        self.accesstoken = args.token\n        if args.threads is None:\n            log.info(\"Using default max threads value of 5\")\n            args.threads = 5\n        #Recursion\n        self.rec = args.rec\n        #Recursion max depth\n        self.depth = args.depth\n        #Test only\n        self.test = args.test\n        #Log dir\n        self.logdir = args.logdir\n        #log progress\n        self.progress = args.progress\n\n        #Initialize\n        self.threads = [None] * args.threads\n        self.end = False\n        self.st = time.time()\n        self.bytestotal = 0\n        self.todownload = deque()\n        self.folders_queue = deque()\n        self.folders_lock = None\n        self.downloader = None\n        self.queuers = []\n        self.done = False\n        self.downloadtime = 0\n        self.copytime = 0\n\n    def process(self):\n        bitc = BitcasaClient(utils.CLIENTID, utils.CLIENTSECRET, \"https://rose-llc.com/bitcasafilelist/\", self.accesstoken)\n        log.debug(\"Getting base folder\")\n        base = None\n        remainingtries = 3\n        if self.test:\n            remainingtries = 1\n        apiratecount = 0\n        while base is None and remainingtries > 0:\n            try:\n                base = bitc.get_folder(self.basefolder)\n            except BitcasaException as e:\n                remainingtries -= 1\n                if e.code == 9006:\n                    apiratecount += 1\n                    remainingtries += 1\n                    log.warn(\"API rate limit reached. Will retry\")\n                else:\n                    log.warn(\"Couldn't get base folder %s. Will retry %s more times\", e.code, remainingtries)\n\n                if remainingtries > 0:\n                    try:\n                        time.sleep(10 * apiratecount)\n                    except KeyboardInterrupt:\n                        log.info(\"Got exit signal. Goodbye\")\n                        return\n                else:\n                    log.error(\"Error could not retreive base folder\")\n                    return\n        log.debug(\"Got base folder\")\n        if self.test:\n            log.info(\"Nothing downloaded because this is a test\")\n            return\n        try:\n            with open(self.successfiles, 'w+') as myfile:\n                myfile.write(\"File path\\n\")\n                myfile.write(time.strftime(\"%Y-%m-%d %H:%M:%S\") + \" Start\\n\")\n\n            with open(self.errorfiles, 'w+') as myfile:\n                myfile.write(\"Type||File path||Base64 Path\\n\")\n                myfile.write(time.strftime(\"%Y-%m-%d %H:%M:%S\") + \" Start\\n\")\n\n            with open(self.skippedfiles, 'w+') as myfile:\n                myfile.write(\"File path||Base64 Path\\n\")\n                myfile.write(time.strftime(\"%Y-%m-%d %H:%M:%S\") + \" Start\\n\")\n        except:\n            log.exception(\"Error. Could not initialize logging files. Ending\")\n            return\n        log.debug(\"Queuing base folder\")\n        folder = {\n            \"folder\": base,\n            \"path\": \"\",\n            \"depth\": 0\n        }\n        self.folders_queue.append(folder)\n        log.debug(\"Starting Queuers and Downloader\")\n        self.downloader = DownloadThreaded(self)\n        self.downloader.start()\n        for qid in xrange(len(self.threads)):\n            qid+=1\n            queue_thread = QueueThreaded(str(qid), self)\n            self.queuers.append({\"thread\":queue_thread, \"processing\":False, \"qid\":qid})\n            queue_thread.start()\n        # Give the queuers time to catch up\n        try:\n            time.sleep(10)\n\n            #wait for threads to finish downoading\n            active = self.getActiveThreads()\n            while len(active) > 0 or len(self.folders_queue) > 0 or len(self.todownload) >0:\n                for thread in active:\n                    if thread and thread.isAlive():\n                        thread.join(5)\n                if len(active) == 0:\n                    time.sleep(5)\n                active = self.getActiveThreads()\n\n            self.done = True\n            for queue_thread in self.queuers:\n                if queue_thread[\"thread\"].isAlive():\n                    queue_thread[\"thread\"].join(5)\n\n        except KeyboardInterrupt:\n            error = True\n            while error:\n                try:\n                    self.end = True\n                    log.info(\"Program received exit signal\")\n                    active = self.getActiveThreads()\n                    for thread in active:\n                        if thread and thread.isAlive():\n                            log.info(\"Waiting for download thread %s to shutdown\", thread.name)\n                            thread.join(1)\n                    for queue_thread in self.queuers:\n                        if queue_thread[\"thread\"].isAlive():\n                            log.info(\"Wating for queuer to shutdown %s\", queue_thread[\"qid\"])\n                            queue_thread[\"thread\"].join(1)\n                    if self.downloader.isAlive():\n                        log.info(\"Waiting for Downloader to shutdown\")\n                        self.downloader.join(1)\n                except KeyboardInterrupt:\n                    pass\n                else:\n                    error = False\n        #Log final speed and statistics\n        speed = utils.get_speed(self.bytestotal, self.downloadtime)\n        copyspeed = utils.get_speed(self.bytestotal, self.copytime)\n        runspeed = utils.get_speed(self.bytestotal, (time.time() - self.st))\n        log.info(\"Total download time: %s\", utils.convert_time(self.downloadtime))\n        log.info(\"Total run time: %s\", utils.convert_time(time.time() - self.st))\n        if self.tmp:\n            log.info(\"Total copy time: %s\", utils.convert_time(self.copytime))\n        log.info(\"Download speed: %s at %s\", utils.convert_size(self.bytestotal), speed)\n        log.info(\"Run speed: %s at %s\", utils.convert_size(self.bytestotal), runspeed)\n        if self.tmp:\n            log.info(\"Copy speed: %s at %s\", utils.convert_size(self.bytestotal), copyspeed)\n\n    def writeSuccess(self, filept):\n        try:\n            with open(self.successfiles, 'a') as myfile:\n                myfile.write(\"%s\\n\" % filept)\n        except:\n            log.exception(\"Error. Could not write to %s. Ending\", self.successfiles)\n            self.end = True\n\n    def writeSkipped(self, tfd, pt, nm):\n        log.debug(\"%s already exists. Skipping\", nm)\n        try:\n            with open(self.skippedfiles, 'a') as myfile:\n                myfile.write(\"%s||%s\\n\" % (tfd, pt))\n        except:\n            log.exception(\"Error. Could not write to %s. Ending\", self.skippedfiles)\n            self.end = True\n\n    def writeError(self, nm, tfd, pt, e):\n        log.error(\"Error processing file %s\\n%s\", nm, e)\n        try:\n            with open(self.errorfiles, 'a') as myfile:\n                myfile.write(\"File||%s||%s\\n\" % (tfd, pt))\n        except:\n            log.exception(\"Error. Could not write to %s. Ending\", self.errorfiles)\n            self.end = True\n\n    def writeErrorDir(self, nm, tfd, pt, e):\n        log.error(\"Error processing folder %s\\n%s\", nm, e)\n        try:\n            with open(self.errorfiles, 'a') as myfile:\n                 myfile.write(\"Folder||%s||%s\\n\" % (tfd, pt))\n        except:\n            log.exception(\"Error. Could not write to %s. Ending\", self.errorfiles)\n            self.end = True\ndef main():\n    args = utils.get_args()\n    log.debug(\"Initializing Bitcasa\")\n    bitc = BitcasaDownload(args)\n    bitc.process()\n    log.info(\"Done\")\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"python/getfiles.py","file_name":"getfiles.py","file_ext":"py","file_size_in_byte":18426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"380388182","text":"from sympy import (plot, pi, sin, cos, Symbol, Integral, summation, sqrt, log,\n                   oo, LambertW, I)\nfrom sympy.plotting.plot import matplotlib\nfrom sympy.utilities.pytest import skip\nfrom tempfile import NamedTemporaryFile\nimport warnings\n\ndef tmp_file(name=''):\n    return NamedTemporaryFile(suffix='.png').name\n\ndef plot_and_save(name):\n    x = Symbol('x')\n    y = Symbol('y')\n    z = Symbol('z')\n\n    ###\n    # Examples from the 'introduction' notebook\n    ###\n\n    p = plot(x, show=False)\n    p = plot(x*sin(x),x*cos(x), show=False)\n    p.extend(p)\n    p[0].line_color = lambda a : a\n    p[1].line_color='b'\n    p.title = 'Big title'\n    p.xlabel = 'the x axis'\n    p[1].label = 'straight line'\n    p.legend = True\n    p.aspect_ratio = (1,1)\n    p.xlim = (-15,20)\n    p.save(tmp_file('%s_basic_options_and_colors.png' % name))\n\n    p.extend(plot(x+1, show=False))\n    p.append(plot((x+3,),(x**2,), show=False)[1])\n    p.save(tmp_file('%s_plot_extend_append.png' % name))\n\n    p[2] = x**2, (x, -2, 3)\n    p.save(tmp_file('%s_plot_setitem.png' % name))\n\n    p = plot(sin(x),(x,-2*pi,4*pi), show=False)\n    p.save(tmp_file('%s_line_explicit.png' % name))\n\n    p = plot(sin(x),(-2*pi,4*pi), show=False)\n    p.save(tmp_file('%s_line_implicit_var.png' % name))\n\n    p = plot(sin(x), show=False)\n    p.save(tmp_file('%s_line_default_range.png' % name))\n\n    p = plot((x,), (x*sin(x), x*cos(x)), show=False)\n    p.save(tmp_file('%s_two_curves.png' % name))\n\n    p = plot(sin(x),cos(x),x, show=False)\n    p.save(tmp_file('%s_3d_line.png' % name))\n\n    p = plot(x*y, show=False)\n    p.save(tmp_file('%s_surface.png' % name))\n\n    p = plot(x*sin(z),x*cos(z),z, show=False)\n    p.save(tmp_file('%s_parametric_surface.png' % name))\n\n    ###\n    # Examples from the 'colors' notebook\n    ###\n\n    p = plot(sin(x), show=False)\n    p[0].line_color = lambda a : a\n    p.save(tmp_file('%s_colors_line_arity1.png' % name))\n\n    p[0].line_color = lambda a, b : b\n    p.save(tmp_file('%s_colors_line_arity2.png' % name))\n\n    p = plot(x*sin(x), x*cos(x), (0,10), show=False)\n    p[0].line_color = lambda a : a\n    p.save(tmp_file('%s_colors_param_line_arity1.png' % name))\n\n    p[0].line_color = lambda a, b : a\n    p.save(tmp_file('%s_colors_param_line_arity2a.png' % name))\n\n    p[0].line_color = lambda a, b : b\n    p.save(tmp_file('%s_colors_param_line_arity2b.png' % name))\n\n    p = plot(sin(x)+0.1*sin(x)*cos(7*x),\n             cos(x)+0.1*cos(x)*cos(7*x),\n             0.1*sin(7*x),\n             (0, 2*pi),\n             show=False)\n    p[0].line_color = lambda a : sin(4*a)\n    p.save(tmp_file('%s_colors_3d_line_arity1.png' % name))\n    p[0].line_color = lambda a, b : b\n    p.save(tmp_file('%s_colors_3d_line_arity2.png' % name))\n    p[0].line_color = lambda a, b, c : c\n    p.save(tmp_file('%s_colors_3d_line_arity3.png' % name))\n\n    p = plot(sin(x)*y, (0,6*pi), show=False)\n    p[0].surface_color = lambda a : a\n    p.save(tmp_file('%s_colors_surface_arity1.png' % name))\n    p[0].surface_color = lambda a, b : b\n    p.save(tmp_file('%s_colors_surface_arity2.png' % name))\n    p[0].surface_color = lambda a, b, c : c\n    p.save(tmp_file('%s_colors_surface_arity3a.png' % name))\n    p[0].surface_color = lambda a, b, c : sqrt((a-3*pi)**2+b**2)\n    p.save(tmp_file('%s_colors_surface_arity3b.png' % name))\n\n    p = plot(x*cos(4*y), x*sin(4*y), y,\n             (-1, 1), (-1, 1), show=False)\n    p[0].surface_color = lambda a : a\n    p.save(tmp_file('%s_colors_param_surf_arity1.png' % name))\n    p[0].surface_color = lambda a, b : a*b\n    p.save(tmp_file('%s_colors_param_surf_arity2.png' % name))\n    p[0].surface_color = lambda a, b, c : sqrt(a**2+b**2+c**2)\n    p.save(tmp_file('%s_colors_param_surf_arity3.png' % name))\n\n    ###\n    # Examples from the 'advanced' notebook\n    ###\n\n    i = Integral(log((sin(x)**2+1)*sqrt(x**2+1)),(x,0,y))\n    p = plot(i,(1,5), show=False)\n    p.save(tmp_file('%s_advanced_integral.png' % name))\n\n    s = summation(1/x**y,(x,1,oo))\n    p = plot(s, (2,10), show=False)\n    p.save(tmp_file('%s_advanced_inf_sum.png' % name))\n\n    p = plot(summation(1/x,(x,1,y)), (2,10), show=False)\n    p[0].only_integers = True\n    p[0].steps = True\n    p.save(tmp_file('%s_advanced_fin_sum.png' % name))\n\n\n    ###\n    # Test expressions that can not be translated to np and generate complex\n    # results.\n    ###\n\n\n    plot(sin(x)+I*cos(x), show=False).save(tmp_file())\n    plot(sqrt(sqrt(-x)), show=False).save(tmp_file())\n    plot(LambertW(x), show=False).save(tmp_file())\n    plot(sqrt(LambertW(x)), show=False).save(tmp_file())\n\n\n    ###\n    # Test all valid input args for plot()\n    ###\n\n    # 2D line with all possible inputs\n    plot(x              , show=False).save(tmp_file())\n    plot(x, (x,        ), show=False).save(tmp_file())\n    plot(x, (   -10, 10), show=False).save(tmp_file())\n    plot(x, (x, -10, 10), show=False).save(tmp_file())\n\n    # two 2D lines\n    plot((x,             ), (x+1, (x, -10, 10)), show=False).save(tmp_file())\n    plot((x, (x,        )), (x+1, (x, -10, 10)), show=False).save(tmp_file())\n    plot((x, (   -10, 10)), (x+1, (x, -10, 10)), show=False).save(tmp_file())\n    plot((x, (x, -10, 10)), (x+1, (x, -10, 10)), show=False).save(tmp_file())\n    plot([x, x+1],               show=False).save(tmp_file())\n    plot([x, x+1], (x,        ), show=False).save(tmp_file())\n    plot([x, x+1], (   -10, 10), show=False).save(tmp_file())\n    plot([x, x+1], (x, -10, 10), show=False).save(tmp_file())\n\n    # 2D and 3D parametric lines\n    plot(x, 2*x, show=False).save(tmp_file())\n    plot(x, 2*x, sin(x), show=False).save(tmp_file())\n\n    # surface and parametric surface\n    plot(x*y, show=False).save(tmp_file())\n    plot(x*y, 1/x, 1/y,show=False).save(tmp_file())\n\n    # some bizarre combinatrions\n    ## two 3d parametric lines and a surface\n    plot(([x, -x], x**2, sin(x)), (x*y,), show=False).save(tmp_file())\n\n\ndef test_matplotlib():\n    if matplotlib:\n        plot_and_save('test')\n    else:\n        skip(\"Matplotlib not the default backend\")\n","sub_path":"sympy/plotting/tests/test_plot.py","file_name":"test_plot.py","file_ext":"py","file_size_in_byte":6014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"334032036","text":"from glob import glob\n\nID_COL = \"id\"\nDATE_COL = \"ydt\"\nDESC_COL = \"description\"\nDEBT_COL = \"debit\"\nCRDT_COL = \"credit\"\nBLNC_COL = \"balance\"\n\n\ndef get_csv(path, out, i):\n    with open(path) as f:\n        for line in f:\n            line = line.replace(',,', ',0,')\n            out.write(str(i) + \",\" + line)\n            i += 1\n    return i\n\n\ndef combine_csvs():\n    paths = glob(\"../statements/*.csv\")\n    i = 1\n    with open(\"../combined_statements.csv\", \"w+\") as output_file:\n        output_file.write(ID_COL+\",\"+DATE_COL+\",\"+DESC_COL+\",\"+DEBT_COL+\",\"+CRDT_COL+\",\"+BLNC_COL)\n        for path in paths:\n            i = get_csv(path, output_file, i)\n\n\nif __name__ == \"__main__\":\n    combine_csvs()\n","sub_path":"src/csv_parser.py","file_name":"csv_parser.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"73688646","text":"import logging\nimport argparse\nimport os\nimport time\nimport cv2\nimport threading\n\nfrom networktables  import NetworkTables\n\nfrom bucketvision.capturers.cv2capture           import Cv2Capture\nfrom bucketvision.capturers.csicapture           import CSICapture\nfrom bucketvision.displays.cv2display            import Cv2Display\nfrom bucketvision.displays.cameraserverdisplay   import CameraServerDisplay\nfrom bucketvision.postprocessors.angryprocessor  import AngryProcesses\nfrom bucketvision.multiplexers.class_mux         import ClassMux\nfrom bucketvision.multiplexers.mux1n             import Mux1N\nfrom bucketvision.sourceprocessors.resizesource  import ResizeSource\nfrom bucketvision.sourceprocessors.overlaysource import OverlaySource\n\nfrom bucketvision.configs                        import configs\n\nlogging.basicConfig(level=logging.DEBUG)\n\ndef connectionListener(connected, info):\n    \"\"\" used to wait till network tables is initalized \"\"\"\n    print(info, '; Connected=%s' % connected)\n    with cond:\n        notified[0] = True\n        cond.notify()\n\nif __name__ == '__main__':\n\n    # add parse arguments\n    parser = argparse.ArgumentParser()\n    \n    parser.add_argument('-ip',   '--ip-address',     required=False, default='10.41.83.2',\n                        help='IP Address for NetworkTable Server')\n\n    parser.add_argument('-t',    '--test',           required=False,\n                        help='Test mode (uses cv2 display)',                 action='store_true')\n\n    parser.add_argument('-cam',  '--num-cam',        required=False, default=1,\n                        help='Number of cameras to instantiate',   type=int, choices=range(1, 10))\n\n    parser.add_argument('-co',   '--offs-cam',       required=False, default=0,\n                        help='First camera index to instantiate',   type=int, choices=range(0, 10))\n                        \n    parser.add_argument('-proc', '--num-processors', required=False, default=4,\n                        help='Number of processors to instantiate', type=int, choices=range(0, 10))\n\n    parser.add_argument('-CSI',  '--CSI', \n                        help='Use CSI interface', action='store_true')\n\n    args = vars(parser.parse_args())\n\n    cond = threading.Condition()\n    notified = [False]\n    # init networktables\n    NetworkTables.initialize(server=args.ip_address)\n    # add a listener for connection\n    NetworkTables.addConnectionListener(connectionListener, immediateNotify=True)\n\n    # if not connected then block and wait\n    with cond:\n        print(\"Waiting\")\n        if not notified[0]:\n            cond.wait()\n\n    VisionTable = NetworkTables.getTable(\"BucketVision\")\n    VisionTable.putString(\"BucketVisionState\", \"Starting\")\n    VisionTable.putNumber(\"Exposure\", 50.0)\n    \n    # create a source per camera\n    source_list = list()\n\n    for i in range(args.num_cam):\n        if not args.CSI:\n            cap = Cv2Capture(camera_num=i+args.offs_cam, network_table=VisionTable, exposure=configs['exposure'], res=configs['camera_res'])\n        else:\n            cap = CSICapture(camera_num=i+args.offs_cam, network_table=VisionTable, exposure=configs['exposure'], res=configs['camera_res'])\n        source_list.append(cap)\n        cap.start()\n\n    # Output is cropped to match vision target search area. \n    # UU would prefer to output full image from camera and adjust traget drawings to croppped target search area.\n    # Right now its not clear how the output matches the input image and the target coordinates.\n    # This is likely why the drawtarget routine has not yet been enabled.\n\n    source_mux     = ClassMux(*source_list)\n    output_mux     = Mux1N(source_mux)\n    \n    process_output = output_mux.create_output()\n    display_output = OverlaySource(ResizeSource(output_mux.create_output(), res=configs['output_res']))\n        \n    VisionTable.putString(\"BucketVisionState\", \"Started Captures\")\n\n    # create post processor threads to process each image before sending it to the CameraServer (or window if testing)\n    # this does the more complex target finding. We create multiple process threads to process the frames\n    # as they are captured\n    proc_list = list()\n    for i in range(args.num_processors):\n        proc = AngryProcesses(process_output, network_table=VisionTable, debug_label=\"Proc{}\".format(i))\n        proc_list.append(proc)\n        proc.start()\n\n    VisionTable.putString(\"BucketVisionState\", \"Started Processes\")\n\n    if args.test:\n        window_display = Cv2Display(source=display_output)\n        window_display.start()\n        VisionTable.putString(\"BucketVisionState\", \"Started CV2 Display\")\n    else:\n        cs_display = CSDisplay(source=display_output, network_table=VisionTable)\n        cs_display.start()\n        VisionTable.putString(\"BucketVisionState\", \"Started CS Display\")\n\n    try:\n        VisionTable.putValue(\"CameraNum\", 0)\n        while True:\n            source_mux.source_num = int(VisionTable.getEntry(\"CameraNum\").value)\n            # not sure how this works and if needed\n            if args.test:\n                if window_display._new_frame:\n                    cv2.imshow(window_display.window_name, window_display._frame)\n                    window_display._new_frame = False\n                cv2.waitKey(1)\n    \n    except KeyboardInterrupt:\n        pass\n    finally:\n        if args.test:\n            window_display.stop()\n            cv2.destroyAllWindows()\n        else:\n            cs_display.stop()\n        for proc in proc_list:\n            proc.stop()\n        for cap in source_list:\n            cap.stop()\n","sub_path":"2019 Pipeline/BucketVision_AngryEyes_2019.py","file_name":"BucketVision_AngryEyes_2019.py","file_ext":"py","file_size_in_byte":5556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"29026462","text":"# while loop in Python\n\n# example 1\n'''\nwhile True:\n    print('hello')\n'''\n# control-C: force the program to stop\n\n# example 2: print a string and strip off its first char every time\nmyString = 'hello'\nwhile myString:\n    print(myString, end = '-> ')\n    myString = myString[1:]\n","sub_path":"basics/loops/while_loop.py","file_name":"while_loop.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"262354212","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution(object):\n    def minDepth(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: int\n        \"\"\"\n        if not root:\n            return 0\n        level = 0\n        currLevel = [root]\n        nextLevel = []\n        while True:\n            while currLevel:\n                node = currLevel[0]\n                currLevel[:] = currLevel[1:]\n                if not node.left and not node.right:\n                    return level + 1\n                else:\n                    if node.left:\n                        nextLevel.append(node.left)\n                    if node.right:\n                        nextLevel.append(node.right)\n            level += 1\n            currLevel = nextLevel\n            nextLevel = []","sub_path":"minimum_depth_of_binary_tree.py","file_name":"minimum_depth_of_binary_tree.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"221548370","text":"import asyncio\nimport unittest\n\nimport www.orm as orm\n\n\nclass User(orm.Model):\n    __table__ = 'users'\n\n    id = orm.IntegerField(primary_key=True)\n    name = orm.StringField()\n\n\nclass ModelTestCase(unittest.TestCase):\n    def test_model_class(self):\n        database = {\n            'host': '127.0.0.1',\n            'port': 3306,\n            'user': 'root',\n            'password': 'root',\n            'db': 'python_test'\n        }\n\n        expect_user = {\n            'id': 1,\n            'name': 'test'\n        }\n\n        async def connenct_database_and_create_data(loop):\n            await orm.create_pool(loop, **database)\n\n            user = await User.find(1)\n            if user is not None:\n                await user.remove()\n\n            user = User()\n            user.id = expect_user['id']\n            user.name = expect_user['name']\n            await user.save()\n            return user\n\n        loop = asyncio.get_event_loop()\n        task = asyncio.ensure_future(\n            connenct_database_and_create_data(loop))\n        actual_user = loop.run_until_complete(task)\n\n        self.assertIsInstance(actual_user, User)\n        self.assertEqual(actual_user.id, expect_user['id'])\n        self.assertEqual(actual_user.name, expect_user['name'])\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"test/www_test/orm_test.py","file_name":"orm_test.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"217009142","text":"from django.urls import path\n\nfrom routes.views import add_route\nfrom .views import (\n    home, TrainCreateView, TrainDeleteView, TrainUpdateView, TrainDetailView,\n)\n\nurlpatterns = [\n    path('delete/<int:pk>/', TrainDeleteView.as_view(), name='delete'),\n    path('update/<int:pk>/', TrainUpdateView.as_view(), name='update'),\n    path('add/', TrainCreateView.as_view(), name='add'),\n    path('details/<int:pk>/', TrainDetailView.as_view(), name='details'),\n    path('add_route/', add_route, name='add_route'),\n    path('', home, name='home'),\n]\n","sub_path":"trains/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"516935156","text":"import random as ran\nimport csv\n\nbreakfast_db = open('breakfast.txt', 'r')\nlunch_db = open('lunch.txt', 'r')\nbreakfast_csv = csv.DictReader(breakfast_db)\nlunch_csv = csv.DictReader(lunch_db)\nbreakfast = sorted(breakfast_csv, key=lambda row: row['recipe_calories'])\nlunch = sorted(lunch_csv, key=lambda row: row['recipe_calories'])\nbreakfast_db.close()\nlunch_db.close()\n\n\ndef random_number(food_list):\n    rand_item = ran.randint(0, len(food_list) - 1)\n    return rand_item\n\n\ndef get_breakfast():\n    rand_num = random_number(breakfast)\n    get_ran_break = breakfast[rand_num]\n    return get_ran_break\n\n\ndef get_lunch():\n    rand_num = random_number(lunch)\n    get_ran_lunch = lunch[rand_num]\n    return get_ran_lunch\n\n\ndef food_menu(calorie_limit):\n    rand_break = random_number(breakfast)\n    rand_lunch = random_number(lunch)\n    get_ran_lunch = lunch[rand_lunch]['recipe_calories']\n    get_ran_break = breakfast[rand_break]['recipe_calories']\n    get_total_calories = int(get_ran_break) + int(get_ran_lunch)\n    if int(calorie_limit) >= get_total_calories >= (int(calorie_limit) - 50):\n        print(\"Today's Breakfast {} and calories are {}\".format(breakfast[rand_break]['recipe_name'],\n                                                                breakfast[rand_break]['recipe_calories']))\n        print(\"Today's Lunch {} and calories are {}\".format(lunch[rand_lunch]['recipe_name'],\n                                                            lunch[rand_lunch]['recipe_calories']))\n\n\nif __name__ == \"__main__\":\n    #  max_calories = input(\"Enter Calorie Limit: \")\n    max_calories = 1130\n    food_menu(max_calories)\n","sub_path":"receipe.py","file_name":"receipe.py","file_ext":"py","file_size_in_byte":1626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"602490833","text":"# -*- coding: utf-8 -*-\n\"\"\"\nConsider a list (list = []). You can perform the following commands:\n\n1. insert i e: Insert integer e at position i.\n2. print: Print the list.\n3. remove e: Delete the first occurrence of integer e.\n4. append e: Insert integer e at the end of the list.\n5. sort: Sort the list.\n6. pop: Pop the last element from the list.\n7. reverse: Reverse the list.\n\nInitialize your list and read in the value of n followed by n lines of commands \nwhere each command will be of the  types listed above. Iterate through each \ncommand in order and perform the corresponding operation on your list.\n\nExample\nN = 4\nappend 1\nappend 2\ninsert 3 1\nprint\n\n- append 1: Append 1 to the list, arr = [1].\n- append 2: Append 2 to the list, arr = [1,2].\n- insert 3 1: Insert 3 at index 1, arr = [1,3,2].\n- print: Print the array.\n\nOutput:\n[1, 3, 2]\n\nInput Format\nThe first line contains an integer, n, denoting the number of commands.\nEach line i of the n subsequent lines contains one of the commands described \nabove.\n\nConstraints\n- The elements added to the list must be integers.\n\nOutput Format\nFor each command of type print, print the list on a new line.\n\nSample Input 0\n12\ninsert 0 5\ninsert 1 10\ninsert 0 6\nprint\nremove 6\nappend 9\nappend 1\nsort\nprint\npop\nreverse\nprint\n\nSample Output 0\n[6, 5, 10]\n[1, 5, 9, 10]\n[9, 5, 1]\n\"\"\" \n \nif __name__ == '__main__':\n    # take in the inputs\n    n = int(input())\n    output = []\n    for _ in range(n):\n        line = input().split()\n        if(line[0] == \"insert\"):\n            output.insert(int(line[1]), int(line[2]))\n        if(line[0] == \"print\"):\n            print(output)\n        if(line[0] == \"remove\"):\n            output.remove(int(line[1]))\n        if(line[0] == \"append\"):\n            output.append(int(line[1]))\n        if(line[0] == \"sort\"):\n            output.sort()\n        if(line[0] == \"pop\"):\n            output.pop(len(output) - 1)\n        if(line[0] == \"reverse\"):\n            output.reverse()","sub_path":"Python/Basic Data Types/Lists.py","file_name":"Lists.py","file_ext":"py","file_size_in_byte":1955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"50022884","text":"#!/usr/bin/env python3\n\n# My solution to 3SUM.\n# The big speed I made was when I was finding the third number...\n# I kept a set of numbers I ALREADY saw instead of looping through the rest\n# of the list every time I was looking for the third number.\n\n# Asks for user input in command line and outputs 3SUMs\ndef main():\n    # User input -> list of ints\n    user_input = input(\"Enter a list of numbers:\\n> \").strip('[').strip(']')\n\n    if (user_input.find(',') > 0):\n        nums = [int(c) for c in user_input.split(',')]\n    else:\n        nums = [int(c) for c in user_input.split(' ')]\n\n    results = set() # Set of tuples\n    seen = set() # Keep track of seen numbers (key improvement)\n\n    for i in range(len(nums) - 1):\n        for j in range(i + 1, len(nums)):\n            # See if third number to sum to 0 exists\n            (n1, n2) = (nums[i], nums[j])\n\n            n3 = -(n1 + n2)\n            if n3 in seen:\n                sum = tuple(sorted([n1, n2, n3]))\n                results.add(sum)\n\n        # Finished with this number, add it to seen\n        seen.add(n1)\n\n    for result in results:\n        print(str(result)[1:-1])\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Challenge #323 [Easy] 3SUM/three_sum.py","file_name":"three_sum.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"152187096","text":"import numpy as np\nimport matplotlib.pyplot as plt \nimport matplotlib.animation as animation\nimport random\n\nN = 100\nON = 255\nOFF = 0\nvals = range(256)\n\n# populate grid with random on/off - more off than on\ngrid = np.random.choice(vals, N*N).reshape(N, N)\n\ndef color(n):\n    if 0 <= n < 64:\n        return 1\n    elif 64 <= n < 128:\n        return 2\n    elif 128 <= n < 192:\n        return 3\n    else:\n        return 4\n\ndef update(data):\n  global grid\n  # copy grid since we require 8 neighbors for calculation\n  # and we go line by line \n  newGrid = grid.copy()\n  for i in range(N):\n    for j in range(N):\n      # compute 8-neghbor sum \n      # using toroidal boundary conditions - x and y wrap around \n      # so that the simulaton takes place on a toroidal surface.\n      colors = {1:0,2:0,3:0,4:0}\n\n      colors[color(grid[i, (j-1)%N])] += 1\n      colors[color(grid[i, (j+1)%N])] += 1\n      colors[color(grid[(i-1)%N, j])] += 1\n      colors[color(grid[(i+1)%N, j])] += 1\n      colors[color(grid[(i-1)%N, (j-1)%N])] += 1\n      colors[color(grid[(i-1)%N, (j+1)%N])] += 1\n      colors[color(grid[(i+1)%N, (j-1)%N])] += 1\n      colors[color(grid[(i+1)%N, (j+1)%N])] += 1\n\n      popularColor = max(colors, key=colors.get)\n      cantidad = max(colors.values())\n      if cantidad >= 7:\n        newGrid[i, j] = abs(popularColor-4)*64\n      elif 4 < cantidad < 7:\n        newGrid[i,j] = popularColor*64 - 1\n      else:\n        newGrid[i, j] = grid[i,j]\n  mat.set_data(newGrid)\n  grid = newGrid\n  return [mat]\n\n# set up animation\nfig, ax = plt.subplots()\nmat = ax.matshow(grid)\nani = animation.FuncAnimation(fig, update, interval=50,\n                              save_count=50)\nplt.show()","sub_path":"more/retarded_game_of_life.py","file_name":"retarded_game_of_life.py","file_ext":"py","file_size_in_byte":1681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"223526638","text":"from __future__ import annotations\n\nfrom copy import deepcopy\nfrom math import log10\nfrom pathlib import Path\nfrom typing import Union\n\nimport librosa\nimport numpy as np\nimport soundfile as sf\nfrom scipy import signal\n\n\nclass AudioFile:\n    \"\"\"\n    A class for representing and modifying audio files.\n    \"\"\"\n\n    def __init__(self, path: Union[str, Path] = None):\n        \"\"\"\n        Initialize an audio file. Load file if path is provided.\n\n        :param path: path to audio file.\n        \"\"\"\n        self.samples: np.ndarray = np.array([])\n        self.sr: int = int()\n        self.filename: str = str()\n        if path is not None:\n            self.load(path)\n\n    @property\n    def length(self) -> int:\n        \"\"\"\n        The length in samples of the audio data.\n        \"\"\"\n        return self.samples.shape[-1]\n\n    @property\n    def peak(self) -> float:\n        \"\"\"\n        The max absolute amplitude value.\n        \"\"\"\n        max_amplitude = self.samples.max(axis=-1, initial=0.0)\n        min_amplitude = self.samples.min(axis=-1, initial=0.0)\n        return max(max_amplitude, abs(min_amplitude))\n\n    @staticmethod\n    def db_to_scale(db: float) -> float:\n        \"\"\"\n        Convert a decibel value to a scalar in [0.0, 1.0]\n\n        :param db: decibel value - 6db of gain is equivalent to scaling by 2.0\n        :return: a scalar in [0.0. 1.0]\n        \"\"\"\n        return 10.0 ** (db / 20.0)\n\n    @staticmethod\n    def scale_to_db(factor: float) -> float:\n        \"\"\"\n        Convert a scalar value in [0.0, 1.0] to a gain adjustment in decibles.\n\n        :param factor: a scalar in [0.0, 1.0]\n        :return: decibel value - 6db of gain is equivalent to scaling by 2.0\n        \"\"\"\n        return 20.0 * log10(factor)\n\n    def copy(self) -> AudioFile:\n        \"\"\"\n        Return a copy of the AudioFile.\n\n        :return: a copy of the current object.\n        \"\"\"\n        return deepcopy(self)\n\n    def load(self, path: Union[str, Path]):\n        \"\"\"\n        Load an audio file.\n\n        :param path: path to audio file.\n        \"\"\"\n        path = Path(path)\n        if not path.exists():\n            raise Exception('file {} does not exist'.format(path))\n        self.samples, self.sr = librosa.load(path=path, sr=None)\n        self.filename = path.name\n\n    def save(self,\n             output_path: Union[str, Path],\n             filename: str = None,\n             subtype: str = 'PCM_16'):\n        \"\"\"\n        Save the current audio samples to a new file.\n\n        :param output_path: output path for audio samples.\n        :param filename: filename for output - will use the original filename of the audio file by default.\n        :param subtype: soundfile subtype - 16-bit PCM by default.\n        \"\"\"\n        if filename is None:\n            filename = self.filename\n        output_path = Path(output_path)\n        if not output_path.exists():\n            output_path.mkdir(parents=True)\n        output_file = output_path.joinpath(filename)\n        sf.write(output_file, self.samples, self.sr, subtype=subtype)\n\n    def resample(self, new_sr: int):\n        \"\"\"\n        Resample to new sample rate.\n\n        :param new_sr: target sample rate.\n        \"\"\"\n        self.samples = librosa.resample(self.samples, self.sr, new_sr)\n        self.sr = new_sr\n        return self\n\n    def invert_polarity(self):\n        \"\"\"\n        Invert the polarity of the waveform.\n        \"\"\"\n        self.scale(-1)\n        return self\n\n    def scale(self, scale: float):\n        \"\"\"\n        Scale amplitudes by a constant.\n\n        :param scale: constant used to scale audio\n        \"\"\"\n        self.samples = self.samples * scale\n        return self\n\n    def gain(self, db: float):\n        \"\"\"\n        Adjust gain of audio file by decibels.\n\n        :param db: number of decibels to adjust gain by.\n        \"\"\"\n        scale = self.db_to_scale(db)\n        self.scale(scale=scale)\n        return self\n\n    def normalize(self):\n        \"\"\"\n        Normalize audio samples.\n        \"\"\"\n        self.samples = librosa.util.normalize(self.samples, axis=-1)\n        return self\n\n    def clip(self, clip_db: float = 0.0):\n        \"\"\"\n        Digitally clip audio samples by scaling beyond [-1., 1.], clipping to [-1., 1.], and reversing the scaling.\n\n        :param clip_db: Number of decibels to clip audio by.\n        \"\"\"\n        peak = min(1.0, self.peak)\n        if peak < 1.0:\n            self.normalize()\n        self.gain(db=clip_db)\n        np.clip(self.samples, -1.0, 1.0, out=self.samples)\n        self.gain(db=-clip_db)\n        self.scale(peak)\n        return self\n\n    def trim_start(self, relative_start: float = 0.0):\n        \"\"\"\n        Trim the beginning of an audio file, leaving the remaining samples.\n\n        :param relative_start: The relative start position of the new audio file.\n        \"\"\"\n        start_sample = int(self.length * relative_start)\n        self.samples = self.samples[start_sample:]\n        return self\n\n    def trim_to_n_samples(self, n: int):\n        \"\"\"\n        Trim an audio file to a specific number of samples.\n\n        :param n: The desired number of samples. Any samples after will be removed.\n        \"\"\"\n        if n >= self.length:\n            self.samples = self.samples[:n]\n        return self\n\n    def add_silence(self,\n                    samples_before: int = None,\n                    samples_after: int = None,\n                    sec_before: float = 0.0,\n                    sec_after: float = 0.0):\n        \"\"\"\n        Add silence to the end of the audio samples.\n\n        :param samples_before: number of samples of silence to add to beginning of the audio data. Takes precedence over sec_before if set.\n        :param samples_after: number of samples of silence to add to end of the audio data. Takes precedence over sec_after if set.\n        :param sec_before: seconds of silence to add the beginning of the audio data.\n        :param sec_after: seconds of silence to add the end of the audio data.\n        \"\"\"\n        if samples_before is None:\n            samples_before = int(self.sr * sec_before)\n        if samples_after is None:\n            samples_after = int(self.sr * sec_after)\n        silence_before = np.zeros((samples_before,), dtype=self.samples.dtype)\n        self.samples = np.concatenate((silence_before, self.samples))\n        silence_after = np.zeros((samples_after,), dtype=self.samples.dtype)\n        self.samples = np.concatenate((self.samples, silence_after))\n        return self\n\n    def varispeed(self, rate: float):\n        \"\"\"\n        Stretch time/pitch of audio data by increasing/decreasing playback speed.\n\n        :param rate: Stretch factor. If rate > 1, then the signal is sped up. If rate < 1, then the signal is slowed down.\n        \"\"\"\n        old_sr = self.sr\n        self.sr *= rate\n        self.resample(old_sr)\n        return self\n\n    def mix(self,\n            audio: AudioFile,\n            relative_start: float = 0.0,\n            maintain_length: bool = False):\n        \"\"\"\n        Combine two audio files.\n\n        :param audio: an AudioFile to add to the current AudioFile.\n        :param relative_start: the start position of audio relative to the current audio - i.e. 0.5 adds audio starting\n            in the middle of the current audio.\n        :param maintain_length: if true, mixed audio will be trimmed to maintain the same length as the original audio\n        \"\"\"\n        assert (self.sr == audio.sr)\n        shape = self.samples.shape\n        audio = audio.copy()\n\n        # delay the start of the new audio\n        start_sample = int(self.length * relative_start)\n        audio.add_silence(samples_before=start_sample)\n\n        # pad end of audio data if needed\n        self.add_silence(samples_after=max(0, audio.length - self.length))\n        audio.add_silence(samples_after=max(0, self.length - audio.length))\n\n        # combine signals\n        self.samples = self.samples + audio.samples\n        if maintain_length:\n            self.samples.resize(shape)\n        return self\n\n    def lpf(self, cutoff: float, order: int = 1):\n        \"\"\"\n        Low-pass filter.\n\n        https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.butter.html#scipy.signal.butter\n\n        :param cutoff: cutoff frequency in Hz.\n        :param order: the order of the filter.\n        \"\"\"\n        sos = signal.butter(order, cutoff, btype='low', analog=False, output='sos', fs=self.sr)\n        filtered = signal.sosfilt(sos, self.samples)\n        self.samples = filtered\n        return self\n\n    def hpf(self, cutoff: float, order: int = 1):\n        \"\"\"\n        High-pass filter.\n\n        https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.butter.html#scipy.signal.butter\n\n        :param cutoff: cutoff frequency in Hz.\n        :param order: the order of the filter.\n        \"\"\"\n        sos = signal.butter(order, cutoff, btype='high', analog=False, output='sos', fs=self.sr)\n        filtered = signal.sosfilt(sos, self.samples)\n        self.samples = filtered\n        return self\n\n    def dynamic_lpf(self,\n                    cutoff: float,\n                    order: int = 1,\n                    relative_start: float = 0.0,\n                    relative_end: float = 1.0,\n                    exponential: float = 1.0):\n        \"\"\"\n        Dynamic low-pass filter.\n\n        :param cutoff: cutoff frequency in Hz.\n        :param order: the order of the filter.\n        :param relative_start: relative position to start filtering, 0.0 is the start of the audio.\n        :param relative_end: relative position to end filtering, 0.0 is the end of the audio.\n        :param exponential: exponential order dictates crossfade shape.\n        \"\"\"\n        filtered = self.copy().lpf(cutoff=cutoff, order=order)\n\n        crossfade = np.zeros(self.samples.shape)\n        start_sample = int(self.length * relative_start)\n        end_sample = int(self.length * relative_end)\n        num = (end_sample - start_sample) // 2\n        fade = np.linspace(0.000001, 1.0, num=num) ** exponential\n\n        for i in range(fade.shape[-1]):\n            crossfade[start_sample+i] = fade[i]\n            crossfade[end_sample-i-1] = fade[i]\n\n        filtered.samples = filtered.samples * crossfade\n        self.samples = self.samples * (1.0 - crossfade)\n        self.mix(filtered)\n        return self\n\n    def conv_reverb(self,\n                    ir: AudioFile,\n                    dry_db: float = 0.0,\n                    wet_db: float = 0.0,\n                    predelay: float = 0.0,\n                    trim_tail: bool = True):\n        \"\"\"\n        Convolution reverb.\n\n        :param ir: AudioFile containing impulse response.\n        :param dry_db: db gain to apply to dry audio.\n        :param wet_db: db gain to apply to wet audio.\n        :param predelay: predelay in ms added to wet audio.\n        :param trim_tail: if True, will trim tail of new audio to maintain length of original audio.\n        \"\"\"\n        ir = ir.copy()\n        if ir.sr != self.sr:\n            ir.resample(self.sr)\n\n        convolution = self.copy()\n        convolution.samples = signal.convolve(self.samples, ir.samples, mode='full')\n\n        if predelay > 0.0:\n            convolution.add_silence(sec_before=predelay / 1000)\n\n        self.gain(dry_db).mix(convolution.gain(wet_db), relative_start=0.0, maintain_length=trim_tail)\n        self.clip()\n        return self\n","sub_path":"AudioFile.py","file_name":"AudioFile.py","file_ext":"py","file_size_in_byte":11345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"416996562","text":"#146. LRU Cache\r\n# Time Complexity : O(1)\r\n# Space Complexity : O(1)\r\n# Did this code successfully run on Leetcode : Yes\r\n# Any problem you faced while coding this : No\r\nclass Node:\r\n    def __init__(self,key,val):\r\n        self.key = key\r\n        self.val = val\r\n        self.prev = None\r\n        self.next = None\r\n    \r\nclass LRUCache:\r\n    head = None\r\n    Tail = None\r\n\r\n    def __init__(self, capacity: int):\r\n        self.mapp = {}\r\n        self.head = Node(-1,-1)\r\n        self.tail = Node(-1,-1)\r\n        self.head.next = self.tail\r\n        self.tail.prev = self.head\r\n        self.capacity = capacity\r\n    def remove(self,node):\r\n        node.prev.next = node.next\r\n        node.next.prev = node.prev\r\n    def insert(self,node):\r\n        node.prev = self.head\r\n        node.next = self.head.next\r\n        self.head.next = node\r\n        node.next.prev = node \r\n\r\n    def get(self, key: int) -> int:\r\n        if key not in self.mapp:\r\n            return -1\r\n        node = self.mapp[key]\r\n        self.remove(node)\r\n        self.insert(node)\r\n        return node.val\r\n\r\n    def put(self, key: int, value: int) -> None:\r\n        if key in self.mapp:\r\n            node = self.mapp[key]\r\n            node.val = value\r\n            self.remove(node)\r\n            self.insert(node)\r\n        else:\r\n            if len(self.mapp) == self.capacity:\r\n                tailnode = self.tail.prev\r\n                self.remove(tailnode)\r\n                self.mapp.pop(tailnode.key)\r\n            newnode = Node(key,value)\r\n            self.mapp[key] = newnode\r\n            self.insert(newnode)\r\n","sub_path":"LRUCache.py","file_name":"LRUCache.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"536259169","text":"import torch.utils.data as data\nfrom PIL import Image\nimport numpy as np\nimport glob\nimport os\nfrom torchvision import transforms\n\n\nclass DataLoaderSegmentation(data.Dataset):\n    def __init__(self, folder_path,transform=None,image_size=[48, 48]):\n        super(DataLoaderSegmentation, self).__init__()\n        self.img_files = glob.glob(os.path.join(folder_path,'image','*.jpg'))\n        self.mask_files = []\n        self.image_size = image_size\n        self.transform= transform\n        for img_path in self.img_files:\n             self.mask_files.append(os.path.join(folder_path,'mask',os.path.basename(img_path)))\n\n    def __getitem__(self, index):\n            img_path = self.img_files[index]\n            mask_path = self.mask_files[index]\n            _img = Image.open(img_path).convert('RGB')\n            _tmp = np.array(Image.open(mask_path).convert('L'), dtype=np.uint8)\n            _target = self.encode_segmap(_tmp)\n            sample={'image': _img,'label': _target}\n            if self.transform:\n                sample = self.transform(sample)\n            return sample #torch.from_numpy(data).float(), torch.from_numpy(label).float()\n\n    def __len__(self):\n        return len(self.img_files)\n    def encode_segmap(self, mask):\n        # Put all void classes to ignore_index\n        mask[mask!=0]=1\n        mask=Image.fromarray(mask)\n        return mask","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"409768967","text":"''' Problem 3\n어떤 수를 소수의 곱으로만 나타내는 것을 소인수분해라 하고, 이 소수들을 그 수의 소인수라고 합니다.\n예를 들면 13195의 소인수는 5, 7, 13, 29 입니다.\n\n600851475143의 소인수 중에서 가장 큰 수를 구하세요.\n'''\nimport time\n\ndef logging_time(original_fn):\n    def wrapper_fn(*args, **kwargs):\n        start_time = time.time()\n        result = original_fn(*args, **kwargs)\n        end_time = time.time()\n        print(\"WorkingTime[{}]: {} sec\".format(original_fn.__name__, end_time-start_time))\n        return result\n    return wrapper_fn\n\n# Answer\n@logging_time\ndef problem3(num):\n    num_list = []\n\n    for i in range(2, num):\n\n        while num % i == 0:\n            num_list.append(i)\n            num = num / i\n            print(num, \" | \", num_list)\n\n        if num == 1:\n            break\n\n    return num_list\n\n# Solution\n@logging_time\ndef prime(num):\n    factor=2\n    while num != 1:\n        if num%factor == 0:\n            print(num, factor)\n            num = num / factor\n        else:\n            factor +=1\n    return factor\n\n\nif __name__ == '__main__':\n\n    print(problem3(13195))\n    # [5, 7, 13, 29]\n\n    print(problem3(600851475143))\n    # [71, 839, 1471, 6857]\n\n    print(prime(600851475143))\n    # 6857\n\n''' Environment\nPyCharm 2019.1 EAP (Community Edition)\nBuild #PC-191.6014.12, built on March 6, 2019\nJRE: 11.0.2+159 amd64\nJVM: OpenJDK 64-Bit Server VM by JetBrains s.r.o\nWindows 7 6.1\nPython 3.6.6\n'''","sub_path":"Algorithm/ProjectEuler/Python/000003.py","file_name":"000003.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"458572445","text":"#!/usr/bin/env python3\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport argparse\nimport gzip\nimport logging\nimport os\nimport random\nimport socket\nimport time\nimport CloudFlare\nimport csv\n\nfrom base64 import b64encode, b64decode\nfrom datetime import datetime, timezone\n\nimport boto3\n\nfrom droidlet.tools.crowdsourcing.servermgr import ping_cuberite\n\nAWS_ACCESS_KEY_ID = os.environ[\"AWS_ACCESS_KEY_ID\"]\nAWS_SECRET_ACCESS_KEY = os.environ[\"AWS_SECRET_ACCESS_KEY\"]\nAWS_DEFAULT_REGION = os.environ[\"AWS_DEFAULT_REGION\"]\n\nec2 = boto3.resource(\"ec2\")\necs = boto3.client(\n    \"ecs\", aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY\n)\n\nlogging.basicConfig(format=\"%(asctime)s [%(levelname)s]: %(message)s\")\nlogging.getLogger().setLevel(logging.INFO)\n\nSUBNET_IDS = [\"subnet-bee9d9d9\"]\nSECURITY_GROUP_IDS = [\"sg-04ec8fa6e1d91d460\"]\n\nwith open(\"../utils/run.withagent.sh\", \"rb\") as f:\n    txt = f.read()\n    txt_flat = txt.replace(b\"diverse_world\", b\"flat_world\")\n    run_sh_gz_b64 = b64encode(gzip.compress(txt)).decode(\"utf-8\")\n    run_flat_sh_gz_b64 = b64encode(gzip.compress(txt_flat)).decode(\"utf-8\")\n\n\ndef register_task_definition(image_tag, task_name):\n    image = f\"492338101900.dkr.ecr.us-west-1.amazonaws.com/craftassist:{image_tag}\"\n    task_definition = ecs.register_task_definition(\n        family=task_name,\n        executionRoleArn=\"arn:aws:iam::492338101900:role/ecsTaskExecutionRole\",\n        networkMode=\"awsvpc\",\n        memory=\"8192\",\n        cpu=\"4096\",\n        containerDefinitions=[\n            {\n                \"name\": task_name,\n                \"image\": image,\n                \"logConfiguration\": {\n                    \"logDriver\": \"awslogs\",\n                    \"options\": {\n                        \"awslogs-group\": f\"/ecs/craftassist\",\n                        \"awslogs-region\": \"us-west-1\",\n                        \"awslogs-stream-prefix\": \"ecs\",\n                    },\n                },\n                \"portMappings\": [\n                    {\"hostPort\": 25565, \"protocol\": \"tcp\", \"containerPort\": 25565},\n                    {\"hostPort\": 2556, \"protocol\": \"tcp\", \"containerPort\": 2556},\n                    {\"hostPort\": 2557, \"protocol\": \"tcp\", \"containerPort\": 2557},\n                    {\"hostPort\": 3000, \"protocol\": \"tcp\", \"containerPort\": 3000},\n                    {\"hostPort\": 5000, \"protocol\": \"tcp\", \"containerPort\": 5000},\n                    {\"hostPort\": 9000, \"protocol\": \"tcp\", \"containerPort\": 9000},\n                ],\n            }\n        ],\n        requiresCompatibilities=[\"EC2\", \"FARGATE\"],\n    )\n    print(f\"Registered task definition: {task_definition}\")\n\n\ndef launch_instance(task=\"craftassist\", config=\"random\", debug=False, batch_id=None):\n    \"\"\"Returns instance id (specifically, ECS task ARN) of a newly launched instance.\n\n    Instance is not yet ready, and may not even have an ip address assigned!\n    \"\"\"\n\n    if config == \"diverse_world\":\n        run_sh = run_sh_gz_b64\n    elif config == \"flat_world\":\n        run_sh = run_flat_sh_gz_b64\n    elif config == \"random\":\n        run_sh = random.choice([run_sh_gz_b64, run_flat_sh_gz_b64])\n    else:\n        raise ValueError(\"Bad config={}\".format(config))\n\n    timestamp = datetime.now(timezone.utc).isoformat()\n    env_var = [\n        {\"name\": \"RUN_SH_GZ_B64\", \"value\": run_sh},\n        {\"name\": \"AWS_ACCESS_KEY_ID\", \"value\": AWS_ACCESS_KEY_ID},\n        {\"name\": \"AWS_SECRET_ACCESS_KEY\", \"value\": AWS_SECRET_ACCESS_KEY},\n        {\"name\": \"TIMESTAMP\", \"value\": timestamp},\n        {\n            \"name\": \"SENTRY_DSN\",\n            \"value\": os.environ.get(\"CRAFTASSIST_SENTRY_DSN\", \"\"),\n        },\n        {\"name\": \"CLOUDFLARE_TOKEN\", \"value\": os.getenv(\"CLOUDFLARE_TOKEN\")},\n        {\"name\": \"CLOUDFLARE_ZONE_ID\", \"value\": os.getenv(\"CLOUDFLARE_ZONE_ID\")},\n    ]\n    if batch_id:\n        env_var.append({\"name\": \"TURK_EXPERIMENT_ID\", \"value\": str(batch_id)})\n\n    r = ecs.run_task(\n        cluster=\"craftassist\",\n        taskDefinition=task,\n        count=1,\n        launchType=\"FARGATE\",\n        networkConfiguration={\n            \"awsvpcConfiguration\": {\n                \"subnets\": SUBNET_IDS,\n                \"securityGroups\": SECURITY_GROUP_IDS,\n                \"assignPublicIp\": \"ENABLED\",\n            }\n        },\n        overrides={\n            \"containerOverrides\": [\n                {\n                    \"name\": task,\n                    \"environment\": env_var,\n                }\n            ]\n        },\n    )\n    logging.info(\"Launched: {}\".format(r))\n    return r[\"tasks\"][0][\"taskArn\"], timestamp\n\n\ndef is_instance_up(instance_id):\n    try:\n        x = ecs.describe_tasks(cluster=\"craftassist\", tasks=[instance_id])\n\n        attachment_id = x[\"tasks\"][0][\"containers\"][0][\"networkInterfaces\"][0][\"attachmentId\"]\n        attachment = next(y for y in x[\"tasks\"][0][\"attachments\"] if y[\"id\"] == attachment_id)\n\n        eni = next(y for y in attachment[\"details\"] if y[\"name\"] == \"networkInterfaceId\")[\"value\"]\n        ip = ec2.NetworkInterface(eni).private_ip_addresses[0][\"Association\"][\"PublicIp\"]\n\n        s = socket.socket()\n        s.settimeout(10)\n        s.connect((ip, 25565))\n        s.close()\n\n        ping_cuberite.ping(ip, 25565, timeout=1)\n\n    except:\n        return False\n\n    return True\n\n\ndef get_instance_ip(instance_id):\n    try:\n        x = ecs.describe_tasks(cluster=\"craftassist\", tasks=[instance_id])\n\n        attachment_id = x[\"tasks\"][0][\"containers\"][0][\"networkInterfaces\"][0][\"attachmentId\"]\n        attachment = next(y for y in x[\"tasks\"][0][\"attachments\"] if y[\"id\"] == attachment_id)\n\n        eni = next(y for y in attachment[\"details\"] if y[\"name\"] == \"networkInterfaceId\")[\"value\"]\n        ip = ec2.NetworkInterface(eni).private_ip_addresses[0][\"Association\"][\"PublicIp\"]\n    except:\n        raise ValueError(f\"This instance {instance_id} should have been up, but failed to get ip\")\n    return ip\n\n\ndef request_instance(instance_num, image_tag, task_name, timeout=-1, batch_id=None):\n    register_task_definition(image_tag, task_name)\n    NUM_RETRIES = 100\n    start_time = time.time()\n    logging.info(f\"[ECS] Requesting {instance_num} instances from AWS, timeout: {timeout}\")\n    cnt = 0\n    instances_ids = []\n    while cnt < instance_num and NUM_RETRIES > 0:\n        try:\n            instance_id, timestamp = launch_instance(\n                task=task_name, config=\"flat_world\", debug=False, batch_id=batch_id\n            )\n        except Exception as e:\n            print(e)\n            NUM_RETRIES -= 1\n            logging.info(\n                f\"[ECS] Err on launching one ecs instance, discard this one. Remaining num retries: {NUM_RETRIES}\"\n            )\n            continue\n        else:\n            instances_ids.append(instance_id)\n            cnt += 1\n        finally:\n            logging.info(f\"[ECS] Progress: {cnt}/{instance_num}\")\n\n    def is_timeout(start_time, timeout):\n        if timeout < 0:\n            return False\n        return (time.time() - start_time) > (timeout * 60)\n\n    instance_status = [False] * instance_num\n    while not all(instance_status) and not is_timeout(start_time, timeout):\n        time.sleep(5)\n        logging.info(\n            f\"[ECS] Checking status of {instance_num} instances... Not ready, remaining time {timeout - (time.time() - start_time) // 60}\"\n        )\n        for i in range(instance_num):\n            instance_status[i] = is_instance_up(instances_ids[i])\n\n    up_instances_ips = []\n    up_instances_ids = []\n    for i in range(len(instance_status)):\n        if is_instance_up(instances_ids[i]):\n            up_instances_ids.append(instances_ids[i])\n            up_instances_ips.append(get_instance_ip(instances_ids[i]))\n    logging.info(\n        f\"[ECS] {len(up_instances_ids)} instances have been launched. Request num: {instance_num}.\\nIP list: {up_instances_ips}\"\n    )\n    return up_instances_ips, up_instances_ids\n\n\ndef register_dashboard_subdomain(cf, zone_id, ip, subdomain):\n    \"\"\"Registers a unique subdomain for craftassist.io\n    that serves proxied HTTP content using cloudflare.\n    Args:\n    cf -- CloudFlare context with R/W permissions.\n    zone_id -- zone ID used to locate DNS records.\n    ip -- IP of the ECS container that runs dashboard.\n    subdomain -- subdomain contains a unique identifier for this task run,\n    which is the batch ID concatenated with the run number.\n    \"\"\"\n    # Check that DNS record does not already exist\n    dns_record_exists = cf.zones.dns_records.get(\n        zone_id, params={\"name\": \"{}.craftassist.io\".format(subdomain)}\n    )\n    if dns_record_exists:\n        print(\"DNS record already exists for {}\".format(subdomain))\n        return\n\n    dns_record = {\"name\": subdomain, \"type\": \"A\", \"content\": ip, \"proxied\": True}\n    try:\n        r = cf.zones.dns_records.post(zone_id, data=dns_record)\n        print(\"Registered IP {} at subdomain {}\".format(ip, subdomain))\n    except Exception as e:\n        raise e\n\n\ndef allocate_instances(\n    instance_num, batch_id, image_tag, task_name, timeout=-1, cf_email=\"rebeccaqian@fb.com\"\n):\n    instance_ips, instance_ids = request_instance(\n        instance_num, image_tag, task_name, timeout, batch_id\n    )\n    if os.getenv(\"CLOUDFLARE_TOKEN\") and os.getenv(\"CLOUDFLARE_ZONE_ID\"):\n        logging.info(\"registering subdomains on craftassist.io\")\n        cloudflare_token = os.getenv(\"CLOUDFLARE_TOKEN\")\n        zone_id = os.getenv(\"CLOUDFLARE_ZONE_ID\")\n        cf = CloudFlare.CloudFlare(email=cf_email, token=cloudflare_token)\n        dns_records = cf.zones.dns_records.get(zone_id)\n\n        # Write the subdomains and batch IDs to input CSV for Mephisto\n        # CSV file headers\n        headers = [\"subdomain\", \"batch\"]\n        with open(\"../../crowdsourcing/droidlet_static_html_task/data.csv\", \"w\") as fd:\n            csv_writer = csv.writer(fd, delimiter=\",\")\n            csv_writer.writerow(headers)\n            for x in range(len(instance_ips)):\n                ip = instance_ips[x]\n                subdomain = \"dashboard-{}-{}\".format(batch_id, x)\n                register_dashboard_subdomain(cf, zone_id, ip, subdomain)\n                # Write record to Mephisto task input CSV\n                csv_writer.writerow([subdomain, batch_id])\n    return instance_ips, instance_ids\n\n\ndef free_ecs_instances(instance_ids):\n    for instance_id in instance_ids:\n        try:\n            ecs.stop_task(\n                cluster=\"craftassist\",\n                task=instance_id,\n            )\n        except:\n            pass\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"--instance_num\", type=int, default=1, help=\"number of instances requested\"\n    )\n    parser.add_argument(\n        \"--batch_id\",\n        type=int,\n        default=0,\n        help=\"ID of the current batch, used to track which group of runs the task was run in\",\n    )\n    parser.add_argument(\n        \"--user\", type=str, default=\"rebeccaqian@fb.com\", help=\"Email of the CloudFlare account\"\n    )\n    parser.add_argument(\n        \"--image_tag\",\n        type=str,\n        help=\"The tag of docker image that will be used to spin up ecs instance\",\n    )\n    parser.add_argument(\n        \"--task_name\", type=str, help=\"Task name of the ecs instance to be requested\"\n    )\n    args = parser.parse_args()\n    instance_ips, instance_ids = request_instance(\n        args.instance_num, args.image_tag, args.task_name, batch_id=\"0643\"\n    )\n    batch_id = args.batch_id\n    # register subdomain to proxy instance IP\n    if os.getenv(\"CLOUDFLARE_TOKEN\") and os.getenv(\"CLOUDFLARE_ZONE_ID\"):\n        logging.info(\"registering subdomains on craftassist.io\")\n        cloudflare_token = os.getenv(\"CLOUDFLARE_TOKEN\")\n        zone_id = os.getenv(\"CLOUDFLARE_ZONE_ID\")\n        cf = CloudFlare.CloudFlare(email=args.user, token=cloudflare_token)\n        dns_records = cf.zones.dns_records.get(zone_id)\n\n        # Write the subdomains and batch IDs to input CSV for Mephisto\n        # CSV file headers\n        headers = [\"subdomain\", \"batch\"]\n        with open(\"../../crowdsourcing/droidlet_static_html_task/data.csv\", \"w\") as fd:\n            csv_writer = csv.writer(fd, delimiter=\",\")\n            csv_writer.writerow(headers)\n            for x in range(len(instance_ips)):\n                ip = instance_ips[x]\n                subdomain = \"dashboard-{}-{}\".format(batch_id, x)\n                register_dashboard_subdomain(cf, zone_id, ip, subdomain)\n                # Write record to Mephisto task input CSV\n                csv_writer.writerow([subdomain, batch_id])\n","sub_path":"droidlet/tools/hitl/utils/allocate_instances.py","file_name":"allocate_instances.py","file_ext":"py","file_size_in_byte":12635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"362800764","text":"'''\nМодуль asyncio доступен для предоставления базовой реализации цикла событий\nпример двух сопрограмм, одна из которых вызывает другую,\nпроизводящую какие-то затратные вычисления\n\nПрименяется устаревший механизм сопрограмм,\nоснованных на генераторах - должен быть применён декоратор @asyncio.coroutine \n'''\nimport asyncio\n\"\"\"\nбудет предупреждение:\nDeprecationWarning: \"@coroutine\" decorator is deprecated since Python 3.8,\nuse \"async def\" instead\n\"\"\"\n@asyncio.coroutine\ndef time_consuming_computation(x):\n    print('Computing {0} ** 2...'.format(x))\n    yield from asyncio.sleep(1)\n    return x ** 2\n\n@asyncio.coroutine\ndef process_data(x):\n    result = yield from time_consuming_computation(x)\n    print('{0} ** 2 = {1}'.format(x, result))\n\n\nif __name__ == '__main__':\n    \"\"\"Функция get_event_loop модуля asyncio возвращает объект цикла событий\"\"\"\n    loop = asyncio.get_event_loop()\n    \"\"\"метод run_until_complete используется для запуска сопрограммы\"\"\"\n    loop.run_until_complete(process_data(238))\n    loop.close()\n","sub_path":"Demo-itmo/mod_thread/asyncDemo/Demo@asyncio_coroutine.py","file_name":"Demo@asyncio_coroutine.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"26328297","text":"import logging\nfrom common import errfunctions\n\nfrom PyQt5 import QtCore\nfrom PyQt5.QtCore import Qt, QEvent\nfrom PyQt5.QtWidgets import QWidget, QVBoxLayout, QHBoxLayout, QAbstractItemView\n\nfrom common.functions import isHighDef\nfrom database.database import DBModelCommon\nfrom widgets.layoutfunctions import saveState, loadColumnLayout\nfrom widgets.messageboxes import JNothingSelectedMessage\nfrom widgets.widgets import JPushButton, JScreenTitle, labelAbove, JDateEdit, JLineEdit, JCheckBox, JComboBox, JTableView\n\n\nclass BasePage(QWidget):\n    def __init__(self, title, parent, showTitleLabel=True):\n        super().__init__(parent)\n\n        self.title = title\n\n        # Add right side buttons to sideBar\n        self.sidebarLayout = QVBoxLayout()\n        self.sidebarLayout.setContentsMargins(15, 24, 15, 15)\n\n        # Add filter lineEdits using labelAbove to filterLayout\n        self.filterLayout = QHBoxLayout()\n        if isHighDef():\n            self.filterLayout.setSpacing(15)\n\n        # Add main contents (probably a grid) to mainLayout\n        self.mainLayout = QVBoxLayout()\n        self.mainLayout.addLayout(self.filterLayout)\n\n        self.outerLayout = QHBoxLayout()\n        self.outerLayout.setContentsMargins(10, 0, 0, 0)\n        self.outerLayout.addLayout(self.mainLayout)\n        self.outerLayout.addLayout(self.sidebarLayout)\n\n        self.outerOuterLayout = QVBoxLayout()\n        self.outerOuterLayout.setContentsMargins(0, 0, 0, 10)\n        if showTitleLabel:\n            self.lMain = JScreenTitle(title, self)\n            self.outerOuterLayout.addWidget(self.lMain)\n        self.outerOuterLayout.addLayout(self.outerLayout)\n        self.setLayout(self.outerOuterLayout)\n\n        self.setFilterActions = []\n        self.filteringEnabled = True\n\n        self.mainGrid = None\n\n        self.window().installEventFilter(self)\n\n    def eventFilter(self, watched, event):\n        if watched == self.window() and event.type() == QEvent.Close:\n            self.saveState()\n\n        return self.parent().eventFilter(watched, event)\n\n    def loadColumnLayout(self):\n        if not loadColumnLayout(self.title, self.mainGrid.header()):\n            self.loadDefaultColumnLayout()\n\n    def loadDefaultColumnLayout(self):\n        errfunctions.print_err(\"No default column layout found for {0}. Implement one by adding a method 'loadDefaultColumnLayout(self)' to the {0} page\".format(self.title))\n\n    def saveState(self):\n        saveState(self.title, self.mainGrid.header())\n\n    def addFilter(self, label, placeholderText, toolTip, columnIndex, width=None, container=None):\n        lineEdit = JLineEdit(self)\n        lineEdit.setPlaceholderText(placeholderText)\n        lineEdit.textChanged.connect(self.invalidateFilter)\n        if width:\n            lineEdit.setFixedWidth(width)\n\n        layout = labelAbove(label, lineEdit, toolTip)\n        if container is not None:\n            container.addLayout(layout)\n        else:\n            self.filterLayout.addLayout(layout)\n        self.setFilterActions.append(lambda: self.setLineEditFilter(columnIndex, lineEdit))\n\n        return lineEdit\n\n    def addDateFilter(self, label, placeholderText, toolTip, columnIndex, width=None, container=None):\n        dateEdit = JDateEdit(self)\n        dateEdit.lineEdit.setPlaceholderText(placeholderText)\n        dateEdit.lineEdit.textChanged.connect(self.invalidateFilter)\n        if width:\n            dateEdit.setFixedWidth(width)\n\n        layout = labelAbove(label, dateEdit, toolTip)\n        if container is not None:\n            container.addLayout(layout)\n        else:\n            self.filterLayout.addLayout(layout)\n        self.setFilterActions.append(lambda: self.setLineEditFilter(columnIndex, dateEdit.lineEdit))\n\n        return dateEdit\n\n    def addComboNonTypeFilter(self, label, defaultText, list, toolTip, columnIndex, typeIsAbbreviation=True, width=None,\n                       container=None):\n        combo = JComboBox(self)\n        combo.addItem(defaultText, None)\n        combo.insertSeparator(1)\n\n        for i, each in enumerate(list):\n            combo.addItem(\"{}\".format(each), None)\n            combo.setItemData(i, i, Qt.UserRole)\n\n        combo.currentIndexChanged.connect(self.invalidateFilter)\n        if width:\n            combo.setFixedWidth(width)\n\n        layout = labelAbove(label, combo, toolTip)\n        if container is not None:\n            container.addLayout(layout)\n        else:\n            self.filterLayout.addLayout(layout)\n\n        if typeIsAbbreviation:\n            self.setFilterActions.append(lambda: self.setAbbreviatedComboFilter(columnIndex, combo))\n        else:\n            self.setFilterActions.append(lambda: self.setFullComboFilter(columnIndex, combo))\n\n        return combo\n\n    def addComboFilter(self, label, defaultText, types, toolTip, columnIndex, typeIsAbbreviation=True, width=None, container=None):\n        combo = JComboBox(self)\n        combo.addItem(defaultText, None)\n        combo.insertSeparator(1)\n\n        for i, type in enumerate(types, 2):\n            if typeIsAbbreviation:\n                combo.addItem(\"{}({})\".format(type.type, type.description[1:]), type)\n            else:\n                combo.addItem(\"{} ({})\".format(type.type, type.description), type)\n            combo.setItemData(i, type.type, Qt.UserRole)\n\n        combo.currentIndexChanged.connect(self.invalidateFilter)\n        if width:\n            combo.setFixedWidth(width)\n\n        layout = labelAbove(label, combo, toolTip)\n        if container is not None:\n            container.addLayout(layout)\n        else:\n            self.filterLayout.addLayout(layout)\n\n        if typeIsAbbreviation:\n            self.setFilterActions.append(lambda: self.setAbbreviatedComboFilter(columnIndex, combo))\n        else:\n            self.setFilterActions.append(lambda: self.setFullComboFilter(columnIndex, combo))\n\n        return combo\n\n    def addCheckboxFilter(self, label, toolTip, columnIndex, container=None):\n        checkBox = JCheckBox(label, self)\n        checkBox.setToolTip(toolTip)\n        checkBox.stateChanged.connect(self.invalidateFilter)\n\n        if container is not None:\n            container.addWidget(checkBox)\n        else:\n            self.filterLayout.addWidget(checkBox)\n\n        self.setFilterActions.append(lambda: self.setCheckBoxFilter(columnIndex, checkBox))\n        return checkBox\n\n    def createGrid(self, model, sort=True):\n        if self.mainGrid is not None:\n            raise Exception(\"Multiple grids not currently supported due to layout saving\")\n\n        mainGrid = JTableView(self, wasJTreeView=True)\n        self.mainGrid = mainGrid\n        self.mainGrid.setAlternatingRowColors(True)\n        self.mainGrid.setModel(model)\n        if sort:\n            DBModelCommon.selectAndSortByColumn(mainGrid, 0, QtCore.Qt.AscendingOrder)\n        self.mainGrid.setEditTriggers(QAbstractItemView.NoEditTriggers)\n        self.mainGrid.doubleClicked.connect(self.gridDoubleClicked)\n        self.mainGrid.setContextMenuPolicy(Qt.CustomContextMenu)\n        self.mainGrid.customContextMenuRequested.connect(self.openContextMenu)\n\n    def addGridAction(self, buttonText, editAction, notSelectedMessage):\n        btnEdit = JPushButton(buttonText, self)\n        btnEdit.clicked.connect(lambda: self.actionIfSelected(editAction, notSelectedMessage))\n\n        return btnEdit\n\n    def actionIfSelected(self, editAction, notSelectedMessage):\n        if not self.checkSelected(notSelectedMessage):\n            return\n\n        editAction(self.mainGrid.selectedIndexes()[0])\n\n    def checkSelected(self, notSelectedMessage):\n        if len(self.mainGrid.selectedIndexes()) < 1:\n            JNothingSelectedMessage(notSelectedMessage, self).exec()\n            return False\n\n        return True\n\n    def invalidateFilter(self):\n        for setFilterAction in self.setFilterActions:\n            setFilterAction()\n\n        self.filterProxyModel.invalidateFilter()\n\n    def setLineEditFilter(self, column, lineEdit):\n        self.filterProxyModel.filterValues[column] = lineEdit.text().strip().upper()\n\n    def setAbbreviatedComboFilter(self, column, combo):\n        self.filterProxyModel.filterValues[column] = combo.currentText()[0].upper() if combo.currentIndex() != 0 else None\n\n    def setFullComboFilter(self, column, combo):\n        self.filterProxyModel.filterValues[column] = combo.currentText().upper()[combo.currentText().find(\"(\") + 1:-1] if combo.currentIndex() != 0 else None\n\n    def setCheckBoxFilter(self, column, checkBox):\n        self.filterProxyModel.filterValues[column] = str(checkBox.isChecked())\n\n    # Sql model\n    @staticmethod\n    def prefixSql(columnName, filterValue):\n        return \" {} LIKE '{}%' \".format(columnName, filterValue) if filterValue else \"\"\n\n    @staticmethod\n    def partialSql(columnName, filterValue):\n        return \" {} LIKE '%{}%' \".format(columnName, filterValue) if filterValue else \"\"\n\n    @staticmethod\n    def exactSql(columnName, filterValue):\n        return \" {} = '{}' \".format(columnName, filterValue) if filterValue else \"\"\n\n    @staticmethod\n    def inSql(columnName, filterValues):\n        return \" {} IN ({}) \".format(columnName, filterValues) if filterValues else \"\"\n\n    @staticmethod\n    def lessThanOrEqualSql(columnName, date):\n        return \" {} <= '{}' \".format(columnName, date) if date else \"\"\n\n    @staticmethod\n    def ifGreaterThan2(string):\n        if len(string) > 2:\n            return string\n        else:\n            return \"\"\n","sub_path":"widgets/basepage.py","file_name":"basepage.py","file_ext":"py","file_size_in_byte":9457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"306830905","text":"# Copyright 2015 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\"\"\"Command to add IAM policy binding for an organization.\"\"\"\n\nimport httplib\n\nfrom googlecloudsdk.api_lib.util import http_retry\nfrom googlecloudsdk.calliope import base\nfrom googlecloudsdk.command_lib.iam import iam_util\nfrom googlecloudsdk.command_lib.organizations import flags\nfrom googlecloudsdk.command_lib.organizations import orgs_base\n\n\n@base.ReleaseTracks(\n    base.ReleaseTrack.GA, base.ReleaseTrack.BETA, base.ReleaseTrack.ALPHA)\nclass AddIamPolicyBinding(orgs_base.OrganizationCommand):\n  \"\"\"Add IAM policy binding for an organization.\n\n  Adds a policy binding to the IAM policy of an organization,\n  given an organization ID and the binding.\n  \"\"\"\n\n  detailed_help = iam_util.GetDetailedHelpForAddIamPolicyBinding('organization',\n                                                                 '123456789')\n\n  @staticmethod\n  def Args(parser):\n    flags.IdArg('to which you want to add a binding').AddToParser(parser)\n    iam_util.AddArgsForAddIamPolicyBinding(parser, 'id',\n                                           'cloudresourcemanager.organizations')\n\n  @http_retry.RetryOnHttpStatus(httplib.CONFLICT)\n  def Run(self, args):\n    messages = self.OrganizationsMessages()\n\n    get_policy_request = (\n        messages.CloudresourcemanagerOrganizationsGetIamPolicyRequest(\n            organizationsId=args.id,\n            getIamPolicyRequest=messages.GetIamPolicyRequest()))\n    policy = self.OrganizationsClient().GetIamPolicy(get_policy_request)\n\n    iam_util.AddBindingToIamPolicy(messages, policy, args.member, args.role)\n\n    set_policy_request = (\n        messages.CloudresourcemanagerOrganizationsSetIamPolicyRequest(\n            organizationsId=args.id,\n            setIamPolicyRequest=messages.SetIamPolicyRequest(policy=policy)))\n\n    return self.OrganizationsClient().SetIamPolicy(set_policy_request)\n","sub_path":"google-cloud-sdk/lib/surface/organizations/add_iam_policy_binding.py","file_name":"add_iam_policy_binding.py","file_ext":"py","file_size_in_byte":2423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"138923717","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 27 19:36:20 2020\n\n@author: jisuk\n\"\"\"\n\n#%% import module\n\nimport os\nimport sys\nimport xml.etree.ElementTree as Et\nfrom xml.etree.ElementTree import Element, ElementTree\nimport tensorflow as tf\n\nimport config as cfg\n\n#%% classes\n\nclass DatasetTool:\n    def __init__(self):\n        self.imagesPath = os.path.join(cfg.VOCdatasetConfig['rootDir'], cfg.VOCdatasetConfig['imageFolder'])\n        \n        self.textFileFolder = cfg.VOCdatasetConfig['textFileFolder']\n        self.trainCasesPath = cfg.VOCdatasetConfig['trainCasesPath']\n        self.valCasesPath = cfg.VOCdatasetConfig['valCasesPath']\n        \n        \n    def splitDataset(self,val = 0.2):\n        ann_root, ann_dir, ann_files = next(os.walk(self.imagesPath))\n        \n        i = 0\n        valBatch = 1/ val\n        trainText = open(os.path.join(self.textFileFolder,\"trainNames.txt\"),'w+')\n        valText = open(os.path.join(self.textFileFolder,\"valNames.txt\"),'w+')\n        for file in ann_files:\n            file = file.split('.')\n            file = file[0]\n            \n            if(i > valBatch):\n                i = 0\n                valText.write(file+\"\\n\")\n            else:\n                trainText.write(file+\"\\n\")\n            \n            i = i + 1\n        \n        trainText.close()\n        valText.close()\n            \n\nclass PascalVOC2007:\n    \n    def __init__(self):\n        \n        self.RootPath = cfg.VOCdatasetConfig['rootDir']\n        self.imageFolderPath = cfg.VOCdatasetConfig['imageFolder']\n        self.imageExtension = cfg.VOCdatasetConfig['imageExtension']\n        self.annotationFolderPath = cfg.VOCdatasetConfig['annotationFolder']\n        self.annotationExtension = cfg.VOCdatasetConfig['annotationExtension']\n        \n        self.imageFolderPath = os.path.join(self.RootPath,self.imageFolderPath)\n        self.annotationFolderPath = os.path.join(self.RootPath,self.annotationFolderPath)\n        \n        self.classNames = cfg.classNames\n        self.textFileFolder = cfg.VOCdatasetConfig['textFileFolder']\n        \n        self.trainCasesPath = cfg.VOCdatasetConfig['trainCasesPath']\n        self.valCasesPath = cfg.VOCdatasetConfig['valCasesPath']\n        \n    \n    def getImagePath(self, imageName):\n        return os.path.join(self.annotationFolderPath, imageName+self.imageExtension)\n    \n    \n    def getAnnotationPath(self, imageName):\n        return os.path.join(self.annotationFolderPath,imageName+self.annotationExtension)\n    \n    \n    def getAnnotation(self, annotationPath, pathtype):\n        if(pathtype == 'fileName'):\n            annotationPath = self.getAnnotationPath(annotationPath)\n        \n        result = dict()\n        \n        xml = open(annotationPath,'r')\n        tree = Et.parse(xml)\n        root = tree.getroot()\n    \n        result['filename'] = root.find(\"filename\").text\n    \n        size = root.find(\"size\") \n        result['width'] = size.find(\"width\").text\n        result['height'] = size.find(\"height\").text\n        result['channels'] = size.find(\"depth\").text\n        \n        \n        objects = root.findall(\"object\")\n        result['objects'] = []\n        for obj in objects:\n            \n            objInfo = dict()\n            \n            objInfo['name'] = obj.find(\"name\").text\n        \n            bndbox = obj.find(\"bndbox\")\n            objInfo['xmin'] = bndbox.find(\"xmin\").text\n            objInfo['ymin'] = bndbox.find(\"ymin\").text\n            objInfo['xmax'] = bndbox.find(\"xmax\").text\n            objInfo['ymax'] = bndbox.find(\"ymax\").text\n            \n            result['objects'].append(objInfo)\n        \n        return result\n    \n    \n    def XML2StrLine(self, objDict):\n        string = objDict['filename']\n        \n        for bndbox in objDict['objects']:\n            string = string +\" \" + str(bndbox['xmin']) + \" \" + str(bndbox['ymin']) + \" \" + str(bndbox['xmax']) + \" \" + str(bndbox['ymax']) + \" \" + str(self.classNames[bndbox['name']])\n        \n        return string\n    \n    def MakeTextFiles(self):\n        trainNames = open(os.path.join(self.textFileFolder,\"trainNames.txt\"),'r')\n        trainText = open(os.path.join(self.textFileFolder,\"train.txt\"),'w+')\n        \n        for line in trainNames.readlines():\n            line = line.strip().split(' ')\n            \n            filename = line[0]\n            annotationDict = self.getAnnotation(filename,'fileName')\n            string = self.XML2StrLine(annotationDict)\n            trainText.write(string+\"\\n\")\n            \n        trainText.close()\n        \n        \n        valNames = open(os.path.join(self.textFileFolder,\"valNames.txt\"),'r')\n        valText = open(os.path.join(self.textFileFolder,\"val.txt\"),'w+')\n        \n        for line in valNames.readlines():\n            line = line.strip().split(' ')\n            \n            filename = line[0]\n            annotationDict = self.getAnnotation(filename,'fileName')\n            string = self.XML2StrLine(annotationDict)\n            valText.write(string+\"\\n\")\n            \n        valText.close()\n    \n    \n\n#%% main code\n\n#d = DatasetTool()\n#d.splitDataset(val=0.2)\n\n            \n            \n            \n    \n    \n    \n    \n    \n        \n        ","sub_path":"Object Detection/YOLOv1/PascalVOC2007Parse.py","file_name":"PascalVOC2007Parse.py","file_ext":"py","file_size_in_byte":5164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"413302744","text":"\"\"\"\r\n------------------------------\r\nBEHAVE PARALLEL FEATURE RUNNER\r\n------------------------------\r\nThis module provides functionality to run Behave features in parallel.\r\nThe amount of features that will run in parallel is determined by\r\nMAX_WORKERS environment variable, set to a default value when not specified.\r\n    Command line usage:\r\n        behave_parallel_runner <tag_args> <feature_args>\r\n    Accepted args:\r\n        <tag_args>\r\n            * --tags=some_tag (same as 'behave' command)\r\n        <feature_args>\r\n            * Single path to a features directory\r\n            * Single path to a feature file\r\n            * Multiple paths to different feature files\r\n    Usage examples:\r\n        * behave_parallel_runner --tags=test ui_tests/admin/features\r\n        * behave_parallel_runner --tags=test ui_tests/admin/features/health.feature\r\n        * behave_parallel_runner --tags=test ui_tests/admin/features/health.feature\r\n            ui_tests/admin/features/apigee.feature\r\n\"\"\"\r\nimport copy\r\nimport gc\r\nimport os\r\nimport subprocess\r\nimport sys\r\nimport tempfile\r\nimport time\r\nfrom datetime import datetime\r\nfrom pytz import timezone\r\n\r\nfrom pteromyini.core.runner.feature_worker import FeatureWorker\r\n\r\n\r\nclass __SubprocessRunner:\r\n    \"\"\"\r\n    main\r\n    \"\"\"\r\n\r\n    def __init__(self, path, rerun_file='rerun.feature', max_workers=3, periodicity_progress_log=60):\r\n        self.rerun_file = rerun_file\r\n        self.MAX_WORKERS = max_workers\r\n        self.active_workers = []\r\n        self.base_command = 'behave --junit {} {} {} {}'\r\n        self.tags = []\r\n        self.feature_args = []\r\n        self.temp_run_id = time.strftime(\"%Y%m%d_%H-%M-%S\")\r\n        self.temp_file_counter = 0\r\n        self.feature_path = path\r\n        self.periodicity_progress_log = periodicity_progress_log\r\n\r\n        self.log_path = os.path.join('logs', self.temp_run_id)\r\n        if not os.path.exists(self.log_path):\r\n            os.makedirs(self.log_path)\r\n\r\n    def run(self):\r\n        \"\"\"\r\n        Behave Parallel Runner main process\r\n        \"\"\"\r\n        print('\\nBEHAVE PARALLEL RUNNER\\n')\r\n\r\n        start_date = datetime.now(timezone('US/Eastern'))\r\n\r\n        self._log('Execution started @ {} EST'.format(\r\n            datetime.now(timezone('US/Eastern')).strftime('%H:%M:%S')))\r\n\r\n        self._set_max_workers()\r\n        self._parse_args()\r\n        self._unify_tags()\r\n        self.worker_controller(self.get_task_list())\r\n        self.create_rerun_feature()\r\n        end_date = datetime.now(timezone('US/Eastern'))\r\n        took = time.strftime('%Hh %Mm %Ss', time.gmtime((end_date - start_date).total_seconds()))\r\n\r\n        self._log('Execution finished @ {} EST'.format(\r\n            datetime.now(timezone('US/Eastern')).strftime('%H:%M:%S')))\r\n        self._log('Took {}'.format(took))\r\n\r\n    def _set_max_workers(self):\r\n        \"\"\"\r\n        Sets the max amount of workers available for the current execution\r\n        Default value will be used if MAX_WORKERS environment variable is not specified\r\n        \"\"\"\r\n        global MAX_WORKERS\r\n        MAX_WORKERS = int(os.getenv('BEHAVE_MAX_WORKERS', self.MAX_WORKERS))\r\n\r\n        self._log('Enabled {} workers'.format(MAX_WORKERS))\r\n\r\n    def _log(self, msg):\r\n        \"\"\"\r\n        Log a message into the behave parallel runner default output\r\n        :param msg:\r\n            A string representing the message to _log\r\n        \"\"\"\r\n        _time = datetime.now(timezone('US/Eastern')).strftime('%H:%M:%S')\r\n        print('[behave-parallel-runner @ {}] {}'.format(_time, msg))\r\n\r\n    def _parse_args(self):\r\n        \"\"\"\r\n        Parses the arguments provided to the behave parallel runner script\r\n        \"\"\"\r\n        self._log('Parsing script arguments')\r\n        args = copy.copy(sys.argv)\r\n        args.pop(0)  # Remove Python file path arg\r\n\r\n        for arg in args:\r\n            if arg[:7] == '--tags=':\r\n                self.tags.append(arg)\r\n            else:\r\n                self.feature_args.append(arg)\r\n\r\n    def _unify_tags(self):\r\n        \"\"\"\r\n        Converts the tags list to a single string\r\n        \"\"\"\r\n        if self.tags:\r\n            tags_as_str = ''\r\n            self._log('Unifying tags')\r\n\r\n            for tag in self.tags:\r\n                tags_as_str = ' '.join((tags_as_str, tag))\r\n\r\n            self.tags = tags_as_str.strip()\r\n        else:\r\n            self.tags = ''\r\n\r\n    def get_task_list(self):\r\n        \"\"\"\r\n            Scans a features directory for feature files\r\n            :returns:\r\n                A list of feature files (relative path & filename)\r\n            \"\"\"\r\n        feature_list = []\r\n\r\n        for root, directories, filenames in os.walk(self.feature_path):\r\n            for file in filenames:\r\n                if file.endswith('.feature'):\r\n                    feature_list.append(os.path.join(root, file))\r\n\r\n        return feature_list\r\n\r\n    def get_rerun_path(self):\r\n        path = os.path.join('temp_parallel_runner', self.temp_run_id, 'rerun')\r\n        if not os.path.exists(path):\r\n            os.makedirs(path)\r\n        return path\r\n\r\n    def _create_temp_log_file(self, file_name):\r\n        \"\"\"\r\n        Creates a unique temporary file\r\n        :param file_name:\r\n            A string representing the temporary file name prefix\r\n        :returns:\r\n            A pair (fd, name) where fd is the file descriptor returned by os.open,\r\n            and name is the filename\r\n        \"\"\"\r\n        dir_name = os.path.join(self.log_path, 'temp')\r\n        if not os.path.exists(dir_name):\r\n            os.makedirs(dir_name)\r\n\r\n        file_descriptor, file_name = tempfile.mkstemp(prefix=f'{file_name}_$run_', dir=os.path.abspath(dir_name),\r\n                                                      suffix='.tmp')\r\n        return file_descriptor, file_name\r\n\r\n    def _trigger_feature(self, feature_path):\r\n        \"\"\"\r\n        Triggers a feature execution\r\n        :param feature_path:\r\n            A string representing the feature (relative path & filename)\r\n        \"\"\"\r\n        self._log('Triggering feature: \"{}\"'.format(feature_path))\r\n\r\n        worker = FeatureWorker(feature_path)\r\n\r\n        log_file_descriptor, log_file_name = self._create_temp_log_file(worker.file_name)\r\n\r\n        outfile = os.path.join(self.get_rerun_path(), f'rerun_{worker.file_name}{self.get_next_temp_file_index()}.features')\r\n        format_rerun = f'--format rerun --outfile {outfile}'\r\n\r\n        cmd = self.base_command.format(self.tags, \" \".join(self.feature_args), feature_path, format_rerun)\r\n        self._log(f'Run feature cmd: \"{cmd}\"')\r\n        process = subprocess.Popen(cmd,\r\n                                   shell=True,\r\n                                   stdout=log_file_descriptor,\r\n                                   stderr=log_file_descriptor)\r\n\r\n        worker.log_file_descriptor = log_file_descriptor\r\n        worker.log_file_name = log_file_name\r\n        worker.subprocess = process\r\n\r\n        self.active_workers.append(worker)\r\n\r\n    def save_log_file(self, log_file_name):\r\n        with open(log_file_name, mode='r', encoding='utf8', errors='ignore') as log_file:\r\n            text_log = log_file.read()\r\n        if text_log.find('Failing scenarios:') < 0:\r\n            file_name_result = ''\r\n        else:\r\n            file_name_result = '_FAILED'\r\n        file_name = os.path.basename(log_file_name).split('_$')[0]\r\n        file_dir = os.path.abspath(self.log_path)\r\n        file_name = f'{file_name}{file_name_result}_{self.get_next_temp_file_index()}.txt'\r\n        file_name = os.path.join(file_dir, file_name)\r\n        with open(file_name, 'w+', encoding='utf8') as file:\r\n            file.write(text_log)\r\n\r\n    def worker_controller(self, feature_list):\r\n        last_time_progress_log = time.time()\r\n\r\n        while True:\r\n            for worker in self.active_workers:\r\n                if worker.subprocess.poll() is not None:\r\n                    # If the worker subprocess finished\r\n                    # Remove worker\r\n                    self._log(f'Releasing worker: \"{worker.name}\" ({worker.run_time/60:.2f} min)')\r\n                    self.active_workers.remove(worker)\r\n                    self.save_log_file(worker.log_file_name)\r\n                    # Print feature log\r\n\r\n                    # Delete worker and call garbage collector\r\n                    del worker\r\n                    gc.collect()\r\n\r\n            if len(feature_list) > 0 and len(self.active_workers) < MAX_WORKERS:\r\n                # If there's pending features\r\n                # And there's an available worker\r\n                # Trigger another feature\r\n                feature = feature_list.pop(0)\r\n                self._trigger_feature(feature)\r\n\r\n            if len(feature_list) == 0 and len(self.active_workers) == 0:\r\n                # If no feature's pending\r\n                # And there are no active workers\r\n                # Terminate the execution\r\n                break\r\n\r\n            if time.time() - last_time_progress_log > self.periodicity_progress_log:\r\n                last_time_progress_log = time.time()\r\n                self._log('---------------------------------------------')\r\n                self._log('IN PROGRESS:')\r\n                for worker in self.active_workers:\r\n                    self._log(f'\\t\"{worker.name}\" ({worker.run_time/60:.2f} min)')\r\n                self._log('---------------------------------------------')\r\n            time.sleep(2)\r\n\r\n        self._log('All workers has completed')\r\n\r\n    def create_rerun_feature(self):\r\n        if not self.rerun_file:\r\n            return\r\n        path = self.get_rerun_path()\r\n        files = os.listdir(path)\r\n        data = []\r\n        for file_name in files:\r\n            file_name = os.path.join(path, file_name)\r\n            with open(file_name, 'r') as file:\r\n                for line in file:\r\n                    line = line.strip()\r\n                    if line and line[0] != '#':\r\n                        data.append(line)\r\n        with open(self.rerun_file, 'w+') as file:\r\n            file.write(f'# -- RERUN: {str(len(data))} failing scenarios during last test run.\\n')\r\n            for d in data:\r\n                file.write(d + '\\n')\r\n\r\n    def get_next_temp_file_index(self):\r\n        self.temp_file_counter += 1\r\n        return str(self.temp_file_counter)\r\n\r\n","sub_path":"custom_library/pteromyini/pteromyini/core/runner/__subprocess_runner.py","file_name":"__subprocess_runner.py","file_ext":"py","file_size_in_byte":10285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"571704392","text":"#! /usr/bin/python\n# Sniff DNS traffic\nfrom scapy.all import *\nimport sys\n\ntry:\n    interface = raw_input(\"[*] Enter desired interface eth0/eth1: \")\nexcept KeyboardInterrupt:\n    print(\"[*] Exit\")\n    sys.exit(1)\n\n\n\ndef monitor_callback(pkt):\n    print(pkt)\n    \nsniff(iface=interface, prn=monitor_callback, filter=\"tcp\", store=0)\nprint(\"[*] Exit\")\n","sub_path":"scapy_scripts/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"239247104","text":"#\n# @lc app=leetcode.cn id=17 lang=python3\n#\n# [17] 电话号码的字母组合\n#\n\n# @lc code=start\nclass Solution:\n    def letterCombinations(self, digits: str) -> List[str]:\n        if not digits: return []\n\n        keyboard = {1: [], \n                    2: ['a', 'b', 'c'], 3: ['d', 'e', 'f'], \n                    4: ['g', 'h', 'i'], 5: ['j', 'k', 'l'], \n                    6: ['m', 'n', 'o'], 7: ['p', 'q', 'r', 's'], \n                    8: ['t', 'u', 'v'], 9: ['w', 'x', 'y', 'z']}\n\n        def helper(index, maxlen, cur):\n            if index == maxlen+1:\n                res.append(cur)\n                return\n            \n            for letter in keyboard[int(digits[index])]:\n                helper(index+1, maxlen, cur+letter)\n\n        cur = ''\n        res = []\n        helper(0, len(digits)-1, cur)\n        return res\n# @lc code=end\n\n","sub_path":"Week_03/实战题目/17.电话号码的字母组合.py","file_name":"17.电话号码的字母组合.py","file_ext":"py","file_size_in_byte":850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"159583677","text":"import itertools\nfrom django.db import models\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.core.validators import RegexValidator\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.core.urlresolvers import reverse\nfrom django.utils.text import slugify\nfrom django.db.models import Q\nfrom django.db import transaction\n\nfrom model_utils.models import TimeStampedModel, StatusModel\nfrom django_extensions.db.models import TitleDescriptionModel\nfrom tags.models import Tag\n# from geopy.geocoders import GoogleV3\nfrom hashid_field import HashidAutoField\n\nfrom .choices import *\nimport itineraries.choices as itin_choices\nfrom .utils import capitalize, is_english, gps_to_address, addr_to_dict, google_place_info, similar\nfrom .merge import merge_objects\nfrom .scrape import scrapeRatings\nfrom core.models import Trip, Image\nfrom addresses.models import AddressField\n# from itineraries.models import ItineraryItem, ItineraryItemImage\n\n\n##########################################\n###      CONTACT Class (abstract)      ###\n##########################################\nclass ContactModel(models.Model):\n\taddress = AddressField(\n\t\ton_delete=models.CASCADE,  \n\t\tnull=True,\n\t\tblank=True)\n\twebsite = models.URLField(_('website'), blank=True)\n\temail = models.EmailField(_('email address'), blank=True)\n\t# phone_regex = RegexValidator(regex=r'^\\+?1?\\d{9,15}$', message=\"Phone/Fax number must be entered in the format: '+999999999'. Up to 15 digits allowed.\")\n\tphone_regex = RegexValidator(regex=r'^\\+?1?[ 0-9-]{9,18}$', message=\"Phone/Fax number must be entered with a '+' sign upfront followed by numbers and spaces only. Up to 15 digits allowed (18 including space/dash).\")\n\tphone = models.CharField(_('phone'), max_length=20, validators=[phone_regex], blank=True) # validators should be a list\n\tfax = models.CharField(_('fax'), max_length=20, validators=[phone_regex], blank=True) # validators should be a list\n\n\tclass Meta:\n\t\tabstract = True\n\n\t# Returns a shortened representation of locality string (up to the set limit of 10 per component)\n\t@property\n\tdef locality_short(self):\n\t\toutput = ''\t\n\t\tif self.address:\n\t\t\tlimit = 10\n\t\t\td = addr_to_dict(self.address)\n\t\t\tif d['locality']:\n\t\t\t\tdata = d['locality']\n\t\t\t\toutput = output + data[:limit] if len(data) > limit else data + ', '\n\t\t\tif d['state_code']:\n\t\t\t\tdata = d['state_code']\n\t\t\t\toutput = output + data + ', '\n\t\t\telif d['state']:\n\t\t\t\tdata = d['state']\n\t\t\t\toutput = output + data[:limit] if len(data) > limit else data + ', '\n\t\t\tif d['country_code']:\n\t\t\t\tdata = d['country_code']\n\t\t\t\toutput = output + data + ', '\n\t\t\telif d['country']:\n\t\t\t\tdata = d['country']\n\t\t\t\toutput = output + data[:limit] if len(data) > limit else data + ', '\n\t\t\tif d['postal_code']:\n\t\t\t\tdata = d['postal_code']\n\t\t\t\toutput = output + data \n\t\treturn output\n\n\n\tdef set_addr_from_gps(self, gps): # gps input should be in a str format 'lat, lng'\n\t\taddr = gps_to_address(gps)\t\n\t\tif addr:\n\t\t\tself.address = addr\n\n\tdef if_same_addr(self, addr):\n\t\tif addr_to_dict(self.address) == addr_to_dict(addr):\n\t\t\treturn True\n\t\treturn False\n\n\n\n\n###################################\n###         PLACE Class         ###\n###################################\nclass Place(ContactModel, TimeStampedModel, StatusModel, TitleDescriptionModel):\n\t# Inherited fields: title, description,created, modified, status, status_changed, website, email, address, phone, fax\n\tid = HashidAutoField(primary_key=True, min_length=10, salt=\"Place Salt 963819\")\n\tslug = models.SlugField(max_length=255)  #default max-length 50\n\t# id_regex = RegexValidator(regex=r'^[A-Za-z0-9_-]{1,255}$', message=\"Must be a valid Google place_id\")\n\t# google_id = models.CharField(_('google place_id'), \n\t# \tmax_length=255,\n\t# \tblank=True,\n\t# \tvalidators=[id_regex],\n\t# \thelp_text=_('Google Places database unique identifier'))\n\tname_local = models.CharField(_('local name'), \n\t\tblank=True,\n\t\tmax_length=255)\n\talias_english = models.TextField(_('english alias names'), \n\t\tblank=True,\n\t\thelp_text=_('Keeps track of a list of English alias names, seperated by \",\"'))\n\talias_local = models.TextField(_('local alias names'), \n\t\tblank=True,\n\t\thelp_text=_('Keeps track of a list of local alias names, seperated by \",\"'))\n\tcategories = models.ManyToManyField('places.PlaceCategory', \n\t\trelated_name='places',  \n\t\tblank=True,\n\t\thelp_text=_('all applicable categories'))\n\tparent = models.ForeignKey('self', \n\t\trelated_name=\"children\", \n\t\ton_delete=models.SET_NULL, # deleion of the ForeignKey object doesn't cascade to the related object \n\t\tnull=True, \n\t\tblank=True)\n\tlast_approved = models.DateTimeField(_('last approved'),\n\t\tblank=True, \n\t\tnull=True, \n\t\thelp_text=_('The last time any change was approved. Compare to the modified field will tell if there is any new unapproved change.'))\n\tSTATUS = PLACE_STATUS_CHOICES\n\t# tags = TagField()\n\n\tclass Meta:\n\t\tunique_together = ('title', 'address',)\n\t\tordering = ['title']\n\n\tdef __str__(self):\n\t\tloc = ''\n\t\tif self.address and self.address.locality:\n\t\t\tif self.address.locality.name:\n\t\t\t\tloc += ', ' + self.address.locality.name \n\t\t\tif self.address.locality.state.country.name:\n\t\t\t\tloc += ', ' + self.address.locality.state.country.name\n\t\treturn self.title + loc\n\n\tdef __init__(self, *args, **kwargs):\n\t\tsuper(Place, self).__init__(*args, **kwargs)\n\t\t# keep a copy of the original value so we can check upon if changed when saving later\n\t\tself.__original_title = self.title\n\n\tdef save(self, *args, **kwargs):\t\n\t\t### TBD ###: shouldn't need to update slug upon every save ... only need to do it when place title or place address (locality & country) has been changed.  \n\t\t# The challenge is how to check when those fields have been changed\n\t\t# Auto-populate the slug field as we need to use it for the absolute url (generate it every time on the fly is very database expensive)\n\t\tmax_length = self.__class__._meta.get_field('slug').max_length\t\n\t\tself.slug = slugify(str(self))[:max_length]\n\n\t\t# check if the title is already included in the alias list and if not, add it\n\t\t# this should take effect only when the Place object is first created and when title is changed\n\t\tif self.__original_title and self.title != self.__original_title:\n\t\t\tself.add_english_alias(self.title)\n\t\t\tself.add_local_alias(self.name_local)\n\n\t\tsuper(Place, self).save(*args, **kwargs)\n\n\n\tdef get_absolute_url(self):\n\t\treturn reverse('places:place-detail', kwargs={'pk':self.pk, 'slug':self.slug})\n\n\t@property\n\tdef related_count(self):\n\t    try:\n\t        count = self.item_place_set.count()\n\t    except ObjectDoesNotExist:\n\t        count = 0\n\t    return count\n\n\t@property\n\tdef display_title(self):\n\t\treturn self.title + (' ('+ self.name_local + ')' if self.name_local else '')\n\n\t@property\n\tdef title_and_locality(self):\n\t\tloc = ' ('+self.address.locality.name+')' if self.address.locality.name else ''\n\t\treturn self.title + loc\n\n\tdef alias_include(self, name):\n\t\tif name:\n\t\t\tif is_english(name):\n\t\t\t\treturn (name.lower() + ',') in self.alias_english.lower()\t\t\t\n\t\t\telse:\n\t\t\t\treturn (name + ',') in self.alias_local\n\t\treturn False\n\n\tdef add_english_alias(self, name):\n\t\tif name and (name.lower() + ',' not in self.alias_english.lower()):\n\t\t\tself.alias_english += name + ','\n\n\tdef add_local_alias(self, name):\n\t\tif name and (name + ',' not in self.alias_local):\n\t\t\tself.alias_local += name + ','\n\n\t# If it's unknown whether the given name is english or local\n\tdef add_alias(self, name):\n\t\tif name:\n\t\t\tif is_english(name):\n\t\t\t\tif (name.lower() + ',') not in self.alias_english.lower():\n\t\t\t\t\tself.alias_english += name + ','\n\t\t\telse: \n\t\t\t\tif (name + ',') not in self.alias_local:\n\t\t\t\t\tself.alias_local += name + ','\n\t\t\t\t\t# print('>>>Checkpoint 2: ' + self.alias_local)\n\n\tdef add_alias_and_save(self, name):\n\t\tself.add_alias(name)\n\t\tself.save(update_fields=['alias_english', 'alias_local'])\t\n\n\t@property\n\tdef all_aliases(self):\n\t\ts = set((self.alias_english+self.alias_local).split(','))\n\t\ts.discard('')\n\t\ts.discard(self.title)\n\t\ts.discard(self.name_local)\n\t\treturn s\n\n\n\tdef merge_duplicates(self, duplicates, keep_old=False, fill_empty=True):\n\t\t# if self in duplicates:\n\t\t# \tduplicates.remove(self)\n\t\treturn merge_objects(self, duplicates, keep_old, fill_empty)\n\n\tdef serialize_for_map(self):\n\t\treturn {\n\t\t\t\"url\": self.get_absolute_url(),\n\t\t\t\"pk\": self.pk.hashid,\n\t\t\t\"title\": self.display_title.replace(\"'\", '&#39;'),\n\t\t\t\"website\": self.website,\n\t\t\t\"phone\": self.phone,\n\t\t\t\"address\": self.address.formatted.replace(\"'\", '&#39;') if self.address else \"\",\n\t\t\t\"latitude\": self.address.latitude if self.address else None,\n\t\t\t\"longitude\": self.address.longitude if self.address else None,\n\t\t}\n\n\n\t# Checking whether a Place object already exists is complicated. All we get from user input is \n\t# a place title, maybe a google_id, and maybe a gps embedded in the image exif\n\t# Address could be a unique identifier but it's possible multiple businesses reside at the same address\n\t# Title is also bad as an identifier becuase difference places around the world can have the same name, \n\t# and also people can mistype or use alias even if it's the same place.\n\t@classmethod\n\tdef create_if_no_match(cls, title, google_id, gps=''): # gps should be in a str format 'lat, lng'\n\t\tplace = None\n\t\tcreated = False\n\t\t\n\t\t# if google_id is given, we either get the existing place with the matching google_id or create a new one. \n\t\tif google_id:\n\t\t\ttry:\n\t\t\t\t# Check whether an exisiting Place with the same google_id already exists. If it does exist, add the title to alias\n\t\t\t\t# TODO: the following lines could raise MultipleObjectsReturned exception \n\t\t\t\trating = PlaceExternalRating.objects.get(site=GOOGLE, site_id=google_id)\n\t\t\t\tplace = rating.place\n\t\t\t\tplace.add_alias_and_save(title)\n\t\t\texcept PlaceExternalRating.DoesNotExist:\n\t\t\t\t# with transaction.atomic():\n\t\t\t\tinfo = google_place_info(google_id)\n\t\t\t\t# Create a new Place object based on google info\n\t\t\t\tplace = cls.objects.create(title=title, address=info.get('address'), website=info.get('website'), phone=info.get('phone'))\n\t\t\t\tplace.add_alias(info.get('name')) # add Google's place name as an alias if different\n\t\t\t\tplace.set_category_by_google_types(info.get('types'))\n\t\t\t\t# set category again by title because sometimes Google gets the type wrong while it could be easily guessed from place title (e.g. Hotel 1898)\n\t\t\t\tplace.set_category_by_place_name(place.title)  \n\t\t\t\tplace.save()\n\t\t\t\t# Create a PlaceExternalRating Google rating for the place\n\t\t\t\tPlaceExternalRating.objects.create(place=place, site=GOOGLE, site_id=google_id,\turl=info.get('url'), rating=info.get('rating'))\n\t\t\t\tcreated = True\n\t\t# if google_id is not given but gps is given, we either get the existing place with the matching address AND a similar title/alias or create a new one\n\t\telif gps:\n\t\t\t# translate the gps to address\n\t\t\taddress = gps_to_address(gps)\n\t\t\tplaces = cls.objects.filter(address=address)\n\t\t\t# Check if the given title is in any alias or similarity between the titles is high (break if found)\n\t\t\tif places:\n\t\t\t\tfor p in places:\n\t\t\t\t\tif p.alias_include(title):\n\t\t\t\t\t\tplace = p \n\t\t\t\t\t\tbreak\n\t\t\t\t\t# QUESTION: is 0.9 a good ratio to determine similarity???\n\t\t\t\t\telif similar(p.title, title) > 0.9:\n\t\t\t\t\t\tplace = p \n\t\t\t\t\t\tplace.add_alias_and_save(title)\n\t\t\t\t\t\tbreak\n\t\t\t# if no place is found with matching address and title/alias, we create a new object with the given title and address.\n\t\t\tif not place:\n\t\t\t\tplace = cls.objects.create(title=title, address=address)\n\t\t\t\tplace.set_category_by_place_name(place.title)\n\t\t\t\tplace.save()\n\t\t\t\tcreated = True\n\t\t# if neither google_id nor gps is given:\n\t\telif title:\n\t\t\t### TBD ### should we check if a Place object with the same title and no address already exists?\n\t\t\t# It might not be prudent to make the judgement of whether any exisitng place is a true match based just on the title\n\t\t\t# place, created = cls.objects.get_or_create(title=title, address=None)\n\t\t\tplace = cls.objects.create(title=title)\n\t\t\tplace.set_category_by_place_name(place.title)\n\t\t\tplace.save()\n\t\t\tcreated = True\n\n\t\treturn (place, created)\n\n\t# update the address field based on the given google_id and gps.  If both are null, \n\t# check if we can find a google_id in existing PlaceExternalRating objects\n\tdef update_address(self, google_id=None, gps=None):\n\t\tif not google_id and gps:\n\t\t\tself.address = gps_to_address(gps)\n\t\t\tself.save()\n\t\t\tmsg = 'address updated according to gps'\n\t\telse: \n\t\t\tgoogle_id = google_id if google_id else self.google_rating.site_id\n\t\t\tinfo = google_place_info(google_id)\n\t\t\tif info:\n\t\t\t\tself.address = info.get('address')\n\t\t\t\tself.save()\n\t\t\t\tmsg = 'address updated according to google_id'\n\t\treturn msg\n\n\n\t# Given Google Place types, set the corresponding category for the place\n\t# types is in a list format, e.g. [\"park\", \"point_of_interest\", \"establishment\"]\n\tdef set_category_by_google_types(self, types):\n\t\tcat_obj = None\n\t\tif types:\n\t\t\tcategory = GOOGLE_TYPES_TO_PLACECATEGORY.get(types[0])\n\t\t\tif category:\n\t\t\t\tl = len(category) - 1\n\t\t\t\tp = None\n\t\t\t\twhile (l >= 0): \n\t\t\t\t\tcat_obj = PlaceCategory.objects.get_or_create(title=category[l], parent=p)[0]\n\t\t\t\t\tl = l - 1\n\t\t\t\t\tp = cat_obj\n\t\t# return cat_obj\n\t\tif cat_obj:\n\t\t\tself.categories.add(cat_obj)\n\n\t# Set place category based on the obvious words in the name (when there is no associated google place info)\n\tdef set_category_by_place_name(self, name):\n\t\tfor key in PLACE_NAME_TO_PLACECATEGORY:\n\t\t\tif any(word in name.lower() for word in PLACE_NAME_TO_PLACECATEGORY[key]):\n\t\t\t\tself.categories.add(PlaceCategory.objects.get(title=key))\n\n\n\n\t# Generate a list of external ratings (used by place_detail.html template)\n\t# [['Google', rating_obj], ['Tripadivsor', rating_obj], ... ]\n\tdef list_ratings(self):\n\t\tl = []\n\t\tfor site in RATING_SITE_CHOICES:\n\t\t\tsite_num = int(site[0])\n\t\t\tsite_name = str(site[1])\n\t\t\tl.append([site_name,])\n\t\tfor rating in self.external_ratings.all():\n\t\t\tl[rating.site].append(rating)\n\t\t\t# try:\n\t\t\t# \trating = self.external_ratings.get(site=site_num)\n\t\t\t# except PlaceExternalRating.DoesNotExist:\n\t\t\t# \trating = None\n\t\t\t# r_tuple = (site_name, rating)\t\n\t\t\t# l.append(r_tuple)\n\t\treturn l\n\n\t\n\t@property\n\tdef google_rating(self):\n\t\ttry:\n\t\t\trating = self.external_ratings.get(site=GOOGLE)\n\t\texcept PlaceExternalRating.DoesNotExist:\n\t\t\trating = None\n\t\treturn rating\n\n\t# Get a list of all trips that visited the place (through attached itinerary items)\n\t@property\n\tdef get_trips(self):\n\t\t# id_list = self.primary_items.filter(~Q(category=itin_choices.TRANSIT)).values_list('trip', flat=True).distinct()\n\t\t# trips = Trip.objects.filter(id__in=id_list).order_by('-score')\n\t\t# try: \n\t\t# \ts1 = set(self.images.values_list('trip', flat=True)) \n\t\t# except ObjectDoesNotExist:\n\t\t# \ts1 = set()\n\t\ttry:\n\t\t\ts2 = set(self.item_place_set.values_list('item__trip', flat=True)) \n\t\texcept ObjectDoesNotExist:\n\t\t\ts2 = set()\n\t\t# trips = Trip.objects.filter(id__in=(s1 | s2)).filter(published__isnull=False).order_by('-score')\n\t\ttrips = Trip.objects.filter(id__in=s2).filter(published__isnull=False).order_by('-score')\n\t\treturn trips\n\n\n\tdef get_images(self):\n\t\t# itinerary_items = self.primary_items.filter(~Q(category=itin_choices.TRANSIT))\n\t\t# images = Image.objects.filter(itinerary__item__in=itinerary_items).order_by('-score', '-taken')\n\t\ttry: \n\t\t\timages = self.images.order_by('-score', '-taken')\n\t\texcept ObjectDoesNotExist:\n\t\t\timages = None\n\t\treturn images\n\n\t# Return sorted tags by count of all the images associated with the place\n\t@property\n\tdef all_image_tags(self):\n\t\ttags = sorted(Tag.objects.usage_for_queryset(self.get_images(), counts=True), key=lambda x: x.count, reverse=True)\n\t\treturn tags\n\n\t# check if a place belongs to a place category (i.e. either assigned the same place category or assigned a subcatgory of the given category)\n\tdef belongs_to_category(self, cat):\n\t\tresult = False\n\t\tfor category in self.categories.all():\n\t\t\tif category.is_category_or_subcategory(cat):\n\t\t\t\tresult = True\n\t\t\t\tbreak\n\t\treturn result\n\n\tdef is_accommodation(self):\n\t\tcat = PlaceCategory.objects.get(title=\"Accommodation\")\n\t\treturn self.belongs_to_category(cat)\n\n\tdef is_food_and_drink(self):\n\t\tcat = PlaceCategory.objects.get(title=\"Food & Drink\")\n\t\treturn self.belongs_to_category(cat)\n\n\tdef is_transportation(self):\n\t\tcat = PlaceCategory.objects.get(title=\"Transportation Service\")\n\t\treturn self.belongs_to_category(cat)\n\n\t# From place and time info, guess the implied itinerary title and category\n\t# This is used when we add/edit the place info of an image, we can add/edit the associated itinerary item accordingly\n\tdef implied_itinerary_info(self, t):\n\t\tif self.is_accommodation():\n\t\t\treturn ('Stay at '+self.title, itin_choices.LODGING)\n\t\t# if it's a food and drink place, we check the time input and decide which meal it is\n\t\telif self.is_food_and_drink():\n\t\t\tif t.hour >= 6 and t.hour < 11:\n\t\t\t\tmeal = \"Breakfast at \" \n\t\t\telif t.hour >= 11 and t.hour < 16: \n\t\t\t\tmeal = \"Lunch at \" \n\t\t\telif t.hour >= 17 and t.hour < 23: \n\t\t\t\tmeal = \"Dinner at \" \n\t\t\telse:\n\t\t\t\tmeal = \"Eat at \"\n\t\t\treturn (meal + self.title, itin_choices.MEAL)\n\t\telif self.is_transportation():\n\t\t\treturn ('Transit from ' + self.title, itin_choices.TRANSIT)\n\t\telse:\n\t\t\treturn ('Visit ' + self.title, itin_choices.ACTIVITY)\n\n\n\n\n###############################\n###  External Rating Class  ###\n###############################\n# Custom validator for Google place_id\nid_regex = RegexValidator(regex=r'^[A-Za-z0-9_-]{1,255}$', message=\"Must be a valid API place id\")\nclass PlaceExternalRating(TimeStampedModel):\n\t# Inherited fields: created, modified\n\tplace = models.ForeignKey('places.Place', \n\t\trelated_name='external_ratings',\n\t\ton_delete=models.CASCADE)  \n\tsite = models.PositiveSmallIntegerField(_('rating site'),\n\t\tchoices=RATING_SITE_CHOICES,\n\t\thelp_text=_('Google / Tripadvisor / Yelp / Facebook'))\n\tsite_id = models.CharField(_('rating site place_id'), # id for the place on external site (e.g. google id)\n\t\tmax_length=255,\n\t\tblank=True,\n\t\tvalidators=[id_regex],\n\t\thelp_text=_('The unique identifier for the place on the external rating site'))\n\turl = models.URLField(_('url'), \n\t\tblank=True,\n\t\thelp_text=_('The page url for the place on the external rating site'))\n\trating = models.FloatField(_('rating'), \n\t\tnull=True,\n\t\tblank=True, \t\t\n\t\thelp_text=_('Average rating'))\n\treview_count = models.PositiveIntegerField(_('review count'),\n\t\tnull=True,\n\t\tblank=True, \n\t\thelp_text=_('Number of reviews/votes'))\n\n\tclass Meta:\n\t\tunique_together = ('place', 'site',)\n\t\tordering = ['place', 'site']\n\t\tverbose_name = _('rating')\n\t\tverbose_name_plural = _('ratings')\n\n\tdef __str__(self):\n\t\treturn self.place.title + ' - ' + str(RATING_SITE_CHOICES[self.site][1]) + ' rating'\n\n\tdef save(self, *args, **kwargs):\n\t\tif (not self.rating) or (not self.review_count):\n\t\t\tif self.site and self.url:\n\t\t\t\tavg, count = scrapeRatings(int(self.site), self.url)\n\t\t\t\tself.rating = avg\n\t\t\t\tself.review_count = count\n\t\tsuper(PlaceExternalRating, self).save(*args, **kwargs)\t\n\n\n\n###################################\n###     PLACECATEGORY Class     ###\n###################################\nclass PlaceCategory(TitleDescriptionModel):\n\t# Inherited fields: title, description\n\tparent = models.ForeignKey('self', \n\t\trelated_name='children', \n\t\ton_delete=models.CASCADE,\n\t\tnull=True, \n\t\tblank=True)\n\tslug = models.SlugField(unique=True)\n\n\tclass Meta:\n\t\tordering = ['slug']\n\t\tverbose_name = _('category')\n\t\tverbose_name_plural = _('categories')\n\n\tdef __str__(self):\n\t\tp_list = self._recurse_for_parents()\n\t\tp_list.append(self.title)\n\t\treturn self.get_separator().join(p_list)\n\n\tdef save(self, *args, **kwargs):\n\t\t# should generate the slug only once upon creating a new object\n\t\tif not self.id:  # this checks whether it's a newly created object\n\t\t\t# if yes, set slug field here\n\t\t\tp_list = self._recurse_for_parents()\n\t\t\tp_list.append(self.title)\n\t\t\tslug_str = '-'.join(p_list)\n\t\t\tself.slug = slugify(slug_str)\n\t\tsuper(PlaceCategory, self).save(*args, **kwargs)\n\n\tdef _recurse_for_parents(self):\n\t\tp_list = []\n\t\tp = self.parent\n\t\twhile p:\n\t\t\tp_list.append(p.title)\n\t\t\tp = p.parent\n\t\tif p_list:\n\t\t\tp_list.reverse()\n\t\treturn p_list\n\n\tdef get_separator(self):\n\t\treturn ' > '\n\n\t# wether self is subcategory of the given cat (including cat = self)\n\tdef is_category_or_subcategory(self, cat):\n\t\tresult = False\n\t\tp = self\n\t\twhile p and p != cat:\n\t\t\tp = p.parent\n\t\tif p:\n\t\t\tresult = True\n\t\treturn result\n\n\n\n\n","sub_path":"places/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":20262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"154967254","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n@author: bananasacks\n\"\"\"\n\n#! /usr/bin/env python3\nimport os\nfrom os import listdir\nfrom os.path import isfile, join\nos.chdir(\"/Users/bananasacks/Desktop/Optimal Transport Internship/Optimal_Transport/pascalle_s_drafts/test_algos_draft\")\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom sklearn import preprocessing\n\nfrom computeK import computeK\nimport sinkhorn_barycenters as sink\n\n\n\n\"\"\"\nintensity = \"minmax\" or \"zeroone\"\n    Will set the plots based off of the minimum and maximum intensity values across all noise levels\n    Or sets the plot's intensity to be between 0 and 1\n    In case of NAN values? Set to 0 and 1\n    \nimgs: number of images used to compute the barycenter\n\nnoise_lvl: choosing to use 4 different or 6 different noise levels for barycenters and plots\n\"\"\"\n\n\n\ndef get_files():\n    onlyfiles = [f for f in listdir(\"./data\") if isfile(join(\"./data\", f))]\n    onlyfiles = [file for file in onlyfiles if file[-4:] == \".npy\"]       \n    onlyfiles.sort()\n    \n    for file in onlyfiles:\n        yield file\n        \n\n\ndef debiased_sink_bary(epsilon = .1, max_iter = int(1000), intensity = \"zeroone\", plot=True, save=True):\n    \n    files = get_files() \n    \n    if plot:\n        plt.figure(1, figsize=(15, 10))\n    vmin = []\n    vmax = []\n \n\n#change on line 78 - first variable in computeK    \n#hs for unbalanced\n#hs_hat for unbalanced and normalized\n#data for just normalized\n    \n    for file in files:    \n        data = np.load(\"./data/\" + file)\n        data = abs((data[:])) \n        #data_pos = data - np.min(data)\n        #mass = np.sum(data_pos, axis=0).max()\n        # unbalanced data\n        #hs = data_pos / mass\n        # normalized data\n        #mass_hs = np.sum(hs, axis=0)\n        #hs_hat = hs / mass_hs\n\n        #normalized\n        data = (data-np.nanmin(data))/(np.nanmax(data)-np.nanmin(data))\n        print(np.nanmin(data), np.nanmax(data), )\n\n        #Computing barycenter\n        \"\"\"using hs for unbalances or hs_hat for normalized\"\"\"\n        P, K = computeK(data, epsilon)     \n        bary = sink.barycenter(P, K, reference=\"debiased\", maxiter = max_iter) \n        print(np.nanmin(bary), np.nanmax(bary))\n\n        #Finding max and min intensities for consistent plotting\n        #Finding the Min intensitywith NAN handler\n        if vmin == []:\n            if torch.isnan(torch.min(bary)) == True:\n                vmin = []\n            else:\n                vmin = torch.min(bary)\n                vmin = vmin.numpy()\n        ##If NAN, do nothing\n        ##If min(bary) > vmin, do nothing\n        else:\n            if torch.isnan(torch.min(bary)) == False:\n                barymin = torch.min(bary)\n                barymin = barymin.numpy()\n                if  barymin < vmin:\n                    vmin = barymin    \n        if np.isnan(vmin):\n            vmin = 0\n        \n        #Finding the Max intensity with NAN handler\n        if vmax == []:\n            if torch.isnan(torch.max(bary)) == True:\n                vmax = []\n            else:\n                vmax = torch.max(bary)\n                vmax = vmax.numpy()\n        ##If NAN, do nothing\n        ##If max(bary) < vmax, do nothing\n        else:\n            if torch.isnan(torch.max(bary)) == False:\n                barymax = torch.max(bary)\n                barymax = barymax.numpy()\n                if  barymax > vmax:\n                    vmax = barymax     \n        if np.isnan(vmax):\n            vmax = 1\n        #print(vmax)\n        #saving the dataset\n        title = \"bary\" + file[15:-4] \n        params = \"_eps_\" + str(epsilon) + \"_iter_\" + str(max_iter) + \"_intensity_\" + str(intensity) \n        if save:\n            np.save(\"./results/debiased_sink_bary/\" + title + params + \".npy\", bary)\n        #print(vmax)\n        \n\n#Choose the intensity scale on the plot to be eith 0/1 or min/max \n    if plot:\n        k = 1\n        params = \"_eps_\" + str(epsilon) + \"_iter_\" + str(max_iter) + \"_intensity_\" + str(intensity)\n        #if noise_lvl == 6:\n        noise_lvls = [\"0.000\", \"0.100\", \"0.200\", \"0.500\", \"1.000\"]\n        m = 3\n        for n in noise_lvls: \n            barys = np.load(\"./results/debiased_sink_bary/bary_noiselvl_\" + n + params + \".npy\")\n            plt.subplot(2, m, k)\n            plt.title(\"bary_lvl_\" + n + \"_mean_0.000\")\n            ##added vmin and vmax so all plots have same itensity scale\n            k += 1\n            if intensity == \"zeroone\":\n                plt.imshow(barys, vmin=0, vmax=1)\n            elif intensity == \"minmax\":\n                plt.imshow(barys, vmin=vmin, vmax=vmax)\n            else:\n                plt.imshow(barys)\n        \n\n    if save:\n        plt.savefig(\"./results/debiased_sink_bary/plots_debiased_sink_bary/\" + title + params + \".png\")\n        \n    if plot:\n        plt.show()\n\n\n\nif __name__ == \"__main__\":\n    #iters = [100, 750, 2000, 10000, 1e8]\n    #for i in iters:\n    debiased_sink_bary(epsilon = .3, max_iter = 100, intensity = \"minmax\") \n\n\"\"\"Notes for Richard\nDebiased:\n#epsilon = .001 max_iter = int(1e6)\n#epsilon = .005 max_iter = int(1e6)\n#epsilon = .01, max_iter = int(1e6), int(1e8) (only works for greater than 1e5)\n#epsilon = .05, max_iter = int(1e7) (anything less than 1e5 doesn't work)\n#epsilon = .2   max_iter = [2500, 3000, 4000]\n#eps = .5 and iters = [500, 750, 1000]\n#eps = .7 and iters = [100, 750, 1000]   \n\"\"\"\n\n\n\n#Notes for me to remember\n#takes a long time to run\n#epsilon = .0001, max_iter = int(1e6)\n#epsilon = .05, max_iter = int(1e7)\n\n#eps = [.0001, .005, .01, .05, .1, .2, .5, .7]    \n#iters = [100, 750, 2000, 1e8]\n    \n#[.001, .025, .05, .075, .1, .15, .2, .3, .5, .7, .9]\n\n\n#ALREADY RUN\n#eps = .05 and iters = [ ] (try with high iterations like 1e6,8,10  [[100, 500, 1000, 2000, 3000, 5000, 1e5] don't work\n#eps = .1 and iters = [100, 10000, ] Need more than 10000 for .1\n#eps = .2 and iters = [2500, 5000] lower than 2500 is no good, 5000 is no good\n#eps = .5 and iters = [500, 750, 1000]\n#eps = .7 and iters = [100, 750, 1000]\n\n\n  ","sub_path":"pascalle_s_drafts/test_algos_draft/debiased_sink_bary.py","file_name":"debiased_sink_bary.py","file_ext":"py","file_size_in_byte":6000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"98170145","text":"\"\"\"Read the experimental CPMG data.\"\"\"\nimport numpy as np\n\nfrom chemex import experiments\n\n\ndef read_profiles(path, filenames, details, model):\n    \"\"\"Read the CPMG profiles.\"\"\"\n\n    details[\"name\"] = name_experiment(details)\n    Profile = experiments.grab(details[\"type\"])\n    dtype = [(\"ncycs\", \"i4\"), (\"intensity\", \"f8\"), (\"error\", \"f8\")]\n\n    profiles = []\n\n    for profile_name, filename in filenames.items():\n        full_path = path / filename\n        data = np.loadtxt(full_path, dtype=dtype)\n        profiles.append(Profile(profile_name, data, details, model))\n\n    error = details.get(\"error\", \"file\")\n\n    if error not in {\"file\", \"duplicates\"}:\n        print(\"Warning: The 'error' option should either be 'file' or \")\n        print(\"'duplicates'. Using the default 'file' option.\")\n        error = \"file\"\n\n    if error == \"duplicates\":\n        # Estimate the uncertainty using the pooled standard deviation\n        # Ref: http://goldbook.iupac.org/html/P/P04758.html\n\n        variances = []\n\n        for profile in profiles:\n            variances.append(profile.get_variance_from_duplicates())\n\n        noise_mean = np.sqrt(np.mean(variances))\n\n        for profile in profiles:\n            profile.data[\"error\"] = noise_mean\n\n    return profiles\n\n\ndef name_experiment(experiment_details=None):\n    \"\"\"Generate a unique name for the experiment.\"\"\"\n    if not experiment_details:\n        experiment_details = dict()\n\n    if \"name\" in experiment_details:\n        name = experiment_details[\"name\"].strip().replace(\" \", \"_\")\n\n    else:\n        exp_type = experiment_details[\"type\"].replace(\".\", \"_\")\n        h_larmor_frq = float(experiment_details[\"h_larmor_frq\"])\n        temperature = float(experiment_details[\"temperature\"])\n        time_t2 = float(experiment_details[\"time_t2\"])\n\n        name = \"{:s}_{:.0f}ms_{:.0f}MHz_{:.0f}C\".format(\n            exp_type, time_t2 * 1e3, h_larmor_frq, temperature\n        ).lower()\n\n    return name\n","sub_path":"chemex/experiments/cpmg/reading.py","file_name":"reading.py","file_ext":"py","file_size_in_byte":1946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"550213817","text":"import requests\nimport single_parser\nimport time\nfrom datetime import datetime\n#runs with python3\n\n# Written by Teddy Rowan\n# READ ME!!!!!!\n# This script prompts the user for a symbol, expiry date, and history range of interest and plots the historic pricing for the selected option.\n# The candlestick binning is 15 minutes if you're going back less than 35 days, or 1 day if you're going back further than 35 days. \n# There is no (?) error handling right now so be careful. \n\n# If you're just going to run it once or whatever this key is fine to use, but if you're going to run it a lot go and sign up for your own key. IT'S FREE, it takes 30 seconds.\n# https://developer.tradier.com/user/sign_up\nAPI_KEY = 'Bearer UNAGUmPNt1GPXWwWUxUGi4ekynpj'\n\nmy_headers = {'Authorization': API_KEY} # Tradier Authorization Header\n\n# TODO: Fix date labels for timesales plots.\n\n# TODO: Validate user inputs\n# TODO: Check API response for error messages. \n# TODO: Add volume line plot below candles\n# TODO: Add technical indicators like moving averages, etc. \n\nprint(\" \")\nprint(\" \")\nprint(\"*********************************************************\")\nprint(\"*********************************************************\")\nprint(\"****** WELCOME TO A HISTORIC OPTIONS DATA PLOTTER *******\")\nprint(\"*********************************************************\")\nprint(\"*********************************************************\")\nprint(\" \")\nprint(\"There is no error-handling in this right now so try to be a grown-up and not fuck everything up.\")\nprint(\"--\")\nprint(\"Also. If you're going to run this more than just to test it out, get your own API key. It's free for fucksake. And that way we don't hit rate-limiting.\")\nprint(\"--\")\nprint(\" \")\n\n# Prompt the user for the underlying symbol of interest\nsymbol = input(\"Select an underlying symbol (that's a stock ticker retard): \")\ntype(symbol)\n\n# Does the user want to look at call options or put options\noptionType = input(\"Type C for Calls or P for Puts: \")\ntype(optionType)\n\nif (optionType == \"C\"):\n    print(\"Selected Call Options for \" + symbol)\nelse:\n    if (optionType == \"P\"):\n        print(\"Selected Put Options for \" + symbol)\n    else:\n        #exit() #let's try to keep the user going a bit.\n        print(\"Invalid input you fucking retard. I guess we're looking at calls.\")\n        optionType = \"C\"\n        \n\n\n# Grab, parse, and print all the available expiry dates for the symbol.\nurl_dates = \"https://sandbox.tradier.com/v1/markets/options/expirations?symbol=\" + symbol\nrDates = requests.get(url_dates, headers=my_headers)\nsingle_parser.parse_multi_quote(rDates.content.decode(\"utf-8\"), \"date\")\n\n# TODO: Error Handling for no option dates. Should probably move the printing of the dates back into this script instead of having it in the parser.\n\n# Prompt the user to pick one of the expiry dates\ndate = input(\"Select an expiry date from the list above: \")\ntype(date)\n\n# TODO: Check whether the input date is in the list of dates.\n\n\n# Grab a list of all the prices available, then parse and format them properly\nurl_strikes = \"https://sandbox.tradier.com/v1/markets/options/strikes?symbol=\" + symbol + \"&expiration=\" + date\nrStrikes = requests.get(url_strikes, headers=my_headers)\nstrikeList = single_parser.parse_strikes(rStrikes.content.decode(\"utf-8\"))\n\nprint(\"List of available strike prices: \")\n\n# Format the price strings to the standard Tradier price format\nupdatedList = []\nprint(strikeList)\n\nselectedPrice = input(\"Select a strike from the list above: \")\ntype(selectedPrice)\n\n# error handling for retards that can't pick a strike from the list\nif not (selectedPrice in strikeList or str(selectedPrice + \".0\") in strikeList):\n    print(\"How hard is it to pick a strike from the list...? Fuck me.\")\n    selectedPrice = strikeList[(int)(len(strikeList)/2)] #pick the middle strike.\n    print(\"I guess I'm choosing for you... Strike = $ \" + selectedPrice)\n    \n# Tradier Formatting is lolz and terrible\ntmp = int(float(selectedPrice)*1000)\nselectedPrice = '{0:08d}'.format(tmp) #edit courtesy: /u/Wallstreet_Fox\n\n\n# Prompt the user for how long of a history they are interested in\nstartDate = input(\"Input a start date for the data range (YYYY-mm-dd): \")\ntype(startDate)\n\n# TODO: Error handling. Make sure that the start date is valid.\n\n\n# Format the date string for Tradier's API formatting\nformat_date = date.replace(\"-\", \"\") # strip out the dashes from the selected date\nformat_date = format_date[2:len(format_date)] # strip the 20 off the front of 2019\n\n# Find out how far back the user wants to look and if it's more than 35 days, use the history endpoint insteal of the timesales endpoint\ndatenum = datetime.strptime(startDate, \"%Y-%m-%d\")\nstartDateTime = time.mktime(datenum.timetuple())\nnowTime = time.mktime(datetime.now().timetuple())\n\nhistory = 0 # should we use the history endpoint.\nif (nowTime - startDateTime > 35*24*60*60): # if it's been more than 35 days, plot daily data\n    history = 1\n\n\n# Set either a /history/ or a /timesales/ url\nif (history):\n    url = \"https://sandbox.tradier.com/v1/markets/history?symbol=\" + symbol + format_date + optionType + selectedPrice + \"&start=\" + startDate\nelse:\n    url = \"https://sandbox.tradier.com/v1/markets/timesales?symbol=\" + symbol + format_date + optionType + selectedPrice + \"&interval=15min&start=\" + startDate\n\ndata_name = \"\" # for plot titles\nif (optionType == \"C\"):\n    data_name = symbol + \" Calls Expiring: \" + date + \" w/ Strike: $\" + str(float(selectedPrice)/1000)\n    print(\"Now grabbing \" + data_name)\nelse:\n    data_name = symbol + \" Puts Expiring: \" + date + \" w/ Strike: $\" + str(float(selectedPrice)/1000)\n    print(\"Now grabbing \" + data_name)\n\nrData = requests.get(url, headers=my_headers) #actually download the data\n\n# parse and plot the data\nif (history):\n    single_parser.parse_history_quote(rData.content.decode(\"utf-8\"), data_name)\nelse:\n    single_parser.parse_timesales_quote(rData.content.decode(\"utf-8\"), data_name)\n\n\n\n","sub_path":"single_plotter.py","file_name":"single_plotter.py","file_ext":"py","file_size_in_byte":5952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"492226881","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom utils.captcha.captcha import Captcha\nfrom utils import restiful\nfrom io import BytesIO\nfrom django.views.decorators.http import require_POST\nfrom utils.smssdk.smssend import send_sms\nimport random\nfrom apps.cms.models import Article, NewsTag, Banners\nfrom django.core.cache import cache\nfrom django.conf import settings\nfrom apps.cms.serializers import ArticleSerialize\nfrom .forms import CommentForms\nfrom .models import Comments\nfrom .serializers import Commentserialize\n# Create your views here.\n\n\ndef index(request):\n    tags = NewsTag.objects.all()\n    end_page = settings.COUNT_PER_PAGE\n    articles = Article.objects.select_related('author','tag').all().order_by('-pub_time')[0:end_page]\n    banners = Banners.objects.all().order_by('-position')\n    context = {\n        \"articles\": articles,\n        \"tags\": tags,\n        \"banners\": banners\n    }\n    return render(request, 'news/index.html',context=context)\n\n\ndef newslist(request):\n    #  djangorestframework 异步新新闻列表\n\n    page = int(request.GET.get('page',1))\n    print(page)\n    start_count = page * settings.COUNT_PER_PAGE\n    end_count = start_count + settings.COUNT_PER_PAGE\n    category_id = int(request.GET.get('category',0))  # 新闻分类的id为0 是默认查找所有新闻分类\n    if category_id != 0:\n        tag = NewsTag.objects.get(pk=category_id)\n        articles = Article.objects.filter(tag=tag)[start_count:end_count]\n        articles_serialize = ArticleSerialize(articles, many=True)  # 使用djangorestframework序列化\n    else:\n        articles = Article.objects.all()[start_count:end_count]\n        articles_serialize = ArticleSerialize(articles, many=True)\n\n    return restiful.success(data=articles_serialize.data)\n\n# 新闻详情页\ndef news_detail(request, news_id):\n    # select_related 将外键关联的模型数据也提取出来减少查询次数。\n    article = Article.objects.select_related('author','tag').get(pk=news_id)\n    comments = Comments.objects.filter(article=article)\n    context = {\n        'article': article,\n        'comments':comments\n    }\n    return render(request, 'news/news_detail.html',context=context)\n\n\n# 新���评论\n@require_POST\ndef news_comment(request):\n    form = CommentForms(request.POST)\n    if form.is_valid():\n        data = form.cleaned_data\n        content = data.get('content')\n        news_id = int(data.get('article'))\n        author = request.user  # 当前默认登陆用户\n        article = Article.objects.get(pk=news_id)\n        try:\n            Comments.objects.create(content=content,author=author,article=article)\n            return restiful.success()\n        except:\n            return restiful.paramserror(message=\"服务器内部错误，添加评论失败\")\n    else:\n        return restiful.paramserror(data=form.get_errors())\n\n\n# 评论列表：\ndef comment_list(request):\n    # 每页显示评论数 per_page_count = 3\n    news_id = int(request.GET.get('article'))\n    per_page_count = 3\n    article = Article.objects.get(pk=news_id)\n    page = int(request.GET.get('page', 1))\n    start = per_page_count * (page-1)\n    end = start + per_page_count\n    print(article)\n    comments = Comments.objects.filter(article=article)[start:end]\n    comments = Commentserialize(comments, many=True)\n    return restiful.success(data=comments.data)\n\n\ndef search(request):\n    return render(request, 'news/search.html')\n\n\ndef draw_example(request):\n    text,image = Captcha.gene_code()\n    out = BytesIO()\n    image.save(out,'png') # 将对象保存到BytesIO中\n    out.seek(0)   # 移动文件指针\n    cache.set('image_text',text.lower())\n    httpresponse = HttpResponse(content_type='image/png')\n    httpresponse.write(out.read())\n    httpresponse[\"Content-length\"] = out.tell()\n    return httpresponse\n\n\ndef sms_send(request):\n    sms_code = random.randint(1000, 9999)\n    cache.set('sms_code', sms_code, 60*5) # 存储验证码 并设置5分钟过期\n    # params = {\n    #     'code': sms_code\n    # }\n    # result = send_sms('17623008502',params)\n    return HttpResponse(\"成功发送验证码：\"+str(sms_code))\n\n","sub_path":"apps/news/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"277312260","text":"# image.py\n\nimport urllib.request\nfrom random import randint\nfrom bs4 import BeautifulSoup\nimport helpers\nimport definitions\n\n\ndef get_image():\n\n    #join the query words, separated by %20\n    base_url = \"https://pixabay.com/en/photos/{}/?image_type=photo&pagi=\".format(\"%20\".join(definitions.search_query))  \n\n    print(\"function get_image called\")\n\n    # gets the total number of pages\n    def get_total_pages(base_url):\n    \n        print(\"...function total_pages called\")\n\n        # get some soup for our url\n        soup = helpers.get_soup(base_url + \"1\")\n\n        total_pages = soup.find('form', {'class': 'add_search_params pure-form hide-xs hide-sm hide-md'}).getText()\n        total_pages = [int(s) for s in total_pages.split() if s.isdigit()][0]\n\n        print(\"...returning total pages: {}\".format(total_pages))\n    \n        return total_pages\n    \n\n    # select a random page from the total pages\n    def get_random_page_url(base_url):\n    \n        print(\"...function get_random_page_url called\")\n    \n        total_pages = get_total_pages(base_url)\n\n        # create random number within range of total pages\n        random_page_number = randint(1, total_pages)\n\n        # append random number to the base url\n        random_page_url = base_url + str(random_page_number)\n\n        print(\"...returning url: {}\".format(random_page_url))\n\n        return random_page_url\n\n\n    # gets all the image urls from a page\n    def get_random_image_url(page_url):\n    \n        print(\"...function get_random_image_url called\")\n    \n        #get the soup\n        soup = helpers.get_soup(page_url)\n\n        # make list of all the image urls\n        links = [element['data-lazy-srcset'] for element in soup.findAll('img', attrs = {'data-lazy-srcset' : True})]\n        \n        # select a random url from the list of links\n        image_url = links[helpers.random_list_number(links)]\n\n        # do some cleanup\n        image_url = image_url.split(',')[1] # split list elements, keep only the second one ([1]), the 480p one\n        image_url = image_url.split(' ')[1]  # split list elements, keep only the first one ([0]), drop the 2x\n        image_url = image_url.replace(\"_480\", \"1280\") # get the larger size images\n\n        print(\"...Using this url: {}\".format(image_url))\n    \n        return image_url\n\n    def download_image(base_url):\n\n        print(\"...function download_image called\")\n\n        # get the page url\n        page_url = get_random_page_url(base_url)\n    \n        # get the image url\n        image_url = get_random_image_url(page_url)\n    \n        # download the image \n        filename =\"temp_image.jpg\"\n        urllib.request.urlretrieve(image_url, filename)\n\n        print(\"...image temp_image.jpg saved\")\n\n\n    # call the download image function\n    download_image(base_url) \n","sub_path":"image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":2792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"351237428","text":"def main():\n    from math import inf, ceil\n    from collections import deque\n\n    N, K = map(int, input().split())\n    for _ in range(K):\n        if N % 200 == 0:\n            N = N // 200\n        else:\n            N = int(f\"{N}200\")\n    print(N)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"abc200/b.py","file_name":"b.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"489074287","text":"\"\"\"\nCopyright [2009-present] EMBL-European Bioinformatics Institute\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n     http://www.apache.org/licenses/LICENSE-2.0\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\n\n\nimport os\nimport glob\nimport re\n\n\nfrom . import config\n\n\ndef generate_model_info(cm_library, rna_type='SSU'):\n    print('Processing files in {}'.format(cm_library))\n\n    all_cm = 'all.cm'  # file with all CMs\n    all_cm_path = os.path.join(cm_library, all_cm)\n\n    cmd = 'rm -f {all_cm_path} && cat {cm_library}/*.cm > {all_cm_path}'.format(\n        cm_library=cm_library,\n        all_cm_path=all_cm_path,\n    )\n    os.system(cmd)\n\n    with open(os.path.join(cm_library, 'modelinfo.txt'), 'w') as f:\n        line = '*all*    -    -    %s\\n' % all_cm\n        f.write(line)\n        for cm in glob.glob('%s/*.cm' % cm_library):\n            if all_cm in cm:\n                continue\n            if re.search(r'RF\\d{5}', cm):\n                with open(cm, 'r') as f_cm:\n                    for line in f_cm:\n                        if line.startswith('NAME '):\n                            model_name = line.strip().split()[-1]\n            else:\n                model_name = os.path.basename(cm).replace('.cm', '')\n            line = \"%s    %s    Bacteria    %s\\n\" % (model_name, rna_type, os.path.basename(cm))\n            f.write(line)\n    print('Done')\n\n\nif __name__ == '__main__':\n    generate_model_info(config.CM_LIBRARY)\n","sub_path":"utils/generate_model_info.py","file_name":"generate_model_info.py","file_ext":"py","file_size_in_byte":1819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"153170564","text":"import math\ntextlength = 0\nclass Word:\n    '''Handle a single word'''\n    def __init__(self, word, index):\n        self.word = word\n        self.indexes = [index]\n        self.count = 1\n        self.rho = 0\n        self.C = 0\n        self.sum = 0\n        self.squaredsum = 0\n        self.rhonor = 0\n    def add_occurence(self, index):\n        self.count = self.count + 1\n        self.sum = self.sum + (index - self.indexes[-1])\n        self.squaredsum = self.squaredsum + math.pow(self.indexes[-1] - index, 2)\n        self.indexes.append(index)\n\n    def __repr__(self):\n        return(\"{word: %d, C: %f, rhonor: %f, rho: %f}\" % (self.word, self.C, self.rhonor, self.rho))\n\n    def __str__(self):\n        return(\"{word: %s, C: %f, rhonor: %f, rho: %f}\" % (self.word, self.C, self.rhonor, self.rho))\n    def nearest_neighbour(self):\n        avgdist =  self.sum / self.count\n        avgsquare = self.squaredsum/self.count\n        s = math.sqrt(avgsquare - (avgdist * avgdist))\n        p = self.count / textlength\n        rhogeo = math.sqrt(1 - p)\n        if avgdist == 0:\n            rho = 0\n        else:\n            rho = s/avgdist\n        self.rho = rho\n        rhonor = rho / rhogeo\n        #print(\"<s>:%f, <s^2>:%f, s: %f, p: %f, rhogeo: %f, rho: %f, rhonor: %f\" %(avgdist, avgsquare,s, p, rhogeo, rho, rhonor))\n        self.rhonor = rhonor\n        self.C = C(rho, self.count)\n        return self.C\n\n    def relative_clustering(self):\n        return self.nearest_neighbour() / self.expected_nearest()\n\n    def expected_nearest(self):\n        return textlength / len(self.indexes)\n\ndef C(rho, n):\n    brarhonor = (2 * n - 1)/(2 * n + 2)\n    sdbrarhonor = 1 / (math.sqrt(n) * (1 + 2.8 * math.pow(n, -0.864)))\n    return (rho - brarhonor)/sdbrarhonor\n\n\n\ndef clean(w):\n    w = w.lower()\n    w = w.replace('\\'s', \"\")\n    w = w.replace(\".\\\"\", \"\")\n    w = w.strip()\n    w = w.strip(\"]\")\n    w = w.strip(\",\")\n    w = w.strip(\".\")\n    w = w.strip(\"\\\"\")\n    w = w.strip(\"(\")\n    w = w.strip(\")\")\n    w = w.strip(\"!\")\n    w = w.strip(\";\")\n    w = w.strip(\"\\\"\")\n    return w\n\nif __name__ == \"__main__\":\n    f = open(\"data/pg996.txt\")\n    data = f.read()\n    data = data.replace(\"--\", \" \")\n    data = data.replace(\"__\", \" \")\n    words = data.split()\n    print(len(words))\n    textlength = len(words)\n    worddict = {}\n    for i in range(0,len(words)):\n        w = clean(words[i])\n        if w in worddict:\n            worddict[w].add_occurence(i)\n        else:\n            worddict[w] = Word(w, i)\n    i = 0\n    for key in sorted(worddict, key = lambda word: worddict[word].count, reverse=True):\n        #print(worddict[key].word, len(worddict[key].indexes), worddict[key].nearest_neighbour(), worddict[key].relative_clustering())\n        worddict[key].nearest_neighbour()\n    print(len(worddict))\n\n    for key in sorted(worddict, key = lambda word: worddict[word].C, reverse=True):\n        #print(worddict[key].word, len(worddict[key].indexes), worddict[key].nearest_neighbour(), worddict[key].relative_clustering())\n        w = worddict[key]\n        print(\"%s: %f\" % (w.word, w.C))\n        i = i + 1\n        if i > 20:\n            break\n","sub_path":"keywords.py","file_name":"keywords.py","file_ext":"py","file_size_in_byte":3128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"530961856","text":"from .BaseDataType import BaseDataTypeClass\nfrom .PrimitiveTypes import Hash\nfrom .ConditionTypes import UnlockHash, UnlockHashType\nfrom .rivine.RivineDataFactory import RivineDataFactory\nfrom hashlib import blake2b\n\nfrom enum import IntEnum\n\n_SIG_Ed25519 = \"ed25519\"\n\n\nclass PublicKeySpecifier(IntEnum):\n    NIL = 0\n    ED25519 = 1\n\n    @classmethod\n    def from_json(cls, obj):\n        if not obj:\n            return PublicKeySpecifier.NIL\n        if obj == _SIG_Ed25519:\n            return PublicKeySpecifier.ED25519\n        raise Exception(\"{} is an invalid Public Key specifier\".format(obj))\n\n    def __str__(self):\n        if self == PublicKeySpecifier.ED25519:\n            return _SIG_Ed25519\n        return \"\"\n\n    __repr__ = __str__\n\n    json = __str__\n\n\nclass PublicKey(BaseDataTypeClass):\n    \"\"\"\n    A PublicKey is a public key prefixed by a Specifier. The Specifier\n        indicates the algorithm used for signing and verification.\n    The other part of the PublicKey is the hash itself.\n    \"\"\"\n\n    def __init__(self, specifier=None, hash=None):\n        self._specifier = PublicKeySpecifier.NIL\n        self._hash = None\n\n        self.specifier = specifier\n        self.hash = hash\n\n    @classmethod\n    def from_json(cls, obj):\n        if not obj:\n            return cls()\n        if not isinstance(obj, str):\n            raise Exception(\"expected JSON-encoded PublicKey to be a string, not {}\".format(type(obj)))\n        parts = obj.split(sep=\":\", maxsplit=2)\n        if len(parts) != 2:\n            raise Exception(\"invalid JSON-encoded PublicKey: {}\".format(obj))\n        pk = cls()\n        pk._specifier = PublicKeySpecifier.from_json(parts[0])\n        pk._hash = Hash.from_json(parts[1])\n        return pk\n\n    @property\n    def specifier(self):\n        return self._specifier\n\n    @specifier.setter\n    def specifier(self, value):\n        if value == None:\n            value = PublicKeySpecifier.NIL\n        elif not isinstance(value, PublicKeySpecifier):\n            raise Exception(\"expected value to be a PublicKeySpecifier, not {}\".format(type(value)))\n        self._specifier = value\n\n    @property\n    def hash(self):\n        if self._hash is None:\n            return Hash()\n        return self._hash\n\n    @hash.setter\n    def hash(self, value):\n        if value is None:\n            self._hash = None\n        elif isinstance(value, Hash):\n            self._hash = value\n        else:\n            self._hash = Hash(value=value)\n\n    def __str__(self):\n        return str(self.specifier) + \":\" + str(self.hash)\n\n    __repr__ = __str__\n\n    json = __str__\n\n    @property\n    def unlockhash(self):\n        \"\"\"\n        Return the unlock hash generated from this public key.\n        \"\"\"\n        e = RivineDataFactory.encoder_sia_get()\n        self.sia_binary_encode(e)\n        # need to encode again to add the length\n        data = e.data\n        e = RivineDataFactory.encoder_sia_get()\n        e.add_slice(data)\n        h = blake2b(e.data, digest_size=32)\n        hash = bytearray.fromhex(h.hexdigest())\n        return UnlockHash(type=UnlockHashType.PUBLIC_KEY, hash=hash)\n\n    @staticmethod\n    def _pad_specifier(specifier):\n        _SPECIFIER_SIZE = 16\n        value = specifier.encode(\"utf-8\")\n        return value + b\"\\0\" * (_SPECIFIER_SIZE - len(value))\n\n    def sia_binary_encode(self, encoder):\n        \"\"\"\n        Encode this binary data according to the Sia Binary Encoding format.\n        \"\"\"\n        encoder.add_array(PublicKey._pad_specifier(str(self.specifier)))\n        encoder.add_slice(self.hash.value)\n\n    def rivine_binary_encode(self, encoder):\n        \"\"\"\n        Encode this binary data according to the Rivine Binary Encoding format.\n        \"\"\"\n        encoder.add_int8(int(self.specifier))\n        encoder.add(self.hash)\n","sub_path":"lib/tfchaintypes/CryptoTypes.py","file_name":"CryptoTypes.py","file_ext":"py","file_size_in_byte":3773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"364760109","text":"#!/usr/bin/python3\n\nimport os\nimport numpy as np\nimport cv2\n\n\ndef train():\n\n\tfaces=[]\n\tIDs=[]\n\n\tRECOG_BASE_DIR = os.path.dirname(os.path.abspath(__file__))\n\timage_dir = os.path.join(RECOG_BASE_DIR, \"images\")\n\n\n\trecognizer=cv2.face.LBPHFaceRecognizer_create()\n\t# print(os.path.dirname(os.path.abspath('manage.py')))\n\n\tfor root, dirs, files in os.walk(image_dir):\n\t\tfor file in files:\n\t\t\tif file.endswith(\"png\") or file.endswith(\"jpg\"):\n\t\t\t\tpath = os.path.join(root, file)\n\t\t\t\t# print(path.split('/')[-2].split('-')[1])\n\n\t\t\t\timage = cv2.imread(path,0)\n\t\t\t\tface_array = np.array(image, \"uint8\")\n\n\n\t\t\t\tfaces.append(face_array)\n\t\t\t\tIDs.append(int(path.split('/')[-2].split('-')[1]))\n\n\n\trecognizer.train(faces,np.array(IDs))\n\t# recognizer.save('recognizer_module/recognizer/trainingdata.yml')\n\trecognizer.write('recognizer_module/recognizer/trainingdata.yml')\n\n\treturn        \t","sub_path":"src/recognizer_module/train_recognizer_model.py","file_name":"train_recognizer_model.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"502153765","text":"from django.urls import path\n\nfrom web1.views import all_comments, create_comment, edit_comment, delete_comment\n\nurlpatterns = [\n    path('', all_comments, name='comments'),\n    path('create_comment', create_comment, name='create-comment'),\n    path('edit_comment/<int:pk>', edit_comment, name='edit-comment'),\n    path('delete_comment/<int:pk>', delete_comment, name='delete-comment'),\n]\n","sub_path":"web/web1/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"471697611","text":"from .. import *\nfrom os.path import dirname\nimport sys\n\nif 'runtests.py' in sys.argv[0] or 'test' in sys.argv:\n    print(\"Using in memory database.\\n\")\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.sqlite3',\n        'NAME': ':memory:',\n    }\n}\n\n# Avoid sending real emails\nEMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend'\n","sub_path":"project_name/settings/env/unittest.py","file_name":"unittest.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"343446384","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport unittest\nimport re\nimport os.path\nimport codecs\nfrom mock import MagicMock, call, patch\nfrom uiautomatorminus import AutomatorDevice, Selector\n\n\ndef fake_jsonrpc_method(rpc_client, method, **kwargs):\n    rpc_method = MagicMock(**kwargs)\n    rpc_client.attach_mock(rpc_method, method)\n    return rpc_method\n\n\nclass TestDevice(unittest.TestCase):\n\n    def setUp(self):\n        self.device = AutomatorDevice()\n        self.device.server = MagicMock()\n        self.device.server.jsonrpc = MagicMock()\n        self.rpc_client = MagicMock()\n        self.device.server.jsonrpc.return_value = self.rpc_client\n\n    def fake_jsonrpc_method(self, method, **kwargs):\n        return fake_jsonrpc_method(self.rpc_client, method, **kwargs)\n\n    def test_info(self):\n        method = self.fake_jsonrpc_method('deviceInfo', return_value={})\n        self.assertEqual(self.device.info, {})\n        method.assert_called_once_with()\n\n    def test_click(self):\n        method = self.fake_jsonrpc_method('click', return_value=True)\n        self.assertEqual(self.device.click(1, 2), True)\n        method.assert_called_once_with(1, 2)\n\n    def test_swipe(self):\n        method = self.fake_jsonrpc_method('swipe', return_value=True)\n        self.assertEqual(self.device.swipe(1, 2, 3, 4, 100), True)\n        method.assert_called_once_with(1, 2, 3, 4, 100)\n\n    def test_long_click(self):\n        method = self.fake_jsonrpc_method('swipe', return_value=True)\n        x, y = 100, 200\n        self.assertEqual(self.device.long_click(x, y), True)\n        method.assert_called_once_with(x, y, x+1, y+1, 100)\n\n    def test_drag(self):\n        method = self.fake_jsonrpc_method('drag', return_value=True)\n        self.assertEqual(self.device.drag(1, 2, 3, 4, 100), True)\n        method.assert_called_once_with(1, 2, 3, 4, 100)\n\n    def test_dump(self):\n        with codecs.open(os.path.join(os.path.dirname(__file__), \"res\", \"layout.xml\"), \"r\", encoding=\"utf8\") as f:\n            xml = f.read()\n            method = self.fake_jsonrpc_method('dumpWindowHierarchy', return_value=xml)\n            self.assertEqual(self.device.dump(\"/tmp/test.xml\"), xml)\n            method.assert_called_once_with(True, None)\n            self.assertEqual(self.device.dump(\"/tmp/test.xml\", False), xml)\n\n            raw_xml = \"\".join(re.split(r\"\\n[ ]*\", xml))\n            method = self.fake_jsonrpc_method('dumpWindowHierarchy', return_value=raw_xml)\n            self.assertTrue(\"\\n  \" in self.device.dump(\"/tmp/test.xml\"))\n\n    def test_screenshot(self):\n        method = self.fake_jsonrpc_method('takeScreenshot', return_value='1.png')\n        self.device.server.adb.cmd = cmd = MagicMock()\n        self.device.server.screenshot = MagicMock()\n        self.device.server.screenshot.return_value = None\n        cmd.return_value.returncode = 0\n        self.assertEqual(self.device.screenshot(\"a.png\", 1.0, 99), \"a.png\")\n        method.assert_called_once_with(\"screenshot.png\", 1.0, 99)\n        self.assertEqual(cmd.call_args_list, [call(\"pull\", \"1.png\", \"a.png\"), call(\"shell\", \"rm\", \"1.png\")])\n\n        method = self.fake_jsonrpc_method('takeScreenshot', return_value=None)\n        self.assertEqual(self.device.screenshot(\"a.png\", 1.0, 100), None)\n\n    def test_freeze_rotation(self):\n        method = self.fake_jsonrpc_method('freezeRotation')\n        self.device.freeze_rotation(True)\n        self.device.freeze_rotation(False)\n        self.assertEqual(method.call_args_list, [call(True), call(False)])\n\n    def test_orientation(self):\n        orientation = {\n            0: \"natural\",\n            1: \"left\",\n            2: \"upsidedown\",\n            3: \"right\"\n        }\n        for i in range(4):\n            method = self.fake_jsonrpc_method('deviceInfo', return_value={'displayRotation': i})\n            self.assertEqual(self.device.orientation, orientation[i])\n        # set\n        orientations = [\n            (0, \"natural\", \"n\", 0),\n            (1, \"left\", \"l\", 90),\n            (2, \"upsidedown\", \"u\", 180),\n            (3, \"right\", \"r\", 270)\n        ]\n        for values in orientations:\n            for value in values:\n                method = self.fake_jsonrpc_method('setOrientation')\n                self.device.orientation = value\n                method.assert_called_once_with(values[1])\n\n        with self.assertRaises(ValueError):\n            self.device.orientation = \"invalid orientation\"\n\n    def test_last_traversed_text(self):\n        method = self.fake_jsonrpc_method('getLastTraversedText', return_value='abcdef')\n        self.assertEqual(self.device.last_traversed_text, \"abcdef\")\n        method.assert_called_once_with()\n\n    def test_clear_traversed_text(self):\n        method = self.fake_jsonrpc_method('clearLastTraversedText')\n        self.device.clear_traversed_text()\n        method.assert_called_once_with()\n\n    def test_open(self):\n        method = self.fake_jsonrpc_method('openNotification')\n        self.device.open.notification()\n        method.assert_called_once_with()\n        method = self.fake_jsonrpc_method('openQuickSettings')\n        self.device.open.quick_settings()\n        method.assert_called_once_with()\n\n    def test_watchers(self):\n        names = [\"a\", \"b\", \"c\"]\n        method = self.fake_jsonrpc_method('getWatchers', return_value=names)\n        self.assertEqual(self.device.watchers, names)\n        method.assert_called_once_with()\n\n        method = self.fake_jsonrpc_method('hasAnyWatcherTriggered', return_value=True)\n        self.assertEqual(self.device.watchers.triggered, True)\n        method.assert_called_once_with()\n\n        method = self.fake_jsonrpc_method('removeWatcher')\n        self.device.watchers.remove(\"a\")\n        method.assert_called_once_with('a')\n        method = self.fake_jsonrpc_method('removeWatcher')\n        self.device.watchers.remove()\n        self.assertEqual(method.call_args_list, [call(name) for name in names])\n\n        method = self.fake_jsonrpc_method('resetWatcherTriggers')\n        self.device.watchers.reset()\n        method.assert_called_once_with()\n\n        method = self.fake_jsonrpc_method('runWatchers')\n        self.device.watchers.run()\n        method.assert_called_once_with()\n\n    def test_watcher(self):\n        method = self.fake_jsonrpc_method('hasWatcherTriggered', return_value=False)\n        self.assertFalse(self.device.watcher(\"name\").triggered)\n        method.assert_called_once_with(\"name\")\n\n        method = self.fake_jsonrpc_method('removeWatcher')\n        self.device.watcher(\"a\").remove()\n        method.assert_called_once_with(\"a\")\n\n        method = self.fake_jsonrpc_method('registerClickUiObjectWatcher')\n        condition1 = {\"text\": \"my text\", \"className\": \"android\"}\n        condition2 = {\"description\": \"my desc\", \"clickable\": True}\n        target = {\"className\": \"android.widget.Button\", \"text\": \"OK\"}\n        self.device.watcher(\"watcher\").when(**condition1).when(**condition2).click(**target)\n        method.assert_called_once_with(\n            \"watcher\",\n            [Selector(**condition1), Selector(**condition2)],\n            Selector(**target)\n        )\n\n        method = self.fake_jsonrpc_method('registerPressKeyskWatcher')\n        self.device.watcher(\"watcher2\").when(**condition1).when(**condition2).press.back.home.power(\"menu\")\n        method.assert_called_once_with(\n            \"watcher2\", [Selector(**condition1), Selector(**condition2)], (\"back\", \"home\", \"power\", \"menu\"))\n\n    def test_press(self):\n        key = [\"home\", \"back\", \"left\", \"right\", \"up\", \"down\", \"center\",\n               \"menu\", \"search\", \"enter\", \"delete\", \"del\", \"recent\",\n               \"volume_up\", \"volume_down\", \"volume_mute\", \"camera\", \"power\"]\n        method = self.fake_jsonrpc_method('pressKey', return_value=True)\n        self.assertTrue(self.device.press.home())\n        self.assertEqual(method.call_args_list, [call(\"home\")])\n        method = self.fake_jsonrpc_method('pressKey', return_value=False)\n        self.assertFalse(self.device.press.back())\n        self.assertEqual(method.call_args_list, [call(\"back\")])\n        method = self.fake_jsonrpc_method('pressKey', return_value=False)\n        for k in key:\n            self.assertFalse(self.device.press(k))\n        self.assertEqual(method.call_args_list, [call(k) for k in key])\n\n        method = self.fake_jsonrpc_method('pressKeyCode', return_value=True)\n        self.assertTrue(self.device.press(1))\n        self.assertTrue(self.device.press(1, 2))\n        self.assertEqual(method.call_args_list, [call(1), call(1, 2)])\n\n    def test_wakeup(self):\n        method = self.fake_jsonrpc_method('wakeUp')\n        self.device.wakeup()\n        method.assert_called_once_with()\n\n        method = self.fake_jsonrpc_method('wakeUp')\n        self.device.screen.on()\n        method.assert_called_once_with()\n\n        method = self.fake_jsonrpc_method('wakeUp')\n        self.device.screen(\"on\")\n        method.assert_called_once_with()\n\n    def test_screen_status(self):\n        method = self.fake_jsonrpc_method('deviceInfo', return_value={\"screenOn\": True})\n        self.assertTrue(self.device.screen == \"on\")\n        self.assertTrue(self.device.screen != \"off\")\n\n        method = self.fake_jsonrpc_method('deviceInfo', return_value={\"screenOn\": False})\n        self.assertTrue(self.device.screen == \"off\")\n        self.assertTrue(self.device.screen != \"on\")\n\n    def test_sleep(self):\n        method = self.fake_jsonrpc_method('sleep')\n        self.device.sleep()\n        method.assert_called_once_with()\n\n        method = self.fake_jsonrpc_method('sleep')\n        self.device.screen.off()\n        method.assert_called_once_with()\n\n        method = self.fake_jsonrpc_method('sleep')\n        self.device.screen(\"off\")\n        method.assert_called_once_with()\n\n    def test_wait_idle(self):\n        method = self.fake_jsonrpc_method('waitForIdle', return_value=True)\n        self.assertTrue(self.device.wait.idle(timeout=10))\n        method.assert_called_once_with(10)\n\n        method = self.fake_jsonrpc_method('waitForIdle', return_value=False)\n        self.assertFalse(self.device.wait(\"idle\", timeout=10))\n        method.assert_called_once_with(10)\n\n    def test_wait_update(self):\n        method = self.fake_jsonrpc_method('waitForWindowUpdate', return_value=True)\n        self.assertTrue(self.device.wait.update(timeout=10, package_name=\"android\"))\n        method.assert_called_once_with(\"android\", 10)\n\n        method = self.fake_jsonrpc_method('waitForWindowUpdate', return_value=False)\n        self.assertFalse(self.device.wait(\"update\", timeout=100, package_name=\"android\"))\n        method.assert_called_once_with(\"android\", 100)\n\n    def test_get_info_attr(self):\n        info = {\"test_a\": 1, \"test_b\": \"string\", \"displayWidth\": 720, \"displayHeight\": 1024}\n        method = self.fake_jsonrpc_method('deviceInfo', return_value=info)\n        for k in info:\n            self.assertEqual(getattr(self.device, k), info[k])\n        self.assertEqual(self.device.width, info[\"displayWidth\"])\n        self.assertEqual(self.device.height, info[\"displayHeight\"])\n        with self.assertRaises(AttributeError):\n            self.device.not_exists\n\n    def test_device_obj(self):\n        with patch(\"uiautomatorminus.AutomatorDeviceObject\") as AutomatorDeviceObject:\n            kwargs = {\"text\": \"abc\", \"description\": \"description...\", \"clickable\": True}\n            self.device(**kwargs)\n            AutomatorDeviceObject.assert_called_once_with(self.device, Selector(**kwargs))\n\n        with patch(\"uiautomatorminus.AutomatorDeviceObject\") as AutomatorDeviceObject:\n            AutomatorDeviceObject.return_value.exists = True\n            self.assertTrue(self.device.exists(clickable=True))\n            AutomatorDeviceObject.return_value.exists = False\n            self.assertFalse(self.device.exists(text=\"...\"))\n\n\nclass TestDeviceWithSerial(unittest.TestCase):\n\n    def test_serial(self):\n        with patch('uiautomatorminus.AutomatorServer') as AutomatorServer:\n            AutomatorDevice(\"abcdefhijklmn\")\n            AutomatorServer.assert_called_once_with(serial=\"abcdefhijklmn\", local_port=None, adb_server_host=None, adb_server_port=None)\n","sub_path":"test/test_device.py","file_name":"test_device.py","file_ext":"py","file_size_in_byte":12122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"609885017","text":"import unittest\nimport logging\nfrom pyrob.loads.p6.loader import P6Loader\nfrom pyrob.schema.ipact_stg import P6ProjectActivity\n\n\nclass TestP6Loader(unittest.TestCase):\n\n    def setUp(self):\n        self.loader = P6Loader(\n            'pyrob/loads/p6/tests/data/fixture.xml',\n            table=P6ProjectActivity\n        )\n\n    def test_fieldnames(self):\n        self.assertEquals(\n            self.loader.fieldnames,\n            [\n                'activity_id',\n                'activity_name',\n                'activity_state',\n                'activity_status',\n                'activity_type',\n                'bl_finish_date',\n                'bl_start_date',\n                'constraint_date',\n                'cpacss_code',\n                'fact_status',\n                'finish_date',\n                'last_modified_by',\n                'last_modified_date',\n                'project_id',\n                'project_name',\n                'resource_ids',\n                'start_date',\n                'update_log',\n                'wbs',\n                'wbs_name'\n            ]\n        )\n\n    def test_source_data(self):\n        self.assertEquals(len(self.loader.source_data()), 11)\n\n    def test_get_rows(self):\n        self.assertEquals(\n            len(list(self.loader.get_rows(process_id='1234'))), 11\n        )\n","sub_path":"pyrob/loads/p6/tests/test_loader.py","file_name":"test_loader.py","file_ext":"py","file_size_in_byte":1322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"479281306","text":"import re\n\ndef allyourbase(s):\n    base = len(set(s))\n    if base == 1:\n        base = 2\n    minval = 1\n    vals = {}\n    vals[s[0]] = minval\n    date = vals[s[0]]*(long(base)**(len(s)-1))\n    for index in xrange(1, len(s)):\n        if not vals.has_key(s[index]):\n            if minval == 1:\n                minval = 0\n            elif minval == 0:\n                minval = 2\n            else:\n                minval += 1\n            vals[s[index]] = minval\n        date += vals[s[index]]*(base**(len(s)-index-1))\n    return date\n\ndef allyourbase_printoutput(filename):\n    fout = file('e-output.dat', 'w')\n    f = file(filename)\n    cases = int(re.compile('([0-9]+)').findall(f.next())[0])\n    for x in xrange(cases):\n        fout.write('Case #%d: ' % (x+1))\n        fout.write(str(allyourbase(f.next().strip('\\n')))+'\\n')","sub_path":"googlecodejam/allyourbase.py","file_name":"allyourbase.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"356009627","text":"\nimport random\n# 花色 红黑方梅\nSUITS = ['H', 'S', 'D', 'C']\n#方片 D 黑桃 S 红桃 H 梅花 C\n# 初始基本牌\nINIT_LIST = ['2', '3', '4', '5', '6', '7', '8', '9', 'T', 'J', 'Q','K','A']\n\nMAPPING_LIST_NUM = '23456789TJQKA'\nMAPPING_LIST_COLOR = 'DCHS'  # 方梅红黑\n#初始牌堆\ndef init_landlords():\n    lis = []\n    for card in INIT_LIST:  # 数字\n        for suit in SUITS:  # 花色\n            lis.append('{0}{1}'.format(card, suit))\n    random.shuffle(lis)\n    # print(lis)\n    return lis\n\n    # print \"初始化牌堆：\", lis\n\ndef exchange_number(cards):\n    number = []\n    for r, s in cards:\n        temp = MAPPING_LIST_NUM.index(r)\n        number.append(temp)\n    return number\n\n\ndef exchange_color(cards):\n    color = []\n    for r, s in cards:\n        temp = MAPPING_LIST_COLOR.index(s)\n        color.append(temp)\n    return color\n\n\n\nif __name__ =='__main__':\n    init_landlords()\n\n","sub_path":"game_config.py","file_name":"game_config.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"174927754","text":"'''\nGrupo: G, Hora: 4º grupo da segunda feira\n'''\n\n\n\ndef modulo(h):\n    if h>=0:\n        h=h\n    if h<0:\n        h=(-1)*h\n    return h\n\nn=int(input('Digite a quantidade de valores: '))\n\nlista=[]\nfor i in range (0, n, 1):\n    lista.append(float(input('Digite um valor para a lista: ')))\n\nmedia = sum(lista)/(float(n))\n\nsomat=0\nfor i in range (0, n, 1):\n    somat+=((lista[i] - media)**2)\n\ndesvpad= float(((1/(n - 1))*somat)**(float(1/2)))\nprint(desvpad)\n\nqntd68=0\nfor i in range (0, n, 1):\n    if modulo(lista[i] - media)<desvpad:\n        qntd68+=1\n    if modulo(lista[i] - media)>=desvpad:\n        qntd68+=0\n        \nqntd95=0\nfor i in range (0, n, 1):\n    if modulo(lista[i] - media)<2*desvpad:\n        qntd95+=1\n    if modulo(lista[i] - media)>=2*desvpad:\n        qntd95+=0\n\nqntd997=0\nfor i in range (0, n, 1):\n    if modulo(lista[i] - media)<3*desvpad:\n        qntd997+=1\n    if modulo(lista[i] - media)>=3*desvpad:\n        qntd997+=0\n        \nif (float(qntd68/n)>=float(68/100)) and (float(qntd95/n)>=float(95/100)) and (float(qntd997/n)>=float(99.7/100)):\n    print('Sim, a distruição é normal.')\nif not (float(qntd68/n)>=float(68/100)) and (float(qntd95/n)>=float(95/100)) and (float(qntd997/n)>=float(99.7/100)):\n    print('Não, a distruição não é normal.')\n\n#-------------------------------------------------------------------------------------------------------------------------------------------------\n'''\ndef crescente(lista):\n    k=0\n    for i in range (0, len(lista)-2, 1):\n        if lista[i]<lista[i+1]:\n            k+=0\n        if lista[i]>=lista[i+1]:\n            k+=1\n    if k==0:\n        cresc=True\n    if not k==0:\n        cresc=False\n    return cresc\n    \nn=int(input('Digite a quantidade de valores: '))\n\nlista=[]\nfor i in range (0, n, 1):\n    lista.append(int(input('Digite um valor para a lista: ')))\n\nif crescente(lista)==True:\n    print('S')\nif crescente(lista)==False:\n    print('N')\n'''","sub_path":"moodledata/vpl_data/303/usersdata/298/97801/submittedfiles/testes.py","file_name":"testes.py","file_ext":"py","file_size_in_byte":1923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"290410649","text":"from math import ceil, log\n\nn = [13, 235, 234, 12, 362, 21, 435, 24, 3456, 675, 2, 3456, 213, 5463]\n\ndef second_largest(n):\n    i = [-1]*len(n)\n    def largest(count, index):\n        while count:\n            comp1 = False\n            for comp2 in n:\n                if i[comp2] != index: continue\n                if comp1 != False:\n                    count -= 1\n                    if n[comp1] > n[comp2]:\n                        i[comp2] = comp1\n                    else:\n                        i[comp1] = comp2\n                    comp1 = False\n                else: comp1 = comp2\n        for l in range(len(n)):\n            if i[l] == index:\n                return l\n    return largest(ceil(log(len(n)))-1, largest(len(n)-1, -1))\n\nprint(second_largest(n))\n","sub_path":"second_largest.py","file_name":"second_largest.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"639633239","text":"import pandas as pd\nimport numpy as np\n\npd.set_option('display.unicode.ambiguous_as_wide', True)\npd.set_option('display.unicode.east_asian_width', True)\nnd = locals()\n\nintput_file = '例4-2.csv'\nf = open(intput_file, encoding='UTF-8')\ndata = pd.read_csv(f,\n                   encoding='UTF-8',\n                   names=['No', 'Nature', 'x1', 'x2', 'x3', 'x4', 'Class'],\n                   header=0)\ndata.reset_index()\nprint(\"原始数据是：\\n\")\nprint(data, \"\\n\")\n\np = 3  # 分类数目\nt = 22  # 测试集数目\nna = np.vstack(np.asarray(data.loc[:t-2:-1, \"Nature\"]))[::-1]  # （国家）标识列表\n\nfor i in range(1, p+1, 1):\n    nd['df_' + str(i)] = data[data['Class'] == i]\n    nd['k'+str(i)] = len(nd['df_' + str(i)])\n    nd['a'+str(i)] = nd['df_'+str(i)].iloc[:, 2:6]\n    nd['matrix_'+str(i)] = np.asmatrix(nd['a'+str(i)])\n    nd['miu_'+str(i)] = np.mean(nd['matrix_'+str(i)], axis=0)\n    nd['deviation_'+str(i)] = nd['matrix_'+str(i)] - nd['miu_'+str(i)]\n    nd['t_deviation_'+str(i)] = nd['deviation_'+str(i)].T\n    nd['sigma_'+str(i)] = (nd['t_deviation_'+str(i)] * nd['deviation_'+str(i)])\n\n# 测试向量从数据框后向前依次提取t个(测试集的类别必须是NaN!)\nfor j in range(1, t+1, 1):\n    nd['test_'+str(j)] = data.iloc[[-j]]\n    nd['t'+str(j)] = nd['test_'+str(j)].iloc[:, 2:6]\n    nd['t_matrix_'+str(j)] = np.asmatrix(nd['t'+str(j)])\n\nnumerator = 0\ndenominator = 0\n\nfor i in range(1, p+1, 1):\n    numerator += nd['sigma_'+str(i)]\n    denominator += nd['k'+str(i)]-1\n\nsigma = numerator/denominator\ninv_sigma = np.linalg.inv(sigma)\n\nn = np.zeros(shape=(t, p))\n\nfor j in range(1, t+1, 1):\n    for i in range(1, p+1, 1):\n        nd['d'+str(j)+str(i)] = (nd['t_matrix_'+str(j)]-nd['miu_'+str(i)]) * \\\n            inv_sigma*((nd['t_matrix_'+str(j)]-nd['miu_'+str(i)]).T)\n        n[j-1, i-1] = nd['d'+str(j)+str(i)]\n\nn = n[::-1]\nn_na = np.concatenate((na, n), axis=1)\np_n = pd.DataFrame(data=np.array(n_na), columns=[\n                   '国家', 'Class1', 'Class2', 'Class3'])\n\nprint(\"每个测试向量对于每一类的距离：\\n\")\nprint(p_n, \"\\n\")\n\ns = np.min(n, axis=1, keepdims=True)\nc = np.vstack(np.argmin(n, axis=1) + 1)\n\nd = np.concatenate((na, s, c), axis=1)\n\np_d = pd.DataFrame(data=np.array(d), columns=['国家', '最短距离', '类别'])\n\nprint(\"最终的结果和归类：\\n\")\nprint(p_d)\n\nq = input(\"Please Enter to quit\")\nif q:\n    quit()\n","sub_path":"college_course/Multivariate Statistical Analysis/multiple discriminant analysis/distance discreminant/DD_1.py","file_name":"DD_1.py","file_ext":"py","file_size_in_byte":2386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"184630844","text":"#!/usr/bin/env python3 -tt\n\nfrom scapy.all import *\nimport struct\nimport codecs\nfrom pyasn1.codec.ber import encoder, decoder\n\n\nMESSAGETYPEOFFSETUDP = 17\nMESSAGETYPEOFFSETTCP = 21\nDEBUG = True\n\nTGS_REP = 13\n\ndef findkerbpayloads(packets, verbose=False):\n\tkploads = []\n\ti = 1\n\tunfinished = {}\n\tfor p in packets:\n\t\t# UDP\n\t\tif p.haslayer(UDP) and p.sport == 88 and p[UDP].load[MESSAGETYPEOFFSETUDP] == TGS_REP:\n\t\t\tif verbose: print(\"found UDP payload of size %i\" % len(p[UDP].load) )\n\t\t\tkploads.append(p[UDP].load)\n\n\t\t#TCP\n\t\telif p.haslayer(TCP) and p.sport == 88 and p[TCP].flags & 23== 16: #ACK Only, ignore push (8), urg (32), and ECE (64+128)\n\t\t\t# assumes that each TCP packet contains the full payload\n\n\t\t\ttry:\n\t\t\t\tpayload = p[TCP].load\n\t\t\texcept:\n\t\t\t\tcontinue\n\n\t\t\tif len(payload) > MESSAGETYPEOFFSETTCP and payload[MESSAGETYPEOFFSETTCP] == TGS_REP:\n\t\t\t\t# found start of new TGS-REP\n\t\t\t\tsize = struct.unpack(\">I\", payload[:4])[0]\n\t\t\t\tif size + 4 == len(payload):\n\t\t\t\t\tkploads.append(payload[4:size+4]) # strip the size field\n\t\t\t\telse:\n\t\t\t\t\t#print('ERROR: Size is incorrect: %i vs %i' % (size, len(payload)))\n\t\t\t\t\tunfinished[(p[IP].src, p[IP].dst, p[TCP].dport)] = (payload[4:size+4], size)\n\t\t\t\tif verbose: print(\"found TCP payload of size %i\" % size)\n\t\t\telif (p[IP].src, p[IP].dst, p[TCP].dport) in unfinished:\n\t\t\t\tticketdata, size = unfinished.pop((p[IP].src, p[IP].dst, p[TCP].dport))\n\t\t\t\tticketdata += payload\n\t\t\t\t#print(\"cont: %i %i\" % (len(ticketdata), size))\n\t\t\t\tif len(ticketdata) == size:\n\t\t\t\t\tkploads.append(ticketdata)\n\t\t\t\telif len(ticketdata) < size:\n\t\t\t\t\tunfinished[(p[IP].src, p[IP].dst, p[TCP].dport)] = (ticketdata, size)\n\t\t\t\telse:\n\t\t\t\t\t# OH NO! Oversized!\n\t\t\t\t\tprint('Too much data received! Source: %s Dest: %s DPort %i' % (p[IP].src, p[IP].dst, p[TCP].dport))\n\n\n\treturn kploads\n\n\n\nif __name__ == '__main__':\n\timport argparse\n\n\tparser = argparse.ArgumentParser(description='Find TGS_REP packets in a pcap file and write them for use cracking')\n\tparser.add_argument('-f', '--pcap', dest='pcaps', action='append', required=True,\n\t\t\t\t\tmetavar='PCAPFILE', #type=file, #argparse.FileType('r'), \n\t\t\t\t\thelp='a file to search for Kerberos TGS_REP packets')\n\tparser.add_argument('-w', '--outputfile', dest='outfile', action='store', required=False, \n\t\t\t\t\tmetavar='OUTPUTFILE', type=argparse.FileType('w'), \n\t\t\t\t\thelp='the output file')\n\tparser.add_argument('-v', '--verbose', dest='verbose', action='store_true', default=False,\n\t\t\t\t\thelp='display verbose messages')\n\n\targs = parser.parse_args()\n\tkploads = []\n\tfor f in args.pcaps:\n\t\tpackets = rdpcap(f)\n\t\tkploads += findkerbpayloads(packets, args.verbose)\n\n\tif len(kploads) == 0:\n\t\tprint('no payloads found')\n\t\tsys.exit(0)\n\n\tif args.outfile:\n\t\tprint('writing %i hex encoded payloads to %s' % (len(kploads), args.outfile.name))\n\t\n\tdecode_hex = codecs.getdecoder(\"hex_codec\")\n\tfor p in kploads:\n\t\tencticket = decoder.decode(p)[0][4][3][2].asOctets().hex()\n\t\tout = \"$krb5tgs$23$*user$realm$test/spn*$\" + encticket[:32] + \"$\" + encticket[32:]\n\n\t\tif args.outfile:\n\t\t\targs.outfile.write(out + '\\n')\n\t\telse:\n\t\t\tprint(out)","sub_path":"krbroast-pcap2hashcat.py","file_name":"krbroast-pcap2hashcat.py","file_ext":"py","file_size_in_byte":3074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"313425165","text":"import argparse\nimport logging\nimport glob\nimport os\nimport json\nimport numpy as np\n\n\nlogging.basicConfig(level=logging.INFO)\n\nparser = argparse.ArgumentParser(description='Launch one synthesizer on one or multiple datasets.')\nparser.add_argument('datasets', type=str, nargs='*',\n                    help='a list of datasets, empty means all datasets.')\nparser.add_argument('--repeat', type=int, default=3,\n                    help='run generater multiple times.')\n\nparser.add_argument('--output', type=str, default='output',\n                    help='output dir')\nparser.add_argument('--name', type=str, default='',\n                    help='model name, default is model class name.')\n\nparser.add_argument('--sample', type=int, default=50000,\n                    help='maximum samples in the synthetic data.')\n\nclass SynthesizerBase(object):\n    \"\"\"docstring for Synthesizer.\"\"\"\n\n    supported_datasets = [\n        'asia', 'alarm', 'child', 'insurance', 'grid', 'gridr', 'ring',\n        'adult', 'credit', 'census',\n        'news', 'covtype', 'intrusion', 'mnist12', 'mnist28']\n\n    def train(self, train_data):\n        pass\n\n    def generate(self, n):\n        pass\n\n    def init(self, meta, working_dir):\n        pass\n\n\ndef run(synthesizer):\n    assert isinstance(synthesizer, SynthesizerBase)\n\n    args = parser.parse_args()\n    datasets = args.datasets\n    name = args.name\n    if name == \"\":\n        name = synthesizer.__class__.__name__\n\n    output = \"{}/{}\".format(args.output, name)\n\n    if not os.path.exists(output):\n        os.makedirs(output)\n    logging.info(\"Use output dir {}\".format(output))\n\n    repeat = args.repeat\n\n    if datasets == []:\n        datasets = synthesizer.supported_datasets\n    else:\n        for item in datasets:\n            if not item in synthesizer.supported_datasets:\n                logging.warning(\"Dataset {} is not supported by {}.\".format(item, name))\n\n\n\n    for dataset in datasets:\n\n        data_filename = glob.glob(\"data/*/{}.npz\".format(dataset))\n        meta_filename = glob.glob(\"data/*/{}.json\".format(dataset))\n\n        if len(data_filename) != 1:\n            logging.warning(\"Skip. Can't find dataset {}. \".format(dataset))\n            continue\n\n        if len(meta_filename) != 1:\n            logging.warning(\"Skip. Can't find meta {}. \".format(dataset))\n            continue\n\n        data = np.load(data_filename[0])['train']\n        with open(meta_filename[0]) as f:\n            meta = json.load(f)\n\n\n        for i in range(repeat):\n            if glob.glob(\"{}/{}_{}_*.npz\".format(output, dataset, i)):\n                logging.warning(\"Skip. {} results on {}_{} exists.\".format(name, dataset, i))\n                continue\n\n            logging.info(\"Generating {} iter {}\".format(dataset, i))\n            working_dir = \"{}/ckpt_{}_{}\".format(output, dataset, i)\n            synthesizer.init(meta, working_dir)\n            synthesizer.train(data)\n            sample = min(args.sample, data.shape[0])\n            generated = synthesizer.generate(sample)\n\n            if len(generated) == 0:\n                logging.warning(\"{} fails on {}. \".format(name, dataset))\n\n            for step, syn in generated:\n                np.savez(\"{}/{}_{}_{}.npz\".format(output, dataset, i, step), syn=syn)\n","sub_path":"synthetic_data_benchmark/synthesizer/synthesizer_base.py","file_name":"synthesizer_base.py","file_ext":"py","file_size_in_byte":3247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"565587545","text":"\ndef programme_candidat(tableau, taille_x, taille_y):\n    out0 = 0\n    for i in range(0, taille_y):\n        for j in range(0, taille_x):\n            out0 += ord(tableau[i][j]) * (i + j * 2)\n            print(\"%c\" % tableau[i][j], end='')\n        print(\"--\\n\", end='')\n    return out0\ntaille_x = int(input())\ntaille_y = int(input())\na = [None] * taille_y\nfor b in range(0, taille_y):\n    a[b] = list(input())\ntableau = a\nprint(\"%d\\n\" % programme_candidat(tableau, taille_x, taille_y), end='')\n\n","sub_path":"out/prologin_template_charmatrix.py","file_name":"prologin_template_charmatrix.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"424441389","text":"# Activity Homework_11_2_Task1\r\n# File: Homework_11_2_Task1_maims\r\n# Date:    05 Nov 2018\r\n# By:      Melanie Mai\r\n# Section: 011\r\n# Team:    122\r\n# \r\n# ELECTRONIC SIGNATURE\r\n# Melanie Mai\r\n#\r\n# The electronic signature above indicates the script\r\n# submitted for evaluation is my individual work, and I\r\n# have a general understanding of all aspects of its\r\n# development and execution.\r\n#\r\n# A BRIEF DESCRIPTION OF WHAT THE SCRIPT OR FUNCTION DOES\r\n# This program will compute the ending distance of a refraction given the indices of refraction\r\n# for two medias, the angle of incidence, and the first and second distance given by the user. \r\n# If there is no refraction in the second media, an error message should occur. \r\nimport math\r\n\r\nimport sys\r\n\r\n#User input of refraction, angle, and distances\r\nRefractionlist = input('Enter indices of refraction for bottom two media: ').split()\r\nAngle = int(input('Enter angle of incidence (in degrees): '))\r\nDistances = input('Enter d1 and d2 (units): ').split()\r\n\r\n#Convert Refractionlist elements to int\r\nRefractionElement1 = float(Refractionlist[0])\r\nRefractionElement2 = float(Refractionlist[1])\r\nProportion = RefractionElement1/RefractionElement2\r\n\r\n#Conver Distances elements to int\r\nDistance1 = float(Distances[0])\r\nDistance2 = float(Distances[1])\r\n\r\nif (Proportion > 1) or (Proportion < -1):\r\n\tprint(\"Error, no refraction in the second media.\")\r\n\tsys.exit()\r\n\r\n#Math conversion\r\nx = math.tan(Angle)*Distance1\r\n\r\nAngle2 = math.asin((RefractionElement1*math.sin(Angle))/RefractionElement2)\r\n\r\ny = math.tan(Angle2)*Distance2\r\n\r\nD3 = (x+y)\r\n\r\nprint(\"The ending distance is: \",D3,\" units.\")\r\n\r\n\r\n\r\n","sub_path":"Homework_11_2_Task1_maims.py","file_name":"Homework_11_2_Task1_maims.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"317941954","text":"\"\"\"Урок 1. Задача 1.\r\nПоработайте с переменными, создайте несколько, выведите на экран,\r\nзапросите у пользователя несколько чисел и строк и сохраните в переменные, выведите на экран.\r\n\"\"\"\r\nname = input('Ваше имя') #функция ввода\r\nprint('Здравствуй', name) #функция вывода\r\n\r\nvar_str = \"Это строка\" #str это строка\r\nvar_str2 = 'ЭТО тоже строка'\r\nvar_str3 = \"\"\"И это тоже\r\nстрока\r\n\"\"\"\r\nvar_int = 44 #любое целое число\r\nvar_float = 44.5 #дробное число\r\nprint(1 , var_str)\r\nprint(2 , var_str2)\r\nprint(3 , var_str3)\r\n#snake_case = 1\r\n\r\nvar_bool = True #True or False\r\nuser_name = 'Ирина'\r\nuser_age = 16\r\nif user_age >= 17:\r\n    print('Доступ разрешен')\r\nelse:\r\n    print('Доступ запрещен')\r\n\r\n\r\n","sub_path":"lesson_1.py","file_name":"lesson_1.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"456138552","text":"import os, sys, shutil\nimport torch\nimport torch.nn as nn\nimport torch.utils.data as data\nimport numpy as np\nfrom time import time\n\nimport DataModule_lstur as data_utils\nimport config_lstur as conf\nfrom metrics import evaluate\nfrom Logging import Logging, check_dir, tensorToScalar\n\n\nif __name__ == '__main__':\n    ############################## CREATE MODEL ##############################\n    from lstur import lstur\n    model = lstur()\n    \n    model.cuda()\n    optimizer = torch.optim.Adam(model.parameters(), lr=conf.learning_rate)\n\n    ############################## PREPARE DATASET ##############################\n    print('System start to load data...')\n    t0 = time()\n    train_data, val_data = data_utils.load_all()\n    t1 = time()\n    print('Data has been loaded successfully, cost:%.4fs' % (t1 - t0))\n\n    ########################### TRAINING STAGE ##################################\n    check_dir('%s/train_log' % conf.out_path)\n    log = Logging('%s/train_%s_lstur.log' % (conf.out_path, conf.data_name))\n    train_model_path = '%s/train_%s_lstur.mod' % (conf.out_path, conf.data_name)\n\n    # prepare data for the training stage\n    train_dataset = data_utils.TrainData(train_data)\n    val_dataset = data_utils.TestData(val_data)\n\n    train_batch_sampler = data.BatchSampler(data.RandomSampler(\n        range(train_dataset.length)), batch_size=conf.batch_size, drop_last=False)\n    val_batch_sampler = data.BatchSampler(data.SequentialSampler(\n        range(val_dataset.length)), batch_size=conf.batch_size, drop_last=True)\n\n    # Start Training !!!\n    max_auc = 0\n    for epoch in range(1, conf.train_epochs+1):\n        t0 = time()\n        model.train()\n\n        train_loss = []\n        for batch_idx_list in train_batch_sampler:\n            user_indexes, his_input_title, pred_input_title, labels = \\\n                train_dataset._get_batch(batch_idx_list)\n            obj_loss = model(user_indexes, his_input_title, pred_input_title, labels)\n            #import pdb; pdb.set_trace()\n            train_loss.append(obj_loss.item())\n            model.zero_grad(); obj_loss.backward(); optimizer.step()\n\n        train_loss = np.mean(train_loss)\n\n        t1 = time()\n\n        # evaluate the performance of the model with following xxx \n        model.eval()\n\n        val_preds, val_labels, val_keys = [], [], []\n        for batch_idx_list in val_batch_sampler:\n            user_indexes, his_input_title, pred_input_title, labels, keys = \\\n                val_dataset._get_batch(batch_idx_list)\n            preds = model.predict(user_indexes, his_input_title, pred_input_title)    \n            val_preds.extend(tensorToScalar(preds))\n            val_labels.extend(labels)\n            val_keys.extend(keys)\n\n        #import pdb; pdb.set_trace()\n        auc, mrr, ndcg_5, ndcg_10 = evaluate(val_labels, val_preds, val_keys)\n        t2 = time()\n\n        # save model when auc > max_auc\n        if epoch == 1:\n            max_auc = auc\n        if auc > max_auc:\n            torch.save(model.state_dict(), train_model_path)\n        max_auc = max(auc, max_auc)\n\n        log.record('Training Stage: Epoch:%d, compute loss cost:%.4fs, evaluation cost:%.4fs' % (epoch, (t1-t0), (t2-t1)))\n        log.record('Train loss:{:.4f}'.format(train_loss))\n        log.record('auc:%.4f, mrr:%.4f, ndcg@5:%.4f, ndcg@10:%.4f' % (auc, mrr, ndcg_5, ndcg_10))","sub_path":"lstur/train_lstru.py","file_name":"train_lstru.py","file_ext":"py","file_size_in_byte":3347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"200832590","text":"#!/usr/bin/env python\n\n##### imports #####\nimport os\nimport sys\nimport time\nimport ConfigParser\nfrom testlib.base.base_utils import get_args\nfrom testlib.scripts.android.adb import adb_steps\nfrom testlib.scripts.android.fastboot import fastboot_steps\nfrom testlib.scripts.android.fastboot import fastboot_utils\n\n##### initialization #####\nglobals().update(vars(get_args(sys.argv)))\nargs = {}\nfor entry in script_args:\n\tkey, val = entry.split(\"=\")\n\targs[key] = val\nflash_files = args[\"flash_files\"]\n\n##### test start #####\ntry:\n\tos.system(\"mkdir -p ./temp/files/flash ./temp/image/n/img\")\n\tfastboot_utils.download_flash_scripts()\n\n\tconf_url = \"https://shstor001.sh.intel.com/artifactory/acs_test_artifacts/OTC_Android_Auto_Test_Suite/resources/EBImage/System_FastBoot/fastboot.conf\"\n\tfastboot_utils.download_file(url=conf_url, local_filename=\"./temp/fastboot.conf\")\n\tconfig = ConfigParser.ConfigParser()\n\tconfig.read(\"./temp/fastboot.conf\")\n\tfastboot_path = config.get(\"fastboot\", \"path\")\n\n\tplatform_name = fastboot_utils.get_platform_name(serial=serial)\n\tif platform_name == \"bxtp_abl\": image_platform = \"image_m_bxt\"\n\tif platform_name == \"gordon_peak\":\n\t\tram_value = fastboot_utils.get_bxt_ram(serial=serial)\n\t\tif ram_value == \"2g\": image_platform = \"image_o_bxt_2g\"\n\t\tif ram_value == \"4g\": image_platform = \"image_o_bxt_4g\"\n\t\tif ram_value == \"8g\": image_platform = \"image_o_bxt_4g\"\n\tsystem_corrupted_verity_path = config.get(image_platform, \"system_corrupted_verity\")\n\tsystem_corrupted_verity_name = system_corrupted_verity_path.split(\"/\")[-1]\n\tfastboot_utils.download_file(url = fastboot_path + system_corrupted_verity_path, local_filename = \"./temp/image/n/img/\" + system_corrupted_verity_name)\n\n\tadb_steps.reboot(command = \"fastboot\", reboot_timeout = 300, serial = serial)()\n\tfastboot_steps.flash_image(partition_name = \"system\", file_name = \"./temp/image/n/img/\"+system_corrupted_verity_name, lock_dut = True, serial = serial)()\n\n\tcheck_point1 = False\n\tcheck_point2 = False\n\n\tfastboot_utils.start_minicom(serial=serial)\n\n\tos.system(\"fastboot reboot > /dev/null 2>&1\")\n\ttime.sleep(60)\n\tfastboot_utils.to_fastboot_by_script(serial=serial)\n\n\tfastboot_utils.kill_minicom()\n\n\tfile_path = \"./temp/files/minicom_result.txt\"\n\treturn_result = open(file_path).readlines()\n\tfor line in return_result:\n\t\tif \"device-mapper: verity\" in line and \"metadata block\" in line and \"is corrupted\" in line: check_point1 = True\n\t\tif \"Restarting system with command 'dm-verity device corrupted'\" in line: check_point2 = True\n\n\tif not check_point1 or not check_point2:\n\t\traise Exception(\"The test result did not achieve the desired results\")\n\n\tfastboot_utils.flash_bxt(zip_file=flash_files, serial=serial)\n\tos.system(\"sudo rm -rf ./temp\")\n\nexcept:\n\tfastboot_utils.kill_minicom()\n\tfastboot_utils.flash_bxt(zip_file=flash_files, serial=serial)\n\tos.system(\"sudo rm -rf ./temp\")\n\traise\n##### test end #####","sub_path":"ACS_v.18.20.4_1/ACS/testlib/scripts/android/fastboot/tests_bxt-p/system_not_mount_if_hashtree_was_altered_o.py","file_name":"system_not_mount_if_hashtree_was_altered_o.py","file_ext":"py","file_size_in_byte":2883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"259719193","text":"'''\nFile that creates a .mp4 file of the change in solution files. \n'''\n\n\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.interpolate as sp\nfrom matplotlib import animation\n\ndef init():\n\tfilename = sys.argv[2] + \"000.txt\"\n\tsolution = np.loadtxt(filename, skiprows = 0)\n\n\t# build data for color plot\n\tX = np.arange(0, x+h, h)\n\tY = np.arange(0, y+h, h)\n\tX, Y = np.meshgrid(X, Y)\n\twrap = solution[:,solution.shape[1]-1]\n\tZ = np.c_[wrap, solution]\n\t\n\tplt.pcolor(X, Y, Z)\n\tplt.xticks(np.arange(0, x+h, .2))\n\tplt.xlabel('X')\n\tplt.yticks(np.arange(-y, y*2 + h, .2))\n\tplt.ylabel('Y')\n\tplt.title('Pseudocolor Plot for {}'.format(filename))\n\ndef animate(i):\n\tfilename = sys.argv[2] + str(i).zfill(3) + \".txt\"\n\tsolution = np.loadtxt(filename, skiprows = 0)\n\n\t# build data for color plot\n\tX = np.arange(0, x+h, h)\n\tY = np.arange(0, y+h, h)\n\tX, Y = np.meshgrid(X, Y)\n\twrap = solution[:,solution.shape[1]-1]\n\tZ = np.c_[wrap, solution]\n\t\n\tplt.pcolor(X, Y, Z)\n\tplt.xticks(np.arange(0, x+h, .2))\n\tplt.xlabel('X')\n\tplt.yticks(np.arange(-y, y*2 + h, .2))\n\tplt.ylabel('Y')\n\tplt.title('Pseudocolor Plot for {}'.format(filename))\n\ndef find_nearest(array,value):\n    idx = (np.abs(array-value)).argmin()\n    return idx\n\nif len(sys.argv) < 4:\n\tprint('Usage:')\n\tprint('  python {} <input file> <solution file> <max iter>'.format(sys.argv[0]))\n\texit()\n\ntry:\n\tf = open(sys.argv[1], 'r')\n\ttext = f.read().splitlines()\n\tline1 = text[0].split(' ')\n\tline2 = text[1].split(' ')\n\tx = float(line1[0])\n\ty = float(line1[1])\n\th = float(line1[2])\n\tTc = float(line2[0])\n\tTh = float(line2[1])\n\tf.close()\nexcept:\n\traise RuntimeError (\"The file {} entered does not exist.\".format(sys.argv[1]))\n\texit()\n\nfig = plt.figure()\nax = plt.axes(xlim=(0, x), ylim=(-y, 2*y))\nline, = ax.plot([], [], lw=2)\n\nmax_iter = int(sys.argv[3])\niter_range = range(10, (max_iter / 10) * 10 + 10, 10)\niter_range.append(max_iter)\n\nanim = animation.FuncAnimation(fig, animate, init_func=init,\n                               frames=iter_range, interval=50, blit=False)\n\nanim.save('bonus_' + sys.argv[1].split('.')[0] + '.mp4', fps=2, extra_args=['-vcodec', 'libx264'])\n","sub_path":"Courses/F15/CME211/homework/project/bonus.py","file_name":"bonus.py","file_ext":"py","file_size_in_byte":2128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"53861342","text":"#!/usr/bin/env python3\n# Principles of Data Mining with Dr. Kinsman: HW 04\n# Author: Alvin Lin (axl1439)\n\nimport math\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nTRAINING_FILE = 'Recipes_For_Release_2181_v202.csv'\nINDENT = '    '\n\nBASE_CODE = \"\"\"#!/usr/bin/env python3\nimport numpy as np\n\nVALIDATION_FILE = 'Recipes_For_VALIDATION_2181_RELEASED_v202.csv'\n\ndef preprocess_training_data_row(row):\n    row = row.split(',')\n    row[0] = 0 if row[0] == 'Muffin' else 1\n    return list(map(float, row))\n\ndef main():\n    with open(VALIDATION_FILE) as f:\n        header = f.readline()\n        for row in f:\n            data = preprocess_training_data_row(row)\n{}\n            print(category)\n\nif __name__ == '__main__':\n    main()\n\"\"\"\n\nclass DecisionTreeNode():\n    def __init__(self):\n        self.data = None\n        self.left = None\n        self.right = None\n\ndef preprocess_training_data_row(row):\n    \"\"\"\n    Preprocessing for a training data row. Zaps Muffin to 0 and Cupcake to 1\n    for numpy processing.\n    \"\"\"\n    row = row.split(',')\n    row[0] = 0 if row[0] == 'Muffin' else 1\n    return list(map(float, row))\n\ndef get_training_data():\n    \"\"\"\n    Returns the training data in the csv file.\n    \"\"\"\n    with open(TRAINING_FILE) as f:\n        header = f.readline().strip().split(',')\n        types = {}\n        for i, label in enumerate(header):\n            types[label] = i\n        return types, np.array([preprocess_training_data_row(row) for row in f])\n\ndef get_mixed_gini_index(lt, gte):\n    \"\"\"\n    Calculates the mixed Gini index based on the cupcake/muffin similarity\n    given the two splits.\n    \"\"\"\n    p_cupcake_lt = np.sum(lt[:,0]) / len(lt)\n    p_cupcake_gte = np.sum(gte[:,0]) / len(gte)\n    gini_index_lt = 1 - (p_cupcake_lt ** 2) - ((1 - p_cupcake_lt) ** 2)\n    gini_index_gte = 1 - (p_cupcake_gte ** 2) - ((1 - p_cupcake_gte) ** 2)\n    return (gini_index_lt + gini_index_gte) / 2\n\ndef get_majority_class(data):\n    \"\"\"\n    Returns the majority class of a given chunk of data.\n    \"\"\"\n    muffin = np.sum(data[:,0] == 0)\n    if muffin > len(data) - muffin:\n        return 'Muffin'\n    return 'Cupcake'\n\ndef train(types, data, root, depth, max_depth=2):\n    \"\"\"\n    Generate a tree of DecisionTreeNode objects on the given data.\n    \"\"\"\n    best_gini_index = math.inf\n    best_split_type = None\n    best_split_value = None\n    for t in range(1, 26):\n        type_values = data[:,t]\n        low = math.floor(min(type_values))\n        high = math.ceil(max(type_values) + 1)\n        for value in range(low, high):\n            lt = data[type_values < value]\n            gte = data[type_values >= value]\n            if len(lt) == 0 or len(gte) == 0:\n                continue\n            gini_index = get_mixed_gini_index(lt, gte)\n            if gini_index < best_gini_index:\n                best_split_type = t\n                best_split_value = value\n                best_gini_index = gini_index\n    split_type_column = data[:,best_split_type]\n    lt = data[split_type_column < best_split_value]\n    gte = data[split_type_column >= best_split_value]\n    root.left = DecisionTreeNode()\n    root.right = DecisionTreeNode()\n    root.data = 'if data[{}] >= {}:\\n'.format(\n        best_split_type, best_split_value)\n    if depth == max_depth:\n        left_majority_class = get_majority_class(lt)\n        right_majority_class = 'Cupcake' if (\n            left_majority_class == 'Muffin') else 'Muffin'\n        root.left.data = 'category = \\'{}\\'\\n'.format(left_majority_class)\n        root.right.data = 'category = \\'{}\\'\\n'.format(right_majority_class)\n    else:\n        train(types, lt, root.left, depth + 1, max_depth)\n        train(types, gte, root.right, depth + 1, max_depth)\n\ndef tree_to_code(root_node, base_indent=''):\n    \"\"\"\n    Generate Python code from a tree of DecisionTreeNode objects.\n    \"\"\"\n    def recursive_helper(root, accum, indent=base_indent):\n        accum += indent + root.data\n        if root.left is not None and root.right is not None:\n            accum += recursive_helper(root.right, '', indent + INDENT)\n            accum += indent + 'else:\\n'\n            accum += recursive_helper(root.left, '', indent + INDENT)\n        return accum\n    return recursive_helper(root_node, '', base_indent)\n\ndef main():\n    types, data = get_training_data()\n    root = DecisionTreeNode()\n    train(types, data, root, 0, max_depth=1)\n    classifier_code = BASE_CODE.format(tree_to_code(root, INDENT * 3))\n    print(classifier_code)\n    with open('HW_05_Lin_Alvin_Classifier.py', 'w') as f:\n        f.write(classifier_code)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"cs420/HW_04_Lin_Alvin/HW_04_Lin_Alvin_Trainer.py","file_name":"HW_04_Lin_Alvin_Trainer.py","file_ext":"py","file_size_in_byte":4595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"148049514","text":"import numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport cv2\r\nfrom joblib import dump, load\r\nimport time\r\n\r\nreferenceType = 'battery'\r\ndesk = 'brisk'\r\ninit_desk = cv2.BRISK_create()\r\nmovie_name = 'videoBattery.mp4'\r\nreference = cv2.imread('originals/referenceBattery.jpg', 0)\r\nreplace = cv2.imread('casperFace.jpg')\r\n\r\nMIN_MATCH_COUNT = 12\r\n\r\nFLANN_INDEX_LSH = 6\r\nindex_params = dict(algorithm=FLANN_INDEX_LSH, table_number=6, key_size=12, multi_probe_level=1)\r\nsearch_params = dict(checks=50)\r\nflann = cv2.FlannBasedMatcher(index_params, search_params)\r\n\r\ndescriptor_list = ['brisk']\r\nkmeans = dict()\r\nfor i in descriptor_list:\r\n    kmeans[i] = load(f'written_models/{i}_kmeans.joblib');\r\nforest = dict()\r\nfor i in descriptor_list:\r\n    forest[i] = load(f'written_models/{i}_forest.joblib')\r\n\r\n\r\nbrisk = cv2.BRISK_create()\r\n\r\ndescriptor = dict()\r\ndescriptor['brisk'] = brisk\r\n\r\nreplace2 = replace\r\nreplace = cv2.cvtColor(replace, cv2.COLOR_BGR2GRAY)\r\n\r\n\r\ndef create_bag(labels_list):\r\n    res = np.zeros((10 * 4,))\r\n    for i in labels_list:\r\n        res[i] += 1\r\n    return res\r\n\r\n\r\ndef preprocess_image(img, estimator):\r\n    img = cv2.resize(img, (img.shape[0] // 4, img.shape[1] // 4))\r\n    kp, des = descriptor[estimator].detectAndCompute(img, None)\r\n    if (des is None):\r\n        return None\r\n    des = des.astype('float32')\r\n    if (des.shape[0] > 500):\r\n        des = des[:500, :]\r\n    kmeans_labels = kmeans[estimator].predict(des)\r\n    bag_of_words = create_bag(kmeans_labels)\r\n    return bag_of_words\r\n\r\n\r\ncap = cv2.VideoCapture(movie_name)\r\n\r\nfps = []\r\ncount = 0\r\nimg = replace\r\nimg1 = reference\r\nkp1, des1 = init_desk.detectAndCompute(img1, None)\r\nstart = time.time()\r\ni = 0\r\n\r\nwhile (cap.isOpened()):\r\n\r\n    ret, frame = cap.read()\r\n    if ret == True:\r\n        frame2 = frame\r\n        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n\r\n        vect = dict()\r\n\r\n        vect[desk] = preprocess_image(frame, desk)\r\n\r\n        temp = vect[desk].reshape((1, vect[desk].shape[0]))\r\n        pred = logreg[desk].predict(temp)[0]\r\n\r\n\r\n        if (pred == referenceType):\r\n\r\n            kp2, des2 = init_desk.detectAndCompute(frame, None)\r\n            matches = flann.knnMatch(des1, des2, k=2)\r\n\r\n            good = []\r\n\r\n            for i in matches:\r\n                if (len(i) > 1):\r\n                    m = i[0]\r\n                    n = i[1]\r\n                    if m.distance < 0.7 * n.distance:\r\n                        good.append(m)\r\n\r\n            if len(good) > MIN_MATCH_COUNT:\r\n                src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)\r\n                dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)\r\n\r\n                M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)\r\n                matchesMask = mask.ravel().tolist()\r\n\r\n                h, w = img1.shape\r\n                pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)\r\n                dst = cv2.perspectiveTransform(pts, M)\r\n                frame = cv2.polylines(frame, [np.int32(dst)], True, 255, 3, cv2.LINE_AA)\r\n                h, w = img.shape  # строим переобзование точек со 100 грн на 10 грн\r\n                pts2 = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)\r\n                matrix = cv2.getPerspectiveTransform(pts2, dst)\r\n                rows, cols = frame.shape\r\n                result = cv2.warpPerspective(replace2, matrix, (cols, rows))\r\n                result = cv2.addWeighted(result, 0.8, frame2, 0.5, 1)  # склеиваем картинки\r\n\r\n                cv2.imshow('Frame', result)\r\n                cv2.imwrite(f'resultIm{count}.jpg', result)\r\n\r\n            else:\r\n                print(\"Not enough matches are found :cry: - %d/%d\" % (len(good), MIN_MATCH_COUNT))\r\n                matchesMask = None\r\n                cv2.imshow('Frame', frame2)\r\n        else:\r\n            cv2.imshow('Frame', frame2)\r\n    else:\r\n        break\r\n    count += 1  # для вывода фпс\r\n    if (time.time() - start > 1):\r\n        fps.append(count)\r\n        count = 0\r\n        start = time.time()\r\n    if cv2.waitKey(1) & 0xFF == ord('q'):\r\n        break\r\n\r\ncap.release()\r\ncv2.destroyAllWindows()\r\n","sub_path":"CVPR L4/lab4.py","file_name":"lab4.py","file_ext":"py","file_size_in_byte":4289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"368722516","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = \"Benny Longwill\"\n__email__ = \"longwill@uw.edu\"\n\n\nfrom nltk import word_tokenize, sent_tokenize, pos_tag, PorterStemmer\n\nfrom nltk.corpus import stopwords\n\nfrom bs4 import BeautifulSoup\nimport document_retriever\nfrom content_realization import get_compressed_sentences\nfrom math import log\nimport os  # os module imported here to open multiple files at once\nimport spacy\n\nspacy_parser = spacy.load('/home/longwill/en_core_web_md/en_core_web_md-2.1.0')\nstop_words = set(stopwords.words('english'))\n\n\n#### Put none for non-existing text\n###############################\nclass Topic:\n    idf_type=None #### Default value\n    tf_type=None\n    def __init__(self, topic_id:str=None ,docsetA_id:str=None,  title:str=None , narrative:str=None, category=None):\n        self.doc_count = 0\n        self.sent_count = 0\n        self.topic_id=topic_id\n        self.docsetA_id=docsetA_id\n        self.raw_counts = {}  ##### Used for counts across all sentences\n        self.tf_norm_values= {}\n        ############### Creates and adds title and narrative and category Sentence Objects To Topic\n        self.title = Sentence.create_sentence(self, title)\n        self.narrative = Sentence.create_sentence(self, narrative) ##### *** Not all topics have this attribute ***\n        self.category=Sentence.create_sentence(self,category) ##### *** Not all topics have this attribute ***\n        ##########################################################\n        self.document_list=[]\n        self.summary = []\n        self.idf={}\n\n\n    #Returns list of all sentences in Topic, including Title sentence, narrative sentences, and headlines\n    def all_sentences(self)->list:\n\n        total_sentences=[sentence for document in self.document_list for sentence in document.sentence_list]\n\n        # checks the title in the topic\n        if self.title:\n            total_sentences.append(self.title)\n        # checks the narrative in the topic\n        if self.narrative:\n            total_sentences.append(self.narrative)\n\n        if self.category:\n            total_sentences.append(self.category)\n\n            # checks all headlines if available in every document in the topic\n        total_sentences += [document.headline for document in self.document_list if document.headline]\n\n        return total_sentences\n\n    ##### Counts the sentences under this topic that contains a token parameter\n    def n_containing(self, token):\n        # checks all tokens in each sentence of every document in the topic\n        count = sum(1 for document in self.document_list for sentence in document.sentence_list if\n            token in sentence)\n\n        # checks all headlines if available in every document in the topic\n        count += sum(1 for document in self.document_list if document.headline if token in document.headline)\n\n        # checks the title in the topic\n        if self.title and token in self.title:\n            count+=1\n        # checks the narrative in the topic\n        if self.narrative and token in self.narrative:\n            count+=1\n\n        return count\n\n\n    #Take the ratio of the total number of documents to the number of documents containing any word.\n    #Then it adds 1 to the devisor to avoid zero division and then takes the log otherwise the weight will be too high\n    def get_smooth_idf(self, token):\n        if token in self.idf:\n           return self.idf[token]\n        else:\n            current_idf = log(self.sent_count / (1 + self.n_containing(token)))\n            self.idf[token]=current_idf\n            return current_idf\n\n    #AKA unary IDF score, i.e. don't use IDF\n    def get_unary_idf(self):\n        return 1\n\n    #he logarithm of the number of documents in the corpus divided by the number of documents the term appears\n    # in (this will lead to negative scores for terms appearing in all documents in the corpus)\n    def get_standard_idf(self, token):\n        if token in self.idf:\n            return self.idf[token]\n        else:\n            current_idf = -log(self.n_containing(token)/self.sent_count)\n\n            self.idf[token] = current_idf\n            return current_idf\n\n    #similar to inverse but substracting the number of documents the term appears in from the\n    ## total number of documents in the training corpus (this can lead to positive and negative scores)\n    def get_probabilistic_idf(self, token):\n        if token in self.idf:\n            return self.idf[token]\n        else:\n            current_idf = log(self.sent_count-self.n_containing(token) / self.n_containing(token))\n            self.idf[token] = current_idf\n            return current_idf\n\n    # Must be used after all Documents, Sentences, and Tokens have been filled.\n    def compute_tf_idf(self):\n\n        cluster_count=sum(self.raw_counts.values())\n\n        #for sentence in doc.sentence_list + [self.title, self.narrative,  doc.headline]:\n        for sentence in self.all_sentences():\n            if sentence:\n\n                for token in sentence.token_list:\n                    token_value=token.token_value\n\n                    if token_value not in self.tf_norm_values.keys():\n                        raw_token_count=self.raw_counts.get(token_value)\n                        try:\n                            self.tf_norm_values.update({token_value: eval(\n                                'Sentence.get_' + Topic.tf_type + '(cluster_count=cluster_count,raw_token_count=raw_token_count)')})\n                        except:\n                            self.tf_norm_values.update({token_value: Sentence.get_term_frequency(cluster_count=cluster_count, raw_token_count=raw_token_count)})\n\n\n                    try:\n                        idf=eval('self.get_' + Topic.idf_type +'(token_value)')\n                    except:\n                        idf=self.get_smooth_idf(token_value)\n\n                    sentence.tf_idf[token_value] = token.raw_count * idf #self.get_standard_idf(token)\n                    sentence.tf_idf_norm[token_value] = sentence.tf_norm_values[token_value] * idf\n\nclass Document:\n    def __init__(self, parent_topic:Topic=None , doc_id:str=None, headline:str=None,date:str=None, category:str=None, document_text:str=None):\n        self.parent_topic = parent_topic\n        self.sent_count = 0\n        self.doc_id=doc_id\n        self.date=date\n        self.category=category ##### *** Not all topics have this attribute ***\n        self.sentence_list = self.create_sentence_list(document_text)\n        ############### Creates and adds headline Sentence Objects To Topic\n        self.headline = Sentence.create_sentence(self,headline) ##### *** Not all topics have this attribute ***\n\n        if parent_topic:\n            self.parent_topic.doc_count+=1 ### Increments parent count When initialized\n\n    def __repr__(self):\n        return str(self.sentence_list)\n    # Less than method allows for sorting\n    def __lt__(self, other):\n        return (self.date < other.date)\n\n    # Takes Document object and the text from doc file. The block of text is separated into sentences as sentence objects and also tokenized using NLTK.\n    def create_sentence_list(self, doc_text)->list:\n        sentence_list=[]\n\n        for doc_sentence in sent_tokenize(doc_text):\n\n            # Get compressed versions of the original sentence\n            compressed_sentences = get_compressed_sentences(doc_sentence, spacy_parser, Document.remove_header, Document.remove_parens, Document.remove_quotes, Document.remove_appos, Document.remove_advcl, Document.remove_relcl, Document.remove_acl)\n            # Add a sentence object for each compressed sentence\n            for sent in compressed_sentences:\n\n                current_sentence=Sentence(self, sent)  #Creates sentence object\n                current_sentence.index=len(sentence_list)\n                sentence_list.append(current_sentence)\n\n        return sentence_list\n\n#All sentences are part of a document of either Topic or Document type\nclass Sentence:\n    ps = PorterStemmer()\n\n    stemming=False\n    lower=False\n\n    def __init__(self,parent_doc:Document or Topic, original_sentence:str):\n        self.score=0\n        self.parent_doc=parent_doc\n        self.original_sentence = original_sentence.replace(\"\\n\", \" \").strip().replace(\"  \", \" \")\n        self.nouns=set()\n        self.sent_len = original_sentence.count(\" \") + 1   #Counts words in original sentence\n        self.raw_counts = {}\n        self.tf_norm_values={}\n        self.token_list=self.create_token_list(self.original_sentence)  ##### *** List of non-duplicate Tokens as Objects ***\n        self.tf_idf={}\n        self.tf_idf_norm={}\n\n        # Increments parent count when initialized. If parent_doc is Document, then Topic sent_count is also incremented\n        self.parent_doc.sent_count+=1\n        if type(self.parent_doc) is Document and self.parent_doc.parent_topic:\n            self.parent_doc.parent_topic.sent_count+=1\n            self.index = -1\n\n    def __repr__(self):\n        return self.original_sentence\n    # Less than method allows for sorting\n    def __lt__(self, other):\n        return (self.score < other.score)\n    def __eq__(self, other):\n        return (self.score == other.score)\n    # allows the use of 'is' operator on Sentence object\n    def __contains__(self, param):\n        return any(token.token_value == param for token in self.token_list)\n\n    ### Wrapper initializer that includes validation\n    @classmethod\n    def create_sentence(cls, self:Topic or Document, original_sentence: str):\n        if original_sentence:\n            sentence = Sentence(self, original_sentence)\n        else:\n            sentence = None\n        return sentence\n\n    @classmethod\n    def get_term_frequency(cls,cluster_count,raw_token_count):\n        return raw_token_count / cluster_count\n    @classmethod\n    def get_raw_count(cls,tokenized_sentence,token):\n        return tokenized_sentence.count(token)\n    @classmethod\n    def get_log_normalization(cls,cluster_count, raw_token_count):\n        return log(1+ raw_token_count )\n\n    ##### I think this would be good to be implemented but reuqires extra planning\n    #def get_augmented_frequency(self,tokenized_sentence,token):\n    #    return .5 + (.5 *tokenized_sentence.count(token)/ argmax word count in sentence    )\n\n\n\n    # Tokenizes a sentences and populates the sentence object with a token object list\n    def create_token_list(self, original_sentence: str)->list:\n\n        # Edits the original sentence to remove article formatting\n        # This location was chosen so that all sentences that are tokenized (i.e., Title sentence) could also be effected by this\n        token_list = []\n\n        '''LOTS OF TOKEN LOOPING WITHIN SENT REQUIRED BECAUSE OF POS TAGGING'''\n        #Tokenize sentence\n        tokenized_sent = word_tokenize(original_sentence)\n\n        #Must tokenize sentence before pos tagging\n        #Captures only Nouns and adds to self.nouns set\n        [self.nouns.add(token) for token,pos in pos_tag(tokenized_sent) if 'NN' in pos]\n\n        #Lowercasing before pos tagging affects tags, so must do after as list\n        if Sentence.lower:\n            tokenized_sent = [token.lower() for token in tokenized_sent]\n\n        #Can only stem one word at a time, can't stem whole sentence\n        if Sentence.stemming:  ##### THe stemmer also lowercases\n            tokenized_sent = [Sentence.ps.stem(token) for token in tokenized_sent]\n\n        for token in set(tokenized_sent):\n            ''''######### Removes stop words #########'''''\n            if token not in stop_words:\n\n                raw_token_count = Sentence.get_raw_count(tokenized_sent, token)\n\n                #Adds raw count to sentence\n                self.raw_counts.update({token:raw_token_count})\n\n                try:\n                    tf_norm = eval('Sentence.get_' + Topic.tf_type + '(cluster_count=len(tokenized_sent),raw_token_count=raw_token_count)')\n                except:\n                    tf_norm = Sentence.get_term_frequency(len(tokenized_sent), raw_token_count)\n\n                #Adds normalalized tf to sentence\n                self.tf_norm_values.update({token: tf_norm})\n\n                ##### Adds tokens raw counts to Parent Topic\n                if type(self.parent_doc)==Topic:\n                    current_topic=self.parent_doc\n                else:\n                    current_topic=self.parent_doc.parent_topic\n\n                current_topic.raw_counts.update({token: current_topic.raw_counts.get(token,0)+raw_token_count})\n                ###############################################\n\n                token_list.append(Token(self, token, raw_token_count ,tf_norm))\n\n        return token_list\n\n\nclass Token:\n    def __init__(self,parent_sentence:Sentence, token_value:str, raw_count, tf_norm):\n        self.raw_count=raw_count\n        self.tf_norm=tf_norm\n        self.parent_sentence=parent_sentence\n        self.token_value=token_value\n    def __repr__(self):\n        return self.token_value\n\n###############################\n### Tag Variables used to avoid hardcoding later in the document or in case of tag changes\n\ntitle_tag='title'\nnarrative_tag='narrative'\ntopic_category_tag='category'\ndocsetA_tag='docseta'\nid_tag='id'\ndoc_tag='doc'\ntopic_tag='topic'\nparser_tag='lxml'\ncategories_file_tag='/categories.txt'\nheadline_tag='headline'\n\n###############################\n\n# Takes a file path, collects all data and stores into a list of class object 'Topic' data structures\ndef get_data(file_path:str, stemming:bool, lower:bool, idf_type, tf_type, remove_header, remove_parens, remove_quotes, remove_appos, remove_advcl, remove_relcl, remove_acl)->list:\n    \"\"\"Extracts database documents and creates data structure objects to hold them. Returns a list of Topic objects\n\n    Args:\n        file_path:str file path on patas that leads to directory that holds training or testing data\n        stemming:bool True enables each sentence to be stored with a stem representation in objects and tokens, False does nothing\n        lower:bool True enables each sentence to be stored in lower case, False does nothing.\n        idf_type:str String input dictates idf representation in objects. Options are: 'smooth_idf', 'probabilistic_idf' , 'standard_idf' , and 'unary_idf'\n        tf_type:str String input dictates tf representation in objects. Options are: 'term_frequency', 'log_normalization'\n        remove_header: True if the header should be removed from the sentence\n        remove_parens: True if parenthetical information should be removed from the sentence\n        remove_quotes: True if unpaired quotes should be removed from the sentence\n        remove_appos: True if appositional modifier should be removed from the sentence\n        remove_advcl: True if adverbial clause modifier should be removed from the sentence\n        remove_relcl: True if relative clause modifier should be removed from the sentence\n        remove_acl: True if a finite or non-finite clausal modifier shoule be removed from the sentence\n\n    \"\"\"\n\n    # Set all the hyperparamaters\n    configure_class_objects(stemming, lower, idf_type, tf_type, remove_header, remove_parens, remove_quotes, remove_appos, remove_advcl, remove_relcl, remove_acl)\n\n    with open(file_path) as f1:\n        task_data = f1.read()\n\n        soup = BeautifulSoup(task_data, parser_tag)\n        raw_topics = soup.findAll(topic_tag)\n\n    return get_topics_list(raw_topics, get_categories(file_path))\n\n# unary_idf smooth_idf standard_idf probabilistic_idf\ndef configure_class_objects(stemming:bool,lower:bool, idf_type:str, tf_type:str, remove_header, remove_parens, remove_quotes, remove_appos, remove_advcl, remove_relcl, remove_acl):\n\n    if stemming:\n        Sentence.stemming = stemming\n    if lower:\n        Sentence.lower=lower\n    if idf_type:\n        Topic.idf_type=idf_type\n    if tf_type:\n        Topic.tf_type=tf_type\n\n    Document.remove_header = remove_header\n    Document.remove_parens = remove_parens\n    Document.remove_quotes = remove_quotes\n    Document.remove_appos = remove_appos\n    Document.remove_advcl = remove_advcl\n    Document.remove_relcl = remove_relcl\n    Document.remove_acl = remove_acl\n\ndef get_categories(file_path:str):\n\n    try:\n        with open(file_path.rsplit(\"/\",maxsplit=1)[0] + categories_file_tag) as f2:\n            categories=f2.read().replace(\":\",\"\").replace(\"(\",\"\").replace(\")\",\"\").replace(\"/\",\" \")\n            #Splits on two empty lines in a row\n            bag_of_words={}\n            for category in categories.split(\"\\n\\n\\n\")[1:]:\n                lines=category.strip().split(\"\\n\")\n                index, words = lines[0].split(\" \", maxsplit= 1)\n                index=index.rstrip(\".\")\n\n                bag_of_words.update({index : words.strip().split(\" \")})\n                for line in lines[1:]:\n                    bag_of_words[index]+=line.split()[1:]\n\n            return bag_of_words\n\n    except FileNotFoundError:\n        return None\n\n\n#Takes a Topic class object, an xml or html document set element, and a document retriever object\n# Itterates all document Id's in html/xml element and uses the doc retriever to get the raw document from database\n#Extracts attributes from raw document and creates Document class object, fills it with sentence objects and adds it to the current topic object\ndef populate_document_list(current_topic, docsetA, doc_ret:document_retriever.Document_Retriever):\n\n    for doc in docsetA.findAll(doc_tag):\n\n        doc_id = doc.attrs[id_tag]\n\n        raw_doc = doc_ret.retrieve_doc(doc_id)\n        headline, category, dateline, doc_text = get_doc_attributes(raw_doc, doc_ret.headline_tag, doc_ret.category_tag, doc_ret.dateline_tag, doc_ret.text_tag)\n\n        current_doc = Document(parent_topic=current_topic, doc_id=doc_id, date=doc_ret.date,headline=headline, category=category, document_text=doc_text)  ########## Creates document object\n\n        current_topic.document_list.append(current_doc)\n\n# Takes the raw topic xml/html and a set of title, narrative, and docset TAGS according to the format of file\n# extracts the respective text including document IDs for Aqcuaint(2) database\ndef get_topic_attributes(raw_topic, title_tag:str , narrative_tag:str, topic_category_tag:str, docsetA_tag:str):\n\n    narrative=None\n    title=None\n    topic_category=None\n\n    topic_id = raw_topic.attrs[id_tag]\n\n    if title_tag:\n        title_element = raw_topic.find(title_tag)\n        if title_element is not None:\n            title = title_element.text\n\n    if narrative_tag:\n        narrative_element = raw_topic.find(narrative_tag)\n        if narrative_element is not None:\n            narrative = narrative_element.text\n\n    if topic_category_tag:\n        category_element=raw_topic.attrs.get(topic_category_tag)\n        if category_element is not None:\n            topic_category=category_element\n\n\n\n    docsetA = raw_topic.find(docsetA_tag)\n\n    docsetA_id = docsetA.attrs[id_tag]\n\n    return topic_id, title, narrative, docsetA_id, docsetA, topic_category\n\n# Requires raw document and the specific tags used according to current HTML or XML.\n# Extracts the headline category dateline and text if available in raw document.\ndef get_doc_attributes(raw_doc, headline_tag:str=\"\", category_tag:str=\"\", dateline_tag:str=\"\", text_tag:str=\"\"):\n\n    ### Instantiate variables in case text not available in raw doc\n    headline = None\n    category = None\n    dateline = None\n    doc_text = None\n\n\n\n    if headline_tag:\n        headline_element = raw_doc.find(headline_tag)\n        if headline_element is not None:\n            headline = headline_element.text\n\n\n    if category_tag:\n        category_element = raw_doc.find(category_tag)\n        if category_element is not None:\n            category = category_element.text\n\n\n    if dateline_tag:\n        dateline_element = raw_doc.find(dateline_tag)\n        if dateline_element is not None:\n            dateline = dateline_element.text\n\n\n    if text_tag:\n        doc_text=' '.join(raw_doc.find(text_tag).itertext())\n\n\n    return headline, category, dateline, doc_text\n\n#Parses Raw data XML file argument and extracts the topic elements.\n# Creates Topic elements and fills withtopic attributes and Document objects\n# Returns these topic objects as a list.\ndef get_topics_list(raw_topics, topic_categories)->list:\n    topics=[]\n    doc_ret = document_retriever.Document_Retriever()\n\n    for raw_topic in raw_topics:\n        topic_id, title, narrative, docsetA_id, docsetA, topic_category = get_topic_attributes(raw_topic, title_tag, narrative_tag,topic_category_tag, docsetA_tag)\n\n        if topic_category:\n            topic_category = \" \".join(topic_categories[topic_category])\n\n        current_topic= Topic(topic_id = topic_id,docsetA_id = docsetA_id, title = title, narrative = narrative, category=topic_category) ########### Creates topic object\n\n        populate_document_list(current_topic, docsetA, doc_ret)\n\n        current_topic.compute_tf_idf()\n\n        topics.append(current_topic)\n\n    return topics\n\n###############################\n\n\n'''''''''''''''''''''''''''''''''''''''''''''\nMethod used to create dummy data structures for testing purposes : Requires formatted Topic file\n'''''''''''''''''''''''''''''''''''''''''''''\ndef build_pseudo_topic(pseudo_document_file_path, stemming:bool=False, lower:bool=False, idf_type:str=None, tf_type:str=None):\n\n    configure_class_objects(stemming,lower, idf_type, tf_type)\n\n    #```doc_id = 1a \\n date = 20110506 \\n ### \\n Sentence 1 would be here. \\n Sentence 2 would be here, etc.```\n    # (where ### is a metadata separator and everything below that would be a sentence on its own line)\n    with open(pseudo_document_file_path) as f1:\n        #Parses whole document by empty line\n        topic_meta_data, pseudo_docs=f1.read().split(\"\\n\\n\", maxsplit=1)\n\n\n        #Puts topic meta-data into dictionary\n        topic_meta_data = dict((x.strip(),y.strip()) for x,y in [data.split(\"=\") for data in topic_meta_data.split(\"\\n\")])\n\n        #Loads empty topic object with topic metadata from dictionary\n        current_topic=None\n        try:\n            current_topic=Topic(**topic_meta_data)\n        except Exception as e:\n\n            print(e)\n\n        #Separates documents then document metadata from sentences and loads them into a dictionary\n        #Calls populate sentences to finish filling remaining data structures\n        for doc in pseudo_docs.split(\"\\n\\n\"):\n            doc_meta_data, sentences=doc.split(\"###\")\n            doc_meta_data=dict([data.split(\"=\") for data in doc_meta_data.replace(\" \",\"\").split()])\n\n\n            try:\n                current_doc = Document(parent_topic=current_topic, document_text=sentences.strip().replace(\"\\n\", \" \"),**doc_meta_data)\n                current_topic.document_list.append(current_doc)\n            except Exception as e:\n                print(e)\n\n\n        current_topic.compute_tf_idf()\n\n\n        return current_topic\n\n\n'''''''''''''''''''''''''''''''''''''''''''''\nMethod used to create dummy data structures for the Gold Standard data\n'''''''''''''''''''''''''''''''''''''''''''''\ndef get_gold_standard_docs(file_path:str)->list:\n    return [Document(parent_topic=Topic(),document_text=open(file_path+\"/\"+file_name).read()) for file_name in os.listdir(file_path)]\n","sub_path":"src/data_input.py","file_name":"data_input.py","file_ext":"py","file_size_in_byte":23227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"251625211","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.template import loader\nimport os\nimport json\n\ndef call(req,mobileNum):\n    os.system(\"termux-telephony-call \"+str(mobileNum))\n    return HttpResponse(\"<h1>hello2</h1>\")\ndef contacts(req):\n    try:\n        contactsList=open('data.json').read()\n    except:\n        print(\"rename data_sample.json to data.json\")\n        contactsList=open('data_sample.json').read()\n    context = {\n        'contactsList': json.loads(contactsList),\n    }\n    return render(req, 'calling/index.html', context)","sub_path":"calling/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"502352867","text":"#!/usr/bin/env python\n\n#Node that is used to control the height of the robot's linear actuators\nfrom math import pi, cos, sin\n\nimport diagnostic_msgs\nimport diagnostic_updater\nimport roboclaw_driver.roboclaw_driver as roboclaw\nimport rospy\nimport tf\nimport signal\nfrom geometry_msgs.msg import Quaternion, Twist\nfrom nav_msgs.msg import Odometry\nfrom sensor_msgs.msg import JointState\nfrom std_msgs.msg import Header\n\n__author__ = \"bwbazemore@uga.edu (Brad Bazemore)\"\n\ndef timeout_handler(signum, frame):\n    raise Exception(\"serial write hanging\")\n\nclass Node:\n    def __init__(self):\n\n        self.ERRORS = {0x0000: (diagnostic_msgs.msg.DiagnosticStatus.OK, \"Normal\"),\n                       0x0001: (diagnostic_msgs.msg.DiagnosticStatus.WARN, \"M1 over current\"),\n                       0x0002: (diagnostic_msgs.msg.DiagnosticStatus.WARN, \"M2 over current\"),\n                       0x0004: (diagnostic_msgs.msg.DiagnosticStatus.ERROR, \"Emergency Stop\"),\n                       0x0008: (diagnostic_msgs.msg.DiagnosticStatus.ERROR, \"Temperature1\"),\n                       0x0010: (diagnostic_msgs.msg.DiagnosticStatus.ERROR, \"Temperature2\"),\n                       0x0020: (diagnostic_msgs.msg.DiagnosticStatus.ERROR, \"Main batt voltage high\"),\n                       0x0040: (diagnostic_msgs.msg.DiagnosticStatus.ERROR, \"Logic batt voltage high\"),\n                       0x0080: (diagnostic_msgs.msg.DiagnosticStatus.ERROR, \"Logic batt voltage low\"),\n                       0x0100: (diagnostic_msgs.msg.DiagnosticStatus.WARN, \"M1 driver fault\"),\n                       0x0200: (diagnostic_msgs.msg.DiagnosticStatus.WARN, \"M2 driver fault\"),\n                       0x0400: (diagnostic_msgs.msg.DiagnosticStatus.WARN, \"Main batt voltage high\"),\n                       0x0800: (diagnostic_msgs.msg.DiagnosticStatus.WARN, \"Main batt voltage low\"),\n                       0x1000: (diagnostic_msgs.msg.DiagnosticStatus.WARN, \"Temperature1\"),\n                       0x2000: (diagnostic_msgs.msg.DiagnosticStatus.WARN, \"Temperature2\"),\n                       0x4000: (diagnostic_msgs.msg.DiagnosticStatus.OK, \"M1 home\"),\n                       0x8000: (diagnostic_msgs.msg.DiagnosticStatus.OK, \"M2 home\")}\n        \n        rospy.init_node(\"roboclaw_node_height\")\n        rospy.on_shutdown(self.shutdown)\n        rospy.loginfo(\"Connecting to roboclaw\")\n        self.dev_name = rospy.get_param(\"~dev\", \"/dev/ttyACM2\") #may need to change the usb port\n\n        self.baud_rate = int(rospy.get_param(\"~baud\", \"38400\")) #may need to change the baud rate. see roboclaw usermanual\n\n        self.address = int(rospy.get_param(\"~address\", \"129\")) #roboclaw is current setup to use address 128 which is setting 7\n\n\n        #Error Handle\n        if self.address > 0x87 or self.address < 0x80:\n            rospy.logfatal(\"Address out of range\")\n            rospy.signal_shutdown(\"Address out of range\")\n        \n        # TODO need someway to check if address is correct\n        try:\n            roboclaw.Open(self.dev_name, self.baud_rate)\n        except Exception as e:\n            rospy.logfatal(\"Could not connect to Roboclaw\")\n            rospy.logdebug(e)\n            rospy.signal_shutdown(\"Could not connect to Roboclaw\")\n        \n        self.updater = diagnostic_updater.Updater()\n        self.updater.setHardwareID(\"Roboclaw\")\n        self.updater.add(diagnostic_updater.FunctionDiagnosticTask(\"Vitals\", self.check_vitals))\n\n        try:\n            version = roboclaw.ReadVersion(self.address)\n        except Exception as e:\n            rospy.logwarn(\"Problem getting roboclaw version\")\n            rospy.logdebug(e)\n            pass\n\n        if not version[0]:\n            rospy.logwarn(\"Could not get version from roboclaw\")\n        else:\n            rospy.logdebug(repr(version[1]))\n\n        roboclaw.SpeedM1M2(self.address, 0, 0)\n        roboclaw.ResetEncoders(self.address)\n\n        self.MAX_SPEED = float(rospy.get_param(\"~max_speed\", \"127\"))\n        self.MAX_DUTY = float(rospy.get_param(\"~max_duty\", \"32767\"))\n\n        self.last_set_speed_time = rospy.get_rostime()\n\n        self.height_vel_sub = rospy.Subscriber(\"roboclaw/height_vel\", Twist, self.cmd_vel_callback)\n        self.height_pub = rospy.Publisher(\"roboclaw/height\", JointState, queue_size=10)\n        \n        self.m1_duty = 0\n        self.m2_duty = 0\n        \n        rospy.sleep(1)\n\n        rospy.loginfo(\"dev %s\", self.dev_name)\n        rospy.loginfo(\"baud %d\", self.baud_rate)\n        rospy.loginfo(\"address %d\", self.address)\n        rospy.loginfo(\"max_speed %f\", self.MAX_SPEED)\n\n    def run(self):\n        rospy.loginfo(\"Starting motor drive\")\n        r_time = rospy.Rate(5)\n        signal.signal(signal.SIGALRM, timeout_handler)\n        \n        while not rospy.is_shutdown():\n            signal.setitimer(signal.ITIMER_REAL, 0.21, 0)\n            \n            try:\n                if (rospy.get_rostime() - self.last_set_speed_time).to_sec() > 0.3:\n                    self.m1_duty = 0\n                    self.m2_duty = 0\n                roboclaw.DutyM1M2(self.address,self.m1_duty,self.m2_duty)\n                self.pub_enc_values()\n                r_time.sleep()\n                \n            except Exception as e:\n                rospy.loginfo(e)\n\n    def pub_enc_values(self):\n            height_state = JointState()\n            height_state.header = Header()\n            height_state.header.stamp = rospy.Time.now()\n            height_state.name = ['m1', 'm2']\n            enc1 = roboclaw.ReadEncM1(self.address)\n            enc2 = roboclaw.ReadEncM2(self.address)\n            height_state.position = [float(enc1[1]),float(enc2[1])]\n            try:\n                self.height_pub.publish(height_state)\n            except Exception as e:\n                rospy.loginfo(\"hanging\")\n                rospy.logdebug(e)\n\n    def cmd_vel_callback(self, twist):\n        #twist 127 full forward, -127 full backward\n        self.last_set_speed_time = rospy.get_rostime() \n        self.m1_duty = self.twist_to_duty(twist.linear.x)\n        self.m2_duty = self.twist_to_duty(twist.linear.y)\n            \n    def twist_to_duty(self, twist):\n        return int((twist / self.MAX_SPEED) * self.MAX_DUTY)\n\n    # TODO: Need to make this work when more than one error is raised\n    def check_vitals(self, stat):\n        try:\n            status = roboclaw.ReadError(self.address)[1]\n        except OSError as e:\n            rospy.logwarn(\"Diagnostics OSError: %d\", e.errno)\n            rospy.logdebug(e)\n            return\n        state, message = self.ERRORS[status]\n        stat.summary(state, message)\n        try:\n            stat.add(\"Main Batt V:\", float(roboclaw.ReadMainBatteryVoltage(self.address)[1] / 10))\n            stat.add(\"Logic Batt V:\", float(roboclaw.ReadLogicBatteryVoltage(self.address)[1] / 10))\n            stat.add(\"Temp1 C:\", float(roboclaw.ReadTemp(self.address)[1] / 10))\n            stat.add(\"Temp2 C:\", float(roboclaw.ReadTemp2(self.address)[1] / 10))\n                       \n            \n        except OSError as e:\n            rospy.logwarn(\"Diagnostics OSError: %d\", e.errno)\n            rospy.logdebug(e)\n        return stat\n\n    # TODO: need clean shutdown so motors stop even if new msgs are arriving\n    def shutdown(self):\n        rospy.loginfo(\"Shutting down\")\n        self.height_vel_sub.unregister()\n        try:\n            roboclaw.ForwardM1(self.address, 0)\n            roboclaw.ForwardM2(self.address, 0)\n\n        except OSError:\n            rospy.logerr(\"Shutdown did not work trying again\")\n            try:\n                roboclaw.ForwardM1(self.address, 0)\n                roboclaw.ForwardM2(self.address, 0)\n                \n            except OSError as e:\n                rospy.logerr(\"Could not shutdown motors!!!!\")\n                rospy.logdebug(e)\n\n\nif __name__ == \"__main__\":\n    try:\n        node = Node()\n        node.run()\n    except rospy.ROSInterruptException:\n        pass\n    rospy.loginfo(\"Exiting\")\n","sub_path":"roboclaw_node/nodes/roboclaw_node_height.py","file_name":"roboclaw_node_height.py","file_ext":"py","file_size_in_byte":7921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"133488163","text":"from pyspark.sql import SparkSession\n\nspark = SparkSession.builder.appName(\"WordCount\").getOrCreate()\ndata = ['cat bat hat', 'bat mat cat', 'bat mat that']\n\n\ndef load_RDD(Spark, Data):\n    myRDD = Spark.sparkContext.parallelize(Data)\n    return myRDD\n\n\ndef wordcount_func(myRDD):\n    words = myRDD.flatMap(lambda x: x.split(' ')).map(lambda a: (a, 1)).reduceByKey(lambda x, y: x + y)\n    return words.collect()\n\n\nwordcount_func(load_RDD(spark, data))\n","sub_path":"com/uday/pyspark/WordCount.py","file_name":"WordCount.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"81895436","text":"import os\nfrom blockchain import Blockchain, Block\nimport discord\nimport asyncio\nimport ast\n\nDATA_DIR = 'data'\nNEW_DATA = 'new_data.txt'\nNOTE_CONVERT = 'CONVERT'\nNOTE_LOAD = 'LOAD'\nNOTE_TRANSFER = 'TRANSFER'\n\n\nclass BlockchainManager(object):\n\n    def __init__(self, client, server_id, blockchain_channel_id):\n        self.client = client\n        self.server_id = server_id\n        self.blockchain_channel_id = blockchain_channel_id\n        self.blockchain = Blockchain()\n\n    async def update_blockchain(self):\n        print('Update blockchain')\n        await self.client.wait_until_ready()\n        server = self.client.get_guild(self.server_id)\n        blockchain_channel = self.client.get_channel(self.blockchain_channel_id)\n        print(self.client.is_closed())\n        while not self.client.is_closed():\n            if NEW_DATA in os.listdir(DATA_DIR):\n                f = open(DATA_DIR + '/' + NEW_DATA, 'r')\n                data = ''\n                for line in f:\n                    data = line\n                f.close()\n                self.blockchain.mine(Block(data))\n                os.remove(DATA_DIR + '/' + NEW_DATA)\n                await blockchain_channel.send(self.blockchain.block)\n            await asyncio.sleep(5)\n\n    async def update_block(self, sender, receiver, amount, note):\n        s_bal = await self.get_balance(sender)\n        r_bal = await self.get_balance(receiver)\n        print(s_bal, r_bal, amount)\n        try:\n            sender_bal = s_bal - amount\n            receiver_bal = r_bal + amount\n            if sender_bal >= 0:\n                f = open(DATA_DIR + '/' + NEW_DATA, 'a+')\n                # [sender_user_id, receiver_id, transver_amount, sender_new_bal]\n                f.write('[\"{}\",\"{}\",\"{}\",\"{}\",\"{}\",\"{}\"],'.format(\n                    sender, receiver, amount, sender_bal, receiver_bal, note))\n                f.close()\n                return(True)\n            else:\n                return(False)\n        except Exception as e:\n            return(False)\n\n    async def load_balance(self, receiver_id, amount):\n        server_bal = await self.get_balance(self.server_id)\n        f = open(DATA_DIR + '/' + NEW_DATA, 'a+')\n        # [sender_user_id, receiver_id, transver_amount, sender_new_bal]\n        f.write('[\"{}\",\"{}\",\"{}\",\"{}\",\"{}\",\"{}\"],'.format(\n            self.server_id, receiver_id, amount, server_bal, amount, NOTE_LOAD))\n        f.close()\n\n    async def get_balance(self, blockchain_id):\n        await self.client.wait_until_ready()\n        server = self.client.get_guild(self.server_id)\n        blockchain_channel = self.client.get_channel(self.blockchain_channel_id)\n        messages = []\n        counter = 0\n        current_bal = None\n        async for message in blockchain_channel.history(limit=None, oldest_first=False):\n            try:\n                data = message.content.split('\\n')[2].replace('Block Data: ', '')[:-1]\n                data = '[' + data + ']'\n                data = ast.literal_eval(data)\n                should_break = False\n                for t in data:\n                    if t[0] == str(blockchain_id):\n                        # print(t)\n                        # print('Current bal: {}'.format(t[3]))\n                        current_bal = float(t[3])\n                        should_break = True\n                    elif t[1] == str(blockchain_id):\n                        # print('id here')\n                        current_bal = float(t[4])\n                        should_break = True\n                if should_break:\n                    break\n            except Exception as e:\n                print('Bad data: ', e)\n                current_bal = None\n        return(current_bal)\n\n\nclass IssueManager(object):\n\n    def __init__(self, bm, commands_channel_id):\n        self.bm = bm\n        self.client = bm.client\n        self.server_id = bm.server_id\n        self.blockchain_channel_id = bm.blockchain_channel_id\n        self.commands_channel_id = commands_channel_id\n        self.issue_in_session = False\n\n    async def get_issue(self, issue_id):\n        issue_data = issue_id.split('-')\n        server = self.client.get_guild(id=int(issue_data[0]))\n        channel = self.client.get_channel(int(issue_data[1]))\n        message = await channel.fetch_message(int(issue_data[2]))\n        return(message.content.replace('!IBDD ', '').replace('\"', ''))\n\n    async def non_voters_transver(self):\n        await self.client.wait_until_ready()\n        server = self.client.get_guild(id=self.server_id)\n        channel = self.client.get_channel(self.blockchain_channel_id)\n        counter = 0\n        current_bal = None\n        print(self.issue_in_session)\n        for member in server.members:\n            print(member.id)\n            voted = False\n            async for message in channel.history(limit=None, oldest_first=False):\n                data = message.content.split('\\n')[2].replace('Block Data: ', '')[:-1]\n                data = '[' + data + ']'\n                data = ast.literal_eval(data)\n                for t in data:\n                    if t[0] == str(member.id) and t[1] == self.issue_in_session:\n                        voted = True\n            if voted:\n                print(member.id, 'voted')\n            else:\n                await self.bm.update_block(member.id, self.issue_in_session, 0, NOTE_CONVERT)\n\n    async def get_converts(self):\n        server = self.client.get_guild(id=self.server_id)\n        channel = self.client.get_channel(self.blockchain_channel_id)\n        converts = 0\n        convert_ids = []\n        async for message in channel.history(limit=None, oldest_first=False):\n            data = message.content.split('\\n')[2].replace('Block Data: ', '')[:-1]\n            data = '[' + data + ']'\n            data = ast.literal_eval(data)\n            for t in data:\n                if t[1] == self.issue_in_session and t[5] == NOTE_CONVERT:\n                    converts += 1\n                    convert_ids.append(t[0])\n        return(converts, convert_ids)\n\n    async def count_votes(self):\n        await self.non_voters_transver()\n        await asyncio.sleep(10)  # change this for end vote check on BC\n        sum_traded_votes, convert_ids = await self.get_converts()\n        server_bal = await self.bm.get_balance(self.server_id)\n        vote_price = round(float(server_bal)/float(sum_traded_votes), 2)\n        for id in convert_ids:\n            await self.bm.update_block(self.server_id, id, vote_price, NOTE_TRANSFER)\n        print(sum_traded_votes, vote_price)\n\n    async def issue_timer(self):\n        server = self.client.get_guild(id=self.server_id)\n        channel = self.client.get_channel(self.commands_channel_id)\n        await asyncio.sleep(60*1)\n        await channel.send(\"**1 min** remaining...\")\n        await asyncio.sleep(60*1)\n        await channel.send(\"*Vote finished*\")\n        await self.count_votes()\n        self.issue_in_session = False\n","sub_path":"discordibdd.py","file_name":"discordibdd.py","file_ext":"py","file_size_in_byte":6899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"247978758","text":"from result.HTMLTestRunner import HTMLTestRunner\nfrom unittest import unittest\nfrom testCase.testcase_login import Login\nfrom testCase.testcase_login_null import LoginNull\nfrom testCase.multilotto_get_config import TestML\n\n\nclass TestResult:\n\n    suite = unittest.TestSuite()\n\n    def get_html_report(self):\n        with open(\"HTMLTestReport.html\", 'w') as f:\n            runner = HTMLTestRunner(stream=f, title='testReport', description='generated by htmlTestReport', verbosity=3)\n            runner.run(self.suite)\n\n    def get_text_result(self):\n        with open(\"textTestResult.txt\", 'a') as f:\n            runner = unittest.TextTestRunner(stream=f, descriptions='generated by textTestReport', verbosity=2)\n            runner.run(self.suite)\n\n\nif __name__ == \"__main__\":\n    h1 = TestResult()\n    h1.suite.addTest(Login('test_login'))\n    tests = [LoginNull('test_login_null'), Login('test_login_error'), TestML('testGetnearbyplace')]\n    h1.suite.addTests(tests)\n    h1.get_html_report()\n\n","sub_path":"ML/result/testResult.py","file_name":"testResult.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"558700619","text":"import ast\nimport sys\n\nfileName = sys.argv[1]\n\nlines = []\nwith open(fileName, \"r\", encoding = 'utf-8', errors = 'ignore') as file:\n    lines = file.readlines()\n#print(lines)\n\nlink = []\nfor line in lines :\n    link.extend(ast.literal_eval(line))\nlink = set(link)\nwith open(\"linkList.txt\", \"a+\", encoding = 'utf-8', errors = 'ignore') as w:\n    for l in link:\n        w.write(str(l)+\"\\n\")\n#print(link)\n","sub_path":"comp6976_security/Source Code/merge.py","file_name":"merge.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"138857948","text":"#!/usr/bin/env python\n\nimport os, sys\nimport argparse\nimport math\nimport numpy as np\nfrom utils import create_dir, SubmitBase\n\ngit_tag=os.popen('git describe --tags --abbrev=0').read()\n\nparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\nparser.add_argument('-t', '--git-tag'     , dest='gittag'     , help='git tag version', default=git_tag)\nparser.add_argument(      '--nRuns'       , dest='nRuns'      , type=int,   help='number of run, 0-indexed', default=-1)\nparser.add_argument('-v', '--version'     , dest='version'    , type=int,   help='detector version', default=3)\nparser.add_argument('-m', '--model'       , dest='model'      , type=int,   help='detector model', default=3)\nparser.add_argument('-a', '--etas'        , dest='etas'       , type=float, help='incidence eta', nargs='+')\nparser.add_argument('-p', '--phi'         , dest='phi'        , type=float, help='incidence phi angle in pi unit' , default=0.5)\nparser.add_argument(      '--shape'       , dest='shape'      , type=int,   help='shape', default=1) # 1 = hexagons, 2=diamonds, 3=triangles, 4=squares\nparser.add_argument('-b', '--Bfield'      , dest='Bfield'     , type=float, help='B field value in Tesla'       , default=0)\nparser.add_argument('-d', '--datatype'    , dest='datatype'   ,             help='data type or particle to shoot', default='e-')\nparser.add_argument('-f', '--datafile'    , dest='datafile'   ,             help='full path to HepMC input file', default='') #data/example_MyPythia.dat\nparser.add_argument('-F', '--datafileeos' , dest='datafileeos',             help='EOS path to HepMC input file', default='') #/eos/cms/store/cmst3/group/hgcal/HGCalMinbias/Pythia8/\nparser.add_argument('-n', '--nevts'       , dest='nevts'      , type=int,   help='number of events to generate' , default=1000)\nparser.add_argument(    '--wcuseed'       , dest='wcuseed'    , type=int,   help='Seed to use when randomizing WCu density' , default=42)\nparser.add_argument(    '--wcuresol'      , dest='wcuresol'   , type=float,  help='Relative resolution to use when randomizing WCu density' , default=-1)\nparser.add_argument('-o', '--out'         , dest='out'        ,             help='output directory'             , default=os.getcwd() )\nparser.add_argument('-e', '--eosOut'      , dest='eos'        ,             help='eos path to save root file to EOS',         default='')\nparser.add_argument('-g', '--gun'         , dest='dogun'      ,             help='use particle gun.', action=\"store_true\")\nparser.add_argument(      '--enList'      , dest='enList'     , type=int,   help='E_T list to use with gun', nargs='+', default=[5,10,20,30,40,60,80,100,150,200])\nparser.add_argument('-S', '--no-submit'   , dest='nosubmit'   ,             help='Do not submit batch job.', action=\"store_true\")\nopt, _ = parser.parse_known_args()\n\nprint(opt)\n\n###################################################################################################\n###################################################################################################\n###################################################################################################\nclass SubmitProd(SubmitBase):\n    def __init__(self, **kwargs):\n        super(SubmitProd, self).__init__(**kwargs)\n\n        self.etaint_tag  = '$(ETAX10)'\n        \n        #variables\n        self.condor_submit_name = 'condorSubmitProd.sub'\n        self.mac_var = '$(MACFILE)'\n        self.mac_name = self._unique_name( (('prefix', 'g4steer'),\n                                           ('en', self.shellify_tag(self.en_tag)),\n                                           ('eta', self.shellify_tag(self.etaint_tag)),\n                                           ('run', self.shellify_tag(self.run_tag)),\n                                           ('ext', 'mac')) )\n\n        self.tags = (self.en_tag, self.eta_tag, self.run_tag, self.gran_tag)\n        self.labels = ('energy', 'eta', 'run', 'granularity')\n        \n    def gen_uniform_int_random_seeds_(self, low, high, size):\n        np.random.seed()\n        r = np.random.uniform(low=low, high=high, size=size)\n        return [int(x) for x in r]\n        \n    def write_shell_script_file(self):\n        with open('{}/runJob.sh'.format(self.outDir), 'w') as s:\n            s.write('#!/usr/bin/env bash\\n')\n\n            #input arguments: energy, eta and run\n            s.write('ARGS=`getopt -o \"\" -l \",energy:,eta:,run:,granularity:\" -n \"getopts_${0}\" -- \"$@\"`\\n')\n            s.write('eval set -- \"$ARGS\"\\n')\n            s.write('while true; do\\n')\n            s.write('case \"$1\" in\\n')\n            for l,t in zip(self.labels, self.tags):\n                s.write('--'+l+')\\n')\n                s.write('if [ -n \"$2\" ]; then\\n')\n                if l=='eta':\n                    tmp = \"$(echo ${2} | sed 's/\\.//');\"\n                    s.write('{}='.format(self.clean_tag(self.etaint_tag))+tmp+'\\n')\n                    s.write('echo \"'+l+'x10: {}\";\\n'.format(self.shellify_tag(self.etaint_tag)))\n                s.write('{}=\"${{2}}\";\\n'.format(self.clean_tag(t)))\n                s.write('echo \"'+l+': {}\";\\n'.format(self.shellify_tag(t)))\n                s.write('fi\\n')\n                s.write('shift 2;;\\n')\n            s.write('--)\\n')\n            s.write('shift\\n')\n            s.write('break;;\\n')\n            s.write('esac\\n')\n            s.write('done\\n\\n')\n            \n            s.write('localdir=`pwd`\\n')\n            s.write('echo \"Job local dir: ${localdir}\"\\n')\n            s.write('{}=\"{}/{}\"\\n'.format(self.clean_tag(self.mac_var),self.outDir,self.mac_name))\n            s.write('export HOME={}\\n'.format(os.environ['HOME']))\n            s.write('cd {}/\\n'.format(os.getcwd()))\n            s.write('source {}/g4env.sh\\n'.format(os.getcwd()))\n            s.write('cd $localdir\\n')\n            if len(self.p.datafileeos)>0:\n                s.write('eos cp {} {}\\n'.format( os.path.join(self.p.datafileeos,self.p.datafile),self.p.datafile))\n\n            cmd = ( 'PFCalEE \"{}\" --model {} --version {} --eta {} --shape {} --wcuseed {} --wcuresol {}'\n                    .format(self.shellify_tag(self.mac_var), self.p.model, self.p.version, self.shellify_tag(self.eta_tag), self.p.shape,self.p.wcuseed,self.p.wcuresol) )\n            s.write('if [ \"${GRAN}\" -eq 0 ]; then\\n')\n            s.write(cmd + ' --fineGranularity\\n')\n            s.write('elif [ \"${GRAN}\" -eq -1 ]; then\\n')\n            s.write(cmd + ' --ultraFineGranularity\\n')\n            s.write('else\\n')\n            s.write(cmd + '\\n')\n            s.write('fi\\n')\n            \n\n            outTag = 'version{}_model{}_{}'.format(self.p.version, self.p.model, self.bfield)\n            outTag += '_en{}_eta{}'.format(self.shellify_tag(self.en_tag),self.shellify_tag(self.etaint_tag)) \n            if self.p.phi != 0.5: outTag += '_phi{n:.{r}f}pi'.format(n=self.p.phi,r=3)\n            outTag += '_run{}'.format(self.shellify_tag(self.run_tag))\n            logfile = os.path.join(self.outDir, 'g4_'+outTag+'.log')\n            s.write('mv PFcal.root HGcal_{}.root\\n'.format(outTag))\n            s.write('echo \"--Local directory is $localdir\" >> {}\\n'.format(logfile))\n            s.write('echo home=$HOME >> {}\\n'.format(logfile))\n            s.write('echo path=$PATH >> {}\\n'.format(logfile))\n            s.write('echo ldlibpath=$LD_LIBRARY_PATH >> {}\\n'.format(logfile))\n            s.write('ls -ltrh * >> {}\\n'.format(logfile))\n            if len(self.p.eos)>0:\n                s.write('eos mkdir -p {}\\n'.format(self.eosDirOut))\n                s.write('eos cp HGcal_{}.root {}/HGcal_{}.root\\n'.format(outTag,self.eosDirOut,outTag))\n                s.write('if (( \"$?\" != \"0\" )); then\\n')\n                s.write('echo \" --- Problem with copy of file PFcal.root to EOS. Keeping locally.\" >> {}\\n'.format(logfile))\n                s.write('else\\n')\n                s.write('eossize=`eos ls -l {}/HGcal_{}.root | awk \\'{{print $5}}\\'`\\n'.format(self.eosDirOut,outTag))\n                s.write('localsize=`ls -l HGcal_{}.root | awk \\'{{print $5}}\\'`\\n'.format(outTag))\n                s.write('if [ \"${eossize}\" != \"${localsize}\" ]; then\\n')\n                s.write('echo \" --- Copy of sim file to eos failed. Localsize = ${{localsize}}, eossize = ${{eossize}}. Keeping locally...\" >> {}\\n'.format(logfile))\n                s.write('else\\n')\n                s.write('echo \" --- Size check done: Localsize = ${{localsize}}, eossize = ${{eossize}}\" >> {}\\n'.format(logfile))\n                s.write('echo \" --- File PFcal.root successfully copied to EOS: {}/HGcal_{}.root\" >> {}\\n'.format(self.eosDirOut,outTag,logfile))\n                s.write('rm HGcal_{}.root\\n'.format(outTag))\n                s.write('fi\\n')\n                s.write('fi\\n')\n\n            s.write('echo \"--deleting core files and hepmc files: too heavy!!\"\\n')\n            s.write('rm core.*\\n')\n            if len(self.p.datafileeos)>0:\n                s.write('rm {}\\n'.format(self.p.datafile))\n            s.write('cp * {}/\\n'.format(self.outDir))\n            s.write('echo \"All done\"\\n')\n\n    def write_geant4_files(self):\n        \"\"\"\n        Writes all required geant4 input files, one\n        for each run (different seed) and energy.\n        \"\"\"\n        niters = self.p.nRuns*len(self.p.enList)*len(self.p.etas)\n        gen_kwargs = dict(low=0, high=100000, size=niters)\n        seeds1 = self.gen_uniform_int_random_seeds_(**gen_kwargs)\n        seeds2 = self.gen_uniform_int_random_seeds_(**gen_kwargs)\n        \n        for run in range(self.p.nRuns):\n            for iet,et in enumerate(self.p.enList):\n                for ieta,eta in enumerate(self.p.etas):\n                    gen_idx = ( ( run * len(self.p.enList) * len(self.p.etas) ) +\n                                ( iet * len(self.p.etas) ) +\n                                ( ieta ) )\n                    assert(gen_idx < niters)\n                    \n                    t = ( ('prefix', 'g4steer'), ('en', et), ('eta', int(eta*10.)), ('run', run), ('ext', 'mac') )\n                    this_mac_name = self._unique_name(t)\n                    with open('{}/{}'.format(self.outDir, this_mac_name), 'w') as s:\n                        s.write('/control/verbose 0\\n')\n                        s.write('/control/saveHistory\\n')\n                        s.write('/run/verbose 0\\n')\n                        s.write('/event/verbose 0\\n')\n                        s.write('/tracking/verbose 0\\n')\n                        s.write('/N03/det/setField {n:.{r}f} T\\n'.format(n=self.p.Bfield,r=1))\n                        s.write('/N03/det/setModel {}\\n'.format(self.p.model))\n                        s.write('/random/setSeeds {} {}\\n'.format(seeds1[gen_idx], seeds2[gen_idx]) )\n                        if self.p.dogun :\n                            s.write('/generator/select particleGun\\n')\n                            s.write('/gun/particle {} \\n'.format(self.p.datatype))\n                            en = et*math.cosh(eta) if eta<5 else et\n                            s.write('/gun/energy {n:.{r}f} GeV\\n'.format(n=en, r=6))\n                            if self.p.model != 2 and eta<5:\n                                alpha = 2*math.atan(math.exp(-1.*eta));\n                                s.write('/gun/direction {} {} {}\\n'.format(math.cos(math.pi*self.p.phi)*math.sin(alpha),math.sin(math.pi*self.p.phi)*math.sin(alpha),math.cos(alpha)))\n                            \n                        else :\n                            s.write('/generator/select hepmcAscii\\n')\n                            s.write('/generator/hepmcAscii/open {}\\n'.format(self.p.datafile))\n                            s.write('/generator/hepmcAscii/verbose 0\\n')\n                        s.write('/run/beamOn {}\\n'.format(self.p.nevts))\n\n\n    def write_condor_submission_file(self):\n        \"\"\"\n        Writes one single condor submission file, which is expanded to multiple\n        jobs for different energies, etas and runs.\n        \"\"\"\n        with open('{}/{}'.format(self.outDir,self.condor_submit_name), 'w') as s:\n            s.write('universe = vanilla\\n')\n            s.write('Executable = {}/runJob.sh\\n'.format(self.outDir))\n            s.write( ('Arguments = --energy {} --eta {} --run {} --granularity {}\\n'\n                      .format(self.en_tag, self.eta_tag, self.run_tag, self.gran_tag)) )\n            #s.write('Requirements = (OpSysAndVer =?= \"CentOS7\")\\n')\n            s.write('MY.WantOS = \"el7\"\\n')\n\n            t = ( ('prefix', 'prod'), ('en', self.en_tag), ('eta', self.eta_tag),\n                  ('run', self.run_tag) )\n            out_name = self._unique_name( t + (('ext', 'out'),) )\n            err_name = self._unique_name( t + (('ext', 'err'),))\n            log_name = self._unique_name( t + (('ext', 'log'),))\n            s.write('Output = {}/{}\\n'.format(self.outDir,out_name))\n            s.write('Error = {}/{}\\n'.format(self.outDir,err_name))\n            s.write('Log = {}/{}\\n'.format(self.outDir,log_name))\n            s.write('RequestMemory = 2GB\\n')\n            s.write('+JobFlavour = \"testmatch\"\\n')            \n            s.write('JobBatchName = prod_' + self.p.gittag + '_' + str(self.p.version) + '_' + self.p.datatype + '\\n')\n            s.write('Queue {nruns} {entag}, {etatag}, {gtag} from (\\n'.format( nruns=self.p.nRuns, entag=self.clean_tag(self.en_tag),\n                                                                               etatag=self.clean_tag(self.eta_tag),\n                                                                               gtag=self.clean_tag(self.gran_tag)))\n            for et in self.p.enList:\n                for eta in self.p.etas:\n                    gran = 1 if eta < 2.4 else 0\n                    if (opt.model==3): gran = -1\n                    s.write('{}, {}, {}\\n'.format(et,str(eta),gran))\n            s.write(')')\n\n###################################################################################################\n###################################################################################################\n###################################################################################################\n\nbval = 'BON' if opt.Bfield>0 else 'BOFF'\nlab = '200u'\nodir = '{}/git{}/version_{}/model_{}/{}/{}/{}'.format(opt.out,opt.gittag,opt.version,opt.model,opt.datatype,bval,lab)\nif opt.phi != 0.5: odir='{out}/phi_{n:.{r}f}pi'.format(out=odir,n=opt.phi,r=3)\neos_partial = opt.eos[1:] if os.path.isabs(opt.eos) else opt.eos\nedir = os.path.join('/eos', 'cms', eos_partial, 'git' + opt.gittag, opt.datatype)\n\nsubprod = SubmitProd(outDir=odir, eosDirOut=edir, bfield=bval, params=opt)\nsubprod.write_shell_script_file()\nsubprod.write_geant4_files()\nsubprod.write_condor_submission_file()\n\nos.system('chmod u+rwx {}/runJob.sh'.format(odir))\nif opt.nosubmit:\n    os.system('echo condor_submit {}/{}'.format(odir, subprod.condor_submit_name)) \nelse:\n    os.system('condor_submit {}/{}'.format(odir, subprod.condor_submit_name))\n","sub_path":"PFCalEE/submitProd.py","file_name":"submitProd.py","file_ext":"py","file_size_in_byte":14891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"426469434","text":"import numpy as np\n\nclass BaseEnvironment1D():\n    def __init__(self, size, num_nodes):\n        self.size = size \n        self.num_nodes = num_nodes\n        self.node_spacing = (size[1] - size[0]) / (num_nodes-1)  \n        self.space_vec = np.linspace(size[0], size[1], num_nodes)\n\nclass BaseEnvironment2D():\n    def __init__(self, size, num_nodes):\n        self.size = size \n        self.num_nodes = num_nodes\n        self.node_spacing = (size[:, 1] - size[:, 0]) / (num_nodes - 1)\n        lin_x = np.linspspace(size[0, 0], size[0, 1], num_nodes[0])\n        lin_y = np.linspspace(size[1, 0], size[1, 1], num_nodes[1])\n        space_x, space_y = np.meshgrid(lin_x, lin_y)\n        self.space_vec = np.array([space_x.flatten(), space_y.flatten()]).T\n\nclass BaseWave1D():\n    def __init__(self, pos_init, mom_init, environment):\n        self.psi = np.zeros(environment.num_nodes, dtype=np.cfloat)\n        self.pos_init = pos_init\n        self.mom_init = mom_init\n        self.environment = environment\n\n\n","sub_path":"quantum_dynamics/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"109518212","text":"import os\nimport subprocess\n\nos.chdir(os.path.dirname(os.path.abspath(__file__)))\n\ndef extract_warc_files():\n    for file in os.listdir('.'):\n        if not file.endswith('warc.gz'):\n            continue\n\n        output_dir = file.replace('.warc.gz', '')\n \n        if not os.path.isdir(output_dir):\n            os.mkdir(output_dir)\n\n        filepath = os.path.dirname( os.path.abspath(__file__))\n\n        filepath = os.path.join(filepath, file)\n\n        command = [\n            'python3',\n            '-m' 'warcat',\n            'extract',\n            '{file}'.format(file=filepath),\n            '--output-dir',\n            '{output_dir}'.format(output_dir=output_dir),\n            '--progress'\n        ]\n\n        extract_warc = subprocess.Popen(command)\n        extract_warc.wait()\n\n\nif __name__ == '__main__':\n    extract_warc_files()\n","sub_path":"extra_docs/warc2files/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"25012313","text":"\"\"\"The Channel class provides a wrapper for interacting with RabbitMQ\nimplementing the methods and behaviors for an AMQP Channel.\n\n\"\"\"\nimport collections\nimport logging\nimport warnings\nimport uuid\n\nfrom . import frame as frame\nfrom . import exceptions\nfrom . import spec as spec\nfrom .utils import is_callable\nfrom .compat import unicode_type, dictkeys, as_bytes\n\n\nLOGGER = logging.getLogger(__name__)\nMAX_CHANNELS = 32768\n\n\nclass Channel(object):\n    \"\"\"A Channel is the primary communication method for interacting with\n    RabbitMQ. It is recommended that you do not directly invoke\n    the creation of a channel object in your application code but rather\n    construct the a channel by calling the active connection's channel()\n    method.\n\n    \"\"\"\n    CLOSED = 0\n    OPENING = 1\n    OPEN = 2\n    CLOSING = 3\n\n    _ON_CHANNEL_CLEANUP_CB_KEY = '_on_channel_cleanup'\n\n    def __init__(self, connection, channel_number, on_open_callback=None):\n        \"\"\"Create a new instance of the Channel\n\n        :param pika.connection.Connection connection: The connection\n        :param int channel_number: The channel number for this instance\n        :param method on_open_callback: The method to call on channel open\n\n        \"\"\"\n        if not isinstance(channel_number, int):\n            raise exceptions.InvalidChannelNumber\n        self.channel_number = channel_number\n        self.callbacks = connection.callbacks\n        self.connection = connection\n\n        # The frame-handler changes depending on the type of frame processed\n        self.frame_dispatcher = ContentFrameDispatcher()\n\n        self._blocked = collections.deque(list())\n        self._blocking = None\n        self._has_on_flow_callback = False\n        self._cancelled = set()\n        self._consumers = dict()\n        self._consumers_with_noack = set()\n        self._on_flowok_callback = None\n        self._on_getok_callback = None\n        self._on_openok_callback = on_open_callback\n        self._pending = dict()\n        self._state = self.CLOSED\n\n        # opaque cookie value set by wrapper layer (e.g., BlockingConnection)\n        # via _set_cookie\n        self._cookie = None\n\n    def __int__(self):\n        \"\"\"Return the channel object as its channel number\n\n        :rtype: int\n\n        \"\"\"\n        return self.channel_number\n\n    def add_callback(self, callback, replies, one_shot=True):\n        \"\"\"Pass in a callback handler and a list replies from the\n        RabbitMQ broker which you'd like the callback notified of. Callbacks\n        should allow for the frame parameter to be passed in.\n\n        :param method callback: The method to call\n        :param list replies: The replies to get a callback for\n        :param bool one_shot: Only handle the first type callback\n\n        \"\"\"\n        for reply in replies:\n            self.callbacks.add(self.channel_number, reply, callback, one_shot)\n\n    def add_on_cancel_callback(self, callback):\n        \"\"\"Pass a callback function that will be called when the basic_cancel\n        is sent by the server. The callback function should receive a frame\n        parameter.\n\n        :param method callback: The method to call on callback\n\n        \"\"\"\n        self.callbacks.add(self.channel_number, spec.Basic.Cancel, callback,\n                           False)\n\n    def add_on_close_callback(self, callback):\n        \"\"\"Pass a callback function that will be called when the channel is\n        closed. The callback function will receive the channel, the\n        reply_code (int) and the reply_text (int) sent by the server describing\n        why the channel was closed.\n\n        :param method callback: The method to call on callback\n\n        \"\"\"\n        self.callbacks.add(self.channel_number, '_on_channel_close', callback,\n                           False, self)\n\n    def add_on_flow_callback(self, callback):\n        \"\"\"Pass a callback function that will be called when Channel.Flow is\n        called by the remote server. Note that newer versions of RabbitMQ\n        will not issue this but instead use TCP backpressure\n\n        :param method callback: The method to call on callback\n\n        \"\"\"\n        self._has_on_flow_callback = True\n        self.callbacks.add(self.channel_number, spec.Channel.Flow, callback,\n                           False)\n\n    def add_on_return_callback(self, callback):\n        \"\"\"Pass a callback function that will be called when basic_publish as\n        sent a message that has been rejected and returned by the server.\n\n        :param method callback: The method to call on callback with the\n                                signature callback(channel, method, properties,\n                                                   body), where\n                                channel: pika.Channel\n                                method: pika.spec.Basic.Return\n                                properties: pika.spec.BasicProperties\n                                body: str, unicode, or bytes (python 3.x)\n\n        \"\"\"\n        self.callbacks.add(self.channel_number, '_on_return', callback, False)\n\n    def basic_ack(self, delivery_tag=0, multiple=False):\n        \"\"\"Acknowledge one or more messages. When sent by the client, this\n        method acknowledges one or more messages delivered via the Deliver or\n        Get-Ok methods. When sent by server, this method acknowledges one or\n        more messages published with the Publish method on a channel in\n        confirm mode. The acknowledgement can be for a single message or a\n        set of messages up to and including a specific message.\n\n        :param int delivery-tag: The server-assigned delivery tag\n        :param bool multiple: If set to True, the delivery tag is treated as\n                              \"up to and including\", so that multiple messages\n                              can be acknowledged with a single method. If set\n                              to False, the delivery tag refers to a single\n                              message. If the multiple field is 1, and the\n                              delivery tag is zero, this indicates\n                              acknowledgement of all outstanding messages.\n        \"\"\"\n        if not self.is_open:\n            raise exceptions.ChannelClosed()\n        return self._send_method(spec.Basic.Ack(delivery_tag, multiple))\n\n    def basic_cancel(self, callback=None, consumer_tag='', nowait=False):\n        \"\"\"This method cancels a consumer. This does not affect already\n        delivered messages, but it does mean the server will not send any more\n        messages for that consumer. The client may receive an arbitrary number\n        of messages in between sending the cancel method and receiving the\n        cancel-ok reply. It may also be sent from the server to the client in\n        the event of the consumer being unexpectedly cancelled (i.e. cancelled\n        for any reason other than the server receiving the corresponding\n        basic.cancel from the client). This allows clients to be notified of\n        the loss of consumers due to events such as queue deletion.\n\n        :param method callback: Method to call for a Basic.CancelOk response\n        :param str consumer_tag: Identifier for the consumer\n        :param bool nowait: Do not expect a Basic.CancelOk response\n        :raises: ValueError\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        if consumer_tag not in self.consumer_tags:\n            return\n        if callback:\n            if nowait is True:\n                raise ValueError('Can not pass a callback if nowait is True')\n            self.callbacks.add(self.channel_number, spec.Basic.CancelOk,\n                               callback)\n        self._cancelled.add(consumer_tag)\n        self._rpc(spec.Basic.Cancel(consumer_tag=consumer_tag,\n                                    nowait=nowait), self._on_cancelok,\n                  [(spec.Basic.CancelOk, {'consumer_tag': consumer_tag})] if\n                  nowait is False else [])\n\n    def basic_consume(self, consumer_callback,\n                      queue='',\n                      no_ack=False,\n                      exclusive=False,\n                      consumer_tag=None,\n                      arguments=None):\n        \"\"\"Sends the AMQP command Basic.Consume to the broker and binds messages\n        for the consumer_tag to the consumer callback. If you do not pass in\n        a consumer_tag, one will be automatically generated for you. Returns\n        the consumer tag.\n\n        For more information on basic_consume, see:\n        http://www.rabbitmq.com/amqp-0-9-1-reference.html#basic.consume\n\n        :param method consumer_callback: The method to callback when consuming\n            with the signature consumer_callback(channel, method, properties,\n                                                 body), where\n                                channel: pika.Channel\n                                method: pika.spec.Basic.Deliver\n                                properties: pika.spec.BasicProperties\n                                body: str, unicode, or bytes (python 3.x)\n\n        :param queue: The queue to consume from\n        :type queue: str or unicode\n        :param bool no_ack: Tell the broker to not expect a response\n        :param bool exclusive: Don't allow other consumers on the queue\n        :param consumer_tag: Specify your own consumer tag\n        :type consumer_tag: str or unicode\n        :param dict arguments: Custom key/value pair arguments for the consume\n        :rtype: str\n\n        \"\"\"\n        self._validate_channel_and_callback(consumer_callback)\n\n        # If a consumer tag was not passed, create one\n        if not consumer_tag:\n            consumer_tag = self._generate_consumer_tag()\n\n        if consumer_tag in self._consumers or consumer_tag in self._cancelled:\n            raise exceptions.DuplicateConsumerTag(consumer_tag)\n\n        if no_ack:\n            self._consumers_with_noack.add(consumer_tag)\n\n        self._consumers[consumer_tag] = consumer_callback\n        self._pending[consumer_tag] = list()\n        self._rpc(spec.Basic.Consume(queue=queue,\n                                     consumer_tag=consumer_tag,\n                                     no_ack=no_ack,\n                                     exclusive=exclusive,\n                                     arguments=arguments or dict()),\n                  self._on_eventok, [(spec.Basic.ConsumeOk,\n                                      {'consumer_tag': consumer_tag})])\n\n        return consumer_tag\n\n    def _generate_consumer_tag(self):\n        \"\"\"Generate a consumer tag\n\n        NOTE: this protected method may be called by derived classes\n\n        :returns: consumer tag\n        :rtype: str\n        \"\"\"\n        return 'ctag%i.%s' % (self.channel_number,\n                              uuid.uuid4().hex)\n\n    def basic_get(self, callback=None, queue='', no_ack=False):\n        \"\"\"Get a single message from the AMQP broker. If you want to\n        be notified of Basic.GetEmpty, use the Channel.add_callback method\n        adding your Basic.GetEmpty callback which should expect only one\n        parameter, frame. For more information on basic_get and its\n        parameters, see:\n\n        http://www.rabbitmq.com/amqp-0-9-1-reference.html#basic.get\n\n        :param method callback: The method to callback with a message that has\n            the signature callback(channel, method, properties, body), where:\n            channel: pika.Channel\n            method: pika.spec.Basic.GetOk\n            properties: pika.spec.BasicProperties\n            body: str, unicode, or bytes (python 3.x)\n        :param queue: The queue to get a message from\n        :type queue: str or unicode\n        :param bool no_ack: Tell the broker to not expect a reply\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        self._on_getok_callback = callback\n        self._send_method(spec.Basic.Get(queue=queue, no_ack=no_ack))\n\n    def basic_nack(self, delivery_tag=None, multiple=False, requeue=True):\n        \"\"\"This method allows a client to reject one or more incoming messages.\n        It can be used to interrupt and cancel large incoming messages, or\n        return untreatable messages to their original queue.\n\n        :param int delivery-tag: The server-assigned delivery tag\n        :param bool multiple: If set to True, the delivery tag is treated as\n                              \"up to and including\", so that multiple messages\n                              can be acknowledged with a single method. If set\n                              to False, the delivery tag refers to a single\n                              message. If the multiple field is 1, and the\n                              delivery tag is zero, this indicates\n                              acknowledgement of all outstanding messages.\n        :param bool requeue: If requeue is true, the server will attempt to\n                             requeue the message. If requeue is false or the\n                             requeue attempt fails the messages are discarded or\n                             dead-lettered.\n\n        \"\"\"\n        if not self.is_open:\n            raise exceptions.ChannelClosed()\n        return self._send_method(spec.Basic.Nack(delivery_tag, multiple,\n                                                 requeue))\n\n    def basic_publish(self, exchange, routing_key, body,\n                      properties=None,\n                      mandatory=False,\n                      immediate=False):\n        \"\"\"Publish to the channel with the given exchange, routing key and body.\n        For more information on basic_publish and what the parameters do, see:\n\n        http://www.rabbitmq.com/amqp-0-9-1-reference.html#basic.publish\n\n        :param exchange: The exchange to publish to\n        :type exchange: str or unicode\n        :param routing_key: The routing key to bind on\n        :type routing_key: str or unicode\n        :param body: The message body\n        :type body: str or unicode\n        :param pika.spec.BasicProperties properties: Basic.properties\n        :param bool mandatory: The mandatory flag\n        :param bool immediate: The immediate flag\n\n        \"\"\"\n        if not self.is_open:\n            raise exceptions.ChannelClosed()\n        if immediate:\n            LOGGER.warning('The immediate flag is deprecated in RabbitMQ')\n        if isinstance(body, unicode_type):\n            body = body.encode('utf-8')\n        properties = properties or spec.BasicProperties()\n        self._send_method(spec.Basic.Publish(exchange=exchange,\n                                             routing_key=routing_key,\n                                             mandatory=mandatory,\n                                             immediate=immediate),\n                          (properties, body))\n\n    def basic_qos(self,\n                  callback=None,\n                  prefetch_size=0,\n                  prefetch_count=0,\n                  all_channels=False):\n        \"\"\"Specify quality of service. This method requests a specific quality\n        of service. The QoS can be specified for the current channel or for all\n        channels on the connection. The client can request that messages be sent\n        in advance so that when the client finishes processing a message, the\n        following message is already held locally, rather than needing to be\n        sent down the channel. Prefetching gives a performance improvement.\n\n        :param method callback: The method to callback for Basic.QosOk response\n        :param int prefetch_size:  This field specifies the prefetch window\n                                   size. The server will send a message in\n                                   advance if it is equal to or smaller in size\n                                   than the available prefetch size (and also\n                                   falls into other prefetch limits). May be set\n                                   to zero, meaning \"no specific limit\",\n                                   although other prefetch limits may still\n                                   apply. The prefetch-size is ignored if the\n                                   no-ack option is set.\n        :param int prefetch_count: Specifies a prefetch window in terms of whole\n                                   messages. This field may be used in\n                                   combination with the prefetch-size field; a\n                                   message will only be sent in advance if both\n                                   prefetch windows (and those at the channel\n                                   and connection level) allow it. The\n                                   prefetch-count is ignored if the no-ack\n                                   option is set.\n        :param bool all_channels: Should the QoS apply to all channels\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        return self._rpc(spec.Basic.Qos(prefetch_size, prefetch_count,\n                                        all_channels), callback,\n                         [spec.Basic.QosOk])\n\n    def basic_reject(self, delivery_tag, requeue=True):\n        \"\"\"Reject an incoming message. This method allows a client to reject a\n        message. It can be used to interrupt and cancel large incoming messages,\n        or return untreatable messages to their original queue.\n\n        :param int delivery-tag: The server-assigned delivery tag\n        :param bool requeue: If requeue is true, the server will attempt to\n                             requeue the message. If requeue is false or the\n                             requeue attempt fails the messages are discarded or\n                             dead-lettered.\n        :raises: TypeError\n\n        \"\"\"\n        if not self.is_open:\n            raise exceptions.ChannelClosed()\n        if not isinstance(delivery_tag, int):\n            raise TypeError('delivery_tag must be an integer')\n        return self._send_method(spec.Basic.Reject(delivery_tag, requeue))\n\n    def basic_recover(self, callback=None, requeue=False):\n        \"\"\"This method asks the server to redeliver all unacknowledged messages\n        on a specified channel. Zero or more messages may be redelivered. This\n        method replaces the asynchronous Recover.\n\n        :param method callback: Method to call when receiving Basic.RecoverOk\n        :param bool requeue: If False, the message will be redelivered to the\n                             original recipient. If True, the server will\n                             attempt to requeue the message, potentially then\n                             delivering it to an alternative subscriber.\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        return self._rpc(spec.Basic.Recover(requeue), callback,\n                         [spec.Basic.RecoverOk])\n\n    def close(self, reply_code=0, reply_text=\"Normal Shutdown\"):\n        \"\"\"Will invoke a clean shutdown of the channel with the AMQP Broker.\n\n        :param int reply_code: The reply code to close the channel with\n        :param str reply_text: The reply text to close the channel with\n\n        \"\"\"\n        if not self.is_open:\n            raise exceptions.ChannelClosed()\n        LOGGER.info('Channel.close(%s, %s)', reply_code, reply_text)\n        if self._consumers:\n            LOGGER.debug('Cancelling %i consumers', len(self._consumers))\n            for consumer_tag in dictkeys(self._consumers):\n                self.basic_cancel(consumer_tag=consumer_tag)\n        self._set_state(self.CLOSING)\n        self._rpc(spec.Channel.Close(reply_code, reply_text, 0, 0),\n                  self._on_closeok, [spec.Channel.CloseOk])\n\n    def confirm_delivery(self, callback=None, nowait=False):\n        \"\"\"Turn on Confirm mode in the channel. Pass in a callback to be\n        notified by the Broker when a message has been confirmed as received or\n        rejected (Basic.Ack, Basic.Nack) from the broker to the publisher.\n\n        For more information see:\n            http://www.rabbitmq.com/extensions.html#confirms\n\n        :param method callback: The callback for delivery confirmations\n        :param bool nowait: Do not send a reply frame (Confirm.SelectOk)\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        if (self.connection.publisher_confirms is False or\n            self.connection.basic_nack is False):\n            raise exceptions.MethodNotImplemented('Not Supported on Server')\n\n        # Add the ack and nack callbacks\n        if callback is not None:\n            self.callbacks.add(self.channel_number, spec.Basic.Ack, callback,\n                               False)\n            self.callbacks.add(self.channel_number, spec.Basic.Nack, callback,\n                               False)\n\n        # Send the RPC command\n        self._rpc(spec.Confirm.Select(nowait), self._on_selectok,\n                  [spec.Confirm.SelectOk] if nowait is False else [])\n\n    @property\n    def consumer_tags(self):\n        \"\"\"Property method that returns a list of currently active consumers\n\n        :rtype: list\n\n        \"\"\"\n        return dictkeys(self._consumers)\n\n    def exchange_bind(self,\n                      callback=None,\n                      destination=None,\n                      source=None,\n                      routing_key='',\n                      nowait=False,\n                      arguments=None):\n        \"\"\"Bind an exchange to another exchange.\n\n        :param method callback: The method to call on Exchange.BindOk\n        :param destination: The destination exchange to bind\n        :type destination: str or unicode\n        :param source: The source exchange to bind to\n        :type source: str or unicode\n        :param routing_key: The routing key to bind on\n        :type routing_key: str or unicode\n        :param bool nowait: Do not wait for an Exchange.BindOk\n        :param dict arguments: Custom key/value pair arguments for the binding\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        return self._rpc(spec.Exchange.Bind(0, destination, source, routing_key,\n                                            nowait, arguments or dict()),\n                         callback, [spec.Exchange.BindOk] if nowait is False\n                         else [])\n\n    def exchange_declare(self,\n                         callback=None,\n                         exchange=None,\n                         exchange_type='direct',\n                         passive=False,\n                         durable=False,\n                         auto_delete=False,\n                         internal=False,\n                         nowait=False,\n                         arguments=None,\n                         type=None):\n        \"\"\"This method creates an exchange if it does not already exist, and if\n        the exchange exists, verifies that it is of the correct and expected\n        class.\n\n        If passive set, the server will reply with Declare-Ok if the exchange\n        already exists with the same name, and raise an error if not and if the\n        exchange does not already exist, the server MUST raise a channel\n        exception with reply code 404 (not found).\n\n        :param method callback: Call this method on Exchange.DeclareOk\n        :param exchange: The exchange name consists of a non-empty\n        :type exchange: str or unicode\n                                     sequence of these characters: letters,\n                                     digits, hyphen, underscore, period, or\n                                     colon.\n        :param str exchange_type: The exchange type to use\n        :param bool passive: Perform a declare or just check to see if it exists\n        :param bool durable: Survive a reboot of RabbitMQ\n        :param bool auto_delete: Remove when no more queues are bound to it\n        :param bool internal: Can only be published to by other exchanges\n        :param bool nowait: Do not expect an Exchange.DeclareOk response\n        :param dict arguments: Custom key/value pair arguments for the exchange\n        :param str type: The deprecated exchange type parameter\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        if type is not None:\n            warnings.warn('type is deprecated, use exchange_type instead',\n                          DeprecationWarning)\n            if exchange_type == 'direct' and type != exchange_type:\n                exchange_type = type\n        return self._rpc(spec.Exchange.Declare(0, exchange, exchange_type,\n                                               passive, durable, auto_delete,\n                                               internal, nowait,\n                                               arguments or dict()), callback,\n                         [spec.Exchange.DeclareOk] if nowait is False else [])\n\n    def exchange_delete(self,\n                        callback=None,\n                        exchange=None,\n                        if_unused=False,\n                        nowait=False):\n        \"\"\"Delete the exchange.\n\n        :param method callback: The method to call on Exchange.DeleteOk\n        :param exchange: The exchange name\n        :type exchange: str or unicode\n        :param bool if_unused: only delete if the exchange is unused\n        :param bool nowait: Do not wait for an Exchange.DeleteOk\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        return self._rpc(spec.Exchange.Delete(0, exchange, if_unused, nowait),\n                         callback, [spec.Exchange.DeleteOk] if nowait is False\n                         else [])\n\n    def exchange_unbind(self,\n                        callback=None,\n                        destination=None,\n                        source=None,\n                        routing_key='',\n                        nowait=False,\n                        arguments=None):\n        \"\"\"Unbind an exchange from another exchange.\n\n        :param method callback: The method to call on Exchange.UnbindOk\n        :param destination: The destination exchange to unbind\n        :type destination: str or unicode\n        :param source: The source exchange to unbind from\n        :type source: str or unicode\n        :param routing_key: The routing key to unbind\n        :type routing_key: str or unicode\n        :param bool nowait: Do not wait for an Exchange.UnbindOk\n        :param dict arguments: Custom key/value pair arguments for the binding\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        return self._rpc(spec.Exchange.Unbind(0, destination, source,\n                                              routing_key, nowait, arguments),\n                         callback, [spec.Exchange.UnbindOk] if nowait is False\n                         else [])\n\n    def flow(self, callback, active):\n        \"\"\"Turn Channel flow control off and on. Pass a callback to be notified\n        of the response from the server. active is a bool. Callback should\n        expect a bool in response indicating channel flow state. For more\n        information, please reference:\n\n        http://www.rabbitmq.com/amqp-0-9-1-reference.html#channel.flow\n\n        :param method callback: The callback method\n        :param bool active: Turn flow on or off\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        self._on_flowok_callback = callback\n        self._rpc(spec.Channel.Flow(active), self._on_flowok,\n                  [spec.Channel.FlowOk])\n\n    @property\n    def is_closed(self):\n        \"\"\"Returns True if the channel is closed.\n\n        :rtype: bool\n\n        \"\"\"\n        return self._state == self.CLOSED\n\n    @property\n    def is_closing(self):\n        \"\"\"Returns True if the channel is closing.\n\n        :rtype: bool\n\n        \"\"\"\n        return self._state == self.CLOSING\n\n    @property\n    def is_open(self):\n        \"\"\"Returns True if the channel is open.\n\n        :rtype: bool\n\n        \"\"\"\n        return self._state == self.OPEN\n\n    def open(self):\n        \"\"\"Open the channel\"\"\"\n        self._set_state(self.OPENING)\n        self._add_callbacks()\n        self._rpc(spec.Channel.Open(), self._on_openok, [spec.Channel.OpenOk])\n\n    def queue_bind(self, callback, queue, exchange,\n                   routing_key=None,\n                   nowait=False,\n                   arguments=None):\n        \"\"\"Bind the queue to the specified exchange\n\n        :param method callback: The method to call on Queue.BindOk\n        :param queue: The queue to bind to the exchange\n        :type queue: str or unicode\n        :param exchange: The source exchange to bind to\n        :type exchange: str or unicode\n        :param routing_key: The routing key to bind on\n        :type routing_key: str or unicode\n        :param bool nowait: Do not wait for a Queue.BindOk\n        :param dict arguments: Custom key/value pair arguments for the binding\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        replies = [spec.Queue.BindOk] if nowait is False else []\n        if routing_key is None:\n            routing_key = queue\n        return self._rpc(spec.Queue.Bind(0, queue, exchange, routing_key,\n                                         nowait, arguments or dict()), callback,\n                         replies)\n\n    def queue_declare(self, callback,\n                      queue='',\n                      passive=False,\n                      durable=False,\n                      exclusive=False,\n                      auto_delete=False,\n                      nowait=False,\n                      arguments=None):\n        \"\"\"Declare queue, create if needed. This method creates or checks a\n        queue. When creating a new queue the client can specify various\n        properties that control the durability of the queue and its contents,\n        and the level of sharing for the queue.\n\n        Leave the queue name empty for a auto-named queue in RabbitMQ\n\n        :param method callback: The method to call on Queue.DeclareOk\n        :param queue: The queue name\n        :type queue: str or unicode\n        :param bool passive: Only check to see if the queue exists\n        :param bool durable: Survive reboots of the broker\n        :param bool exclusive: Only allow access by the current connection\n        :param bool auto_delete: Delete after consumer cancels or disconnects\n        :param bool nowait: Do not wait for a Queue.DeclareOk\n        :param dict arguments: Custom key/value arguments for the queue\n\n        \"\"\"\n        if queue:\n            condition = (spec.Queue.DeclareOk,\n                         {'queue': queue})\n        else:\n            condition = spec.Queue.DeclareOk\n        replies = [condition] if nowait is False else []\n        self._validate_channel_and_callback(callback)\n        return self._rpc(spec.Queue.Declare(0, queue, passive, durable,\n                                            exclusive, auto_delete, nowait,\n                                            arguments or dict()), callback,\n                         replies)\n\n    def queue_delete(self,\n                     callback=None,\n                     queue='',\n                     if_unused=False,\n                     if_empty=False,\n                     nowait=False):\n        \"\"\"Delete a queue from the broker.\n\n        :param method callback: The method to call on Queue.DeleteOk\n        :param queue: The queue to delete\n        :type queue: str or unicode\n        :param bool if_unused: only delete if it's unused\n        :param bool if_empty: only delete if the queue is empty\n        :param bool nowait: Do not wait for a Queue.DeleteOk\n\n        \"\"\"\n        replies = [spec.Queue.DeleteOk] if nowait is False else []\n        self._validate_channel_and_callback(callback)\n        return self._rpc(spec.Queue.Delete(0, queue, if_unused, if_empty,\n                                           nowait), callback, replies)\n\n    def queue_purge(self, callback=None, queue='', nowait=False):\n        \"\"\"Purge all of the messages from the specified queue\n\n        :param method callback: The method to call on Queue.PurgeOk\n        :param queue: The queue to purge\n        :type queue: str or unicode\n        :param bool nowait: Do not expect a Queue.PurgeOk response\n\n        \"\"\"\n        replies = [spec.Queue.PurgeOk] if nowait is False else []\n        self._validate_channel_and_callback(callback)\n        return self._rpc(spec.Queue.Purge(0, queue, nowait), callback, replies)\n\n    def queue_unbind(self,\n                     callback=None,\n                     queue='',\n                     exchange=None,\n                     routing_key=None,\n                     arguments=None):\n        \"\"\"Unbind a queue from an exchange.\n\n        :param method callback: The method to call on Queue.UnbindOk\n        :param queue: The queue to unbind from the exchange\n        :type queue: str or unicode\n        :param exchange: The source exchange to bind from\n        :type exchange: str or unicode\n        :param routing_key: The routing key to unbind\n        :type routing_key: str or unicode\n        :param dict arguments: Custom key/value pair arguments for the binding\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        if routing_key is None:\n            routing_key = queue\n        return self._rpc(spec.Queue.Unbind(0, queue, exchange, routing_key,\n                                           arguments or dict()), callback,\n                         [spec.Queue.UnbindOk])\n\n    def tx_commit(self, callback=None):\n        \"\"\"Commit a transaction\n\n        :param method callback: The callback for delivery confirmations\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        return self._rpc(spec.Tx.Commit(), callback, [spec.Tx.CommitOk])\n\n    def tx_rollback(self, callback=None):\n        \"\"\"Rollback a transaction.\n\n        :param method callback: The callback for delivery confirmations\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        return self._rpc(spec.Tx.Rollback(), callback, [spec.Tx.RollbackOk])\n\n    def tx_select(self, callback=None):\n        \"\"\"Select standard transaction mode. This method sets the channel to use\n        standard transactions. The client must use this method at least once on\n        a channel before using the Commit or Rollback methods.\n\n        :param method callback: The callback for delivery confirmations\n\n        \"\"\"\n        self._validate_channel_and_callback(callback)\n        return self._rpc(spec.Tx.Select(), callback, [spec.Tx.SelectOk])\n\n    # Internal methods\n\n    def _add_callbacks(self):\n        \"\"\"Callbacks that add the required behavior for a channel when\n        connecting and connected to a server.\n\n        \"\"\"\n        # Add a callback for Basic.GetEmpty\n        self.callbacks.add(self.channel_number, spec.Basic.GetEmpty,\n                           self._on_getempty, False)\n\n        # Add a callback for Basic.Cancel\n        self.callbacks.add(self.channel_number, spec.Basic.Cancel,\n                           self._on_cancel, False)\n\n        # Deprecated in newer versions of RabbitMQ but still register for it\n        self.callbacks.add(self.channel_number, spec.Channel.Flow,\n                           self._on_flow, False)\n\n        # Add a callback for when the server closes our channel\n        self.callbacks.add(self.channel_number, spec.Channel.Close,\n                           self._on_close, True)\n\n    def _add_on_cleanup_callback(self, callback):\n        \"\"\"For internal use only (e.g., Connection needs to remove closed\n        channels from its channel container). Pass a callback function that will\n        be called when the channel is being cleaned up after all channel-close\n        callbacks callbacks.\n\n        :param method callback: The method to call on callback with the\n            signature: callback(channel)\n\n        \"\"\"\n        self.callbacks.add(self.channel_number, self._ON_CHANNEL_CLEANUP_CB_KEY,\n                           callback, one_shot=True, only_caller=self)\n\n    def _add_pending_msg(self, consumer_tag, method_frame, header_frame, body):\n        \"\"\"Add the received message to the pending message stack.\n\n        :param str consumer_tag: The consumer tag for the message\n        :param pika.frame.Method method_frame: The received method frame\n        :param pika.frame.Header header_frame: The received header frame\n        :param body: The message body\n        :type body: str or unicode\n\n        \"\"\"\n        self._pending[consumer_tag].append((self, method_frame.method,\n                                            header_frame.properties, body))\n\n    def _cleanup(self):\n        \"\"\"Remove all consumers and any callbacks for the channel.\"\"\"\n        self.callbacks.process(self.channel_number,\n                               self._ON_CHANNEL_CLEANUP_CB_KEY, self,\n                               self)\n        self._consumers = dict()\n        self.callbacks.cleanup(str(self.channel_number))\n        self._cookie = None\n\n    def _cleanup_consumer_ref(self, consumer_tag):\n        \"\"\"Remove any references to the consumer tag in internal structures\n        for consumer state.\n\n        :param str consumer_tag: The consumer tag to cleanup\n\n        \"\"\"\n        if consumer_tag in self._consumers_with_noack:\n            self._consumers_with_noack.remove(consumer_tag)\n        if consumer_tag in self._consumers:\n            del self._consumers[consumer_tag]\n        if consumer_tag in self._pending:\n            del self._pending[consumer_tag]\n        self._cancelled.discard(consumer_tag)\n\n    def _get_cookie(self):\n        \"\"\"Used by the wrapper implementation (e.g., `BlockingChannel`) to\n        retrieve the cookie that it set via `_set_cookie`\n\n        :returns: opaque cookie value that was set via `_set_cookie`\n        \"\"\"\n        return self._cookie\n\n    def _get_pending_msg(self, consumer_tag):\n        \"\"\"Get a pending message for the consumer tag from the stack.\n\n        :param str consumer_tag: The consumer tag to get a message from\n        :rtype: tuple(pika.frame.Header, pika.frame.Method, str|unicode)\n\n        \"\"\"\n        return self._pending[consumer_tag].pop(0)\n\n    def _handle_content_frame(self, frame_value):\n        \"\"\"This is invoked by the connection when frames that are not registered\n        with the CallbackManager have been found. This should only be the case\n        when the frames are related to content delivery.\n\n        The frame_dispatcher will be invoked which will return the fully formed\n        message in three parts when all of the body frames have been received.\n\n        :param pika.amqp_object.Frame frame_value: The frame to deliver\n\n        \"\"\"\n        try:\n            response = self.frame_dispatcher.process(frame_value)\n        except exceptions.UnexpectedFrameError:\n            return self._unexpected_frame(frame_value)\n\n        if response:\n            if isinstance(response[0].method, spec.Basic.Deliver):\n                self._on_deliver(*response)\n            elif isinstance(response[0].method, spec.Basic.GetOk):\n                self._on_getok(*response)\n            elif isinstance(response[0].method, spec.Basic.Return):\n                self._on_return(*response)\n\n    def _has_content(self, method_frame):\n        \"\"\"Return a bool if it's a content method as defined by the spec\n\n        :param pika.amqp_object.Method method_frame: The method frame received\n\n        \"\"\"\n        return spec.has_content(method_frame.INDEX)\n\n    def _on_cancel(self, method_frame):\n        \"\"\"When the broker cancels a consumer, delete it from our internal\n        dictionary.\n\n        :param pika.frame.Method method_frame: The method frame received\n\n        \"\"\"\n        if method_frame.method.consumer_tag in self._cancelled:\n            # User-initiated cancel is waiting for Cancel-ok\n            return\n\n        self._cleanup_consumer_ref(method_frame.method.consumer_tag)\n\n    def _on_cancelok(self, method_frame):\n        \"\"\"Called in response to a frame from the Broker when the\n         client sends Basic.Cancel\n\n        :param pika.frame.Method method_frame: The method frame received\n\n        \"\"\"\n        self._cleanup_consumer_ref(method_frame.method.consumer_tag)\n\n    def _on_close(self, method_frame):\n        \"\"\"Handle the case where our channel has been closed for us\n\n        :param pika.frame.Method method_frame: The close frame\n\n        \"\"\"\n        LOGGER.info('%s', method_frame)\n        LOGGER.warning('Received remote Channel.Close (%s): %s',\n                       method_frame.method.reply_code,\n                       method_frame.method.reply_text)\n        if self.connection.is_open:\n            self._send_method(spec.Channel.CloseOk())\n        self._set_state(self.CLOSED)\n        self.callbacks.process(self.channel_number, '_on_channel_close', self,\n                               self, method_frame.method.reply_code,\n                               method_frame.method.reply_text)\n        self._cleanup()\n\n    def _on_closeok(self, method_frame):\n        \"\"\"Invoked when RabbitMQ replies to a Channel.Close method\n\n        :param pika.frame.Method method_frame: The CloseOk frame\n\n        \"\"\"\n        self._set_state(self.CLOSED)\n        self.callbacks.process(self.channel_number, '_on_channel_close', self,\n                               self, 0, '')\n        self._cleanup()\n\n    def _on_deliver(self, method_frame, header_frame, body):\n        \"\"\"Cope with reentrancy. If a particular consumer is still active when\n        another delivery appears for it, queue the deliveries up until it\n        finally exits.\n\n        :param pika.frame.Method method_frame: The method frame received\n        :param pika.frame.Header header_frame: The header frame received\n        :param body: The body received\n        :type body: str or unicode\n\n        \"\"\"\n        consumer_tag = method_frame.method.consumer_tag\n        if consumer_tag in self._cancelled:\n            if self.is_open and consumer_tag not in self._consumers_with_noack:\n                self.basic_reject(method_frame.method.delivery_tag)\n            return\n        if consumer_tag not in self._consumers:\n            return self._add_pending_msg(consumer_tag, method_frame,\n                                         header_frame, body)\n        while self._pending[consumer_tag]:\n            self._consumers[consumer_tag](*self._get_pending_msg(consumer_tag))\n        self._consumers[consumer_tag](self, method_frame.method,\n                                      header_frame.properties, body)\n\n    def _on_eventok(self, method_frame):\n        \"\"\"Generic events that returned ok that may have internal callbacks.\n        We keep a list of what we've yet to implement so that we don't silently\n        drain events that we don't support.\n\n        :param pika.frame.Method method_frame: The method frame received\n\n        \"\"\"\n        LOGGER.debug('Discarding frame %r', method_frame)\n\n    def _on_flow(self, method_frame_unused):\n        \"\"\"Called if the server sends a Channel.Flow frame.\n\n        :param pika.frame.Method method_frame_unused: The Channel.Flow frame\n\n        \"\"\"\n        if self._has_on_flow_callback is False:\n            LOGGER.warning('Channel.Flow received from server')\n\n    def _on_flowok(self, method_frame):\n        \"\"\"Called in response to us asking the server to toggle on Channel.Flow\n\n        :param pika.frame.Method method_frame: The method frame received\n\n        \"\"\"\n        self.flow_active = method_frame.method.active\n        if self._on_flowok_callback:\n            self._on_flowok_callback(method_frame.method.active)\n            self._on_flowok_callback = None\n        else:\n            LOGGER.warning('Channel.FlowOk received with no active callbacks')\n\n    def _on_getempty(self, method_frame):\n        \"\"\"When we receive an empty reply do nothing but log it\n\n        :param pika.frame.Method method_frame: The method frame received\n\n        \"\"\"\n        LOGGER.debug('Received Basic.GetEmpty: %r', method_frame)\n\n    def _on_getok(self, method_frame, header_frame, body):\n        \"\"\"Called in reply to a Basic.Get when there is a message.\n\n        :param pika.frame.Method method_frame: The method frame received\n        :param pika.frame.Header header_frame: The header frame received\n        :param body: The body received\n        :type body: str or unicode\n\n        \"\"\"\n        if self._on_getok_callback is not None:\n            callback = self._on_getok_callback\n            self._on_getok_callback = None\n            callback(self, method_frame.method, header_frame.properties, body)\n        else:\n            LOGGER.error('Basic.GetOk received with no active callback')\n\n    def _on_openok(self, frame_unused):\n        \"\"\"Called by our callback handler when we receive a Channel.OpenOk and\n        subsequently calls our _on_openok_callback which was passed into the\n        Channel constructor. The reason we do this is because we want to make\n        sure that the on_open_callback parameter passed into the Channel\n        constructor is not the first callback we make.\n\n        :param pika.frame.Method frame_unused: Unused Channel.OpenOk frame\n\n        \"\"\"\n        self._set_state(self.OPEN)\n        if self._on_openok_callback is not None:\n            self._on_openok_callback(self)\n\n    def _on_return(self, method_frame, header_frame, body):\n        \"\"\"Called if the server sends a Basic.Return frame.\n\n        :param pika.frame.Method method_frame: The Basic.Return frame\n        :param pika.frame.Header header_frame: The content header frame\n        :param body: The message body\n        :type body: str or unicode\n\n        \"\"\"\n        if not self.callbacks.process(self.channel_number, '_on_return', self,\n                                      self,\n                                      method_frame.method,\n                                      header_frame.properties,\n                                      body):\n            LOGGER.warning('Basic.Return received from server (%r, %r)',\n                           method_frame.method, header_frame.properties)\n\n    def _on_selectok(self, method_frame):\n        \"\"\"Called when the broker sends a Confirm.SelectOk frame\n\n        :param pika.frame.Method method_frame: The method frame received\n\n        \"\"\"\n        LOGGER.debug(\"Confirm.SelectOk Received: %r\", method_frame)\n\n    def _on_synchronous_complete(self, method_frame_unused):\n        \"\"\"This is called when a synchronous command is completed. It will undo\n        the blocking state and send all the frames that stacked up while we\n        were in the blocking state.\n\n        :param pika.frame.Method method_frame_unused: The method frame received\n\n        \"\"\"\n        LOGGER.debug('%i blocked frames', len(self._blocked))\n        self._blocking = None\n        while len(self._blocked) > 0 and self._blocking is None:\n            self._rpc(*self._blocked.popleft())\n\n    def _rpc(self, method_frame, callback=None, acceptable_replies=None):\n        \"\"\"Shortcut wrapper to the Connection's rpc command using its callback\n        stack, passing in our channel number.\n\n        :param pika.amqp_object.Method method_frame: The method frame to call\n        :param method callback: The callback for the RPC response\n        :param list acceptable_replies: The replies this RPC call expects\n\n        \"\"\"\n        # Make sure the channel is open\n        if self.is_closed:\n            raise exceptions.ChannelClosed\n\n        # If the channel is blocking, add subsequent commands to our stack\n        if self._blocking:\n            return self._blocked.append([method_frame, callback,\n                                         acceptable_replies])\n\n        # Validate we got None or a list of acceptable_replies\n        if acceptable_replies and not isinstance(acceptable_replies, list):\n            raise TypeError(\"acceptable_replies should be list or None\")\n\n        # Validate the callback is callable\n        if callback and not is_callable(callback):\n            raise TypeError(\"callback should be None, a function or method.\")\n\n        # Block until a response frame is received for synchronous frames\n        if method_frame.synchronous:\n            self._blocking = method_frame.NAME\n\n        # If acceptable replies are set, add callbacks\n        if acceptable_replies:\n            for reply in acceptable_replies or list():\n                if isinstance(reply, tuple):\n                    reply, arguments = reply\n                else:\n                    arguments = None\n                LOGGER.debug('Adding in on_synchronous_complete callback')\n                self.callbacks.add(self.channel_number, reply,\n                                   self._on_synchronous_complete,\n                                   arguments=arguments)\n                if callback:\n                    LOGGER.debug('Adding passed in callback')\n                    self.callbacks.add(self.channel_number, reply, callback,\n                                       arguments=arguments)\n\n        self._send_method(method_frame)\n\n    def _send_method(self, method_frame, content=None):\n        \"\"\"Shortcut wrapper to send a method through our connection, passing in\n        the channel number\n\n        :param pika.object.Method method_frame: The method frame to send\n        :param tuple content: If set, is a content frame, is tuple of\n                              properties and body.\n\n        \"\"\"\n        self.connection._send_method(self.channel_number, method_frame, content)\n\n    def _set_cookie(self, cookie):\n        \"\"\"Used by wrapper layer (e.g., `BlockingConnection`) to link the\n        channel implementation back to the proxy. See `_get_cookie`.\n\n        :param cookie: an opaque value; typically a proxy channel implementation\n            instance (e.g., `BlockingChannel` instance)\n        \"\"\"\n        self._cookie = cookie\n\n    def _set_state(self, connection_state):\n        \"\"\"Set the channel connection state to the specified state value.\n\n        :param int connection_state: The connection_state value\n\n        \"\"\"\n        self._state = connection_state\n\n    def _unexpected_frame(self, frame_value):\n        \"\"\"Invoked when a frame is received that is not setup to be processed.\n\n        :param pika.frame.Frame frame_value: The frame received\n\n        \"\"\"\n        LOGGER.warning('Unexpected frame: %r', frame_value)\n\n    def _validate_channel_and_callback(self, callback):\n        if not self.is_open:\n            raise exceptions.ChannelClosed()\n        if callback is not None and not is_callable(callback):\n            raise ValueError('callback must be a function or method')\n\n\nclass ContentFrameDispatcher(object):\n    \"\"\"Handle content related frames, building a message and return the message\n    back in three parts upon receipt.\n\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Create a new instance of the Dispatcher passing in the callback\n        manager.\n\n        \"\"\"\n        self._method_frame = None\n        self._header_frame = None\n        self._seen_so_far = 0\n        self._body_fragments = list()\n\n    def process(self, frame_value):\n        \"\"\"Invoked by the Channel object when passed frames that are not\n        setup in the rpc process and that don't have explicit reply types\n        defined. This includes Basic.Publish, Basic.GetOk and Basic.Return\n\n        :param Method|Header|Body frame_value: The frame to process\n\n        \"\"\"\n        if (isinstance(frame_value, frame.Method) and\n            spec.has_content(frame_value.method.INDEX)):\n            self._method_frame = frame_value\n        elif isinstance(frame_value, frame.Header):\n            self._header_frame = frame_value\n            if frame_value.body_size == 0:\n                return self._finish()\n        elif isinstance(frame_value, frame.Body):\n            return self._handle_body_frame(frame_value)\n        else:\n            raise exceptions.UnexpectedFrameError(frame_value)\n\n    def _finish(self):\n        \"\"\"Invoked when all of the message has been received\n\n        :rtype: tuple(pika.frame.Method, pika.frame.Header, str)\n\n        \"\"\"\n        content = (self._method_frame, self._header_frame,\n                   b''.join(self._body_fragments))\n        self._reset()\n        return content\n\n    def _handle_body_frame(self, body_frame):\n        \"\"\"Receive body frames and append them to the stack. When the body size\n        matches, call the finish method.\n\n        :param Body body_frame: The body frame\n        :raises: pika.exceptions.BodyTooLongError\n        :rtype: tuple(pika.frame.Method, pika.frame.Header, str)|None\n\n        \"\"\"\n        self._seen_so_far += len(body_frame.fragment)\n        self._body_fragments.append(body_frame.fragment)\n        if self._seen_so_far == self._header_frame.body_size:\n            return self._finish()\n        elif self._seen_so_far > self._header_frame.body_size:\n            raise exceptions.BodyTooLongError(self._seen_so_far,\n                                              self._header_frame.body_size)\n        return None\n\n    def _reset(self):\n        \"\"\"Reset the values for processing frames\"\"\"\n        self._method_frame = None\n        self._header_frame = None\n        self._seen_so_far = 0\n        self._body_fragments = list()\n","sub_path":"aio_pika/pika/channel.py","file_name":"channel.py","file_ext":"py","file_size_in_byte":53073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"627547504","text":"# -*- coding: UTF-8 -*-\nfrom tkinter import *\nfrom Unit import *\nfrom Flag import *\nfrom Helper import *\nimport tkinter.messagebox as mb\nimport subprocess\n\nclass View():              \n    def __init__(self, parent):\n        self.parent = parent                  \n        self.root=Tk()\n        self.root.title(\"Orion\")\n        self.root.resizable(0,0)\n        #la taille du jeu se resize selon la résolution de l'écran, niceshithum?\n        self.taille=self.root.winfo_screenheight()-125\n        if self.taille>800:\n            self.taille=800\n        self.root.geometry('+25+5')\n        self.dragging = False\n        self.hpBars=False\n        self.selectStart = [0,0]\n        self.selectEnd = [0,0]\n        self.positionMouse = [0,0,0]\n        self.mainMenuBG = PhotoImage(file='images\\\\Menus\\\\MainMenuBG.gif')\n        self.mainMenu = self.fMainMenu()\n        self.mainMenu.pack()\n        self.pLobby = None\n        self.currentFrame = self.mainMenu\n        self.firstTime = True\n        self.gameFrame = None\n        self.joinGame = self.fJoinGame()\n        self.createServer = self.fCreateServer()\n        self.sun=PhotoImage(file='images\\\\Galaxy\\\\sun.gif')\n        self.sunFOW = PhotoImage(file='images\\\\Galaxy\\\\sunFOW.gif')\n        self.planet=PhotoImage(file='images\\\\Galaxy\\\\planet.gif')\n        self.planetFOW = PhotoImage(file='images\\\\Galaxy\\\\planetFOW.gif')\n        self.nebula=PhotoImage(file='images\\\\Galaxy\\\\nebula.gif')\n        self.nebulaFOW=PhotoImage(file='images\\\\Galaxy\\\\nebulaFOW.gif')\n        self.explosion=PhotoImage(file='images\\\\explosion.gif')\n        self.attacking = False\n        self.asteroid=PhotoImage(file='images\\\\Galaxy\\\\asteroid.gif')\n        self.asteroidFOW=PhotoImage(file='images\\\\Galaxy\\\\asteroidFOW.gif')\n        self.mineral = PhotoImage(file='images\\\\Planet\\\\crystal.gif')\n        self.gifStop = PhotoImage(file='images\\\\icones\\\\stop2.gif')\n        self.gifMove = PhotoImage(file='images\\\\icones\\\\move2.gif')\n        self.gifCancel = PhotoImage(file='images\\\\icones\\\\delete2.gif')\n        self.gifAttack = PhotoImage(file='images\\\\icones\\\\attack2.gif')\n        self.gifIcone2 = PhotoImage(file='images\\\\icones\\\\icone2.gif')\n        self.gifPatrol = PhotoImage(file='images\\\\icones\\\\patrol2.gif')\n        self.gifChat = PhotoImage(file='images\\\\icones\\\\boutonChat.gif')\n        self.gifTrade = PhotoImage(file='images\\\\icones\\\\boutonTrade.gif')\n        self.gifTeam = PhotoImage(file='images\\\\icones\\\\boutonTeam.gif')\n        self.planetBackground = PhotoImage(file='images\\\\Planet\\\\background.gif')\n        self.galaxyBackground = PhotoImage(file='images\\\\Galaxy\\\\night-sky.gif')\n        self.gifCadreMenuAction = PhotoImage(file='images\\\\cadreMenuAction2.gif')\n        self.attacking = False\n        self.selectAllUnits = False\n        # Quand le user ferme la fenêtre et donc le jeu, il faut l'enlever du serveur\n        self.root.protocol('WM_DELETE_WINDOW', self.parent.removePlayer)\n    \n    def changeFrame(self, frame):\n        self.currentFrame.pack_forget()\n        frame.pack()\n        self.currentFrame = frame\n\n    #Frame principal du jeu    \n    def fGame(self):\n        gameFrame = Frame(self.root, bg=\"black\")\n        self.scoutShips = []\n        self.attackShips = []\n        self.motherShips = []\n        self.trasportShips = []\n        for i in range(0,8):\n            self.scoutShips.append(PhotoImage(file='images\\\\Ships\\\\Scoutships\\\\Scoutship'+str(i)+'.gif'))\n            self.attackShips.append(PhotoImage(file='images\\\\Ships\\\\Attackships\\\\Attackship'+str(i)+'.gif'))\n            self.motherShips.append(PhotoImage(file='images\\\\Ships\\\\Motherships\\\\Mothership'+str(i)+'.gif'))\n            self.trasportShips.append(PhotoImage(file='images\\\\Ships\\\\Transport\\\\Transport'+str(i)+'.gif'))\n        Label(gameFrame, text=\"Mineraux: \", bg=\"black\", fg=\"white\", width=10, anchor=E).grid(column=0, row=0)\n        self.showMinerals=Label(gameFrame, text=self.parent.players[self.parent.playerId].mineral, fg=\"white\", bg=\"black\", anchor=W)\n        self.showMinerals.grid(column=1,row=0)\n        Label(gameFrame, text=\"Gaz: \", bg=\"black\", fg=\"white\", width=10, anchor=E).grid(column=2, row=0)\n        self.showGaz=Label(gameFrame, text=self.parent.players[self.parent.playerId].gaz, fg=\"white\", bg=\"black\", anchor=W)\n        self.showGaz.grid(column=3,row=0)\n        self.gameArea=Canvas(gameFrame, width=self.taille, height=self.taille-200, background='Black', relief='ridge')\n        self.gameArea.grid(column=0,row=1, columnspan=5)#place(relx=0, rely=0,width=taille,height=taille)\n        self.minimap= Canvas(gameFrame, width=200,height=200, background='Black', relief='raised')\n        self.minimap.grid(column=0,row=2, rowspan=4)\n        self.menuModes=Canvas(gameFrame, width=self.taille, height=200, background='black', relief='ridge')\n        self.menuModes.grid(row=2,column=2)       \n        self.Actionmenu = Canvas(gameFrame,width=200,height=200,background='black')\n        self.Actionmenu.grid(column=3,row=2, rowspan=4)\n        self.changeBackground('GALAXY')\n        self.drawWorld()\n        self.createActionMenu()\n        self.menuChat(gameFrame)\n        self.assignControls()\n        return gameFrame\n    \n    def menuTeam(self):\n        self.menuModesremove() \n        self.menuModes.create_text(150,50,text='voici le menu Team',fill='white')\n        \n    def menuTrade(self):\n        self.menuModesremove()\n        self.menuModes.create_text(150,50,text='voici le menu Tradeeee',fill='red')\n        \n    def menuChat(self,gameFrame):\n        self.menuModesremove()\n        self.menuModes.chat = Label(gameFrame, anchor=W, justify=LEFT, width=75, background='black', fg='white', relief='raised')\n        self.menuModes.chat.grid(row=2, column=2)\n        self.menuModes.entryMess = Entry(gameFrame, width=60)\n        self.menuModes.entryMess.grid(row=5, column=2)\n        \n    # delete tout ce qu'il y a dans le canvas menuModes + affiche les 3 menus\n    def menuModesremove(self):\n        self.menuModes.delete(ALL)\n        self.menuModes.create_image(0,0,image=self.gifChat,anchor = NW,tag='bouton_chat')\n        self.menuModes.create_image(77,0,image=self.gifTrade,anchor = NW,tag='bouton_trade')\n        self.menuModes.create_image(150,0,image=self.gifTeam,anchor = NW,tag='bouton_team')\n\n    #Creation du menu Action\n    def createActionMenu(self):\n        self.Actionmenu.delete(ALL)\n        units = self.parent.players[self.parent.playerId].selectedObjects\n        self.Actionmenu.create_image(0,0,image=self.gifCadreMenuAction,anchor = NW)\n        if len(units) > 0:\n            if isinstance(units[0], Unit):\n                self.Actionmenu.create_image(13,35,image=self.gifMove,anchor = NW)\n                self.Actionmenu.create_image(76,35,image=self.gifStop,anchor = NW)\n                if isinstance(units[0], SpaceAttackUnit):\n                    self.Actionmenu.create_image(140,35,image=self.gifAttack,anchor = NW)\n            if isinstance(units[0],Mothership):\n                self.Actionmenu.create_image(140,35,image=self.gifAttack,anchor = NW)\n                self.Actionmenu.create_image(13,89,image=self.gifCancel,anchor = NW)\n                self.Actionmenu.create_image(76,89,image=self.gifPatrol,anchor = NW)\n\n\t#Frame du menu principal    \n    def fMainMenu(self):\n        mainMenuFrame = Frame(self.root, bg=\"black\")\n        panel1 = Label(mainMenuFrame, image=self.mainMenuBG)\n        panel1.grid(row=0, column=0, rowspan=10)\n        Button(mainMenuFrame, text='Créer une partie', command=lambda:self.changeFrame(self.createServer)).grid(row=7, column=0)\n        Button(mainMenuFrame, text='Rejoindre une partie', command=lambda:self.changeFrame(self.joinGame)).grid(row=8, column=0)\n        return mainMenuFrame\n    \n    #Frame permettant de rejoindre une partie\n    def fJoinGame(self):\n        joinGameFrame = Frame(self.root, bg=\"black\")\n        Label(joinGameFrame, text=\"Nom de joueur:\", fg=\"white\", bg=\"black\").grid(row=0, column=0)\n        self.entryLogin = Entry(joinGameFrame, width=20)\n        self.entryLogin.focus_set()\n        self.entryLogin.grid(row=0, column=1)\n        #self.entryLogin.delete(0,END)\n        Label(joinGameFrame, text=\"Adresse du serveur:\", fg=\"white\", bg=\"black\").grid(row=1, column=0)\n        self.entryServer = Entry(joinGameFrame, width=20)\n        self.entryServer.grid(row=1, column=1)\n        #self.entryServer.delete(0,END)\n        widget = Button(joinGameFrame, text='Connecter', command=lambda:self.lobbyEnter(0, self.entryLogin.get(), self.entryServer.get()))\n        widget.grid(row=2, column=1)\n\t\t#Crée un bouton de retour au menu principal\n        widget = Button(joinGameFrame, text='Retour', command=lambda:self.changeFrame(self.mainMenu), width=10)\n        widget.grid(row=3, column=1, columnspan=2, pady=10)\n        self.entryServer.bind(\"<Return>\",self.lobbyEnter)\n        return joinGameFrame\n    \n    #Frame de création de nouveau serveur\n    def fCreateServer(self):\n        #Crée le frame\n        createServerFrame = Frame(self.root, bg=\"black\")\n        #Crée le label de l'IP du serveur\n        Label(createServerFrame, text=\"Adresse du serveur:\", fg=\"white\", bg=\"black\").grid(row=0, column=0)\n        #Crée le champ texte pour l'IP du serveur\n        self.entryCreateServer = Entry(createServerFrame, width=20)\n        #On met l'adresse de l'hôte comme valeur par défaut\n        self.entryCreateServer.insert(0,self.parent.playerIp)\n        self.entryCreateServer.grid(row=0, column=1)\n        #Crée le label du nom du joueur\n        Label(createServerFrame, text=\"Nom de joueur:\", fg=\"white\", bg=\"black\").grid(row=1, column=0)\n        #Crée le champ texte pour le nom du joueur\n        self.entryCreateLogin = Entry(createServerFrame, width=20)\n        self.entryCreateLogin.focus_set()\n        self.entryCreateLogin.grid(row=1, column=1)\n        #Crée le bouton de confirmation\n        widget = Button(createServerFrame, text='Créer', command=lambda:self.startServer(False))\n        widget.grid(row=2, column=1)\n        #Crée le bouton de confirmation et se connecte\n        widget = Button(createServerFrame, text='Créer et connecter', command=lambda:self.startServer(True))\n        widget.grid(row=3, column=1)\n\t\t#Crée un bouton de retour au menu principal\n        widget = Button(createServerFrame, text='Retour', command=lambda:self.changeFrame(self.mainMenu), width=10)\n        widget.grid(row=5, column=1, columnspan=2, pady=10)\n        return createServerFrame\n\n    def startServer(self, connect):\n        serverAddress= self.entryCreateServer.get()\n        #Démarre le serveur dans un autre processus avec l'adresse spécifiée\n        child = subprocess.Popen(\"C:\\python32\\python.exe server.py \" + serverAddress, shell=True)\n        #On vérifie si le serveur s'est terminé en erreur et si oui, on affiche un message à l'utilisateur\n        if child.poll():\n            if child.returncode != None:\n                child.terminate()\n                self.serverNotCreated()\n        else:\n            self.serverCreated(serverAddress)\n            #Si l'usager veut se connecter en créant le serveur, on le connecte\n            if connect:\n                self.parent.connectServer(self.entryCreateLogin.get(), self.entryCreateServer.get())\n            else:\n                self.changeFrame(self.mainMenu)\n\n    \n    #Frame du lobby\n    def fLobby(self):\n        self.entryServer.unbind(\"<Return>\")\n        lobbyFrame = Frame(self.root, bg=\"black\")\n        if self.parent.server != None:\n            pNum = len(self.parent.server.getSockets())\n            for i in range(0, pNum):\n                Label(lobbyFrame, text=self.parent.server.getSockets()[i][1], fg=\"white\", bg=\"black\").grid(row=i,column=0)\n            Label(lobbyFrame, text='Admin : '+self.parent.server.getSockets()[0][1], fg=\"white\", bg=\"black\").grid(row=(pNum+1), column=1)\n            if self.parent.playerId == 0:\n                Button(lobbyFrame, text='Demarrer la partie', command=self.parent.startGame, bg=\"black\", fg=\"white\").grid(row=(pNum+2), column=1)\n        self.chatLobby = Label(lobbyFrame, anchor=W, justify=LEFT, width=45, background='black', fg='white', relief='raised')\n        self.chatLobby.grid(row=(pNum+11), column=1)\n        self.entryMessLobby = Entry(lobbyFrame, width=35)\n        self.entryMessLobby.grid(row=(pNum+12), column=1)\n        self.entryMessLobby.bind(\"<Return>\", self.sendMessLobby)\n        #Choix de couleur\n        self.variableColor = StringVar(lobbyFrame)\n        self.colorOPTIONS = [\"Orange\",\"Rouge\",\"Bleu\",\"Vert\",\"Jaune\",\"Brun\",\"Blanc\",\"Rose\"]\n        self.variableColor.set(self.colorOPTIONS[0]) # default value\n        self.colorChoice = OptionMenu(lobbyFrame, self.variableColor, *self.colorOPTIONS, command=self.parent.choiceColor)\n        self.colorChoice.grid(row=(self.parent.playerId), column=1)\n        self.buttonColor = Button(lobbyFrame, text=\"OK\", command=self.parent.choiceColor, bg=\"black\", fg=\"white\")\n        self.buttonColor.grid(row=(self.parent.playerId), column=2)\n        return lobbyFrame\n\n    def redrawLobby(self ,lobbyFrame):\n        if self.parent.server.getSockets()[self.parent.playerId][3] == -1:\n            listOfColors = self.parent.server.getColorChoices()\n            self.colorChoice['menu'].delete(0, END)\n            for i in listOfColors:\n                if i[1] == False:\n                    self.colorChoice['menu'].add_command(label=i[0], command=lambda temp = i[0]: self.colorChoice.setvar(self.colorChoice.cget(\"textvariable\"), value = temp))\n        else:\n            self.buttonColor.configure(state=DISABLED, background='white')\n            self.colorChoice['menu'].delete(0, END)\n        if self.parent.server != None:\n            pNum = len(self.parent.server.getSockets())\n            for i in range(0, pNum):\n                Label(lobbyFrame, text=self.parent.server.getSockets()[i][1], fg=\"white\", bg=\"black\").grid(row=i,column=0)\n            Label(lobbyFrame, text='Admin : '+self.parent.server.getSockets()[0][1], fg=\"white\", bg=\"black\").grid(row=(pNum+1), column=1)\n            if self.parent.playerId == 0:\n                Button(lobbyFrame, text='Demarrer la partie', command=self.parent.startGame, bg=\"black\", fg=\"white\").grid(row=(pNum+2), column=1)\n\n    def sendMessLobby(self, eve):\n        if self.entryMessLobby.get() != \"\":\n            self.parent.sendMessageLobby(self.entryMessLobby.get(), self.parent.server.getSockets()[self.parent.playerId][1])\n            self.entryMessLobby.delete(0,END)\n        \n    def loginFailed(self):\n        mb.showinfo('Erreur de connection', 'Le serveur est introuvable. Veuillez reessayer.')\n    \n    def colorAlreadyChosen(self):\n        mb.showinfo('Trop tard!', 'La couleur sélectionnée a déjà été choisie.')\n\n    def gameHasBeenStarted(self):\n        mb.showinfo('Erreur de connection', 'La partie a déjà débutée. Veuillez attendre sa fin.')\n    \n    def showGameIsFinished(self):\n        mb.showinfo('Fin de la partie', 'L\\'administrateur de la partie a quitté prématurément la partie, la partie est donc terminée.')\n\n    def serverCreated(self, serverIP):\n        mb.showinfo('Serveur créé', 'Le serveur a été créé à l\\'adresse ' + serverIP + '.')\n        \n    def serverNotCreated(self):\n        mb.showinfo('Serveur non créé', 'Une erreur est survenue lors de la création du serveur.\\nVeuillez vérifier que les informations entrées sont exactes.')\n    #Methode pour dessiner la vue d'un planete\n    def drawPlanetGround(self, planet):\n        self.gameArea.delete('deletable')\n        for i in planet.minerals:\n            self.gameArea.create_image(i.position[0], i.position[1], image=self.mineral, tag='deletable')\n        for i in planet.gaz:\n            self.gameArea.create_oval(i.position[0]-12, i.position[1]-12, i.position[0]+12, i.position[1]+12, fill='green', tag='deletable')\n\n    def changeBackground(self, type):\n        self.gameArea.delete('background')\n        if type == 'PLANET':\n            self.gameArea.create_image(0,0,image=self.planetBackground, anchor=NW, tag='background')\t\t\n        else:\n            self.gameArea.create_image(0,0,image=self.galaxyBackground, anchor=NW, tag='background')\n\n    #Methode pour dessiner la galaxie\n    def drawWorld(self):\n        self.gameArea.delete('deletable')\n        sunList = self.parent.galaxy.solarSystemList\n        players = self.parent.players \n        id = self.parent.playerId\n        for i in sunList:\n            if self.parent.players[self.parent.playerId].inViewRange(i.sunPosition):\n                if not i.discovered:\n                    i.discovered = True\n                    self.redrawMinimap()\n                self.drawSun(i.sunPosition, players[id], True)\n            else:\n                if i.discovered:\n                    self.drawSun(i.sunPosition, players[id], False)\n            for j in i.planets:\n                if self.parent.players[self.parent.playerId].inViewRange(j.position):\n                    if not j.discovered:\n                        j.discovered = True\n                        self.redrawMinimap()\n                    self.drawPlanet(j, players[id], True)\n                else:\n                    if j.discovered:\n                        self.drawPlanet(j, players[id], False)\n            for j in i.nebulas:\n                if self.parent.players[self.parent.playerId].inViewRange(j.position):\n                    if not j.discovered:\n                        j.discovered = True\n                        self.redrawMinimap()\n                    self.drawNebula(j, players[id], True)\n                else:\n                    if j.discovered:\n                        self.drawNebula(j, players[id], False)\n            for j in i.asteroids:\n                if self.parent.players[self.parent.playerId].inViewRange(j.position):\n                    if not j.discovered:\n                        j.discovered = True\n                        self.redrawMinimap()\n                    self.drawAsteroid(j, players[id], True)\n                else:\n                    if j.discovered:\n                        self.drawAsteroid(j, players[id], False)\n        for i in players:\n            for j in i.units:\n                if j.isAlive:\n                    if self.parent.players[self.parent.playerId].inViewRange(j.position):\n                        if j.name == 'Mothership':\n                            j.discovered = True\n                        self.drawUnit(j, i, False)\n        if self.dragging:\n            self.drawSelectionBox()\n        self.drawMinimap()\n        \n    #Pour dessiner un soleil     \n    def drawSun(self, sunPosition, player, isInFOW):\n        if player.camera.isInFOV(sunPosition):\n            distance = player.camera.calcDistance(sunPosition)\n            if isInFOW:\n                self.gameArea.create_image(distance[0],distance[1], image=self.sun, tag='deletable')\n            else:\n                self.gameArea.create_image(distance[0],distance[1], image=self.sunFOW, tag='deletable')\n            #self.gameArea.create_oval(distance[0]-20, distance[1]-20, distance[0]+20, distance[1]+20, fill='RED')\n    \n    #pour dessiner une planete        \n    def drawPlanet(self, planet, player, isInFOW):\n        planetPosition = planet.position\n        if player.camera.isInFOV(planetPosition):\n            distance = player.camera.calcDistance(planetPosition)\n            if isInFOW:\n                if planet in player.selectedObjects:\n                    self.gameArea.create_oval(distance[0]-10, distance[1]-10, distance[0]+10, distance[1]+10,outline=\"green\", tag='deletable')\n                    mVariable = \"Mineral :\" + str(planet.mineralQte)\n                    gVariable = \"Gaz :\" + str(planet.gazQte)\n                    self.gameArea.create_text(distance[0]-20, distance[1]-25,fill=\"cyan\",text=mVariable, tag='deletable')\n                    self.gameArea.create_text(distance[0]-20, distance[1]-40,fill=\"green\",text=gVariable, tag='deletable')\n                self.gameArea.create_image(distance[0],distance[1],image=self.planet, tag='deletable')\n            else:\n                self.gameArea.create_image(distance[0], distance[1], image=self.planetFOW, tag='deletable')\n            #self.gameArea.create_oval(distance[0]-10, distance[1]-10, distance[0]+10, distance[1]+10, fill='BLUE', tag=\"planet\")\n\n    def drawNebula(self,nebula,player, isInFOW):\n        nebulaPosition = nebula.position\n        if player.camera.isInFOV(nebulaPosition):\n            distance = player.camera.calcDistance(nebulaPosition)\n            if isInFOW:\n                if nebula in player.selectedObjects:\n                    self.gameArea.create_oval(distance[0]-10, distance[1]-10, distance[0]+10, distance[1]+10,outline=\"green\", tag='deletable')\n                    mVariable = \"Gaz :\" + str(nebula.gazQte)\n                    self.gameArea.create_text(distance[0]-20, distance[1]-25,fill=\"green\",text=mVariable, tag='deletable')\n                self.gameArea.create_image(distance[0],distance[1],image=self.nebula, tag='deletable')\n            else:\n                self.gameArea.create_image(distance[0], distance[1], image=self.nebulaFOW, tag='deletable')\n    \n    def drawAsteroid(self,asteroid,player, isInFOW):\n        asteroidPosition = asteroid.position\n        if player.camera.isInFOV(asteroidPosition):\n            distance = player.camera.calcDistance(asteroidPosition)\n            if isInFOW:\n                if asteroid in player.selectedObjects:\n                    self.gameArea.create_oval(distance[0]-10, distance[1]-10, distance[0]+10, distance[1]+10,outline=\"green\", tag='deletable')\n                    mVariable = \"Mineral :\" + str(asteroid.mineralQte)\n                    self.gameArea.create_text(distance[0]-20, distance[1]-25,fill=\"cyan\",text=mVariable, tag='deletable')\n                self.gameArea.create_image(distance[0],distance[1],image=self.asteroid, tag='deletable')\n            else:\n                self.gameArea.create_image(distance[0],distance[1],image=self.asteroidFOW, tag='deletable')\n    \n    #pour dessiner un vaisseau        \n    def drawUnit(self, unit, player, isInFOW):\n        unitPosition = unit.position\n        if self.parent.players[self.parent.playerId].camera.isInFOV(unitPosition):\n            distance = self.parent.players[self.parent.playerId].camera.calcDistance(unitPosition)\n            if not isInFOW:\n                if unit.name.find('Scout') != -1:\n                    if unit in player.selectedObjects:\n                        self.gameArea.create_oval(distance[0]-8,distance[1]-8,distance[0]+8,distance[1]+8, outline=\"green\", tag='deletable')\n                    self.gameArea.create_image(distance[0]+1, distance[1], image=self.scoutShips[player.colorId],tag='deletable')#On prend l'image dependamment du joueur que nous sommes\n                if unit.name.find('Attack') != -1:\n                    if unit.attackcount <= 5:\n                        d2 = self.parent.players[self.parent.playerId].camera.calcDistance(unit.flag.finalTarget.position)\n                        self.gameArea.create_line(distance[0],distance[1], d2[0], d2[1], fill=\"yellow\", tag='deletable')\n                    if unit in player.selectedObjects:\n                        self.gameArea.create_oval(distance[0]-13,distance[1]-13,distance[0]+13,distance[1]+13, outline=\"green\", tag='deletable')\n                    self.gameArea.create_image(distance[0]+1, distance[1], image=self.attackShips[player.colorId], tag='deletable')#On prend l'image dependamment du joueur que nous sommes\n                elif unit.name == 'Mothership':\n                    if unit in player.selectedObjects:\n                        self.gameArea.create_oval(distance[0]-65,distance[1]-65,distance[0]+65,distance[1]+65, outline=\"green\", tag='deletable')\n                    self.gameArea.create_image(distance[0]+1, distance[1], image = self.motherShips[player.colorId], tag='deletable')\n                elif unit.name == 'Transport':\n                    if unit in player.selectedObjects:\n                        self.gameArea.create_oval(distance[0]-18,distance[1]-18,distance[0]+18,distance[1]+18, outline=\"green\", tag='deletable')\n                    self.gameArea.create_image(distance[0]+1, distance[1], image = self.trasportShips[player.colorId], tag='deletable')\n                if unit.hitpoints <= 5:\n                    self.gameArea.create_image(distance[0]+1, distance[1], image=self.explosion, tag='deletable')\n                if self.hpBars:\n                    self.drawHPBars(distance, unit)\n                else:\n                    self.drawHPHoverUnit(unit, distance)\n     \n    def drawHPHoverUnit(self, unit, distance):\n        posSelected=self.parent.players[self.parent.playerId].camera.calcPointInWorld(self.positionMouse[0],self.positionMouse[1])\n        if unit.position[0] >= posSelected[0]-8 and unit.position[0] <= posSelected[0]+8:\n            if unit.position[1] >= posSelected[1]-8 and unit.position[1] <= posSelected[1]+8:\n                hpLeft=((unit.hitpoints/unit.maxHP)*30)-15\n                hpLost=(hpLeft+(((unit.maxHP-unit.hitpoints)/unit.maxHP)*30))\n                self.gameArea.create_rectangle(distance[0]-15,distance[1]-11,distance[0]+hpLeft,distance[1]-11, outline=\"green\", tag='deletable')\n                if int(unit.hitpoints) != int(unit.maxHP):\n                    self.gameArea.create_rectangle(distance[0]+hpLeft,distance[1]-11,distance[0]+hpLost,distance[1]-11, outline=\"red\", tag='deletable')\n    \n    def drawHPBars(self, distance, unit):\n        hpLeft=((unit.hitpoints/unit.maxHP)*30)-15\n        hpLost=(hpLeft+(((unit.maxHP-unit.hitpoints)/unit.maxHP)*30))\n        self.gameArea.create_rectangle(distance[0]-15,distance[1]-11,distance[0]+hpLeft,distance[1]-11, outline=\"green\", tag='deletable')\n        if int(unit.hitpoints) != int(unit.maxHP):\n            self.gameArea.create_rectangle(distance[0]+hpLeft,distance[1]-11,distance[0]+hpLost,distance[1]-11, outline=\"red\", tag='deletable')\n       \n                    \n    #Dessine la minimap\n    def drawMinimap(self):\n        self.minimap.delete('deletable')\n        sunList = self.parent.galaxy.solarSystemList\n        players = self.parent.players\n        if self.firstTime:\n            for i in sunList:\n                self.drawMiniSun(i)\n                for j in i.planets:\n                    self.drawMiniPlanet(j)\n                for n in i.nebulas:\n                    self.drawMiniNebula(n)\n                for q in i.asteroids:\n                    self.drawMiniAsteroid(q)\n            self.firstTime = False\n        for i in players:\n            for j in i.units:\n                if j.isAlive:\n                    if players[self.parent.playerId].inViewRange(j.position):\n                        self.drawMiniUnit(j)\n        self.drawMiniFOV()\n        \n    def redrawMinimap(self):\n        self.minimap.delete(ALL)\n        sunList = self.parent.galaxy.solarSystemList\n        players = self.parent.players\n        for i in sunList:\n            self.drawMiniSun(i)\n            for j in i.planets:\n                self.drawMiniPlanet(j)\n            for n in i.nebulas:\n                self.drawMiniNebula(n)\n            for q in i.asteroids:\n                self.drawMiniAsteroid(q)\n        for i in players:\n            for j in i.units:\n                if j.isAlive:\n                    if players[self.parent.playerId].inViewRange(j.position):\n                        self.drawMiniUnit(j)\n        self.drawMiniFOV()\n\n    #Dessine le carrer de la camera dans la minimap    \n    def drawMiniFOV(self):\n        cameraX = (self.parent.players[self.parent.playerId].camera.position[0]-(self.taille/2) + self.parent.galaxy.width/2) / self.parent.galaxy.width * 200\n        cameraY = (self.parent.players[self.parent.playerId].camera.position[1]-((self.taille/2)-self.taille/8) + self.parent.galaxy.height/2) / self.parent.galaxy.height * 200\n        width = self.taille / self.parent.galaxy.width * 200\n        height = self.taille / self.parent.galaxy.height * 150\n        self.minimap.create_rectangle(cameraX, cameraY, cameraX+width, cameraY+height, outline='GREEN', tag='deletable')\n\n    #Dessine un soleil dans la minimap    \n    def drawMiniSun(self, sun):\n        sunPosition = sun.sunPosition\n        sunX = (sunPosition[0] + self.parent.galaxy.width/2) / self.parent.galaxy.width * 200\n        sunY = (sunPosition[1] + self.parent.galaxy.height/2) / self.parent.galaxy.height * 200\n        if sun.discovered:\n            self.minimap.create_oval(sunX-3, sunY-3, sunX+3, sunY+3, fill='ORANGE')\n\n    #Dessine une planete dans la minimap        \n    def drawMiniPlanet(self, planet):\n        planetPosition = planet.position\n        planetX = (planetPosition[0] + self.parent.galaxy.width/2) / self.parent.galaxy.width * 200\n        planetY = (planetPosition[1] + self.parent.galaxy.height/2) / self.parent.galaxy.height * 200\n        if planet.discovered:\n            self.minimap.create_oval(planetX-1, planetY-1, planetX+1, planetY+1, fill='LIGHT BLUE')\n            \n    #dessine une nebula dans la minimap\n    def drawMiniNebula(self, nebula):\n        nebulaPosition = nebula.position\n        nebulaX = (nebulaPosition[0] + self.parent.galaxy.width/2) / self.parent.galaxy.width * 200\n        nebulaY = (nebulaPosition[1] + self.parent.galaxy.height/2) / self.parent.galaxy.height * 200\n        if nebula.discovered:\n            self.minimap.create_oval(nebulaX-1, nebulaY-1, nebulaX+1, nebulaY+1, fill='PURPLE')\n        \n    #dessine un asteroid dans la minimap\n    def drawMiniAsteroid(self, asteroid):\n        asteroidPosition = asteroid.position\n        asteroidX = (asteroidPosition[0] + self.parent.galaxy.width/2) / self.parent.galaxy.width * 200\n        asteroidY = (asteroidPosition[1] + self.parent.galaxy.height/2) / self.parent.galaxy.height * 200\n        if asteroid.discovered:\n            self.minimap.create_oval(asteroidX-1, asteroidY-1, asteroidX+1, asteroidY+1, fill='CYAN')\n        \n    #Dessine une unite dans la minimap        \n    def drawMiniUnit(self, unit):\n        unitX = (unit.position[0] + self.parent.galaxy.width/2) / self.parent.galaxy.width * 200\n        planetY = (unit.position[1] + self.parent.galaxy.height/2) / self.parent.galaxy.height * 200\n        if unit.name != \"Mothership\":\n            if unit in self.parent.players[self.parent.playerId].units:\n                self.minimap.create_polygon((unitX-2, planetY+2, unitX, planetY-2, unitX+2, planetY+2),fill='GREEN', tag='deletable')\n            else:\n                self.minimap.create_polygon((unitX-2, planetY+2, unitX, planetY-2, unitX+2, planetY+2),fill='RED', tag='deletable')\n        else:\n            if unit in self.parent.players[self.parent.playerId].units:\n                self.minimap.create_polygon((unitX-4, planetY+2, unitX, planetY-2, unitX+4, planetY+2),fill='WHITE', tag='deletable')\n            else:\n                self.minimap.create_polygon((unitX-4, planetY+2, unitX, planetY-2, unitX+4, planetY+2),fill='RED', tag='deletable')\n\n    #Dessine la boite de selection lors du clic-drag\t\n    def drawSelectionBox(self):\n        self.gameArea.create_rectangle(self.selectStart[0], self.selectStart[1], self.selectEnd[0], self.selectEnd[1], outline='WHITE', tag='deletable')\n\n    #Actions quand on clic sur les fleches du clavier\n    def keyPressUP(self, eve):\n        if 'UP' not in self.parent.players[self.parent.playerId].camera.movingDirection:\n            if self.parent.players[self.parent.playerId].currentPlanet == None:\n                self.parent.players[self.parent.playerId].camera.movingDirection.append('UP')\n                self.drawWorld()\n\n    def keyPressDown(self, eve):\n        if 'DOWN' not in self.parent.players[self.parent.playerId].camera.movingDirection:\n            if self.parent.players[self.parent.playerId].currentPlanet == None:\n                self.parent.players[self.parent.playerId].camera.movingDirection.append('DOWN')\n                self.drawWorld()\n\n    def keyPressLeft(self, eve):\n        if 'LEFT' not in self.parent.players[self.parent.playerId].camera.movingDirection:\n            if self.parent.players[self.parent.playerId].currentPlanet == None:\n                self.parent.players[self.parent.playerId].camera.movingDirection.append('LEFT')\n                self.drawWorld()\n\n    def keyPressRight(self, eve):\n        if 'RIGHT' not in self.parent.players[self.parent.playerId].camera.movingDirection:\n            if self.parent.players[self.parent.playerId].currentPlanet == None:\n                self.parent.players[self.parent.playerId].camera.movingDirection.append('RIGHT')\n                self.drawWorld()\n\n    #Actions quand on lache les touches\n    def keyReleaseUP(self, eve):\n        if self.parent.players[self.parent.playerId].currentPlanet == None:\n            self.parent.players[self.parent.playerId].camera.movingDirection.remove('UP')\n            self.drawWorld()\n\n    def keyReleaseDown(self, eve):\n        if self.parent.players[self.parent.playerId].currentPlanet == None:\n            self.parent.players[self.parent.playerId].camera.movingDirection.remove('DOWN')\n            self.drawWorld()\n\n    def keyReleaseLeft(self, eve):\n        if self.parent.players[self.parent.playerId].currentPlanet == None:\n            self.parent.players[self.parent.playerId].camera.movingDirection.remove('LEFT')\n            self.drawWorld()\n\n    def keyReleaseRight(self, eve):\n        if self.parent.players[self.parent.playerId].currentPlanet == None:\n            self.parent.players[self.parent.playerId].camera.movingDirection.remove('RIGHT')\n            self.drawWorld()\n\n    #Actions avec la souris    \n    def rightclic(self, eve):\n        self.attacking = False\n        x = eve.x\n        y = eve.y\n        canva = eve.widget\n        if x > 0 and x < self.taille:\n            if y > 0 and y < self.taille-200:\n                if canva == self.gameArea:\n                    pos = self.parent.players[self.parent.playerId].camera.calcPointInWorld(x,y)\n                    self.parent.rightClic(pos)\n                elif canva == self.minimap and self.parent.players[self.parent.playerId].currentPlanet == None:\n                    pos = self.parent.players[self.parent.playerId].camera.calcPointMinimap(x,y)\n                    self.parent.setMovingFlag(pos[0], pos[1])\n                    self.drawWorld()\n\n    #Quand on fait un clic gauche (peu importe ou)\n    def leftclic(self, eve):\n        x = eve.x\n        y = eve.y\n        canva = eve.widget\n        if canva == self.gameArea:\n            pos = self.parent.players[self.parent.playerId].camera.calcPointInWorld(x,y)\n            if self.attacking:\n                self.parent.setAttackFlag(pos[0],pos[1])\n            else:\n                if not self.selectAllUnits:\n                    self.parent.select(pos)\n                else:\n                    self.parent.selectAll(pos)\n        elif canva == self.minimap:\n            self.parent.quickMove(x,y,canva)\n\n    def selectAll(self, eve):\n        self.selectAllUnits = True\n    def unSelectAll(self, eve):\n        self.selectAllUnits = False\n\n    #Quand on fait un clic gauche et qu'on bouge\n    def clicDrag(self,eve):\n        self.attacking = False\n        if self.dragging == False:\n            self.selectStart = [eve.x, eve.y]\n            self.selectEnd = [eve.x, eve.y]\n            self.dragging = True\n        else:\n            self.selectEnd = [eve.x, eve.y]\n\n    #Quand on clicDrag et qu'on lache la souris\n    def endDrag(self, eve):\n        self.attacking = False\n        if self.dragging:\n            self.dragging = False\n            self.selectEnd = [eve.x, eve.y]\n            self.parent.boxSelect(self.selectStart, self.selectEnd)\n\n    def posMouse(self, eve):\n        self.positionMouse[0] = eve.x\n        self.positionMouse[1] = eve.y\n    \n    def ctrlPressed(self, eve):\n        self.hpBars=True\n    \n    def ctrlDepressed(self, eve):\n        self.hpBars=False\n\n    #methode test attack\n    def attack(self,eve):\n        self.attacking = True\n        \n    #Quand on appui sur enter dans le chat\t\t\n    def enter(self, eve):\n        self.parent.sendMessage(self.menuModes.entryMess.get())\n        self.menuModes.entryMess.delete(0,END)\n        self.gameArea.focus_set()\n\n    #Quand on appui sur enter dans le login\n    def lobbyEnter(self, eve, login=\"\", server=\"\"):\n        if login==\"\" and server==\"\":\n            login = self.entryLogin.get()\n            server = self.entryServer.get()\n        self.parent.connectServer(login,server)\n\t\t\t\n    def stop(self, eve):\n        self.attacking = False\n        self.parent.setStandbyFlag()\n\n    def delete(self, eve):\n        self.attacking = False\n        self.parent.eraseUnit()\n        \n    #Pour la selection multiple\t\n    def shiftPress(self, eve):\n        self.parent.multiSelect = True\n\n    def shiftRelease(self, eve):\n        self.parent.multiSelect = False\n\t\n    def checkMotherSip(self, eve):\n        self.parent.players[self.parent.playerId].currentPlanet = None\n        self.changeBackground('GALAXY')\n        self.drawWorld()\n        cam = self.parent.players[self.parent.playerId].camera\n        cam.position = cam.defaultPos\n        \n                \n    def clickMenuModes(self,eve):\n        Button_pressed = (eve.widget.gettags(eve.widget.find_withtag('current')))[0]\n        if (Button_pressed == \"bouton_chat\"):\n            print('do some shit nigga 1')\n            self.menuChat(self.gameFrame)\n        elif (Button_pressed == \"bouton_trade\"):\n            print('do some shit nigga 2')\n            self.menuTrade()\n        elif (Button_pressed == \"bouton_team\"):\n            print('do some shit nigga 3')\n            self.menuTeam()\n\n    #Assignation des controles\t\n    def assignControls(self):\n        self.gameArea.focus_set()\n        #Bindings des fleches\n        self.gameArea.bind (\"<Key-Up>\", self.keyPressUP)\n        self.gameArea.bind(\"<Key-Down>\", self.keyPressDown)\n        self.gameArea.bind(\"<Key-Left>\", self.keyPressLeft)\n        self.gameArea.bind(\"<Key-Right>\", self.keyPressRight)\n        self.gameArea.bind (\"<KeyRelease-Up>\", self.keyReleaseUP)\n        self.gameArea.bind (\"<KeyRelease-Down>\", self.keyReleaseDown)\n        self.gameArea.bind (\"<KeyRelease-Left>\", self.keyReleaseLeft)\n        self.gameArea.bind (\"<KeyRelease-Right>\", self.keyReleaseRight)\n        #Bindings de shift pour la multiselection\n        self.gameArea.bind(\"<Shift_L>\", self.shiftPress)\n        self.gameArea.bind(\"<KeyRelease-Shift_L>\", self.shiftRelease)\n        #BINDINGS POUR LES SHORTCUTS CLAVIERS\n        self.gameArea.bind(\"s\", self.stop)\n        self.gameArea.bind(\"S\", self.stop)\n        self.gameArea.bind(\"<Delete>\", self.delete)\n        self.gameArea.bind(\"a\",self.attack)\n        self.gameArea.bind(\"A\",self.attack)\n        self.gameArea.bind(\"c\", self.selectAll)\n        self.gameArea.bind(\"<KeyRelease-c>\", self.unSelectAll)\n        self.gameArea.bind(\"1\", self.checkMotherSip)\n        self.gameArea.bind(\"<Control_L>\",self.ctrlPressed)\n        self.gameArea.bind(\"<KeyRelease-Control_L>\",self.ctrlDepressed)\n        #Bindings des boutons de la souris\n        self.gameArea.bind(\"<Button-3>\", self.rightclic)\n        self.gameArea.bind(\"<B3-Motion>\", self.rightclic)\n        self.minimap.bind(\"<Button-3>\", self.rightclic)\n        self.gameArea.bind(\"<Button-1>\", self.leftclic)\n        self.minimap.bind(\"<B1-Motion>\",self.leftclic)\n        self.minimap.bind(\"<Button-1>\",self.leftclic)\n        self.gameArea.bind(\"<B1-Motion>\", self.clicDrag)\n        self.gameArea.bind(\"<ButtonRelease-1>\", self.endDrag)\n        self.gameArea.bind(\"<Motion>\", self.posMouse)\n        self.menuModes.entryMess.bind(\"<Return>\",self.enter)\n        self.menuModes.bind(\"<Button-1>\",self.clickMenuModes)\n\n","sub_path":"src/Aghi/View.py","file_name":"View.py","file_ext":"py","file_size_in_byte":39670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"20576701","text":"import cv2 \r\nimport sys\r\n\r\nif not sys.warnoptions:\r\n    import warnings\r\n    warnings.simplefilter(\"ignore\")\r\nimport keras\r\nfrom keras.models import load_model\r\nimport frame_enhancer as fe\r\nmodel=load_model('val96p.h5')\r\n\r\ncap=cv2.VideoCapture('Fire video 1.mp4')\r\nwhile True:\r\n\tret,frame=cap.read()\r\n\tif not ret:\r\n\t\tbreak;\r\n\tframe1=fe.enhance(frame)\r\n\ta=cv2.resize(frame,(50,50))\r\n\ta=a.reshape(1,50,50,3)\r\n\tp=model.predict(a)\r\n\tcv2.imshow('Original video',frame);\r\n\tcv2.imshow('Enhanced video',frame1);\r\n\ts=p[0]\r\n\tif s[0]>s[1]:\r\n\t\tprint(\"No Fire detected\",end=\" \")\r\n\t\tprint(p);\r\n\telse:\r\n\t\tprint(\"FIRE!!!\",end=\" \")\r\n\t\tprint(p);\r\n\tif cv2.waitKey(1000//24) & 0xFF == ord('q'):\r\n\t\tbreak\r\ncap.release()\r\ncv2.destroyAllWindows()\r\n","sub_path":"Video_test.py","file_name":"Video_test.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"449757182","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n\tpath('directoryreport', views.listpage, name='listpage'),\n\tpath('errorreport', views.errpage, name = 'errpage'),\n\tpath('home', views.homepage, name = 'home'),\n\tpath('<path:mvolfolder_name>', views.hierarch, name = \"hierarch\"),\n]","sub_path":"reportapp/reperrapp/listpage/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"498513957","text":"\"\"\"\nDate: 16.06.2015\nAuthor: Can Mekik\n\nUnit tests datautils.LabelMe module.\n\nNeeds to be run from top-level datautils folder.\n\"\"\"\n\nfrom datautils.processors.LabelMe import LabelMe2Visuo, LabelMe3D2Visuo\nfrom collections import defaultdict\nfrom numpy import array, sqrt, allclose\nfrom copy import deepcopy\nfrom os import remove\nimport unittest\n\n\nclass LabelMe2VisuoPreprocessTestCase(unittest.TestCase):\n\n    def setUp(self):\n\n        def rm_polygon(entries):\n            for entry in entries:\n                entries[entry].pop('polygon', None)\n            return entries\n\n        def add_stuff(x):\n            out = {}\n            for item in x:\n                out[item] = defaultdict(dict, {'added': 'stuff'})\n            return out\n\n        labelme2visuo = LabelMe2Visuo(rm_polygon, add_stuff)\n        labelme2visuo.destination = './test/processors/LabelMe/'\n        labelme2visuo.filename = 'out'\n        test = './test/processors/LabelMe/test_db/Annotations/test/test.xml'\n        with open(test) as f:\n            self.preprocessed = labelme2visuo.preprocess(f)\n        #Need to copy preprocessed as dicts are mutable.\n        self.processed = labelme2visuo.process(\n            deepcopy(self.preprocessed))\n        labelme2visuo.publish(self.processed)\n\n    def tearDown(self):\n\n        del self.preprocessed, self.processed\n        remove('./test/processors/LabelMe/out.visuo')\n\n    def test_preprocess(self):\n        \"\"\"Tests preprocess with artificial data.\"\"\"\n\n        radius = 0.5*sqrt(2)*512.0\n        expected = {0: defaultdict(dict,\n                                   {'name': 'triangle',\n                                    'polygon': array([[0, 0],\n                                                      [0, 1/radius],\n                                                      [1/radius, 0]])})}\n        for key in self.preprocessed:\n            self.assertEqual(self.preprocessed[key]['name'],\n                             expected[key]['name'])\n            self.assertTrue(allclose(self.preprocessed[key]['polygon'],\n                                     expected[key]['polygon']))\n\n    def test_process(self):\n        \"\"\"Tests process with some simple processes.\"\"\"\n\n        expected = {0: defaultdict(dict,\n                                   {'name': 'triangle',\n                                    'added': 'stuff'})}\n        for index in range(len(self.processed)):\n            self.assertEqual(self.processed[index]['name'],\n                             expected[index]['name'])\n            self.assertEqual(self.processed[index]['added'],\n                             expected[index]['added'])\n\n    def test_publish(self):\n        \"\"\"Test publish with artificial data and simple processes.\"\"\"\n\n        expected = 'name = triangle\\nadded = stuff\\n\\n'\n        with open('./test/processors/LabelMe/out.visuo') as f:\n            lines = ''.join(f.readlines())\n        self.assertEqual(lines, expected)\n\n\nclass LabelMe3D2VisuoTestCase(unittest.TestCase):\n\n    def setUp(self):\n\n        labelme3d2visuo = LabelMe3D2Visuo()\n        test = './test/processors/LabelMe/test_db/Annotations/test/test.xml'\n        with open(test) as f:\n            self.preprocessed = labelme3d2visuo.preprocess(f)\n\n    def tearDown(self):\n\n        del self.preprocessed\n\n    def test_preprocess(self):\n\n        expected = {0: defaultdict(dict,\n                                   {'name': 'triangle',\n                                    'polygon': array([[0., 0., 0.],\n                                                      [0., 1., 0.],\n                                                      [1., 0., 0.]])})}\n        for key in self.preprocessed:\n            self.assertEqual(self.preprocessed[key]['name'],\n                             expected[key]['name'])\n            self.assertTrue(allclose(self.preprocessed[key]['polygon'],\n                                     expected[key]['polygon']))","sub_path":"datautils/test/processors/LabelMe/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"591454838","text":"#!/usr/bin/env python\n# coding=utf-8\n'''\nAuthor: wjm\nDate: 2020-10-20 16:40:30\nLastEditTime: 2021-08-22 10:48:55\n@Description: batch_size=16, patch_size=48, L1 loss, lr=1e-4, epoch=500, ADAM, decay=150, n_feats = 128, depth = 12\n'''\nimport os\nimport torch.nn as nn\nimport torch.optim as optim\nfrom model.base_net import *\nfrom torchvision.transforms import *\nimport torch\nimport numpy as np\nfrom PIL import Image\nimport torch.nn.functional as F\n\nclass Net(nn.Module):\n    def __init__(self, args):\n        super(Net, self).__init__()\n        \n        self.args = args\n        num_channels = self.args['data']['n_colors']\n        scale_factor = self.args['data']['upsacle']\n        \n        n_feats = 128\n        self.depth = 12\n        kernel_size = 3 \n        scale = scale_factor      \n        self.scale = scale_factor\n        # rgb_mean = (0.4488, 0.4371, 0.4040)\n        # rgb_std = (1.0, 1.0, 1.0)\n        # self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std)\n        \n        # define head module\n        m_head = [ConvBlock(num_channels, n_feats, 3, 1, 1, activation='prelu', bias=True, norm=None),\n        ConvBlock(n_feats, n_feats, 3, 1, 1, activation='prelu', bias=True, norm=None)]\n\n        # define Self-Exemplar Mining Cell\n        self.SEM = RecurrentProjection(n_feats, scale = scale)\n\n        # define tail module\n        m_tail = [\n            nn.Conv2d(\n                n_feats*self.depth, num_channels, kernel_size,\n                padding=(kernel_size//2)\n            )\n        ]\n\n        # self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1)\n\n        self.head = nn.Sequential(*m_head)\n        self.tail = nn.Sequential(*m_tail)\n\n    def forward(self,input):\n        # x = self.sub_mean(input)\n        x = self.head(input)\n        bag = []\n        for i in range(self.depth):\n            x, h_estimate = self.SEM(x)\n            bag.append(h_estimate)\n        h_feature = torch.cat(bag,dim=1)\n        h_final = self.tail(h_feature)\n        # return self.add_mean(h_final)\n        return h_final\n        \nclass CrossScaleAttention(nn.Module):\n    def __init__(self, channel=128, reduction=2, ksize=3, scale=3, stride=1, softmax_scale=10, average=True):\n        super(CrossScaleAttention, self).__init__()\n        \n        self.ksize = 3\n        self.stride = 1\n        self.softmax_scale = 10\n        \n        self.scale = scale\n        self.average = True\n        escape_NaN = torch.FloatTensor([1e-4])\n        self.register_buffer('escape_NaN', escape_NaN)\n        self.base_filter = channel\n\n        self.conv_match_1 = ConvBlock(self.base_filter, self.base_filter // 2, 1, 1, 0, activation='prelu', bias=True, norm=None)\n        self.conv_match_2 = ConvBlock(self.base_filter, self.base_filter // 2, 1, 1, 0, activation='prelu', bias=True, norm=None)\n        self.conv_assembly = ConvBlock(self.base_filter, self.base_filter, 1, 1, 0, activation='prelu', bias=True, norm=None)\n\n    def forward(self, input):\n        #get embedding\n        embed_w = self.conv_assembly(input)\n        match_input = self.conv_match_1(input)\n        \n        # b*c*h*w\n        shape_input = list(embed_w.size())   # b*c*h*w\n        input_groups = torch.split(match_input,1,dim=0)\n        # kernel size on input for matching \n        kernel = self.scale*self.ksize\n        \n        # raw_w is extracted for reconstruction \n        raw_w = extract_image_patches(embed_w, ksizes=[kernel, kernel],\n                                      strides=[self.stride*self.scale,self.stride*self.scale],\n                                      rates=[1, 1],\n                                      padding='same') # [N, C*k*k, L]\n        # raw_shape: [N, C, k, k, L]\n        raw_w = raw_w.view(shape_input[0], shape_input[1], kernel, kernel, -1)\n        raw_w = raw_w.permute(0, 4, 1, 2, 3)    # raw_shape: [N, L, C, k, k]\n        raw_w_groups = torch.split(raw_w, 1, dim=0)\n        \n    \n        # downscaling X to form Y for cross-scale matching\n        ref  = F.interpolate(input, scale_factor=1./self.scale, mode='bilinear')\n        ref = self.conv_match_2(ref)\n        w = extract_image_patches(ref, ksizes=[self.ksize, self.ksize],\n                                  strides=[self.stride, self.stride],\n                                  rates=[1, 1],\n                                  padding='same')\n        shape_ref = ref.shape\n        # w shape: [N, C, k, k, L]\n        w = w.view(shape_ref[0], shape_ref[1], self.ksize, self.ksize, -1)\n        w = w.permute(0, 4, 1, 2, 3)    # w shape: [N, L, C, k, k]\n        w_groups = torch.split(w, 1, dim=0)\n\n\n        y = []\n        scale = self.softmax_scale  \n          # 1*1*k*k\n        #fuse_weight = self.fuse_weight\n\n        for xi, wi, raw_wi in zip(input_groups, w_groups, raw_w_groups):\n            # normalize\n            wi = wi[0]  # [L, C, k, k]\n            max_wi = torch.max(torch.sqrt(reduce_sum(torch.pow(wi, 2),\n                                                     axis=[1, 2, 3],\n                                                     keepdim=True)),\n                               self.escape_NaN)\n            wi_normed = wi/ max_wi\n\n            # Compute correlation map\n            xi = same_padding(xi, [self.ksize, self.ksize], [1, 1], [1, 1])  # xi: 1*c*H*W\n            yi = F.conv2d(xi, wi_normed, stride=1)   # [1, L, H, W] L = shape_ref[2]*shape_ref[3]\n\n            yi = yi.view(1,shape_ref[2] * shape_ref[3], shape_input[2], shape_input[3])  # (B=1, C=32*32, H=32, W=32)\n            # rescale matching score\n            yi = F.softmax(yi*scale, dim=1)\n            if self.average == False:\n                yi = (yi == yi.max(dim=1,keepdim=True)[0]).float()\n            \n            # deconv for reconsturction\n            wi_center = raw_wi[0]           \n            yi = F.conv_transpose2d(yi, wi_center, stride=self.stride*self.scale, padding=self.scale)\n            \n            yi =yi/6.\n            y.append(yi)\n      \n        y = torch.cat(y, dim=0)\n        return y\n\n#in-scale non-local attention\nclass NonLocalAttention(nn.Module):\n    def __init__(self, channel=128, reduction=2, ksize=3, scale=3, stride=1, softmax_scale=10, average=True):\n        super(NonLocalAttention, self).__init__()\n        self.conv_match_1 = ConvBlock(channel, channel // 2, 1, 1, 0, activation='prelu', bias=True, norm=None)\n        self.conv_match_2 = ConvBlock(channel, channel // 2, 1, 1, 0, activation='prelu', bias=True, norm=None)\n        self.conv_assembly = ConvBlock(channel, channel, 1, 1, 0, activation='prelu', bias=True, norm=None)\n        \n    def forward(self, input):\n        x_embed_1 = self.conv_match_1(input)\n        x_embed_2 = self.conv_match_2(input)\n        x_assembly = self.conv_assembly(input)\n\n        N,C,H,W = x_embed_1.shape\n        x_embed_1 = x_embed_1.permute(0,2,3,1).view((N,H*W,C))\n        x_embed_2 = x_embed_2.view(N,C,H*W)\n        score = torch.matmul(x_embed_1, x_embed_2)\n        score = F.softmax(score, dim=2)\n        x_assembly = x_assembly.view(N,-1,H*W).permute(0,2,1)\n        x_final = torch.matmul(score, x_assembly)\n        return x_final.permute(0,2,1).view(N,-1,H,W)\n        \ndef extract_image_patches(images, ksizes, strides, rates, padding='same'):\n    \"\"\"\n    Extract patches from images and put them in the C output dimension.\n    :param padding:\n    :param images: [batch, channels, in_rows, in_cols]. A 4-D Tensor with shape\n    :param ksizes: [ksize_rows, ksize_cols]. The size of the sliding window for\n     each dimension of images\n    :param strides: [stride_rows, stride_cols]\n    :param rates: [dilation_rows, dilation_cols]\n    :return: A Tensor\n    \"\"\"\n    assert len(images.size()) == 4\n    assert padding in ['same', 'valid']\n    batch_size, channel, height, width = images.size()\n    \n    if padding == 'same':\n        images = same_padding(images, ksizes, strides, rates)\n    elif padding == 'valid':\n        pass\n    else:\n        raise NotImplementedError('Unsupported padding type: {}.\\\n                Only \"same\" or \"valid\" are supported.'.format(padding))\n\n    unfold = torch.nn.Unfold(kernel_size=ksizes,\n                             dilation=rates,\n                             padding=0,\n                             stride=strides)\n    patches = unfold(images)\n    return patches  # [N, C*k*k, L], L is the total number of such blocks\n\ndef reduce_mean(x, axis=None, keepdim=False):\n    if not axis:\n        axis = range(len(x.shape))\n    for i in sorted(axis, reverse=True):\n        x = torch.mean(x, dim=i, keepdim=keepdim)\n    return x\n\n\ndef reduce_std(x, axis=None, keepdim=False):\n    if not axis:\n        axis = range(len(x.shape))\n    for i in sorted(axis, reverse=True):\n        x = torch.std(x, dim=i, keepdim=keepdim)\n    return x\n\n\ndef reduce_sum(x, axis=None, keepdim=False):\n    if not axis:\n        axis = range(len(x.shape))\n    for i in sorted(axis, reverse=True):\n        x = torch.sum(x, dim=i, keepdim=keepdim)\n    return x\n\ndef same_padding(images, ksizes, strides, rates):\n    assert len(images.size()) == 4\n    batch_size, channel, rows, cols = images.size()\n    out_rows = (rows + strides[0] - 1) // strides[0]\n    out_cols = (cols + strides[1] - 1) // strides[1]\n    effective_k_row = (ksizes[0] - 1) * rates[0] + 1\n    effective_k_col = (ksizes[1] - 1) * rates[1] + 1\n    padding_rows = max(0, (out_rows-1)*strides[0]+effective_k_row-rows)\n    padding_cols = max(0, (out_cols-1)*strides[1]+effective_k_col-cols)\n    # Pad the input\n    padding_top = int(padding_rows / 2.)\n    padding_left = int(padding_cols / 2.)\n    padding_bottom = padding_rows - padding_top\n    padding_right = padding_cols - padding_left\n    paddings = (padding_left, padding_right, padding_top, padding_bottom)\n    images = torch.nn.ZeroPad2d(paddings)(images)\n    return images\n\n#projection between attention branches\nclass MultisourceProjection(nn.Module):\n    def __init__(self, in_channel,kernel_size = 3,scale=2):\n        super(MultisourceProjection, self).__init__()\n        deconv_ksize, stride, padding, up_factor = {\n            2: (6,2,2,2),\n            3: (9,3,3,3),\n            4: (6,2,2,2)\n        }[scale]\n        self.up_attention = CrossScaleAttention(channel=in_channel, scale = up_factor)\n        self.down_attention = NonLocalAttention(channel=in_channel)\n        self.upsample = nn.Sequential(*[nn.ConvTranspose2d(in_channel,in_channel,deconv_ksize,stride=stride,padding=padding),nn.PReLU()])\n        self.encoder = ResnetBlock(in_channel, 3, 1, 1, 1, activation='prelu', norm=None)\n    \n    def forward(self,x):\n        down_map = self.upsample(self.down_attention(x))\n        up_map = self.up_attention(x)\n\n        err = self.encoder(up_map-down_map)\n        final_map = down_map + err\n        \n        return final_map\n\n#projection with local branch\nclass RecurrentProjection(nn.Module):\n    def __init__(self, in_channel,kernel_size = 3, scale = 2):\n        super(RecurrentProjection, self).__init__()\n        self.scale = scale\n        stride_conv_ksize, stride, padding = {\n            2: (6,2,2),\n            3: (9,3,3),\n            4: (6,2,2)\n        }[scale]\n\n        self.multi_source_projection = MultisourceProjection(in_channel,kernel_size=kernel_size,scale = scale)\n        self.down_sample_1 = nn.Sequential(*[nn.Conv2d(in_channel,in_channel,stride_conv_ksize,stride=stride,padding=padding),nn.PReLU()])\n        if scale != 4:\n            self.down_sample_2 = nn.Sequential(*[nn.Conv2d(in_channel,in_channel,stride_conv_ksize,stride=stride,padding=padding),nn.PReLU()])\n        self.error_encode = nn.Sequential(*[nn.ConvTranspose2d(in_channel,in_channel,stride_conv_ksize,stride=stride,padding=padding),nn.PReLU()])\n        self.post_conv = ConvBlock(in_channel, in_channel, 3, 1, 1, activation='prelu', bias=True, norm=None)\n        if scale == 4:\n            self.multi_source_projection_2 = MultisourceProjection(in_channel,kernel_size=kernel_size,scale = scale)\n            self.down_sample_3 = nn.Sequential(*[nn.Conv2d(in_channel,in_channel,8,stride=4,padding=2),nn.PReLU()])\n            self.down_sample_4 = nn.Sequential(*[nn.Conv2d(in_channel,in_channel,8,stride=4,padding=2),nn.PReLU()])\n            self.error_encode_2 = nn.Sequential(*[nn.ConvTranspose2d(in_channel,in_channel,8,stride=4,padding=2),nn.PReLU()])\n\n\n    def forward(self, x):\n        x_up = self.multi_source_projection(x)\n        x_down = self.down_sample_1(x_up)\n        error_up = self.error_encode(x-x_down)\n        h_estimate = x_up + error_up\n        if self.scale == 4:\n            x_up_2 = self.multi_source_projection_2(h_estimate)\n            x_down_2 = self.down_sample_3(x_up_2)\n            error_up_2 = self.error_encode_2(x-x_down_2)\n            h_estimate = x_up_2 + error_up_2\n            x_final = self.post_conv(self.down_sample_4(h_estimate))\n        else:\n            x_final = self.post_conv(self.down_sample_2(h_estimate))\n\n        return x_final, h_estimate","sub_path":"model/csnla.py","file_name":"csnla.py","file_ext":"py","file_size_in_byte":12854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"245034459","text":"\"\"\"wtf\n\nRevision ID: 85664cd33531\nRevises: e032cee1827b\nCreate Date: 2020-04-22 15:44:45.018749\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '85664cd33531'\ndown_revision = 'e032cee1827b'\nbranch_labels = None\ndepends_on = None\n\n\n# Enum 'type' for PostgreSQL\nenum_name = 'genderderenum'\n# Set temporary enum 'type' for PostgreSQL\ntmp_enum_name = 'tmp_' + enum_name\n\n# Options for Enum\nold_options = ('hombre', 'mujer')\nnew_options = ('nulo', 'hombre', 'mujer')\n\n# Create enum fields\nold_type = sa.Enum(*old_options, name=enum_name)\nnew_type = sa.Enum(*new_options, name=enum_name)\n\ndef upgrade():\n    # Rename current enum type to tmp_\n    op.execute('ALTER TYPE ' + enum_name + ' RENAME TO ' + tmp_enum_name)\n    # Create new enum type in db\n    new_type.create(op.get_bind())\n    # Update column to use new enum type\n    op.execute('ALTER TABLE public.\"user\" ALTER COLUMN gender TYPE ' + enum_name + ' USING gender::text::' + enum_name)\n    # Drop old enum type\n    op.execute('DROP TYPE ' + tmp_enum_name)\n\n\ndef downgrade():\n    # Instantiate db query\n    audit = sa.sql.table('user', sa.Column('gender', new_type, nullable=False))\n    # Rename enum type to tmp_\n    op.execute('ALTER TYPE ' + enum_name + ' RENAME TO ' + tmp_enum_name)\n    # Create enum type using old values\n    old_type.create(op.get_bind())\n    # Set enum type as type for event_type column\n    op.execute('ALTER TABLE public.\"user\"  ALTER COLUMN gender TYPE ' + enum_name + ' USING gender::text::' + enum_name)\n    # Drop temp enum type\n    op.execute('DROP TYPE ' + tmp_enum_name)\n\n","sub_path":"migrations/versions/85664cd33531_wtf.py","file_name":"85664cd33531_wtf.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"545657326","text":"# -*- coding: UTF-8 -*-  \nimport urllib.request\nimport urllib.parse\nimport geohash\nimport threading\nimport ogr, osr, gdal\nimport os\nimport subprocess\nimport numpy as np\nfrom PIL import Image\n\ndef makeimagegcp(image_height, image_width, corners):\n    pixels = image_width - 1\n    lines = image_height - 1\n    imagegcp = \" \".join( [\"-gcp 0 0 \" + str(corners['leftup'][0]) + \" \" + str(corners['leftup'][1]),\n                        \"-gcp \" + str(pixels) + \" 0 \" + str(corners['rightup'][0]) + \" \" + str(corners['rightup'][1]), \n                        \"-gcp 0 \" + str(lines) + \" \" + str(corners['leftdown'][0]) + \" \" + str(corners['leftdown'][1]), \n                        \"-gcp \" + str(pixels) + \" \" + str(lines) + \" \" + str(corners['rightdown'][0]) + \" \" + str(corners['rightdown'][1])] )\n    print(\"make gcp done.\")\n    return imagegcp\n\ndef gdal_translate(image, imagegcp, temptiff):\n    cmd = \" \".join([\"gdal_translate\", imagegcp, \"-a_srs EPSG:4326\", image, temptiff])    \n    os.system(cmd)\n    print(\"gdal_translate done.\")\n    return\n\ndef gdalwarp(temptiff, outtiff):\n    cmd = \" \".join([\"gdalwarp\", \"-tps\", \"-r bilinear\", temptiff, outtiff])\n    print(cmd)\n    os.system(cmd)\n    print(\"gdalwarp done.\")\n    return\n\ndef __GetGeometryGeohashes(geom, precision = 5):\n    lon_step = geohash.decode_exactly(geohash.encode(40, 120, precision))[3]\n    lat_step = geohash.decode_exactly(geohash.encode(40, 120, precision))[2]\n    simply_range = lon_step / 2 if lat_step > lon_step else lat_step / 2\n    geohashnumbers = []\n    geohashnumbersandcoveragepercent = {}\n    minx, maxx, miny, maxy = geom.GetEnvelope()\n    simplygeom = geom.Buffer(simply_range).Simplify(simply_range)\n    min_centery, min_centerx, lat_err, lon_err = geohash.decode_exactly(geohash.encode(miny, minx, precision))\n    lonstepnum = int((maxx - minx) / lon_step + 1)\n    latstepnum = int((maxy - miny) / lat_step + 1)\n    for i in range(lonstepnum + 2):\n        for j in range(latstepnum + 2):\n            current_centerx = i * lon_step + min_centerx\n            current_centery = j * lat_step + min_centery\n            geohashnumber = geohash.encode(current_centery, current_centerx, precision)\n            if not geohashnumber in geohashnumbers:\n                # Create ring\n                ring = ogr.Geometry(ogr.wkbLinearRing)\n                result = geohash.decode_exactly(geohashnumber)\n                ring.AddPoint_2D(result[1] - result[3], result[0] - result[2])\n                ring.AddPoint_2D(result[1] - result[3], result[0] + result[2])\n                ring.AddPoint_2D(result[1] + result[3], result[0] + result[2])\n                ring.AddPoint_2D(result[1] + result[3], result[0] - result[2])\n                ring.AddPoint_2D(result[1] - result[3], result[0] - result[2])        \n                # Create polygon\n                poly = ogr.Geometry(ogr.wkbPolygon)\n                poly.AddGeometry(ring) \n                if poly.Intersect(simplygeom):\n                    geohashnumbers.append(geohashnumber)\n                    geohashnumbersandcoveragepercent[geohashnumber] = poly.Intersection(geom).GetArea() / poly.GetArea()\n    print(geohashnumbersandcoveragepercent)\n    return geohashnumbersandcoveragepercent\n\ndef __WriteGeoHash2Shapefile(geohashnumber, outshapefile):\n    if len(geohashnumber) < 1:\n        return\n    srs = osr.SpatialReference()\n    srs.ImportFromEPSG(4326)\n    # Create the output shapefile\n    shpDriver = ogr.GetDriverByName(\"ESRI Shapefile\")\n    if os.path.exists(outshapefile):\n        shpDriver.DeleteDataSource(outshapefile)\n    outDataSource = shpDriver.CreateDataSource(outshapefile)\n    outLayer = outDataSource.CreateLayer('layername', srs)\n    outLayer.CreateField(ogr.FieldDefn(\"geohash\", ogr.OFTString))\n                    \n    outFeature = ogr.Feature(outLayer.GetLayerDefn())\n    outFeature.SetField(\"geohash\", geohashnumber)\n    # Create ring\n    ring = ogr.Geometry(ogr.wkbLinearRing)\n    result = geohash.decode_exactly(geohashnumber)\n    ring.AddPoint_2D(result[1] - result[3], result[0] - result[2])\n    ring.AddPoint_2D(result[1] - result[3], result[0] + result[2])\n    ring.AddPoint_2D(result[1] + result[3], result[0] + result[2])\n    ring.AddPoint_2D(result[1] + result[3], result[0] - result[2])\n    ring.AddPoint_2D(result[1] - result[3], result[0] - result[2])        \n    # Create polygon\n    poly = ogr.Geometry(ogr.wkbPolygon)\n    poly.AddGeometry(ring)        \n    outFeature.SetGeometry(poly)\n    outLayer.CreateFeature(outFeature)\n    return\n\ndef __clipbygeohashgird(inputtiff, outfolder, geohashnumber):\n    geohashgridshp = os.path.join(outfolder, geohashnumber + '.shp')\n    geohashgridtiff = os.path.join(outfolder, '#' + geohashnumber  + '.tiff')\n    geohashgridimg = os.path.join(outfolder, '#' + geohashnumber + '.jpg')\n    geohashgridimgxml = geohashgridimg + \".aux.xml\"\n    __WriteGeoHash2Shapefile(geohashnumber, geohashgridshp)\n    args = [\"gdalwarp\",\n            '--config', 'GDAL_FILENAME_IS_UTF8', 'NO',\n            '--config', 'CPL_DEBUG', 'NO',\n            \"-r\", \"bilinear\",\n            \"-cutline\", geohashgridshp,\n            '-crop_to_cutline',\n            \"-dstnodata\", \"0\",\n            \"-s_srs\", \"EPSG:4326\",\n            \"-t_srs\", \"EPSG:4326\",\n            #'-ts', '256', '256',\n            inputtiff, geohashgridtiff ]\n    if not os.path.exists(geohashgridimg):\n        newp = subprocess.Popen(args)\n        newp.wait()\n        args2 = [\"gdal_translate\",\n                \"-of\", \"JPEG\",\n                #'-ts', '256', '256',\n                geohashgridtiff, geohashgridimg ]\n        newp2 = subprocess.Popen(args2)\n        newp2.wait()\n\n    shpDriver = ogr.GetDriverByName(\"ESRI Shapefile\")\n    if os.path.exists(geohashgridshp):\n        shpDriver.DeleteDataSource(geohashgridshp)\n    if os.path.exists(geohashgridtiff):\n        os.remove(geohashgridtiff)\n    if os.path.exists(geohashgridimgxml):\n        os.remove(geohashgridimgxml)\n    return geohashgridimg\n\ndef geogridise(corners, inputtiff, outfolder, precision= 1):\n    # Create ring\n    ring = ogr.Geometry(ogr.wkbLinearRing)\n    ring.AddPoint(corners['leftup'][0], corners['leftup'][1])\n    ring.AddPoint(corners['rightup'][0], corners['rightup'][1])\n    ring.AddPoint(corners['rightdown'][0], corners['rightdown'][1])\n    ring.AddPoint(corners['leftdown'][0], corners['leftdown'][1])\n    ring.AddPoint(corners['leftup'][0], corners['leftup'][1])\n\n    # Create polygon\n    geom = ogr.Geometry(ogr.wkbPolygon)\n    geom.AddGeometry(ring)\n\n    geohashnumbersandcoveragepercent = __GetGeometryGeohashes(geom, precision)\n    gridimagesandcoveragepercent = {}\n    for geohashnumber, coveragepercent in geohashnumbersandcoveragepercent.items():\n        gridimage = __clipbygeohashgird(inputtiff, outfolder, geohashnumber)\n        gridimagesandcoveragepercent[gridimage] = coveragepercent\n    print(\"geogridise done.\")\n    return gridimagesandcoveragepercent\n\nif __name__ == \"__main__\":\n    testimage = \"e:/mortal/data/GeoHash_0.jpg\"\n    corners = {\n            \"leftup\": [-180, 90],\n            \"rightup\": [180, 90],\n            \"rightdown\": [180, -90],\n            \"leftdown\": [-180, -90]\n            }\n    jpggcp = makeimagegcp(1024, 2048, corners)\n    print(jpggcp)\n    temptiff = \"e:/mortal/test/temp.tiff\"\n    outtiff = \"e:/mortal/test/out.tiff\"\n    gdal_translate(testimage, jpggcp, temptiff)\n    gdalwarp(temptiff, outtiff)\n    outfolder = 'e:/mortal/test'    \n    geohashgridimages = geogridise(corners, outtiff, outfolder)\n    if os.path.exists(temptiff):\n        os.remove(temptiff)\n    if os.path.exists(outtiff):\n        os.remove(outtiff)","sub_path":"experiment003.py","file_name":"experiment003.py","file_ext":"py","file_size_in_byte":7576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"114688470","text":"# Given a singly linked list where elements are sorted in ascending order, convert it to a height balanced BST.\n#\n# For this problem, a height-balanced binary tree is defined as a binary tree in which the depth of the two subtrees\n# of every node never differ by more than 1.\n#\n# Example:\n#\n# Given the sorted linked list: [-10,-3,0,5,9],\n#\n# One possible answer is: [0,-3,9,-10,null,5], which represents the following height balanced BST:\n#\n#        0\n#       / \\\n#     -3   9\n#     /   /\n#   -10  5\n\n\n# Definition for singly-linked list.\nclass ListNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\n\n# Definition for a binary tree node.\nclass TreeNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\n\nclass Solution(object):\n    def sortedListToBST(self, head):\n        \"\"\"\n        :type head: ListNode\n        :rtype: TreeNode\n        \"\"\"\n        if not head:\n            return None\n        return self.createBST(head, None)\n\n    def createBST(self, head, tail):\n        sp = head\n        fp = head\n        if head == tail:\n            return None\n\n        while fp != tail and fp.next != tail:\n            fp = fp.next.next\n            sp = sp.next\n\n        root = TreeNode(sp.val)\n        root.left = self.createBST(head, sp)\n        root.right = self.createBST(sp.next, tail)\n\n        return root\n\n# Note:\n# We find middle element of linked list which is root using fast pointer slow pointer mechanism. After that we recurse\n# to set left and right node of the root node on each step\n","sub_path":"LeetCode/109-M-ConvertSortedListToBinarySearchTree.py","file_name":"109-M-ConvertSortedListToBinarySearchTree.py","file_ext":"py","file_size_in_byte":1589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"137915643","text":"def datetime_from_timestamp(timestamp):\n\tx = datetime.datetime.fromtimestamp(int(timestamp)).strftime('%Y-%m-%d %H:%M:%S')\n\treturn x\n\n\ndef writeData2csv(filename, pair='BTC_ZEC', period=300, days=230):\n\tfrom poloniex import Poloniex\n\timport time, datetime\n\n\tcsvfile = open(filename, \"a\")\n\tconn = Poloniex()\n\n\tstartTime = time.time() - (86400 * days)\n\t#startTime = time.time() - (period * 50)\n\tendTime = time.time() #End at current time\n\n\tdata = conn.returnChartData(pair, period, startTime, endTime)\n\n\n\tfor tick in data:\n\t\t#print(str(datetime_from_timestamp(tick['date'])) + \",\" + tick['close'], file=csvfile)\n\t\tprint(tick['close'], file=csvfile)\n\treturn\n\n\nwriteData2csv('test.csv')","sub_path":"Experimental/getData.py","file_name":"getData.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"296703152","text":"from AccessControl import getSecurityManager\nfrom AccessControl.Permissions import view as View\nfrom OFS.interfaces import IApplication\nfrom Products.CMFCore.permissions import ManagePortal\nfrom Products.CMFPlone.factory import _DEFAULT_PROFILE\nfrom Products.CMFPlone.factory import addPloneSite\nfrom Products.CMFPlone.interfaces import INonInstallable\nfrom Products.CMFPlone.interfaces import IPloneSiteRoot\nfrom Products.Five.browser.pagetemplatefile import ViewPageTemplateFile\nfrom Products.GenericSetup import BASE, EXTENSION\nfrom Products.GenericSetup import profile_registry\nfrom Products.GenericSetup.upgrade import normalize_version\nfrom ZPublisher.BaseRequest import DefaultPublishTraverse\nfrom operator import itemgetter\nfrom plone.i18n.locales.interfaces import IContentLanguageAvailability\nfrom plone.keyring.interfaces import IKeyManager\nfrom plone.protect.authenticator import check as checkCSRF\nfrom plone.protect.interfaces import IDisableCSRFProtection\nfrom zope.component import adapts\nfrom zope.component import getAllUtilitiesRegisteredFor\nfrom zope.component import getUtility\nfrom zope.component import queryMultiAdapter\nfrom zope.component import queryUtility\nfrom zope.i18n.interfaces import IUserPreferredLanguages\nfrom zope.i18n.locales import locales, LoadLocaleError\nfrom zope.interface import Interface\nfrom zope.interface import alsoProvides\nfrom zope.publisher.browser import BrowserView\nfrom zope.publisher.interfaces import IRequest\nfrom zope.schema.interfaces import IVocabularyFactory\n\nimport logging\nLOGGER = logging.getLogger('Products.CMFPlone')\n\n\nclass AppTraverser(DefaultPublishTraverse):\n    adapts(IApplication, IRequest)\n\n    def publishTraverse(self, request, name):\n        if name == 'index_html':\n            view = queryMultiAdapter(\n                (self.context, request), Interface, 'plone-overview')\n            if view is not None:\n                return view\n        return DefaultPublishTraverse.publishTraverse(self, request, name)\n\n\nclass Overview(BrowserView):\n\n    def sites(self, root=None):\n        if root is None:\n            root = self.context\n\n        result = []\n        secman = getSecurityManager()\n        for obj in root.values():\n            if obj.meta_type is 'Folder':\n                result = result + self.sites(obj)\n            elif IPloneSiteRoot.providedBy(obj):\n                if secman.checkPermission(View, obj):\n                    result.append(obj)\n            elif obj.getId() in getattr(root, '_mount_points', {}):\n                result.extend(self.sites(root=obj))\n        return result\n\n    def outdated(self, obj):\n        mig = obj.get('portal_migration', None)\n        if mig is not None:\n            return mig.needUpgrading()\n        return False\n\n    def can_manage(self):\n        secman = getSecurityManager()\n        return secman.checkPermission(ManagePortal, self.context)\n\n    def upgrade_url(self, site, can_manage=None):\n        if can_manage is None:\n            can_manage = self.can_manage()\n        if can_manage:\n            return site.absolute_url() + '/@@plone-upgrade'\n        else:\n            return self.context.absolute_url() + '/@@plone-root-login'\n\n\nclass RootLoginRedirect(BrowserView):\n    \"\"\" @@plone-root-login\n\n    This view of the Zope root forces authentication via the root\n    acl_users and then redirects elsewhere.\n    \"\"\"\n\n    def __call__(self, came_from=None):\n        if came_from is None:\n            came_from = self.context.absolute_url()\n        self.request.response.redirect(came_from)\n\n\nclass RootLogout(BrowserView):\n    \"\"\" @@plone-root-logout \"\"\"\n\n    logout = ViewPageTemplateFile('templates/plone-logged-out.pt')\n\n    def __call__(self):\n        response = self.request.response\n        realm = response.realm\n        response.setStatus(401)\n        response.setHeader('WWW-Authenticate', 'basic realm=\"%s\"' % realm, 1)\n        response.setBody(self.logout())\n        return\n\n\nclass FrontPage(BrowserView):\n\n    index = ViewPageTemplateFile('templates/plone-frontpage.pt')\n\n\nclass AddPloneSite(BrowserView):\n\n    # Profiles that are installed by default,\n    # but can be removed later.\n    default_extension_profiles = (\n        'plone.app.caching:default',\n        'plonetheme.barceloneta:default',\n    )\n\n    def profiles(self):\n        base_profiles = []\n        extension_profiles = []\n\n        # profiles available for install/uninstall, but hidden at the time\n        # the Plone site is created\n        not_installable = [\n            'Products.CMFPlacefulWorkflow:CMFPlacefulWorkflow',\n        ]\n        utils = getAllUtilitiesRegisteredFor(INonInstallable)\n        for util in utils:\n            not_installable.extend(util.getNonInstallableProfiles())\n\n        for info in profile_registry.listProfileInfo():\n            if info.get('type') == EXTENSION and \\\n               info.get('for') in (IPloneSiteRoot, None):\n                profile_id = info.get('id')\n                if profile_id not in not_installable:\n                    if profile_id in self.default_extension_profiles:\n                        info['selected'] = 'selected'\n                    extension_profiles.append(info)\n\n        def _key(v):\n            # Make sure implicitly selected items come first\n            selected = v.get('selected') and 'automatic' or 'manual'\n            return '%s-%s' % (selected, v.get('title', ''))\n        extension_profiles.sort(key=_key)\n\n        for info in profile_registry.listProfileInfo():\n            if info.get('type') == BASE and \\\n               info.get('for') in (IPloneSiteRoot, None):\n                base_profiles.append(info)\n\n        return dict(\n            base=tuple(base_profiles),\n            default=_DEFAULT_PROFILE,\n            extensions=tuple(extension_profiles),\n        )\n\n    def browser_language(self):\n        language = 'en'\n        pl = IUserPreferredLanguages(self.request)\n        if pl is not None:\n            languages = pl.getPreferredLanguages()\n            for httplang in languages:\n                parts = (httplang.split('-') + [None, None])[:3]\n                if parts[0] == parts[1]:\n                    # Avoid creating a country code for simple languages codes\n                    parts = [parts[0], None, None]\n                elif parts[0] == 'en':\n                    # Avoid en-us as a language\n                    parts = ['en', None, None]\n                try:\n                    locale = locales.getLocale(*parts)\n                    language = locale.getLocaleID().replace('_', '-').lower()\n                    break\n                except LoadLocaleError:\n                    # Just try the next combination\n                    pass\n        return language\n\n    def languages(self, default='en'):\n        util = queryUtility(IContentLanguageAvailability)\n        if '-' in default:\n            available = util.getLanguages(combined=True)\n        else:\n            available = util.getLanguages()\n        languages = [(code, v.get(u'native', v.get(u'name'))) for\n                     code, v in available.items()]\n        languages.sort(key=itemgetter(1))\n        return languages\n\n    def timezones(self):\n        tz_vocab = getUtility(\n            IVocabularyFactory,\n            'plone.app.vocabularies.CommonTimezones'\n        )(self.context)\n        tz_list = [it.value for it in tz_vocab]\n        return tz_list\n\n    def __call__(self):\n        context = self.context\n        form = self.request.form\n        submitted = form.get('form.submitted', False)\n        if submitted:\n            site_id = form.get('site_id', 'Plone')\n\n            # CSRF protect. DO NOT use auto CSRF protection for adding a site\n            alsoProvides(self.request, IDisableCSRFProtection)\n\n            # check if keyring is installed on root, disable CSRF protection\n            # if it is because it is not installed until a plone site\n            # is created\n            if queryUtility(IKeyManager) is None:\n                LOGGER.info('CSRF protection disabled on initial site '\n                            'creation')\n            else:\n                # we have a keymanager, check csrf protection manually now\n                checkCSRF(self.request)\n            site = addPloneSite(\n                context, site_id,\n                title=form.get('title', ''),\n                profile_id=form.get('profile_id', _DEFAULT_PROFILE),\n                extension_ids=form.get('extension_ids', ()),\n                setup_content=form.get('setup_content', False),\n                default_language=form.get('default_language', 'en'),\n                portal_timezone=form.get('portal_timezone', 'UTC')\n            )\n            self.request.response.redirect(site.absolute_url())\n\n        return self.index()\n\n\nclass Upgrade(BrowserView):\n\n    def upgrades(self):\n        ps = getattr(self.context, 'portal_setup')\n        return ps.listUpgrades(_DEFAULT_PROFILE)\n\n    def versions(self):\n        pm = getattr(self.context, 'portal_migration')\n        result = {}\n        result['instance'] = pm.getInstanceVersion()\n        result['fs'] = pm.getFileSystemVersion()\n        result['equal'] = result['instance'] == result['fs']\n        instance_version = normalize_version(result['instance'])\n        fs_version = normalize_version(result['fs'])\n        result['instance_gt'] = instance_version > fs_version\n        result['instance_lt'] = instance_version < fs_version\n        result['corelist'] = pm.coreVersions()\n        return result\n\n    def __call__(self):\n        form = self.request.form\n        submitted = form.get('form.submitted', False)\n        if submitted:\n            # CSRF protect. DO NOT use auto CSRF protection for upgrading sites\n            alsoProvides(self.request, IDisableCSRFProtection)\n\n            pm = getattr(self.context, 'portal_migration')\n            report = pm.upgrade(\n                REQUEST=self.request,\n                dry_run=form.get('dry_run', False),\n            )\n            qi = getattr(self.context, 'portal_quickinstaller')\n            pac_installed = qi.isProductInstalled('plone.app.contenttypes')\n            pac_installable = qi.isProductInstallable('plone.app.contenttypes')\n            advertise_dx_migration = pac_installable and not pac_installed\n\n            return self.index(\n                report=report,\n                advertise_dx_migration=advertise_dx_migration\n            )\n\n        return self.index()\n","sub_path":"buildout-cache/eggs/Products.CMFPlone-5.0b2-py2.7.egg/Products/CMFPlone/browser/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":10387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"333295702","text":"import dash_bootstrap_components as dbc\nimport dash_html_components as html\n\nitems = [\n    dbc.DropdownMenuItem(\"First\"),\n    dbc.DropdownMenuItem(divider=True),\n    dbc.DropdownMenuItem(\"Second\"),\n]\n\ndropdown = html.Div(\n    [\n        dbc.DropdownMenu(\n            label=\"large dropdown\",\n            bs_size=\"lg\",\n            children=items,\n            className=\"mb-3\",\n        ),\n        dbc.DropdownMenu(\n            label=\"normal dropdown\", children=items, className=\"mb-3\"\n        ),\n        dbc.DropdownMenu(label=\"small dropdown\", bs_size=\"sm\", children=items),\n    ]\n)\n","sub_path":"docs/components_page/components/dropdown/size.py","file_name":"size.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"246158833","text":"# -*- coding: utf-8 -*-\n\nimport simple_draw as sd\n\n# Шаг 1: Реализовать падение снежинки через класс. Внести в методы:\n#  - создание снежинки с нужными параметрами\n#  - отработку изменений координат\n#  - отрисовку\n\nsd.resolution = (1200, 800)\n\n\nclass Snowflake:\n    down_snowflakes = []\n\n    def __init__(self):\n        self.length = sd.random_number(5, 15)\n        self.x = sd.random_number(0, sd.resolution[0])\n        self.y = 700\n        self.factor_a = sd.random_number(1, 10) / 10\n        self.factor_b = sd.random_number(1, 10) / 10\n        self.factor_c = sd.random_number(10, 120)\n\n    def clear_previous_picture(self):\n        self.draw(color=sd.background_color)\n\n    def move(self):\n        self.x += sd.random_number(0, 2)\n        self.y -= self.length + sd.random_number(-5, 5)\n\n    def draw(self, color=sd.COLOR_WHITE):\n        start_point = sd.get_point(x=self.x, y=self.y)\n        sd.snowflake(center=start_point,\n                     length=self.length,\n                     color=color,\n                     factor_a=self.factor_a,\n                     factor_b=self.factor_b,\n                     factor_c=self.factor_c)\n\n    def can_fall(self):\n        if self.y > 0:\n            return True\n\n\n# flake = Snowflake()\n#\n# while True:\n#     flake.clear_previous_picture()\n#     flake.move()\n#     flake.draw()\n#     if not flake.can_fall():\n#         break\n#     sd.sleep(0.1)\n#     if sd.user_want_exit():\n#         break\n\n# шаг 2: создать снегопад - список объектов Снежинка в отдельном списке, обработку примерно так:\n# flakes = get_flakes(count=N)  # создать список снежинок\n# while True:\n#     for flake in flakes:\n#         flake.clear_previous_picture()\n#         flake.move()\n#         flake.draw()\n#     fallen_flakes = get_fallen_flakes()  # подчитать сколько снежинок уже упало\n#     if fallen_flakes:\n#         append_flakes(count=fallen_flakes)  # добавить еще сверху\n#     sd.sleep(0.1)\n#     if sd.user_want_exit():\n#         break\n\ndef get_flakes(count):\n    _flakes = {}\n    for i in range(count):\n        _flakes[i + 1] = Snowflake()\n    return _flakes\n\n\nN = 20\nfallen_flakes = []\n\nflakes = get_flakes(count=N)\n\n\ndef get_fallen_flakes(flakes):\n    for number, flake_parameter in flakes.items():\n        if flake_parameter.y < 0:\n            fallen_flakes.append(number)\n    return fallen_flakes\n\n\ndef append_flakes(fallen_flakes=fallen_flakes):\n    for fallen in fallen_flakes:\n        flakes[fallen] = Snowflake()\n    fallen_flakes.clear()\n    return fallen_flakes\n\n\nwhile True:\n    for num, flake in flakes.items():\n        flake.clear_previous_picture()\n        flake.move()\n        flake.draw()\n        if not flake.can_fall():\n            break\n    fallen_flakes = get_fallen_flakes(flakes=flakes)  # подчитать сколько снежинок уже упало\n    if fallen_flakes:\n        append_flakes(fallen_flakes=fallen_flakes)  # добавить еще сверху\n    sd.sleep(0.1)\n    if sd.user_want_exit():\n        break\n","sub_path":"lesson_007/01_snowfall.py","file_name":"01_snowfall.py","file_ext":"py","file_size_in_byte":3253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"452901844","text":"from collections import defaultdict\n\n\ndef numMatchingSubseq(S, words):\n    # keys of this map are individual chars and the value is a list of words\n    map = defaultdict(list)\n    count = 0\n\n    # build map with initial words\n    for word in words:\n        map[word[0]].append(word)\n\n    # loop through large string\n    for char in S:\n        # grab all the current words that start with the char\n        wordsExpectingChar = map[char]\n\n        # we've stored the words so you can empty the list here\n        map[char] = []\n\n        # go through the words that start with char\n        for word in wordsExpectingChar:\n            # its a full word\n            if len(word) == 1:\n                count += 1\n            else:\n                # slice the word minues the first char and put it in the map\n                map[word[1]].append(word[1:])\n\n    return count\n\n\nprint(numMatchingSubseq(\"abcde\", [\"a\", \"bb\", \"acd\", \"ace\"]))  # 3\n","sub_path":"Hashmaps/NumberOfMatchingSubsequences.py","file_name":"NumberOfMatchingSubsequences.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"409534870","text":"import os.path\nimport logging\nimport sys\nimport inspect\nimport mitmproxy.websocket\n\n# This file is executed by `mitmdump' with `execfile'. Therefore, in\n# order to import submodules under the directory where this file exists,\n# we need some tricks. See http://stackoverflow.com/questions/3718657\n# for the details of the tricks used in the following lines.\nTHIS_FILENAME = inspect.getframeinfo(inspect.currentframe()).filename\nTHIS_DIR_PATH = os.path.dirname(os.path.abspath(THIS_FILENAME))\nsys.path.append(THIS_DIR_PATH)\nimport mahjongsoul_sniffer.logging as logging_\nfrom mahjongsoul_sniffer.redis_mirroring import RedisMirroring\n\n\nlogging_.initialize(\n    module_name='game_abstract_crawler', service_name='sniffer')\n\n\n_REDIS_MIRRORING_CONFIG = {\n    'websocket': {\n        '.lq.Lobby.loginBeat': {\n            'request_direction': 'outbound',\n            'action': {\n                'command': 'SET',\n                'key': 'login-beat'\n            }\n        },\n        '.lq.Lobby.fetchGameLiveList': {\n            'request_direction': 'outbound',\n            'action': {\n                'command': 'RPUSH',\n                'key': 'game-abstract-list'\n            }\n        }\n    }\n}\n\n\nredis_mirroring = RedisMirroring(\n    module_name='game_abstract_crawler', config=_REDIS_MIRRORING_CONFIG)\n\n\ndef websocket_message(flow: mitmproxy.websocket.WebSocketFlow) -> None:\n    try:\n        redis_mirroring.on_websocket_message(flow)\n    except Exception as e:\n        logging.exception(\n            '`RedisMirroring.on_websocket_message` threw an exception.')\n","sub_path":"game-abstract-crawler/sniffer.py","file_name":"sniffer.py","file_ext":"py","file_size_in_byte":1558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"579690378","text":"# Import some useful libraries\nimport nibabel as nib \nimport numpy as np\nimport os.path\n\n\n# Function to read *.nii files and store as 3D arrays in a list\n# \n# input:\tdata_path: string with path to file folder. usually \"data/set_test/\" or \"data_/set_train/\"\n# \n# return:\tdata: array with some length. The entries of data are numpy arrays with shape (176, 208, 176, 1)\n# \t\t\t-1: if data_path doesn't exist\ndef read_image_data(data_path):\n\t# Assert path\n\tif data_path != \"data/set_train/\" and data_path != \"data/set_test/\":\n\t\tprint(\"Error: Couldn't find path \" + data_path)\n\t\treturn -1\n\n\t# Allocate data\n\tdata = []\n\n\t# Differ between training and test data set\n\tif data_path == \"data/set_train/\":\n\t\t# Load each file in the folder\n\t\tprint(\"Loading files\")\n\t\tfile_number = 1\n\t\twhile os.path.isfile(data_path + \"train_\" + str(file_number) + \".nii\"):\n\t\t\t# Construct file path and load file\n\t\t\tfile = data_path + \"train_\" + str(file_number) + \".nii\"\n\t\t\timg = nib.load(file)\n\t\t\tprint(\"    Load file: \" + file)\n\n\t\t\t# Add imgage data to data list\n\t\t\tdata.append(np.squeeze(img.get_data()))\n\n\t\t\t# Increase counter\n\t\t\tfile_number += 1\n\n\telif data_path == \"data/set_test/\":\n\t\t# Load each file in the folder\n\t\tprint(\"Loading files\")\n\t\tfile_number = 1\n\t\twhile os.path.isfile(data_path + \"test_\" + str(file_number) + \".nii\"):\n\t\t\t# Construct file path and load file\n\t\t\tfile = data_path + \"test_\" + str(file_number) + \".nii\"\n\t\t\timg = nib.load(file)\n\t\t\tprint(\"    Load file: \" + file)\n\n\t\t\t# Add imgage data to data list\n\t\t\tdata.append(np.squeeze(img.get_data()))\n\n\t\t\t# Increase counter\n\t\t\tfile_number += 1\n\n\t# Return list with data\n\treturn data\n\n\n# Function to read the target values from .csv file\n# \n# input:\tfile_path: string with name of target file\n# \n# output: \tfile_values: numpy array with target values\n# \t\t\t-1: If file is not found\ndef read_file(file_path):\n\t# Assert if the file is in folder\n\tif not os.path.isfile(file_path):\n\t\tprint(\"Error: Couldn't find file \" + file_path)\n\t\treturn -1\n\n\t# Read values from file\n\tfile_values = np.genfromtxt(file_path, delimiter=\",\")\n\n\t# Return the array\n\treturn file_values","sub_path":"project1/src/readData.py","file_name":"readData.py","file_ext":"py","file_size_in_byte":2100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"911448","text":"#!/usr/bin/env python\n\"\"\"\nAn OSX tool to automate downloads from showRSS\n\"\"\"\n\nfrom dateutil import parser\nfrom os import path\nfrom subprocess import call\nfrom xml.dom import minidom\n\ntry:\n    from urllib2 import build_opener\nexcept ImportError:\n    from urllib.request import build_opener\n\n\nRSS_FEED_URL = \"\"\"FEED_URL_CONFIG\"\"\"\n\nSTAMP_FILE = path.join(path.expanduser(\"~\"), '.showrss')\n\n\ndef read_url(url):\n    opener = build_opener()\n    opener.addheaders = [('User-agent', 'Mozilla/5.0')]\n    return opener.open(url, timeout=10).read()\n\n\ndef parse_feed(feed):\n    get = minidom.parseString(feed).getElementsByTagName\n    dates = (parser.parse(z.firstChild.nodeValue) for z in get('pubDate'))\n    links = (z.firstChild.nodeValue for z in get('link')[1:])\n    return zip(dates, links)\n\n\ndef get_timestamp():\n    dt = '2000-01-01 00:00:00+00:00'\n    if path.exists(STAMP_FILE):\n        with open(STAMP_FILE, 'r') as stamp_file:\n            dt = stamp_file.read()\n    return parser.parse(dt)\n\n\ndef write_timestamp(dt):\n    with open(STAMP_FILE, 'w') as stamp_file:\n        stamp_file.write(str(dt))\n\n\nif __name__ == '__main__':\n    timestamp = get_timestamp()\n    all_shows = parse_feed(read_url(RSS_FEED_URL))\n    new_shows = [x for x in all_shows if x[0] > timestamp]\n\n    for _, link in new_shows:\n        call(['open', '-g', link])\n\n    if new_shows:\n        times, _ = zip(*new_shows)\n        write_timestamp(max(times))\n","sub_path":"showrss_helper.py","file_name":"showrss_helper.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"24368080","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2019-2020 CERN.\n#\n# CDS Books is free software; you can redistribute it and/or modify it under\n# the terms of the MIT License; see LICENSE file for more details.\n\n\"\"\"Literature covers.\"\"\"\n\nimport os\nfrom functools import partial\n\nfrom invenio_app_ils.literature.covers_builder import build_placeholder_urls\n\n\ndef is_record_with_cover(record):\n    \"\"\"Check if this type of record has cover.\"\"\"\n    if '$schema' in record:\n        schema = record[\"$schema\"]\n        if schema.endswith(\"document-v1.0.0.json\") or schema.endswith(\n            \"series-v1.0.0.json\"\n        ):\n            return True\n    return False\n\n\ndef has_already_cover(record):\n    \"\"\"Check if record has already valid cover in cover_metadata.\"\"\"\n    cover_metadata = record.get(\"cover_metadata\", {})\n    return cover_metadata.get(\"ISBN\", False) or cover_metadata.get(\n        \"ISSN\", False\n    )\n\n\ndef preemptively_set_first_isbn_as_cover(sender, *args, **kwargs):\n    \"\"\"Update cover metadata of the record with identifier.\"\"\"\n    record = kwargs.get(\"record\", {})\n    if not is_record_with_cover(record):\n        return\n\n    if has_already_cover(record):\n        return\n\n    identifiers = record.get(\"identifiers\")\n    if not identifiers:\n        return\n\n    record.setdefault(\"cover_metadata\", {})\n    schema = record.get(\"$schema\")\n\n    if schema.endswith(\"series-v1.0.0.json\"):\n        for ident in identifiers:\n            if ident[\"scheme\"] == \"ISSN\":\n                record[\"cover_metadata\"][\"ISSN\"] = ident[\"value\"]\n                return\n\n    for ident in identifiers:\n        if ident[\"scheme\"] == \"ISBN\":\n            record[\"cover_metadata\"][\"ISBN\"] = ident[\"value\"]\n            return\n\n\ndef build_syndetic_cover_urls(metadata):\n    \"\"\"Decorate literature with cover urls for all sizes.\"\"\"\n    client = os.environ.get(\"SYNDETIC_CLIENT\")\n    url = \"https://secure.syndetics.com/index.aspx\"\n\n    cover_metadata = metadata.get(\"cover_metadata\", {})\n\n    issn = cover_metadata.get(\"ISSN\", \"\")\n    if issn:\n        scheme = \"ISSN\"\n        scheme_value = issn\n\n    isbn = cover_metadata.get(\"ISBN\", \"\")\n    if isbn:\n        scheme = \"ISBN\"\n        scheme_value = isbn\n\n    if issn or isbn:\n        _url = \"{url}?client={client}&{sheme}={value}/{size}.gif\"\n        partial_url = partial(\n            _url.format,\n            url=url,\n            client=client,\n            sheme=scheme,\n            value=scheme_value,\n        )\n        return {\n            \"is_placeholder\": False,\n            \"small\": partial_url(size=\"SC\"),\n            \"medium\": partial_url(size=\"MC\"),\n            \"large\": partial_url(size=\"LC\"),\n        }\n    return build_placeholder_urls()\n","sub_path":"cds_books/literature/covers.py","file_name":"covers.py","file_ext":"py","file_size_in_byte":2680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"543354373","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('products', '0002_topic_in_aaq'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='product',\n            name='image_alternate',\n            field=models.ImageField(help_text='Used everywhere except the home page. Must be 96x96.', max_length=250, null=True, upload_to=b'uploads/products/', blank=True),\n        ),\n        migrations.AlterField(\n            model_name='product',\n            name='image',\n            field=models.ImageField(help_text='Used on the the home page. Must be 484x244.', max_length=250, null=True, upload_to=b'uploads/products/', blank=True),\n        ),\n    ]\n","sub_path":"kitsune/products/migrations/0003_auto_20181001_1347.py","file_name":"0003_auto_20181001_1347.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"189996609","text":"from typing import Tuple, Dict, Any, List, Union\n\nPropName = str\nPropValue = str\nPropFuncParamName = str\nPropFuncParamValue = Any\nPropFuncParams = Dict[PropFuncParamName, PropFuncParamValue]\nPropFuncBody = str\nPropFuncResult = Tuple[PropFuncParams, PropFuncBody]\nPropDef = Tuple[PropValue, PropFuncParams, PropFuncBody]\n\nTypeName = str\nTypeDef = Dict[PropName, PropDef]\nTypeDefs = Dict[TypeName, TypeDef]\n\nVarName = str\nVarDefs = Dict[VarName, TypeName]\n\nRule = str\nRules = List[Rule]\n","sub_path":"dectree/types.py","file_name":"types.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"280269287","text":"###################################################################################################\n# Cerebri AI CONFIDENTIAL\n# Copyright (c) 2017-2020 Cerebri AI Inc., All Rights Reserved.\n#\n# NOTICE: All information contained herein is, and remains the property of Cerebri AI Inc.\n# and its subsidiaries, including Cerebri AI Corporation (together “Cerebri AI”).\n# The intellectual and technical concepts contained herein are proprietary to Cerebri AI\n# and may be covered by U.S., Canadian and Foreign Patents, patents in process, and are\n# protected by trade secret or copyright law.\n# Dissemination of this information or reproduction of this material is strictly\n# forbidden unless prior written permission is obtained from Cerebri AI. Access to the\n# source code contained herein is hereby forbidden to anyone except current Cerebri AI\n# employees or contractors who have executed Confidentiality and Non-disclosure agreements\n# explicitly covering such access.\n#\n# The copyright notice above does not evidence any actual or intended publication or\n# disclosure of this source code, which includes information that is confidential and/or\n# proprietary, and is a trade secret, of Cerebri AI. ANY REPRODUCTION, MODIFICATION,\n# DISTRIBUTION, PUBLIC PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE OF THIS SOURCE\n# CODE WITHOUT THE EXPRESS WRITTEN CONSENT OF CEREBRI AI IS STRICTLY PROHIBITED, AND IN\n# VIOLATION OF APPLICABLE LAWS AND INTERNATIONAL TREATIES. THE RECEIPT OR POSSESSION OF\n# THIS SOURCE CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS TO\n# REPRODUCE, DISCLOSE OR DISTRIBUTE ITS CONTENTS, OR TO MANUFACTURE, USE, OR SELL\n# ANYTHING THAT IT MAY DESCRIBE, IN WHOLE OR IN PART.\n###################################################################################################\n#!/usr/bin/env python3\n\"\"\"\n@author:\n@version: 1.2\nConfidential and Proprietary (c) Copyright 2015-2019 Cerebri AI Inc. and Cerebri AI Corporation\n\"\"\"\nmodule_name = 'Recursive Feature Elimination'\nversion_name = '1.2'\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.feature_selection import RFE\n\nfrom base_pd.base_feature_selection_pd import PdBaseFeatureSelect as BaseFeatureSelect\n\n\nclass RFE_TE(BaseFeatureSelect):\n    \"\"\"\n    This is a class for Recursive Feature Elimination (RFE) feature selection.\n\n    Attributes:\n        rack_order (int): the order of the rack (i.e. 7).\n        data_plus_meta (dict): dictionary contining data and meta data.\n        racks_map (dict): dictionary of all racks and their orders.\n    \"\"\"\n\n    def __init__(self, rack_order, data_plus_meta, racks_map):\n        \"\"\"\n        Constructor for RFE feature selection.\n\n        Parameters:\n            rack_order (int): the order of the rack (i.e. 7).\n            data_plus_meta (dict): dictionary contining data and meta data.\n            racks_map (dict): dictionary of all racks and their orders.\n\n        Returns:\n           ises none\n        \"\"\"\n\n        super(RFE_TE, self).__init__(module_name, version_name, rack_order, data_plus_meta, racks_map)\n\n\n    def fit(self, df, cfg):\n        \"\"\"\n        Perfomrs RFE feature selection.\n\n        Parameters:\n            df (dataframe): dataframe.\n            cfg (dict): configuration dictionary.\n\n        Returns:\n            selected_features: list of selected variable names.\n        \"\"\"\n\n        date_cols = [col for col in df.columns if df[col].dtype == 'datetime64[ns]']\n\n        all_features = [x for x in df.columns if x not in cfg['drop_cols']+[cfg['ID_COL'], cfg['CTU_COL']]+date_cols]\n\n        for i in all_features:\n            if sum(df[i].isnull()) > 0:\n                df[i] = df[i].fillna(0)\n\n        X = df[all_features]\n        y = df[cfg['TE_TARGET_COL']]\n\n        if (sum(y)/len(y)) < 0.1:\n            class_ratio = (len(y) - sum(y))/sum(y)\n            print (\"Class Ratio:\", class_ratio)\n            class_weight = dict({1:class_ratio, 0:1.5})\n            max_depth = 4\n            n_estimators = 100\n        else:\n            class_weight = None\n            max_depth = 4\n            n_estimators = 100\n\n        param = {\n                     'bootstrap':True,\n                     'class_weight':class_weight,\n                     'criterion':'gini',\n                     'max_depth': max_depth, 'max_features':'auto', 'max_leaf_nodes':None,\n                     'min_impurity_decrease' :0.0, 'min_impurity_split':None,\n                     'min_samples_leaf':2, 'min_samples_split':10,\n                     'min_weight_fraction_leaf':0.0, 'n_estimators':n_estimators,\n                     'oob_score':False,\n                     'random_state':121,\n                     'verbose':0,\n                     'warm_start':False\n            }\n\n\n        estimator = RandomForestClassifier(**param)\n\n        feat_selector = RFE(estimator, cfg['n_features'], cfg['n_step'])\n        feat_selector.fit(X, y)\n\n        selected_features = [col for (col, id_bool) in zip(all_features, feat_selector.support_) if id_bool]\n\n        return selected_features\n\n\n    def transform(self, df, selected_feature):\n        \"\"\"\n        Removes variables in selected_feature from dataframe.\n\n        Parameters:\n            df (dataframe): dataframe.\n            selected_feature (list): list of variable names to be removed.\n\n        Returns:\n            filtered_data: dataframe excluding the variables in selected_feature.\n        \"\"\"\n\n        feature_name = selected_feature\n        filtered_data = df[feature_name]\n\n        return filtered_data\n","sub_path":"te_code_v_2_7/terminal/ATLAS_1.2/feature_selection/RFE.py","file_name":"RFE.py","file_ext":"py","file_size_in_byte":5553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"446742015","text":"import codecs\nimport cStringIO\nimport logging\nimport os\n\nfrom celery.task.control import inspect\nfrom django.core.paginator import Paginator, PageNotAnInteger, EmptyPage\nfrom django.core.servers.basehttp import FileWrapper\nfrom django.core.urlresolvers import reverse\nfrom django.db import transaction\nfrom django.db.models import Q\nfrom django.http import HttpResponse\n\nfrom sparky.api import ExceptionMixin\nfrom sparky.api.serializers import (ProjectSerializer, ResourceSerializer, EntrySerializer, TermSerializer,\n                                    HistorySerializer, TodoSerializer)\nfrom sparky.translation import service, sizeof_fmt, checksum\nfrom sparky.translation.exporters import create_exporter\nfrom sparky.translation.models import Locale, Project, Resource, Term, Entry, Todo, Conflict, History\nfrom sparky.translation.service import calculate_project_stats\n\nfrom rest_framework import views, status, viewsets, mixins\nfrom rest_framework.parsers import MultiPartParser, JSONParser\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.response import Response\n\nfrom time import time\nfrom zipfile import ZipFile, ZIP_DEFLATED\n\nlogger = logging.getLogger(__name__)\n\n\nclass IndexView(ExceptionMixin, views.APIView):\n    \"\"\"\n    Simple foobar index page\n    \"\"\"\n    def get(self, request):\n        data = {'msg': 'A question that sometimes drives me hazy: am I or are the others crazy?'}\n        return Response(data=data)\n\n    def post(self, request):\n        data = {'msg': 'The important thing is not to stop questioning; curiosity has its own reason for existing.'}\n        return Response(data=data)\n\n\nclass ProjectListView(views.APIView):\n    permission_classes = (IsAuthenticated, )\n\n    def get(self, request):\n        projects = Project.objects.all()\n        serializer = ProjectSerializer(projects, many=True)\n        data = {'projects': serializer.data}\n        return Response(data)\n\n\nclass ProjectView(ExceptionMixin, views.APIView):\n    \"\"\"\n    \"\"\"\n    permission_classes = (IsAuthenticated, )\n\n    def get(self, request, project_slug):\n        project = Project.objects.get(slug=project_slug)\n        serializer = ProjectSerializer(project, many=False)\n        return Response(serializer.data)\n\n    def post(self, request, project_slug):\n        try:\n            Project.objects.get(slug=project_slug)\n            return Response(data={'detail': 'Object already exists'}, status=status.HTTP_400_BAD_REQUEST)\n        except Project.DoesNotExist:\n            pass\n\n        serializer = ProjectSerializer(data=request.POST, many=False)\n\n        if serializer.is_valid():\n            # Fix for the source_locale, could be fixed in the serializer maybe?\n            source_locale_slug = request.DATA.get('source_locale', 'en')\n            source_locale = Locale.objects.get(iso=source_locale_slug)\n\n            project = serializer.object\n            project.slug = project_slug\n            project.user = request.user\n            project.source_locale = source_locale\n            project.save()\n\n            # Add the source locale of this resource as a locale to the project\n            service.bind_locale(source_locale.iso, project=project)\n\n            if 'locales' in request.POST:\n                for slug in request.POST.getlist('locales'):\n                    try:\n                        project.locales.add(Locale.objects.get(iso=slug))\n                    except Locale.DoesNotExist:\n                        pass\n                project.save()\n            return Response(data=ProjectSerializer(project, many=False).data, status=status.HTTP_201_CREATED)\n\n        logger.debug(\"POST: Project errors: %s\", serializer.errors)\n        data = {'detail': 'Invalid project data'}\n        return Response(data=data, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass ProjectDownloadView(ExceptionMixin, views.APIView):\n    permission_classes = (IsAuthenticated, )\n\n    def get(self, request, project_slug):\n        project = Project.objects.get(slug=project_slug)\n        # Create po file here\n        response = HttpResponse(content_type='Content-Type: application/zip')\n        response['Content-Disposition'] = 'attachment; filename=%s.zip' % project.slug\n\n        # Create a zip file, the output will be written to StringIO stream\n        fobj = cStringIO.StringIO()\n        zipfile = ZipFile(fobj, 'w', ZIP_DEFLATED)\n        exporter = create_exporter('po')\n\n        # Add all the resources of all languages\n        for resource in project.resources:\n            for locale in resource.locales.all():\n                fdr, pathr = exporter.create_resource(resource, locale)\n                zipfile.write(pathr, '%s/%s/%s/LC_MESSAGES/%s.po' % (project.slug, resource.slug.replace('-js', ''),\n                                                                     locale.iso, resource.get_filename()))\n                exporter.cleanup(fdr, pathr)\n\n            # Create the source resource file\n            fdr, pathr = exporter.create_source_resource(resource)\n            zipfile.write(pathr, '%s/%s/en/LC_MESSAGES/%s.po' % (project.slug, resource.slug.replace('-js', ''),\n                                                                 resource.get_filename()))\n            exporter.cleanup(fdr, pathr)\n\n        zipfile.close()                     # Close the zipfile\n        response.write(fobj.getvalue())     # write the the output of the StringIO stream\n        fobj.close()                        # close the StringIO Stream\n\n        # Okido, the zip is created let's serve a nice download\n        return response\n\n\nclass ProjectStatsView(ExceptionMixin, views.APIView):\n    permission_classes = (IsAuthenticated,)\n\n    def get(self, request, project_slug, locale_slug):\n        \"\"\"\n        Returns the current progress of translations\n\n        :param request:\n        :param project_slug:\n        :param locale_slug:\n        :return:\n        \"\"\"\n        project = Project.objects.get(slug=project_slug)\n        locale = Locale.objects.get(iso=locale_slug)\n        return Response(data=calculate_project_stats(project, locale))\n\n\nclass ResourceView(ExceptionMixin, views.APIView):\n    \"\"\"\n    Fetches the info of a resource or creates/updates one.\n    \"\"\"\n    permission_classes = (IsAuthenticated,)\n    parser_classes = (MultiPartParser, JSONParser, )  # support multipart/form-data and application/json bodies\n\n    def get(self, request, project_slug, resource_slug):\n        \"\"\"\n        Returns a serialized view of a resource and all its available languages\n\n        :param request:\n        :param project_slug:\n        :param resource_slug:\n        :return:\n        \"\"\"\n        project = Project.objects.get(slug=project_slug)\n        resource = Resource.objects.get(slug=resource_slug, project__id=project.id)\n        data = ResourceSerializer(resource, many=False).data\n        return Response(data=data)\n\n    def post(self, request, project_slug, resource_slug):\n        \"\"\"\n        If the resource object doesn't exist yet, it will be created.  It will return status 400 if it already exists.\n        To upload a file with terms, you'll have to invoke a PUT request.\n\n        :param request:\n        :param project_slug:\n        :param resource_slug:\n        :return:\n        \"\"\"\n        try:\n            Resource.objects.get(project__slug=project_slug, slug=resource_slug)\n            return Response(data={'detail': 'Object already exists'}, status=status.HTTP_400_BAD_REQUEST)\n        except Resource.DoesNotExist:\n            pass\n\n        project = Project.objects.get(slug=project_slug)\n        serializer = ResourceSerializer(data=request.DATA, many=False)\n\n        if serializer.is_valid():\n            resource = serializer.object\n            resource.slug = resource_slug\n            resource.project = project\n            resource.save()\n\n            if 'locales' in request.DATA:\n                # for slug in request.DATA.getlist('locales'):\n                if hasattr(request.DATA, 'getlist'):\n                    # Works for posting form data\n                    arr = request.DATA.getlist('locales')\n                else:\n                    # works for posting json data\n                    arr = request.DATA['locales']\n\n                for slug in arr:\n                    try:\n                        resource.locales.add(Locale.objects.get(iso=slug))\n                    except Locale.DoesNotExist:\n                        pass\n                resource.save()\n            return Response(data=ResourceSerializer(resource, many=False).data, status=status.HTTP_201_CREATED)\n\n        logger.debug(\"POST: Resource errors: %s\", serializer.errors)\n        data = {'detail': 'Invalid resource data', 'errors': serializer.errors}\n        return Response(data=data, status=status.HTTP_400_BAD_REQUEST)\n\n    @transaction.autocommit\n    def put(self, request, project_slug, resource_slug):\n        \"\"\"\n        The uploaded file with terms will be processed. The actual Resource record won't be updated.\n\n        Activate the autocommit to avoid race conditions with celery tasks,\n\n        :param request:\n        :param project_slug:\n        :param resource_slug:\n        :return:\n        \"\"\"\n        logger.debug(\"Uploading terms %s/%s\", project_slug, resource_slug)\n        project = Project.objects.get(slug=project_slug)\n        resource = Resource.objects.get(project=project, slug=resource_slug)\n\n        if 'file' in request.FILES:\n            _file = request.FILES['file']\n            logger.debug(\"File uploaded %s (%s), filename=%s\", _file.name, sizeof_fmt(_file.size), _file.name)\n            content = codecs.EncodedFile(_file, \"utf-8\").read()\n            # The user should be authenticated, cfr authentication_classes\n            user = request.user\n            task_id = service.process_upload_terms(content, resource, user, filename=_file.name)\n            logger.debug(\"Uploaded the shizzle %s/%s\", project_slug, resource_slug)\n\n            # state = status.HTTP_200_OK\n            state = status.HTTP_202_ACCEPTED\n            return Response(status=state, headers={'Location': reverse('api.queue.task', kwargs={'task_id': task_id})})\n\n        data = {'detail': 'Bummer, no file uploaded'}\n        return Response(data=data, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass ResourceTermsView(ExceptionMixin, views.APIView):\n    model = Term\n    permission_classes = (IsAuthenticated, )\n    serializer_class = TermSerializer\n\n    def post(self, request, project_slug, resource_slug):\n        project = Project.objects.get(slug=project_slug)\n        resource = Resource.objects.get(project=project, slug=resource_slug)\n        msgid = request.DATA.get('msgid', None)\n        msgctxt = request.DATA.get('msgctxt', None)\n\n        if msgid:\n            term = Term()\n            term.resource = resource\n            term.msgid = msgid\n            term.msgctxt = msgctxt\n            term.checksum = checksum(msgid, msgctxt)\n            term.user = request.user\n            term.save()\n\n            for locale in resource.locales.all():\n                Entry.objects.get_or_create(term=term, locale=locale, defaults={\n                    'user': request.user\n                })\n\n            data = TermSerializer(term, context={'request': request}, many=False).data\n            return Response(status=status.HTTP_201_CREATED, data=data)\n\n        return Response(status=status.HTTP_400_BAD_REQUEST, data={'msg': \"'msgid' is required\"})\n\n\nclass ResourceTodosView(ExceptionMixin, views.APIView):\n    permission_classes = (IsAuthenticated,)\n\n    def get(self, request, project_slug, resource_slug):\n        project = Project.objects.get(slug=project_slug)\n        resource = Resource.objects.get(slug=resource_slug, project__id=project.id)\n        todos = Todo.objects.filter(term__resource=resource).exclude(state=Todo.SOLVED)\n        data = TodoSerializer(todos, many=True).data\n        return Response(data=data)\n\n\nclass ResourceTranslationView(ExceptionMixin, views.APIView):\n    \"\"\"\n    The PUT requests allows to upload a translation file. This will create the resource if doesn't exist, otherwise it\n    will be updated.\n\n    It's better to use the MultiPartParser, better support for web-base clients.\n    http://django-rest-framework.org/api-guide/parsers.html#multipartparser\n    http://django-rest-framework.org/api-guide/parsers.html#fileuploadparser\n    \"\"\"\n    permission_classes = (IsAuthenticated,)\n    # parser_classes = (FileUploadParser,)\n    parser_classes = (MultiPartParser,)\n\n    def get(self, request, project_slug, resource_slug, locale_slug=None):\n        project = Project.objects.get(slug=project_slug)\n        resource = Resource.objects.get(slug=resource_slug, project__id=project.id)\n        locale = Locale.objects.get(iso=locale_slug)\n        resource.assert_locale(locale)\n        resource_serializer = ResourceSerializer(resource, many=False)\n\n        data = {\n            'resource': resource_serializer.data,\n            'locale': locale.iso,\n        }\n        return Response(data=data)\n\n    @transaction.autocommit\n    def put(self, request, project_slug, resource_slug, locale_slug):\n        \"\"\"\n        Activate the autocommit to avoid race conditions with celery tasks,\n\n        Troubleshooting with utf-8\n        http://stackoverflow.com/questions/4729832/proccessing-a-django-uploadedfile-as-utf-8-with-universal-newlines\n        \"\"\"\n        logger.debug(\"Project: %s \", project_slug)\n        logger.debug(\"Resource: %s\", resource_slug)\n        logger.debug(\"Locale: %s\", locale_slug)\n\n        if 'file' in request.FILES:\n            _file = request.FILES['file']\n            logger.debug(\"File uploaded %s (%s), filename=%s\", _file.name, sizeof_fmt(_file.size), _file.name)\n            content = codecs.EncodedFile(_file, \"utf-8\").read()\n            locale = Locale.objects.get(iso=locale_slug)\n\n            project = Project.objects.get(slug=project_slug)\n            resource = Resource.objects.get(slug=resource_slug, project=project)\n            # Add the locale to the project and resource\n            service.bind_locale(locale_slug, project=project, resource=resource)\n\n            if locale not in resource.locales.all():\n                resource.locales.add(locale)\n                resource.save()\n\n            # The user should be authenticated, cfr authentication_classes\n            user = request.user\n\n            logging.debug(\"Processing upload: %s, %s, %s\", project_slug, resource_slug, locale_slug)\n            task_id = service.process_upload_translation(content, resource, locale, user, filename=_file.name)\n\n            # state = status.HTTP_200_OK if not created else status.HTTP_201_CREATED\n            state = status.HTTP_202_ACCEPTED\n            return Response(status=state, headers={'Location': reverse('api.queue.task', kwargs={'task_id': task_id})})\n\n        data = {'error': 'Unprocessable Entity, not a valid po file'}\n        return Response(data=data, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass QueueView(ExceptionMixin, views.APIView):\n    permission_classes = (IsAuthenticated, )\n\n    def get(self, request, task_id=None):\n        i = inspect()\n        reserved = i.reserved()\n        active = i.active()\n\n        # Get an overview of the current running and reserved tasks\n        if not task_id:\n            data = {\n                'reserved': reserved,\n                'active': active\n            }\n            return Response(data=data, status=status.HTTP_200_OK)\n        else:\n            def _iter(resultset):\n                for k, v in resultset.iteritems():\n                    for task in v:\n                        if task_id == task['id']:\n                            return task\n                return None\n\n            if active:\n                task = _iter(active)\n                if not task:\n                    task = _iter(reserved)\n\n                if task:\n                    data = task\n                    return Response(data=data, status=status.HTTP_200_OK)\n\n            return Response(data={'detail': 'Task not found'}, status=status.HTTP_404_NOT_FOUND)\n\n            # raw = ResourceRaw.objects.get(id=task_id)\n            # data = {\n            #     'project': raw.resource.project.slug,\n            #     'resource': raw.resource.slug,\n            #     'processed': raw.processed,\n            #     'created': raw.created,\n            #     'locale': raw.locale.iso,\n            #     'user': raw.user.username\n            # }\n            #\n            # if raw.processed:\n            #     data['stats'] = raw.stats\n            #\n            # return Response(data=data, status=status.HTTP_200_OK)\n\n\nclass ResourceDownloadView(ExceptionMixin, views.APIView):\n    \"\"\"\n    Download a resource.  With the optional parameter you can change the filename of the downloaded file.\n    \"\"\"\n    permission_classes = (IsAuthenticated, )\n\n    def get(self, request, project_slug, resource_slug, locale_slug):\n        \"\"\"\n        # text/x-gettext-translation\n        \"\"\"\n        project = Project.objects.get(slug=project_slug)\n        resource = Resource.objects.get(slug=resource_slug, project__id=project.id)\n        locale = Locale.objects.get(iso=locale_slug)\n\n        exporter_type = request.GET.get('type', 'po')\n        exporter = create_exporter(exporter_type)\n\n        if locale_slug != 'en':\n            resource.assert_locale(locale)\n            fd, path = exporter.create_resource(resource, locale)\n        else:\n            fd, path = exporter.create_source_resource(resource)\n\n        # Create po file here\n        response = HttpResponse(FileWrapper(file(path, 'r')), content_type=exporter.get_content_type())\n        response['Content-Disposition'] = 'attachment; filename=%s' % \\\n                                          exporter.get_filename(resource, request.GET.get('filename', None))\n        response['Content-Length'] = os.path.getsize(path)\n        #exporter.cleanup(fd, path)\n        return response\n\n\nclass TermViewSet(mixins.RetrieveModelMixin,\n                  mixins.CreateModelMixin,\n                  mixins.UpdateModelMixin,\n                  viewsets.GenericViewSet):\n    model = Term\n    permission_classes = (IsAuthenticated, )\n    serializer_class = TermSerializer\n    queryset = Term.objects.all()\n\n    def update(self, request, *args, **kwargs):\n        data = request.DATA\n        msgid = data.get('msgid', None)\n        self.object = self.get_object_or_none()\n\n        check1 = checksum(msgid)\n        check2 = checksum(self.object.msgid)\n\n        if check1 == check2:\n            logger.debug(\"Just update\")\n            request.DATA['checksum'] = check2\n\n            # If the new value has been changed and equal to the original, delete the existing Todo object attached\n            # to it\n            try:\n                Todo.objects.get(term=self.object).delete()\n            except:\n                pass\n        else:\n            logger.debug(\"TODO update\")\n            request.DATA['msgid'] = self.object.msgid\n            request.DATA['checksum'] = check2\n\n            # Create an update for the Term\n            todo, created = Todo.objects.get_or_create(term=self.object,\n                                                       defaults={'user': request.user})\n            todo.term = self.object\n            todo.state = Todo.CHANGE_SOURCE\n            todo.value = msgid\n            todo.user = request.user\n            todo.save()\n\n        return super(TermViewSet, self).update(request, *args, **kwargs)\n\n\nclass MessageViewSet(mixins.RetrieveModelMixin,\n                     mixins.UpdateModelMixin,\n                     viewsets.GenericViewSet):\n    model = Entry\n    permission_classes = (IsAuthenticated, )\n    serializer_class = EntrySerializer\n    queryset = Entry.objects.all()\n\n    def pre_save(self, obj):\n        \"\"\"\n        Will set the authentication user as author for an entry before it is created/updated\n        \"\"\"\n        obj.user = self.request.user\n        super(MessageViewSet, self).pre_save(obj)\n\n    def update(self, request, *args, **kwargs):\n        #/api/terms/<pk>/\tsparky.api.views.TermViewSet\tterm-detail\n        # A workaround, for updating an entry without specifying the term in request.DATA\n        _object = self.get_object_or_none()\n        if _object is not None and 'term' not in request.DATA:\n            from rest_framework.reverse import reverse as reverse_rest\n            term_url = reverse_rest('term-detail', args=[_object.term_id], request=request)\n            request.DATA['term'] = term_url\n\n        return super(MessageViewSet, self).update(request, args, kwargs)\n\n\nclass MessageHistoryView(ExceptionMixin, views.APIView):\n    permission_classes = (IsAuthenticated, )\n\n    def get(self, request, message_id):\n        entry = Entry.objects.get(id=message_id)\n        histories = History.objects.filter(entry=entry).order_by('-created')\n        data = HistorySerializer(histories, many=True).data\n        return Response(data=data, status=status.HTTP_200_OK)\n\n\nclass SolveConflictView(ExceptionMixin, views.APIView):\n    permission_classes = (IsAuthenticated, )\n\n    def post(self, request, conflict_id):\n        which = request.DATA.get('which', None)\n        conflict = Conflict.objects.get(id=conflict_id)\n\n        if which == 'current':\n            conflict.delete()\n            return Response(data={'status': 'done'}, status=status.HTTP_200_OK)\n        elif which == 'new':\n            entry = conflict.entry\n            entry.value = conflict.value\n            entry.user = request.user\n            entry.save()\n            conflict.delete()\n            return Response(data={'status': 'done'}, status=status.HTTP_200_OK)\n\n        return Response(data={'detail': 'The status field \\'which\\' has to be \\'current\\' or \\'new\\''},\n                        status=status.HTTP_400_BAD_REQUEST)\n\n\nclass TodoView(ExceptionMixin, views.APIView):\n    permission_classes = (IsAuthenticated, )\n\n    def delete(self, request, todo_id):\n        try:\n            Todo.objects.get(id=int(todo_id)).delete()\n        except Todo.DoesNotExist:\n            return Response(status=status.HTTP_404_NOT_FOUND)\n\n        return Response(status=status.HTTP_204_NO_CONTENT)\n\n    def put(self, request, todo_id):\n        # Only support to update the done field\n        if 'done' in request.DATA:\n            todo = Todo.objects.get(pk=todo_id)\n            todo.done = request.DATA.get('done', False)\n            todo.save()\n        return Response(status=status.HTTP_200_OK)\n\n\nclass SearchView(ExceptionMixin, views.APIView):\n    \"\"\"\n    Possible filters:\n\n     - Text search\n     - only translated\n     - only not translated\n     - fuzzy\n     - obsolete\n     - ignored\n\n    Useful links:\n\n     - http://www.niwi.be/2012/10/09/postgresql-fulltextsearch-integration-with-django/\n     - https://django-orm.readthedocs.org/en/latest/orm-pg-fulltext.html\n\n    TODO:\n\n     - full text search\n     - stats in the result set\n    \"\"\"\n    permission_classes = (IsAuthenticated, )\n\n    RESULTS_PER_PAGE = 50\n\n    def get(self, request):\n        parse_bool = lambda request, param: request.GET.get(param) in ['True', 'true', 1]\n\n        q = request.GET.get('q')\n        is_fuzzy = parse_bool(request, 'fuzzy')\n        is_ignored = parse_bool(request, 'ignored')\n        is_obsolete = parse_bool(request, 'obsolete') # XXX: we don't use this one??\n        is_translated = parse_bool(request, 'translated')\n        is_not_translated = parse_bool(request, 'not_translated')\n        reference = request.GET.get('reference', None)\n        results_per_page = request.GET.get('count', self.RESULTS_PER_PAGE)\n\n        project = Project.objects.get(slug=request.GET.get('p'))\n        locale = Locale.objects.get(iso=request.GET.get('l'))\n\n        query = Q(term__resource__project=project) & Q(locale=locale)\n        query_category = Q()\n\n        if q:\n            # Making queries\n            # https://docs.djangoproject.com/en/1.6/topics/db/queries/\n            #query = query & Q(term__msgid__contains=q)     # Case insensitive\n            #query = query & Q(term__msgid__istartswith=q)   # Case insensitive\n            query = query & (Q(term__msgid__icontains=q) | Q(value__icontains=q))\n\n        # We have 4 different categories: not translated, translated, fuzzy, ignored. They are all optional.\n        if is_not_translated:\n            query_category = query_category | (Q(value__isnull=True) | Q(value__exact=''))\n\n        if is_translated:\n            query_category = query_category | Q(value__isnull=False)\n\n        if is_fuzzy:\n            query_category = query_category | Q(fuzzy=True)\n\n        if is_ignored:\n            query_category = query_category | Q(ignore=True)\n        else:\n            query = query & Q(ignore=False)\n\n        # Filter per reference\n        if reference:\n            query = query & Q(term__references__filename=reference)\n\n        # Do the search\n        start = time()\n        #entries = Entry.objects.filter(query).order_by('term__msgid')\n        entries = Entry.objects.filter(query & (query_category)).order_by('-novelty_date', '-created', '-modified')\n        count = entries.count()\n        paginator = Paginator(entries, results_per_page)\n        logger.info(\"Query %2.2f sec\" % (time() - start))\n        logger.debug(\"Query: %s \" % entries.query)\n\n        # Do the pagination\n        try:\n            # defaults to the first page if the 'page' param is not specified\n            page_param = int(request.GET.get('page', 1))\n            page = paginator.page(page_param)\n        except (ValueError, PageNotAnInteger, EmptyPage):\n            page_param = -1\n            page = None\n\n        paging = {\n            'page': page_param,\n            'per_page': self.RESULTS_PER_PAGE,\n            'num_pages': paginator.num_pages,\n            'total': count,\n        }\n\n        if page:\n            results = EntrySerializer(page.object_list, many=True, context={'request': request}).data\n            full_path = request.build_absolute_uri(None)\\\n                .replace('&page=%s' % request.GET.get('page'), '')\n\n            if not page.has_previous():\n                url_previous = None\n            else:\n                url_previous = full_path + \"&page=%s\" % page.previous_page_number()\n\n            if not page.has_next():\n                url_next = None\n            else:\n                url_next = full_path + \"&page=%s\" % page.next_page_number()\n\n            paging.update({\n                'previous': url_previous,\n                'next': url_next,\n                'count': len(page.object_list)\n            })\n        else:\n            results = []\n            paging.update({\n                'previous': None,\n                'next': None\n            })\n\n        data = {\n            'results': results,\n            'paging': paging\n        }\n\n        return Response(data=data)\n","sub_path":"translations/sparky/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":26767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"252036902","text":"#anime news network rss feed to channel\n#Need to make it start by itself somehow.\nimport feedparser\nfrom datetime import datetime\nimport time\n\ndef parse(now):\n        new = []\n        allnew = ''\n        url = 'http://www.animenewsnetwork.com/news/rss.xml'\n        x = feedparser.parse(url)\n        for items in x.entries:\n                if now < items.published_parsed:\n                        if items.title == 'Daily Briefs' or 'Ranking' in items.title:\n                                continue\n                        else:\n                                new.append(items.title)\n        if new:\n                allnew = \" | \".join(new)\n                return allnew\n\ndef rss(phenny, input):\n        now = time.gmtime()\n        time.sleep(3600)\n        while True:\n                time.sleep(7200)\n                out = parse(now)\n                if out:\n                        phenny.say(out)\n                now = time.gmtime()\nrss.commands = ['startrss']\nrss.priority = 'medium'\n","sub_path":"modules/annrss.py","file_name":"annrss.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"316262503","text":"# Programa para leer un archivo de texto sin importar si contiene saltos de\r\n# linea o no y cuenta la cantidad de palabras que contiene\r\n\r\n# Librerias\r\nimport argparse\r\nimport time\r\nfrom tabulate import tabulate\r\n\r\n# == Variables ==\r\n# Globales\r\n# args / Objeto con los parámetros que se indican al ejecutar el programa\r\n# args.nombre / Dirección del archivo de texto\r\n# args.time / Indica si se reproduce o no el tiempo de ejecución\r\n# palabra / Lista que va a contener las palabas del archivo que se selecciona\r\n# cantElementos / Lista que contiene el número de repetición de las palabras\r\n# ordenPalabra / Lista que contiene todas las palabras del archivo\r\n# Local de la funcion leerArchivo\r\n# nombre / Dirección del archivo .txt a leer\r\n# tiempo / Booleana para indicar si se indica o no el tiempo de ejecución\r\n# archivo / Objeto donde se almacena la información del archivo\r\n# palabra / Lista temporal donde se almacena una palabra por elemento de lista\r\n# cantPalabras / Permite indicar si ya se analizaron todas las palabras\r\n# del archivo y salir de un ciclo while\r\n# numPalabra / Variable temporal para almacenar el número de repeticiones\r\n# de una palabra en la lista palabra\r\n# t1 / Almacena el tiempo inicial de ejecución\r\n# t2 / Almacena el tiempo final de ejecución\r\n\r\npalabra = list()\r\ncantElementos = list()\r\nordenPalabra = list()\r\n\r\n\r\n# Funcion encargada de leer el archivo contar las palabras en el archivo\r\n# y luego imprimirlas como una tabla con su respectiva frecuencia\r\n# Parámetros de entrada:\r\n# nombre / Dirección del archivo .txt a analizar\r\n# tiempo / Indica si se imprime el tiempo de ejecución\r\ndef leerArchivo(nombre, tiempo):\r\n    if(tiempo):\r\n        t1 = time.time()\r\n    archivo = open(nombre, \"r\")\r\n    for lineas in archivo:\r\n        palabra.extend(lineas.split())\r\n    for i in range(len(palabra)):\r\n        palabra[i] = palabra[i].replace(\",\", \"\")\r\n        palabra[i] = palabra[i].replace(\".\", \"\")\r\n        palabra[i] = palabra[i].lower()\r\n    cantPalabras = len(palabra)\r\n    while (cantPalabras > 0):\r\n        delPalabra = palabra[0]\r\n        numPalabra = palabra.count(palabra[0])\r\n        cantElementos.append(numPalabra)\r\n        ordenPalabra.append(palabra[0])\r\n        for i in range(numPalabra):\r\n            palabra.remove(delPalabra)\r\n        cantPalabras = len(palabra)\r\n    print(tabulate({\"Palabra\": ordenPalabra, \"Frecuencia\": cantElementos},\r\n          headers=\"keys\"))\r\n    if (tiempo):\r\n        t2 = time.time() - t1\r\n        print(\"Tiempo de ejecucion:\", t2, \"segundos\")\r\n\r\n\r\n# Parte del argparse\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument(\"nombre\", help=\"Nombre del archivo .txt\", type=str)\r\nparser.add_argument(\"-t\", \"--time\", help=\"Indica si se ocupa el tiempo de ejecucion\",\r\n                    action=\"store_true\")\r\nargs = parser.parse_args()\r\n\r\n# Inicio del programa\r\ndef contTexto():\r\n    # Se llama a la función que lee el archivo\r\n    leerArchivo(args.nombre, args.time)\r\n\r\nif __name__ == '__main__':\r\n    contTexto()\r\n","sub_path":"tarea3/contTexto.py","file_name":"contTexto.py","file_ext":"py","file_size_in_byte":3016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"579333307","text":"import FDBA\nimport numpy\nimport matplotlib.pyplot as plt\nfrom tslearn.datasets import CachedDatasets\nfrom tslearn.preprocessing import TimeSeriesScalerMeanVariance, TimeSeriesResampler\nimport timeit\n'''\npoly1=numpy.polynomial.polynomial.Polynomial((3,4,1,-1,0.5,1,-1/3,1))\npoly2=numpy.polynomial.polynomial.Polynomial((-1,-3,2,0.1,0.7,-2))\nX_train=[]\n\nfor i in range(0,40):\n    X_train.append(poly1.linspace(3000,(i,i+20))[1])\n    X_train.append(poly2.linspace(3000,(i,i+20))[1])\n\nX_train=numpy.array(X_train)\nprint (X_train.shape)\nseed = 0\nnumpy.random.seed(seed)\n'''\nseed = 0\nnumpy.random.seed(seed)\nX_train, y_train, X_test, y_test = CachedDatasets().load_dataset(\"Trace\")\nX_train = X_train[y_train < 4]  # Keep first 3 classes\nnumpy.random.shuffle(X_train)\nX_train = TimeSeriesScalerMeanVariance().fit_transform(X_train[:50])  # Keep only 50 time series\nX_train = TimeSeriesResampler(sz=40).fit_transform(X_train)  # Make time series shorter\nX_train=numpy.squeeze(X_train)\n\nsz = X_train.shape[1]\n\ndef main():\n    centers = FDBA.FDBA_clustering(X_train,n_iterations=6,k=2,verbose=False)\n    y_pred=FDBA.assign(centers,X_train)\n    print (y_pred)\n    '''for yi in range(2):\n        #plt.subplot(3, 3, 4 + yi)\n        for xx in X_train[y_pred == yi]:\n            plt.plot(xx.ravel(), \"k-\", alpha=.2)\n        plt.plot(centers[yi].ravel(), \"r-\")\n        plt.xlim(0, sz)\n        plt.ylim(-4, 4)\n        if yi == 1:\n            plt.title(\"FDBA $k$-means\")\n\n    #plt.tight_layout()\n    plt.show()'''\n\nif __name__== \"__main__\":\n    #timeit.timeit(\"main()\",number=3)\n    main()\n","sub_path":"dtw_stuff/testing_clustering_python.py","file_name":"testing_clustering_python.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"540683963","text":"import inspect\nimport tkinter\nfrom tkinter.filedialog import askopenfilename\nimport configparser\nimport re\nfrom loguru import logger\n\ndef assign_config(config, section, val):\n    \n    val = {}\n\n    if not section in config.keys():\n        logger.error(f\"No {section} in config => {config.keys()}\")\n        return None\n    for key, value in config[section].items():\n        x = re.search(\"^example-\", key)\n        if x:\n            logger.warning(\"Fill your paths for the programm into \"\n                           f\"[{section}]\")\n            return None\n        try:\n            val[key] = value\n        except ValueError as err:\n            logger.error(f\"Wrong value in config for => {key} : {value}\")\n\n    return val\n\n\ndef create_config(path: str):\n    \"\"\"\n        Parameter:\n            path            [str]       -       Path to the config file\n\n        Create a empty config and ask user for input to fill values\n    \"\"\"\n\n    config = configparser.ConfigParser()\n    config['PATH'] = {\n            'example-path' : 'example/string'\n    }\n    config['EMAIL'] = {\n        'example-email':'example-string'\n    }\n\n\n    with open(path, 'w') as configfile:\n        config.write(configfile)\n\n    logger.info(\"Fill out the Path and Email field of the config\")\n\n    return config","sub_path":"Test/packages/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"366664978","text":"import numpy as np\n\n\n\n# This is our initial data; one entry per \"sample\"\n# (in this toy example, a \"sample\" is just a sentence, but\n# it could be an entire document).\nsamples = ['The cat sat on the mat.', 'The dog ate my homework.']\n\n\n\n\n# First, build an index of all tokens in the data.\ntoken_index = {}\nfor sample in samples:\n    # We simply tokenize the samples via the `split` method.\n    # in real life, we would also strip punctuation and special characters\n    # from the samples.\n    for word in sample.split():\n        if word not in token_index:\n        # Assign a unique index to each unique word\n            token_index[word] = len(token_index) + 1\n# Note that we don't attribute index 0 to anything.\n\n\n\n\n\n# Next, we vectorize our samples. We will change each token index into a N vector!\n# We will only consider the first `max_length` words in each sample. (Assume we only consider the first max_length in this case)\nmax_length = 10\n\n\n# This is where we store our results:\nresults = np.zeros((len(samples), max_length, max(token_index.values()) + 1)) # why the last one +1 because we do not assign 0 to any token\nresults_shape = results.shape\n\n\n\nfor i, sample in enumerate(samples):\n    for j, word in list(enumerate(sample.split()))[:max_length]:\n        index = token_index.get(word)\n        results[i, j, index] = 1\n\n\n# Therefore, after one-hot encoding, each sample will become : 10 times N matrix.\n\n","sub_path":"deeplearning/oneHotEncoding.py","file_name":"oneHotEncoding.py","file_ext":"py","file_size_in_byte":1417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"303353155","text":"import tensorflow as tf\nimport numpy as np,sys\nfrom sklearn import datasets\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \nnp.random.seed(789)\ntf.set_random_seed(789)\n\nclass TF_PCA():\n    \n    def __init__(self,data,label):\n        \n        self.data = data\n        self.dtype = tf.float32\n        self.target = label         \n\n    def fit(self):\n        self.graph = tf.Graph()\n        with self.graph.as_default():\n            self.X = tf.placeholder(self.dtype, shape=self.data.shape)\n            # Perform SVD\n            singular_values, u, v = tf.svd(self.X,full_matrices=False)\n            # Create sigma matrix\n            sigma = tf.diag(singular_values)\n\n            print('singular_values:',singular_values.shape)\n            print('u:',u.shape)\n            print('v:',v.shape)\n            print('sigma:',sigma.shape)\n\n            # sigma1 = tf.slice(sigma, [0, 0], [self.data.shape[1], 512])\n            # print('-------f1------')\n            # print(u.shape)\n            # print(sigma1.shape)\n            # print('-------f1------')\n            # pca = tf.matmul(u,sigma1)\n\n        with tf.Session(graph=self.graph) as session:\n            self.u, self.v,self.singular_values, self.sigma = session.run([u,v, singular_values, sigma],feed_dict={self.X: self.data})\n\n\n    def reduce(self, n_dimensions=None, keep_info=None):\n\n        with self.graph.as_default():\n            # Cut out the relevant part from sigma\n            sigma = tf.slice(self.sigma, [0, 0], [self.data.shape[1], n_dimensions])\n            # PCA\n            pca = tf.matmul(self.u, sigma)\n        with tf.Session(graph=self.graph) as session:\n            return session.run(pca, feed_dict={self.X: self.data})\n\n\n\n\n\n\n# x = tf.placeholder(shape=[50,32,32,3],dtype=tf.float32)\n# ss = tf.reshape(x,[50,-1])\n\n# ff = tf_pca(ss)\n\n# with tf.Session() as sess:\n\n#     sess.run(tf.global_variables_initializer())\n    \n#     s  = sess.run([ff],feed_dict={x:np.random.randn(50,32,32,3).astype(np.float32)})\n\n#     print(s[0].shape)\n\n# sys.exit()\n\niris_dataset = datasets.load_iris()\nrow = 250\n# cal = 16 * 16 * 4\ncal = 8 * 8 * 3\n# cal = 8*8*16   \n# cal = 4*4*64   \ntemp = np.random.randn(row,cal)\n# temp = np.random.randn(cal,row)\n#  8 8 4 = 256\n#  4 4 4 = 64\ntemps = np.ones((row,) )\nprint(iris_dataset.data.shape)\nprint(iris_dataset.target.shape)\nprint(temp.shape)\nprint(temps.shape)\n\n\n\n\nprint('------------------------------------')\n\ntf_pca2 = TF_PCA(temp, temps)\n# tf_pca2 = TF_PCA(iris_dataset.data, iris_dataset.target)\ntf_pca2.fit()\npca = tf_pca2.reduce(n_dimensions=cal//4)  # Results in 2 dimensions\n\nprint(temp.shape)\nprint(pca.shape)\n\nsys.exit()\n\nprint('------------------------------------')\nprint('------------------------------------')\nprint('------------------------------------')\nprint('------------------------------------')\nprint('------------------------------------')\nprint('------------------------------------')\ntf_pca = TF_PCA(temp, temps)\ntf_pca.fit()\npca = tf_pca.reduce(n_dimensions=64)  # Results in 2 dimensions\nsys.exit()\n\npca = tf_pca.reduce(keep_info=0.5)  # Results in 2 dimensions\n\nsys.exit()\n\ncolor_mapping = {0: sns.xkcd_rgb['bright purple'], 1: sns.xkcd_rgb['lime'], 2: sns.xkcd_rgb['ochre']}\ncolors = list(map(lambda x: color_mapping[x], tf_pca.target))\nplt.scatter(pca[:, 0], pca[:, 1], c=colors)\nplt.show()\n\n\n\n\n\n\n# -- end code --","sub_path":"NeuralNetwork/IPCA/z.py","file_name":"z.py","file_ext":"py","file_size_in_byte":3396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"303685969","text":"#Bài 13: Input và Đọc ghi file trong Python\r\n\r\n'''\r\n1, Input.\r\n- Trong Python có cung cấp cho chúng ta hàm input để nhận dữ liệu từ người dùng nhập vào trong commandline. Sử dụng với cú pháp như sau:\r\n            input(something)\r\n\r\n'''\r\n\r\nname = input(\"What is your name ?\")\r\nprint(\"Name: \"+name)\r\n\r\n'''\r\n2, Đọc ghi file.\r\n- File là một thứ rất cần thiết trong các dự án, ví dụ như chúng ta cần phải ghi log ra một file để sau này có thể kiểm soát được....\r\n- Và ngôn ngữ lập trình nào cũng hỗ trợ chúng ta làm việc với file.\r\n\r\nMở file.\r\nĐể mở file trong Python chúng ta sử dụng hàm open với cú pháp như sau:\r\n\r\nopen(filePath, mode, buffer)\r\n    Trong đó:\r\n\r\n- filePath là đường dẫn đến địa chỉ của file.\r\n- mode là thông số thiết lập chế độ chúng ta mở file được cấp những quyền gì? Mặc địn mode sẽ bằng r (xem các mode ở dưới).\r\n- buffer là thông số đệm cho file mặc định thì nó sẽ là 0.\r\n\r\n    Các chế độ mode.\r\n\r\nMode\tChú thích\r\nr\tChế độ chỉ được phép đọc.\r\nrb\tChế độ chỉ được phép đọc nhưng cho định dạn nhị phân.\r\nr+\tChế độ này cho phép đọc và ghi file, con trỏ nó sẽ nằm ở đầu file.\r\nrb+\tChế độ này cho phép đọc và ghi file ở dạng nhị phân, con trỏ sẽ nằm ở đầu file.\r\nw\tChế độ ghi file, nếu như file không tồn tại thì nó sẽ tạo mới file và ghi nội dung, còn nếu như file đã tồn tại nó sẽ ghi đè nội dung lên file cũ.\r\nwb\tTương tự chế độ w nhưng đối với nhị phân.\r\nw+\tMở file trong chế độ đọc và ghi. còn lại như w.\r\nwb+\tGiống chế độ w+ nhưng đối với nhị phân\r\na\tMở file trong chế độ ghi tiếp. Nếu file đã tồn tại rồi thì nó sẽ ghi tiếp nội dung, và nếu như file chưa tồn tại thì nó sẽ tạo một file mới và ghi nội dung vào đó.\r\nab\tTương tự a nhưng đối với nhị phân.\r\na+\tMở file trong chế độ đọc và ghi tiếp nội dung, còn lại cơ chế giống chế độ a.\r\nab+\tTương tự chế độ a+ nhưng đối với nhị phân.\r\n\r\n\r\n\r\n'''\r\n\r\nfopen = open(r\"test.txt\")\r\nfor i in fopen :\r\n    print(i)\r\nfopen.close()\r\n\r\n'''\r\nĐóng file.\r\n- Để đóng một file đang được mở, thì chúng ta sử dụng phương thức close() với cú pháp như sau:\r\n\r\nfileObject.close()\r\n- Trong đó, fileObject là đối tượng mà chúng ta thu được khi sử dụng hàm open().\r\n\r\n- Để đảm bảo quy chế đóng mở và giải phóng bộ nhớ cho chương trình thì các bạn phải luôn nhớ đống file khi kết thúc phiên làm việc.\r\n'''\r\n\r\n\r\n'''\r\nĐọc file.\r\n- Sau khi đã mở được file ra rồi, để đọc được file thì chúng ta sử dụng phương thức read với cú pháp:\r\n\r\n            fileObject.read(length);\r\n-Trong đó:\r\n\r\n- fileObject là đối tượng mà chúng ta thu được khi sử dụng hàm open().\r\n- length là dung lượng của dữ liệu mà chúng ta muốn đọc, nếu để trống tham số này thì nó sẽ đọc hết file hoặc nếu file lớn quá thì nó sẽ đọc đến khi giới hạn của bộ nhớ cho phép.\r\n'''\r\n\r\nfw =  open(\"test.txt\",\"w\")\r\n\r\nfw.write(\"Mai Dac Viet\")\r\n\r\nfw.close()","sub_path":"B13.py","file_name":"B13.py","file_ext":"py","file_size_in_byte":3469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"259547064","text":"from django.utils.translation import ugettext_lazy as _\nfrom rest_framework import serializers\nfrom rest_framework.exceptions import ValidationError\n\n\nclass HasCreateRequired(serializers.ModelSerializer):\n    default_error_message = _('{field_name} is required.')\n\n    def check_create_required_fields(self, attrs):\n        error_list = {}\n        for field in self.create_required_fields:\n            parsed_field, data = self._get_data(field, attrs)\n            if not data:\n                error_list[parsed_field] = self.default_error_message.format(field_name=parsed_field)\n        if error_list:\n            raise ValidationError(error_list)\n\n    def _get_data(self, field, attrs):\n        keys = field.split('.')\n        parsed_field = keys[0]\n        parent = attrs.get(keys[0], None)\n        if len(keys) == 2:\n            parsed_field = keys[1]\n            if parent:\n                return parsed_field, parent.get(keys[1], None)\n            else:\n                return parsed_field, None\n        return parsed_field, parent\n\n    def validate(self, attrs):\n        super(HasCreateRequired, self).validate(attrs)\n        if not self.instance:\n            self.check_create_required_fields(attrs)\n        return attrs","sub_path":"mobile_framework/core/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"291395172","text":"from states.BaseState import *\nfrom states.StartState import StartState\nfrom states.PlayState import PlayState\n\nclass StateMachine:\n    def __init__(self, states):\n        self.states = states\n        self.current = None\n        self.returnButton = pygame.Rect(0, 10, 80, 30)\n        self.returnText = fonts[\"small\"].render(u\"\\u25c4 BACK\", True, GRAY)\n        self.returnTextRect = self.returnText.get_rect(center = self.returnButton.center)\n\n\n    def current_state(self):\n        return str(self.current)\n\n\n    def change(self, state, args=None):\n        self.current = self.states[state]\n\n        if self.current_state() == \"PlayState\":\n            self.current.enter(args)\n\n\n    def update(self, mouse, click):\n        screen.fill(DARK_GRAY)\n\n        # return button\n        if self.current_state() != \"StartState\":\n            if self.returnButton.collidepoint(mouse):\n                if click:\n                    self.current.exit()\n                    self.change(\"StartState\")\n                else:\n                    pygame.draw.rect(screen, WHITE, self.returnButton, border_top_right_radius = 7, border_bottom_right_radius = 7)\n                    screen.blit(self.returnText, self.returnTextRect)\n            else:\n                pygame.draw.rect(screen, DARK_GRAY, self.returnButton)\n                screen.blit(self.returnText, self.returnTextRect)\n\n        self.current.update(self, mouse, click)\n","sub_path":"StateMachine.py","file_name":"StateMachine.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"244925479","text":"#Linear Logistic Regresssion\r\n\r\n#inporting libraries\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\n#importing dataset\r\ndataset = pd.read_csv(\"Social_Network_Ads.csv\")\r\nX = pd.DataFrame(dataset.iloc[:, [2, 3]])\r\nY = pd.DataFrame(dataset.iloc[:, 4])\r\n\r\n#adding X_0\r\n#X.insert(0, \"X_0\", 1)          #not required, handled by the package.\r\n\r\n#performing feature scaling\r\nfrom sklearn.preprocessing import StandardScaler\r\nX_sc = StandardScaler()\r\nX = X.astype('float')\r\nX = pd.DataFrame(X_sc.fit_transform(X))\r\n\r\n#splitting the data\r\nfrom sklearn.model_selection import train_test_split\r\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state = 0, test_size = 0.25)\r\n\r\n#fitting the Logistic Regression\r\nfrom sklearn.linear_model import LogisticRegression\r\nregressor = LogisticRegression(random_state = 0, solver = 'liblinear')\r\nregressor.fit(X_train, Y_train.values.ravel())\r\n\r\n#visualising Logistic Regression\r\nY_train = Y_train.values\r\nfor i in range(0, len(Y_train)):\r\n    if Y_train[i] == 1:\r\n        Y_train[i] = 25\r\n    else:\r\n        Y_train[i] = -25\r\nclass_no = np.ma.masked_where(Y_train > 0, Y_train)       # important class_yes and class_no appear to be interchanged.\r\nclass_yes = np.ma.masked_where(Y_train < 0, Y_train)\r\nplt.scatter(X_train[0], X_train[1], marker = 'o', s = abs(class_yes), color = \"blue\")\r\nplt.scatter(X_train[0], X_train[1], marker = 'x', s = abs(class_no), color = \"red\")\r\nplt.title(\"Social Network training data\")\r\nplt.xlabel(\"Age\")\r\nplt.ylabel(\"Salary\")\r\n\r\n#ploting the regression line\r\ntemp = regressor.coef_              #returns two values, thea_0 and theta_1\r\nslope, intercept = temp[0][0], temp[0][1]\r\nx = np.arange(min(X_train[0]), max(X_train[0]), 0.1)\r\ny = -slope*x + intercept            #values for slope and intercept were unfortunately found using trial and error.\r\nplt.plot(x, y)\r\nplt.show()\r\n\r\n#predicting values\r\nY_pred = regressor.predict(X_test)\r\n\r\n#confusion matrix\r\nfrom sklearn.metrics import confusion_matrix\r\nprint(confusion_matrix(Y_test, Y_pred))\r\n\r\n#visualising Logistic Regression on test data\r\nY_test = Y_test.values\r\nfor i in range(0, len(Y_test)):\r\n    if Y_test[i] == 1:\r\n        Y_test[i] = 25\r\n    else:\r\n        Y_test[i] = -25\r\nclass_no = np.ma.masked_where(Y_test > 0, Y_test)       # important class_yes and class_no appear to be interchanged.\r\nclass_yes = np.ma.masked_where(Y_test < 0, Y_test)\r\nplt.scatter(X_test[0], X_test[1], marker = 'o', s = abs(class_yes), color = \"blue\")\r\nplt.scatter(X_test[0], X_test[1], marker = 'x', s = abs(class_no), color = \"red\")\r\nplt.title(\"Social Network test data\")\r\nplt.xlabel(\"Age\")\r\nplt.ylabel(\"Salary\")\r\n\r\n#ploting the regression line\r\ntemp = regressor.coef_              #returns two values, thea_0 and theta_1\r\nslope, intercept = temp[0][0], temp[0][1]\r\nx = np.arange(min(X_train[0]), max(X_train[0]), 0.1)\r\ny = -slope*x + intercept            #values for slope and intercept were unfortunately found using trial and error.\r\nplt.plot(x, y)                      #line can be varified from the confusion matrix.\r\nplt.show()","sub_path":"1.) Logistic_Regression.py","file_name":"1.) Logistic_Regression.py","file_ext":"py","file_size_in_byte":3077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"146174941","text":"lookingFor = set()\nlookingFor.add(\"shiny gold bag\")\nfullSet = set()\n\ndef recursiveBagSearcher(myList, LFSet):\n    newLFSet = set()\n    for i in myList:\n        for j in LFSet:\n            if j in i.split(\"s contain\")[1]:\n                newLFSet.add(i.split(\"s contain\")[0])\n    helpSet = newLFSet.difference(fullSet)\n    if len(helpSet) == 0:\n        return\n    fullSet.update(helpSet)\n    recursiveBagSearcher(myList, helpSet)\n    return\n\ndef calculateNoOfBags(myList, bag):\n    counter = 0\n    for i in myList:\n        if bag in i.split(\"s contain\")[0]:\n            bagsLine = i.split(\"s contain \")[1]\n            bagsLine = bagsLine.replace('.','')\n            bagsList = bagsLine.split(\", \")\n            if \"no other bag\" in bagsLine:\n                return 1\n            for j in bagsList:\n                number = int(j.split(\" \", 1)[0])\n                newBag = j.split(\" \", 1)[1]\n                newBag = newBag.replace(\"bags\", \"bag\")\n                print(newBag)\n                nob = int(calculateNoOfBags(myList, newBag))\n                if nob == 1 and number == 1:\n                    counter += number+nob\n                    continue\n                counter += number*nob\n            return counter\n    return 1\n\n\nf = open(\"D:\\Documents\\AoCinputs\\inputday7a.txt\")\nlista = list(map(lambda x: x.rstrip(), f.readlines()))\nrecursiveBagSearcher(lista, lookingFor)\nprint(len(fullSet))\nprint(calculateNoOfBags(lista, \"shiny gold bag\"))\n","sub_path":"AoC7PY/AoC7PY.py","file_name":"AoC7PY.py","file_ext":"py","file_size_in_byte":1446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"308363944","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport sys\nimport time\n\nR = 1.0\ntestdir = sys.argv[1]\nfilebase= sys.argv[2]\nwallfile = sys.argv[3]\ntsave   = int(sys.argv[4])\ntfinal  = int(sys.argv[5])\n\nNT    = tfinal/tsave;\ntheta = np.linspace(0,2*np.pi,100);\nxcirc = R*np.cos(theta);\nycirc = R*np.sin(theta);\n\nxyw = np.genfromtxt(testdir + wallfile, delimiter=',');\nfig = plt.figure(1);\nplt.ion();\nplt.show()\nfor i in range(NT):\n    xy = np.genfromtxt(testdir + filebase + str(tsave*(i+1)) + '.csv', delimiter=',')\n    for j in range(xy.shape[0]):\n        plt.plot(xcirc + xy[j,0] , ycirc + xy[j,1] , 'b');\n    for j in range(xyw.shape[0]):\n        plt.plot(xcirc + xyw[j,0] , ycirc + xyw[j,1] , 'r');\n    plt.gca().set_aspect('equal');\n    plt.xlim([-40,40])\n    plt.ylim([-4,76])\n    fig.canvas.draw()\n    time.sleep(0.01)\n    plt.clf()\n\nplt.ioff(); plt.show();\nfig = plt.figure(2);\nfor i in range(NT):\n    em = np.genfromtxt(testdir + filebase + \"EM_\" + str(tsave*(i+1)) + '.csv', delimiter=',')\n    plt.plot(i,em[0],'bo'); plt.plot(i,em[1],'ro'); plt.plot(i,em[2],'go');\n\nplt.show();\n","sub_path":"CPP/vis/plotSim.py","file_name":"plotSim.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"515777667","text":"#Import Flask\nfrom flask import Flask, jsonify\nimport pandas as pd\nimport numpy as np\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func, inspect, desc\nfrom datetime import datetime as DateTime, timedelta as TimeDelta\nfrom datetime import date\nfrom dateutil.parser import parse\n\n\n\n\n\n\n\n\n\n\n# 2. Create an app\napp = Flask(__name__)\n\nengine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")\n# reflect an existing database into a new model\nBase = automap_base()\n# reflect the tables\nBase.prepare(engine, reflect=True)\n\n# Save references to each table\nMeasurement = Base.classes.measurement\nStation = Base.classes.station\n\n# 3. Define Menu\n@app.route(\"/\")\ndef index():\n    return (\n                f\"Welcome to Mustafa's Weather API Services\"\n\t\tf\"/api/v1.0/precipitation<br/>\"                \n\t\tf\"/api/v1.0/stations<br/>\"\n\t\tf\"/api/v1.0/tobs<br/>\"\n                f\"/api/v1.0<start>/<end><br/>\"\n\t)\n\n@app.route(\"/about\")\ndef about():\n    name = \"Mustafa\"\n    location = \"Houston, TX\"\n\n    return f\"Hi, my name is {name}, I live in {location}.\"\n\n@app.route(\"/precipitation/\")\ndef precipitation():\n    session = Session(engine)\n    results = session.query(Measurement.date, Measurement.prcp).all()\n    \n    session.close()\n    precipy = list(np.ravel(results))\n\n    return jsonify(precipy)\n\n@app.route(\"/station/\")\ndef station():\n    session = Session(engine)\n    results2 =engine.execute('SELECT * FROM station LIMIT 5').fetchall()\n\n    session.close()\n    stationlist = list(np.ravel(results2))\n\n    return jsonify(stationlist)\n\n@app.route(\"/tobs/\")\ndef tobs():\n    session = Session(engine)\n    results3 =session.query(Station.name, Measurement.station, Station.latitude, Station.longitude, Station.elevation, Measurement.prcp,)\\\n        .filter(Measurement.date >= '2016-01-30', Measurement.date <= '2017-01-30')\\\n        .order_by((Measurement.prcp).desc())\\\n        .all()\n\n    session.close()\n    thetemps = list(np.ravel(results3))\n\n    return jsonify(thetemps)\n\n\n@app.route(\"/api/v1.0/<start>\")\ndef start_trip_temp(start_date):\n\tstart_trip = []\n\n\tresults_min = session.query(func.min(Measurement.tobs)).filter(Measurement.date == start_date).all()\n\tresults_max = session.query(func.max(Measurement.tobs)).filter(Measurement.date == start_date).all()\n\tresults_avg = session.query(func.avg(Measurement.tobs)).filter(Measurement.date == start_date).all()\n\n\tstart_trip = list(np.ravel(results_min,results_max, results_avg))\n\n\treturn jsonify(start_trip)\n\n\n\n@app.route(\"/<start>\")\ndef firstoption(start):\n    session = Session(engine)\n    option1result = session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\\\n        filter(Measurement.date >= start).all()\n    \n    session.close()\n    option1temp = list(np.ravel(option1result))\n\n    return jsonify(option1temp)\n\n\n@app.route(\"/<start>/<end>\")\ndef secondoption(start,end):\n    session = Session(engine)\n    option2result = session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\\\n        filter(Measurement.date >= start).filter(Measurement.date <= end).all()\n   \n    session.close()\n    option2temp = list(np.ravel(option2result))\n    \n    return jsonify(option2temp)\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n\n\n\n\n\n\n\n\n\n\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"117570145","text":"import sys, json, os\nfrom functions.clearing      import clearing\nfrom functions.normalization import normalization\nfrom functions.translation   import translation\nfrom functions.statistics    import statistics\n\n\nfrom flask import Flask, render_template, redirect, jsonify\nfrom flask import session, request, url_for, flash, make_response\nfrom dicty import *\n\n\n@app.route('/')\n@app.route('/index')\ndef index():\n\n    return render_template('index.html')\n\n\n@app.route(\"/download\", methods=['POST', 'GET'])\ndef download():\n\n    words = request.values.get('input_text').split(' ')\n\n    word_ranking = open('dicty/functions/eng_freq_55000_dict.txt')\n    word_ranking = json.load(word_ranking)\n\n    dictionary   = open('dicty/functions/translation/muller_dict.txt')\n    dictionary   = json.load(dictionary)\n\n    cleared_words    = clearing.words_clearing(words)\n    pos_tagged_words = normalization.pos_tagging(cleared_words)\n    lemmatized_words = normalization.lemmatizing(pos_tagged_words)\n    complexity_rank  = statistics.complexity_rank(lemmatized_words, word_ranking)\n    translated_words = dict(zip(lemmatized_words, translation.translating(lemmatized_words, dictionary)))\n\n    ranked_words     = statistics.word_ranking(translated_words, word_ranking)\n    text_statistics  = statistics.text_statistics(ranked_words)\n    #return jsonify(a = text_statistics)\n\n    text  = '' \n    text += 'Total number of words in raw text: {0}{1}' .format(str(len(cleared_words)),   '\\n'     )\n    text += 'Total number of unique words: {0}{1}'      .format(str(len(lemmatized_words)),'\\n'     )\n    text += 'Text complexity: {0}{1}{2}'                .format(str(complexity_rank),      '\\n','\\n')\n    text += 'Easy words: {0}{1}'                        .format(str(text_statistics[0]),   '\\n'     )\n    text += 'Difficult words: {0}{1}'                   .format(str(text_statistics[1]),   '\\n'     )\n    text += 'Uncategorized words: {0}{1}{2}'            .format(str(text_statistics[2]),   '\\n','\\n')\n\n    \n    for i in ranked_words:\n\n        text += '{0:10}{1:15}{2:15}' .format(str(i[2]), str(i[0]), str(i[1].encode('utf-8')))\n        text += '\\n'\n\n    response = make_response(text)\n    response.headers['Content-Disposition'] = \"attachment; filename = books.txt\"\n    \n    return response","sub_path":"dicty/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"451348771","text":"from django.apps import AppConfig\nimport pandas as pd\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report, mean_absolute_error\nfrom sklearn import preprocessing\nimport pickle\n\n\nclass ServerConfig(AppConfig):\n    name = 'server'\n\n    def calcul_pertinence(self, row):\n        temps_estime = int(row.pge_estimated_time)\n        temps_passe = int(row.vst_time)\n        pourcentage_pertinence = {\n            1: [20, 30, 40],\n            2: [20, 30, 40],\n            3: [20, 30, 40],\n            4: [20, 30, 40]\n        }\n\n        ecart = (temps_passe - temps_estime) / temps_estime * 100\n        if ecart < 0:\n            ecart = ecart * -1\n\n        for i in range(3):\n            if ecart <= pourcentage_pertinence[row.pge_type_id][i]:\n                return i + 1\n        return 3\n\n    def ready(self):\n        print(\"Début du ready()\")\n\n        #Récupération des fichiers\n        df_dataset = pd.read_csv(r\"C:\\Users\\trist\\Desktop\\IA\\dataset.csv\", sep=\";\")\n\n\n        #Ajoute de features\n        df_dataset['pertinence_computed'] = df_dataset.apply(self.calcul_pertinence, axis=1)\n\n        features = [\n            'app_id',\n            'vst_time',\n            'pge_estimated_time',\n            'pge_type_id'\n        ]\n        # On n'ajoute pas pertinence_computed car c'est ce que l'on cherche à prédire.\n        # J'ajoute pas la colonne pge_name car elle n'a pas d'intérêt pour l'apprentissage de l'IA\n\n        train_features = df_dataset[features]  # X_train\n\n        label = ['pertinence_computed']  # ce que l'on cherche à prédire\n\n        train_label = df_dataset[label]  # y_train\n\n\n        #découpage du jeux en 2\n        train, test = train_test_split(df_dataset, test_size=0.3, random_state=42)\n\n        #RandomForrest\n        rf = RandomForestClassifier(n_estimators=100,\n                                    max_depth=8,\n                                    random_state=0)\n\n        rf.fit(train_features, train_label)  # apprentissage\n\n\n        #sauvegarde\n        filename = 'finalized_model.sav'\n        pickle.dump(rf, open(filename, 'wb'))\n\n","sub_path":"REST_IA_Server/server/apps.py","file_name":"apps.py","file_ext":"py","file_size_in_byte":2203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"445718196","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nThis program implements Historical Value at Risk calculation for a \r\nportfolio of tickers with given weights. To simulate returns, it draws \r\n(with replacement) from historical returns for each ticker. \r\nChange tickers, weights, time for lookback at the top.\r\n\r\nCreated on Mon Feb 3 11:03:15 2020\r\n\r\n@author: mroll\r\n\"\"\"\r\n# import some packages and use some shorthand (data, plt, pd)\r\nfrom pandas_datareader import data \r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt \r\nimport numpy as np\r\nimport statistics as stats # need this to take standard deviations\r\nfrom random import choice # choice will select an item from list at random\r\n\r\ndef MR_get(tickers,s,e):\r\n    stock_data = data.DataReader(tickers, data_source='yahoo', start = s, \r\n                       end = e)['Adj Close']\r\n    \r\n    return stock_data\r\n\r\n# Initialize all the stuff you might want to change later\r\ntickers = ['F', 'AAPL', 'GOOG'] # stock tickers go here\r\n# Need some weights for where we have our money\r\nweights = pd.Series(index = tickers, dtype = float)\r\nweights[tickers]=0.33333333\r\nstart_date = '2018-1-27' # start day for lookback\r\nend_date = '2020-1-27' # end day for lookback\r\n\r\n# Initialize some Monte Carlo paramters\r\nmonte_carlo_runs = 1000\r\ndays_to_simulate = 5\r\nloss_cutoff = 0.99 # count any losses larger than 5% (or -5%)\r\n\r\n# Call that simple function we wrote above\r\nwhat_we_got = MR_get(tickers, start_date, end_date)\r\n# Compute returns from those Adjusted Closes\r\nreturns = what_we_got[tickers].pct_change()\r\n    # This is basically what bt.get returned, just the returns\r\n# Remove the NA from returns; we always get 1 fewer returns than data\r\nreturns = returns.dropna() # pretty easy command\r\n\r\n# Calculate mu and sigma\r\nmu = returns.mean() # mu was very easy using .mean method\r\n# sigma was harder, as I couldn't do it in one line\r\n#   the statistics package didn't know what to do without the loop\r\n#   So I copied the mu data structure and filled it in a loop\r\nsigma=mu.copy() # copy the mu series (a pandas datatype)\r\n# Loop over tickers and fill sigma up with the calculated standard deviation\r\nfor i in tickers: # python loops over tickers no problem\r\n    sigma[i]=stats.stdev(returns[i]) # fill up sigma with stdev's\r\n# There is probably a cleaner way to do that \r\n'''sigma = np.std()\r\nprint(sigma)'''\r\n\r\n# Now we can start the Monte Carlo VaR loop\r\n\r\ncompound_returns = sigma.copy()\r\ntotal_simulations = 0\r\nbad_simulations = 0\r\nfor run_counter in range(0,monte_carlo_runs): # Loop over runs    \r\n    for i in tickers: # loop over tickers, below is done once per ticker\r\n        # Loop over simulated days:\r\n        compounded_temp = 1\r\n        for simulated_day_counter in range(0,days_to_simulate): # loop over days\r\n            simulated_return = choice(returns[i])\r\n            compounded_temp = compounded_temp * (simulated_return + 1)        \r\n        compound_returns[i]=compounded_temp # store compounded returns\r\n    # Now see if those returns are bad by combining with weights\r\n    portfolio_return = compound_returns.dot(weights) # dot product\r\n    if(portfolio_return<loss_cutoff):\r\n        bad_simulations = bad_simulations + 1\r\n    total_simulations = total_simulations + 1\r\n\r\nprint(\"Your portfolio will lose\",round((1-loss_cutoff)*100,3),\"%\",\r\n      \"over\",days_to_simulate,\"days\", \r\n      bad_simulations/total_simulations, \"of the time\")\r\n        \r\n        \r\n            \r\n        \r\n            ","sub_path":"Historical_VaR.py","file_name":"Historical_VaR.py","file_ext":"py","file_size_in_byte":3450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"586492479","text":"import re\n\ndef getTime(line, pattern):\n    ret = pattern.search(line)\n    if ret is not None:\n        ret = int(float(ret.group(1)) * 10.0)\n\n    return ret\n\nif __name__ == '__main__':\n    pattern = re.compile(r'时间：(\\d+\\.\\d+)$')\n    with open('hjspider.log', mode='r', encoding='utf-8') as f:\n        while True:\n            line = f.readline()\n            if line is '':\n                break\n            time = getTime(line, pattern)\n            if time is not None:\n                print('#' * time)","sub_path":"HJSpider2ED/statics.py","file_name":"statics.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"346286721","text":"import matplotlib; matplotlib.use('agg')\n\nfrom argparse import ArgumentParser\nfrom datetime import datetime\nfrom os.path import join, isfile, basename\n\nimport matplotlib.pyplot as plt\nimport netCDF4\nimport numpy as np\nfrom pyclamps.plotting import rhi_plot\n\nterrain_nc = '/Users/tbell/Data/perdigao/terrain/newa_perdigao_map_topo.nc'\n\ndlr_lat_lons = {'DLR85': (39.714533, -7.731142),\n                'DLR86': (39.712467, -7.735108),\n                'DLR89': (39.709867, -7.746794)}\n\nparser = ArgumentParser()\nparser.add_argument('-i', dest='in_files', nargs='*')\nparser.add_argument('-o', dest='out_dir')\nparser.add_argument('-t', dest='terrain', default=terrain_nc)\nargs = parser.parse_args()\nfor f in args.in_files:\n    nc = netCDF4.Dataset(f)\n\n    # Get the coords of the appropriate lidar\n    coord_key = basename(f).split('_')[0]\n    lon, lat = dlr_lat_lons[coord_key]\n\n    for key in nc.groups.keys():\n        sweep = nc.groups[key]\n\n        # Get the datetime of the first ray\n        time = datetime.utcfromtimestamp(sweep['time'][:][0])\n\n        # Get the grid figured out\n        elev = np.deg2rad(sweep['elevation'])\n        rng_m = sweep['range'][:][0]\n        az = np.round(np.mean(sweep['azimuth']))\n\n        # Get the data\n        vel = sweep['rws']\n        # intensity = nc['intensity'][\n        # vel = np.ma.masked_where(intensity < 1.01, vel)\n\n        # Figure out the image name\n        image_name = '{}_RHI_horiz_{}_{}.png'.format(coord_key, az, time.strftime(\"%Y%m%d_%H%M\"))\n        # image_name = 'RHI_{}_{}.png'.format(az, time.strftime(\"%Y%m%d_%H%M\"))\n        image_name = join(args.out_dir, image_name)\n\n        # Testing horiz component view\n        tmp_elev = np.meshgrid(elev, rng_m)[0].transpose()\n        h_vel = vel * np.cos(tmp_elev)\n        if az > 180.: h_vel = -h_vel\n        del tmp_elev\n\n        # if not isfile(image_name):\n        print(image_name)\n\n        rhi_plot(elev, rng_m, -h_vel, az, time, vmax=10, vmin=-10, xlim=(-500, 2000), ylim=(0, 1500),\n                 terrain_file=args.terrain, lat_0=lon, lon_0=lat)\n        plt.savefig(image_name)\n        plt.close()\n\n    nc.close()\n","sub_path":"rhi_DLR_plot.py","file_name":"rhi_DLR_plot.py","file_ext":"py","file_size_in_byte":2126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"132096249","text":"'''\nCreated on Mar 28, 2019\n@title: pure MCTS\n@tudo: Monte-Carlo Tree Search without machine learning\n@author: martha\n'''\nimport psutil\nimport os\nfrom math import sqrt, log\n# import numpy as np\n# from random import choice\nfrom play.play_rules import findNeighbor, possibleBlockLocation\nfrom play.board import Board\nfrom MCTS.AStar import eveluation_func\n\nclass TreeNode():\n    \"\"\"\n    BASIC class TreeNode\n    initialization :\n        instance: turn whose turn for this step\n                  parent: parent node for this node\n                  action: this node's action\n                  state: temp state related on this node\n    \"\"\"\n    def __init__(self, turn, parent, action, state):\n        self._turn = turn\n        self._action = action\n        self._parent_node = parent\n        self._child_action = []  # a map from action to TreeNode\n        self._child_node = []\n        self._wins = 0\n        self._visits = 0\n        self._state = state\n        self._reset_piece_location = []\n\n    def possible_moves(self):\n        \"\"\"\n        method: possible moves\n        possible moves for this player\n        continue with piece list with length of 2\n        and block list with length of 3\n        \"\"\"\n        location = self._state._player[self._turn][:2]\n        enemy_location = self._state._player[1-self._turn][:2]\n        state = self._state\n        piece_list = findNeighbor(location, state.board)\n        block_list = possibleBlockLocation(enemy_location,\n                                           state.board,\n                                           state.block_list)\n        self.move_list = piece_list + block_list\n#         self.move_list = block_list\n\n    def UCT_select_child(self):\n        \"\"\" Use the UCB1 formula to select a child node.\n            Often a constant UCTK is applied so we have\n            lambda c: c.wins/c.visits + UCTK * sqrt(2*log(self.visits)/c.visits\n            to vary the amount of\n            exploration versus exploitation.\n        \"\"\"\n        max_value = -0x3f3f3f\n        for temp_node in self._child_node:\n            value = temp_node._wins/temp_node._visits \\\n                + sqrt(2*log(self._visits)/temp_node._visits)\n            if value > max_value:\n                max_value = value\n                action = temp_node._action\n                select_node = temp_node\n        self._state.get_move(self._turn, action)\n        select_node._turn = 1-self._turn\n        select_node._parent_node = self\n        select_node._action = action\n        return select_node\n\n    def expend_child(self):\n        \"\"\"\n        if this node is leaf node than it should be expended:\n        expend with possible_action within possible_moves list and not searched yet\n        \"\"\"\n        for possible_action in reversed(self.move_list):\n            if possible_action not in self._child_action:\n                expend_state = self._state\n                if len(possible_action) == 3 and self._state._player[self._turn][2] == 0:\n                    self.move_list.remove(possible_action)\n                    continue\n                expend_state.get_move(self._turn, possible_action)\n                expend_node = TreeNode(1-self._turn,\n                                       self, possible_action,\n                                       expend_state)\n                self._child_action.append(possible_action)\n                self._child_node.append(expend_node)\n                return expend_node\n#                 break\n\n    def simulation_(self):\n        \"\"\"\n        simulation:\n        board judge with enemy's step minus my step\n        if it's enemy's turn than return -point\n        \"\"\"\n        point = eveluation_func(self._state)\n#         return point\n        if self._turn:\n            return point\n        else:\n            return -point\n\n    def update(self, value):\n        \"\"\"\n        update value to all parent's value \n        \"\"\"\n        if self._parent_node is not None:\n            self._parent_node.update(value)\n        self._visits += 1\n        self._wins += value\n\n    def reset_board(self, turn, reset_location, action):\n        \"\"\"\n        after simulation reset board as board before simulation\n        \"\"\"\n        if len(action) == 2:\n            self._state.move_piece(turn, reset_location)\n        else:\n            self._state.remove_blocks(turn, action)\n\n    def is_leaf(self):\n        \"\"\"\n        if this node is leaf node return 1 else return 0\n        \"\"\"\n        return self._child_action == []\n\n    def is_root(self):\n        \"\"\"\n        if this node is root node return 1 else return 0\n        \"\"\"\n        return self._parent_node is None\n\n    def is_full_expended(self):\n        \"\"\"\n        if this node is full expended(all possible moves have been calculated)\n         return 1 else return 0)\n        \"\"\"\n        if len(self._child_action) == len(self.move_list):\n            return 1\n        else:\n            return 0\n\n    def remove_illegal_child(self, child_node):\n        self._child_action.remove(child_node._action)\n        self._child_node.remove(child_node)\n        self.move_list.remove(child_node._action)\n\ndef my_MCTS_fun():\n    \"\"\"\n    function in\n    \"\"\"\n    testBoard = Board()  # initialization board\n    node = TreeNode(0, None, [6, 3], testBoard)  # root node\n    node.possible_moves()  # find all possible moves of root node\n    node._reset_piece_location = node._state._player[node._turn][:2]\n    # save note node's origin location\n    i = 0  # set timeout!\n    while(i < 500):\n        while node.is_full_expended():   \n            #  if all possible moves has been calculated\n            #  select node begin with root node\n            i = i + 1\n#             if i == 435:\n#                 print(\"break point\")\n            while not node.is_root():\n                action = node._action\n                node = node._parent_node  # located to root node\n                node.reset_board(node._turn, node._reset_piece_location,\n                                 action)  # reset noard\n            while not node.is_leaf():  # select child node until leaf node\n                action = node._state._player[1-node._turn][:2]\n                node = node.UCT_select_child()\n                node._reset_piece_location = action\n            node.possible_moves()  # search possible moves for leaf node\n        child_node = node.expend_child()  # expended child\n        child_node._reset_piece_location = node._reset_piece_location\n        value = child_node.simulation_()  # calculate board judge point\n        if value == 1000 or value == -1000:  # not root available illegal movement\n#             print(\"need remove child node\")\n            node.remove_illegal_child(child_node)\n            child_node.reset_board(1-child_node._turn, child_node._reset_piece_location,\n                               child_node._action)  # reset noard\n            continue\n        child_node.update(value)  # update value\n        child_node.reset_board(1-child_node._turn, child_node._reset_piece_location,\n                               child_node._action)  # reset noard\n    while not node.is_root():\n        node = node._parent_node  # located to root node\n    node = node.UCT_select_child()\n    print(node._action)\n\n\"\"\"\ntest Function in\n\"\"\"\nif __name__ == '__main__':\n    my_MCTS_fun()\n    info = psutil.virtual_memory()\n    print('内存使用：', psutil.Process(os.getpid()).memory_info().rss)\n    print('内存占比：',info.percent)","sub_path":"src/MCTS/pure_MCTS.py","file_name":"pure_MCTS.py","file_ext":"py","file_size_in_byte":7424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"330842091","text":"import os\nimport datetime\nimport requests\nimport json\nfrom flask import render_template, flash, redirect, request, url_for, jsonify\nfrom app import app, db, api\nfrom app.models import User, Staff, Pos, ValletTransaction, ValletProduct\nfrom config import Config\n# from app.addons import addon_purchase\nfrom app.farer import authorizestaff, authorize\nfrom werkzeug.utils import secure_filename\nfrom werkzeug.urls import url_parse\nfrom flask_restplus import Resource, Api\nfrom sqlalchemy.sql import func\nfrom sqlalchemy import or_\n\nvallet = api.namespace('vallet', description=\"Vallet service\")\n\ndef valletbalance(vid):\n    topup = db.session.execute(\"select sum(amt) from vallet_transaction where vid=\"+str(vid)+\" and typ=1\").scalar()\n    # topup = ValletTransaction.query.with_entities(func.sum(ValletTransaction.amt)).filter(ValletTransaction.vid==vid).filter(ValletTransaction.typ==1)\n    spent = db.session.execute(\"select sum(amt) from vallet_transaction where vid=\"+str(vid)+\" and typ=2\").scalar()\n    print(topup, spent)\n    # spent = ValletTransaction.query.with_entities(func.sum(ValletTransaction.amt)).filter(ValletTransaction.vid==vid).filter(ValletTransaction.typ==2)\n    if topup == None:\n        topup = 0\n    if spent == None:\n        spent = 0\n    balance = topup - spent\n    return balance\n\n@vallet.route('/transaction/recharge')\nclass VTransaction(Resource):\n    @authorize(request)\n    def get(u, self):\n        vt = ValletTransaction.query.filter_by(vid=u.vid).all()\n        return jsonify(vt)\n\n    @authorizestaff(request, \"pay\", 3)\n    @api.doc(params={\n        'vid':'Vidyut ID of the staff',\n        'pos':'Point of sale ID',\n        'amt':'Transaction Amount (when pos < 100)'\n    })\n    def post(u, self):\n        data = request.get_json()\n        if data.get('amt') is None or data.get('vid') is None or data.get('pos') is None:\n            responseObject = {\n                'status':'fail',\n                'message':'Inadequate data'\n            }\n            return jsonify(responseObject)\n        try:\n            vid = data.get('vid')\n            truser = User.query.filter_by(vid=vid).first()\n            if truser is None:\n                responseObject = {\n                    'status':'fail',\n                    'message':'Invalid User'\n                }\n                return jsonify(responseObject)\n        except Exception as e:\n            print(e)\n            responseObject = {\n                'status':'fail',\n                'message':'Error: '+str(e)\n            }\n            return jsonify(responseObject)\n        try:\n            pos = Pos.query.filter_by(posid=data.get('pos')).first()\n            if pos is None:\n                responseObject = {\n                    'status':'fail',\n                    'message':'Invalid point of sale'\n                }\n                return jsonify(responseObject)\n        except Exception as e:\n            print(e)\n        try:\n            typ = 2\n            amt = data.get('amt')\n            truser.balance = truser.balance + amt\n            typ = 1\n            vt = ValletTransaction(vid=data.get('vid'),\n                                typ=typ,\n                                pos=data.get('pos'),\n                                notes=data.get('notes'),\n                                amt=data.get('amt'),\n                                by=u.vid\n                                )\n            db.session.add(vt)\n            db.session.commit()\n        except Exception as e:\n            responseObject = {\n                'status':'fail',\n                'message':'No balance or database error. Error: '+str(e)\n            }\n        try:\n            # mail the user and send message on the transaction\n            payload = {\n                # 'ip':\n                'key':'vidyuth1908030559',\n                'num':truser.phno,\n                'message':'Vallet Transaction of Rs. '+str(amt)+' at '+str(pos.title)+'.'\n            }\n            r = requests.get('http://sms.amrita.ac.in/', data=payload)\n            # Uncomment only when needed\n        except Exception as e:\n            responseObject = {\n                'status':'success',\n                'message':'Transaction succesful with errors (message not sent). Please inform user the transaction is successful. Error: '+str(e)\n            }\n            return jsonify(responseObject)\n        responseObject = {\n            'status':'success',\n            'tid':vt.tid\n        }\n        return jsonify(responseObject)\n\n@vallet.route('/transaction')\nclass TransactionManagement(Resource):\n    @authorizestaff(request, \"pay\", 3)\n    @api.doc(params={\n        'vid':'Vidyut ID of the staff',\n        'pos':'Point of sale ID (>=100)',\n        'products':'Products (ID) + count array. eg: {{1, 2}, {2, 1}, {3, 4}}'\n    })\n    def post(u, self):\n        data = request.get_json()\n        if data.get('vid') is None or data.get('pos') is None:\n            responseObject = {\n                'status':'fail',\n                'message':'Inadequate data'\n            }\n            return jsonify(responseObject)\n        try:\n            vid = data.get('vid')\n            truser = User.query.filter_by(vid=vid).first()\n            if truser is None:\n                responseObject = {\n                    'status':'fail',\n                    'message':'Invalid User'\n                }\n                return jsonify(responseObject)\n        except Exception as e:\n            print(e)\n            responseObject = {\n                'status':'fail',\n                'message':'Error: '+str(e)\n            }\n            return jsonify(responseObject)\n        try:\n            pos = Pos.query.filter_by(posid=data.get('pos')).first()\n            if pos is None:\n                responseObject = {\n                    'status':'fail',\n                    'message':'Invalid point of sale'\n                }\n                return jsonify(responseObject)\n        except Exception as e:\n            print(e)\n        try:\n            typ = 2\n            amt = data.get('amt')\n            products \n            vt = ValletTransaction(vid=data.get('vid'),\n                                typ=typ,\n                                pos=data.get('pos'),\n                                notes=data.get('notes'),\n                                amt=data.get('amt'),\n                                by=u.vid\n                                )\n            db.session.add(vt)\n            db.session.commit()\n        except Exception as e:\n            responseObject = {\n                'status':'fail',\n                'message':'No balance or database error. Error: '+str(e)\n            }\n        try:\n            # mail the user and send message on the transaction\n            payload = {\n                # 'ip':\n                'key':'vidyuth1908030559',\n                'num':truser.phno,\n                'message':'Vallet Transaction of Rs. '+str(amt)+' at '+str(pos.title)+'.'\n            }\n            r = requests.get('http://sms.amrita.ac.in/', data=payload)\n            # Uncomment only when needed\n        except Exception as e:\n            responseObject = {\n                'status':'success',\n                'message':'Transaction succesful with errors (message not sent). Please inform user the transaction is successful. Error: '+str(e)\n            }\n            return jsonify(responseObject)\n        responseObject = {\n            'status':'success',\n            'transaction ID':vt.tid\n        }\n        return jsonify(responseObject)\n\n@vallet.route('/deliver/pos/<int:id>')\nclass VDeliver(Resource):\n    @authorizestaff(request)\n    # Sends all undelivered transactions\n    def get(u, self, id):\n        if pos < 100:\n            responseObject = {\n                'status':'fail',\n                'message':'No deliveries for recharge station'\n            }\n            return jsonify(responseObject)\n        und = ValletTransaction.query.filter_by(pos=id, delivered=False).all()\n        resp = []\n        for i in und:\n            resp.append({\n                'tid':i.tid,\n                'vid':i.vid,\n                'notes':i.notes,\n                'amt':i.amt,\n                'by':i.by\n            })\n        responseObject = {\n            'status':'success',\n            'data':jsonify(resp)\n        }\n        return jsonify(responseObject)\n\n@vallet.route('/products/<int:id>')\nclass PosProducts(Resource):\n    def get(self, id):\n        p = ValletProduct.query.filter_by(pod=id).all()\n        pr = []\n        for i in p:\n            pr.append({\n                'prodid':i.prodid,\n                'title':i.title,\n                'desc':i.desc,\n                'amount':i.amount\n            })\n        responseObject = {\n            'status':'success',\n            'response':jsonify(pr)\n        }\n        return jsonify(responseObject)\n\n    @authorizestaff(request, \"pay\", 4)\n    def post(u, self, id):\n        try:\n            data = request.get_json()\n            product = ValletProduct(pos=id,\n                                    title=data.get('title'),\n                                    desc=data.get('desc'),\n                                    amount=data.get('amount'),\n                                    by=u.vid\n                                )\n            db.session.add(product)\n            db.session.commit()\n            responseObject = {\n                'status':'success',\n                'message':'Product successfully added'\n            }\n            return jsonify(responseObject)\n        except Exception as e:\n            print(e)\n            responseObject = {\n                'status':'fail',\n                'message':'Inadequate data / Database error',\n                'error':str(e)\n            }\n            return jsonify(responseObject)\n\n@vallet.route('/deliver')\nclass VDeliverNow(Resource):\n    @authorizestaff(request)\n    @api.doc(params={\n        'tid':'Transaction ID'\n    })\n    def post(u, self):\n        try:\n            data = request.get_json()\n            tid = data.get('tid')\n        except Exception as e:\n            responseObject = {\n                'status':'fail',\n                'message':'Invalid data'\n            }\n            return jsonify(responseObject)\n        try:\n            t = ValletTransaction.query.filter_by(tid=tid).first()\n            t.delivered = True\n            db.session.commit()\n        except Exception as e:\n            print(e)\n            responseObject = {\n                'status':'fail',\n                'message':'Error: '+str(e)\n            }\n            return jsonify(responseObject)\n        responseObject = {\n            'status':'success',\n            'message':'Successfully delivered'\n        }\n        return jsonify(responseObject)\n\n@vallet.route('/balance')\nclass VBalance(Resource):\n    @authorize(request)\n    def get(u, self):\n        try:\n            balance = valletbalance(u.vid)\n        except Exception as e:\n            print(e)\n            responseObject = {\n                'status':'fail',\n                'message':'Error: '+str(e)\n            }\n            return jsonify(responseObject)\n        responseObject = {\n            'status':'success',\n            'balance':balance\n        }\n        return jsonify(responseObject)\n\n@vallet.route('/pos')\nclass ValletPOS(Resource):\n    def get(self):\n        try:\n            pos = Pos.query.all()\n            resp = []\n            for i in pos:\n                resp.append({\n                    'posid':i.posid,\n                    'title':i.title,\n                    'descr':i.descr\n                })\n        except Exception as e:\n            print(e)\n            responseObject = {\n                'status':'fail',\n                'message':'Database error: '+str(e)\n            }\n        responseObject = {\n            'status':'success',\n            'data':jsonify(resp)\n        }\n        return jsonify(responseObject)\n\n    @api.doc(params={\n        'title':'POS title',\n        'descr':'Description'\n    })\n    @authorizestaff(request, \"pay\", 4)\n    def post(u, self):\n        data = request.get_json()\n        pos = Pos(title=data.get('title'), descr=data.get('descr'))\n        db.session.add(pos)\n        db.session.commit()\n        responseObject = {\n            'status':'success',\n            'message':'POS added'\n        }\n        return jsonify(responseObject)\n","sub_path":"app/vallet.py","file_name":"vallet.py","file_ext":"py","file_size_in_byte":12299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"623895930","text":"# saa1_psb31_2\nfrom saa1_psb31_1 import *\n# Helper Funcitons#\ndef sequenceToindexList(seq):\n\t# Return a sequence of indeces seq (0-N)\n\tseq = np.array(seq)\n\treturn sequenceToindexArray(seq)\ndef sequenceToindexArray(seq):\n\t# Return a sequence of indeces seq (0-N)\n\tV = np.unique(seq)\n\tfor i,v in enumerate(V):\n\t\tindex = np.where(seq == v)[0]\n\t\tseq[index] = i \n\tseq.astype(int)\n\tseq = seq.tolist()\n\tseq = [int(i) for i in seq]\n\treturn seq\ndef followCount(tup,chain):\n\n\tx1 = tup[0]\n\tx2 = tup[1]\n\t# Count the of times that x2 follows x1 / number \n\t# getIndices of x1 and x2\n\ti1 = np.where(np.array(chain) == x1)[0]\n\ti2 = np.where(np.array(chain) == x2)[0]-1\n\tcommonIndeces =  [item for item in i1 if item in i2]\t# how many i1 == i2\n\tif x1 == chain[-1]:\n\t\tpercentage = (float(len(commonIndeces)))/float(len(i1)-1)\n\telse:\n\t\tpercentage = (float(len(commonIndeces)))/float(len(i1))\n\treturn percentage\n# end helper functions#\ndef mlmChain(chain):\n# A function which reads a sequence of state indices (i.e. elements of\n# f1; : : : ;Kg) and infers a maximum likelihood Markov chain transition matrix and\n# initial distribution.\n\tS =  list(set(chain))\n\t# Get set of states\n\tK = len(S)\n\t# Given a A trans matrix and mu init dist, calc the probability of the chain occuring\n\tmu = [0]*K # zeros \n\tmu[S.index(chain[0])] = 1\n\t# now MLE for A\n\tplist = []\n\tfor i in S:\n\t\tfor j in S:\n\t\t\tfc = followCount((i,j),chain)\n\t\t\tplist.append(fc)\n\t# Generate the matrix\n\tA =  np.array(plist).reshape(K,K)\n\treturn A.tolist(),mu\n# print mlmChain(chain=mChain(muFile='initialDistribution.csv',Afile='transitionMatrix.csv',N=1000))\n# ([[0.8063492063492064, 0.19365079365079366], [0.09064327485380116, 0.9093567251461988]], [0, 1])\n\n","sub_path":"saa1_psb31_2.py","file_name":"saa1_psb31_2.py","file_ext":"py","file_size_in_byte":1697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"207796159","text":"try:\n    import neovim\n    import os\n    sock = os.environ.get('NVIM_LISTEN_ADDRESS')\n    vim = neovim.attach('socket', sock)\nexcept:\n    import vim\n\nfrom pudb.settings import load_breakpoints\nfrom pudb import NUM_VERSION\n\nfilename = vim.eval('expand(\"%:p\")')\n\nargs = () if NUM_VERSION >= (2013, 1) else (None,)\nbps = load_breakpoints(*args)\n\nfor bp in bps:\n    if bp[0] != filename:\n        continue\n\n    sign_id = vim.eval(\"s:next_sign_id\")\n    vim.command(\"sign place %s line=%s name=PudbBreakPoint file=%s\"\n                % (sign_id, bp[1], filename))\n    vim.eval(\"add(b:pudb_sign_ids, s:next_sign_id)\")\n    vim.command(\"let s:next_sign_id += 1\")\n","sub_path":"plugin/update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"555156388","text":"# -*- coding: utf-8 -*-\n\nfrom django.db import models\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom wagtail.contrib.settings.models import BaseSetting, register_setting\n\n\n@register_setting\nclass FaviconSettings(BaseSetting):\n    favicon_small = models.ImageField(\n        _('favicon small'),\n        help_text=_(\"Your favicon, 32x32 .ico file\"),\n        blank=True)\n    favicon_large = models.ImageField(\n        _('favicon large'),\n        help_text=_(\"Your favicon, 128x128 .png file\"),\n        blank=True)\n","sub_path":"wagtail_favicon/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"85035011","text":"from django.urls import reverse\nfrom rest_framework import status\nfrom posts.models import Post\nfrom users.models import CustomUser\nfrom .models import Comment\nfrom rest_framework.test import APITestCase\nimport json\n\n\nclass CommentTests(APITestCase):\n    def setUp(self):\n        self.user = CustomUser.objects.create(username='zach')\n        self.post = Post.objects.create(title='', content='')\n        self.client.force_authenticate(self.user)\n\n    def test_create_comment(self):\n        url = reverse('comments-list')\n        data = {\n            'content': 'Test comment!',\n            'post': self.post.id\n        }\n\n        res = self.client.post(url, data=json.dumps(data), content_type='application/json')\n        self.assertEqual(res.status_code, status.HTTP_201_CREATED, 'Unable to create comment!')\n\n        comment_objects = Comment.objects.all()\n        self.assertGreater(comment_objects.count(), 0, 'No comments in database!')\n\n    def test_empty_comment(self):\n        url = reverse('comments-list')\n        data = {\n            'content': '',\n            'post': self.post.id\n        }\n\n        res = self.client.post(url, data=json.dumps(data), content_type='application/json')\n        self.assertNotEqual(res.status_code, status.HTTP_201_CREATED, 'Why was this empty comment created??')\n\n        comment_objects = Comment.objects.all()\n        self.assertEqual(comment_objects.count(), 0, 'Should be zero comments')\n\n    def test_invalid_post_comment(self):\n        url = reverse('comments-list')\n        data = {\n            'content': 'Test comment!',\n            'post': -1\n        }\n\n        res = self.client.post(url, data=json.dumps(data), content_type='application/json')\n        self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST, 'Why was this shitty request accepted?')\n\n        comment_objects = Comment.objects.all()\n        self.assertEqual(comment_objects.count(), 0, \"There shouldn't be any comments in the database\")\n\n    def test_delete_comment(self):\n        # First, let's actually create the comment\n        url = reverse('comments-list')\n        data = {\n            'content': 'Test comment!',\n            'post': self.post.id\n        }\n\n        # Send the request\n        res = self.client.post(url, data=json.dumps(data), content_type='application/json')\n        self.assertEqual(res.status_code, status.HTTP_201_CREATED, 'Unable to create comment!')\n\n        # Okay, now that we're created the comment, let's make sure it exists...\n        comment_objects = Comment.objects.all()\n        self.assertGreater(comment_objects.count(), 0, 'No comments in database!')\n\n        # Let's get the specific comment we just created... pk of 1 should suffice\n        detail_url = reverse('comments-detail', kwargs={'pk': 1})\n        res = self.client.delete(detail_url)\n        self.assertLess(res.status_code, 300, 'Unable to delete comment!')\n\n        # Let's make sure that it's deleted now\n        comment_objects = Comment.objects.all()\n        self.assertEqual(comment_objects.count(), 0, 'There shouldn\\'t be any comments! But there are...')\n","sub_path":"comments/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":3094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"207769110","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Feb  4 17:31:40 2019\r\n\r\n@author: jbgab\r\n\"\"\"\r\n\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Nov 23 11:37:23 2018\r\n\r\n@author: jbgab\r\n\"\"\"\r\n\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\n\r\ndef spatial(x,y,c,title=''):\r\n    maxX=max(x)\r\n    minX=min(x)\r\n    xdev=(maxX-minX)\r\n    #xlim=xdev*.05\r\n    xbound=xdev\r\n    while xbound < 5:\r\n        xbound+=xdev*(5-int(xbound))\r\n    xbound=int(xbound)\r\n    \r\n    maxY=max(y)\r\n    minY=min(y)\r\n    ydev=(maxY-minY)\r\n    #ylim=xdev*.05\r\n    #ybound=ydev\r\n    ybound=xbound*(ydev/xdev)\r\n    ybound=int(ybound)\r\n    \r\n    figsize=(xbound,ybound)\r\n    plt.title=(title)\r\n\r\n    plt.figure(figsize=figsize,dpi=100)\r\n    plt.xlim(minX-xdev,maxX+xdev)\r\n    plt.ylim(minY-ydev,maxY+ydev)\r\n\r\n    plt.scatter(x,y,s=10,c=c)\r\n    plt.ticklabel_format(style='plain',axis='both', useOffset=False)\r\n    \r\n    plt.axis('equal')\r\n    plt.grid(True)\r\n    plt.show()\r\n\r\n\r\n#%%  Example of use\r\n\r\n\r\nlonColumn='Lon'\r\nlatColumn='Lat'\r\n\r\nsoilsensors = pd.read_csv('eucfacesoilsensorfiles\\\\convertedLocations.csv',index_col=0)\r\nsoilsensors=soilsensors.rename(columns={'Latitude':latColumn,'Longitude':lonColumn})\r\n#soilsensorsCleaned=soilsensors.loc[[lonColumn,latColumn],:].values.astype(float)\r\nsoils = pd.read_csv('sensorLocations.csv_out.csv',index_col=0)\r\nringColler=pd.read_csv('out_EucFACE_exact_mapgrid_ring_coller.csv',index_col=0)\r\nlocations = pd.concat([soilsensors, soils,ringColler], ignore_index=True,sort=False)\r\nlocations.index=locations.UniqueID\r\n\r\nimport glob\r\nimport re\r\n#files like FACE_R1_B1_SoilVars_20180831.dat\r\nfilePath='eucfacesoilsensorfiles\\\\'\r\noutPath='eucfacesoilsensorFigures\\\\'\r\nfiles = glob.glob(filePath+'*SoilVars*.dat')#uses psuedo regex to access a list of files\r\nprint(files)\r\n\r\ntesting=True\r\n\r\ni=0\r\nwhile (i < len(files)):\r\n    title=files[i].strip(filePath)\r\n    file=pd.read_csv(files[i], header=[1],index_col=0)#read file i\r\n    plotterVals=file.loc[file.index[2]:,file.columns[1]:].astype(float)\r\n    plotterVals.index=pd.to_datetime(plotterVals.index)\r\n    ring=float(re.findall('R([0-9])',title)[0])\r\n    currentlocations=locations[locations.loc[:,'Ring']==ring]\r\n    minimum=dict()\r\n    maximum=dict()\r\n    deviation=dict()\r\n    for column in plotterVals:\r\n        minimum[column]=plotterVals[column].min()\r\n        maximum[column]=plotterVals[column].max()\r\n        deviation[column]=plotterVals[column].max()-plotterVals[column].min()\r\n    x=[]\r\n    y=[]\r\n    c=[]\r\n    for timestamp,data in plotterVals.iterrows():\r\n        i=0\r\n        while i<len(data):\r\n            try:\r\n                sensorname=data.index[i]\r\n                normalizedsensor=(data[i]-minimum[sensorname])/deviation[sensorname]\r\n                x.append(currentlocations.loc[sensorname,lonColumn].values[0].astype(float))\r\n                y.append(currentlocations.loc[sensorname,latColumn].values[0].astype(float))\r\n                c.append(normalizedsensor)\r\n            except:\r\n                e=1\r\n            i+=1\r\n        spatial(x,y,c,title=str(timestamp))\r\n        \r\n        \r\n    i+=1\r\n\r\n","sub_path":"spatial.py","file_name":"spatial.py","file_ext":"py","file_size_in_byte":3089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"106816527","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nCreated on Thu Aug 18 14:26 2018\n\n@author: bdus\n@ Auto Encoder Dense\n\n@env py3.x\n\"\"\"\n\n\nimport tensorflow as tf\nimport input_data\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n#para\n\nlearning_rate = 0.01\ntraining_epochs = 10\nbatch_size = 256\n\n## dataset\n\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n\n## model\ndef weight_variable(shape):\n    tmpVar = tf.truncated_normal(shape, stddev=0.1)\n    return tf.Variable(tmpVar)\n\n\ndef bias_variable(shape):\n    tmpVar = tf.constant(0.1,shape=shape)\n    return tf.Variable(tmpVar)\n\ndef encoder(x):\n    #Dense hidden layer 1\n    W1 = weight_variable([784,256])\n    b1 = bias_variable([256])\n    h1 = tf.nn.sigmoid(tf.add(tf.matmul(x,W1),b1))\n    #2\n    W2 = weight_variable([256,128])\n    b2 = bias_variable([128])\n    return tf.nn.sigmoid(tf.add(tf.matmul(h1,W2),b2))\n\ndef decoder(x):\n    #Dense hidden layer 1\n    W1 = weight_variable([128,256])\n    b1 = bias_variable([256])\n    h1 = tf.nn.sigmoid(tf.add(tf.matmul(x,W1),b1))\n    #2\n    W2 = weight_variable([256,784])\n    b2 = bias_variable([784])\n    return tf.nn.sigmoid(tf.add(tf.matmul(h1,W2),b2))\n\nx = tf.placeholder(\"float\",[None,784])\n#y = tf.placeholder(\"float\",[None,128])\n#x_hat = tf.placeholder(\"float\",[None,784])\n\ny = encoder(x)\nx_hat = decoder(y)\n\n#loss\n\nloss = tf.reduce_mean( tf.pow(x-x_hat,2) )\n\n#train\n\noptimizer = tf.train.AdamOptimizer(learning_rate)\ntrain = optimizer.minimize(loss)\n\n## run\n\ninit = tf.global_variables_initializer()\nsess = tf.Session()\nsess.run(init)\n\nepoch_list = []\nloss_list = []\nacc_list = []\n\ntotal_batch = int(mnist.train.num_examples/batch_size)\nfor epoch in range(training_epochs):\n    for i in range(total_batch):\n        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n        sess.run(train,feed_dict={x:batch_xs})\n        if i%200 == 0:\n            tmpi = epoch*total_batch+i\n            myloss = sess.run(loss,feed_dict={x:batch_xs})\n            epoch_list.append(tmpi)\n            loss_list.append(myloss)\n            print(\"step %d ,loss %g\"%(tmpi,myloss))\n\nencode_decode = sess.run(x_hat,feed_dict={x:mnist.test.images[:10]})\n\n\n\nplt.figure(1)\nplt.plot(epoch_list,loss_list/np.max(loss_list),'r-',label='loss')\nplt.legend()\n\n\nf, a = plt.subplots(2,10,figsize=(10,2))\nfor i in range(10):\n    a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))\n    a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))\n\nplt.show()\nplt.waitforbuttonpress()\n\n","sub_path":"mnist/mnist5_0_AutoEncoder.py","file_name":"mnist5_0_AutoEncoder.py","file_ext":"py","file_size_in_byte":2477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"417866135","text":"def FizzBuzz():\n    #variables\n    num1=3\n    num2=5\n    range_start=1\n    range_end=101\n\n    for i in xrange(range_start,range_end):\n        if i%num1==0 and i%num2==0:\n            print('FizzBuzz')\n        elif i%num1==0:\n            print('Fizz')\n        elif i%num2==0:\n            print('Buzz')\n        else:\n            print(i)\n","sub_path":"students/rob_sanchez/Lesson 2/Fizz_Buzz.py","file_name":"Fizz_Buzz.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"237693350","text":"# # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n# The goal of this python file is to get the email        #\n# address of activated users.                             #\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n\nimport xlrd\n\n\n# Define the function to process the date inside the excel file\ndef data_processing(filename, sheet_number):\n\n    workbook = xlrd.open_workbook(filename)\n\n    # Get the name of the sheets\n    sheet_names = workbook.sheet_names()\n\n    # Change to the sheet you want to use\n    working_sheet = workbook.sheet_by_index(sheet_number)\n\n    # Read the 'Name' 'email address' 'Status' row\n    for i in range(1):\n        Name = working_sheet.col_values(i)[1:]\n        email_address = working_sheet.col_values(i + 1)[1:]\n        Status = working_sheet.col_values(i + 2)[1:]\n\n    # Check the status of users to see whether to send the email to him/her or not\n    index = [i for i, j in enumerate(Status) if j == 'activated']\n\n    # Get the users' email address whose status is activated\n    receiver = []\n    for i in index:\n        receiver.append(email_address[i])\n\n    return sheet_names, receiver\n","sub_path":"Data_Processing.py","file_name":"Data_Processing.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"373839195","text":"# Iterative DFS:\nclass Solution(object):\n    def findTarget(self, root, k):\n        \"\"\"\n        :type root: TreeNode\n        :type k: int\n        :rtype: bool\n        \"\"\"\n        stack, seen = [root], set()\n        \n        while stack:\n            curr = stack.pop()\n            # If we've seen k - curr.val,\n            # we have k - curr.val + curr.val = k\n            if k - curr.val in seen:\n                return True\n            seen.add(curr.val)\n            \n            # Visit children\n            if curr.left:\n                stack.append(curr.left)\n            if curr.right:\n                stack.append(curr.right)\n                \n        return False\n\n# inorder traversal, then two pointers\nclass Solution(object):\n    def findTarget(self, root, k):\n        \"\"\"\n        :type root: TreeNode\n        :type k: int\n        :rtype: bool\n        \"\"\"\n        def inorder(root):\n            if not root: return []\n            return inorder(root.left) + [root.val] + inorder(root.right)\n        arr = inorder(root)\n        p1, p2 = 0, len(arr)-1\n        while p1 < p2:\n            if arr[p1] + arr[p2] < k:\n                p1 += 1\n            elif arr[p1] + arr[p2] == k:\n                return True\n            else:\n                p2 -= 1\n        return False","sub_path":"653. Two Sum IV - Input is a BST.py","file_name":"653. Two Sum IV - Input is a BST.py","file_ext":"py","file_size_in_byte":1274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"158343138","text":"class Solution:\n    def kSmallestPairs(self, nums1: List[int], nums2: List[int], k: int) -> List[List[int]]:\n        #0221  16:03 复杂度高：构建字典，根据大小排序\n        dic=collections.defaultdict(list)\n        for i in range(len(nums1)):\n            for j in range(len(nums2)):\n                dic[nums1[i]+nums2[j]].append([nums1[i],nums2[j]])\n        res=[]\n        count=0\n        for s in sorted(list(dic.keys())):#升序的\n            for j in range(len(dic[s])):\n                res.append(dic[s][j])\n                count+=1\n                if count==k:#也可能达不到\n                    return res\n        return res\n    \n'''给定两个以升序排列的整形数组 nums1 和 nums2, 以及一个整数 k。\n\n定义一对值 (u,v)，其中第一个元素来自 nums1，第二个元素来自 nums2。\n\n找到和最小的 k 对数字 (u1,v1), (u2,v2) ... (uk,vk)。\n\n示例 1:\n\n输入: nums1 = [1,7,11], nums2 = [2,4,6], k = 3\n输出: [1,2],[1,4],[1,6]\n解释: 返回序列中的前 3 对数：\n     [1,2],[1,4],[1,6],[7,2],[7,4],[11,2],[7,6],[11,4],[11,6]\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/find-k-pairs-with-smallest-sums\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。'''","sub_path":"leetcode_solution/leetcode类别/4堆median/中等/.ipynb_checkpoints/373 查找和最小的K对数字-checkpoint.py","file_name":"373 查找和最小的K对数字-checkpoint.py","file_ext":"py","file_size_in_byte":1302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"523616390","text":"# -*- coding: utf-8 -*-\r\n# == Tank Battle of SESSDSA course ==\r\n# == 2016 SESS.PKU.EDU.CN\r\n# == Data Structure and Algorithm\r\n# == Program Practice Group Work\r\n\r\nf=open('win.txt','w')\r\nimport sys\r\nimport subprocess\r\nfrom public import *\r\nimport time\r\nimport xmlrpc.client\r\n\r\nif len(sys.argv) > 1:  # 有命令行参数，代替round.ini\r\n    iniFilename = sys.argv[1]\r\nelse:\r\n    iniFilename = 'round.ini'\r\nbattleRound = Round(iniFilename)  # 一轮中的多场战斗\r\nserver = xmlrpc.client.ServerProxy(battleRound.battleServerURI, allow_none=True)\r\nbattleNumber = len(battleRound.battles)\r\nbattleCount = 1\r\nwaitOnAir = battleRound.waitOnAir\r\nplayerPID = list()\r\n\r\nprint(\"Starting tank.sessdsa Simulator...\")\r\nprint(\"Config file: %s\" % iniFilename)\r\nprint(\"Server URI : %s\" % battleRound.battleServerURI)\r\nprint(\"Battle Number: %d\" % battleNumber)\r\nprint(\"Battle On Air: %s\" % waitOnAir)\r\n\r\nprint('===ROUND BEGIN===')\r\nfor battle in battleRound.battles:\r\n    players = dict()\r\n    for player in battle.players.values():\r\n        players[player.color] = player.playerID\r\n    battle.setBattleFieldConfig()\r\n    battleData = BattleData(battle.UUID, players,\r\n                            {key:dict(battle.battleConfig[key]) for key in dict(battle.battleConfig)})\r\n\r\n    # 0. 如果需要直播，则等待可视化程序放行\r\n    if waitOnAir:\r\n        print('   Waiting for vtankonair...', end='', flush=True)\r\n        server.setBattleOnAir(False)\r\n        while not server.getBattleOnAir():\r\n            print('.', end='', flush=True)\r\n            time.sleep(0.5)\r\n        print('OK!')\r\n\r\n    # 1. 开始战斗\r\n    print('1> [%d/%d]Starting battle...(%s)' % (battleCount, battleNumber, battle.UUID), end='')\r\n    server.startBattle(battle.UUID, battleData)\r\n    print('  OK!')\r\n\r\n    # 2. 启动所有player进程\r\n    print('2> Staring player...')\r\n    playerPID.clear()\r\n    for player in battle.players.values():\r\n        print('    >>%s:%s' % (player.color, player.playerID))\r\n        #pid = os.popen('\"%s\" player.py %s %s %s %s' % (sys.executable, player.color, player.playerID,\r\n        #               battleRound.battleServerURI, battle.configFile))\r\n        proc = subprocess.Popen([sys.executable, \"player.py\", player.color, player.playerID,\r\n                                battleRound.battleServerURI, battle.configFile], shell=False)\r\n    #        pid= os.fork()\r\n    #        if pid== 0:\r\n    #            # 子进程，启动player进程\r\n    #            # 启动player进程：python player.py red alpha http://localhost:8000/tank.sessdsa battle.ini\r\n    #            os.execl(sys.executable, 'python', 'player.py', player.color, player.playerID,\r\n    #                                                        battleRound.battleServerURI, battle.configFile)\r\n    #        else:\r\n    #            # 父进程，将pid加入，以便后面清理\r\n        playerPID.append(proc)\r\n    print('    OK!')\r\n\r\n    # 3. 等待所有player注册完毕\r\n    print('3> Waiting for player submit tanks...', flush=True)\r\n    while not server.allPlayerReady(battle.UUID):\r\n        print('...', end='', flush=True)\r\n        time.sleep(0.5)\r\n    print('Simulator OK!')\r\n\r\n    # 4. 取回坦克布局\r\n    print('4> Getting player tanks...', end='', flush=True)\r\n    playerTanks = server.getPlayerTanks(battle.UUID)\r\n    print('OK!')\r\n\r\n    # 5. 初始化战场\r\n    print('5> Setting up battle field...', end='', flush=True)\r\n    tanks = list()\r\n    for playerID in playerTanks:\r\n        for tankData in playerTanks[playerID]:\r\n            tanks.append(Tank(tankData[\"playerID\"], Position(tankData[\"position\"][\"x\"], tankData[\"position\"][\"y\"]),\r\n                              tankData[\"radarDirection\"], tankData[\"radarAngle\"], tankData[\"HP\"], tankData[\"fuel\"],\r\n                              tankData[\"shellPower\"], tankData[\"UUID\"]))\r\n    battle.battleField.deployTanks(tanks)\r\n    battle.battleField.deployMines()\r\n    print('OK!')\r\n\r\n    # 6. 向服务器更新战场数据\r\n    print('6> Updating server battle field data...', end='', flush=True)\r\n\r\n    server.initBattleField(battle.UUID, BattleFieldData(battle.battleField))\r\n    print('OK!')\r\n\r\n    # 7. 进入主循环\r\n    print('7> Entering main loop...')\r\n    while not battle.isOver():\r\n        commandBuffer = server.getPlayerCommands(battle.UUID)\r\n        battle.battleField.submitCommands(commandBuffer)\r\n        battle.battleField.timeGo()\r\n        battleData.winner = battle.battleField.winner\r\n        battleData.winType = battle.battleField.winType\r\n        battleData.winInfo = battle.battleField.winInfo\r\n        battleData.duration = battle.battleField.timeTick\r\n        battleData.battleFieldData = [BattleFieldData(battle.battleField)]\r\n        server.updateBattleData(battle.UUID, battleData)  # 服务器更新战场数据\r\n        print('%3d|' % battleData.duration, end='', flush=True)\r\n        time.sleep(0.5)  # delay if needed\r\n    print('\\n8> [%d/%d]Battle over! winner=%s Ending/Saving...' % (battleCount, battleNumber, battle.getWinInfo()), end='', flush=True)\r\n    Playerlist=list(battle.players.keys())\r\n    f.write('winner=\\'%s\\' (\\'%s\\' vs \\'%s\\')   %s\\n'%(battle.getWinner(),Playerlist[0],Playerlist[1],battle.getWinInfo()))\r\n    server.endBattle(battle.UUID)\r\n    time.sleep(1)  # 等待player子进程结束退出\r\n    # 杀死所有可能的子进程\r\n    for proc in playerPID:\r\n        proc.kill()\r\n        #os.kill(pid, 9)\r\n    print('OK!\\n========================================')\r\n    battleCount += 1\r\nprint(\"===ROUND END===\")\r\n# 0. 如果是直播，等待可视化程序放行结束\r\nif waitOnAir:\r\n    print('   Waiting for vtankonair...', end='', flush=True)\r\n    server.setBattleOnAir(False)\r\n    while not server.getBattleOnAir():\r\n        print('.', end='', flush=True)\r\n        time.sleep(0.5)\r\n    print('OK! Bye!')\r\nsys.exit(0)\r\n","sub_path":"simulator.py","file_name":"simulator.py","file_ext":"py","file_size_in_byte":5838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"594877243","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[3]:\n\n\n# writing functions - A leap year\ndef isYearLeap(year):\n    if year < 1582:\n        return None\n    elif year % 4 != 0:\n        return False\n    elif year % 100 != 0:\n        return True\n    elif year % 400 != 0:\n        return False\n    else:\n        return True\ntestData = [1580,1900, 2000, 2016, 1987]\ntestResults = [None,False, True, True, False]\nfor i in range(len(testData)):\n\tyr = testData[i]\n\tprint(yr,\"->\",end=\"\")\n\tresult = isYearLeap(yr)\n\tif result == testResults[i]:\n\t\tprint(\"OK\")\n\telse:\n\t\tprint(\"Failed\")\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"testLeapYear.py","file_name":"testLeapYear.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"182724467","text":"# Ivan Carvalho\n# Solution to https://www.beecrowd.com.br/judge/problems/view/1436\n#!/usr/bin/env python\n# encoding : utf-8\nordem = int(input())\n\n\ndef mediana(matriz):\n    array = sorted(matriz)\n    tamanho = len(array)\n    if tamanho % 2 == 0:\n        return int(array[tamanho / 2] + array[1 + tamanho / 2])\n    else:\n        return int(array[tamanho / 2])\n\n\nfor i in range(ordem):\n    entrada = [int(k) for k in input().split(\" \") if k != \"\"]\n    print(\"Case %d: %d\" % (i + 1, mediana(entrada[1:])))\n","sub_path":"beecrowd/1436.py","file_name":"1436.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"414614479","text":"#!/usr/bin/env py.test-3\n\nimport os\nimport pytest\nimport re\n\n\ndef test_add_file_to_default_repo(cli, gitrepo):\n    src = 'default-git-repository.tar.gz'\n\n    with gitrepo.from_archive(src) as git:\n        # configure git user\n        git.config('--local', 'user.email', 'me@example.com')\n        git.config('--local', 'user.name', 'pytest robot')\n        print('Using git {}'.format(git.version))\n\n        # git status\n        ex, out, err = git.status()\n        assert ex == 0\n        assert 'On branch' in out\n\n        # create new file\n        newfile = os.path.join(git.root, 'newfile')\n        with open(newfile, 'w') as fd:\n            fd.write('#!/bin/sh\\necho \"Hello World\"\\n')\n\n        # adding file to repository\n        assert git.branch == 'master'\n        git.add(newfile)\n        git.commit('-m', 'Adding a new file')\n\n        # checking log\n        ex, out, err = git.log()\n        assert ex == 0\n        assert 'Adding a new file' in out\n\n\n@pytest.mark.tt_11776\ndef test_status_output(cli, gitrepo):\n    # we mock an existing repository by loading it from the default archive\n    with gitrepo.from_archive(gitrepo.default) as git:\n        # now we work with \"existing\" repository with path given in git.root\n        with gitrepo.in_folder(git.root) as git:\n            ex, out, err = git.status()\n            assert ex == 0\n            # git >=v2.9.3 uses \"working tree\",\n            # before it was \"working directory\"\n            regex = re.compile(r\"nothing to commit, working (tree|directory)\")\n            assert re.search(regex, out)\n\n            ex, out, error = git.status('--porcelain=v2')\n            assert out == \"\"\n            assert error == \"\"\n            assert ex == 0\n\n            # create new file\n            newfile = os.path.join(git.root, 'newfile')\n            with open(newfile, 'w') as fd:\n                fd.write('#!/bin/sh\\necho \"Hello World\"\\n')\n\n            ex, out, error = git.status('--porcelain=v2')\n            assert out == '''? newfile\\n'''\n            assert error == \"\"\n            assert ex == 0\n","sub_path":"t/test_git.py","file_name":"test_git.py","file_ext":"py","file_size_in_byte":2053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"123691126","text":"# Standard Library\nimport time\n\n# Third Party Stuff\nimport splinter\nfrom PIL import Image\n\n\ndef is_transparent(image):\n    \"\"\"\n    Check to see if an image is transparent.\n    \"\"\"\n    if not isinstance(image, Image.Image):\n        # Can only deal with PIL images, fall back to the assumption that that\n        # it's not transparent.\n        return False\n    return (image.mode in ('RGBA', 'LA') or\n            (image.mode == 'P' and 'transparency' in image.info))\n\n\ndef webkit2png(url, image_file_path, browser=None, wait_time=0):\n    new_browser = False\n    try:\n        if not browser:\n            browser = splinter.Browser('phantomjs')\n            new_browser = True\n        browser.visit(url)\n        if browser.status_code.is_success():\n            if wait_time > 0:\n                time.sleep(wait_time)\n            browser.driver.save_screenshot(image_file_path)\n            image = Image.open(image_file_path)\n            image.load()\n            if is_transparent(image) and False:\n                no_alpha = Image.new('L', image.size, (255))\n                no_alpha.paste(image, mask=image.split()[-1])\n            else:\n                no_alpha = image.convert('L')\n            # Convert to black and white imageage.\n            bw = no_alpha.convert('L')\n            # bw = bw.filter(ImageFilter.MedianFilter)\n            # White background.\n            bg = Image.new('L', image.size, 255)\n            bbox = ImageChops.difference(bw, bg).getbbox()\n            if bbox:\n                image = image.crop(bbox)\n            image.save(image_file_path)\n    finally:\n        if new_browser:\n            browser.quit()\n","sub_path":"src/html2latex/webkit2png.py","file_name":"webkit2png.py","file_ext":"py","file_size_in_byte":1631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"453811874","text":"#! /usr/bin/env python\n## Author : Atif Haider <mail@atifhaider.com>\n\nimport re\n\nfrom wtforms import *\nfrom wtforms.validators import ValidationError\n\n# Regex borrowed from Django forms\nemail_re = re.compile(\n    r\"(^[-!#$%&'*+/=?^_`{}|~0-9A-Z]+(\\.[-!#$%&'*+/=?^_`{}|~0-9A-Z]+)*\"  # dot-atom\n    r'|^\"([\\001-\\010\\013\\014\\016-\\037!#-\\[\\]-\\177]|\\\\[\\001-011\\013\\014\\016-\\177])*\"' # quoted-string\n    r')@(?:[A-Z0-9-]+\\.)+[A-Z]{2,6}$', re.IGNORECASE)  # domain\n\nurl_re = re.compile(\n    r'^(https?://)?' # http:// or https://\n    r'(?:(?:[A-Z0-9-]+\\.)+[A-Z]{2,6})' #domain...\n    r'(?:/?|/\\S+)$', re.IGNORECASE)\n\ndef is_valid_email(form, field):\n    \"\"\"Validates email field\n    \"\"\"\n    email = field.data\n    if email:\n        if not email_re.match(email):\n            raise ValidationError(u'Enter a valid email')\n    else:\n        raise ValidationError(u'This field is required')\n    return field\n\ndef is_valid_url(form, field):\n    \"\"\"Validates url field\n    \"\"\"\n    url = field.data\n    if url:\n        if not url_re.match(url):\n            raise ValidationError(u'Enter a valid url')\n    return field\n\n\nclass RegistrationForm(Form):\n    \"\"\"A simple class for registration form\n    \"\"\"\n    username = TextField('Username', [validators.length(min=4, max=25)])\n    password = PasswordField('Password', [validators.length(min=5, max=25)])\n    first_name = TextField(u'First Name', validators=[validators.required()])\n    last_name  = TextField(u'Last Name', validators=[validators.optional()])\n    email = TextField('Email Address', [validators.Email(message=\"Enter a valid email address\")])\n    avatar = FileField(u'Image File')\n    # Could have used URL validators but doesn't work\n    website = TextField('Website', [is_valid_url])\n    birthday = DateTimeField('Your Birthday',  validators=[validators.optional()], format='%m/%d/%y')\n\n","sub_path":"wtforms-test/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"207236177","text":"import time\nimport sys\nfrom models.modules.callouts import Callout\nfrom models.modules.columns import Columns\nfrom models.modules.moduleEnum import ModuleEnum\nfrom models.modules.html import HTML\nfrom models.modules.image import Image\nfrom models.modules.tiles import Tiles\nfrom models.modules.video import Video\nfrom selenium import webdriver\nfrom selenium.webdriver.support.select import Select\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.common.by import By\n\n# All methods below are designed to automatically build a Wordpress site\n# given a list of 'Site' objects generated by running migrate.py.\n# This is accomplished through robotic automation using Selenium.\n# In its current state, this code does not work. Roadblocks were encountered when\n# trying to edit the 'Header' input field in the Wordpress modules. \n# Below is a list of known issues that were encountered during development and testing\n# of this process:\n#   - 'Header' input field of all modules is disabled in the HTML. Thus the value cannot be updated via automation.\n#       Any attempts to circumvent this were unsuccessful.\n#   - HTML ID's of all modules' 'Content' iFrames are always different. There are strings of numbers within the ID that \n#       change whenever a new module is added. This makes selecting them by ID difficult,\n#       and requires the use of regular expressions by XPATH. That being said, the numbers do not change significantly\n#       which further complicates the use of regular expressions as there is no clear pattern to search for.\n#       Also, the numbers do not simply increment in value as a new one is added, as we initially thought. \n#       This is not a problem when adding a single module, but the purpose of this process is to automate the build \n#       of a whole site, and pages within a site generally have more than one module.\n#\n# The above is a summary of the main difficulties that were encountered. \n# If these issues can be solved then this solution will be well on its way to completion.\n#\n__author__ = 'Berkay Kaplan, Anthony Vanadia'\n__status__ = 'Prototype'\n__date__ =   '11/2019'\n\n'''\nAdds a single Callout module to a Wordpress page.\n\nParameters:\n    callout: Callout -- the callout object to be added. Contains fields 'callout.header' and 'callout.content'\n    driver: WebDriver -- Selenium 'WebDriver' object necessary for browser manipulation.\n\nRequires -- the 'callout' parameter be a well-formed Callout object\n'''\ndef addCallout(callout, driver):\n    driver.find_element_by_link_text(\"Add module\").click()\n    driver.find_element_by_link_text(\"Callouts\").click()\n    driver.switch_to_frame(driver.find_element_by_id('acf-editor-121_ifr'))\n    driver.find_element_by_id('tinymce').find_element_by_tag_name('p').send_keys(callout.content)\n    driver.switch_to.default_content()\n\n'''\nAdds a single Column module to a Wordpress page.\n\nParameters:\n    column: Columns -- the column object to be added. Contains fields 'column.headers', \n            'column.contents', and 'column.tag'.\n    driver: WebDriver -- Selenium 'WebDriver' object necessary for browser manipulation.\n\nRequires -- the 'column' parameter be a well-formed Columns object \n'''\ndef addColumn(column, driver):\n    driver.find_element_by_link_text(\"Add module\").click()\n    driver.find_element_by_link_text(\"Text Columns (Content)\").click()\n    driver.switch_to_frame(driver.find_element_by_id('acf-editor-121_ifr'))\n    driver.find_element_by_id('tinymce').find_element_by_tag_name('p').send_keys(column.content)\n    driver.switch_to.default_content()\n\n'''\nAdds a single Html module to a Wordpress page.\n\nParameters:\n    html: HTML -- the html element to be added. Contains fields 'html.content'.\n    driver: WebDriver -- Selenium 'WebDriver' object necessary for browser manipulation.\n\nRequires -- the 'html' parameter be a well-formed Html object.\n'''\ndef addHtml(html, driver):\n    driver.find_element_by_link_text(\"Add module\").click()\n    driver.find_element_by_link_text(\"Custom HTML Embed\").click()\n    driver.switch_to_frame(driver.find_element_by_id('acf-editor-121_ifr'))\n    driver.find_element_by_id('tinymce').find_element_by_tag_name('p').send_keys(html.content)\n    driver.switch_to.default_content()\n\n'''\nAdds a single Image module to a Wordpress page.\n\nParameters:\n    image: Image -- the image element to be added. Contains fields 'image.tag', 'image.header', 'image.content'.\n    driver: WebDriver -- Selenium 'WebDriver' object necessary for browser manipulation.\n\nRequrires -- the 'image' parameter be a well-formed Image object\n'''\ndef addImage(image, driver):\n    driver.find_element_by_link_text(\"Add module\").click()\n    driver.find_element_by_link_text(\"Image\").click()\n    driver.switch_to_frame(driver.find_element_by_id('acf-editor-121_ifr'))\n    driver.find_element_by_id('tinymce').find_element_by_tag_name('p').send_keys(image.content)\n    driver.switch_to.default_content()\n\n'''\nAdds a single Tile module to a Wordpress page.\n\nParameters:\n    tiles: Tiles -- the tile element to be added. Contains fields 'tiles.header' and tiles.content.\n    driver: WebDriver -- Selenium 'WebDriver' object necessary for browser manipulation.\n\nRequires -- the 'tiles' parameter be a well-formed Tiles object.\n'''\ndef addTiles(tiles, driver):\n    driver.find_element_by_link_text(\"Add module\").click()\n    driver.find_element_by_link_text(\"Tiles\").click()\n    driver.switch_to_frame(driver.find_element_by_id('acf-editor-121_ifr'))\n    driver.find_element_by_id('tinymce').find_element_by_tag_name('p').send_keys(tiles.content)\n    driver.switch_to.default_content()\n\n'''\nAdds a single Video module to a Wordpress page.\n\nParameters:\n    video: Video -- the video element to be added. Contains fields 'video.tag', 'video.content',\n             'video.title', 'video.duration', and  'video.link'.\n    driver: WebDriver -- Selenium 'WebDriver' object necessary for browser manipulation.\n\nRequires -- the 'video' parameter be a well-formed Video object.\n'''\ndef addVideo(video, driver):\n    driver.find_element_by_link_text(\"Add module\").click()\n    driver.find_element_by_link_text(\"Video (Full-width / Video list)\").click()\n    driver.switch_to_frame(driver.find_element_by_id('acf-editor-121_ifr'))\n    driver.find_element_by_id('tinymce').find_element_by_tag_name('p').send_keys(video.content)\n    driver.switch_to.default_content()\n\n'''\nIterates through all modules for a given page and builds them out on Wordpress accordingly.\n\nParameters:\n    moduleList: List -- a list of all modules contained in a particular page.\n    driver: WebDriver -- Selenium 'WebDriver' object necessary for browser manipulation.\n\nRequires -- parameter 'moduleList' either be empty OR contain only elements consisting of \n            well-formed objects from the set of types: [Callout, Tiles, Columns, HTML, Image, Video]\n'''\ndef buildModules(moduleList, driver):\n    for each in moduleList:\n        if each.type == ModuleEnum.CALLOUT:\n            addCallout(each, driver)\n        elif each.type == ModuleEnum.COLUMN:\n            addColumn(each, driver)\n        elif each.type == ModuleEnum.HTML:\n            addHtml(each, driver)\n        elif each.type == ModuleEnum.IMAGE:\n            addImage(each, driver)\n        elif each.type == ModuleEnum.TILE:\n            addTiles(each, driver)\n        elif each.type == ModuleEnum.VIDEO:\n            addVideo(each, driver)\n        else:\n            print(\"No corresponding method for type of \" + each)\n\n'''\nInitializes the session, opens Wordpress in a browser and logs in automatically.\nThen begins the actual building process by calling available methods for Wordpress components.\n\nParameters:\n    site -- the Site object to be built out.\n\nRequires -- the 'site' parameter is a well-formed Site object\n'''\n\ndriver = webdriver.Chrome(executable_path = r'C:\\Users\\Paris\\Downloads\\chromedriver.exe')\nurl = 'https://mccofva.kaminotest.magellanhealth.com/wp-admin/post-new.php?post_type=page'\n# opens the url provided\ndriver.get(url)\n#Authentication to Wordpress\ntime.sleep(3)\n# Set the below username and password to your own credentials to automate the log-in process.\n# Be sure to delete/hide the password value if sharing this code.\ndriver.find_element_by_id(\"okta-signin-username\").send_keys('vanadiaa')\ndriver.find_element_by_id (\"okta-signin-password\").send_keys('Password1!')\ndriver.find_element_by_id(\"okta-signin-submit\").click()\ntime.sleep(10)\n\ndriver.find_element_by_id (\"title\").send_keys(\"This is the title\")\n\ndriver.find_element_by_link_text(\"Add module\").click()\ndriver.find_element_by_link_text(\"Callouts\").click()\ndriver.switch_to_frame(driver.find_element_by_id('acf-editor-121_ifr'))\ndriver.find_element_by_id('tinymce').find_element_by_tag_name('p').send_keys(\"Anthony is a genius\")\ndriver.switch_to.default_content()   ","sub_path":"RPA.py","file_name":"RPA.py","file_ext":"py","file_size_in_byte":8883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"572867144","text":"##                   pyKratos\n##\n##  License:\t\t BSD License\n##\t\t\t\t\t pyKratos default license: pyKratos/LICENSE\n##\n##  Main authors:    Andreas Riedl, Technical University of Munich, Chair of Structural Analysis\n##\n##  contributors:    Máté Pentek, M.Sc., Technical University of Munich, Chair of Structural Analysis\n##\n##  description:     bernoulli beam element for the linear calculation of the deformation\n\nfrom __future__ import print_function, absolute_import, division \nimport math\nfrom pyKratos import *\nfrom numpy import *\nfrom scipy import linalg\n\ndef Create(Id, prop, list_of_nodes):\n    geom = line2d.Line2D(list_of_nodes)   \n    return BernoulliBeamElement(Id, prop, geom)\n\n\nclass BernoulliBeamElement:\n    integration_order = 3  # this is like a c \"static variable\" one for all of the objects of this type\n\n    def __init__(self, Id, prop, geometry):\n        self.Id = Id\n        self.prop = prop\n        self.geometry = geometry\n        self.K_static_last = 0      # last calculated stiffness matrix\n        self.T = 0                  # transformation matrix\n        self.RHSstatic = 0          # load vector\n        self.length = 0\n\n        self.variables = []\n        \n        #mark as lagrangian all of the nodes in the element\n        for node in self.geometry:\n            node.SetSolutionStepValue(IS_LAGRANGIAN,0,True)\n\n    def GetDofsPerNode(self):\n        return 3\n    \n        #this _auxiliary function computes the stiffness contribution from bending\n    def _ComputeStiffnessContributionBending(self,ProcessInfo):\n        order = self.integration_order\n        [gpc, weights] = self.geometry.GaussPoints(order)\n        [Ns, dderivatives, ddderivatives] = self.geometry.ShapeFunctionsBending(gpc)        \n\n        RHSstatic = zeros(4)    # preallocate load vector\n        K = zeros((4, 4))       # preallocate stiffness matrix\n               \n        nnodes = self.geometry.GetNumberOfNodes()\n        number_of_gauss = len(Ns)\n        E = self.prop[YOUNG_MODULUS]    # in N/m^2\n        I = self.prop[MOMENT_INERTIA_AREA]    # in m^4     \n\n        # # preforming, imposed values - TO DO\n        # u0_1 = self.geometry.nodes[0].variables[0][\"DISPLACEMENT_X\"]\n        # w0_1 = self.geometry.nodes[0].variables[0][\"DISPLACEMENT_Y\"]\n        # u0_2 = self.geometry.nodes[1].variables[0][\"DISPLACEMENT_X\"]\n        # w0_2 = self.geometry.nodes[1].variables[0][\"DISPLACEMENT_Y\"]           \n\n        #loop over all gauss points                \n        for gauss in range(0, number_of_gauss):\n            N = Ns[gauss]\n            N_dderivate = dderivatives[gauss] #second derivate of N\n            GPW = weights[gauss]\n\n            # local stiffness matrix bending\n            for i in range(0,4):\n                for j in range(0,4):\n                    K[i,j] += N_dderivate[i] * N_dderivate[j] * E*I * GPW   #GPW = WGP * L                  \n        \n        return [K, RHSstatic]\n\n     #this _auxiliary function computes the stiffness contribution from stretching\n    def _ComputeStiffnessContributionLinear(self,ProcessInfo):\n        order = self.integration_order\n        [gpc, weights] = self.geometry.GaussPoints(order=1)\n        [Ns, derivatives] = self.geometry.ShapeFunctionsLinear(gpc)\n\n        RHSstatic = zeros(2)    # preallocate load vector\n        K = zeros((2, 2))       # preallocate stiffness matrix\n               \n        number_of_gauss = len(Ns)\n        E = self.prop[YOUNG_MODULUS]    # in N/m^2\n        A = self.prop[SECTION_TYPE]     # in m^               \n\n        #loop over all gauss points                \n        for gauss in range(0, number_of_gauss):\n            N = Ns[gauss]\n            N_derivate = derivatives[gauss]\n            GPW = weights[gauss]\n\n            # local stiffness matrix\n            for i in range(0,2):\n                for j in range(0,2):\n                    K[i,j] += N_derivate[i] * N_derivate[j] * E*A * GPW   #GPW = WGP * L               \n\n        return [K, RHSstatic]\n\n    \n    # this function computes the selfweight of the beam for the RHS\n    def _CalculateSelfWeight(self, ProcessInfo):\n\n        RHSstatic = zeros(6)    # preallocate load vector  \n\n        order = self.integration_order\n        [gpc, weights] = self.geometry.GaussPoints(order=1)\n        [Ns_bending, dderivatives, ddderivatives] = self.geometry.ShapeFunctionsBending(gpc)\n        [Ns_linear, derivatives] = self.geometry.ShapeFunctionsLinear(gpc)     \n               \n        number_of_gauss = len(gpc)\n        A = self.prop[SECTION_TYPE]     # in m^            \n\n         # try Self weight in X Dir\n        try:\n            force_x = self.prop[BODY_FORCE_X] #in m/s^2\n            density = self.prop[DENSITY]      #in kg/m^3         \n\n            qx_glob = density * A * force_x\n            \n        except:\n            qx_glob = 0\n            print(\"No Selfweight in X-Dir\")\n\n        # try Self weight in Y Dir\n        try:\n            force_y = self.prop[BODY_FORCE_Y] #in m/s^2\n            density = self.prop[DENSITY]      #in kg/m^3         \n\n            qy_glob = density * A * force_y\n\n        except:\n            qy_glob = 0\n            print(\"No Selfweight in Y-Dir\")\n       \n       \n        # transfer Selfweight in Lokal Forces\n        lokal_val = dot(self.T[0:3,0:3],[qx_glob, qy_glob,0])\n        qx_lok = lokal_val[0]\n        qy_lok = lokal_val[1]\n\n        #loop over all gauss points                \n        for gauss in range(0, number_of_gauss):\n            GPW = weights[gauss]\n\n            # local load vector from linear element\n            for i,j in zip( range(0, 2), array((0, 3)) ):\n                RHSstatic[j] += qx_lok * Ns_linear[gauss][i] * GPW #GPW = WGP * L     \n\n            # local load vector from bending\n            for i,j in zip( range(0, 4), array((1, 2, 4, 5)) ):\n                RHSstatic[j] += qy_lok * Ns_bending[gauss][i] * GPW #GPW = WGP * L   \n\n        return RHSstatic\n\n        \n            \n\n    # called from each element and added to the system matrix\n    def CalculateLocalSystem(self,ProcessInfo):\n        self._CalculateTransformationMatrix()\n\n        RHS_selfweight = self._CalculateSelfWeight(ProcessInfo)\n\n        K_beam, RHSstatic_beam = self._ComputeStiffnessContributionBending(ProcessInfo)\n        K_truss, RHSstatic_truss = self._ComputeStiffnessContributionLinear(ProcessInfo)\n\n        # build 6x6 local stiffness matrix\n        K = K_beam\n        K = insert(K,[0,2],0,1) #from 4x4 to 6x6\n        K = insert(K,[0,2],0,0)\n        K[0,0] = K_truss[0,0]\n        K[3,0] = K_truss[1,0]\n        K[0,3] = K_truss[0,1]\n        K[3,3] = K_truss[1,1]\n\n        # build 6x1 local rhs\n        RHSstatic = RHSstatic_beam\n        RHSstatic = insert(RHSstatic,0,RHSstatic_truss[0],0)\n        RHSstatic = insert(RHSstatic,3,RHSstatic_truss[1],0)\n        RHSstatic += RHS_selfweight\n\n        # global stiffness matrix\n        K_glob = dot(transpose(self.T),dot(K,self.T))\n        \n        # global load vector\n        RHSstatic_glob = dot(transpose(self.T),RHSstatic)\n\n        # # preforming, imposed values - TO DO\n        # u0_1 = self.geometry.nodes[0].variables[0][\"DISPLACEMENT_X\"]\n        # w0_1 = self.geometry.nodes[0].variables[0][\"DISPLACEMENT_Y\"]\n        # u0_2 = self.geometry.nodes[1].variables[0][\"DISPLACEMENT_X\"]\n        # w0_2 = self.geometry.nodes[1].variables[0][\"DISPLACEMENT_Y\"]\n\n        # preforming\n        # preforming = array([u0_1, w0_1, u0_2, w0_2])   \n        # RHS_preforming = -dot(K_glob, preforming)        \n        # RHSstatic_glob = RHSstatic_glob + RHS_preforming\n\n        self.RHSstatic = RHSstatic # save newest local rhs for sgr calculation\n        self.K_static_last = K # save newest local K for sgr calculation\n\n        return [K_glob,RHSstatic_glob]\n\n\n        # this function calculates the transformation matrix from local to global\n    def _CalculateTransformationMatrix(self):\n        # coordinates\n        x_1 = self.geometry.nodes[0].coordinates[0]\n        y_1 = self.geometry.nodes[0].coordinates[1]\n        x_2 = self.geometry.nodes[1].coordinates[0]\n        y_2 = self.geometry.nodes[1].coordinates[1]\n\n        # length, sin, cos\n        length_0 = math.sqrt((x_2 - x_1)**2 + (y_2 - y_1)**2)   # original length\n        self.length = length_0   \n        c = (x_2 - x_1)/ length_0 # cos_alpha\n        s = (y_2 - y_1)/ length_0 # sin_alpha\n        alpha = (arcsin(s)*180)/pi\n\n        # transformation matrix\n        T = array([ [c, s, 0, 0, 0, 0],\n                    [-s, c, 0, 0, 0, 0],\n                    [0, 0, 1, 0, 0, 0],\n                    [0, 0, 0, c, s, 0],\n                    [0, 0, 0, -s, c, 0],\n                    [0, 0, 0, 0, 0, 1] ]) \n\n        self.T = T\n\n\n        # this function returns a list with the node and unkowns to be solved for\n    def GetDofList(self):\n        unknowns = []\n        unknowns.append(Dof(self.geometry[0], DISPLACEMENT_X))  \n        unknowns.append(Dof(self.geometry[0], DISPLACEMENT_Y)) \n        unknowns.append(Dof(self.geometry[0], ROTATION)) \n        unknowns.append(Dof(self.geometry[1], DISPLACEMENT_X))  \n        unknowns.append(Dof(self.geometry[1], DISPLACEMENT_Y))  \n        unknowns.append(Dof(self.geometry[1], ROTATION)) \n        return unknowns\n\n        # this function returns a list with the equationIds to be solved for\n    def EquationId(self):   \n        equation_ids = []\n        equation_ids.append(self.geometry[0].EquationId(DISPLACEMENT_X))\n        equation_ids.append(self.geometry[0].EquationId(DISPLACEMENT_Y))  \n        equation_ids.append(self.geometry[0].EquationId(ROTATION))  \n        equation_ids.append(self.geometry[1].EquationId(DISPLACEMENT_X))    \n        equation_ids.append(self.geometry[1].EquationId(DISPLACEMENT_Y))  \n        equation_ids.append(self.geometry[1].EquationId(ROTATION))    \n        return equation_ids\n\n\n    #calculates the local displacments from the global\n    def CalculateLocalDisplacement(self):\n        # displacement\n        u_1 = self.geometry.nodes[0].variables[0][\"DISPLACEMENT_X\"]\n        w_1 = self.geometry.nodes[0].variables[0][\"DISPLACEMENT_Y\"]\n        r_1 = self.geometry.nodes[0].variables[0][\"ROTATION\"]\n        u_2 = self.geometry.nodes[1].variables[0][\"DISPLACEMENT_X\"]\n        w_2 = self.geometry.nodes[1].variables[0][\"DISPLACEMENT_Y\"]\n        r_2 = self.geometry.nodes[1].variables[0][\"ROTATION\"]\n\n        # transform to lokal\n        u_glob = [u_1, w_1, r_1, u_2, w_2, r_2]\n        u_lok = dot(self.T,u_glob)\n\n        # internal forces at the ends\n        s_lok = dot(self.K_static_last,u_lok) - self.RHSstatic\n        sgr_lok = s_lok\n        sgr_lok[0]=sgr_lok[0]*-1\n        sgr_lok[1]=sgr_lok[1]*-1\n        sgr_lok[2]=sgr_lok[2]*-1\n\n        return [u_lok]\n\n    # Calculates the internal forces N,V,M for 50 steps of the beam\n    def CalculateInternalForces(self):\n\n        [u_lok] = self.CalculateLocalDisplacement() #calculate local displacement\n        xi = linspace(0,1,num=51)\n        x = xi * self.length\n\n        N = zeros(51)\n        V = zeros(51)\n        M = zeros(51)\n\n        E = self.prop[YOUNG_MODULUS]    # in N/m^2\n        A = self.prop[SECTION_TYPE]     # in m^    \n        I = self.prop[MOMENT_INERTIA_AREA]    # in m^4  \n\n        [Ns_linear, derivatives_linear] = self.geometry.ShapeFunctionsLinear(xi)\n        [Ns_bend, dderivatives_bend, ddderivatives_bend] = self.geometry.ShapeFunctionsBending(xi)\n\n        #internal forces from Boundaries\n        for i in range(0,51):\n            M[i] = -1 * E * I *( u_lok[1]*dderivatives_bend[i][0] + u_lok[2]*dderivatives_bend[i][1] + u_lok[4]*dderivatives_bend[i][2] + u_lok[5]*dderivatives_bend[i][3] )\n            V[i] = -1 * E * I *( u_lok[1]*ddderivatives_bend[i][0] + u_lok[2]*ddderivatives_bend[i][1] + u_lok[4]*ddderivatives_bend[i][2] + u_lok[5]*ddderivatives_bend[i][3] )\n            N[i] = E * A * (u_lok[0]*derivatives_linear[i][0] + u_lok[3]*derivatives_linear[i][1])\n\n            qx = self.GetSolutionStepValue(LINE_LOAD_X,0)\n            qy = self.GetSolutionStepValue(LINE_LOAD_Y,0)  \n\n        #forces from const loads \n        for i in range(0,51):\n            M[i] += -1 * E * I * qy * self.length**2 / (24 * E * I) * (2 - 12 *xi[i] + 12 * xi[i]**2)\n            V[i] += ((-1) * E * I * qy * self.length**2 / (24 * E * I)) * (1 / self.length) * (-12 + 24 * xi[i])\n            N[i] += E * A * qx * self.length / (E * A * 2) * (1 - 2 * xi[i])       \n   \n        return [N,V,M]\n\n\n    # calculates the coordinates of the deformed beam\n    def CalculateDeflectionCurve(self, scale):\n\n        [u_lok] = self.CalculateLocalDisplacement() #calculate local displacement\n        xi = linspace(0, 1, num=51)\n\n        E = self.prop[YOUNG_MODULUS]    # in N/m^2\n        A = self.prop[SECTION_TYPE]     # in m^    \n        I = self.prop[MOMENT_INERTIA_AREA]    # in m^4  \n\n        u_curve_local = zeros(51)\n        w_curve_local = zeros(51)\n\n        [Ns_linear, derivatives_linear] = self.geometry.ShapeFunctionsLinear(xi)\n        [Ns_bend, dderivatives_bend, ddderivatives_bend] = self.geometry.ShapeFunctionsBending(xi)\n\n        qx = self.GetSolutionStepValue(LINE_LOAD_X,0)\n        qy = self.GetSolutionStepValue(LINE_LOAD_Y,0)  \n\n        for i in range(0,51):\n            # deformation from boundaries\n            u_curve_local[i] = u_lok[0]*Ns_linear[i][0] + u_lok[3]*Ns_linear[i][1]\n            w_curve_local[i] = u_lok[1]*Ns_bend[i][0] + u_lok[2]*Ns_bend[i][1] + u_lok[4]*Ns_bend[i][2] + u_lok[5]*Ns_bend[i][3]\n\n            # deformation from const load\n            u_curve_local[i] += qx * self.length**2 / (E * A * 2) * ( xi[i]- xi[i]**2)\n            w_curve_local[i] += qy * self.length**2 * self.length**2 / (24 * E * I) * xi[i]**2 * (1 - 2 * xi[i] + xi[i]**2)\n\n        # transform to global\n        curve_global = dot(transpose(self.T[0:2,0:2]), array((u_curve_local,w_curve_local)))\n        u_curve_global = curve_global[0]\n        w_curve_global = curve_global[1]\n\n        # coordinates for global plot - x_def and y_def\n        x_1 = self.geometry.nodes[0].coordinates[0]\n        y_1 = self.geometry.nodes[0].coordinates[1]\n        x_2 = self.geometry.nodes[1].coordinates[0]\n        y_2 = self.geometry.nodes[1].coordinates[1]\n\n        x_def = zeros(51)\n        y_def = zeros(51)\n        for i in range(0,51):\n            x_def[i] = x_1 * (1-xi[i]) + x_2 * xi[i] + u_curve_global[i] * scale\n            y_def[i] = y_1 * (1-xi[i]) + y_2 * xi[i] + w_curve_global[i] * scale\n\n        return [x_def, y_def]\n    \n\n    def GetSolutionStepValue(self, variable_name, step):\n        try: return self.variables[step][variable_name]\n        except: return 0 \n\n    def SetSolutionStepValue(self, variable_name, step, value):        \n        try: self.variables[step][variable_name] = value\n        except:\n            self.variables.append(dict())\n            self.variables[step][variable_name] = value\n","sub_path":"bernoulli_beam.py","file_name":"bernoulli_beam.py","file_ext":"py","file_size_in_byte":14743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"184803489","text":"import numpy as np\nfrom copy import deepcopy\nimport pandas as pd\n\nclass Client:\n    def __init__(self, model, X, y, X_test, y_test):\n        self.model = model\n        self.X = X\n        self.y = y\n        self.X_test = X_test\n        self.y_test = y_test\n        self.datasetSize = X.shape[0]\n        self.classes = np.unique(y, axis=0).size\n\n        self.testSize = X_test.shape[0]\n\n    def update(self, B, E, W):\n        self.model.set_weights(deepcopy(W))\n        if B is None:\n            B = self.datasetSize\n        self.model.fit(x=self.X, y=self.y, verbose=0, batch_size=B, epochs=E)\n\n    def evaluate(self, W):\n        self.model.set_weights(deepcopy(W))\n        _, accuracy = self.model.evaluate(self.X_test, self.y_test, verbose=0)\n        return accuracy\n\n    def gen_stats(self, path, client_index):\n        y_pred = self.model.predict(self.X_test)\n        y_pred = np.argmax(y_pred, axis=1)\n        y_test = np.argmax(self.y_test, axis=1)\n        df = pd.DataFrame(data={'y_pred': y_pred, 'y_true': y_test})\n        path += \"client_\" + str(client_index+1) + \".csv\"\n        df.to_csv(path, index=False)\n\n\n","sub_path":"MNIST/Client.py","file_name":"Client.py","file_ext":"py","file_size_in_byte":1119,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"501110949","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jul  2 23:01:13 2018\n\n@author: VISHAL-PC\n\"\"\"\n\nfrom nltk.stem import WordNetLemmatizer\n\nfrom stanfordcorenlp import StanfordCoreNLP\nfrom word2number import w2n\nimport sqlite3\n\nnlp = 1\n\ndef startnlp():\n    global nlp\n    nlp = StanfordCoreNLP(r'stanford-corenlp-full-2018-02-27')\n#print(nlp)\n#startnlp()\n#print(nlp)\n\ndef stopnlp():\n    nlp.close()\n\ndef englishToSQL(sentence):\n\n    #nlp = StanfordCoreNLP(r'D:\\IIIT\\stanford-corenlp-full-2018-02-27')\n\n    #sentence = 'ratings of top 2 movies of Tom Hardy and James Cameron'\n    #sentence = 'best director and actor of action movies'\n    #sentence = 'Top 5 action and adventure movies after 1980, greater than 100 mins and rating above 5 of Tom Hardy and James Cameron'\n    #sentence = 'movies rated above 8'\n    \n    \n    pos = nlp.pos_tag(sentence)\n    i = 0\n    names = []\n    sentence = ''\n    while i<len(pos):\n        strr = pos[i][0]\n        name = strr\n        if pos[i][1]=='NNP':\n            i += 1\n            while((i<len(pos)) and pos[i][1]=='NNP' ):\n                strr = strr + pos[i][0]\n                name = name + ' ' + pos[i][0]\n                i += 1\n            names.append(name)\n            i = i-1\n        else:\n            strr = strr.lower()\n        i += 1\n        sentence = sentence + strr + ' '  \n    tok = nlp.word_tokenize(sentence)\n    pos = nlp.pos_tag(sentence)\n    #ner = nlp.ner(sentence)\n    #cparse = nlp.parse(sentence)\n    dparse = nlp.dependency_parse(sentence)\n    print('Completed')\n    #nlp.close()\n    \n    parsedict = {}\n    for line in dparse:\n        print(line[0]+\" \"+tok[line[1]-1]+\" \"+tok[line[2]-1])\n        if line[0] not in parsedict:\n            parsedict[line[0]] = [[tok[line[1]-1],tok[line[2]-1]]]\n        else:\n            parsedict[line[0]].append([tok[line[1]-1],tok[line[2]-1]])\n    \n    pos_tags = {}\n    for d in pos:\n        if d[1] in pos_tags:\n            pos_tags[d[1]].append(d[0])\n        else:\n            pos_tags[d[1]] = [d[0]]\n    \n    wordnet_lemmatizer = WordNetLemmatizer()\n    tokl = []\n    for t in tok:\n        tokl.append(wordnet_lemmatizer.lemmatize(t))\n    \n    list1 = ['compound', 'conj', 'appos', 'amod']\n    list2 = ['cc', 'punct']\n    \n    def conj(data):\n        for i in range(len(dparse)):\n            if dparse[i][0] in list1:\n                if tok[dparse[i][2]-1] in data:\n                    k = data.index(tok[dparse[i][2]-1])\n                    if dparse[i-1][0] == 'neg':\n                        data[k] = 'and not ' + data[k]\n                    if dparse[i-1][0] in list2:\n                        data[k] = 'or ' + data[k]\n        return data\n                \n    countrynames = ['usa', 'uk', 'new zealand', 'canada', 'australia']\n    languages = ['english']\n        \n    \n    per = []\n    perr = []\n    cou = []\n    couu = []\n    lan = []\n    lann = []\n    i = 0\n    while i < len(names):\n        if names[i].lower() in countrynames:\n            cou.append(pos_tags['NNP'][i])\n            couu.append(names[i])\n        elif names[i].lower() in languages:\n            lan.append(pos_tags['NNP'][i])\n            lann.append(names[i])\n        else:\n            per.append(pos_tags['NNP'][i])\n            perr.append(names[i])\n        i += 1\n    \n    #To find languages\n    for t in tokl:\n        if t in languages:\n            lan.append(t)\n            lann.append(t)\n    if len(lan) > 0:\n        lan = conj(lan)\n    \n    #To find countries\n    if len(cou) > 0:\n        cou = conj(cou)\n    \n    #To find person's name\n    if len(per) > 0:\n        per = conj(per)\n    \n    #To combine NN and NNS and lemmatize\n    nounslist = []\n    if 'NN' in pos_tags:\n        for d in pos_tags['NN']:\n            nounslist.append(wordnet_lemmatizer.lemmatize(d))\n    if 'NNS' in pos_tags:\n        for d in pos_tags['NNS']:\n            nounslist.append(wordnet_lemmatizer.lemmatize(d))\n    \n    #To find genres\n    genrelist = ['action', 'adventure', 'fantasy', 'sci-fi', 'thriller', \n               'romance', 'animation', 'comedy', 'family', 'musical', \n               'mystery', 'western', 'drama', 'history', 'sport', 'crime', 'horror']\n    \n    gen = []\n    for d in tokl:\n        if d in genrelist:\n            gen.append(d)\n    \n    gen = conj(gen)\n    \n    #For handling not\n    notflag = 0\n    gen1 =[]\n    for g in gen:\n        gen1.append(g)\n    i = 0\n    while i < len(gen1):\n        ii = gen1[i].split()\n        if len(ii) == 2:\n            gen1[i] = \" and not \"+ii[1]\n        elif len(ii) == 3:\n            gen1[i] = \"and \"+ii[2]\n            notflag = 1\n        else:\n            gen1[i] = \"not \" + gen1[i]\n        i += 1\n    \n    \n    #To find member table coloums\n    memlabel = 0\n    actorlist = ['actor','hero','heroine', 'cast']\n    directorlist = ['director', 'crew'] \n    \n    mem = {'actor':0, 'director':0}\n    bestcount = 0\n    for d in nounslist:\n        if d in actorlist:\n            if 'NN' in pos_tags and d in pos_tags['NN']:\n                bestcount = 1\n            mem['actor'] = 1\n            memlabel = 1\n        elif d in directorlist:\n            if 'NN' in pos_tags and d in pos_tags['NN']:\n                bestcount = 1\n            mem['director'] = 1\n            memlabel = 1\n    \n    \n    #To find movie table coloums\n    movielist = ['movie', 'cinema']\n    ratinglist = ['rating', 'score', 'rated']\n    durationlist = ['time', 'duration', 'length', 'hour', 'minute', 'hr', 'min', 'runtime']\n    languagelist = ['language']\n    yearlist = ['year', 'date', 'when']\n    linklist = ['link', 'url', 'website']\n    countrylist = ['country', 'city', 'place', 'area', 'where']\n    \n    #==============================================================================\n    #     beforelist = ['before', 'prior', 'early']\n    #     afterlist = ['after','follow','later']\n    #     inlist = ['in', 'of']\n    #     betweenlist = ['between', 'middle', 'later']\n    #==============================================================================\n    \n    lesslist = ['le', 'below', 'before', 'prior', 'early']\n    greatlist = ['greater', 'above', 'after','follow','later', 'more', 'long', 'longer']\n    \n    #==============================================================================\n    #     hrlist = ['hr', 'hour']\n    #==============================================================================\n    minlist = ['min', 'minute']\n    \n    \n    mcon = ''\n    #To get condition on countries\n    if(len(cou)>0):\n        mcon = 'where ('\n        for i in range(len(cou)):\n            ii = cou[i].split()\n            for j in range(len(ii)-1):\n                mcon  = mcon +' '+ii[j]\n            mcon = mcon +' lower(t.COUNTRY) = \"'+couu[i].lower()+'\" '\n        mcon += ') '\n    \n    if(len(lan)>0):\n        if mcon == '':\n            mcon = 'where ('\n        else:\n            mcon += 'and ('\n        for i in range(len(lan)):\n            ii = lan[i].split()\n            for j in range(len(ii)-1):\n                mcon  = mcon +' '+ii[j]\n            mcon = mcon +' lower(t.LANGUAGE) = \"'+lann[i].lower()+'\" '\n        mcon += ') '\n    \n    if 'CD' in pos_tags:\n        #Movie table conditions\n        def comp(strr):\n            if(strr in greatlist):\n                return '> '\n            elif(strr in lesslist):\n                return '< '\n            else:\n                return '= '\n        \n        i = 0\n        while i < len(dparse):\n            fl = 0\n            mstr = ''\n            if(dparse[i][0] == 'case'):\n                if (tokl[dparse[i][1]-1] in pos_tags['CD'] and w2n.word_to_num(tokl[dparse[i][1]-1])>1500):\n                    if(mcon == ''):\n                        mstr = 'where t.YEAR '\n                    else:\n                        mstr = mstr + ' and t.YEAR '\n                    mstr = mstr + comp(tokl[dparse[i][2]-1])\n                    if tokl[dparse[i][1]-1] in pos_tags['CD']:\n                        mstr = mstr + str(w2n.word_to_num(tokl[dparse[i][1]-1]))\n                        fl = 1\n                elif(dparse[i+1][0] == 'nmod'):\n                    if(dparse[i-1][0] == 'amod' or (dparse[i-1][0] == 'conj' and tokl[dparse[i-1][2]-1] in ratinglist)):\n                        if(tokl[dparse[i-1][1]-1] in ratinglist or tokl[dparse[i-1][2]-1] in ratinglist):\n                            if(mcon == ''):\n                                mstr = 'where t.RATING '\n                            else:\n                                mstr = mstr + ' and t.RATING '\n                            if dparse[i-1][0] == 'amod':\n                                mstr = mstr + comp(tokl[dparse[i-1][2]-1])\n                            else:\n                                mstr = mstr + comp(tokl[dparse[i][2]-1])\n                            if tokl[dparse[i][1]-1] in pos_tags['CD']:\n                                mstr = mstr + str(w2n.word_to_num(tokl[dparse[i][1]-1]))\n                                fl = 1\n                    else:\n                        print(tokl[dparse[i][1]-1])\n                        if(tokl[dparse[i+1][1]-1] in ratinglist):\n                            if(mcon == ''):\n                                mstr = 'where t.RATING '\n                            else:\n                                mstr = mstr + ' and t.RATING'\n                            mstr = mstr + comp(tokl[dparse[i][2]-1])\n                            if tokl[dparse[i][1]-1] in pos_tags['CD']:\n                                mstr = mstr + str(w2n.word_to_num(tokl[dparse[i][1]-1]))\n                            if(i+3 < len(dparse) and dparse[i+2][0] == 'cc' and dparse[i+3][0] == 'case'):\n                                mstr = mstr + ' and t.RATING'\n                                mstr = mstr + comp(tokl[dparse[i+3][2]-1])\n                                if tokl[dparse[i+3][1]-1] in pos_tags['CD']:\n                                    mstr = mstr + str(w2n.word_to_num(tokl[dparse[i+3][1]-1]))\n                                fl = 1\n                        elif (tokl[dparse[i+1][1]-1] in durationlist):\n                            if(mcon == ''):\n                                mstr = 'where t.DURATION '\n                            else:\n                                mstr = mstr + ' and t.DURATION '\n                            mstr = mstr + comp(tokl[dparse[i][2]-1])\n                            if tokl[dparse[i][1]-1] in pos_tags['CD']:\n                                if tokl[dparse[i+1][1]-1] in minlist:\n                                    mstr = mstr + str(w2n.word_to_num(tokl[dparse[i][1]-1]))\n                                else:\n                                    mstr = mstr + str(w2n.word_to_num(tokl[dparse[i][1]-1])*60)\n                                fl = 1\n                elif(dparse[i+1][0] == 'nummod'):\n                    if (tokl[dparse[i+1][1]-1] in durationlist):\n                        if(mcon == ''):\n                            mstr = 'where t.DURATION '\n                        else:\n                            mstr = mstr + ' and t.DURATION '\n                        if(dparse[i-1][0] == 'amod' or dparse[i-1][0] == 'conj'):\n                            mstr = mstr + comp(tokl[dparse[i-1][2]-1])\n                        else:\n                            mstr = mstr + comp(tokl[dparse[i][2]-1])\n                        if tokl[dparse[i+1][2]-1] in pos_tags['CD']:\n                            if tokl[dparse[i+1][1]-1] in minlist:\n                                mstr = mstr + str(w2n.word_to_num(tokl[dparse[i+1][2]-1]))\n                            else:\n                                mstr = mstr + str(w2n.word_to_num(tokl[dparse[i+1][2]-1])*60)\n                            fl = 1\n                elif(dparse[i+1][0] == 'dep'):\n                    if(dparse[i-1][0] == 'conj'):\n                        if(tokl[dparse[i-1][2]-1] in ratinglist):\n                            if(mcon == ''):\n                                mstr = 'where t.RATING '\n                            else:\n                                mstr = mstr + ' and t.RATING'\n                            mstr = mstr + comp(tokl[dparse[i][2]-1])\n                            if tokl[dparse[i+1][2]-1] in pos_tags['CD']:\n                                mstr = mstr + str(w2n.word_to_num(tokl[dparse[i+1][2]-1]))\n                                fl = 1\n            elif (dparse[i][0] == 'advmod' and (i+3 < len(dparse) and (dparse[i+2][0] == 'nummod' or dparse[i+3][0] == 'dep')) ):\n                if dparse[i+2][0] == 'nummod':\n                    dname = tokl[dparse[i+2][1]-1]\n                else:\n                    dname = tokl[dparse[i+3][2]-1]\n                if (dname in durationlist):\n                    if(mcon == ''):\n                        mstr = 'where t.DURATION '\n                    else:\n                        mstr = mstr + ' and t.DURATION '\n                    mstr = mstr + comp(tokl[dparse[i][2]-1])\n                    if tokl[dparse[i][1]-1] in pos_tags['CD']:\n                        if dname in minlist:\n                            mstr = mstr + str(w2n.word_to_num(tokl[dparse[i][1]-1]))\n                        else:\n                            mstr = mstr + str(w2n.word_to_num(tokl[dparse[i][1]-1])*60)\n                        fl = 1\n            if fl == 1:\n                mcon = mcon + mstr\n            i += 1\n                    \n    \n    \n    mov = {'name':0, 'rating':0, 'duration':0, 'language':0, 'year':0, 'country':0, 'link':0}\n    \n    for d in tokl:\n        if d in movielist:\n            mov['name'] = 1\n        elif d in ratinglist:\n            mov['rating'] = 1\n        elif d in durationlist:\n            mov['duration'] = 1\n        elif d in languagelist:\n            mov['language'] = 1\n        elif d in yearlist:\n            mov['year'] = 1\n        elif d in countrylist:\n            mov['country'] = 1\n        elif d in linklist:\n            mov['link'] = 1\n              \n    \n    \n        \n    num = 0\n    i=0\n    while i < len(dparse):\n        if dparse[i][0] == 'nummod' and (dparse[i-1][0] != 'case' and dparse[i-1][0] != 'mwe'):\n            num = w2n.word_to_num(pos_tags['CD'][0])\n        i += 1\n    if 'NNS' not in pos_tags:\n        num = 1\n    \n    \n    order = 'desc'\n    if('JJ' in pos_tags):\n        if'worst' in tokl: \n            order = 'asc'\n    \n    genre = ''\n    if(len(gen)>0):\n        genre = 'where'\n        for i in range(len(gen)):\n            ii = gen[i].split()\n            for j in range(len(ii)-1):\n                genre  = genre +' '+ii[j]\n            genre = genre +' lower(g.NAME) = \"'+ii[-1].lower()+'\"'\n    \n    genre1 = ''\n    if(len(gen1)>0):\n        genre1 = 'where'\n        for i in range(len(gen1)):\n            ii = gen1[i].split()\n            for j in range(len(ii)-1):\n                genre1  = genre1 +' '+ii[j]\n            genre1 = genre1 +' lower(g.NAME) = \"'+ii[-1].lower()+'\"'\n    \n    memdata = ''\n    if len(genre)<1 and len(per)>0 and mov['name'] == 0:\n        genre = 'where '\n        for i in range(len(per)):\n            ii = per[i].split()\n            for j in range(len(ii)-1):\n                genre  = genre +' '+ii[j]\n            genre = genre +' lower(m.NAME) = \"'+names[i].lower()+'\"'\n    else:\n        if(len(per)>0):\n            memdata = 'where ('\n            for i in range(len(per)):\n                ii = per[i].split()\n                for j in range(len(ii)-1):\n                    memdata  = memdata +' '+ii[j]\n                memdata = memdata +' lower(mem.NAME) = \"'+perr[i].lower()+'\"'\n    \n    \n    column = 'distinct '\n    col = []\n    for i in mov:\n        if mov[i] == 1:\n            col.append(i)\n            column = column + 't.'+i+', '\n    if 'genre' in tokl:\n        col.append('genre')\n        column = column + 't.gname, '\n    column = column[:-2]\n    \n    if memlabel == 1:\n        num = bestcount\n        col = ['person name','profession']\n        column = 'distinct mem.NAME, mem.PROFESSION'\n        memdata = 'where (mem.PROFESSION = '\n        if mem['actor'] == 1 and mem['director'] == 1:\n            memdata = memdata + '\"director\" or mem.PROFESSION = \"actor\"'\n            num = bestcount * 2\n        else:\n            if mem['actor'] == 1:\n                memdata = memdata + '\"actor\"'\n            else:\n                memdata = memdata + '\"director\"'\n    \n    #==============================================================================\n    # if column == 'distinct ':\n    #     column = 't.*'\n    #==============================================================================\n    \n    if ('how' in tokl and 'many' in tokl) or (('number' in tokl or 'count' in tokl) and 'of' in tokl):\n        column = 'COUNT(distinct t.name)'\n        col = ['Count']\n    \n    cgname = ', g.NAME as gname'\n    if notflag == 1:\n        cgname = ''\n    command = 'select '+column+' from\\n'\n    command += '(select distinct m.*' + cgname + ' from MOVIE m\\n'\n    command += 'left join MOVIE_GENRE mg on mg.MOVIE_ID = m.ID\\n'\n    command += 'left join GENRE g on g.ID = mg.GENRE_ID '+genre+'\\n'\n    if genre != '' and notflag == 1:\n        command += 'except\\n'\n        command += 'select distinct m.*' + cgname + ' from MOVIE m\\n'\n        command += 'left join MOVIE_GENRE mg on mg.MOVIE_ID = m.ID\\n'\n        command += 'left join GENRE g on g.ID = mg.GENRE_ID '+genre1+')t\\n'\n    else:\n        command += ')t\\n'\n    command += 'left join MOVIE_MEMBERS mm on mm.MOVIE_ID = t.ID\\n'\n    command += 'left join MEMBERS mem on mem.ID = mm.MEMBERS_ID '+memdata\n    if memdata != '':\n        command += ')\\n'\n    if len(mcon) > 0:\n        if memdata != '':\n            command = command + 'and' + mcon[5:] + '\\n'\n        else:\n            command = command + mcon + '\\n'\n    command += 'order by t.RATING '+order+'\\n'\n    \n    \n    if(num>0):\n        command += 'limit '+str(num)\n    #==============================================================================\n    # else:\n    #     command += ' limit '+str(5)\n    #==============================================================================\n    \n    \n    \n    print(command)\n    \n    conn = sqlite3.connect('Data/MovieDB.db')\n    print (\"Opened database successfully\\n\")\n    \n    try:\n        cursor = conn.execute(command)\n    except sqlite3.OperationalError:\n        return [command,{}]\n    \n    rowcount = 0\n    result = {}\n    for i in col:\n        result[i] = []\n    for row in cursor:\n        rowcount += 1\n        for i in range(len(col)):\n            result[col[i]].append(row[i])\n            print( \"\"+col[i]+\":\", row[i])\n       \n    print(\"\\n\" + str(rowcount) + \" rows fetched\\n\")\n    \n    print (\"Operation done successfully\")\n    conn.close()\n    \n    return [command,result]\n    \n#print(englishToSQL('top adventure movie directors'))\n\n#==============================================================================\n# with open('Questions.txt') as f:\n#     lines = f.read().split('\\n')\n# f.close()\n# \n# \n# i = 0\n# sql = []\n# nlp = StanfordCoreNLP(r'D:\\IIIT\\stanford-corenlp-full-2018-02-27')\n# for line in lines:\n#     i += 1\n#     sql.append(englishToSQL(line))\n#     print(i)\n#     #dd = input(\"Press any key to continue\")\n# nlp.close()    \n# \n# f= open(\"quires.txt\",\"w+\")\n# for i in range(len(lines)):\n#     f.write(lines[i]+'\\n\\n')\n#     f.write(sql[i][0]+'\\n\\n')\n#     #f.write(sql[i][1]+'\\n\\n')\n#     strr = ''\n#     keys = []\n#     for j in sql[i][1]:\n#         keys.append(j)\n#     for j in range (len(sql[i][1][keys[0]])):\n#         for k in range(len(keys)):\n#             strr += keys[k]+\" : \"+str(sql[i][1][keys[k]][j])+'\\n'\n#         strr += '\\n'\n#     strr += str(len(sql[i][1][keys[0]]))+' rows fetched\\n'\n#     f.write(strr+'\\n\\n')\n# f.close()\n#==============================================================================\n    ","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":19576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"2922564","text":"## Matthew Hall\r\n## ISY150\r\n## Chapter 3\r\n## Exercise 10\r\n## 09/08/2013\r\n## Last modified: 09/18/2013\r\n\r\n## A retail company must file a monthly sales tax report listing the total\r\n## sales for the month, and the amount of state and county sales tax collected.\r\n## The state sales tax rate is 4 percent and the county sales tax rate is\r\n## 2 percent. Write a program that asks the user to enter the total sales\r\n## for the month. From this figure, the application should calculate and\r\n## dispaly the following: amount of county sales tax, smount of state sales\r\n## tax, total sales tax(county plus state).\r\n\r\ndef main():\r\n#get input from user\r\n    sales = float(input(\"What is the amount of sales for the month?\\n\"))\r\n#calculate sales tax\r\n    county_sales_tax = float(sales) * 0.02\r\n    state_sales_tax = float(sales) * 0.04\r\n    total_sales_tax = float(state_sales_tax) + float(county_sales_tax)\r\n#display totals\r\n    print(\"The total amount of sales is $\" + format(float(sales),'.2f') + \".\")\r\n    print(\"The county sales tax for the sales amount is $\" + format(float(county_sales_tax),'.2f') + \".\")\r\n    print(\"The state sales tax for the sales amount is $\" + format(float(state_sales_tax),'.2f') + \".\")\r\n    print(\"The total sales tax for the sales amount is $\" + format(float(total_sales_tax),'.2f') + \".\")\r\n\r\n\r\nmain()\r\n","sub_path":"wip/ch3exr10.wip1.py","file_name":"ch3exr10.wip1.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"51075300","text":"#!/usr/bin/env python3\n\nfrom __future__ import print_function\nimport numpy as np\nnp.random.seed(1337)  # for reproducibility\n\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Dropout, Activation\nfrom keras.optimizers import SGD\nfrom keras.utils import np_utils\nfrom get_data_new import get_data\n\ndata_set = get_data()\nX_train,Y_train = data_set.get_train()\nX_test,Y_test = data_set.get_test()\n\n\nmodel = Sequential()\nmodel.add(Dense(200, input_dim=8,init='uniform'))\nmodel.add(Activation('tanh'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(200))\nmodel.add(Activation('tanh'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(6))\nmodel.add(Activation('softmax'))\n\nsgd = SGD(lr=0.1, decay=0.0, momentum=0.0, nesterov=False)\nmodel.compile(loss='mean_squared_error', optimizer=sgd)\nmodel.fit(X_train, Y_train,batch_size=128, nb_epoch=20,\\\n        show_accuracy=True, verbose=1,\\\n        validation_data=(X_test,Y_test))\n","sub_path":"pm2.5_sort.py","file_name":"pm2.5_sort.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"94984827","text":"# -*- coding:utf-8 -*-\r\n# Author: washing\r\n# DateTime: 2021/11/20 10:40\r\n# File: 0594.py\r\n# Desc: \r\n\r\nclass Solution:\r\n    def findLHS(self, nums: List[int]) -> int:\r\n        c = collections.Counter(nums)\r\n        ret = 0\r\n        for num in nums:\r\n            if num+1 in c.keys():\r\n                ret = max(ret, c[num] + c[num+1])\r\n        return ret\r\n","sub_path":"Solutions/0594/0594.py","file_name":"0594.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"470486386","text":"from config import Config\nimport pymysql\nimport json\n\n\nclass PDBC:\n\n    @staticmethod\n    def open_db():\n        return pymysql.connect(Config.db_address,\n                               Config.db_user,\n                               Config.db_passwd,\n                               Config.db_database,\n                               charset='utf8')\n\n    @staticmethod\n    def open_cs(db):\n        return db.cursor\n\n    @staticmethod\n    def get_class(db):\n        data = []\n\n        cursor = db.cursor()\n        sql = \"SELECT distinct species FROM catalog;\"\n        cursor.execute(sql)\n        results = cursor.fetchall()\n\n        for item in results:\n            pet = {'index': 0, 'species': item[0], 'variety':[]}\n            sql = \"SELECT distinct cid,variety FROM catalog WHERE species = '{}';\".format(item[0])\n            cursor.execute(sql)\n            results = cursor.fetchall()\n            for it in results:\n                pet['variety'].append({\n                    'cid': it[0],\n                    'name': it[1]})\n            data.append(pet)\n\n        return data\n\n    @staticmethod\n    def get_list(db, cid):\n        data = []\n\n        cursor = db.cursor()\n        sql = \"SELECT pid,title,price,photo FROM pets WHERE cid = {};\".format(cid)\n        cursor.execute(sql)\n        results = cursor.fetchall()\n\n        for item in results:\n            pet = {\n                'img_link': item[3],\n                'title': item[1],\n                'price': item[2],\n                'pid': item[0]\n            }\n            data.append(pet)\n        return data\n\n    @staticmethod\n    def get_animal(db, pid):\n        data = []\n\n        cursor = db.cursor()\n        sql = \"SELECT title,price,photo,intro,num FROM pets WHERE pid = {};\".format(pid)\n        cursor.execute(sql)\n        results = cursor.fetchall()\n\n        for item in results:\n            pet = {\n                'img_link': item[2],\n                'title': item[0],\n                'intro': item[3],\n                'price': item[1],\n                'num': item[4]\n            }\n            data.append(pet)\n        return data\n\n    @staticmethod\n    def get_order(db, uid):\n        data = []\n\n        cursor = db.cursor()\n        sql = \"SELECT distinct lid FROM user_list WHERE uid = {};\".format(uid)\n        cursor.execute(sql)\n        results = cursor.fetchall()\n\n        for item in results:\n            it = {\n                'lid': item[0],\n                'time': '',\n                'list': []\n            }\n            data.append(it)\n\n        for itema in data:\n            sql = \"SELECT title,price,num,time FROM user_list WHERE lid = {}\".format(itema['lid'])\n            cursor.execute(sql)\n            results = cursor.fetchall()\n\n            for itemb in results:\n                it = {\n                    'title': itemb[0],\n                    'price': itemb[1],\n                    'num': itemb[2]\n                }\n                itema['time'] = itemb[3]\n                itema['list'].append(it)\n        return data\n\n    @staticmethod\n    def get_user(db, uid):\n        data = {\n            'user_name': '',\n            'user_phone': '',\n            'user_area': [],\n            'user_address': ''\n        }\n        if int(uid) < 1:\n            return data\n        cursor = db.cursor()\n        sql = \"SELECT name,phone,province,area,city,address FROM user_info WHERE uid = {};\".format(uid)\n        cursor.execute(sql)\n        results = cursor.fetchone()\n        data = {\n            'user_name': results[0],\n            'user_phone': results[1],\n            'user_area': [results[2], results[3], results[4]],\n            'user_address': results[5]\n        }\n        return data\n\n    @staticmethod\n    def pick_pet(db, uid, data):\n        result = \"error\"\n        cursor = db.cursor()\n        sql = \"START TRANSACTION;\"\n        cursor.execute(sql)\n\n        sql = \"SELECT max(lid) as max FROM user_list;\"\n        cursor.execute(sql)\n        lid = cursor.fetchone()\n        if lid[0] is None:\n            lid = 1\n        else:\n            lid = int(lid[0])+1\n\n        for item in data:\n            line = json.loads(item)\n            sql = \"INSERT INTO user_list (uid,lid,title,price,num) VALUES ({},{},'{}',{},{});\"\\\n                .format(uid, lid, line['title'], line['price'], line['num'])\n            cursor.execute(sql)\n\n        sql = \"COMMIT;\"\n        cursor.execute(sql)\n        result = 'success'\n        return result\n\n    @staticmethod\n    def login(db, username, password):\n        cursor = db.cursor()\n        sql = \"SELECT uid,password FROM user WHERE username = '{}';\".format(username)\n        cursor.execute(sql)\n        results = cursor.fetchall()\n\n        for item in results:\n            if password == item[1]:\n                return item[0]\n        return -1\n\n    @staticmethod\n    def sure_user(db, uid, data):\n        cursor = db.cursor()\n        sql = \"UPDATE user_info SET name='{}',\" \\\n              \"phone='{}',province='{}',area='{}',\" \\\n              \"city='{}',address='{}' WHERE uid = '{}';\"\\\n            .format(data[0], data[1], data[2], data[3], data[4], data[5], uid)\n        cursor.execute(sql)\n        return {'status': 'success'}\n\n","sub_path":"pdbc.py","file_name":"pdbc.py","file_ext":"py","file_size_in_byte":5159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"108695618","text":"# ADRS Application Class\n# Written by Shragovich Eugene 2014\n\nimport config\nimport generics\nimport inspect\nfrom sikuli import *\nfrom app import App\n\nclass ADRS(App):\n\n\tdef __init__(self, testName):\n\t\tself.link = config.ADRS_link\n\t\tself.debug = config.ADRS_debug\n\t\tself.retry = config.ADRS_retry\n\t\tself.application = config.ADRS_appName\n\t\tself.appName1 = config.ADRS_appName1\n\t\tself.appType = config.ADRS_appType\n\t\tsuper(ADRS, self).__init__(testName)\n\t\t\t\t\n\tdef _specificAccess(self):\n\t\t\"\"\"\n\t\t\"\"\"\n\t\t# First time fail workaround\n\t\tgenerics.windowClose(0, self.application)\n\t\tgenerics.windowsRun(self.link)\n\t\t\n\t\t#Maximize window\n\t\tgenerics.maxActiveWin()\n\n\t\t# Verify access\n\t\tgenerics.tryDetect(\"ADRS_Logo.png\", self.retry, self.application)\n\t\t\n\tdef _specificLogOut(self):\n\t\t\"\"\"\n\t\t\"\"\"\n\t\t# Close active window (Alt + F4)\n\t\tgenerics.windowClose(0, self.application)","sub_path":"Lib/ADRS.py","file_name":"ADRS.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"524493202","text":"from helper_functions import *\nimport os\n\n# TODO:\n# submission\n\nclass Login(Resource):\n\t\n\t#Checking log in credentials\n\t\"\"\"\n\t-2: password incorrect\n\t-1: username does not exist\n\t0: student\n\t1: faculty\n\t\"\"\"\n\tdef get(self):\n\t\tdetails = request.args\n\t\tusn = details[\"Usn\"]\n\t\tpassword = details[\"Password\"]\n\t\tuser_type = get_user_type(usn)\n\n\t\tif user_type==None:\n\t\t\tresponse = jsonify([-1])\n\t\t\tresponse.status_code = 400\n\t\t\treturn response\n\n\t\tvalid = credential_exists(usn, password, user_type)\n\t\tif valid==1:\n\t\t\tresponse = jsonify([int(user_type==\"Faculty\")])\n\t\t\tresponse.status_code = 200\n\t\telse:\n\t\t\tresponse = jsonify([-2])\n\t\t\tresponse.status_code = 400\n\t\treturn response\n\n\t\n\t#Validate sign up credentials\n\t\"\"\"\n\t-1: user exists\n\t0: succesfully signed up\n\t\"\"\"\n\tdef post(self):\n\t\tdetails = request.form\n\t\tusn = details[\"Usn\"]\n\t\tprint(get_user_type(usn))\n\t\tif get_user_type(usn)==None:\n\t\t\tpassword = details[\"Password\"]\n\t\t\tname = details[\"Name\"]\n\t\t\tsection = details[\"Section\"]\n\t\t\tbatch = details[\"Batch\"]\n\t\t\tdepartment = details[\"Department\"]\n\n\t\t\tcur = mysql.connection.cursor()\n\t\t\tresult = cur.execute(f\"INSERT INTO Student(Student_ID, Student_Name, Batch, Department, Section) VALUES ('{usn}', '{name}', {batch}, '{department}', '{section}')\")\n\t\t\tresult = cur.execute(f\"INSERT INTO Student_login(Student_ID, Student_password) VALUES ('{usn}', '{password}')\")\n\t\t\tmysql.connection.commit()\n\t\t\tcur.close()\n\t\t\tresponse = jsonify([0])\n\t\t\tresponse.status_code = 200\n\n\t\telse:\n\t\t\tresponse = jsonify([-1])\n\t\t\tresponse.status_code = 400\n\n\t\treturn response\n\n\nclass Student(Resource):\n\t# Get relevant details of a student\n\tdef get(self, usn):\n\t\tprint(\"Getting Student Details \"+usn)\n\n\t\tresponse = jsonify({})\n\t\tresponse.status_code = 400\n\n\t\tif user_exists(usn, \"Student\"):\n\t\t\tcur = mysql.connection.cursor()\n\t\t\tcur.execute(f\"SELECT * FROM Student WHERE Student_ID='{usn}'\")\n\t\t\tresult = cur.fetchone()\n\t\t\tcur.close()\n\n\t\t\tdata = {\"Name\":result[1], \"Section\":result[2], \"Batch\":str(result[3]), \"Department\":result[4]}\n\n\t\t\tresponse = jsonify(data)\n\t\t\tresponse.status_code = 200\n\n\t\treturn response\n\t\n\t# Post the student usn and relevant details to the server\n\tdef put(self, usn):\n\t\tprint(\"Adding new student\", usn)\n\n\t\tresponse = jsonify({})\n\t\tresponse.status_code = 400\n\n\t\tif not user_exists(usn, \"Student\"):\n\t\t\tdetails = request.form\n\t\t\tname = details[\"Name\"]\n\t\t\tsection = details[\"Section\"]\n\t\t\tbatch = details[\"Batch\"]\n\t\t\tdepartment = details[\"Department\"]\n\t\t\t\n\t\t\tcur = mysql.connection.cursor()\n\t\t\tresult = cur.execute(f\"INSERT INTO Student(Student_ID, Student_Name, Batch, Department, Section) VALUES ('{usn}', '{name}', {batch}, '{department}', '{section}')\")\n\t\t\tmysql.connection.commit()\n\t\t\tcur.close()\n\n\t\t\tresponse.status_code = 200\n\n\t\treturn response\n\n\nclass Faculty(Resource):\n\t# Get relevant details of a faculty member\n\tdef get(self, f_id):\n\t\tprint(\"Getting Faculty Details \"+f_id)\n\n\t\tresponse = jsonify({})\n\t\tresponse.status_code = 400\n\n\t\tif user_exists(f_id, \"Faculty\"):\n\t\t\tcur = mysql.connection.cursor()\n\t\t\tcur.execute(f\"SELECT * FROM Faculty WHERE Faculty_ID='{f_id}'\")\n\t\t\tresult = cur.fetchone()\n\t\t\tcur.close()\n\n\t\t\tdata = {\"Name\":result[1], \"Department\":result[2]}\n\n\t\t\tresponse = jsonify(data)\n\t\t\tresponse.status_code = 200\n\n\t\treturn response\n\n\t# Post the faculty id and relevant details to the server\n\tdef put(self, f_id):\n\t\tprint(\"Adding new faculty\", f_id)\n\n\t\tresponse = jsonify({})\n\t\tresponse.status_code = 400\n\n\t\tif not user_exists(f_id, \"Faculty\"):\n\t\t\tdetails = request.form\n\t\t\tname = details[\"Name\"]\n\t\t\tdepartment = details[\"Department\"]\n\t\t\t\n\t\t\tcur = mysql.connection.cursor()\n\t\t\tresult = cur.execute(f\"INSERT INTO Faculty(Faculty_ID, Faculty_Name, Department) VALUES ('{f_id}', '{name}', '{department}')\")\n\t\t\tmysql.connection.commit()\n\t\t\tcur.close()\n\n\t\t\tresponse.status_code = 200\n\n\t\treturn response\n\n\nclass Question(Resource):\n\t# Gets the description of the question\n\t\"\"\"\n\t-1: user does not exist\n\t-2: question does not exist\n\t\"\"\"\n\tdef get(self):\n\t\tdetails = request.args\n\t\tq_id = details[\"Q_id\"]\n\t\tusn = details[\"Usn\"]\n\n\t\tuser_type = get_user_type(usn)\n\n\t\tif user_type==None:\n\t\t\tresponse = jsonify([-1])\n\t\t\tresponse.status_code = 400\n\t\t\treturn response\n\n\t\tcur = mysql.connection.cursor()\n\t\tcur.execute(f\"SELECT Description_Pathway, Number_Testcases, Question_name FROM Questions WHERE Question_ID={q_id}\")\n\t\tresult = cur.fetchone()\n\t\tcur.close()\n\n\t\tif result==None:\n\t\t\tresponse = jsonify([-2])\n\t\t\tresponse.status_code = 400\n\t\t\treturn response\t\n\n\t\tresponse = {}\n\t\tresponse[\"number_testcases\"] = result[1]\n\t\tresponse[\"name\"] = result[2]\n\t\twith open(result[0], \"r\") as file:\n\t\t\tresponse[\"description\"] = file.read()\n\n\t\tresponse = jsonify(response)\n\t\tresponse.status_code = 200\n\n\t\treturn response\n\n\tdef post(self):\n\t\tfiles = request.files\n\t\tdetails = request.form\n\n\t\tfile_names = list(files.keys())\n\n\t\tprint(details)\n\t\t\n\t\tf_id = details[\"Usn\"]\n\t\tname = details[\"Question_name\"]\n\t\ttags = details[\"Tags\"].split(\" \")\n\t\tip = [name for name in file_names if name[:2]=='ip']\n\t\top = [name for name in file_names if name[:2]=='op']\n\t\tip.sort()\n\t\top.sort()\n\n\t\tif get_user_type(f_id)!=\"Faculty\":\n\t\t\tresponse = jsonify([-1])\n\t\t\tresponse.status_code = 400\n\t\t\treturn response\n\n\t\tcur = mysql.connection.cursor()\n\t\tcur.execute(f\"INSERT INTO Questions (List_Testcases_Pathway, Description_Pathway, Faculty_ID, Question_name) VALUES ('unassigned','unassigned','unassigned', '{name}')\")\n\t\tcur.connection.commit()\n\t\tcur.execute(f\"SELECT Question_ID FROM Questions WHERE Description_Pathway = 'unassigned' AND Question_name='{name}'\")\n\t\tq_id = cur.fetchone()[0]\n\t\tcur.close()\n\n\t\tfile_path = \"./Questions/\"+str(q_id)+\"/\"\n\t\ttry:\n\t\t\tos.stat(file_path)\n\t\texcept:\n\t\t\tos.mkdir(file_path)\n\n\t\tfiles[\"description\"].save(file_path+\"description.txt\")\n\n\t\tfor ind in range(len(ip)):\n\t\t\tfiles[ip[ind]].save(file_path+\"ip\"+str(ind+1)+\".txt\")\n\t\t\tfiles[op[ind]].save(file_path+\"op\"+str(ind+1)+\".txt\")\n\n\t\tcur = mysql.connection.cursor()\n\t\tcur.execute(f\"UPDATE Questions SET Number_Testcases={len(ip)}, List_Testcases_Pathway='{file_path}',Description_Pathway='{file_path+'description.txt'}', Faculty_ID='{f_id}' WHERE Question_ID={q_id}\")\n\t\tcur.connection.commit()\n\t\tfor tag in tags:\n\t\t\tcur.execute(f\"INSERT INTO Question_tag VALUES({q_id}, '{tag}')\")\n\t\tcur.connection.commit()\t\n\t\tcur.close()\n\n\t\tresponse = jsonify([0])\n\t\tresponse.status_code = 200\n\t\treturn response\n\n\nclass Questions(Resource):\n\t\"\"\"\n\tsend first=-1 to get the first 10\n\t\"\"\"\n\tdef get(self):\n\t\tdetails = request.args\n\n\t\tfirst = int(details[\"First\"])\n\t\tnumber = int(details[\"Number\"])\n\t\ttag = details[\"Tag\"]\n\t\tfaculty = details[\"Faculty\"]\n\n\t\tquestions = []\n\n\t\tcur = mysql.connection.cursor()\n\t\ttag_cur = mysql.connection.cursor()\n\t\tquery = f\"SELECT * FROM Questions WHERE Question_ID>{first}\" \n\t\tif faculty!=\"\":\n\t\t\tquery = query+f\" AND Questions.Faculty_ID='{faculty}'\"\n\t\tif tag!=\"\":\n\t\t\tquery = query+f\" AND Questions.Question_ID IN (SELECT Question_ID from Question_tag WHERE Tag_name='{tag}')\"\n\t\tquery = query+f\" ORDER BY Question_ID DESC LIMIT {number}\"\n\t\tprint(query)\n\t\tcur.execute(query)\n\t\trow_count = cur.rowcount\n\t\tfor row_ind in range(row_count):\n\t\t\tresult = cur.fetchone()\n\t\t\tprint(\"res \",result)\n\t\t\ttag_cur.execute(f\"SELECT Tag_name from Question_tag WHERE Question_ID={result[1]}\")\n\t\t\ttags = []\n\t\t\tfor ind in range(tag_cur.rowcount):\n\t\t\t\ttags.append(str(tag_cur.fetchone()[0]))\n\t\t\tprint(tags)\n\t\t\tquestions.append(\n\t\t\t\t{ \"name\":result[0], \"id\":result[1], \"number\": result[4], \"faculty\": result[5], \"tags\": \" \".join(tags) }\n\t\t\t\t)\n\t\ttag_cur.connection.commit()\n\t\tcur.close()\n\n\t\tresponse = jsonify(questions)\n\t\tresponse.status_code = 200\n\n\t\treturn response\n\nclass Testcase(Resource):\n\t\"\"\"\n\t-1: question id incorrect\n\t-2: testcase number does not exist\n\t\"\"\"\n\tdef get(self, q_id, file_type, t_num):\n\t\tcur = mysql.connection.cursor()\n\t\tcur.execute(f\"SELECT * FROM Questions WHERE Question_ID={q_id}\")\n\t\tresult = cur.fetchone()\n\t\tif result==None or file_type not in ['op', 'ip']:\n\t\t\tresponse = jsonify([-1])\n\t\t\tresponse.status_code = 400\n\t\t\treturn response\n\t\tif result[4]<int(t_num):\n\t\t\tresponse = jsonify([-2])\n\t\t\tresponse.status_code = 400\n\t\t\treturn response\n\t\treturn send_file(f\"./Questions/{q_id}/{file_type}{t_num}.txt\")\n\nclass Submission(Resource):\n\t\"\"\"\n\t\t-1: compilation error\n\t\t-2: user does not exist\n\t\t-3: question does not exits\n\t\tn: number of passed test cases\n\t\"\"\"\n\tdef get(self, q_id, usn):\n\t\tuser_type = get_user_type(usn)\n\n\t\tif user_type==None:\n\t\t\tresponse = jsonify([-2])\n\t\t\tresponse.status_code = 400\n\t\t\treturn response\n\n\t\tcur = mysql.connection.cursor()\n\t\tcur.execute(f\"SELECT Number_Testcases FROM Questions WHERE Question_ID={q_id}\")\n\t\tresult = cur.fetchone()\n\t\tcur.close()\n\n\t\tif result==None:\n\t\t\tresponse = jsonify([-3])\n\t\t\tresponse.status_code = 400\n\t\t\treturn response\n\n\t\tresult_val = 0\n\t\tcur = mysql.connection.cursor()\n\t\tcur.execute(f\"SELECT Correct_testcases FROM Submissions WHERE Question_ID={q_id} and Student_ID='{usn}'\")\n\t\tresult = cur.fetchone()\n\t\tcur.close()\n\n\t\tif result!=None:\n\t\t\tresult_val = result[0]\n\n\t\tresponse = jsonify([result_val])\n\t\tresponse.status_code = 200\n\t\treturn response\n\n\n\n\t\"\"\"\n\t\t-1: usn does not exist\n\t\t-2: question does not exist\n\t\t0: no update\n\t\t1: updated\n\t\"\"\"\n\tdef post(self, q_id, usn):\n\t\textension = {\"cpp\": \"cpp\", \"c\": \"c\", \"python\": \"py\"}\n\n\t\tfiles = request.files\n\t\tdetails = request.form\n\n\t\tlanguage = details[\"language\"]\n\t\tstatuses = [int(val)for val in details[\"status\"].split(\"_\") if val!='']\n\t\tstatus = len([a for a in statuses if a==0])\n\t\tstatuses = \" \".join([str(a) for a in statuses])\n\n\t\tuser_type = get_user_type(usn)\n\n\t\tif user_type==None:\n\t\t\tresponse = jsonify([-1])\n\t\t\tresponse.status_code = 400\n\t\t\treturn response\n\n\t\tcur = mysql.connection.cursor()\n\t\tcur.execute(f\"SELECT Number_Testcases FROM Questions WHERE Question_ID={q_id}\")\n\t\tresult = cur.fetchone()\n\t\tcur.close()\n\n\t\tif result==None:\n\t\t\tresponse = jsonify([-2])\n\t\t\tresponse.status_code = 400\n\t\t\treturn response\n\n\t\tcur = mysql.connection.cursor()\n\t\tcur.execute(f\"SELECT Correct_testcases FROM Submissions WHERE Question_ID={q_id} and Student_ID='{usn}'\")\n\t\tresult = cur.fetchone()\n\t\tcur.close()\n\n\t\tresult_val = 0\n\t\tcur = mysql.connection.cursor()\n\t\tif result!=None:\n\t\t\tif int(result[0])<int(status):\n\t\t\t\tresult_val = 1\n\t\t\t\tcur.execute(f\"UPDATE Submissions SET Correct_testcases={status}, Language='{language}' WHERE Question_ID={q_id} and Student_ID='{usn}'\")\n\n\t\telse:\n\t\t\tcur.execute(f\"INSERT INTO Submissions(Student_ID, Question_ID, Correct_testcases, Language) VALUES ('{usn}', {q_id}, {status}, '{language}')\")\n\t\t\n\t\tcur.connection.commit()\n\t\tcur.close()\n\n\t\tfile_path = f\"./Submissions/{q_id}_{usn}/\"\n\t\ttry:\n\t\t\tos.stat(file_path)\n\t\texcept:\n\t\t\tos.mkdir(file_path)\n\t\t\topen(file_path+\"logs\", \"a\").close()\n\t\t\topen(file_path+\"code.\"+extension[language], \"a\").close()\n\n\t\tfiles[\"code\"].save(file_path+\"code.\"+extension[language])\n\n\t\twith open(file_path+\"logs\", \"a\") as logs:\n\t\t\tlogs.write(f\"{statuses}\\n\")\n\n\t\tresponse = jsonify([result_val])\n\t\tresponse.status_code = 200\n\t\treturn response\n\t\t\n\n\n\n\n\napi.add_resource(Student, \"/codecouch/student/<usn>\")\napi.add_resource(Faculty, \"/codecouch/faculty/<f_id>\")\napi.add_resource(Login, \"/codecouch/login/\")\napi.add_resource(Question, \"/codecouch/question/\")\napi.add_resource(Questions, \"/codecouch/questions/\")\napi.add_resource(Testcase, \"/codecouch/testcases/<q_id>/<file_type>/<t_num>\")\napi.add_resource(Submission, \"/codecouch/submission/<q_id>/<usn>\")\n\n\n\nif __name__==\"__main__\":\n\tapp.run(debug=True)\n","sub_path":"flask/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":11409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"42403803","text":"import PySimpleGUI as sg\r\n\r\n\r\ndef layout():  # Define la interfaz grafica\r\n    # Ejemplo de Pantalla\r\n    lista = [\r\n        [sg.Text(\"Nombre: \", size=(10, 1)), sg.Input(key=\"nombre\")],\r\n        [sg.Text(\"Edad: \", size=(10, 1)), sg.Input(key=\"edad\")],\r\n        [sg.Button(\"Ok\", bind_return_key=True)],\r\n    ]\r\n    lista.append([sg.Button(\"Ok\", bind_return_key=True)]) #agrega otro boton\r\n    return lista\r\n\r\n\r\ndef main(window):\r\n    while True:\r\n        event, values = window.read()\r\n        if event in (None, \"Quit\"):\r\n            break\r\n        print(event, values)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    w = sg.Window(\"Title\", layout())\r\n    main(w)\r\n    w.close()","sub_path":"base_simple.py","file_name":"base_simple.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"116880575","text":"import random\nimport string\n\nfrom django.shortcuts import render\nfrom tethys_sdk.routing import controller\n\nfrom tethys_sdk.gizmos import *\nfrom .app import GeoserverApp as app\n\n\nWORKSPACE = 'geoserver_app'\nGEOSERVER_URI = 'http://www.example.com/geoserver-app'\n\n\n@controller\ndef home(request):\n    \"\"\"\n    Controller for the app home page.\n    \"\"\"\n    # Retrieve a geoserver engine\n    geoserver_engine = app.get_spatial_dataset_service(name='main_geoserver', as_engine=True)\n\n    # Check for workspace and create workspace for app if it doesn't exist\n    response = geoserver_engine.list_workspaces()\n\n    if response['success']:\n        workspaces = response['result']\n\n        if WORKSPACE not in workspaces:\n            geoserver_engine.create_workspace(workspace_id=WORKSPACE, uri=GEOSERVER_URI)\n\n    # Case where the form has been submitted\n    if request.POST and 'submit' in request.POST:\n        # Verify files are included with the form\n        if request.FILES and 'files' in request.FILES:\n            # Get a list of the files\n            file_list = request.FILES.getlist('files')\n\n            # Upload shapefile\n            store = ''.join(random.choice(string.ascii_lowercase + string.digits) for _ in range(6))\n            store_id = WORKSPACE + ':' + store\n            geoserver_engine.create_shapefile_resource(\n                store_id=store_id,\n                shapefile_upload=file_list,\n                overwrite=True\n            )\n\n    context = {}\n\n    return render(request, 'geoserver_app/home.html', context)\n\n\n@controller\ndef map(request):\n    \"\"\"\n    Controller for the map page\n    \"\"\"\n    geoserver_engine = app.get_spatial_dataset_service(name='main_geoserver', as_engine=True)\n\n    options = []\n\n    response = geoserver_engine.list_layers(with_properties=False)\n\n    if response['success']:\n        for layer in response['result']:\n            options.append((layer.title(), layer))\n\n    select_options = SelectInput(\n        display_text='Choose Layer',\n        name='layer',\n        multiple=False,\n        options=options\n    )\n\n    map_layers = []\n\n    if request.POST and 'layer' in request.POST:\n        selected_layer = request.POST['layer']\n        legend_title = selected_layer.title()\n\n        geoserver_layer = MVLayer(\n            source='ImageWMS',\n            options={\n                'url': 'http://localhost:8181/geoserver/wms',\n                'params': {'LAYERS': selected_layer},\n                'serverType': 'geoserver'\n            },\n            legend_title=legend_title,\n            legend_extent=[-114, 36.5, -109, 42.5],\n            legend_classes=[\n                MVLegendClass('polygon', 'County', fill='#999999'),\n        ])\n\n        map_layers.append(geoserver_layer)\n\n\n    view_options = MVView(\n        projection='EPSG:4326',\n        center=[-100, 40],\n        zoom=4,\n        maxZoom=18,\n        minZoom=2\n    )\n\n    map_options = MapView(\n        height='500px',\n        width='100%',\n        layers=map_layers,\n        legend=True,\n        view=view_options\n    )\n\n    context = {\n        'map_options': map_options,\n        'select_options': select_options\n    }\n\n    return render(request, 'geoserver_app/map.html', context)\n\n\n@controller\ndef draw(request):\n    drawing_options = MVDraw(\n        controls=['Modify', 'Move', 'Point', 'LineString', 'Polygon', 'Box'],\n        initial='Polygon'\n    )\n\n    map_options = MapView(\n        height='450px',\n        width='100%',\n        layers=[],\n        draw=drawing_options\n    )\n\n    geometry = ''\n\n    if request.POST and 'geometry' in request.POST:\n        geometry = request.POST['geometry']\n\n    context = {'map_options': map_options,\n               'geometry': geometry}\n\n    return render(request, 'geoserver_app/draw.html', context)\n","sub_path":"tethysapp/geoserver_app/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":3763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"250404585","text":"# coding: utf-8\n\nimport urllib2,json\nfrom requests_oauthlib import OAuth1Session\nimport os\nimport bitly\n\nif os.path.exists('livejson') is False:\n        os.mkdir('livejson')\n\njsonurl = 'http://livetube.cc/index.live.json'\n\nr = urllib2.urlopen(jsonurl)\n\nroot = json.loads(r.read())\n\ndef livetweet(title,liveurl):\n    CK = 'XXXXXXXXXXXXXXXXXX'                             # Consumer Key\n    CS = 'XXXXXXXXXXXXXXXXXX'         # Consumer Secret\n    AT = 'XXXXXXXXXXXXXXXXXX' # Access Token\n    AS = 'XXXXXXXXXXXXXXXXXX'         # Accesss Token Secert\n\n    bitlylogin = 'XXXXXXXXXXXXXXXXXX'\n    apikey = 'XXXXXXXXXXXXXXXXXX'\n\n    #bitly認証\n    bitly_api = bitly.Api(login=bitlylogin, apikey=apikey)\n\n    # ツイート投稿用のURL\n    url_media = \"https://upload.twitter.com/1.1/media/upload.json\"\n    url_text = \"https://api.twitter.com/1.1/statuses/update.json\"\n\n    # OAuth認証で POST method で投稿\n    twitter = OAuth1Session(CK, CS, AT, AS)\n\n    short_url = bitly_api.shorten(liveurl)\n    req = twitter.post(url_text, params = {\"status\": \"【配信開始】\" + title + \"\\n\" + short_url})\n\n    # レスポンスを確認\n    if req.status_code == 200:\n        print (\"OK\")\n    else:\n        print (\"Error: %d\" % req.status_code)\n\n# 画像のダウンロード\ndef imgdownload(url):\n    img = urllib2.urlopen(url)\n    f = open(\"thumbnail.jpg\", \"wb\")\n    f.write(img.read())\n    f.close()\n\nfor entry in xrange(len(root)):\n    if root[entry]['author'] == u'げぇーく':\n        entrynum = entry\n        entryid = root[entry]['id']\n\n        if os.path.exists('livejson\\live.json') is False:\n            f = open(\"livejson\\live.json\", \"w\")\n            dict = {\"author\": \"\",\"id\": \"\",\"link\":\"\",\"title\":\"\"}\n            json.dump(dict, f, sort_keys=True, indent=4)\n            f.close()\n\n        with open('livejson\\live.json', 'r') as f:\n            fenrifja_dic = json.load(f)\n\n        if fenrifja_dic['id'] == entryid:\n            f.close()\n            exit()\n\n        elif fenrifja_dic['id'] != entryid:\n            with open('livejson\\live.json', 'w') as f:\n                json.dump(root[entry], f, sort_keys=True, indent=4)\n                f.close()\n\n            title = root[entry]['title']\n            liveurl = 'http://livetube.cc/' + root[entry]['link']\n\n            livetweet(title,liveurl)\n\n\n\n","sub_path":"livetube-tweet.py","file_name":"livetube-tweet.py","file_ext":"py","file_size_in_byte":2310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"537603229","text":"#EJERCICIO 1.13\n\n# Escribí un programa llamado esfera.py que le pida al usuario que ingrese por teclado \n# el radio r de una esfera y calcule e imprima el volumen de la misma. \n# Sugerencia: recordar que el volúmen de una esfera es 4/3 πr^3.\n\n# Finalmente, ejecutá el programa desde la línea de comandos para responder \n# ¿cuál es el volumen de una esfera de radio 6? Debería darte 904.7786842338603.\n\nimport math\n# r -> Radio de la esfera \nr = int(input(\"Ingrese radio de esfera: \"))\nvolumen= (4/3)* math.pi*(r**3)\nprint(volumen)\n\n#INPUT\n# Ingrese radio de esfera: 6\n# 904.7786842338603\n","sub_path":"Ejercicios/Clase01/esfera.py","file_name":"esfera.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"624026222","text":"#pe_9.py\n#DESCRIPTION\n#A Pythagorean triplet is a set of three natural numbers, a  b  c, for which,\n#\n#a2 + b2 = c2\n#For example, 32 + 42 = 9 + 16 = 25 = 52.\n#\n#There exists exactly one Pythagorean triplet for which a + b + c = 1000.\n#Find the product abc.\n\ndef fun(a,b):\n\treturn a**2+b**2-(1000-a-b)**2\n\ndef nat_num(start=0):\n\twhile(True):\n\t\tyield start\n\t\tstart+=1\n\ngen=nat_num(start=1)\nb=gen.__next__()\n\nbool=True\nwhile(bool):\n\tfor a in range(1,b+1):\n\t\tprint(\"a={0} and b={1}\".format(a,b))\n\t\tif fun(a,b)==0:\n\t\t\tprint(\"a*b*c is {0}\".format(a*b*(1000-a-b)))\n\t\t\tbool=False\n\t\t\tbreak\n\tb=gen.__next__()\n\t\n","sub_path":"pe_9.py","file_name":"pe_9.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"491531285","text":"# vim: ts=2:sw=2:tw=80:nowrap\nimport re\nfrom .register import register_converter\n\ndef to_1_0_0( vardict ):\n  \"\"\"\n  Convert configuration data from version 0.1.9 to 1.0.0\n  \"\"\"\n  from ...processor import messages as msg\n  msg.info(\n    'Converted configuration file from version '\n    '<span color=\"red\">0.1.9</span> to version '\n    '<span color=\"blue\">1.0.0</span>\\n'\n    '<span color=\"green\">Changes:</span>\\n'\n    '  Channels now permit plot scaling and offset.\\n'\n    '  Ensuring each channel config includes plot scaling/offset...\\n'\n    , buttons='ok', ignore_result=True\n  )\n\n  for chlabel, chcfg in vardict['channels'].items():\n    chcfg.setdefault('plot_scale_factor', 1.0)\n    chcfg.setdefault('plot_scale_offset', 0.0)\n  return vardict\n\nregister_converter( '0.1.9', '1.0.0', to_1_0_0 )\n","sub_path":"python/arbwave/tools/compatibility/from_0_1_9.py","file_name":"from_0_1_9.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"3988201","text":"# coding: utf-8\n\n# # Mask R-CNN - Train on Shapes Dataset\n# \n# \n# This notebook shows how to train Mask R-CNN on your own dataset. To keep things simple we use a synthetic dataset of shapes (squares, triangles, and circles) which enables fast training. You'd still need a GPU, though, because the network backbone is a Resnet101, which would be too slow to train on a CPU. On a GPU, you can start to get okay-ish results in a few minutes, and good results in less than an hour.\n# \n# The code of the *Shapes* dataset is included below. It generates images on the fly, so it doesn't require downloading any data. And it can generate images of any size, so we pick a small image size to train faster. \n\n# In[1]:\n\n\nimport os\nimport pickle\nimport traceback\nimport tensorflow as tf\n\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\nfrom keras.backend.tensorflow_backend import set_session\n\nconfig = tf.ConfigProto()\n# config.gpu_options.per_process_gpu_memory_fraction = 0.3\nset_session(tf.Session(config=config))\nimport sys\nimport random\nimport skimage.io\nimport math\nimport re\nimport time\nimport numpy as np\nimport cv2\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nfrom config import Config\nimport utils\nimport model as modellib\nimport visualize\nfrom model import log\nfrom USDataset import USDataset\n\n# get_ipython().run_line_magic('matplotlib', 'inline')\n\n# Root directory of the project\nROOT_DIR = os.getcwd()\n\n# Directory to save logs and trained model\nMODEL_DIR = os.path.join(ROOT_DIR, \"logs\")\n\ndirs = os.listdir(MODEL_DIR)\ndirs = [os.path.join(MODEL_DIR, filename) for filename in dirs]\nctimes = [os.path.getctime(filename) for filename in dirs]\nMODEL_DIR = dirs[ctimes.index(max(ctimes))]\n\n# Path to COCO trained weights\nCOCO_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_coco.h5\")\n\n\n# ## Configurations\n\n# In[2]:\n\n\nclass ShapesConfig(Config):\n    \"\"\"Configuration for training on the toy shapes dataset.\n    Derives from the base Config class and overrides values specific\n    to the toy shapes dataset.\n    \"\"\"\n    # Give the configuration a recognizable name\n    NAME = \"ultar_sound\"\n\n    # Train on 1 GPU and 8 images per GPU. We can put multiple images on each\n    # GPU because the images are small. Batch size is 8 (GPUs * images/GPU).\n    GPU_COUNT = 1\n    IMAGES_PER_GPU = 1\n\n    # Number of classes (including background)\n    NUM_CLASSES = 1 + 1  # background + 3 shapes\n\n    # Use smaller anchors because our image and objects are small\n    RPN_ANCHOR_SCALES = (8, 16, 32, 64, 128)  # anchor side in pixels\n    RPN_ANCHOR_SCALES = (32, 64, 128)  # anchor side in pixels\n\n    # # Reduce training ROIs per image because the images are small and have\n    # # few objects. Aim to allow ROI sampling to pick 33% positive ROIs.\n    TRAIN_ROIS_PER_IMAGE = 320\n    #\n    # # Use a small epoch since the data is simple\n    STEPS_PER_EPOCH = 100\n    #\n    # # use small validation steps since the epoch is small\n    # VALIDATION_STEPS = 5\n\n\nconfig = ShapesConfig()\n\n\n# config.display()\n\n\n# ## Notebook Preferences\n\n# In[3]:\n\n\ndef get_ax(rows=1, cols=1, size=8):\n    \"\"\"Return a Matplotlib Axes array to be used in\n    all visualizations in the notebook. Provide a\n    central point to control graph sizes.\n\n    Change the default size attribute to control the size\n    of rendered images\n    \"\"\"\n    _, ax = plt.subplots(rows, cols, figsize=(size * cols, size * rows))\n    return ax\n\n\n# In[5]:\n\n\n# Training dataset\ndataset_train = USDataset('train.txt')\ndataset_train.prepare()\n\n# Validation dataset\ndataset_val = USDataset('data/label.txt')\ndataset_val.prepare()\n\n\n# In[6]:\n\n\n# Load and display random samples\n# image_ids = np.random.choice(dataset_train.image_ids, 4)\n# for image_id in image_ids:\n#     image = dataset_train.load_image(image_id)\n#     mask, class_ids = dataset_train.load_mask(image_id)\n#     visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names)\n\n# ## Ceate Model\n\n# In[ ]:\n\n\n# ## Detection\n\n# In[11]:\n\n\nclass InferenceConfig(ShapesConfig):\n    GPU_COUNT = 1\n    IMAGES_PER_GPU = 1\n\n\ninference_config = InferenceConfig()\nprint(MODEL_DIR)\n\n# Recreate the model in inference mode\nmodel = modellib.MaskRCNN(mode=\"inference\",\n                          config=inference_config,\n                          model_dir=MODEL_DIR)\n\n# Get path to saved weights\n# Either set a specific path or find last trained weights\n# model_path = os.path.join(MODEL_DIR, \"mask_rcnn_ultar_sound_000%s.h5\" % sys.argv[-1])\nmodel_path = os.path.join(MODEL_DIR, \"mask_rcnn_ultar_sound_000%s.h5\" % 8)\n# model_path = os.path.join(\"mask_rcnn_shapes.h5\")\n# model_path = model.find_last()[1]\n\n# Load trained weights (fill in path to trained weights here)\nassert model_path != \"\", \"Provide path to trained weights\"\nprint(\"Loading weights from \", model_path)\nmodel.load_weights(model_path, by_name=True)\n\n# In[12]:\n\n\n# Test on a random image\n# image_id = random.choice(dataset_val.image_ids)\n# original_image, image_meta, gt_class_id, gt_bbox, gt_mask =    modellib.load_image_gt(dataset_val, inference_config,\n#                            image_id, use_mini_mask=False)\n#\n# log(\"original_image\", original_image)\n# log(\"image_meta\", image_meta)\n# log(\"gt_class_id\", gt_bbox)\n# log(\"gt_bbox\", gt_bbox)\n# log(\"gt_mask\", gt_mask)\n#\n# visualize.display_instances(original_image, gt_bbox, gt_mask, gt_class_id,\n#                             dataset_train.class_names, figsize=(8, 8))\n\n\n# In[13]:\n\n\n# results = model.detect([original_image], verbose=1)\n#\n# r = results[0]\n# visualize.display_instances(original_image, r['rois'], r['masks'], r['class_ids'],\n#                             dataset_val.class_names, r['scores'], ax=get_ax())\n\n\n# ## Evaluation\n\n# In[14]:\n\n\n# Compute VOC-Style mAP @ IoU=0.5\n# Running on 10 images. Increase for better accuracy.\n# image_ids = np.random.choice(dataset_val.image_ids, 330)\nimage_ids = dataset_val.image_ids\nAPs = []\nfor image_id in image_ids:\n    try:\n        # Load image and ground truth data\n        image, image_meta, gt_class_id, gt_bbox, gt_mask = modellib.load_image_gt(dataset_val, inference_config,\n                                                                                  image_id, use_mini_mask=False)\n        # print(image.shape)\n        molded_images = np.expand_dims(modellib.mold_image(image, inference_config), 0)\n        # Run object detection\n        results = model.detect([image], verbose=0)\n\n        r = results[0]\n        rois = r['rois']\n        area = (rois[:, 2] - rois[:, 0]) * (rois[:, 3] - rois[:, 1])\n        ids = np.transpose(np.asarray(np.where(area > 1000)), (0, 1))[0]\n        rois = np.asarray(r['rois'])[ids, :]\n        class_ids = np.asarray(r['class_ids'])[ids]\n        scores = np.asarray(r['scores'])[ids]\n        masks = np.asarray(r['masks'])[:, :, ids]\n        print(ids.shape[0])\n        if ids.shape[0] < 5:\n            ids = np.argsort(scores)[::-1][0: ids.shape[0]]\n        else:\n            ids = np.argsort(scores)[::-1][0: 5]\n        rois = rois[ids, :]\n        class_ids = class_ids[ids]\n        scores = scores[ids]\n        masks = masks[:, :, ids]\n        r = {'rois': rois, 'scores': scores, 'masks': masks, 'class_ids': class_ids}\n\n        IMAGE_DIR = ''\n        file_names = dataset_val.image_info[image_id]['path']\n        # print(file_names)\n        # image = skimage.io.imread(file_names)\n\n        # # Run detection\n        # results = model.detect([image], verbose=1)\n\n        # Visualize results\n        result_name = file_names.split('.')\n        fr = open(result_name[0] + '.dat', 'wb')\n        pickle.dump([image, r, ['mass']], fr)\n        fr.flush()\n        fr.close()\n        print(result_name[0] + '.dat')\n        # Compute AP\n        # AP, precisions, recalls, overlaps = utils.compute_ap(gt_bbox, gt_class_id,\n        #                                                      r[\"rois\"], r[\"class_ids\"], r[\"scores\"])\n        # print(\"************************************\")\n    except Exception as e:\n        traceback.print_exc()\n        # print(AP)\n        # print(precisions)\n        # print(recalls)\n        # print(overlaps)\n        # APs.append(AP)\n\nprint(\"mAP: \", np.mean(APs))\n","sub_path":"test_us.py","file_name":"test_us.py","file_ext":"py","file_size_in_byte":8151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"432683887","text":"from city import *\n\ndef get_from_object_to_list(list_of_obj):\n    \"\"\"Function that changes list of objects to list od lists\"\"\"\n\n    list_of_all = []\n    for key, values in list_of_obj.items():\n        list=[str(values), key]\n        list_of_all.append(list)\n    return list_of_all\n\n\n\ndef print_table(table, title_list):\n    \"\"\"\n    Prints table with data. Sample output:\n        /-----------------------------------\\\n        |   id   |      title     |   type  |\n        |--------|----------------|---------|\n        |   0    | Counter strike |   fps   |\n        |--------|----------------|---------|\n        |   1    |       fo       |   fps   |\n        \\-----------------------------------/\n    Args:\n        table: list of lists - table to display\n        title_list: list containing table headers\n    Returns:\n        This function doesn't return anything it only prints to console.\n    \"\"\"\n    table.insert(0, title_list)\n    width_list = []\n    for i in range(len(table[0])):\n        longest_string = 0\n        for row in table:\n            if len(row[i]) > longest_string:\n                longest_string = len(row[i])\n        width_list.append(longest_string)\n    print(\"╔\", end=\"\")\n    for column in range(len(table[0])):\n        print(\"{0:═^{w}}\".format(\"═\", w=width_list[column]+2), end=\"\")\n        if column+1 != len(table[0]):\n            print(\"╦\", end=\"\")\n    print(\"╗\\n\", end=\"\")\n    '''content'''\n    for row_number, row in enumerate(table):\n        for column, cell in enumerate(row):\n            print(\"║{0:^{w}}\".format(cell, w=width_list[column]+2), end=\"\")\n        print(\"║\\n\", end=\"\")\n        if row_number+1 != len(table):\n            print(\"╠\", end=\"\")\n            for column, cell in enumerate(row):\n                print(\"{0:═^{w}}\".format(\"═\", w=width_list[column]+2), end=\"\")\n                if column+1 != len(table[0]):\n                    print(\"╬\", end=\"\")\n            print(\"╣\\n\", end=\"\")\n        '''footer'''\n        if row_number+1 == len(table):\n            print(\"╚\", end=\"\")\n            for column, cell in enumerate(row):\n                print(\"{0:═^{w}}\".format(\"═\", w=width_list[column]+2), end=\"\")\n                if column+1 != len(table[0]):\n                    print(\"╩\", end=\"\")\n            print(\"╝\")\n    table.remove(table[0])\n\n\n","sub_path":"UI.py","file_name":"UI.py","file_ext":"py","file_size_in_byte":2311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"649082186","text":"from django.db import models\n\nfrom core.metamodels import (\n    BaseModel,\n    IsActiveModel,\n)\n\nfrom costcentre.models import DepartmentalGroup\n\n\n# salaries data\n# define a choice field for this\nclass Grade(IsActiveModel):\n    grade = models.CharField(primary_key=True, max_length=10)\n    gradedescription = models.CharField(\"Grade Description\", max_length=50)\n    order = models.IntegerField\n\n    def __str__(self):\n        return self.grade\n\n    class Meta:\n        verbose_name = \"Grade\"\n        verbose_name_plural = \"Grades\"\n\n\nclass GiftAndHospitalityClassification(IsActiveModel):\n    GIFT = \"Gift\"\n    HOSPITALITY = \"Hospitality\"\n    GF_TYPE = ((GIFT, \"Gift\"), (HOSPITALITY, \"Hospitality\"))\n\n    gift_type = models.CharField(\n        max_length=20, choices=GF_TYPE, default=HOSPITALITY, verbose_name=\"Type\"\n    )\n    gif_hospitality_classification = models.CharField(\"Classification\", max_length=100)\n    sequence_no = models.IntegerField(null=True)\n\n    def __str__(self):\n        return str(self.gif_hospitality_classification)\n\n    class Meta:\n        verbose_name = \"Gift and Hospitality Type\"\n        verbose_name_plural = \"Gift and Hospitality Types\"\n        ordering = [\"sequence_no\"]\n\n\nclass GiftAndHospitalityCategory(IsActiveModel):\n    gif_hospitality_category = models.CharField(\"Category\", max_length=100)\n    sequence_no = models.IntegerField(null=True)\n\n    def __str__(self):\n        return str(self.gif_hospitality_category)\n\n    class Meta:\n        verbose_name = \"Gift and Hospitality Category\"\n        verbose_name_plural = \"Gift and Hospitality Categories\"\n        ordering = [\"sequence_no\"]\n\n\nGIFT_RECEIVED = \"Received\"\nGIFT_OFFERED = \"Offered\"\nOFFER_CHOICE = (\n    (GIFT_RECEIVED, \"Received by DIT Staff\"),\n    (GIFT_OFFERED, \"Given by DIT Staff\"),\n)\n\n\nclass GiftAndHospitalityCompany(IsActiveModel):\n    gif_hospitality_company = models.CharField(\"Company\", max_length=100)\n    sequence_no = models.IntegerField(null=True)\n\n    def __str__(self):\n        return str(self.gif_hospitality_company)\n\n    class Meta:\n        verbose_name = \"Gift and Hospitality Company\"\n        verbose_name_plural = \"Gift and Hospitality Companies\"\n        ordering = [\"sequence_no\"]\n\n    # Gift and Hospitality\n\n\nclass GiftAndHospitality(BaseModel):\n    \"\"\"Model used to keep information of gifts/hospitality received/offered by DIT people.\n    On purpose, I am not using foreign key for people and group,\n    because we need to have a record of details\n    when the gift was registered, not later on.\"\"\"\n\n    id = models.AutoField(\"Record ID\", primary_key=True)\n    old_id = models.IntegerField(null=True, blank=True)\n    classification = models.ForeignKey(\n        GiftAndHospitalityClassification,\n        on_delete=models.PROTECT,\n        limit_choices_to={\"active\": True},\n        verbose_name=\"Type\",\n    )\n\n    group_name = models.CharField(\"Group\", max_length=200, blank=True, null=True)\n    date_agreed = models.DateField(\"Date of event /  gift received\",)\n    venue = models.CharField(max_length=1000)\n    reason = models.CharField(\"Description of offer and reason\", max_length=1000)\n    value = models.IntegerField(\"Estimated value of offer (£)\")\n    rep = models.CharField(\n        \"DIT representative offered to/from\", max_length=200, blank=True\n    )\n\n    group = models.ForeignKey(\n        DepartmentalGroup,\n        on_delete=models.SET_NULL,\n        null=True,\n        blank=True,\n        verbose_name=\"DIT Group\",\n    )\n\n    # rep = models.CharField(\"DIT representative offered to/from\", max_length=255)\n    # group = models.CharField(\"DIT Group offered to/from\", max_length=255)\n    offer = models.CharField(max_length=200, choices=OFFER_CHOICE)\n    company_rep = models.CharField(\n        \"Company representative offered to/from\", max_length=200\n    )\n    company = models.ForeignKey(\n        GiftAndHospitalityCompany,\n        on_delete=models.SET_NULL,\n        limit_choices_to={\"active\": True},\n        null=True,\n        blank=True,\n        verbose_name=\"company\",\n    )\n    company_name = models.CharField(\n        \"Other company\", max_length=200, blank=True, default='')\n    ACTION_TYPE = (\n        (\"Action1\", \"Rejected\"),\n        (\"Action2\", \"Accepted (difference paid to Department)\"),\n        (\"Action3\", \"Accepted (surrendered to Department)\"),\n        (\"Action0\", \"Accepted\"),\n    )\n    action_taken = models.CharField(\n        max_length=200, choices=ACTION_TYPE, verbose_name=\"Action taken\", blank=True\n    )\n    entered_by = models.CharField(max_length=100)\n    entered_date_stamp = models.DateField(\"Date entered\")\n    category = models.ForeignKey(\n        GiftAndHospitalityCategory,\n        on_delete=models.PROTECT,\n        limit_choices_to={\"active\": True},\n        verbose_name=\"category\",\n    )\n    grade = models.ForeignKey(Grade, on_delete=models.PROTECT, verbose_name=\"grade\",\n                              null=True)\n\n    class Meta:\n        verbose_name = \"Gift and Hospitality\"\n        verbose_name_plural = \"Gift and Hospitality\"\n        ordering = [\"-id\"]\n\n    def __str__(self):\n        return str(self.date_agreed)\n\n\nclass GiftHospitalityPermissions(models.Model):\n    class Meta:\n        permissions = ((\"can_view_all_gifthospitality\",\n                        \"Can view all Gift Hospitality entries\"),)\n","sub_path":"gifthospitality/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"269220893","text":"from talon import actions, cron, scope\nfrom talon.scripting import Dispatch\nimport time\n\nmax_log_length = 50\n\n# Contains the state of the content inside of the head up display\n# Widget data like hover states are contained within the widget\n# NOTE - THIS USES A TALON API THAT IS SUBJECT TO CHANGE AND MIGHT BREAK IN FUTURE VERSIONS\nclass HeadUpDisplayContent(Dispatch):\n\n    # Default content to be displayed\n    content = {\n        'mode': 'command',\n        'language': {\n            'ext': None,\n            'forced': False\n        }\n    }\n    \n    # Event log for things like history etc.\n    log = []\n        \n    # Update the content and sends an event if the state has changed\n    def update(self, dict):\n        updated = False\n        for key in dict:\n            if (key in self.content):\n                if not updated and self.content[key] != dict[key]:\n                    updated = True\n                self.content[key] = dict[key]\n            \n            if (key not in self.content):\n                updated = True\n                self.content[key] = dict[key]\n        \n        if updated:\n            self.dispatch(\"content_update\", self.content)\n        \n    def append_to_log(self, type, log_message):\n        self.log.append({'type': type, 'message': log_message, 'time': time.monotonic()})\n        self.log[-max_log_length:]\n        self.dispatch(\"log_update\", self.log)\n        \nhud_content = HeadUpDisplayContent()","sub_path":"state.py","file_name":"state.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"543219752","text":"# -*- coding: utf-8 -*-\nimport math\nimport time\nfrom openerp import netsvc\nfrom openerp.osv import fields, osv\n\nclass wf_stat_seller(osv.osv):\n   _name = \"wf_stat.seller\"\n   _columns = {\n\t'date': fields.date('date'),\n\t'user_id': fields.integer('user_id'),\n\t'company_id': fields.many2one('res.company', 'Company'),\n\t'name': fields.char('name', size=10),\n\t'amount_pos': fields.float('amount_pos'),\n\t'amount_inv': fields.float('amount_inv'),\n\t'amount_total': fields.float('amount_total'),\n\t'shop_id': fields.many2one('sale.shop', 'Shop'),\n\n\t}\nwf_stat_seller()\n\n\n\n","sub_path":"wf_statistik/wf_stat_seller.py","file_name":"wf_stat_seller.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"160022128","text":"# Задание 4 - peak.html http://www.pythonchallenge.com/pc/def/linkedlist.php?nothing=peak.html\nimport requests\nfrom bs4 import BeautifulSoup\n\n\ndef get_html(url, params=None):\n    r = requests.get(url, params=params)\n    return r\n\n\ndef get_content(html):\n    soup = BeautifulSoup(html, 'html.parser')\n    # items = soup.find_all('a', class_='na-card-item')\n    return soup\n\n\ndef parse(url):\n    html = get_html(url)\n    while html.status_code == 200:\n        data = str(get_content(html.text))\n        u = url.split('=')\n        URL = f'{u[0]}={data.split()[-1]}'\n        print(data, URL)\n        html = get_html(URL)\n\n\nparse('http://www.pythonchallenge.com/pc/def/linkedlist.php?nothing=90231')\n","sub_path":"level4.py","file_name":"level4.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"114790760","text":"from flask import url_for\nfrom piggychallenge.utils.tests import assert_status_with_message\n\nclass TestContact(object):\n\tdef test_contact_page(self,client):\n\t\t\"\"\"Contact page should respond with a success 200 \"\"\"\n\t\tresponse = client.get(url_for('contact.mail'))\n\t\tassert response.status_code == 200\n\n\tdef test_contact_form(self,client):\n\t\t\"\"\" Post to contact form \"\"\"\n\t\tform = {\n\t\t\t'email': 'foo@bar.com',\n\t\t\t'message': 'Test message from Piggy Challenge'\n\t\t}\n\n\t\tresponse = client.post(url_for('contact.mail'), data=form, follow_redirects=True)\n\t\tassert_status_with_message(200, response, 'Thank you')","sub_path":"piggychallenge/tests/contact/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"324441229","text":"# coding: UTF-8\nimport torch\nimport torch.nn as nn\n#import torch.nn.functional as F\n#import numpy as np\n\n\nclass Config(object):\n\n    \"\"\"配置参数\"\"\"\n    def __init__(self, dataset):\n        self.model_name = 'RNN'\n        self.train_path = dataset + '/data/data.csv'                                # 训练集\n\n        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'        # 模型训练结果\n\n        self.device = torch.device('cpu')\n        #self.device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')   # 设备\n\n        self.dropout = 0.5                                              # 随机失活\n        self.require_improvement = 10\n        self.num_classes = 1                        # 类别数\n        self.k_folds = 5            #5\n\n        self.num_epochs = 28                                           # epoch数\n        self.batch_size = 35                                      # mini-batch大小\n\n        self.learning_rate = 0.1                                        # 学习率 0.1\n\n        self.hidden_size = 256                                         # lstm隐藏层\n        self.num_layers = 1                                           # lstm层数\n        self.embed = 1\n\n\n\n\nclass Model(nn.Module):\n    def __init__(self, config):\n        super(Model, self).__init__()\n        self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers,\n                            bidirectional=True, batch_first=True, dropout=config.dropout)\n        self.fc1 = nn.Linear(config.hidden_size * 2, config.num_classes)\n\n    def forward(self, x):\n        out = x.type(torch.float32).clone().detach().requires_grad_(True)        #输出的x为[64，4,1]的矩阵\n        out, _ = self.lstm(out)    #3维矩阵输入3维矩阵输出，lstm输出（output,（hn,cn）） output(sep_len,batch,num_diction*hidden_size)\n        #output的形状为[batch，seq_len，hidden_size]取seq_len最后一个位置，形状为（64,512）去做线性变换\n        out = self.fc1(out[:, -1, :])  # 句子最后时刻的 hidden state    out[seq_len，batch，hidden_size]    从隐藏层到输出层的变换\n        return out\n","sub_path":"Codes/RNN.py","file_name":"RNN.py","file_ext":"py","file_size_in_byte":2175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"252899727","text":"#!/usr/bin/env python\n'''\n@File      :favorite_languages_1.py\n@Copyright :luoming\n@Date      :\n@Desc      :\n'''\n\nfavorite_languages = {\n    'jen': ['python', 'ruby'],\n    'sarah': ['c'],\n    'edward':['ruby','go'],\n    'phil': ['python','haskell'],\n}\nfor name, languages in favorite_languages.items():\n    if len(languages)<2:\n        print(\"/n\"+\n          name.title()+\n          \"'s favorite language is\")\n    else:\n        print(\"/n\"+\n          name.title()+\n          \"'s favorite language are\")\n    for language in languages:\n        print(language.title())","sub_path":"favorite_languages_1.py","file_name":"favorite_languages_1.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"103893417","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jun  5 19:11:22 2016\n\n@author: ceciliaLee\n\"\"\"\nfrom numpy import *\nimport operator\nfrom os import listdir\n\n              \n# create a dataset which contains 4 samples with 2 classes\ndef createDataSet(): \n    # create a matrix: each row as a sample  \n    group=array([[1.0, 0.8], [1.0, 1.0], [0.2, 0.3], [0.0, 0.1]]) \n    labels=['A', 'A', 'B', 'B'] # four samples and two classes \n    return group,labels\n\n######################## Codes for KNN Clasifier  \n# method: KNNClasifier(unLabel,dataSet,labels,k)\n# Input:      unLabel:  feature vector of the sample you need to clasify\n#             dataSet:  dataset (N*M samples) with known label (class)\n#             labels:   data set labels (1xM vector)  \n#             k:        number of neighbors to use for comparison   \n              \n# Output:     the label(class) of the unknown sample\n              \ndef KNNClasifier(unLabel,dataSet,labels,k):\n    numSamples=dataSet.shape[0]  # shape[0] stands for the number of rows , axis=1 get number of columns \n\n    ## Step1: calculate Euclidean distance  \n    ## tile(A, reps): Construct an array by repeating A for reps times \n    diff=tile(unLabel, (numSamples, 1)) - dataSet \n    sqDiff=diff**2  # square of difference\n    sqDist = sum(sqDiff, axis = 1) \n    distance=sqDist**0.5  ## sqrt\n    \n    ## Step 2: Sort the distances (ascending)  \n    sortedDistIndex=argsort(distance) #returns the indices that would sort an array in a ascending order\n    \n    classCount={} ## Define a dictionary\n    \n    ## Step 3 : get class label; Choose the min k distance\n    for i in range(k):\n        voteILabel=labels[sortedDistIndex[i]]\n    ## Step 4: Count the tiems labels occur\n        classCount[voteILabel]=classCount.get(voteILabel,0)+1 # when the key voteLabel is not in dictionary classCount, get() will return 0 \n        \n    ## Step 5: The most voted class will return\n    maxCount=0\n    for key, value in classCount.items():\n        if value>maxCount:\n            maxCount=value\n            maxIndex=key\n    return maxIndex","sub_path":"KNN.py","file_name":"KNN.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"451506082","text":"import sqlite3\nconn = sqlite3.connect('vreme.db')\nc = conn.cursor()\nizbrane_minute=[]\nizbrane_ure=[]\nizbrane_temperatura=[]\nx1=[]\ny1=[]\nprint(\"Vnesite dan in mesec katerega želite da bo izrisan graf temperature tistega dneva\")\n\nker_dan = int(input(\"Vnesi željeni dan: \"))\nker_mesec = int(input(\"Vnesi željen mesec: \"))\n\nprint(str(ker_dan) + \". \" + str(ker_mesec) +\".\")\n\n\nfor row in c.execute('SELECT minute FROM poenostavljeno WHERE dan=? AND mesec=? ORDER BY ura, minute', (ker_dan, ker_mesec)):\n    izbrane_minute.append(row)\n\nfor row in c.execute('SELECT ura FROM poenostavljeno WHERE dan=? AND mesec=? ORDER BY ura, minute', (ker_dan, ker_mesec)):\n    izbrane_ure.append(row)\n\nfor row in c.execute('SELECT temperatura FROM poenostavljeno WHERE dan=? AND mesec=? ORDER BY ura, minute', (ker_dan, ker_mesec)):\n    izbrane_temperatura.append(row)\n\nprint(izbrane_minute)\nprint(izbrane_ure)\nprint(izbrane_temperatura)\n\nfor j in range(len(izbrane_ure)):\n    a=(str(izbrane_ure[j][0]) + \":\" + str(izbrane_minute[j][0]))\n    x1.append(a)\n\nfor i in range(len(izbrane_temperatura)):\n    b=(str(izbrane_temperatura[i][0]))\n    y1.append(b)\n","sub_path":"main_različica/podpogrami/poisce_po_podatkovni_bazi.py","file_name":"poisce_po_podatkovni_bazi.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"145421778","text":"from tika import parser\nimport math\nimport json\nfrom shared import transofrmareString\nfrom math import log2\ncarti = ['../carti/carte2.pdf', '../carti/carte3.pdf', '../carti/carte4.pdf', '../carti/carte5.pdf', '../carti/carte6.pdf', '../carti/carte7.pdf'\n, '../carti/carte8.pdf', '../carti/carte9.pdf', '../carti/carte10.pdf', '../carti/carte11.pdf', '../carti/carte12.pdf']\n\ndictionarPrefix = {}\n\nprefix = 3\n\nfor carte in carti:\n\n    raw = parser.from_file(carte)\n\n    raw = raw['content']\n    if raw:\n        for cuvant in raw.split(' '):\n            cuv = transofrmareString(cuvant)\n            if cuv != -1 and len(cuv) > prefix:\n                if cuv[:prefix] in dictionarPrefix:\n                    dictionarPrefix[cuv[:prefix]]+=1\n                else:\n                    dictionarPrefix[cuv[:prefix]] = 0\n                    dictionarPrefix[cuv[:prefix]]+=1\n            elif cuv != -1 and len(cuv) == 3:\n                if cuv in dictionarPrefix:\n                    dictionarPrefix[cuv]+=1\n                else:\n                    dictionarPrefix[cuv] = 1\n            elif cuv != -1 and len(cuv) == 2:\n                if cuv in dictionarPrefix:\n                    dictionarPrefix[cuv]+=1\n                else:\n                    dictionarPrefix[cuv] = 1\n            elif cuv != -1 and len(cuv) == 1:\n                cuv = '00' + cuv\n                if cuv in dictionarPrefix:\n                    dictionarPrefix[cuv]+=1\n                else:\n                    dictionarPrefix[cuv] = 1\n\n\nwith open('Dataset.json') as json_file:\n    data = json.load(json_file)\n    for cuv in data:\n        cuvNou = transofrmareString(cuv)\n        if cuvNou!=-1 and len(cuvNou) > prefix and not(cuvNou[:prefix] in dictionarPrefix):\n            dictionarPrefix[cuvNou[:prefix]] = 1\n            # daca prefixul meu nu apare in 10 romane analizate, atunci ii consider totalul de aparitii fiind 1 .\n        elif cuvNou != -1 and len(cuvNou) == 3:\n            if cuvNou in dictionarPrefix:\n                dictionarPrefix[cuvNou]+=1\n            else:\n                dictionarPrefix[cuvNou] = 1\n        elif cuvNou != -1 and len(cuvNou) == 2:\n            cuvNou = '0' + cuvNou\n            if cuvNou in dictionarPrefix:\n                dictionarPrefix[cuvNou]+=1\n            else:\n                dictionarPrefix[cuvNou] = 1\n        elif cuvNou != -1 and len(cuvNou) == 1:\n            cuvNou = '00' + cuvNou\n            if cuvNou in dictionarPrefix:\n                dictionarPrefix[cuvNou]+=1\n            else:\n                dictionarPrefix[cuvNou] = 1\n        \n            \n\nwith open(\"prefixeCuvinteFrecvente.json\", \"w\") as f:\n    json.dump(dictionarPrefix, f)\n\n\n\n\n","sub_path":"generators/generatorForPrefixXLetters.py","file_name":"generatorForPrefixXLetters.py","file_ext":"py","file_size_in_byte":2659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"478838388","text":"\"\"\"\nAuthor: Austin Bren\n\nDescription: Contains sample courses to evaluate\n\nSample Course:\n\nSTATES\n=========================\n|(3,1),(3,2),(3,3),(3,4)|\n|(2,1),(2,2),(2,3),(2,4)|\n|(1,1),(1,2),(1,3),(1,4)|\n=========================\n\nACTIONS:\n      N\n     W E\n      S\n\"\"\"\n\nfrom robot_data import Data\nimport numpy as np\nimport os\nimport pandas as pd\n\ndef make_numeric(in_mat):\n      mat = in_mat\n      for i in range(len(mat)):\n            for j in range(len(mat[0])):\n                  if mat[i][j] != 'b' and type(mat[i][j]) == str:\n                      mat[i][j] = eval(mat[i][j])\n      return mat\n\n\nclass Courses():\n      def lava(self, l_err = 0.1, r_err = 0.1):\n            n_rows = 5\n            n_columns = 5\n            blocked_states = [(2,3),(2,4),(3,4),(4,4)]\n            profit_mod = {(1,1):-100,(2,1):-100,(3,1):-100,(4,1):-100,(5,1):-100,(3,3):-100,(4,3):-100,(5,3):10}\n            \n            return Data(n_rows, n_columns, l_err, r_err, blocked_states, profit_mod)\n      \n      def rand(self, l_err = 0.1, r_err = 0.1, n_rows = 15, n_columns = 8, seed = 15, num_blocked_states = 30, num_value_states = 3, num_cost_states = 25):            \n            np.random.seed(seed)\n            \n            potential_states = [(i,j) for i in range(1,n_rows+1) for j in range(1,n_columns+1)]\n            blocked_state_indices = -np.sort(-np.random.choice(range(len(potential_states)), num_blocked_states, replace=False))\n            blocked_states = []\n            for i in blocked_state_indices:\n                  blocked_states.append(potential_states[i])\n                  potential_states.remove(potential_states[i])\n                        \n            profit_mod = {}\n            for state in potential_states:\n                  profit_mod[state]=0\n                  \n            value_state_indices = np.random.choice(range(len(potential_states)), num_value_states, replace=False)\n            for state in value_state_indices:\n                  profit_mod[potential_states[state]]=np.random.randint(1,5)\n                  \n            cost_state_indices = np.random.choice(range(len(potential_states)), num_value_states, replace=False)\n            for state in cost_state_indices:\n                  profit_mod[potential_states[state]]=-np.random.randint(1,5)\n                  \n            return Data(n_rows, n_columns, l_err, r_err, blocked_states, profit_mod)\n\n      def small(self, l_err = 0.1, r_err = 0.1):\n            n_rows = 2\n            n_columns = 2\n            blocked_states = []\n            profit_mod =  {(1,1):1,(2,1):0.5,(2,2):-1}\n            \n            return Data(n_rows, n_columns, l_err, r_err, blocked_states, profit_mod)\n      \n      def medium(self, l_err = 0.1, r_err = 0.1):\n            n_rows = 3\n            n_columns = 3\n            blocked_states = [(2,2)]\n            profit_mod = {(1,1):1,(2,1):0.5,(3,3):-1}\n\n            return Data(n_rows, n_columns, l_err, r_err, blocked_states, profit_mod)\n\n      def large(self, l_err = 0.1, r_err = 0.1):\n            n_rows = 4\n            n_columns = 4\n            blocked_states = [(2,2)]\n            profit_mod = {(1,1):1,(4,4):0.5,(4,2):-1}\n\n            return Data(n_rows, n_columns, l_err, r_err, blocked_states, profit_mod)\n\n      def classic(self, l_err = 0.1, r_err = 0.1):\n            n_rows = 3\n            n_columns = 4\n            blocked_states = [(2,2)]\n            profit_mod = {}\n            for i in range(1,4):\n                  for j in range(1,5):\n                        if (i,j)!=(2,2):\n                              profit_mod[(i,j)] = -0.01\n            profit_mod[(2,4)] = -1\n            profit_mod[(3,4)] = 1\n\n            return Data(n_rows, n_columns, l_err, r_err, blocked_states, profit_mod)\n      \n      def custom(self, l_err = 0.1, r_err = 0.1, name = 'custom_ex1.csv'):           \n            df = pd.read_csv(os.getcwd()+\"\\\\Input_Data\\\\\"+name, header=None)\n            mat = make_numeric(df.values.tolist())\n            \n            n_rows = len(mat)\n            n_columns = len(mat[0])\n            blocked_states = []\n            profit_mod = {}\n            for i in range(1,n_rows+1):\n                  for j in range(1,n_columns+1):\n                        if mat[i-1][j-1] == 'b':\n                              blocked_states.append((n_rows+1-i,j))\n                        else:\n                              profit_mod[(n_rows+1-i,j)] = mat[i-1][j-1]\n                              \n            return Data(n_rows, n_columns, l_err, r_err, blocked_states, profit_mod)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"robot_courses.py","file_name":"robot_courses.py","file_ext":"py","file_size_in_byte":4508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"232032596","text":"from django.contrib import admin\nfrom django.urls import path\n\nfrom movies import views\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('', views.index, name='index'),\n\n    ## 재현님\n    path('AgeChart', views.AgeChart, name='AgeChart'),\n    path('ages', views.ages, name='ages'),\n    path('ScatterPlot', views.ScatterPlot, name='ScatterPlot'),\n    path('sp', views.sp, name='sp'),\n    path('imrate', views.imrate, name='imrate'),\n    path('imdbrate', views.imdbrate, name='imdbrate'),\n\n    ## 현수님\n    path('tp', views.tp, name='tp'),\n    path('chs1', views.chs1, name='chs1'),\n    path('tablechs', views.tablechs, name='tablechs'),\n    path('tablechs2', views.tablechs2, name='tablechs2'),\n\n    ## 운기님\n    path('matrix', views.matrix, name='matrix'),\n\n    ## 청하님\n    path('tp1', views.tp1, name='tp1'),\n    path('TP1', views.TP1, name='TP1'),\n    path('tp32', views.tp32, name='tp32'),\n    path('TP32', views.TP32, name='TP32'),\n    path('tpex', views.tpex, name='tpex'),\n    path('TPex', views.TPex, name='TPex')\n]\n","sub_path":"movies/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"28547352","text":"#!/usr/bin/python3\n\nimport sys\nsys.path.append('../ry')\nfrom libry import *\n\nK = Config()\nD = K.view()\n\nK.addFile('../test/lgp-example.g');\n\nlgp = K.lgp(\"../test/fol.g\");\n\nlgp.optimizeFixedSequence(\"(grasp baxterR stick) (push stickTip redBall table1) (grasp baxterL redBall) \");\n\ninput(\"Press Enter to continue...\")\n","sub_path":"examples/lgp-example.py","file_name":"lgp-example.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"608722155","text":"\"\"\"Typelib provides functions to work with types.\"\"\"\nimport itertools\n\nfrom . import errors, astlib, defs, utils\nfrom .context import context\nfrom .utils import A\n\n\npytype = type\n\n\nclass InferenceResult:\n    __slots__ = (\"type\", \"casted\")\n\n    def __init__(self, type, casted):\n        self.type = type\n        self.casted = casted\n\n    def __iter__(self):\n        return iter((self.type, self.casted))\n\n\ndef infer_default_value(type):\n    \"\"\"Infers default value from type.\"\"\"\n    # TODO: write\n    errors.not_implemented(infer_default_value, \"write me\")\n\n\ndef create_type_placeholder() -> astlib.TypePlaceholder:\n    \"\"\"Creates new type placeholder in type placeholder env and\n    returns it's object.\n    \"\"\"\n    type_placeholder_name = str(context.type_placeholder_n)\n    type_placeholder = astlib.TypePlaceholder(type_placeholder_name)\n    context.type_placeholders[type_placeholder_name] = type_placeholder\n    context.type_placeholder_n += 1\n    return type_placeholder\n\n\ndef implements_interface(implemented_infs: list, interface_name: str):\n    \"\"\"Searches for interface in implemented_infs.\n    Supports interface inheritance.\n    \"\"\"\n    def check_implementation(interface):\n        if interface in A(astlib.Name):\n            if str(interface) == interface_name:\n                return True\n            impled_info = context.env.get_interface(interface)\n            if impled_info.interfaces:\n                if implements_interface(\n                        impled_info.interfaces, interface_name):\n                    return True\n        elif interface in A(astlib.GenericType):\n            return check_implementation(interface.base)\n        elif interface in A(astlib.Annotated):\n            return check_implementation(interface.node)\n        else:\n            errors.unrecognized_node(check_implementation, interface)\n\n    return any((check_implementation(impled) for impled in implemented_infs))\n\n\ndef _emulate_is_py_type(subtype, supertype):\n    if supertype in A(astlib.PyType):\n        return (str(subtype.name) == str(supertype.name) and\n                all((\n                    emulate_is(p1, p2)\n                    for p1, p2 in zip(subtype.params, supertype.params)\n                )))\n    else:\n        return False\n\n\ndef _emulate_is_generic_type(subtype, supertype):\n    if subtype in A(astlib.GenericType):\n        subtype_info = context.env.get_type(subtype.base)\n        subtype_params = subtype.params\n    elif subtype in A(astlib.Name):\n        subtype_info = context.env.get_type(subtype)\n        subtype_params = []\n    else:\n        errors.unrecognized_node(_emulate_is_generic_type, subtype)\n\n    if supertype in A(astlib.GenericType):\n        supertype_info = context.env.get_type(supertype.base)\n        supertype_params = supertype.params\n        supertype_str = str(supertype.base)\n    elif supertype in A(astlib.Name):\n        supertype_info = context.env.get_type(supertype)\n        supertype_params = []\n        supertype_str = str(supertype)\n    else:\n        errors.unrecognized_node(_emulate_is_generic_type, supertype)\n\n    if (subtype_info.entryt == astlib.EntryT.struct and\n            supertype_info.entryt == astlib.EntryT.struct):\n        if not subtype_params or not supertype_params:\n            return False\n        return (\n            str(subtype.base) == str(supertype.base) and all([\n                emulate_is(subtype_param, supertype_param)\n                for subtype_param, supertype_param in zip(\n                    subtype_params, supertype_params)\n            ]))\n    elif (subtype_info.entryt == astlib.EntryT.adt and\n            supertype_info.entryt == astlib.EntryT.adt):\n        if not subtype_params or not supertype_params:\n            return False\n        return (\n            str(subtype.base) == str(supertype.base) and all([\n                emulate_is(subtype_param, supertype_param)\n                for subtype_param, supertype_param in zip(\n                    subtype_params, supertype_params)\n            ]))\n    elif (subtype_info.entryt == astlib.EntryT.struct and\n            supertype_info.entryt == astlib.EntryT.interface):\n        return implements_interface(subtype_info.interfaces, supertype_str)\n    else:\n        errors.unrecognized_node(_emulate_is_generic_type, subtype)\n\n\ndef emulate_is(subtype, supertype):\n    \"\"\"Statically emulates Adrian's is operator.\n    Is operator rules:\n     0. (Internal names):\n        1. Void is Void -> True\n           Void is Object -> False\n           Void is Other -> False\n           Other is Void -> False\n        2. Bool is Bool -> True\n           Bool is Object -> True\n           Bool is Other -> False\n           Other is Bool -> False\n        3. Object is Object -> True\n           Object is Other -> False\n           Other is Object -> True (if Other != Void)\n     1. `subtype` is struct and `supertype` is struct ->\n        return `subtype` == `supertype`\n     2. `subtype` is struct and `supertype` is interface ->\n        return `subtype` implements `supertype`\n     3. `subtype` is interface and `supertype` is interface ->\n        return `subtype` == `supertype` or `subtype` inherits from `supertype`\n     4. `subtype` is adt and `supertype` is adt ->\n        return `subtype` == `supertype`\n     TODO: write more rules\n    \"\"\"\n    if subtype in A(astlib.GenericType) or supertype in A(astlib.GenericType):\n        return _emulate_is_generic_type(subtype, supertype)\n    if subtype in A(astlib.Name):\n        if str(subtype) in defs.INTERNAL_NAMES:\n            if supertype in A(astlib.Name):\n                return (str(subtype) == str(supertype) or\n                        (str(supertype) == defs.OBJECT and\n                         str(subtype) != defs.VOID))\n            return False\n    if subtype in A(astlib.TypePlaceholder):\n        return True\n    elif subtype in A(astlib.PyType):\n        return _emulate_is_py_type(subtype, supertype)\n    subtype_info = context.env.get(subtype)\n    supertype_info = context.env.get_type(supertype)\n    if subtype_info.entryt in (\n            astlib.EntryT.var, astlib.EntryT.let, astlib.EntryT.arg):\n        return emulate_is(\n            infer_type(subtype).type, supertype)\n    elif (subtype_info.entryt == astlib.EntryT.struct and\n            supertype_info.entryt == astlib.EntryT.struct):\n        return str(subtype) == str(supertype)\n    elif (subtype_info.entryt == astlib.EntryT.struct and\n            supertype_info.entryt == astlib.EntryT.interface):\n        return implements_interface(subtype_info.interfaces, str(supertype))\n    elif (subtype_info.entryt == astlib.EntryT.interface and\n            supertype_info.entryt == astlib.EntryT.interface):\n        return (str(subtype) == str(supertype) or\n                implements_interface(subtype_info.interfaces, str(supertype)))\n    elif (subtype_info.entryt == astlib.EntryT.adt and\n            supertype_info.entryt == astlib.EntryT.adt):\n        return str(subtype) == str(supertype)\n    else:\n        errors.unrecognized_node(emulate_is, subtype)\n\n\ndef unify(t1, t2, e1=None, e2=None, comment=None):\n    \"\"\"Checks types for equality and returns the most specific type.\n    Call type errors. If one of types is a type placeholder ->\n    replaces it's type to other type\n\n    Rules to determine the most specific type:\n    1. struct or adt is more specific than interface\n    2. anything is more specific than type placeholder\n    3. else -> type error\n    \"\"\"\n    def _unify(t1, t2, try_to_recover=True):\n        \"\"\"Same as unify, but doesn't call type errors.\n        Returns None if cannot unify types\n        \"\"\"\n        # TODO: maybe if t is type placeholder and\n        # e is None -> not write to env, but return?\n        if t1 in A(astlib.TypePlaceholder):\n            context.type_placeholders[t1.name] = t2\n            return t2\n        elif t2 in A(astlib.TypePlaceholder):\n            context.type_placeholders[t2.name] = t1\n            return t1\n        elif t1 in A(astlib.PyType):\n            if t2 in A(astlib.TypeExpr):\n                if t2.op == defs.AND:\n                    # Py type cannot be something more then py type.\n                    return None\n                return _unify(t1, t2.left) or _unify(t1, t2.right)\n            elif t2 in A(astlib.Name):\n                if str(t2) == defs.OBJECT:\n                    # Anything is Object.\n                    return t1\n            elif t2 in A(astlib.PyType):\n                if t1.name == t2.name:\n                    params = []\n                    for p1, p2 in zip(t1.params, t2.params):\n                        param = _unify(p1, p2)\n                        if param is None:\n                            return None\n                        params.append(param)\n                    return astlib.PyType(t1.name, params)\n            return None\n        elif t1 in A(astlib.Name):\n            if t2 in A(astlib.Name):\n                if emulate_is(subtype=t1, supertype=t2):\n                    return t1\n                return None\n            elif t2 in A(astlib.Is):\n                for interface in t2.infs:\n                    if not emulate_is(t1, interface):\n                        return False\n                return _unify(t1, t2.base)\n            elif t2 in A(astlib.GenericType):\n                if emulate_is(subtype=t1, supertype=t2):\n                    return t1\n                return None\n            errors.unrecognized_node(_unify, t2)\n        elif t1 in A(astlib.GenericType):\n            if str(t1.base) == defs.FUNCTION_TYPE:\n                if t2 in A(astlib.GenericType) and str(t2.base) == str(t1.base):\n                    base = t1.base\n                    params = []\n                    for p1, p2 in itertools.zip_longest(t1.params, t2.params):\n                        if p1 is None or p2 is None:\n                            return None\n                        params.append(_unify(p1, p2))\n                    return astlib.GenericType(base, params)\n                return None\n            t1_info = context.env.get_type(t1.base)\n            if t2 in A(astlib.GenericType):\n                t2_info = context.env.get_type(t2.base)\n                if t1_info.entryt == astlib.EntryT.struct:\n                    if t2_info.entryt == astlib.EntryT.interface:\n                        t2_impl_type = get_interface_impl_decl(t1_info, t2)\n                        if t2_impl_type is None:\n                            return None\n                        param_mapping = build_param_mapping(\n                            construct_type(t1_info.name, t1_info.params), t1)\n                        applied = apply_param_mapping(\n                            t2_impl_type, param_mapping)\n                        return _unify(applied, t2)\n                    elif t2_info.entryt == astlib.EntryT.struct:\n                        base = _unify(t1.base, t2.base)\n                        return astlib.GenericType(\n                            base, [\n                                _unify(p1, p2)\n                                for p1, p2 in zip(t1.params, t2.params)\n                            ])\n                elif t1_info.entryt == astlib.EntryT.interface:\n                    base = _unify(t1.base, t2.base)\n                    return astlib.GenericType(\n                        base, [\n                            _unify(p1, p2)\n                            for p1, p2 in zip(t1.params, t2.params)\n                        ])\n                elif t1_info.entryt == astlib.EntryT.adt:\n                    if t2_info.entryt == astlib.EntryT.adt:\n                        base = _unify(t1.base, t2.base)\n                        return astlib.GenericType(\n                            base, [\n                                _unify(p1, p2)\n                                for p1, p2 in zip(t1.params, t2.params)\n                            ])\n            elif t2 in A(astlib.Name):\n                t2_info = context.env.get_type(t2)\n                if t1_info.entryt == astlib.EntryT.struct:\n                    if t2_info.entryt == astlib.EntryT.interface:\n                        t2_impl_type = get_interface_impl_decl(t1_info, t2)\n                        if t2_impl_type is None:\n                            return None\n                        return _unify(t2_impl_type, t2)\n                elif t1_info.entryt == astlib.EntryT.interface:\n                    return None\n            elif t2 in A(astlib.Annotated):\n                return _unify(t1, t2.node)\n            errors.unrecognized_node(_unify, t2)\n        else:\n            if try_to_recover:\n                return _unify(t2, t1, try_to_recover=False)\n            errors.unrecognized_node(_unify, t1)\n\n    result = _unify(t1, t2)\n    if result is None:\n        errors.type_error(t1, t2, e1, e2, comment)\n    return result\n\n\ndef construct_type_from_name(name):\n    \"\"\"Searches for name in env. If it doesn't have params -> returns name\n    else -> returns generic type parameterized with params.\n    Name not found -> error\n    \"\"\"\n    info = context.env.get_type(name)\n    if info.entryt == astlib.EntryT.adt_constructor:\n        return name\n    if info.params:\n        return astlib.GenericType(name, info.params)\n    return name\n\n\ndef construct_type(name, params: list):\n    \"\"\"If params is empty -> returns name\n    else -> returns generic type parameterized with params\n    \"\"\"\n    if params:\n        return astlib.GenericType(name, params)\n    return name\n\n\ndef is_py_type(type):\n    \"\"\"Returns type_of_node(type) == PyType.\"\"\"\n    return type in A(astlib.PyType)\n\n\ndef to_real_type(name):\n    \"\"\"Looks for name in env,\n    if name has parameters -> returns generic type using parameters\n    elif not found -> error\n    else -> returns name\n    \"\"\"\n    info = context.env.get_type(name)\n    if info.entryt == astlib.EntryT.adt_constructor:\n        return name\n    return construct_type(name, info.params)\n\n\ndef get_parameter_of_maybe(maybe_type):\n    \"\"\"Returns parameter of Maybe type.\n    Example:\n    in = Maybe(Number)\n    out = Number\n    \"\"\"\n    return maybe_type.params[0]\n\n\ndef get_interface_impl_decl(type_info, interface_type):\n    \"\"\"Returns type that maps to interface_type in interface implementations of\n    type. Returns None if type does not support interface.\n    Example:\n    type_info = info of Vector(Number), interface_type = Subscriptable(a, b)\n    result = Subscriptable(Number, a)\n    (because Vector(a) is Subscriptable(Number, a))\n    \"\"\"\n    def check(interface):\n        if interface_type in A(pytype(interface)):\n            if interface_type in A(astlib.GenericType):\n                if str(interface_type.base) == str(interface.base):\n                    return interface\n            elif interface_type in A(astlib.Name):\n                if str(interface_type) == str(interface):\n                    return interface\n            else:\n                errors.unrecognized_node(\n                    get_interface_impl_decl, interface_type)\n        elif interface in A(astlib.Annotated):\n            node = interface.node\n            if node is None:\n                return node\n            return astlib.Annotated(node, interface.annotations)\n        return None\n\n    for interface in type_info.interfaces:\n        result = check(interface)\n        if result is not None:\n            return result\n    return None\n\n\ndef get_interface_impl(type, interface_name: str):\n    \"\"\"Returns implementations of all methods from interface implemented in\n    type. If type doesn't implement interface or method -> error.\n    \"\"\"\n    interface_info = context.env.get(interface_name)\n    if type in A(astlib.Name):\n        info = context.env.get(type)\n        implemented_methods = {}\n        if info.entryt == astlib.EntryT.struct:\n            if not implements_interface(info.interfaces, interface_name):\n                errors.doesnt_support_interface(type, interface_name)\n            for method_name, method_info in {\n                    **interface_info.inherited_methods,\n                    **interface_info.methods}.items():\n                method_impl = info.methods.get(method_name)\n                if method_impl is None and not method_info.has_annotation(\n                        defs.ANNOTATION_NOT_REQUIRED):\n                    errors.doesnt_implement_interface_in_full(\n                        type, interface_name, method_name)\n                # TODO: check types in signatures\n                implemented_methods[method_name] = method_impl\n            return implemented_methods\n        else:\n            errors.not_implemented(\n                \"cannot get interface implementation from non-struct name\")\n    elif type in A(astlib.GenericType):\n        info = context.env.get(type.base)\n        implemented_methods = {}\n        if info.entryt == astlib.EntryT.struct:\n            if not implements_interface(info.interfaces, interface_name):\n                errors.doesnt_support_interface(type, interface_name)\n            for method_name, method_info in {\n                    **interface_info.inherited_methods,\n                    **interface_info.methods}.items():\n                method_impl = info.methods.get(method_name)\n                if method_impl is None and not method_info.has_annotation(\n                        defs.ANNOTATION_NOT_REQUIRED):\n                    errors.doesnt_implement_interface_in_full(\n                        type, interface_name, method_name)\n                # TODO: check types in signatures\n                implemented_methods[method_name] = method_impl\n            return implemented_methods\n    else:\n        errors.unrecognized_node(get_interface_impl, type)\n\n\ndef is_param(type):\n    \"\"\"Returns bool = type is not declarated or type is not a type.\"\"\"\n    info = context.env[str(type)]\n    if info is None:\n        return True\n    return info.entryt not in (\n        astlib.EntryT.struct, astlib.EntryT.interface, astlib.EntryT.adt)\n\n\ndef replace_type_placeholders_with_types(type):\n    \"\"\"Replaces type placeholders in type with types they map to (in env).\n    If type placeholder doesn't map to type ->\n        same type placeholder will be used.\n    \"\"\"\n    if type in A(astlib.Name):\n        return type\n    elif type in A(astlib.TypePlaceholder):\n        return context.type_placeholders.get(type.name, type)\n    elif type in A(astlib.Is):\n        return astlib.Is(\n            replace_type_placeholders_with_types(type.base),\n            [replace_type_placeholders_with_types(inf) for inf in type.infs])\n    elif type in A(astlib.GenericType):\n        return astlib.GenericType(\n            type.base, [\n                replace_type_placeholders_with_types(param)\n                for param in type.params\n            ])\n    else:\n        errors.unrecognized_node(replace_type_placeholders_with_types, type)\n\n\ndef replace_params_with_type_placeholders(signature):\n    \"\"\"Replaces parameters with type placeholders in signature.\n    Non-param arg types will be the same.\n    Returns new signature.\n    Use for function or method signatures to replace params introduced in\n    `signature`.\n    \"\"\"\n    param_to_type_placeholder = {}\n\n    def produce_new_type_for_signature(type):\n        \"\"\"Produces new type based on `type` argument, but replaces params with\n        type placeholders. Same params -> same type placeholders\n        \"\"\"\n        if type in A(astlib.Name):\n            if is_param(type):\n                found = param_to_type_placeholder.get(str(type), None)\n                if found:\n                    return astlib.TypePlaceholder(found)\n                t_placeholder = create_type_placeholder()\n                param_to_type_placeholder[str(type)] = t_placeholder.name\n                return t_placeholder\n            return type\n        elif type in A(astlib.Is):\n            return astlib.Is(\n                produce_new_type_for_signature(type.base),\n                [produce_new_type_for_signature(inf) for inf in type.infs])\n        elif type in A(astlib.GenericType):\n            return astlib.GenericType(\n                type.base, [\n                    produce_new_type_for_signature(param)\n                    for param in type.params\n                ])\n        elif type in A(astlib.PyType):\n            return astlib.PyType(\n                type.name, [\n                    produce_new_type_for_signature(param)\n                    for param in type.params\n                ])\n        elif type in A(astlib.TypePlaceholder):\n            # TODO: maybe we should create new placeholder? or placeholder here\n            # is an error?\n            return type\n        else:\n            errors.unrecognized_node(produce_new_type_for_signature, type)\n\n    args = [\n        astlib.Arg(arg.name, produce_new_type_for_signature(arg.type), arg.expr)\n        for arg in signature.args]\n    return_type = produce_new_type_for_signature(signature.return_type)\n    return astlib.FuncSignature(args, return_type)\n\n\ndef apply_param_mapping_on_signature(signature, mapping):\n    \"\"\"Replaces params in signature with values in mapping (if found).\n    Returns new signature.\n    \"\"\"\n    args = [\n        astlib.Arg(arg.name, apply_param_mapping(arg.type, mapping), arg.expr)\n        for arg in signature.args]\n    return_type = apply_param_mapping(signature.return_type, mapping)\n    return astlib.FuncSignature(args, return_type)\n\n\ndef apply_param_mapping(type, mapping):\n    \"\"\"Replaces params in type with values in mapping (if found).\n    Returns new type.\n    \"\"\"\n\n    def apply(t):\n        if t in A(astlib.Name):\n            return mapping.get(str(t), t)\n        elif t in A(astlib.GenericType):\n            return astlib.GenericType(\n                t.base, [apply(param) for param in t.params])\n        elif t in A(astlib.PyType):\n            return astlib.PyType(t.name, [apply(param) for param in t.params])\n        elif t in A(astlib.Annotated):\n            return astlib.Annotated(apply(t.node), t.annotations)\n        else:\n            errors.unrecognized_node(apply, t)\n\n    return apply(type)\n\n\ndef build_param_mapping(decl_type, type):\n    \"\"\"Builds parameter mapping based on {type_param: decl_type_param}.\n    Example:\n    decl_type = Maybe(a)\n    type = Maybe(Number)\n    result = {a: Number}\n    \"\"\"\n    mapping = {}\n\n    def replace(t_decl, t):\n        if t_decl in A(astlib.Name):\n            if is_param(t_decl):\n                mapping[str(t_decl)] = t\n        elif t_decl in A(astlib.GenericType):\n            assert(t in A(astlib.GenericType))\n            for p_decl, p in itertools.zip_longest(t_decl.params, t.params):\n                assert(p_decl is not None and p is not None)\n                replace(p_decl, p)\n        else:\n            errors.unrecognized_node(replace, t_decl)\n\n    replace(decl_type, type)\n    return mapping\n\n\ndef change_type(expr, to_type):\n    \"\"\"Changes type of expr to to_type.\"\"\"\n    if expr in A(astlib.Name):\n        context.env.get(expr).casted = to_type\n    else:\n        errors.unrecognized_node(change_type, expr)\n\n\ndef _infer_type_from_py_type_call(name, args):\n    if name in defs.PY_GENERIC_TYPES:\n        if name == defs.PY_LIST:\n            initial_type_placeholder = create_type_placeholder()\n            if not utils.contains_exactly_n_elements(args, n=1):\n                errors.wrong_number_of_args(\n                    func=astlib.PyFunc(name), exp=1, got=len(args))\n            elements = args[0].text\n            for elem in elements:\n                initial_type_placeholder = unify(\n                    infer_type(elem).type, initial_type_placeholder, e1=elem,\n                    e2=None, comment=(\n                        \"you passed element (e1) of a wrong type (t1) to \"\n                        \"the list of type t2\"\n                    ))\n            return astlib.PyType(defs.PY_LIST, [initial_type_placeholder])\n        elif name == defs.PY_DICT:\n            key_type_placeholder = create_type_placeholder()\n            val_type_placeholder = create_type_placeholder()\n            if not utils.contains_exactly_n_elements(args, n=1):\n                errors.wrong_number_of_args(\n                    func=astlib.PyFunc(name), exp=1, got=len(args))\n            elements = args[0].text\n            for (key, val) in elements:\n                key_type_placeholder = unify(\n                    infer_type(key).type, key_type_placeholder, e1=key,\n                    e2=None, comment=(\n                        \"you passed key (e1) of a wrong type (t1) to \"\n                        \"the dict with key type = t2\"\n                    ))\n                val_type_placeholder = unify(\n                    infer_type(val).type, val_type_placeholder, e1=val,\n                    e2=None, comment=(\n                        \"you passed value (e1) of a wrong type (t1) to \"\n                        \"the dict with value type = t2\"\n                    ))\n            return astlib.PyType(\n                defs.PY_DICT, [key_type_placeholder, val_type_placeholder])\n        else:\n            errors.no_such_py_generic_type(name)\n    return astlib.PyType(name)\n\n\ndef _unify_from_signature(signature, args, func, error_comment=None):\n    \"\"\"If all args have right types (and the right number of args is passed) ->\n    returns signature's return type\n    `func` arg is passed to wrong_number_of_args error if it will occur.\n    `error_comment` arg is passed to type_error if it will occur.\n    \"\"\"\n    declarated_arguments = signature.args\n    for passed, declarated_arg in itertools.zip_longest(\n            args, declarated_arguments, fillvalue=None):\n        passed = passed or declarated_arg.expr\n        if passed is None or declarated_arg is None:\n            errors.wrong_number_of_args(\n                func, exp=len(declarated_arguments), got=len(args))\n        t_passed, _ = infer_type(passed)\n        unify(\n            t_passed, declarated_arg.type, e1=passed, e2=None,\n            comment=error_comment)\n    return signature.return_type\n\n\ndef signature_from_function_type(type, base, args):\n    if (type not in A(astlib.GenericType) or\n            str(type.base) != defs.FUNCTION_TYPE):\n        errors.call_on_non_callable(base, args)\n    args = [astlib.Arg(None, param, None) for param in type.params[:-1]]\n    return_type = type.params[-1]\n    return astlib.FuncSignature(args, return_type)\n\n\ndef _infer_type_from_py_method_call(t_base, base, method_name, args):\n    if method_name.startswith(defs.MANGLING_NAME_PREFIX):\n        method_name = utils.real_method_name(method_name)\n    if method_name == defs.PY_DICT_ITEMS:\n        return t_base\n    else:\n        errors.no_such_py_method(t_base, method_name)\n\n\ndef _infer_type_from_py_func_call(name, args):\n    signature = defs.PY_FUNC_TO_SIGNATURE.get(name, None)\n    if signature is None:\n        errors.no_such_py_function(name)\n    return _unify_from_signature(\n        signature, args, func=astlib.PyFunc(name), error_comment=(\n            \"you passed argument (e1) with wrong type (t1) to \"\n            \"py function '{0}'\"\n        ).format(name))\n\n\ndef _infer_type_from_method_call(base, method_name, args):\n    t_base, _ = infer_type(base)\n    if t_base in A(astlib.PyType):\n        return _infer_type_from_py_method_call(t_base, base, method_name, args)\n    t_base_info = context.env.get_type(t_base)\n    param_mapping = build_param_mapping(\n        construct_type(t_base_info.name, t_base_info.params), t_base)\n    method_info = context.env.get_method_from_info(t_base_info, method_name)\n    signature = replace_params_with_type_placeholders(\n        apply_param_mapping_on_signature(method_info.signature, param_mapping))\n    return replace_type_placeholders_with_types(_unify_from_signature(\n        signature, args, func=astlib.Field(base, astlib.Name(method_name)),\n        error_comment=(\n            \"you passed argument (e1) with wrong type (t1) to '{0}' method of \"\n            \"'{1}' type (call base = '{2}')\"\n        ).format(method_name, t_base, base)))\n\n\ndef _infer_type_from_call(base, args):\n    if base in A(astlib.Name):\n        name_info = context.env.get(base)\n        if name_info.entryt == astlib.EntryT.struct:\n            init_method_info = context.env.get_method_from_info(\n                name_info, utils.method_name(defs.SPEC_METHOD_INIT))\n            signature = replace_params_with_type_placeholders(\n                init_method_info.signature)\n            return replace_type_placeholders_with_types(\n                _unify_from_signature(\n                    signature, args, func=base, error_comment=(\n                        \"you passed argument (e1) with wrong type (t1) to \"\n                        \"__init__ method from struct '{0}'\"\n                    ).format(base)))\n        elif name_info.entryt == astlib.EntryT.func:\n            signature = replace_params_with_type_placeholders(\n                name_info.signature)\n            return replace_type_placeholders_with_types(\n                _unify_from_signature(\n                    signature, args, func=base, error_comment=(\n                        \"you passed argument (e1) with wrong type (t1) to \"\n                        \"'{0}' function\"\n                    ).format(base)))\n        elif name_info.entryt in (\n                astlib.EntryT.var, astlib.EntryT.let, astlib.EntryT.arg):\n            signature = replace_params_with_type_placeholders(\n                signature_from_function_type(name_info.type, base, args))\n            return replace_type_placeholders_with_types(\n                _unify_from_signature(\n                    signature, args, func=base, error_comment=(\n                        \"you passed argument (e1) with wrong type (t1) to \"\n                        \"'{0}' function\"\n                    ).format(base)))\n        else:\n            errors.call_on_non_callable(base, args)\n    else:\n        errors.call_on_non_callable(base, args)\n\n\ndef _infer_type_from_field(base, field):\n    t_base, t_casted = infer_type(base)\n    t_base_info = context.env.get_type(t_base)\n    param_mapping = build_param_mapping(\n        construct_type(t_base_info.name, t_base_info.params), t_base)\n    if t_base_info.entryt == astlib.EntryT.adt:\n        if t_casted is None:\n            errors.no_cast(t_base, base, field)\n        t_base_info = context.env.get_constructor_from_info(\n            t_base_info, t_casted.constructor)\n    while field in A(astlib.Field):\n        field_info = context.env.get_field_from_info(t_base_info, field.base)\n        t_base = apply_param_mapping(field_info.type, param_mapping)\n        t_base_info = context.env.get_type(t_base)\n        param_mapping = build_param_mapping(\n            construct_type(t_base_info.name, t_base_info.params), t_base)\n        base = field.base\n        field = field.field\n    field_info = context.env.get_field_from_info(t_base_info, field)\n    return apply_param_mapping(field_info.type, param_mapping)\n\n\ndef infer_type(expr):\n    \"\"\"Infers type from expr.\n    Returns (inferred_type, inferred_casted (can be None)).\n    \"\"\"\n    # TODO: support more nodes (not_implemented must be implemented)\n    if expr in A(astlib.PyTypeCall):\n        return InferenceResult(\n            _infer_type_from_py_type_call(expr.name, expr.args), None)\n    elif expr in A(astlib.PyFuncCall):\n        return InferenceResult(\n            _infer_type_from_py_func_call(expr.name, expr.args), None)\n    elif expr in A(astlib.Call):\n        return InferenceResult(\n            _infer_type_from_call(expr.base, expr.args), None)\n    elif expr in A(astlib.MethodCall):\n        return InferenceResult(\n            _infer_type_from_method_call(expr.base, expr.name, expr.args),\n            None)\n    elif expr in A(astlib.Subscript):\n        t_base, _ = infer_type(expr.base)\n        t_base_info = context.env.get_type(t_base)\n        interface_name = utils.prelude_name(defs.PRELUDE_SUBSCRIPTABLE)\n        method_name = utils.method_name(defs.SPEC_METHOD_GETELEM)\n        method_impls = get_interface_impl(t_base, interface_name)\n        param_mapping = build_param_mapping(\n            construct_type(t_base_info.name, t_base_info.params), t_base)\n        signature = replace_params_with_type_placeholders(\n            apply_param_mapping_on_signature(\n                method_impls[method_name].signature, param_mapping))\n        return InferenceResult(\n            replace_type_placeholders_with_types(\n                _unify_from_signature(\n                    signature, [expr.key], func=method_name, error_comment=(\n                        \"you passed expression (e1) of wrong type t1 to \"\n                        \"subscription (base expression = '{0}')\"\n                    ).format(expr.base))), None)\n    elif expr in A(astlib.Field):\n        return InferenceResult(\n            _infer_type_from_field(expr.base, expr.field), None)\n    elif expr in A(astlib.AdtConstructorCall):\n        adt_info = context.env.get_adt(expr.base)\n        constructor_info = context.env.get_constructor_from_info(\n            adt_info, expr.constructor)\n        init_method_info = context.env.get_method_from_info(\n            constructor_info, utils.method_name(defs.SPEC_METHOD_INIT))\n        func = astlib.AdtConstructor(expr.base, expr.constructor)\n        signature = replace_params_with_type_placeholders(\n            init_method_info.signature)\n        result = replace_type_placeholders_with_types(_unify_from_signature(\n            signature, expr.args, func=func, error_comment=(\n                \"you passed argument (e1) with wrong type (t1) to \"\n                \"__init__ method from adt constructor '{0}'\"\n            ).format(func)))\n        casted = astlib.AdtConstructor(result, expr.constructor)\n        return InferenceResult(result, casted)\n    elif expr in A(astlib.Name):\n        info = context.env.get(expr)\n        if info in A(astlib.DeclInfo):\n            return InferenceResult(info.type, info.casted)\n        elif info in A(astlib.FuncLikeInfo):\n            params = [arg.type for arg in info.signature.args]\n            type = astlib.GenericType(\n                astlib.Name(defs.FUNCTION_TYPE),\n                params + [info.signature.return_type])\n            return InferenceResult(type, None)\n        else:\n            errors.not_implemented(\n                \"cannot infer type from name that is a struct or \"\n                \"something else\"\n            )\n    elif expr in A(astlib.UnaryExpr):\n        if expr.op == defs.NOT:\n            return InferenceResult(unify(\n                infer_type(expr.expr).type, astlib.Name(defs.BOOL),\n                e1=expr.expr, e2=None, comment=(\n                    \"operator '{0}' work only on expression with Bool type, \"\n                    \"but you passed e1 with type t1 as expression\"\n                ).format(expr.op)), None)\n        else:\n            errors.not_implemented(\n                \"cannot infer type from unary expression that uses non-bool \"\n                \"operator\"\n            )\n    elif expr in A(astlib.BinaryExpr):\n        if expr.op == defs.IS:\n            return InferenceResult(astlib.Name(defs.BOOL), None)\n        t_left, _ = infer_type(expr.left)\n        if expr.op in (defs.AND, defs.OR):\n            unify(\n                t_left, astlib.Name(defs.BOOL), e1=expr.left, e2=None,\n                comment=(\n                    \"operator '{0}' work only on expressions with Bool types, \"\n                    \"but you passed e1 with type t1 as left expression\"\n                ).format(expr.op))\n            t_right, _ = infer_type(expr.right)\n            return InferenceResult(unify(\n                t_right, astlib.Name(defs.BOOL), e1=expr.right, e2=None,\n                comment=(\n                    \"operator '{0}' work only on expressions with Bool types, \"\n                    \"but you passed e1 with type t1 as right expression\"\n                ).format(expr.op)), None)\n        if is_py_type(t_left):\n            t_right, _ = infer_type(expr.right)\n            result = unify(\n                t_left, t_right, e1=expr.left, e2=expr.right, comment=(\n                    \"binary operators on py types work only on \"\n                    \"equal types, but you tried to do e1 {0} e2\"\n                ).format(expr.op))\n            if expr.op in defs.BOOL_OPERATORS:\n                return InferenceResult(astlib.Name(defs.BOOL), None)\n            return InferenceResult(result, None)\n        else:\n            if expr.op in (\"<=\", \">=\"):\n                # e1 <= e2 is the same as (e1 < e2) or (e1 == e2)\n                return infer_type(\n                    astlib.BinaryExpr(\n                        astlib.BinaryExpr(expr.left, expr.op[0], expr.right),\n                        defs.OR,\n                        astlib.BinaryExpr(expr.left, \"==\", expr.right)))\n            (interface_name, method_name) = defs.OP_TO_INTERF_AND_METHOD.get(\n                expr.op, (None, None))\n            if interface_name is None:\n                errors.no_such_operator(expr.op)\n            interface_name = utils.prelude_name(interface_name)\n            method_name = utils.method_name(method_name)\n            method_impls = get_interface_impl(t_left, interface_name)\n            signature = replace_params_with_type_placeholders(\n                method_impls[method_name].signature)\n            return InferenceResult(\n                replace_type_placeholders_with_types(_unify_from_signature(\n                    signature, [expr.right], func=method_name, error_comment=(\n                        \"you passed expression (e1) of wrong type t1 to \"\n                        \"binary operator '{0}' (left expression = '{1}')\"\n                    ).format(expr.op, expr.left))), None)\n    elif expr in A(astlib.PyConstant):\n        if expr.name in (defs.PY_TRUE, defs.PY_FALSE):\n            return InferenceResult(astlib.Name(defs.BOOL), None)\n        elif expr.name == defs.PY_ARGV:\n            return InferenceResult(\n                astlib.PyType(defs.PY_LIST, [astlib.PyType(defs.PY_STR)]),\n                None)\n        else:\n            errors.no_such_py_constant(expr.name)\n    elif expr in A(astlib.Cast):\n        type = infer_type(expr.expr).type\n        return InferenceResult(\n            type, astlib.AdtConstructor(type, expr.constructor))\n    elif expr in A(astlib.KeywordArg):\n        return infer_type(expr.expr)\n    else:\n        errors.unrecognized_node(infer_type, expr)\n","sub_path":"solyanka/solyanka/typelib.py","file_name":"typelib.py","file_ext":"py","file_size_in_byte":38158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"294354784","text":"import pytest\n\nfrom secret import API_KEY\nfrom carto.auth import APIKeyAuthClient\nfrom carto.exceptions import CartoException\nfrom conftest import USR_BASE_URL, DEFAULT_PUBLIC_API_KEY\nfrom carto.auth import AuthAPIClient\n\n\ndef test_wrong_url():\n    with pytest.raises(CartoException):\n        APIKeyAuthClient('https://wrongurl', API_KEY).username\n\n\ndef test_cloud_personal_url():\n    user1 = APIKeyAuthClient('https://user1.carto.com/a/b/c',\n                             API_KEY).username\n    user2 = APIKeyAuthClient('https://www.user2.carto.com',\n                             API_KEY).username\n    user3 = APIKeyAuthClient('http://www.user3.carto.com/a/b/c',\n                             API_KEY).username\n    assert user1 == 'user1'\n    assert user2 == 'user2'\n    assert user3 == 'user3'\n\n\ndef test_on_prem_url():\n    user1 = APIKeyAuthClient('https://carto.com/user/user1/a/b/c',\n                             API_KEY).username\n    user2 = APIKeyAuthClient('https://www.carto.com/user/user2',\n                             API_KEY).username\n    user3 = APIKeyAuthClient('http://www.carto.com/user/user3/a/b/c',\n                             API_KEY).username\n    assert user1 == 'user1'\n    assert user2 == 'user2'\n    assert user3 == 'user3'\n\nUSER1_BASE_URL = 'https://user1.carto.com/'\nUSER1_USERNAME = 'user1'\n\n\ndef test_api_key_auth_client_username():\n    conf_username = APIKeyAuthClient(USER1_BASE_URL, API_KEY).username\n    assert conf_username == USER1_USERNAME\n\n\ndef test_api_key_auth_client_me_endpoint():\n    client = APIKeyAuthClient(USER1_BASE_URL, API_KEY)\n    username = client.send('/api/v3/me', 'get').json()['config']['user_name']\n    assert username == USER1_USERNAME\n\n\ndef test_auth_api_client_username():\n    conf_username = AuthAPIClient(USER1_BASE_URL, API_KEY).username\n    assert conf_username == USER1_USERNAME\n\n\ndef test_auth_api_client_me_endpoint():\n    client = AuthAPIClient(USER1_BASE_URL, API_KEY)\n    username = client.send('/api/v3/me', 'get').json()['config']['user_name']\n    assert username == USER1_USERNAME\n\n\ndef test_api_key_auth_cant_read_api_keys_with_default_public():\n    client = APIKeyAuthClient(USER1_BASE_URL, DEFAULT_PUBLIC_API_KEY)\n    response = client.send('/api/v3/api_keys', 'get')\n    assert response.status_code == 401\n\n\ndef test_auth_api_can_read_api_keys_with_default_public():\n    client = AuthAPIClient(USER1_BASE_URL, DEFAULT_PUBLIC_API_KEY)\n    response = client.send('/api/v3/api_keys', 'get')\n    assert response.status_code == 200\n    assert response.json()['count'] == 1\n\n\ndef test_auth_api_is_valid_api_key_with_wrong_key():\n    assert AuthAPIClient(USER1_BASE_URL, 'wadus').is_valid_api_key() is False\n\n\ndef test_auth_api_is_valid_api_key_with_default_public_key():\n    assert AuthAPIClient(USER1_BASE_URL, DEFAULT_PUBLIC_API_KEY). \\\n               is_valid_api_key()\n\n\ndef test_auth_api_is_valid_api_key_with_master_key():\n    if API_KEY == 'mockmockmock':\n        pytest.skip(\"Can't be tested with mock api key\")\n    assert AuthAPIClient(USR_BASE_URL, API_KEY).is_valid_api_key()\n","sub_path":"tests/test_auth.py","file_name":"test_auth.py","file_ext":"py","file_size_in_byte":3054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"304845915","text":"# References:\n# http://developer.download.nvidia.com/books/HTML/gpugems/gpugems_ch38.html\n# https://github.com/PavelDoGreat/WebGL-Fluid-Simulation\n# https://www.bilibili.com/video/BV1ZK411H7Hc?p=4\n# https://github.com/ShaneFX/GAMES201/tree/master/HW01\n\nimport argparse\n\nimport numpy as np\n\nimport taichi as ti\n\nti.init(arch=ti.vulkan)\n\nres = 512\ndt = 0.03\np_jacobi_iters = 500  # 40 for a quicker but less accurate result\nf_strength = 10000.0\ncurl_strength = 0\ntime_c = 2\nmaxfps = 60\ndye_decay = 1 - 1 / (maxfps * time_c)\nforce_radius = res / 2.0\n\n\nclass TexPair:\n    def __init__(self, cur, nxt):\n        self.cur = cur\n        self.nxt = nxt\n\n    def swap(self):\n        self.cur, self.nxt = self.nxt, self.cur\n\n\n@ti.func\ndef sample(qf: ti.template(), u, v):\n    I = ti.Vector([int(u), int(v)])\n    I = ti.max(0, ti.min(res - 1, I))\n    return qf[I]\n\n\n@ti.func\ndef lerp(vl, vr, frac):\n    # frac: [0.0, 1.0]\n    return vl + frac * (vr - vl)\n\n\n@ti.func\ndef bilerp(vf: ti.template(), p):\n    u, v = p\n    s, t = u - 0.5, v - 0.5\n    # floor\n    iu, iv = ti.floor(s), ti.floor(t)\n    # fract\n    fu, fv = s - iu, t - iv\n    a = sample(vf, iu, iv)\n    b = sample(vf, iu + 1, iv)\n    c = sample(vf, iu, iv + 1)\n    d = sample(vf, iu + 1, iv + 1)\n    return lerp(lerp(a, b, fu), lerp(c, d, fu), fv)\n\n\n# 3rd order Runge-Kutta\n@ti.func\ndef backtrace(vf: ti.template(), p, dt_: ti.template()):\n    v1 = bilerp(vf, p)\n    p1 = p - 0.5 * dt_ * v1\n    v2 = bilerp(vf, p1)\n    p2 = p - 0.75 * dt_ * v2\n    v3 = bilerp(vf, p2)\n    p -= dt_ * ((2 / 9) * v1 + (1 / 3) * v2 + (4 / 9) * v3)\n    return p\n\n\n@ti.kernel\ndef advect(\n    vf: ti.types.ndarray(ndim=2),\n    qf: ti.types.ndarray(ndim=2),\n    new_qf: ti.types.ndarray(ndim=2),\n):\n    for i, j in vf:\n        p = ti.Vector([i, j]) + 0.5\n        p = backtrace(vf, p, dt)\n        new_qf[i, j] = bilerp(qf, p) * dye_decay\n\n\n@ti.kernel\ndef apply_impulse(\n    vf: ti.types.ndarray(ndim=2),\n    dyef: ti.types.ndarray(ndim=2),\n    imp_data: ti.types.ndarray(ndim=1),\n):\n    g_dir = -ti.Vector([0, 9.8]) * 300\n    for i, j in vf:\n        omx, omy = imp_data[2], imp_data[3]\n        mdir = ti.Vector([imp_data[0], imp_data[1]])\n        dx, dy = (i + 0.5 - omx), (j + 0.5 - omy)\n        d2 = dx * dx + dy * dy\n        # dv = F * dt\n        factor = ti.exp(-d2 / force_radius)\n\n        dc = dyef[i, j]\n        a = dc.norm()\n\n        momentum = (mdir * f_strength * factor + g_dir * a / (1 + a)) * dt\n\n        v = vf[i, j]\n        vf[i, j] = v + momentum\n        # add dye\n        if mdir.norm() > 0.5:\n            dc += ti.exp(-d2 * (4 / (res / 15) ** 2)) * ti.Vector([imp_data[4], imp_data[5], imp_data[6]])\n\n        dyef[i, j] = dc\n\n\n@ti.kernel\ndef divergence(vf: ti.types.ndarray(ndim=2), velocity_divs: ti.types.ndarray(ndim=2)):\n    for i, j in vf:\n        vl = sample(vf, i - 1, j)\n        vr = sample(vf, i + 1, j)\n        vb = sample(vf, i, j - 1)\n        vt = sample(vf, i, j + 1)\n        vc = sample(vf, i, j)\n        if i == 0:\n            vl.x = -vc.x\n        if i == res - 1:\n            vr.x = -vc.x\n        if j == 0:\n            vb.y = -vc.y\n        if j == res - 1:\n            vt.y = -vc.y\n        velocity_divs[i, j] = (vr.x - vl.x + vt.y - vb.y) * 0.5\n\n\n@ti.kernel\ndef pressure_jacobi(\n    pf: ti.types.ndarray(ndim=2),\n    new_pf: ti.types.ndarray(ndim=2),\n    velocity_divs: ti.types.ndarray(ndim=2),\n):\n    for i, j in pf:\n        pl = sample(pf, i - 1, j)\n        pr = sample(pf, i + 1, j)\n        pb = sample(pf, i, j - 1)\n        pt = sample(pf, i, j + 1)\n        div = velocity_divs[i, j]\n        new_pf[i, j] = (pl + pr + pb + pt - div) * 0.25\n\n\n@ti.kernel\ndef subtract_gradient(vf: ti.types.ndarray(ndim=2), pf: ti.types.ndarray(ndim=2)):\n    for i, j in vf:\n        pl = sample(pf, i - 1, j)\n        pr = sample(pf, i + 1, j)\n        pb = sample(pf, i, j - 1)\n        pt = sample(pf, i, j + 1)\n        vf[i, j] -= 0.5 * ti.Vector([pr - pl, pt - pb])\n\n\ndef solve_pressure_jacobi():\n    for _ in range(p_jacobi_iters):\n        pressure_jacobi(pressures_pair.cur, pressures_pair.nxt, _velocity_divs)\n        pressures_pair.swap()\n\n\ndef step_orig(mouse_data):\n    advect(velocities_pair.cur, velocities_pair.cur, velocities_pair.nxt)\n    advect(velocities_pair.cur, dyes_pair.cur, dyes_pair.nxt)\n    velocities_pair.swap()\n    dyes_pair.swap()\n\n    apply_impulse(velocities_pair.cur, dyes_pair.cur, mouse_data)\n\n    divergence(velocities_pair.cur, _velocity_divs)\n\n    solve_pressure_jacobi()\n\n    subtract_gradient(velocities_pair.cur, pressures_pair.cur)\n\n\nmouse_data_ti = ti.ndarray(ti.f32, shape=(8,))\n\n\nclass MouseDataGen:\n    def __init__(self):\n        self.prev_mouse = None\n        self.prev_color = None\n\n    def __call__(self, gui):\n        # [0:2]: normalized delta direction\n        # [2:4]: current mouse xy\n        # [4:7]: color\n        mouse_data = np.zeros(8, dtype=np.float32)\n        if gui.is_pressed(ti.GUI.LMB):\n            mxy = np.array(gui.get_cursor_pos(), dtype=np.float32) * res\n            if self.prev_mouse is None:\n                self.prev_mouse = mxy\n                # Set lower bound to 0.3 to prevent too dark colors\n                self.prev_color = (np.random.rand(3) * 0.7) + 0.3\n            else:\n                mdir = mxy - self.prev_mouse\n                mdir = mdir / (np.linalg.norm(mdir) + 1e-5)\n                mouse_data[0], mouse_data[1] = mdir[0], mdir[1]\n                mouse_data[2], mouse_data[3] = mxy[0], mxy[1]\n                mouse_data[4:7] = self.prev_color\n                self.prev_mouse = mxy\n        else:\n            self.prev_mouse = None\n            self.prev_color = None\n        mouse_data_ti.from_numpy(mouse_data)  # false+, pylint: disable=no-member\n        return mouse_data_ti\n\n\ndef reset():\n    velocities_pair.cur.fill(0)\n    pressures_pair.cur.fill(0)\n    dyes_pair.cur.fill(0)\n\n\ndef main():\n    global velocities_pair, pressures_pair, dyes_pair, curl_strength\n\n    paused = False\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--baseline\", action=\"store_true\")\n    args, _ = parser.parse_known_args()\n\n    gui = ti.GUI(\"Stable Fluid\", (res, res))\n    md_gen = MouseDataGen()\n\n    _velocities = ti.Vector.ndarray(2, float, shape=(res, res))\n    _new_velocities = ti.Vector.ndarray(2, float, shape=(res, res))\n    _velocity_divs = ti.ndarray(float, shape=(res, res))\n    _pressures = ti.ndarray(float, shape=(res, res))\n    _new_pressures = ti.ndarray(float, shape=(res, res))\n    _dye_buffer = ti.Vector.ndarray(3, float, shape=(res, res))\n    _new_dye_buffer = ti.Vector.ndarray(3, float, shape=(res, res))\n\n    if args.baseline:\n        velocities_pair = TexPair(_velocities, _new_velocities)\n        pressures_pair = TexPair(_pressures, _new_pressures)\n        dyes_pair = TexPair(_dye_buffer, _new_dye_buffer)\n    else:\n        print(\"running in graph mode\")\n        velocities_pair_cur = ti.graph.Arg(ti.graph.ArgKind.NDARRAY, \"velocities_pair_cur\", dtype=ti.math.vec2, ndim=2)\n        velocities_pair_nxt = ti.graph.Arg(ti.graph.ArgKind.NDARRAY, \"velocities_pair_nxt\", dtype=ti.math.vec2, ndim=2)\n        dyes_pair_cur = ti.graph.Arg(ti.graph.ArgKind.NDARRAY, \"dyes_pair_cur\", dtype=ti.math.vec3, ndim=2)\n        dyes_pair_nxt = ti.graph.Arg(ti.graph.ArgKind.NDARRAY, \"dyes_pair_nxt\", dtype=ti.math.vec3, ndim=2)\n        pressures_pair_cur = ti.graph.Arg(ti.graph.ArgKind.NDARRAY, \"pressures_pair_cur\", dtype=ti.f32, ndim=2)\n        pressures_pair_nxt = ti.graph.Arg(ti.graph.ArgKind.NDARRAY, \"pressures_pair_nxt\", dtype=ti.f32, ndim=2)\n        velocity_divs = ti.graph.Arg(ti.graph.ArgKind.NDARRAY, \"velocity_divs\", dtype=ti.f32, ndim=2)\n        mouse_data = ti.graph.Arg(ti.graph.ArgKind.NDARRAY, \"mouse_data\", dtype=ti.f32, ndim=1)\n\n        g1_builder = ti.graph.GraphBuilder()\n        g1_builder.dispatch(advect, velocities_pair_cur, velocities_pair_cur, velocities_pair_nxt)\n        g1_builder.dispatch(advect, velocities_pair_cur, dyes_pair_cur, dyes_pair_nxt)\n        g1_builder.dispatch(apply_impulse, velocities_pair_nxt, dyes_pair_nxt, mouse_data)\n        g1_builder.dispatch(divergence, velocities_pair_nxt, velocity_divs)\n        # swap is unrolled in the loop so we only need p_jacobi_iters // 2 iterations.\n        for _ in range(p_jacobi_iters // 2):\n            g1_builder.dispatch(pressure_jacobi, pressures_pair_cur, pressures_pair_nxt, velocity_divs)\n            g1_builder.dispatch(pressure_jacobi, pressures_pair_nxt, pressures_pair_cur, velocity_divs)\n        g1_builder.dispatch(subtract_gradient, velocities_pair_nxt, pressures_pair_cur)\n        g1 = g1_builder.compile()\n\n        g2_builder = ti.graph.GraphBuilder()\n        g2_builder.dispatch(advect, velocities_pair_nxt, velocities_pair_nxt, velocities_pair_cur)\n        g2_builder.dispatch(advect, velocities_pair_nxt, dyes_pair_nxt, dyes_pair_cur)\n        g2_builder.dispatch(apply_impulse, velocities_pair_cur, dyes_pair_cur, mouse_data)\n        g2_builder.dispatch(divergence, velocities_pair_cur, velocity_divs)\n        for _ in range(p_jacobi_iters // 2):\n            g2_builder.dispatch(pressure_jacobi, pressures_pair_cur, pressures_pair_nxt, velocity_divs)\n            g2_builder.dispatch(pressure_jacobi, pressures_pair_nxt, pressures_pair_cur, velocity_divs)\n        g2_builder.dispatch(subtract_gradient, velocities_pair_cur, pressures_pair_cur)\n        g2 = g2_builder.compile()\n\n    swap = True\n\n    while gui.running:\n        if gui.get_event(ti.GUI.PRESS):\n            e = gui.event\n            if e.key == ti.GUI.ESCAPE:\n                break\n            elif e.key == \"r\":\n                paused = False\n                reset()\n            elif e.key == \"s\":\n                if curl_strength:\n                    curl_strength = 0\n                else:\n                    curl_strength = 7\n            elif e.key == \"p\":\n                paused = not paused\n\n        if not paused:\n            _mouse_data = md_gen(gui)\n            if args.baseline:\n                step_orig(_mouse_data)\n                gui.set_image(dyes_pair.cur.to_numpy())\n            else:\n                invoke_args = {\n                    \"mouse_data\": _mouse_data,\n                    \"velocities_pair_cur\": _velocities,\n                    \"velocities_pair_nxt\": _new_velocities,\n                    \"dyes_pair_cur\": _dye_buffer,\n                    \"dyes_pair_nxt\": _new_dye_buffer,\n                    \"pressures_pair_cur\": _pressures,\n                    \"pressures_pair_nxt\": _new_pressures,\n                    \"velocity_divs\": _velocity_divs,\n                }\n                if swap:\n                    g1.run(invoke_args)\n                    gui.set_image(_dye_buffer.to_numpy())  # false+, pylint: disable=no-member\n                    swap = False\n                else:\n                    g2.run(invoke_args)\n                    gui.set_image(_new_dye_buffer.to_numpy())  # false+, pylint: disable=no-member\n                    swap = True\n        gui.show()\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"python/taichi/examples/graph/stable_fluid_graph.py","file_name":"stable_fluid_graph.py","file_ext":"py","file_size_in_byte":10945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"637709254","text":"import sys\r\nsys.path.append('../../../../utils/')\r\nfrom matplotlib import pyplot as plt\r\nimport numpy as np\r\nfrom PIL import Image\r\nimport tensorflow as tf\r\nfrom inference_utils import get_dataset_colormap\r\n\r\n\r\nLABEL_NAMES = np.asarray([\r\n    'background', 'dynamic', 'ground', 'road', 'sidewalk', 'parking', 'rail track',\r\n    'building', 'wall', 'fence', 'guard rail', 'bridge', 'tunnel', 'pole', 'polegroup',\r\n    'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky', 'person', 'rider',\r\n    'car', 'truck', 'bus', 'caravan', 'trailer', 'train', 'motorcycle', 'bicycle', 'license plate'\r\n])\r\nFULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)\r\nFULL_COLOR_MAP = get_dataset_colormap.label_to_color_image(FULL_LABEL_MAP)\r\nclass DeepLabModel(object):\r\n    \"\"\"Class to load deeplab model and run inference.\"\"\"\r\n    INPUT_TENSOR_NAME = 'ImageTensor:0'\r\n    OUTPUT_TENSOR_NAME = 'SemanticPredictions:0'\r\n    INPUT_SIZE = 513\r\n    def __init__(self, model_path):\r\n        \"\"\"Creates and loads pretrained deeplab model.\"\"\"\r\n        self.graph = tf.Graph()\r\n        with open(model_path, \"rb\") as fd:\r\n            graph_def = tf.GraphDef.FromString(fd.read())\r\n        with self.graph.as_default():\r\n            tf.import_graph_def(graph_def, name='')\r\n        self.sess = tf.Session(graph=self.graph)\r\n\r\n    def run(self, image):\r\n        \"\"\"Runs inference on a single image.\r\n        Args:\r\n            image: A PIL.Image object, raw input image.\r\n        Returns:\r\n            resized_image: RGB image resized from original input image.\r\n            seg_map: Segmentation map of `resized_image`.\r\n        \"\"\"\r\n        width, height = image.size\r\n        resize_ratio = 1.0 * self.INPUT_SIZE / max(width, height)\r\n        target_size = (int(resize_ratio * width), int(resize_ratio * height))\r\n        resized_image = image.convert('RGB').resize(target_size,\r\n                                                    Image.ANTIALIAS)\r\n        batch_seg_map = self.sess.run(\r\n            self.OUTPUT_TENSOR_NAME,\r\n            feed_dict={\r\n                self.INPUT_TENSOR_NAME: [np.asarray(resized_image)]\r\n            })\r\n        seg_map = batch_seg_map[0]\r\n        return resized_image, seg_map\r\ndef vis_segmentation(image, seg_map):\r\n    plt.figure()\r\n    plt.subplot(221)\r\n    plt.imshow(image)\r\n    plt.axis('off')\r\n    plt.title('input image')\r\n    plt.subplot(222)\r\n    seg_image = get_dataset_colormap.label_to_color_image(\r\n        seg_map, get_dataset_colormap.get_cityscapes_name()).astype(np.uint8)\r\n    plt.imshow(seg_image)\r\n    plt.axis('off')\r\n    plt.title('segmentation map')\r\n    plt.subplot(223)\r\n    plt.imshow(image)\r\n    plt.imshow(seg_image, alpha=0.7)\r\n    plt.axis('off')\r\n    plt.title('segmentation overlay')\r\n    unique_labels = np.unique(seg_map)\r\n    ax = plt.subplot(224)\r\n    plt.imshow(\r\n        FULL_COLOR_MAP[unique_labels].astype(np.uint8),\r\n        interpolation='nearest')\r\n    ax.yaxis.tick_right()\r\n    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])\r\n    plt.xticks([], [])\r\n    ax.tick_params(width=0)\r\n    plt.savefig('output.png')\r\n\r\n    plt.show()\r\nif __name__ == '__main__':\r\n    if len(sys.argv) < 2:\r\n        print('Usage: python {} image_path '.format(sys.argv[0]))\r\n        exit()\r\n    image_path = sys.argv[1]\r\n    model_path = 'models/Final/cityscapes_xception_65.pb'\r\n    model = DeepLabModel(model_path)\r\n    orignal_im = Image.open(image_path)\r\n    resized_im, seg_map = model.run(orignal_im)\r\n    vis_segmentation(resized_im, seg_map)\r\n","sub_path":"inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":3535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"521120578","text":"# 给定两个大小为 m 和 n 的有序数组 nums1 和 nums2。#\n# 请你找出这两个有序数组的中位数，并且要求算法的时间复杂度为 O(log(m + n))。#\n# 你可以假设 nums1 和 nums2 不会同时为空。#\n# 示例 1:#\n# nums1 = [1, 3]\n# nums2 = [2]#\n# 则中位数是 2.0\n# 示例 2:#\n# nums1 = [1, 2]\n# nums2 = [3, 4]#\n# 则中位数是 (2 + 3)/2 = 2.5\n#\nclass Solution:\n    def findMedianSortedArrays(self, nums1, nums2) -> float:\n        \"\"\"\n        参考线性k选取的算法，之前算法是针对无序数组的，需要划分判断，现在针对有序列表，不需要进行划分\n        如果长度和为奇数，直接寻找中间数，如果为偶数，寻找中间两个数求平均\n        \"\"\"\n\n        def find_k(list1, list2, k):  # k从0开始\n            # print(list1, list2, k)\n            if len(list1) == 0:\n                return list2[k - 1]\n            if len(list2) == 0:\n                return list1[k - 1]\n            if k == 1:\n                return min(list1[0], list2[0])\n            m = n = k // 2 - 1  # index从0开始\n            if len(list1) - 1 <= m:\n                m = len(list1) - 1\n            if len(list2) - 1 <= n:\n                n = len(list2) - 1\n            if list1[m] <= list2[n]:\n                return find_k(list1[m + 1:], list2, k - m - 1)\n            if list1[m] > list2[n]:\n                return find_k(list1, list2[n + 1:], k - n - 1)\n\n        # return find_k(nums1, nums2, 5)\n        length = len(nums1) + len(nums2)\n        if length % 2 == 1:\n            return find_k(nums1, nums2, (length >> 1) + 1)\n        else:\n            return (find_k(nums1, nums2, length >> 1) + find_k(nums1, nums2, (length >> 1) + 1)) / 2\n\n\nnums1 = [1, 2, 3, 4]\nnums2 = [-1, 5, 7, 8, 10]\nprint(Solution.findMedianSortedArrays(Solution(), nums1, nums2))\nprint(sorted(nums1 + nums2))\n","sub_path":"4. 寻找两个有序数组的中位数.py","file_name":"4. 寻找两个有序数组的中位数.py","file_ext":"py","file_size_in_byte":1863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"204158948","text":"# =============================================================================\r\n# Field measurements from the horizontal and vertical electrodes\r\n# CH1 - CH2 gives horizontal field\r\n# CH1 - CH3 gives vertical field \r\n# unit is uv/mm\r\n# branch\r\ndef efield_surface(trialno):\r\n    \r\n    import numpy as np\r\n    from functools import reduce\r\n    import scipy.io as sio\r\n\r\n    mat = sio.loadmat('trial{}.mat'.format(trialno))\r\n    data = mat['data']; \r\n    \r\n    ch1 = np.array(list(map(lambda y: y/10, reduce(lambda x,y: x+y,data[:,0].reshape(10,-1)))))\r\n    ch2 = np.array(list(map(lambda y: y/10, reduce(lambda x,y: x+y,data[:,1].reshape(10,-1)))))\r\n    ch3 = np.array(list(map(lambda y: y/10, reduce(lambda x,y: x+y,data[:,2].reshape(10,-1)))))\r\n    \r\n    horizontal = 100*(ch1.max()-np.mean(ch1[2475:2495])-ch2.max()-np.mean(ch2[2475:2495]))/250  \r\n    vertical = 100*(ch1.max()-np.mean(ch1[2475:2495])-ch3.max()-np.mean(ch3[2475:2495]))/250\r\n    \r\n    return [horizontal, vertical]\r\n\r\n# =============================================================================\r\n# Field measurements from the helical electrode \r\n# Two trial (from 125 um and 375 um) are necessary to calculate the field through the depth.\r\n# =============================================================================\r\ndef efield_depth(trialno):\r\n\r\n    import numpy as np\r\n    from functools import reduce\r\n    import scipy.io as sio\r\n    \r\n    all_chs = []\r\n    for i in trialno:\r\n        mat = sio.loadmat('trial{}.mat'.format(i))\r\n        data = mat['data']; \r\n\r\n        ch1 = np.array(list(map(lambda y: y/10, reduce(lambda x,y: x+y,data[:,0].reshape(10,-1)))))\r\n        ch2 = np.array(list(map(lambda y: y/10, reduce(lambda x,y: x+y,data[:,1].reshape(10,-1)))))\r\n        ch3 = np.array(list(map(lambda y: y/10, reduce(lambda x,y: x+y,data[:,2].reshape(10,-1)))))\r\n        \r\n        all_ch = [ch1.max()-np.mean(ch1[2475:2495]),ch2.max()-np.mean(ch2[2475:2495]),ch3.max()-np.mean(ch3[2475:2495])];\r\n        all_chs.append(all_ch)\r\n\r\n    all_chs = np.array(all_chs)\r\n    ch_results = 100*(all_chs[0,:]-all_chs[1,:])/250\r\n    \r\n    return ch_results\r\n\r\ntest\r\n","sub_path":"e_field.py","file_name":"e_field.py","file_ext":"py","file_size_in_byte":2134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"252425097","text":"import random\nimport time\nimport pygame as pg\n\nfrom EventManager import *\nimport Const\n\nclass StateMachine(object):\n    '''\n    Manages a stack based state machine.\n    peek(), pop() and push() perform as traditionally expected.\n    peeking and popping an empty stack returns None.\n    '''\n    def __init__(self):\n        self.statestack = []\n\n    def peek(self):\n        '''\n        Returns the current state without altering the stack.\n        Returns None if the stack is empty.\n        '''\n        try:\n            return self.statestack[-1]\n        except IndexError:\n            # empty stack\n            return None\n\n    def pop(self):\n        '''\n        Returns the current state and remove it from the stack.\n        Returns None if the stack is empty.\n        '''\n        try:\n            return self.statestack.pop()\n        except IndexError:\n            # empty stack\n            return None\n\n    def push(self, state):\n        '''\n        Push a new state onto the stack.\n        Returns the pushed value.\n        '''\n        self.statestack.append(state)\n        return state\n\n    def clear(self):\n        '''\n        Clear the stack.\n        '''\n        self.statestack = []\n\n\nclass GameEngine:\n    '''\n    The main game engine. The main loop of the game is in GameEngine.run()\n    '''\n\n    def __init__(self, ev_manager: EventManager):\n        '''\n        This function is called when the GameEngine is created.\n        For more specific objects related to a game instance\n            , they should be initialized in GameEngine.initialize()\n        '''\n        self.ev_manager = ev_manager\n        ev_manager.register_listener(self)\n        self.state_machine = StateMachine()\n        self.changetime = Const.CHANGE_TIME\n        self.timeleft = Const.GAME_LENGTH\n\n    def initialize(self):\n        '''\n        This method is called when a new game is instantiated.\n        '''\n        self.clock = pg.time.Clock()\n        self.state_machine.push(Const.STATE_MENU)\n        self.players = [Player(0), Player(1)]\n\n    def notify(self, event: BaseEvent):\n        '''\n        Called by EventManager when a event occurs.\n        '''\n        if isinstance(event, EventInitialize):\n            self.initialize()\n\n        elif isinstance(event, EventEveryTick):\n            cur_state = self.state_machine.peek()\n            if cur_state == Const.STATE_MENU:\n                self.update_menu()\n            elif cur_state == Const.STATE_PLAY:\n                self.update_objects()\n\n                self.timer -= 1\n                if self.timer == 0:\n                    self.ev_manager.post(EventTimesUp())\n            elif cur_state == Const.STATE_ENDGAME:\n                self.update_endgame()\n\n        elif isinstance(event, EventStateChange):\n            if event.state == Const.STATE_POP:\n                if self.state_machine.pop() is None:\n                    self.ev_manager.post(EventQuit())\n            else:\n                self.state_machine.push(event.state)\n\n        elif isinstance(event, EventQuit):\n            self.running = False\n\n        elif isinstance(event, EventPlayerMove):\n            self.players[event.player_id].move_direction(event.direction)\n\n        elif isinstance(event, EventTimesUp):\n            self.state_machine.push(Const.STATE_ENDGAME)\n\n    def update_menu(self):\n        '''\n        Update the objects in welcome scene.\n        For example: game title, hint text\n        '''\n        pass\n\n    def update_objects(self):\n        '''\n        Update the objects not controlled by user.\n        For example: obstacles, items, special effects\n        '''\n        pass\n\n    def update_endgame(self):\n        '''\n        Update the objects in endgame scene.\n        For example: scoreboard\n        '''\n        pass\n\n    def run(self):\n        '''\n        The main loop of the game is in this function.\n        This function activates the GameEngine.\n        ''' \n        self.running = True\n        self.ev_manager.post(EventInitialize())\n        self.timer = Const.GAME_LENGTH\n        while self.running:\n            self.ev_manager.post(EventEveryTick())\n            self.clock.tick(Const.FPS)\n            if (self.state_machine.peek() == Const.STATE_STOP):\n                continue\n            \n            self.changetime -= 1;\n            self.timeleft -= 1;\n            if ((self.players[1].position.x - self.players[0].position.x) ** 2 + (self.players[1].position.y - self.players[0].position.y) ** 2 <= (2 * Const.PLAYER_RADIUS) ** 2):\n                if (self.players[0].player_id == 1):\n                    self.players[0].score += 1;\n                else:\n                    self.players[1].score += 1;\n            if (self.changetime == 0):\n                (self.players[0].speed, self.players[1].speed) = (self.players[1].speed, self.players[0].speed)\n                (self.players[0].player_id, self.players[1].player_id) = (self.players[1].player_id, self.players[0].player_id)\n                self.changetime = Const.CHANGE_TIME\n            if (self.timeleft == 0):\n                self.ev_manager.post(EventTimesUp())\n        \nclass Player:\n    def __init__(self, player_id):\n        self.player_id = player_id\n        self.position = Const.PLAYER_INIT_POSITION[player_id] # is a pg.Vector2\n        self.speed = Const.SPEED_ATTACK if player_id == 1 else Const.SPEED_DEFENSE\n        self.score = 0\n    def move_direction(self, direction: str):\n        '''\n        Move the player along the direction by its speed.\n        Will automatically clip the position so no need to worry out-of-bound moving.\n        '''\n        self.position += self.speed / Const.FPS * Const.DIRECTION_TO_VEC2[direction]\n        \n        # clipping\n        self.position.x = max(0, min(Const.ARENA_SIZE[0], self.position.x))\n        self.position.y = max(0, min(Const.ARENA_SIZE[1], self.position.y))\n\n","sub_path":"Model.py","file_name":"Model.py","file_ext":"py","file_size_in_byte":5836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"403365486","text":"from random import randint\nfrom multiprocessing.connection import Client\nimport pandas as pd\n\n#print(randint(0,2))\t\t\t#give random number --> both inclusive\naddress = ('localhost', 6000)\nconn = Client(address, authkey=b'secret password')\n\ncsv_file_path = \"distanceFile.csv\"\ndf = pd.read_csv(csv_file_path)\n\n\ndef random_neighbour(currentCityId , visited):\n\n\tdist = df.loc[currentCityId,\"dist0\":\"dist10\"]\n\tprint(\"distance matrix of \",currentCityId , \" is \\n\" ,dist)\n\n\tfor cityIndex in range(len(dist)):\n\t\tif(dist[cityIndex] != 0):\n\t\t\ttoCheck.append(cityIndex)\n\n\tprint(\"toCheck array is \",toCheck)\n\n\tfor city in toCheck:\n\t\tif city not in visited:\n\t\t\tunVisited.append(city)\n\n\tprint(\"unVisited array is \",unVisited)\n\n\trandom_number = randint(0 , len(unVisited) - 1)\t\t\t\t#both inclusive\n\n\tnewCity = unVisited[random_number]\n\tprint(\"new city selected is \",newCity)\n\treturn newCity\n\nvisited = []\ninitialStateAdded = False\n\nwhile(len(visited) < 11):\n\tagent1_state = str(conn.recv())\n\tagent2_state = str(conn.recv())\n\tvisited = conn.recv()\n\n\t#if(initialStateAdded == False):\n\t#\tvisited.append(int(agent1_state))\n\t#\tinitialStateAdded = True\n\n\ttoCheck = []\n\tunVisited = []\n\n\tnewCity = random_neighbour(int(agent1_state) , visited)\n\n\t#visited.append(newCity)\n\n\tconn.send(newCity)\n\tprint(\"visited array is \",visited)\n\n\tinput(\"\")\n\nconn.close()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"random_neighbour.py","file_name":"random_neighbour.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"159883374","text":"from fastapi import FastAPI\nimport uuid, random, requests, json, hashlib, os, rsa, sys, datetime, trace, threading, time\nfrom Crypto.PublicKey import RSA\nfrom pydantic import BaseModel\nfrom typing import Optional, List\nfrom kafka import KafkaProducer\nfrom fastapi.responses import JSONResponse\nfrom sqlalchemy import *\nfrom sqlalchemy import create_engine\nfrom sqlalchemy_utils import database_exists, create_database\nfrom sqlalchemy.orm import scoped_session, sessionmaker, relationship\nfrom sqlalchemy.ext.declarative import declarative_base\nimport sqlalchemy.dialects.postgresql as postgresql\n\nPOSTGRES_USER = os.environ[\"POSTGRES_USER\"]\nPOSTGRES_PW = os.environ[\"POSTGRES_PW\"]\nPOSTGRES_URL = os.environ[\"POSTGRES_URL\"]\nPOSTGRES_DB = os.environ[\"POSTGRES_DB\"]\npublicKeyOperator = os.environ.get(\"OPERATOR_PUBLIC_KEY\")\n\nclass Metric_Model(BaseModel):\n\tmetricName: str\n\tmetricType: str\n\taggregationMethod: str = None\n\ttimestampStep: str\n\nclass Config_Model(BaseModel):\n\tbusinessID: int\n\ttopic: str\n\tnetworkID: int\n\tmetrics: List[Metric_Model]\n\ttimestampStart: Optional[str] = datetime.datetime.now()\n\ttimestampEnd: Optional[str] = None\n\nclass Update_Config_Model(BaseModel):\n\ttimestampStart: Optional[str] = None\n\ttimestampEnd: Optional[str] = None\n\tmetrics: Optional[List[Metric_Model]] = None\n\nclass Response_Config_Model(BaseModel):\n\tid: uuid.UUID\n\tcreated_at: datetime.datetime\n\tupdated_at: datetime.datetime\n\tbusinessID: int\n\ttopic: str\n\tnetworkID: int\n\ttimestampStart: datetime.datetime\n\ttimestampEnd: datetime.datetime\n\tmetrics: List[Metric_Model]\n\tstatus: int\n\nclass Response_Error_Model(BaseModel):\n\tstatus: str\n\tmessage: str\n\nfrom .database import *\n\nclass thread_with_trace(threading.Thread): \n\tdef __init__(self, *args, **keywords): \n\t\tthreading.Thread.__init__(self, *args, **keywords) \n\t\tself.killed = False\n\n\tdef start(self): \n\t\tself.__run_backup = self.run \n\t\tself.run = self.__run       \n\t\tthreading.Thread.start(self) \n\n\tdef __run(self): \n\t\tsys.settrace(self.globaltrace) \n\t\tself.__run_backup() \n\t\tself.run = self.__run_backup \n\n\tdef globaltrace(self, frame, event, arg): \n\t\tif event == 'call': \n\t\t\treturn self.localtrace \n\t\telse: \n\t\t\treturn None\n\n\tdef localtrace(self, frame, event, arg): \n\t\tif self.killed: \n\t\t\tif event == 'line': \n\t\t\t\traise SystemExit() \n\t\treturn self.localtrace \n\n\tdef kill(self): \n\t\tself.killed = True\n\napp = FastAPI()\n\n# Json response example\njson_response_enable = {\"id\": \"ab51f3e1-7b61-4f9d-85a4-9e9f366b593b\",\"created_at\": \"2021-03-11T11:34:00.402075\",\"updated_at\": \"null\",\"businessID\": 36574564,\"topic\": \"postData\",\"networkID\": 232,\"timestampStart\": \"2021-03-11T11:34:00\",\"timestampEnd\": \"null\",\"metrics\": [{\"metricName\": \"cpu_utilization\",\"metricType\": \"float\",\"aggregationMethod\": \"agg\",\"timestampStep\": \"1h\"}],\"status\": 1}\njson_response_disable = json_response_enable.copy()\njson_response_disable['status'] = 0\n\n\nseconds_per_unit = {\"s\": 1, \"m\": 60, \"h\": 3600, \"d\": 86400, \"w\": 604800}\nthreads = {}\n\ndef convert_to_seconds(s):\n\treturn int(s[:-1]) * seconds_per_unit[s[-1]]\n\ndef validate_uuid4(uuid_string):\n\ttry:\n\t\tuuid.UUID(uuid_string).hex\n\texcept ValueError:\n\t\treturn False\n\treturn True\n\ndef request_OSM(request_metric, request_start):\n\tendpoint = 'http://osm:4500/monitoringData?'\n\trequest_url = endpoint + request_metric + request_start\n\tresponse = requests.get(request_url)\n\tif response.status_code != 200:\n\t\treturn ('Error in fetching data!', 200)\n\tresp = response.text\n\tjson_data = json.loads(response.text)\n\tprint(f'Response from OSM: {resp} + operator public key: {publicKeyOperator}')\n\n\tpayload_encoded = {k: str(v).encode('utf-8') for k,v in json_data.items()}\n\thashData = {k: hashlib.sha256(v).hexdigest() for k,v in payload_encoded.items()}\n\tprint(f'Hashed Data: {hashData}')\n\tpKey = RSA.importKey(publicKeyOperator)\n\tprint(f'Operator Public Key: {pKey}')\n\n\tdataHashEncrypt =  {rsa.encrypt(k.encode(), pKey): rsa.encrypt(v.encode(), pKey) for k,v in hashData.items()}\n\tprint(f'Signup Data: {dataHashEncrypt}')\n\n\t# Data Lake Kafka Topic\n\tproducer = KafkaProducer(bootstrap_servers=['kafka:9092'], api_version=(0,10,1))\n\t#producer = KafkaProducer(bootstrap_servers=['kafka:9092'], value_serializer=lambda x:json.dumps(x).encode('utf-8'), api_version=(0,10,1))\n\tprint(\"Post Data into Kafka Topic\")\n\t\n\tproducer.send('topic_test', key=list(dataHashEncrypt.keys())[1], value=list(dataHashEncrypt.values())[1])\n\treturn \"Post Data into Data Lake sucessfull\"\n\n\n# curl 'http://localhost:9090/api/v1/query?query=cpu_utilization&time=2015-07-01T20:10:51.781Z'\ndef fetch_values(metric_id, metric_name, starttime , timeout, status, step):\n\t''' 2 situations that can happen\n\t\t1. VS sends with timestamp fields filled (not null):\n\t\t\t- verify start time\n\t\t\t- verify end time\n\t\t\t- verify status\n\t\t2. VS sends null timestamp fields:\n\t\t\t- timestamp start: datetime.datetime.now() - default\n\t\t\t- timestamp end: null (expects disable order)\n\t\t\t- verify status \n\t'''\n\trequest_metric = \"match=\"+metric_name+\"&\"\n\trequest_start = \"start=\"+str(starttime)\n\tprint(f'Start Fetching values of metric: {metric_id} - {metric_name}')\n\tif (timeout != None):\n\t\twhile((datetime.datetime.now() >= starttime) and (datetime.datetime.now() < timeout) and status==1):\n\t\t\tprint(f'{datetime.datetime.now()} - S1: Fetching values from OSM, metric: {metric_name}, step: {step}')\n\t\t\trequest_OSM(request_metric, request_start)\n\t\t\ttime.sleep(convert_to_seconds(step))\n\telse:\n\t\twhile(status == 1):\n\t\t\t#print('entraste')\n\t\t\tprint(f'{datetime.datetime.now()} - S2: Fetching values from OSM, metric: {metric_name}, step: {step}')\n\t\t\trequest_OSM(request_metric, request_start)\n\t\t\ttime.sleep(convert_to_seconds(step))\n\treturn f'Done Fetching values of metric: {metric_id} - {metric_name}'\n\n\t'''while((datetime.datetime.now() < timeout) and int(status)==1):\n\t\t\t\t\tprint(f'{datetime.datetime.now()} - Fetching values from OSM, metric: {metric[\"metricName\"]}, step: {metric[\"timestampStep\"]}')\n\t\t\t\n\t\t\t\t\trequest_metric = \"query=\"+metric[\"metricName\"]+\"&\"\n\t\t\t\t\trequest_start = \"time=\"+str(datetime.datetime.now())\n\t\t\t\n\t\t\t\t\trequest_OSM(request_metric, request_start)\n\t\t\t\t\ttime.sleep(convert_to_seconds(metric[\"timestampStep\"]))\n\t\t\t\treturn f'Done Fetching values of metric: {metric[\"id\"]} - {metric[\"metricName\"]}'''\n\ndef transform_config(metric_id, metric_name, timestampStart, timestampEnd, status, step):\n\tglobal threads\n\tobj = thread_with_trace(target = fetch_values, args=(metric_id, metric_name, timestampStart, timestampEnd, status, step))\n\tthreads[str(metric_id)] = obj\n\tthreads[str(metric_id)].start()\n\tprint(f'{datetime.datetime.now()} - Active Threads {threading.active_count()}')\n\ndef delete_thread(metric_id):\n\tglobal threads\n\t# stop thread and remove from global dict the assign\n\tthreads[str(metric_id)].kill()\n\tthreads[str(metric_id)].join()\n\tprint(f'Stop Fetching values of metric: {metric_id}')\n\tif metric_id in threads.keys():\n\t\tdel threads[metric_id]\n\treturn\n\n\n\n'''def run_thread(config: Config_Model):\n\t# Request get metric values from OSM\n\trequest_metrics = ''\n\tfor i in range(len(config.metrics)):\n\t\tmetric = \"match=\"+config.metrics[i]['metricName']+\"&\"\n\t\trequest_metrics += metric\n\trequest_metrics = request_metrics + \"start=\" + config.timestamp.replace('\"','')\n\tif config.timestamp_final != None:\n\t\trequest_metrics = request_metrics + \"&end=\" + config.timestamp_final\n\tif config.step != None:\n\t\trequest_metrics = request_metrics + \"&step=\" + config.step\n\tendpoint = 'http://osm:4500/monitoringData?'\n\trequest_url = endpoint + request_metrics\n\tresponse = requests.get(request_url)\n\tif response.status_code != 200:\n\t\treturn ('Error in fetching data!', 200)\n\tresp = response.text\n\tpayload_encoded = {k: str(v).encode('utf-8') for k,v in resp.items()}\n\thashData = {k: hashlib.sha256(v).hexdigest() for k,v in payload_encoded.items()}\n\tpKey = RSA.importKey(publicKeyOperator)\n\tdataHashEncrypt =  {k: rsa.encrypt(v.encode(), pKey) for k,v in hashData.items()}\n\t# Data Lake Kafka Topic\n\tproducer = KafkaProducer(bootstrap_servers=['kafka:9092'], value_serializer=lambda x:json.dumps(x).encode('utf-8'), api_version=(0,10,1))\n\tprint(\"send to DL Topic\")\n\tdata = {'counter':2}\n\tproducer.send('topic_test', value=dataHashEncrypt)\n\treturn \"Pipeline completed\"'''\n\n# ----------------------------------------------------------------#\n@app.on_event(\"shutdown\")\ndef shutdown_event():\n\tprint('exit')\n\tglobal threads\n\tfor i in threads.keys():\n\t\tdel threads[i]\n\n# ----------------------------------------------------------------#\n\n@app.post(\"/settings\", status_code=201, responses={201: {\"model\": Response_Config_Model,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"content\": {\"application/json\": {\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"example\": json_response_enable}}},\n\t\t\t\t\t\t\t\t\t\t\t\t   404: {\"model\": Response_Error_Model,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"content\": {\"application/json\": {\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"example\": {\"status\": \"Error\", \"message\": \"Error message.\"}}}}})\nasync def set_param(config: Config_Model):\n\t# Save config in database\n\tresp = add_config(config)\n\tif resp == -1:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Error in create config in database.\"})\n\n\t# Run threads\n\tfor metric in resp['metrics']:\n\t  transform_config(metric['id'], metric['metricName'], resp['timestampStart'], resp['timestampEnd'], resp['status'], metric['timestampStep'])\n\t  del metric['id']\n\n\treturn resp\n\n@app.get(\"/settings/{config_id}\", responses={200: {\"model\": Response_Config_Model,\n\t\t\t\t\t\t\t\t\t\t\t\t   \"content\": {\"application/json\": {\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t   \"example\": json_response_enable}}},\n\t\t\t\t\t\t\t\t\t\t\t 404: {\"model\": Response_Error_Model,\n\t\t\t\t\t\t\t\t\t\t\t\t   \"content\": {\"application/json\": {\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t   \"example\": {\"status\": \"Error\", \"message\": \"Error message.\"}}}}})\nasync def get_config_id(config_id):\n\t# Get config by id\n\tif validate_uuid4(config_id) is False:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Config id invalid.\"})\n\tresp = get_config(config_id)\n\tif resp == 0:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Config id invalid.\"})\n\tif resp == -1:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Error in get config in database.\"})\n\n\tfor metric in resp['metrics']:\n\t\t  del metric['id']\n\n\treturn resp\n\n@app.get(\"/settings\", responses={200: {\"model\": List[Response_Config_Model],\n\t\t\t\t\t\t\t\t\t   \"content\": {\"application/json\": {\n\t\t\t\t\t\t\t\t\t\t\t\t   \"example\": [json_response_enable]}}},\n\t\t\t\t\t\t\t\t 404: {\"model\": Response_Error_Model,\n\t\t\t\t\t\t\t\t\t   \"content\": {\"application/json\": {\n\t\t\t\t\t\t\t\t\t\t\t\t   \"example\": {\"status\": \"Error\", \"message\": \"Error message.\"}}}}})\nasync def get_all_configs():\n\t# Get configs\n\t#print('teste1')\n\tresp = get_configs()\n\t#print('teste2')\n\tif resp == -1:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Error in get config in database.\"})\n\treturn resp\n\n@app.put(\"/settings/{config_id}\", responses={200: {\"model\": Response_Config_Model,\n\t\t\t\t\t\t\t\t\t\t\t\t   \"content\": {\"application/json\": {\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t   \"example\": json_response_enable}}},\n\t\t\t\t\t\t\t\t\t\t\t 404: {\"model\": Response_Error_Model,\n\t\t\t\t\t\t\t\t\t\t\t\t   \"content\": {\"application/json\": {\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t   \"example\": {\"status\": \"Error\", \"message\": \"Error message.\"}}}}})\nasync def update_config_id(config_id, config: Update_Config_Model):\n\t# Update config by id\n\tif validate_uuid4(config_id) is False:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Config id invalid.\"})\n\tresp1 = get_config(config_id)\n\tresp2 = update_config(config_id, config)\n\tif resp1 == 0 or resp2 == 0:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Config id invalid.\"})\n\tif resp1 == 1:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"No body data to update.\"})\n\tif resp1 == -1 or resp2 == -1:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Error in update config in database.\"})\n\n\t# Delete old threads\n\tfor metric in resp1['metrics']:\n\t  delete_thread(metric['id'])\n\t  del metric['id']\n\n\t# Run threads\n\tfor metric in resp2['metrics']:\n\t  transform_config(metric['id'], metric['metricName'], resp['timestampStart'], resp['timestampEnd'], resp['status'], metric['timestampStep'])\n\t  del metric['id']\n\n\treturn resp2\n\n@app.put(\"/settings/{config_id}/enable\", responses={200: {\"model\": Response_Config_Model,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t  \"content\": {\"application/json\": {\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t  \"example\": json_response_enable}}},\n\t\t\t\t\t\t\t\t\t\t\t\t\t404: {\"model\": Response_Error_Model,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t  \"content\": {\"application/json\": {\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t  \"example\": {\"status\": \"Error\", \"message\": \"Error message.\"}}}}})\nasync def enable_config_id(config_id):\n\t# Enable config by id\n\tif validate_uuid4(config_id) is False:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Config id invalid.\"})\n\tresp = enable_config(config_id)\n\tif resp == 0:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Config id invalid.\"})\n\tif resp == 1:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Config already enabled.\"})\n\tif resp == -1:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Error in enable config in database.\"})\n\n\t# Run threads\n\tfor metric in resp['metrics']:\n\t  transform_config(metric['id'], metric['metricName'], resp['timestampStart'], resp['timestampEnd'], resp['status'], metric['timestampStep'])\n\t  del metric['id']\n\n\treturn resp\n\n@app.put(\"/settings/{config_id}/disable\", responses={200: {\"model\": Response_Config_Model,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t   \"content\": {\"application/json\": {\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t   \"example\": json_response_disable}}},\n\t\t\t\t\t\t\t\t\t\t\t\t\t 404: {\"model\": Response_Error_Model,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t   \"content\": {\"application/json\": {\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t   \"example\": {\"status\": \"Error\", \"message\": \"Error message.\"}}}}})\nasync def disable_config_id(config_id):\n\t# Disable config by id\n\tif validate_uuid4(config_id) is False:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Config id invalid.\"})\n\tresp = disable_config(config_id)\n\tif resp == 0:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Config id invalid.\"})\n\tif resp == 1:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Config already disabled.\"})\n\tif resp == -1:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Error in disable config in database.\"})\n\n\t# Delete old threads\n\tfor metric in resp['metrics']:\n\t  delete_thread(metric['id'])\n\t  del metric['id']\n\n\treturn resp\n\n@app.delete(\"/settings/{config_id}\", status_code=204,\n\t\t\t\t\t\t\t\t\t responses={404: {\"model\": Response_Error_Model,\n\t\t\t\t\t\t\t\t\t\t\t\t\t  \"content\": {\"application/json\": {\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t  \"example\": {\"status\": \"Error\", \"message\": \"Error message.\"}}}}})\nasync def delete_config_id(config_id):\n\t# Get config by id\n\tif validate_uuid4(config_id) is False:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Config id invalid.\"})\n\tresp1 = get_config(config_id)\n\tresp2 = delete_config(config_id)\n\tif resp1 == 0 or resp2 == 0:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Config id invalid.\"})\n\tif resp1 == -1 or resp2 == -1:\n\t  return JSONResponse(status_code=404, content={\"status\": \"Error\", \"message\": \"Error in delete config in database.\"})\n\n\t# Delete old threads\n\tfor metric in resp1['metrics']:\n\t  delete_thread(metric['id'])\n\t  del metric['id']\n\n\treturn JSONResponse(status_code=204, content=None)\n","sub_path":"mda/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":15485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"638672766","text":"\"\"\"Self-Delimiting Numeric Values (RFC 6256)\"\"\"\n\n\ndef sdnv_encode(value):\n    \"\"\"Returns the SDNV-encoded byte string representing value\n\n    Args:\n        value (int): Non-negative integer that should be encoded\n    Returns:\n        bytes: SDNV-encoded number\n    Raises:\n        ValueError: If value not an integer or negative\n    \"\"\"\n    if value < 0 or not isinstance(value, int):\n        raise ValueError(\"Only non-negative integers can be SDNV-encoded\")\n\n    # Special case: zero whould produce an empty byte string\n    if value == 0:\n        return b\"\\x00\"\n\n    result = bytearray()\n    while value != 0:\n        result.append((value & 0x7f) | 0x80)\n        value >>= 7\n\n    # Clear MSB in the first byte that was set by the XOR with 0x80\n    result[0] &= 0x7f\n\n    # Network byte order\n    return bytes(reversed(result))\n\n\ndef sdnv_decode(buffer):\n    \"\"\"Decodes a byte string (or any iterable over bytes) assumed to be a an\n    SDNV and returns the non-negative integer representing the numeric value\n\n    Args:\n        buffer (bytes): Encoded SDNV\n    Returns:\n        int: Decoded non-negative integer\n    Raises:\n        ValueError: If the buffer contains insufficient bytes (not the complete\n        SDNV)\n    \"\"\"\n    n = 0\n    for i, byte in enumerate(buffer, 1):\n        n = (n << 7) | (byte & 0x7f)\n\n        if byte >> 7 == 0:\n            return n, i\n\n    raise ValueError(\"Insufficient bytes\")\n\n\ndef test_sdnv_encode():\n    assert sdnv_encode(0xabc) == b\"\\x95\\x3c\"\n    assert sdnv_encode(0x4234) == b\"\\x81\\x84\\x34\"\n\n\ndef test_sdnv_decode():\n    import pytest\n\n    assert sdnv_decode(b\"\\xa4\\x34\") == (0x1234, 2)\n    assert sdnv_decode(b\"\\xa4\\x34\\x00\\x00\") == (0x1234, 2)\n    assert sdnv_decode(b\"\\x7f\") == (0x7f, 1)\n\n    with pytest.raises(ValueError):\n        assert sdnv_decode(b\"\\xa4\")\n","sub_path":"pyupcn/sdnv.py","file_name":"sdnv.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"133173585","text":"# Adam Vert\n# Feb 2020\n\n\n############################# Imports ##########################################################################################################\nimport os\n\nimport pyedflib\nimport numpy as np\nimport pandas as pd\nfrom scipy import signal\nimport datetime\n\n\n################################################################################################################################################\ndef combine_edf(path1, path2, output_path):\n    '''\n\n    Args:\n        path1: Full path to EDF1\n        path2: Full path to EDF2\n        output_path: Full path to where you want the combined edf outputted\n\n    Returns:\n        An edf with the data points and time stamps combined from EDF1 and EDF2\n\n    Notes:\n        Both files must have the same sample rate and the intervals must not overlap\n    '''\n    #  Read EDF's\n    edf_a = pyedflib.EdfReader(path1)\n    edf_b = pyedflib.EdfReader(path2)\n\n    #  Find order of EDF's\n    if edf_a.getStartdatetime() < edf_b.getStartdatetime():\n        edf_1 = edf_a\n        edf_2 = edf_b\n    else:\n        edf_1 = edf_b\n        edf_2 = edf_a\n\n    #  Get Durations\n    edf_1_duration = edf_1.getFileDuration()\n    edf_2_duration = edf_2.getFileDuration()\n\n    # Get Signal Headers\n    sig_labels_1 = edf_1.getSignalLabels()\n    sig_labels_2 = edf_2.getSignalLabels()\n    if not np.array_equal(sig_labels_1, sig_labels_2):\n        print(sig_labels_1)\n        print(sig_labels_2)\n        print(\"ERROR: FILES HAVE DIFFERENT SIGNALS\")\n        return\n\n    # Get Signal values\n    data1 = []\n    data2 = []\n    for n in range(len(sig_labels_1)):\n        data1.append(edf_1.readSignal(n))\n        data2.append(edf_2.readSignal(n))\n\n    data1 = np.array(data1)\n    data2 = np.array(data2)\n\n    # Get Frequencies\n    freq_1 = edf_1.getSampleFrequencies()\n    freq_2 = edf_2.getSampleFrequencies()\n\n    if not np.array_equal(freq_1, freq_2):\n        for n in range(len(freq_1)):\n            if freq_1[n] > freq_2[n]:\n                freq_1[n] = freq_2[n]\n                data1[n] = signal.resample(data1[n], edf_1_duration * freq_2[n])\n            elif freq_2[n] > freq_1[n]:\n                freq_1[n] = freq_2[n]\n                len(data2[n])\n                data2[n] = signal.resample(data2[n], edf_2_duration * freq_1[n])\n                len(data2[n])\n\n    #  Calculate end times\n    edf_1_endtime = edf_1.getStartdatetime() + datetime.timedelta(seconds=edf_1_duration)\n    edf_2_endtime = edf_2.getStartdatetime() + datetime.timedelta(seconds=edf_2_duration)\n\n    #  Get time difference\n    print((edf_2_endtime - edf_1.getStartdatetime()).total_seconds())\n    time_diff = edf_2.getStartdatetime() - edf_1_endtime\n    time_diff = time_diff.seconds\n    if edf_2.getStartdatetime() < edf_1_endtime:  # If end time over lap with start time, return\n        print(\"ERROR, SAMPLES OVERLAP\")\n        return\n\n    #  Make data set\n    data_final = data1\n    for n in range(len(freq_1)):\n        data_final[n] = np.append(data_final[n], [0] * (time_diff * freq_1[n]))\n        data_final[n] = np.append(data_final[n], data2[n])\n\n    print(edf_a.getHeader())\n    print(edf_a.getSignalHeaders())\n\n    # Write EDF\n    header = edf_a.getHeader()\n    signal_headers = edf_a.getSignalHeaders()\n    pyedflib.highlevel.write_edf(edf_file=output_path, signals=data_final, signal_headers=signal_headers, header=header,\n                                 digital=False)\n\n\n#combine_edf(r\"D:\\Adam PC\\PycharmProjects\\owcurate\\STEPS_UOW_7914_01_SE01_BF\\08-57-53.EDF\",\n#            r\"D:\\Adam PC\\PycharmProjects\\owcurate\\STEPS_UOW_7914_01_SE01_BF\\11-13-17.EDF\",\n#            r\"D:\\Adam PC\\PycharmProjects\\owcurate\\STEPS_UOW_7914_01_SE01_BF\\COMBINED_7914.EDF\")\n# combine_edf(r\"O:\\Data\\STEPS\\Raw data\\Bittium\\STEPS_UOW_1611_01_SE01_BF\\20200211\\14-04-34.EDF\",r\"O:\\Data\\STEPS\\Raw data\\Bittium\\STEPS_UOW_1611_01_SE01_BF\\20200211\\14-39-49.EDF\",r\"D:\\Adam PC\\PycharmProjects\\owcurate\\combined_edf_1611.edf\")\n","sub_path":"MiscScripts/combine_2_edf.py","file_name":"combine_2_edf.py","file_ext":"py","file_size_in_byte":3928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"4913369","text":"#!/usr/bin/env python3\n\n#Load necessary libriaries\nimport numpy as np\nimport gym\nimport random\n\n\n#Main class\nclass Algo():\n  '''\n  Creates object which can compute qtable\n  for given game from gym library\n  :param:\n    total_episodes: number of episode to play to create qtable\n\n  :param:\n    learning_rate: how agressive we are gonna learn (0, 1)\n\n  :param:\n    max_steps: maximum number of steps per episode\n\n  :param:\n    gamma: how much You wanna value immediate reward (0, 1)\n\n  :param:\n    epsilon: for epsilon-greedy strategy\n\n  :param:\n    min_epsilon: min value for epsilon\n\n  :param:\n    max_epsilon: max value for epsilon\n\n  :param:\n    decay_rate: how fast You wanna decrease epsilon\n\n  :param:\n    game_name: name of the game from env e.g. 'FrozeLake-v0'\n  '''\n\n\n  def __init__(self, total_episodes,\n               learning_rate, max_steps, gamma, epsilon,\n               min_epsilon, max_epsilon, decay_rate, game_name):\n\n    '''\n    init all necessary variables\n    '''\n    self._total_episodes = total_episodes\n    self._learning_rate = learning_rate\n    self._max_steps = max_steps\n    self._gamma = gamma\n    self._epsilon = epsilon\n    self._min_epsilon = min_epsilon\n    self._max_epsilon = max_epsilon\n    self._decay_rate = decay_rate\n    self._env = gym.make(game_name)\n    self._action_size = self._env.action_space.n\n    self._state_size= self._env.observation_space.n\n    self._rewards = []\n    self._qtable = np.zeros(shape=(self._state_size, self._action_size))\n\n\n  def q_learning(self):\n    '''\n    q_learning algorithm to 'solve' qtable\n    '''\n\n    #we iterate through all episodes\n    for epiosde in range(self._total_episodes):\n      state = self._env.reset()\n      done = False\n      total_rewards = 0\n      #we iterate through all steps\n      for step in range(self._max_steps):\n        #epsilon-greedy strategy\n        tradeoff = random.uniform(0, 1)\n        if tradeoff > self._epsilon:\n          #greedy move\n          action = np.argmax(self._qtable[state, :])\n        else:\n          #random action\n          action = self._env.action_space.sample()\n        #making move according to picked action\n        new_state, reward, done, info = self._env.step(action)\n\n        #Update qtable according to bellman equation!!!\n        self._qtable[state, action] = self._qtable[state, action] + self._learning_rate * \\\n                                      (reward + self._gamma * (np.max(self._qtable[new_state, :]) -\n                                      self._qtable[state, action]))\n        total_rewards += reward\n        state = new_state\n\n        if done:\n          break\n      #Update epsilon after each episode\n      self._epsilon = self._min_epsilon + (self._max_epsilon - self._min_epsilon)  * \\\n                                          np.exp(-self._decay_rate * epiosde)\n      self._rewards.append(total_rewards)\n\n    print(f\"Score over time: {str(sum(self._rewards) / self._total_episodes)}\")\n    print(self._qtable)\n\n\n\n\ndef main():\n  FrozenLake = Algo(15000, 0.8, 99, 0.95, 1.0, 0.001, 1.0, 0.005, 'FrozenLake-v0')\n  FrozenLake.q_learning()\n\n\nif __name__ == '__main__':\n  main()\n","sub_path":"Q_learning.py","file_name":"Q_learning.py","file_ext":"py","file_size_in_byte":3126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"593105001","text":"import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output\nfrom app_def import app\nimport dash_bootstrap_components as dbc\nfrom dash.dependencies import State\nfrom choroplethmapbox import get_area_in_km2_for_stat_zones\nfrom pre_process import *\nimport pickle\n\n#### Create scores #####\ndf_salaries_t1 = dfs_dict['df_salaries_t1']\ndf_salaries_t0 = dfs_dict['df_salaries_t0']\ndf_conflicts_t1 = dfs_dict['df_conflicts_t1']\ndf_conflicts_t0 = dfs_dict['df_conflicts_t0']\ndf_cameras_t1 = dfs_dict['df_cameras_t1']\ndf_cameras_t0 = dfs_dict['df_cameras_t0']\ndf_aband_t1 = dfs_dict['df_aband_t1']\ndf_aband_t0 = dfs_dict['df_aband_t0']\ndf_106_t1 = dfs_dict['df_106_t1']\ndf_106_t0 = dfs_dict['df_106_t0']\ndf_crimes_t1 = dfs_dict['df_crime_t1']\ndf_crimes_t0 = dfs_dict['df_crime_t0']\nStatZone_size_dict = get_area_in_km2_for_stat_zones()\n\n### choose initial score for each weight\nscores = [3] * 12\n\n\ndef calc_safety_scores(StatZone, df_salary, df_conflicts, df_cameras, df_aband, df_106,\n                       df_crimes):\n    df_area_salary = df_salary[df_salary['StatZone'] == StatZone]\n    df_area_cameras = df_cameras[df_cameras['StatZone'] == StatZone]\n    df_ares_aband = df_aband[df_aband['StatZone'] == StatZone]\n    df_area_106_security = df_106[(df_106['StatZone'] == StatZone) &\n                                  (df_106['בעל המשימה'] == 'בטחון')]\n    df_area_106_social = df_106[(df_106['StatZone'] == StatZone) &\n                                (df_106['בעל המשימה'] == 'רווחה הדר/עיר תחתית')]\n\n    df_area_crimes = df_crimes[df_crimes['StatZone'] == StatZone]\n\n    # ******* Variables ********#\n    population = df_area_salary['ResNum']\n    Tot_crimes = df_crimes[df_crimes.columns[2:]].sum().sum()\n    Tot_area_crimes = df_area_crimes[df_area_crimes.columns[2:]].sum(axis=1)\n    HaifaAvgIncome = 13110\n    Demographic_density_haifa = 271291 / 63.67\n\n    # ******************* PART 1 SCORES **********************#\n    conflicts_s = 1 - (df_conflicts[df_conflicts['StatZone'] == StatZone]['Conflicts'] / population)\n    cameras_s = ((len(df_area_cameras) * 1000) / population)\n    aband_s = (1 - len(df_ares_aband) / population)\n    security_106_s = (1 - len(df_area_106_security) / population)\n    social_106_s = (1 - len(df_area_106_social) / population)\n\n    # ******************* PART 2 -CRIME SCORES **********************#\n    crime_s = 1 - Tot_area_crimes / Tot_crimes\n    crime_thefts_s = 1 - df_area_crimes['Thefts'] / Tot_area_crimes\n    crime_BodyAssaults_s = 1 - df_area_crimes['BodyAssaults'] / Tot_area_crimes\n    crime_SexualAssaults_s = 1 - df_area_crimes['SexualAssaults'] / Tot_area_crimes\n    crime_Robbery_s = 1 - df_area_crimes['Robbery'] / Tot_area_crimes\n\n    # ******************* PART 3 -INCOME SCORES **********************#\n    income_avg_s = df_area_salary['IncSelfAve'] / HaifaAvgIncome\n    Demographic_density_area = population / StatZone_size_dict[StatZone]\n    Demographic_density_s = Demographic_density_area / Demographic_density_haifa\n\n    X_features = [conflicts_s, cameras_s, aband_s, security_106_s, social_106_s, crime_s, \\\n                  crime_thefts_s, crime_BodyAssaults_s, crime_SexualAssaults_s, crime_Robbery_s, \\\n                  income_avg_s, Demographic_density_s]\n    X_features = [x.values[0] for x in X_features]\n    return X_features\n\n\ndef create_safety_table(df_salaries, df_conflicts, df_cameras, df_aband, df_106, df_crimes):\n    values_dict = dict.fromkeys([611, 612, 613, 621, 622, 623, 631, 632, 633, 634, 641, 642, 643, 644], 0.)\n    data = dict.fromkeys([611, 612, 613, 621, 622, 623, 631, 632, 633, 634, 641, 642, 643, 644], 0.)\n\n    for StatZone in values_dict.keys():\n        X_features = calc_safety_scores(StatZone, df_salaries, df_conflicts, df_cameras, df_aband, df_106,\n                                        df_crimes)\n        data[StatZone] = X_features\n\n    df_score = pd.DataFrame(data.values(),\n                            columns=['conflicts_s', 'cameras_s', 'aband_s', 'security_106_s', 'social_106_s', 'crime_s', \\\n                                     'crime_thefts_s', 'crime_BodyAssaults_s', 'crime_SexualAssaults_s',\n                                     'crime_Robbery_s', 'income_avg_s', 'Demographic_density_s'])\n    df_score['StatZone'] = data.keys()\n    for col in df_score.columns[:-1]:\n        df_score[col] = (df_score[col] - df_score[col].min()) / (df_score[col].max() - df_score[col].min())\n\n    return df_score, values_dict\n\n\ndf_score_t1, values_dict_t1 = create_safety_table(df_salaries_t1, df_conflicts_t1, df_cameras_t1, df_aband_t1,\n                                                  df_106_t1, df_crimes_t1)\ndf_score_t0, values_dict_t0 = create_safety_table(df_salaries_t0, df_conflicts_t0, df_cameras_t0, df_aband_t0,\n                                                  df_106_t0, df_crimes_t0)\n\nlayout = html.Div(children=[\n    html.Div([html.H4(\n        'ציון הביטחון האישי הוא קומבינציה של 12 רכיבים שונות המציגים הביטים שונים של ביטחון אישי. אנא בחר את החשיבות של כל רכיב בהתאם להעדפותיך '\n        ,\n        style={'text-align': 'right', 'text-transform': 'none', 'font-family': 'sans-serif',\n               'letter-spacing': '0em'}, )],\n        className='pretty_container'\n    ),\n\n    html.Div([html.Div([\n        html.Div([html.Div([\n            dbc.Button('אפס חשיבות רכיבים', id='reset_weights_weights', n_clicks=0, color='dark',\n                       style={'text-align': 'right', 'text-transform': 'none', 'font-family': 'sans-serif',\n                              'letter-spacing': '0em', 'font-size': 15}\n                       ),\n            html.H4(\"5: רמת חשיבות נמוכה :1, רמת חשיבות גבוהה\", style={'text-align': 'right', 'text-transform': 'none',\n                                                                       'font-family': 'sans-serif',\n                                                                       'letter-spacing': '0em', 'font-size': 15,\n                                                                       'font-weight': 'bold'}),\n        ], className='row_rapper'), ], className='pretty_container'),\n        html.Div([html.P(id=\"slider-text1\", children=\"קונפליקטים בין שכנים\",\n                         style={'color': 'black', 'text-align': 'center'},\n                         ),\n                  dcc.Slider(id=\"Weight 1_weights\", min=1, max=5, value=scores[0], step=None,\n                             marks={\n                                 str(num): {\"label\": str(num),\n                                            \"style\": {\"color\": \"#7fafdf\"}, }\n                                 for num in [1, 2, 3, 4, 5]\n                             }),\n                  html.P(id=\"slider-text2\", children=\"מצלמות אבטחה\",\n                         style={'color': 'black', 'text-align': 'center'},\n                         ),\n                  dcc.Slider(id=\"Weight 2_weights\", min=1, max=5, value=scores[1], step=None,\n                             marks={\n                                 str(num): {\"label\": str(num),\n                                            \"style\": {\"color\": \"#7fafdf\"}, }\n                                 for num in [1, 2, 3, 4, 5]\n                             }),\n                  html.P(id=\"slider-text3\", children=\"בתים נטושים\",\n                         style={'color': 'black', 'text-align': 'center'},\n                         ),\n                  dcc.Slider(id=\"Weight 3_weights\", min=1, max=5, value=scores[2], step=None,\n                             marks={\n                                 str(num): {\"label\": str(num),\n                                            \"style\": {\"color\": \"#7fafdf\"}, }\n                                 for num in [1, 2, 3, 4, 5]\n                             }),\n                  html.P(id=\"slider-text4\", children=\"שיחות למוקד העירייה בנושא ביטחון\",\n                         style={'color': 'black', 'text-align': 'center'},\n                         ),\n                  dcc.Slider(id=\"Weight 4_weights\", min=1, max=5, value=scores[3], step=None,\n                             marks={\n                                 str(num): {\"label\": str(num),\n                                            \"style\": {\"color\": \"#7fafdf\"}, }\n                                 for num in [1, 2, 3, 4, 5]\n                             }),\n                  ], className=\"slider-container\"),\n        html.Div([html.P(id=\"slider-text5\", children=\"שיחות למוקד העירייה בנושא סוציאלי\",\n                         style={'color': 'black', 'text-align': 'center'},\n                         ),\n                  dcc.Slider(id=\"Weight 5_weights\", min=1, max=5, value=scores[4], step=None,\n                             marks={\n                                 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max=5, value=scores[6], step=None,\n                             marks={\n                                 str(num): {\"label\": str(num),\n                                            \"style\": {\"color\": \"#7fafdf\"}, }\n                                 for num in [1, 2, 3, 4, 5]\n                             }),\n                  html.P(id=\"slider-text8\", children=\"תקיפות גוף\",\n                         style={'color': 'black', 'text-align': 'center'},\n                         ),\n                  dcc.Slider(id=\"Weight 8_weights\", min=1, max=5, value=scores[7], step=None,\n                             marks={\n                                 str(num): {\"label\": str(num),\n                                            \"style\": {\"color\": \"#7fafdf\"}, }\n                                 for num in [1, 2, 3, 4, 5]\n                             }),\n                  ], className=\"slider-container\"),\n        html.Div([html.P(id=\"slider-text9\", children=\"תקיפות מיניות\",\n                         style={'color': 'black', 'text-align': 'center'},\n                         ),\n                  dcc.Slider(id=\"Weight 9_weights\", min=1, max=5, value=scores[8], step=None,\n                             marks={\n                                 str(num): {\"label\": str(num),\n                                            \"style\": {\"color\": \"#7fafdf\"}, }\n                                 for num in [1, 2, 3, 4, 5]\n                             }),\n                  html.P(id=\"slider-text10\", children=\"שודים\",\n                         style={'color': 'black', 'text-align': 'center'},\n                         ),\n                  dcc.Slider(id=\"Weight 10_weights\", min=1, max=5, value=scores[9], step=None,\n                             marks={\n                                 str(num): {\"label\": str(num),\n                                            \"style\": {\"color\": \"#7fafdf\"}, }\n                                 for num in [1, 2, 3, 4, 5]\n                             }),\n                  html.P(id=\"slider-text11\", children=\"הכנסה ממוצעת\",\n                         style={'color': 'black', 'text-align': 'center'},\n                         ),\n                  dcc.Slider(id=\"Weight 11_weights\", min=1, max=5, value=scores[10],\n                             step=None,\n                             marks={\n                                 str(num): {\"label\": str(num),\n                                            \"style\": {\"color\": \"#7fafdf\"}, }\n                                 for num in [1, 2, 3, 4, 5]\n                             }),\n                  html.P(id=\"slider-text12\", children=\"צפיפות דמוגרפית\",\n                         style={'color': 'black', 'text-align': 'center'},\n                         ),\n                  dcc.Slider(id=\"Weight 12_weights\", min=1, max=5, value=scores[11],\n                             step=None,\n                             marks={\n                                 str(num): {\"label\": str(num),\n                                            \"style\": {\"color\": \"#7fafdf\"}, }\n                                 for num in [1, 2, 3, 4, 5]\n                             }), ], className=\"slider-container\")],\n    )]),\n    html.Div(id='fake_output')]\n)\n\n\n@app.callback(\n    Output(component_id='fake_output', component_property='value'),\n    Input(component_id='Weight 1_weights', component_property='value'),\n    Input(component_id='Weight 2_weights', component_property='value'),\n    Input(component_id='Weight 3_weights', component_property='value'),\n    Input(component_id='Weight 4_weights', component_property='value'),\n    Input(component_id='Weight 5_weights', component_property='value'),\n    Input(component_id='Weight 6_weights', component_property='value'),\n    Input(component_id='Weight 7_weights', component_property='value'),\n    Input(component_id='Weight 8_weights', component_property='value'),\n    Input(component_id='Weight 9_weights', component_property='value'),\n    Input(component_id='Weight 10_weights', component_property='value'),\n    Input(component_id='Weight 11_weights', component_property='value'),\n    Input(component_id='Weight 12_weights', component_property='value'),\n)\ndef update_output_div(w1, w2, w3, w4, w5, w6, w7, w8, w9, w10, w11, w12):\n    \"\"\"\n    create all the graphs that rely on the input values (weights and area)\n    :param w1: weight value\n    :param w2: weight value\n    :param area: statistic ares (or 'All')\n    :return: all wanted figures\n    \"\"\"\n    W = [w1, w2, w3, w4, w5, w6, w7, w8, w9, w10, w11, w12]\n    W = [float(i) / sum(W) for i in W]\n\n    # Save weights locally by user choice\n    weights = open(\"weights.pkl\", \"wb\")\n    pickle.dump(W, weights)\n    weights.close()\n\n    return 0\n\n\n@app.callback(\n    [Output(f\"collapse-1_weights\", \"is_open\")],\n    [Input(f\"group-1-toggle\", \"n_clicks\")],\n    [State(f\"collapse-1_weights\", \"is_open\")],\n)\ndef toggle_accordion(n1, is_open1):\n    ctx = dash.callback_context\n\n    if not ctx.triggered:\n        return [False]\n    else:\n        button_id = ctx.triggered[0][\"prop_id\"].split(\".\")[0]\n\n    if button_id == \"group-1-toggle\" and n1:\n        return [not is_open1]\n    return [False]\n\n\n@app.callback(\n    Output(component_id='Weight 1_weights', component_property='value'),\n    Output(component_id='Weight 2_weights', component_property='value'),\n    Output(component_id='Weight 3_weights', component_property='value'),\n    Output(component_id='Weight 4_weights', component_property='value'),\n    Output(component_id='Weight 5_weights', component_property='value'),\n    Output(component_id='Weight 6_weights', component_property='value'),\n    Output(component_id='Weight 7_weights', component_property='value'),\n    Output(component_id='Weight 8_weights', component_property='value'),\n    Output(component_id='Weight 9_weights', component_property='value'),\n    Output(component_id='Weight 10_weights', component_property='value'),\n    Output(component_id='Weight 11_weights', component_property='value'),\n    Output(component_id='Weight 12_weights', component_property='value'),\n    Input(component_id='reset_weights_weights', component_property='n_clicks'),\n)\ndef reset_all_weights(n1):\n    ctx = dash.callback_context\n    if not ctx.triggered:\n        return scores\n    else:\n        return scores\n","sub_path":"apps/weights.py","file_name":"weights.py","file_ext":"py","file_size_in_byte":16222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"158549942","text":"from rest_framework import viewsets\nfrom mainapp.models import WarningDevice\nfrom mainapp.api.serializers import WarningDeviceSerializer\nfrom rest_framework import filters\nimport django_filters\n\n\nclass WarningDeviceFilter(django_filters.FilterSet):\n    zone_radius_min = django_filters.NumberFilter(field_name='zone_radius',\n                                                  lookup_expr='gte')\n    zone_radius_max = django_filters.NumberFilter(field_name='zone_radius',\n                                                  lookup_expr='lte')\n\n    class Meta:\n        model = WarningDevice\n        fields = ['zone_radius_min', 'zone_radius_max', 'device_type']\n\n\nclass WarningDeviceViewSet(viewsets.ModelViewSet):\n    serializer_class = WarningDeviceSerializer\n    filter_backends = [django_filters.rest_framework.DjangoFilterBackend,\n                       filters.SearchFilter]\n    search_fields = ['name', 'location_address']\n    filterset_class = WarningDeviceFilter\n\n    def get_queryset(self):\n        queryset = WarningDevice.objects.all()\n        params = self.request.query_params\n        if 'right_bottom' in params and params.get('right_bottom') != '':\n            right_bottom_coords = params.get('right_bottom').replace(' ', '')\\\n                .split(',')\n\n            if len(right_bottom_coords) == 2:\n                queryset = queryset.filter(\n                    latitude__lte=right_bottom_coords[0],\n                    longitude__lte=right_bottom_coords[1]\n                )\n            else:\n                queryset = WarningDevice.objects.none()\n        if 'left_top' in params and params.get('left_top') != '':\n            left_top_coords = params.get('left_top').replace(' ', '')\\\n                .split(',')\n            if len(left_top_coords) == 2:\n                queryset = queryset.filter(\n                    latitude__gte=left_top_coords[0],\n                    longitude__gte=left_top_coords[1]\n                )\n            else:\n                queryset = WarningDevice.objects.none()\n        return queryset\n","sub_path":"infodev/mainapp/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2041,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"106207683","text":"#    (C) Copyright 2018 Anthony D. Dutoi\n# \n#    This file is part of Qode.\n# \n#    Qode is free software: you can redistribute it and/or modify\n#    it under the terms of the GNU General Public License as published by\n#    the Free Software Foundation, either version 3 of the License, or\n#    (at your option) any later version.\n# \n#    Qode 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 Qode.  If not, see <http://www.gnu.org/licenses/>.\n#\n# This module is for TEST PURPOSE\n# It generates a NUMPY Transition Dipole Matrix in such form:\n#    <i'|<j'| V |i >|j > = <i'|<j'| xi xj |i >|j >\n#\n# Non-Zero Condition:  i' = i +/- 1  AND j' = j +/- 1\n#\n# The multiplier used is   1.0/ Leading Dimension    to scale off-diagonal numbers down.\n#\n\n\n\nimport numpy as np\n\ndef gen_two_body_x_matrix( ld1, ld2 ):\n    lda = ld1 * ld2\n    A = np.matrix( np.zeros((lda,lda)) )\n    for i in range(ld1):\n        for j in range(ld2):\n            for k in range(ld1):\n                for l in range(ld2):\n                    if abs(i-k) == 1 and abs(j-l) == 1:\n                        A[i*ld2 + j ,k*ld2 + l] = (i+j+k+l)*(1.0/lda)\n    return A\n\n\n\nif __name__ == \"__main__\":\n    n1 = 5\n    n2 = 6\n    A = gen_two_body_x_matrix(n1,n2)\n    print(A)\n    print(\"show one non-zero element =\",A[0,7])","sub_path":"QODE/qode/coupled_oscillators/gen_rand_transition_dipole_mat.py","file_name":"gen_rand_transition_dipole_mat.py","file_ext":"py","file_size_in_byte":1554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"383919397","text":"from nn_constructor import *\nimport numpy as np\nimport tensorflow as tf\nimport os\n\n\ndef encoder(inputs, hidden_size, dims, activations, layer_type, n_layers, keep_prob,\n            phase=True, num_filters=None, ksize=None, kind_pool=\"max\", r=0.3):\n    inputs, prev_dense, _, _ = build_nn(inputs=inputs,\n                                        dims=dims,\n                                        activations=activations,\n                                        layer_type=layer_type,\n                                        n_layers=n_layers,\n                                        keep_prob=keep_prob,\n                                        phase=phase,\n                                        num_filters=num_filters,\n                                        ksize=ksize,\n                                        kind_pool=kind_pool,\n                                        r=r)\n\n    # Output layers\n    z_mean = dense(inputs, neurons=hidden_size, num_layers=1,\n                   prev_dense=prev_dense, activation=None)\n    prev_dense += 1\n\n    z_log_sigma_sq = dense(inputs, neurons=hidden_size, num_layers=1,\n                           prev_dense=prev_dense, activation=None)\n    prev_dense += 1\n\n    return z_mean, z_log_sigma_sq\n\n\ndef decoder(inputs, dims, activations, layer_type, n_layers, keep_prob,\n            phase=True, num_filters=None, ksize=None, kind_pool=\"max\", r=0.3):\n    outputs, _, _, _ = build_nn(inputs=inputs,\n                                dims=dims,\n                                activations=activations,\n                                layer_type=layer_type,\n                                n_layers=n_layers,\n                                keep_prob=keep_prob,\n                                phase=phase,\n                                num_filters=num_filters,\n                                ksize=ksize,\n                                kind_pool=kind_pool,\n                                r=r)\n\n    return outputs\n\n\ndef compiler(inputs, hidden_size,\n             dims_enc, activations_enc,\n             dims_dec, activations_dec,\n             layer_type, batch_size=100, n_layers=[1], keep_prob=[1], phase=True):\n\n    z_prior = tf.random_normal((batch_size, hidden_size), 0, 1)  # normal dist for z\n    eps = tf.random_normal((batch_size, hidden_size), 0, 1)  # normal dist for VAE reparametrization\n\n    with tf.variable_scope(\"Encoder\"):\n        z_cond_mean, z_cond_log_sigma_sq = encoder(inputs=inputs,\n                                                   hidden_size=hidden_size,\n                                                   dims=dims_enc,\n                                                   activations=activations_enc,\n                                                   layer_type=layer_type,\n                                                   n_layers=n_layers,\n                                                   keep_prob=keep_prob,\n                                                   phase=phase)  # get z from the input\n    with tf.variable_scope(\"Decoder\"):\n        # Reparametrization\n        z_cond = tf.add(z_cond_mean,\n                        tf.multiply(tf.sqrt(tf.exp(z_cond_log_sigma_sq)), eps))  # grab our actual z\n        # Generator with the output from encoder\n        x_tilde = decoder(inputs=z_cond,\n                          dims=dims_dec,\n                          activations=activations_dec,\n                          layer_type=layer_type,\n                          n_layers=n_layers,\n                          keep_prob=keep_prob,\n                          phase=phase)\n    with tf.variable_scope(\"Decoder\", reuse=True):\n        # Generator from a prior Z\n        x_prior = decoder(inputs=z_prior,\n                          dims=dims_dec,\n                          activations=activations_dec,\n                          layer_type=layer_type,\n                          n_layers=n_layers,\n                          keep_prob=keep_prob,\n                          phase=phase)\n    return z_cond_mean, z_cond_log_sigma_sq, z_cond, x_tilde, x_prior, z_prior\n\n\ndef loss(inputs, x_tilde, z_cond_log_sigma_sq, z_cond_mean, kind=\"binary\"):\n    # KL divergence\n    kl_loss = tf.reduce_mean(-0.5 * tf.reduce_sum(1 + z_cond_log_sigma_sq -\n                                                  tf.square(z_cond_mean) -\n                                                  tf.exp(z_cond_log_sigma_sq), 1))\n    # Decoder Loss\n    recon_loss = 0\n\n    if kind == \"binary\":\n        # log-loss\n        recon_loss = tf.reduce_mean(- tf.reduce_sum(inputs * tf.log(x_tilde + 1e-12) +\n                                                    (1. - inputs) * tf.log(1. - x_tilde + 1e-12), 1))\n    elif kind == \"squared\":\n        # Squared error\n        recon_loss = tf.reduce_mean(tf.reduce_sum(tf.squared_difference(inputs, x_tilde), axis=[1]))\n\n    # Total loss\n    vae_loss = kl_loss + recon_loss\n\n    return vae_loss, kl_loss, recon_loss\n\n# Discriminator Loss\n#     D_loss = tf.reduce_mean(-1. * (tf.log(tf.clip_by_value(score_x,1e-5,1.0)) +\n#                                    tf.log(tf.clip_by_value(1.0 - score_x_prior,1e-5,1.0)) +\n#                                    tf.log(tf.clip_by_value(1.0 - score_x_tilde,1e-5,1.0))\n#                                   )\n#                            )\n\n# Lth Layer Loss - the 'learned similarity measure'\n#     LL_loss = tf.reduce_mean(tf.reduce_sum(llikNorm(lay_x_tilde, lay_x), [1, 2, 3]))\n\n#     E_loss = KL_loss / (latent_dim * batch_size) - LL_loss / np.prod(tf.cast(lay_x_tilde.shape, tf.float32))\n\n\ndef build_graph_vae(threshold, hidden_size,\n                    dims_enc, activations_enc, dims_dec, activations_dec,\n                    layer_type, n_layers=[1], keep_prob=[1], batch_size=100,\n                    learning_rate=1e-4,  restore=False, model_dir=None):\n    # Design graph VAE\n    tf.reset_default_graph()\n\n    real_data = tf.placeholder(tf.float32, shape=[batch_size, threshold])\n    phase = tf.placeholder_with_default(True, shape=())\n\n    # Compile models\n    z_cond_mean, z_cond_log_sigma_sq, z_cond, x_tilde, \\\n        x_prior, z_prior = compiler(inputs=real_data,\n                                    hidden_size=hidden_size,\n                                    batch_size=batch_size,\n                                    dims_enc=dims_enc,\n                                    activations_enc=activations_enc,\n                                    dims_dec=dims_dec,\n                                    activations_dec=activations_dec,\n                                    layer_type=layer_type,\n                                    n_layers=n_layers,\n                                    keep_prob=keep_prob,\n                                    phase=phase)\n\n    # Compute loss\n    vae_loss, kl_loss, recon_loss = loss(inputs=real_data,\n                                         x_tilde=x_tilde,\n                                         z_cond_log_sigma_sq=z_cond_log_sigma_sq,\n                                         z_cond_mean=z_cond_mean,\n                                         kind=\"binary\")\n\n    # List of variables to optimize\n    params = tf.trainable_variables()\n    d_vars = [var for var in params if \"Decoder\" in var.name]\n    e_vars = [var for var in params if \"Encoder\" in var.name]\n\n    # List of variables to save\n    vars_glob = tf.global_variables()\n    lst_vars = []\n    for v in vars_glob:\n        lst_vars.append(v)\n\n    # VAE Optimizer\n    trainer_vae = tf.train.AdamOptimizer(learning_rate=learning_rate)\n    gradients_vae = trainer_vae.compute_gradients(vae_loss, var_list=[d_vars] + [e_vars])\n    optimize_vae = trainer_vae.apply_gradients(gradients_vae)\n\n    config = tf.ConfigProto()\n    config.gpu_options.allow_growth = True\n    session = tf.Session(config=config)\n    session.run(tf.global_variables_initializer())\n    saver = tf.train.Saver(var_list=lst_vars)\n\n    if restore:\n        saver.restore(session, model_dir)\n\n    return session, z_cond_mean, z_cond_log_sigma_sq, z_cond, x_tilde, \\\n        x_prior, z_prior, vae_loss, kl_loss, recon_loss, optimize_vae, real_data, phase, saver\n\n\ndef train_vae(train, valid, session, optimize_vae, vae_loss, kl_loss, recon_loss, real_data, saver,  batch_size=100,\n              count_vae=0, iters=1000, checkpoint_iters=0,\n              early_stopping_iters=100, early_stopping_tol=1e-6,\n              kl_ls=[], recon_ls=[], vae_ls=[], val_ls=[], log_dir=None):\n    n = train.shape[0]\n    n_val = valid.shape[0]\n    for epoch in range(iters):\n        # Shuffle rows of the data matrix\n        perm = np.random.permutation(n)\n        train = train[perm, :]\n\n        # Increase counter and reset statistics\n        count_vae += 1\n        print(count_vae, end=\" \")\n        loss_batch = 0\n        loss_kl = 0\n        loss_recon = 0\n        loss_val = 0\n\n        # Mini-batch training of the NNs\n        for start, end in zip(range(0, n, batch_size), range(batch_size, n + 1, batch_size)):\n            _, loss_v, kl, recon = session.run([optimize_vae, vae_loss, kl_loss, recon_loss],\n                                               {real_data: train[start:end, :]})\n            loss_batch += loss_v\n            loss_kl += kl\n            loss_recon += recon\n\n        # Mini-batch validation of the NNs\n        for start, end in zip(range(0, n_val, batch_size), range(batch_size, n_val + 1, batch_size)):\n            loss_val += session.run(vae_loss, {real_data: valid[start:end, :]})\n\n        # Aggregate statistics\n        vae_ls.append(loss_batch * batch_size / n)\n        kl_ls.append(loss_kl * batch_size / n)\n        recon_ls.append(loss_recon * batch_size / n)\n        val_ls.append(loss_val * batch_size / n_val)\n\n        # Save checkpoints\n        if checkpoint_iters and count_vae % checkpoint_iters == 0:\n            os.makedirs(log_dir, exist_ok=True)\n            saver.save(session, os.path.join(log_dir, \"vae.ckpt\"))\n\n        # Early stopping\n        if early_stopping_iters and epoch % early_stopping_iters == 0 and epoch > 0:\n            diff = val_ls[-early_stopping_iters] - val_ls[-1]\n            if diff <= early_stopping_tol:\n                break\n\n    # Return variables\n    return session, vae_ls, kl_ls, recon_ls, val_ls, count_vae\n\n\n","sub_path":"build_vae.py","file_name":"build_vae.py","file_ext":"py","file_size_in_byte":10127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"591553980","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.utils.timezone\nimport availableworks.core.models.mixins\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('uprofile', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='RequestedInvite',\n            fields=[\n                ('created_at', models.DateTimeField(default=django.utils.timezone.now, editable=False)),\n                ('updated_at', availableworks.core.models.mixins.AutoDateTimeField(default=django.utils.timezone.now)),\n                ('email_address', models.CharField(max_length=100, serialize=False, primary_key=True)),\n                ('name', models.CharField(max_length=100)),\n                ('website', models.CharField(max_length=100)),\n                ('profile', models.OneToOneField(related_name='requested_invite', null=True, to='uprofile.UserProfile')),\n            ],\n            options={\n                'abstract': False,\n            },\n        ),\n    ]\n","sub_path":"availableworks/registration/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"360595028","text":"\"\"\"\n Program: Trinity College Quantitative Equity Fund\n Author:  Samuel Oyebefun\n Date:    12/30/2016\n\n Trinity College ‘19\n Economics, Computer Science and Mathematics\n Bantam Track and Field\n (860) 212-2344 | samueloyebefun@gmail.com\n http://soyebefun.com\n\n Created by Samuel Oyebefun on 12/30/16.\n Copyright © 2016 Samuel Oyebefun. All rights reserved.\n\"\"\"\nfrom quantopian.pipeline import Pipeline\nfrom quantopian.algorithm import attach_pipeline, pipeline_output\nfrom quantopian.pipeline.data import morningstar\nfrom quantopian.pipeline.filters import Q500US\nfrom quantopian.pipeline import filters, classifiers\nfrom quantopian.pipeline.data.builtin import USEquityPricing\nfrom quantopian.pipeline.factors import CustomFactor, AverageDollarVolume, Returns, Latest\nimport pandas as pd\nimport numpy as np\n\ndef initialize(context):\n    \n    set_long_only()\n    #set_commission(commission.PerTrade(cost=0))\n    #set_slippage(slippage.FixedSlippage(spread=0))\n     \n    context.lookback = 252   \n    context.rf_asset = sid(23911)\n    \n    # Create our dynamic stock selector.    \n    attach_pipeline(make_pipeline(context), 'pipe')\n    \n    # Rebalance\n    schedule_function(\n        rebalance,\n        date_rules.week_start(), \n        time_rules.market_open())\n    \n    # Record variables at the end of each day.\n    schedule_function(\n        my_record_vars, \n        date_rules.every_day(),\n        time_rules.market_close())\n\nclass factors():  \n    class Valuation(CustomFactor):\n\n        inputs = [morningstar.valuation_ratios.pe_ratio,\n                  morningstar.valuation_ratios.pb_ratio,\n                  morningstar.valuation_ratios.ev_to_ebitda]  \n\n        window_length = 1\n\n        def compute(self, today, assets, out, pe_ratio, pb_ratio, ev_to_ebitda):\n            out[:] = (pe_ratio[-1] + pb_ratio[-1] + ev_to_ebitda[-1] ) / 3.0\n            \n    class Value(CustomFactor):\n\n        inputs = [morningstar.valuation_ratios.book_value_yield,\n                  morningstar.valuation_ratios.sales_yield,\n                  morningstar.valuation_ratios.fcf_yield] \n\n        window_length = 1\n\n        def compute(self, today, assets, out, book_value, sales, fcf):\n            out[:] = (book_value[-1] + sales[-1] + fcf[-1]) / 3.0\n            \n    class Momentum(CustomFactor):\n\n        inputs = [USEquityPricing.close]\n        \n        window_length = 252\n\n        def compute(self, today, assets, out, close):\n            out[:] = close[-20] / close[0]\n            \nclass Comparative_Multiples():\n    class Industry_valuation(CustomFactor):  \n\n        inputs = [morningstar.valuation_ratios.pe_ratio,\n                  morningstar.valuation_ratios.pb_ratio,\n                  morningstar.valuation_ratios.ev_to_ebitda,\n                  morningstar.asset_classification.morningstar_industry_code]  \n\n        window_length = 1\n\n        def compute(self, today, assets, out, pe_ratio, pb_ratio, ev_to_ebitda, industry_codes):\n            ratio = (pe_ratio[-1] + pb_ratio[-1] + ev_to_ebitda[-1] ) / 3.0\n            df = pd.DataFrame(index=assets, data={\"ratio\": ratio,  \n                                                  \"industry_codes\": industry_codes[-1]})  \n            out[:] = df.groupby(\"industry_codes\").transform(np.mean).values.flatten() \n\ndef make_pipeline(context):\n\n    # Financial Analysis\n    market_cap = morningstar.valuation.market_cap.latest > 1e6\n    country_id = morningstar.company_reference.country_id.latest != \"CHN\"\n    business_country_id = morningstar.company_reference.business_country_id.latest != \"CHN\"\n    operating_cash_flow = morningstar.cash_flow_statement.operating_cash_flow.latest > 0\n    total_debt_equity_ratio = morningstar.operation_ratios.total_debt_equity_ratio.latest < 1\n    current_ratio = morningstar.operation_ratios.current_ratio.latest > 1\n\n    # Comparitive Multiples     \n    industry_valuation = Comparative_Multiples.Industry_valuation()\n    valuation = factors.Valuation()\n    value = factors.Value()\n    momentum = factors.Momentum()\n\n    # Factors\n    industry_factor = (industry_valuation.notnan()) & (industry_valuation > 0) \n    valuation_factor = (valuation.notnan()) & (valuation > 0) & (valuation < industry_valuation)\n    value_factor = (value.notnan()) & (value > 0)\n    momentum_factor = (momentum.notnan()) & (momentum > 0)\n\n    # Base fundamental filter    \n    base_filter = (\n        market_cap\n        & country_id\n        & business_country_id\n        & operating_cash_flow\n        & total_debt_equity_ratio\n        & current_ratio\n        & industry_factor\n        & valuation_factor\n        & value_factor\n        & momentum_factor\n        )\n    \n    columns = {\n        'Valuation': valuation,\n        'Value': value,\n        'Momentum': momentum}\n    \n    pipe = Pipeline(columns=columns, screen=base_filter)\n \n    return pipe\n\ndef before_trading_start(context, data):\n    \"\"\"\n    Called every day before market open.\n    \"\"\"            \n    log.info(\"---------Before Trading---------\")\n\n    # Call pipeline_output to get the output\n    context.longs = pipeline_output('pipe')\n\n    # Long Stocks\n    context.longs = context.longs.sort_values(['Valuation'], ascending=False).tail(80)\n    context.longs = context.longs.sort_values(['Value'], ascending=False).head(50)\n    context.longs = context.longs.sort_values(['Momentum'], ascending=False).head(20)\n    context.longs = context.longs.index.tolist()\n    \ndef rebalance(context,data):\n    \"\"\"\n    Execute orders according to our schedule_function() timing.\n    \"\"\"                    \n    log.info(\"---------Rebalance---------\")  \n    \n    log.info(\"Today's longs: \"  +\", \".join([long_.symbol for long_ in context.longs]))\n    \n    # Create weights for each stock\n    for security in context.portfolio.positions:\n        if (security not in context.longs) and (data.can_trade(security)):\n            order_target_percent(security, 0)\n            \n    context.long_weight = 1.0/len(context.longs)\n            \n    for security in context.longs:\n        if data.can_trade(security):\n            order_target_percent(security, context.long_weight)\n            \ndef my_record_vars(context, data):\n    \"\"\"\n    Record variables at the end of each day.\n    \"\"\"\n    record(leverage=context.account.leverage)\n    record(cash=context.portfolio.cash)    \n    \ndef adjust_exposure(context, data, stock):\n    \n    context.assets = [stock]\n    \n    context.base_weight = 1.0 / len(context.assets)\n    \n    context.DPMs = []\n    for security in context.assets:\n        context.DPMs.append(DownsideProtectionModel(security))\n        \n    context.all_assets = context.assets + [context.rf_asset]\n\n    prices = data.history(context.all_assets, \"price\", context.lookback, \"1d\")\n    rf_asset_target = 0\n    for dpm in context.DPMs:\n        dpm.update_prices(prices[dpm.asset])\n        dpm.update_rf_return((prices[context.rf_asset][-1] / prices[context.rf_asset][0]) - 1 )\n        rf_asset_target += dpm.non_asset_exposure\n            \n    rf_asset_exposure = rf_asset_target * context.base_weight\n    record(bond_exposure=rf_asset_exposure)\n    return rf_asset_exposure            \n           \nclass DownsideProtectionModel(object):\n    \n    def __init__(self, asset, historical_prices=None, rf=0):\n        self.asset = asset\n        self.rf = rf\n        self.historical_prices = historical_prices\n        \n    @property\n    def exposure(self):\n        return np.mean([self._TMOM_Rule(), self._MA_Rule()])\n    \n    @property\n    def non_asset_exposure(self):\n        return 1 - self.exposure\n        \n    def update_prices(self, new_prices):\n        self.historical_prices = new_prices\n        \n    def update_rf_return(self, new_return):\n        self.rf = new_return\n        \n    # Returns False if you should reduce exposure, True if you should increase it.\n    def _TMOM_Rule(self):\n        starting_price = self.historical_prices[0]\n        current_price = self.historical_prices[-1]\n        twelve_month_return = (current_price / starting_price) - 1\n        excess_return = twelve_month_return - self.rf\n        return excess_return > 0\n        \n    # Returns False if you should reduce exposure, True if you should increase it.\n    def _MA_Rule(self):\n        current_price = self.historical_prices[-1]\n        moving_average_price = self.historical_prices.mean()\n        return current_price > moving_average_price\n","sub_path":"TCQEF.py","file_name":"TCQEF.py","file_ext":"py","file_size_in_byte":8342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"156548121","text":"# -*-coding:utf-8-*-\n\n'''\n# ----------------------------------------------------------------------------\n#  Purpose:     轮询模式 · 数据生成获取\n#\n#  Author:      半片叶\n#\n#  Created:     2020.08.13\n#\n#        _       __    _     ___   ____\n#       | |\\/|  / /\\  \\ \\_/ | |_) | |_\n#       |_|  | /_/--\\  |_|  |_|_) |_|__\n# ----------------------------------------------------------------------------\n'''\n\nfrom typing import Any,List,Dict\n\n\nclass CirclePattern():\n    def __init__(self):\n        \"\"\"\n        轮询遍历类\n        外部参数:\n            :param 无\n        内部参数:\n            :private data: 轮询数据源，目前只支持 List 和 Dict两种类型\n            :private _total: 数据源条目总数\n            :private _circle_index: 当前轮询索引下标值\n        \"\"\"\n        self.data = None\n        self._total = 0\n        self._circle_index = -1\n\n    def add(self, data:[List[Any], Dict]):\n        \"\"\"\n        添加数据集\n            :param data: 数据集，目前只支持 List 和 Dict两种类型\n            Dict类型只返回 Key 值，Value通过数据源自行取值\n        \"\"\"\n        if isinstance(data, list):\n            self.data = data\n        elif isinstance(self.data, dict):\n            self.data = list(data.keys())\n        self._total = len(self.data)\n\n    def get(self) -> Any:\n        \"\"\"\n        遍历数据集，获取一个可用的数据\n        返回值:\n            任意类型的数据单元\n        \"\"\"\n        if not self._total:\n            return None\n        self._circle_index = self._circle_index + 1\n        if self._circle_index + 1 > self._total:\n            self._circle_index = 0\n        return self.data[self._circle_index]\n","sub_path":"backend/maybe/patterns/circle_pattern.py","file_name":"circle_pattern.py","file_ext":"py","file_size_in_byte":1726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"14663242","text":"\"\"\"\n1- start at A, initialize distances to inf\n2- update paths [min(old, w+pathlen)] of adjacent nodes then add them to priority queue\n3- pick node from queue with min path that hasn't been processed then do BFS -> 2 \n4- keep going until all nodes are visited (queue is empty)\n\n--- basically (greedy) bfs that uses a priority queue + aux ds to store/lookup paths\n \"\"\"\n\n\nimport heapq\n\n\nNODES = ['A', 'B', 'C', 'D', 'E']\n\nG = [\n    [0, 7,  2, -1, -1],\n    [7, 0,  3,  5,  2],\n    [2, 3,  0,  1, -1],\n    [-1, 5,  1,  0,  1],\n    [-1, 2, -1,  1,  0],\n]\n\nINV = -1\n\n\ndef shortest_path(g, s):\n    def update_path(_v, d, src):\n        minpath = min(_v[0], d+vertices[src][0])\n        if minpath != _v[0]:\n            updated_v = [minpath, _v[1], src]\n            vertices[_v[1]] = updated_v\n            _v[1] = INV\n            heapq.heappush(pq, updated_v)\n\n    vertices = [0] * len(g)\n    visited = [0] * len(g)\n    pq = []\n    s = NODES.index(s.upper())\n    # initialize priority queue\n    for v in range(len(g)):\n        vertex = [float('inf') if v != s else 0, v, v]  # store ptrs to vertices for later updates\n        vertices[v] = vertex\n        heapq.heappush(pq, vertex)\n\n    while pq:\n        curr = heapq.heappop(pq)[1]\n        if curr == INV:\n            continue\n\n        for v, w in enumerate(g[curr]):\n            if visited[v] or w == -1:\n                continue\n            v_entry = vertices[v]\n            update_path(v_entry, w, curr)\n\n        visited[curr] = 1\n\n    print_paths(vertices, s)\n\n\ndef print_paths(vertices, src):\n    for idx, v in enumerate(vertices):\n        if idx == src:\n            continue\n        pathlen = v[0]\n        path = [NODES[idx]]\n        while True:\n            path.append(NODES[v[2]])\n            if v[2] == src:\n                break\n            v = vertices[v[2]]\n\n        print(f'{ \" => \".join(path[::-1])} ({pathlen})')\n\n\nshortest_path(G, 'C')\n","sub_path":"algo-practice/dijkstra.py","file_name":"dijkstra.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"643703095","text":"from scipy.spatial.distance import euclidean\nimport numpy as np\n\nfrom services import (\n    getCoordinates,\n    countFullLength,\n)\n\nclass Legs():\n    def __init__(self, df) -> None:\n        self.min, self.max, self.mean = countFullLength(self.getLegsPoint(df))\n        self.stepLength = self.getMeanStepLength(df)\n\n    # Рассчет длинны ног по аналогии с руками\n    def getLegsPoint(self, df):\n        series = getCoordinates(df)\n        for seria in series:\n            subSeria = seria[[\n                            'Таз слева',\n                            'Левое колено',\n                            'Левая лодыжка',\n                            'Таз справа',\n                            'Правое колено',\n                            'Правая лодыжка'\n                            ]]\n            \n            left_hip = euclidean(subSeria[0], subSeria[1])\n            left_ankle = euclidean(subSeria[1], subSeria[2])\n            right_hip = euclidean(subSeria[3], subSeria[4])\n            right_ankle = euclidean(subSeria[4], subSeria[5])\n\n            yield (left_hip + left_ankle, right_hip + right_ankle)\n\n    # Просчет всех шагов по фреймам\n    def getStepLength(self, df):\n        series = getCoordinates(df)\n        for seria in series:\n            subSeria = seria[[\n                            'Левая лодыжка',\n                            'Правая лодыжка'\n                            ]]\n\n            step_length = euclidean(subSeria[0], subSeria[1])\n            yield step_length\n\n    # Просчет среднего шага в каждом фрейме\n    def getMeanStepLength(self, df):\n        steps = self.getStepLength(df)\n        return np.mean(list(steps))","sub_path":"walkSupport/SMP/metrics/legs.py","file_name":"legs.py","file_ext":"py","file_size_in_byte":1821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"190587566","text":"\n# coding: utf-8\n\n# You are given list l = [1,1,2,5, 3, 2, 1, 1, 7, 2], print a table with the value occuring and their count in sorted order of count.\n\n# In[1]:\n\n\nl= [1, 1, 2, 5, 3, 2, 1, 1, 7, 2] # List\nl.sort() #sorting the list\nd = {} # Dictionary\nfor item in l:\n    if item in d:\n        #d[item] = d.get(item)+1\n        d[item] = d[item]+1\n    else:\n        d[item] = 1\nfor k,v in d.items():\n    print(str(k)+':'+str(v))\n\n","sub_path":"Raju_Shrestha_Assignment_1_Week_1.py","file_name":"Raju_Shrestha_Assignment_1_Week_1.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"277546434","text":"\n\nfrom xai.brain.wordbase.nouns._naysayer import _NAYSAYER\n\n#calss header\nclass _NAYSAYERS(_NAYSAYER, ):\n\tdef __init__(self,): \n\t\t_NAYSAYER.__init__(self)\n\t\tself.name = \"NAYSAYERS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"naysayer\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_naysayers.py","file_name":"_naysayers.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"199605874","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Filename: progression_boxplot.py\n# Authors: #cf\n# Version 0.1.0 (2016-10)\n\n\n\"\"\"\nBoxplot visualisation of topics over text time, with variation.\n\"\"\"\n\nimport os\nimport glob\nimport pandas as pd\nimport numpy as np\nimport pygal\n\n\n##########################\n# Shared functions\n##########################\n\n\n\ndef get_firstwords(FirstwordsFile):\n    \"\"\"\n    Function to load list of top topic words into dataframe.\n    \"\"\"\n    with open(FirstwordsFile, \"r\") as InFile: \n        Firstwords = pd.read_csv(InFile, header=None)\n        Firstwords.drop(0, axis=1, inplace=True)\n        Firstwords.rename(columns={1:\"topicwords\"}, inplace=True)\n        Firstwords.index = Firstwords.index.astype(np.int64)        \n        #print(Firstwords)\n        return(Firstwords)\n\n\n\ndef get_mastermatrix(MastermatrixFile):\n    with open(MastermatrixFile, \"r\") as InFile:\n        Mastermatrix = pd.read_csv(InFile)\n        #print(Mastermatrix.head())\n        return Mastermatrix\n\n\n##########################\n# progression (simple)\n##########################\n\n\npg_style = pygal.style.Style(\n  background='white',\n  plot_background='white',\n  font_family = \"FreeSans\",\n  title_font_size = 20,\n  legend_font_size = 16,\n  label_font_size = 12,\n  colors=[\"#1d91c0\",\"#225ea8\",\"#253494\",\"#081d58\", \"#071746\"])\n\n\n\ndef get_binneddata(Mastermatrix):\n    BinnedData = Mastermatrix.groupby(by=[\"binID\"])\n    return BinnedData\n\n\ndef select_data(BinnedData, Topic):\n    SelectedData = []\n    for name, group in BinnedData:\n        group = group.loc[:,str(Topic)]\n        SelectedData.append(group)\n    return SelectedData\n\n\ndef make_boxplot(SelectedData, Topic, Firstwords, GraphFolder):\n    if not os.path.exists(GraphFolder):\n        os.makedirs(GraphFolder)\n    Boxplot = pygal.Box(legend_at_bottom=True,\n                        legend_at_bottom_columns=5,\n                        pretty_print=True,\n                        style=pg_style,\n                        box_mode=\"tukey\",\n                        title = str(Firstwords.iloc[Topic,0]))\n    for i in range(0,5): \n        Boxplot.add(\"Segment\"+str(i+1), SelectedData[i])\n    Boxplot.render_to_file(GraphFolder+\"boxplot_\"+\"{:03d}\".format(Topic)+\".svg\")\n\n\ndef progression(MastermatrixFile,\n                FirstwordsFile,\n                GraphFolder,\n                NumTopics):\n    \"\"\"\n    Function to plot topic development over textual progression using boxplots.\n    \"\"\"\n    print(\"Launched progression.\")\n    Firstwords = get_firstwords(FirstwordsFile)\n    Mastermatrix = get_mastermatrix(MastermatrixFile)\n    BinnedData = get_binneddata(Mastermatrix)\n    for Topic in range(0,NumTopics): \n        SelectedData = select_data(BinnedData, Topic)\n        make_boxplot(SelectedData, Topic, Firstwords, GraphFolder)\n    print(\"Done.\")\n    \n\n\n\n##########################\n# complex progression\n##########################\n\n\ncx_style = pygal.style.Style(\n  background='white',\n  plot_background='white',\n  font_family = \"FreeSans\",\n  title_font_size = 20,\n  legend_font_size = 16,\n  label_font_size = 12,\n  colors=[\"#1d91c0\", \"#1d91c0\", \"#1d91c0\", \"#1d91c0\", \"#1d91c0\", \"#071746\", \"#071746\", \"#071746\", \"#071746\", \"#071746\"])\n\n\ndef get_complexbinneddata(Mastermatrix):\n    BinnedData = Mastermatrix.groupby(by=[\"tc_subgenre\", \"binID\"])\n    #print(BinnedData.head())\n    return BinnedData\n\n\ndef select_complexdata(BinnedData, Topic):\n    SelectedData = []\n    Labels = []\n    for label, group in BinnedData:\n        if \"Comédie\" in label or \"Tragédie\" in label:\n            Labels.append(label)\n            group = group.loc[:,str(Topic)]\n            SelectedData.append(list(group))\n    #print(Labels)\n    return SelectedData, Labels\n\n\ndef make_complex(SelectedData, Labels, Topic, Firstwords, GraphFolder):\n    if not os.path.exists(GraphFolder):\n        os.makedirs(GraphFolder)\n    XLabels = [str(Label[1]+1) for Label in Labels]\n    Boxplot = pygal.Box(show_legend = False,\n                        #legend_at_bottom = True,\n                        #legend_at_bottom_columns = 15,\n                        x_labels = XLabels,\n                        x_label_rotation=0,\n                        pretty_print=True,\n                        style = cx_style,\n                        box_mode = \"tukey\",\n                        title = str(Firstwords.iloc[Topic,0]),\n                        x_title = \"comedies _________________ tragedies\")\n    for i in range(0,len(XLabels)): \n        #print(str(Labels[i]))\n        Boxplot.add(str(Labels[i]), SelectedData[i])\n    Boxplot.render_to_file(GraphFolder+\"boxplot_\"+\"{:03d}\".format(Topic)+\"-2017.svg\")\n\n\ndef check_significance(SelectedData, Labels):\n    indices = list(range(0, len(Labels)))\n    from scipy import stats\n    print(\"comedies\")\n    combinations = [[0,1],[0,2],[0,3],[0,4],[1,2],[1,3],[1,4],[2,3],[2,4],[3,4]]\n    for combination in combinations:\n        x = SelectedData[combination[0]]\n        y = SelectedData[combination[1]] \n        result = stats.mannwhitneyu(x, y, use_continuity=True, alternative=None)\n        print(\"   mwu\", combination[0]+1, \"/\", combination[1]+1, \"p-value\", result[1])\n    print(\"tragedies\")\n    combinations = [[5,6],[5,7],[5,8],[5,9],[6,7],[6,8],[6,9],[7,8],[7,9],[8,9]]\n    for combination in combinations:\n        x = SelectedData[combination[0]]\n        y = SelectedData[combination[1]] \n        result = stats.mannwhitneyu(x, y, use_continuity=True, alternative=None)\n        print(\"   mwu\", combination[0]-4, \"/\", combination[1]-4, \"p-value\", result[1])\n    \n    \n    \n    \n\n\n\ndef complex(MastermatrixFile,\n            FirstwordsFile,\n            GraphFolder,\n            NumTopics):\n    \"\"\"\n    Function to plot topic development over textual progression using boxplots.\n    \"\"\"\n    print(\"Launched complex.\")\n    Firstwords = get_firstwords(FirstwordsFile)\n    Mastermatrix = get_mastermatrix(MastermatrixFile)\n    BinnedData = get_complexbinneddata(Mastermatrix)\n    for Topic in range(39,40):\n        print(\"topic\", Topic)\n        SelectedData, Labels = select_complexdata(BinnedData, Topic)\n        make_complex(SelectedData, Labels, Topic, Firstwords, GraphFolder)\n        check_significance(SelectedData, Labels)\n    print(\"Done.\")\n\n\n\n\n\n##########################\n# comparison\n##########################\n\n\ncp_style = pygal.style.Style(\n  background='white',\n  plot_background='white',\n  font_family = \"FreeSans\",\n  title_font_size = 20,\n  legend_font_size = 16,\n  label_font_size = 12,\n  colors=[\"#4078a5\", \"#7eaacd\", \"#1d3549\"])\n\n\ndef get_comparisonbinneddata(Mastermatrix):\n    BinnedData = Mastermatrix.groupby(by=[\"tc_subgenre\"])\n    return BinnedData\n\n\ndef select_comparisondata(BinnedData, Topic):\n    SelectedData = []\n    Labels = []\n    for label, group in BinnedData:\n        Labels.append(label)\n        group = group.loc[:,str(Topic)]\n        SelectedData.append(group)\n    return SelectedData, Labels\n\n\ndef make_comparisonboxplot(SelectedData, Labels, Topic, Firstwords, GraphFolder):\n    if not os.path.exists(GraphFolder):\n        os.makedirs(GraphFolder)\n    XLabels = [str(Label) for Label in Labels]\n    Boxplot = pygal.Box(show_legend=False, \n                        #legend_at_bottom=False,\n                        #legend_at_bottom_columns=5,\n                        pretty_print=True,\n                        style=cp_style,\n                        box_mode=\"tukey\", #tukey|1.5IQR|stdev\n                        title = str(Firstwords.iloc[Topic,0]),\n                        x_labels = XLabels)\n    for i in range(0,3): \n        Boxplot.add(\"Segment\"+str(i+1), SelectedData[i])\n    Boxplot.render_to_file(GraphFolder+\"boxplot_\"+\"{:03d}\".format(Topic)+\".svg\")\n\n\ndef comparison(MastermatrixFile,\n                FirstwordsFile,\n                GraphFolder,\n                NumTopics):\n    \"\"\"\n    Function to plot topic development over textual progression using boxplots.\n    \"\"\"\n    print(\"Launched comparison.\")\n    Firstwords = get_firstwords(FirstwordsFile)\n    Mastermatrix = get_mastermatrix(MastermatrixFile)\n    BinnedData = get_comparisonbinneddata(Mastermatrix)\n    for Topic in range(0,NumTopics): \n        SelectedData, Labels = select_comparisondata(BinnedData, Topic)\n        make_comparisonboxplot(SelectedData, Labels, Topic, Firstwords, GraphFolder)\n    print(\"Done.\")\n\n\n\n\n","sub_path":"2015/gddh/code/boxplot.py","file_name":"boxplot.py","file_ext":"py","file_size_in_byte":8273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"357512972","text":"import os\nimport pandas as pd\nimport numpy as np\nimport netCDF4 as nc\nimport urllib\n\n\ndef read_data():\n\n    def num_missing(x):\n        return sum(x.isnull())\n\n    lonW = -73.99  # Longitude Oeste.\n    lonE = -33.86  # Longitude Leste.\n    latS = -28.63  # Latitude Sul.\n    latN = 5.29  # Latitude Norte.\n    dgrad = .25  # Resolução do píxel.\n\n    df = pd.read_csv('focos.csv')\n\n    states = df.groupby(['estado']).size()\n\n    print(states)\n\n\nread_data()","sub_path":"data_analise/teste.py","file_name":"teste.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"198077838","text":"import sys\n\nwith open(sys.argv[3], 'r') as blkf:\n\tblkd={}\n\tfor line in blkf:\n\t\tblkd[line[:-1]]=None\n\ninp=open(sys.argv[1], 'r')\nlog=open('.'.join(sys.argv[1].split('.')[:-1])+'.filter_reads_out.log', 'w')\n\nif sys.argv[2]=='-': \n\toutp=sys.stdout\n\toutpname='stdout'\nelse:\n\toutpname=sys.argv[2] \n\toutp=open(outpname, 'w') \nlog.write('Filtering reads (matching those listed in '+sys.argv[3]+') out from '+sys.argv[1]+'\\n')\ncnttot=cntrem=cntout=0\nfor line in inp:\n\tcnttot=cnttot+1\n\tif line.split()[0] not in blkd: outp.write(line); cntout=cntout+1\ncntrem=cnttot-cntout\nlog.write('# reads in '+sys.argv[1]+': '+str(cnttot)+'\\n# reads in '+sys.argv[3]+': '+str(len(blkd))+'\\n# reads removed: '+str(cntrem)+'\\n# reads left after filtering and written to '+outpname+': '+str(cntout)+'\\n')\ninp.close()\nlog.close()\noutp.close()\n\n","sub_path":"analysis_for_AlleleHMM_manuscript/alleledb_pipeline_mouse/filter_reads_out.py","file_name":"filter_reads_out.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"321281275","text":"import unittest\n\nfrom city_functions import city_country\n\n\nclass CityTestCase(unittest.TestCase):\n    \"\"\"城市和国家的测试用例\"\"\"\n\n    def test_city_country(self):\n        \"\"\"处理 Guangxi,China 这样的输入\"\"\"\n        ret = city_country('guangxi', 'china')\n        self.assertEqual(ret, 'Guangxi,China')\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"11/11.1/test_cities.py","file_name":"test_cities.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"380711268","text":"#!/usr/bin/python3 -OO\n# coding=UTF-8\n\nimport re, sys\n\nunihanReadings = \"Unihan_Readings.txt\"\ndictInput = \"/dev/stdin\" if sys.argv[1] == '-' else sys.argv[1]\ndictOutput = \"/dev/stdout\" if sys.argv[2] == '-' else sys.argv[2]\ndictPy = {}\n\n## Functions to process Unihan data.\n\ndef codepoint_to_chr(s):\n\tpatcCodeptHdr = re.compile(r\"^U\\+\")\n\treturn chr(int(patcCodeptHdr.sub(\"\", s), 16))\n\ndef get_unihan_properties(prop, fname):\n\tpatLine = '\\t'.join([r\"(U\\+[0-9A-F]+)\", \"(\" + prop + \")\", \"([^\\t]+)\"])\n\tpatcLine = re.compile(\"^\" + patLine + \"$\")\n\treturn map(\n\t\t(lambda ss: (codepoint_to_chr(ss[0]), ss[2])),\n\t\t[m.groups() for m in filter(\n\t\t\t(lambda x: x != None),\n\t\t\tmap(patcLine.match, open(fname))\n\t\t)]\n\t)\n\ndef add_entry_info_to_dict(prop, py_proc):\n\tfor (c, rawPys) in get_unihan_properties(prop, unihanReadings):\n\t\tif not c in dictPy and ord(c) < 0x20000:\n\t\t\tdictPy[c] = set()\n\t\t\tdictPy[c].update(py_proc(rawPys))\n\n## Functions to process pinyin strings.\n\ndef py_translate(py):\n\tdictPyTrans = str.maketrans(\n\t\t\"āáǎàōóǒòēéěèīíǐìūúǔùǖǘǚǜü\", \"aaaaooooeeeeiiiiuuuuuuuuu\"\n\t)\n\treturn py.translate(dictPyTrans)\n\ndef py_utov(py):\n\tpatcUtoV = re.compile(r\"([ln])[ǖǘǚǜü]\")\n\treturn patcUtoV.sub(r\"\\1v\", py)\n\ndef py_rm_tone(py):\n\tpatcPyTone = re.compile(r\"[1-5]$\")\n\treturn patcPyTone.sub(\"\", py)\n\n## Process related Unihan entries.\n\ndef py_proc_kHanyuPinlu(pys):\n\tpatcKHanyuPinlu = re.compile(r\"([^0-9]+[1-5])\\(([0-9]+)\\)\")\n\treturn [py_utov(py_rm_tone(patcKHanyuPinlu.match(py).group(1)))\n\t\tfor py in pys.split()]\nadd_entry_info_to_dict(\"kHanyuPinlu\", py_proc_kHanyuPinlu)\n\ndef py_proc_kXHC1983(pys):\n\tpatcKHanyuPinyin = re.compile(r\"(?<=:).*\")\n\treturn [py_translate(py_utov(patcKHanyuPinyin.search(py).group(0)))\n\t\tfor py in pys.split()]\nadd_entry_info_to_dict(\"kXHC1983\", py_proc_kXHC1983)\n\ndef py_proc_kHanyuPinyin(pyss):\n\tpatcKHanyuPinyin = re.compile(r\"(?<=:).*\")\n\treturn [py_translate(py_utov(py)) for pys in pyss.split()\n\t\tfor py in patcKHanyuPinyin.search(pys).group(0).split(',')]\nadd_entry_info_to_dict(\"kHanyuPinyin\", py_proc_kHanyuPinyin)\n\n## Format dictPy to output.\n\ndef fmt_chr_info(c):\n\treturn ' '.join([c] + [py for py in dictPy[c]])\n\npatcMinus = re.compile(r\"-\")\nwith open(dictOutput, \"w\") as outputFile:\n\tfor l in open(dictInput):\n\t\tif len(l) == 0:\n\t\t\tcontinue\n\t\tc = l.split()[0]\n\t\tif len(c) == 1 and c in dictPy and not patcMinus.search(l):\n\t\t\toutputFile.write(fmt_chr_info(c) + \"\\n\")\n\t\telse:\n\t\t\toutputFile.write(l)\n\t\tif c in dictPy:\n\t\t\tdictPy.pop(c)\n\tfor c in dictPy:\n\t\toutputFile.write(fmt_chr_info(c) + \"\\n\")\n\n","sub_path":"tools/utils/sunpinyin-hanzi-table-extend.py","file_name":"sunpinyin-hanzi-table-extend.py","file_ext":"py","file_size_in_byte":2551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"469662971","text":"import library\nimport base64\nfrom time import sleep\nimport requests\nclass cape:\n\tdef __init__(self):\n\t\tvar = []\n\t\tself.method = 'https'\n\t\tself.proxies = self.CheckProxyFile()\n\t\twith open('file.txt', 'r') as f:\n\t\t\topen('Skin.txt', 'w')\n\t\t\tff = f.read().split('\\n')\n\t\tfor x in range(len(ff)):\n\t\t\tvar.append(ff[x].split(':'))\n\t\tm = open('Skin.txt', 'a')\n\t\tfor x in range(len(var)):\n\t\t\ttry:\n\t\t\t\tvar[x][2]\n\t\t\t\twhile True:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tresponse = requests.get('https://sessionserver.mojang.com/session/minecraft/profile/' + str(var[x][2]), proxies={})\n\t\t\t\t\t\tprint(response.json())\n\t\t\t\t\t\tprint('\\n')\n\t\t\t\t\t\tvar = base64.b64decode(response.json()['properties'][0]['value'])\n\t\t\t\t\texcept:\n\t\t\t\t\t\tsleep(6)\n\t\t\t\t\telse:\n\t\t\t\t\t\tif 'CAPE' in str(var):\n\t\t\t\t\t\t\tm.write(var[x][0]+\":\"+var[x][1]+\":     \" + str(1) + \"\\n\")\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tm.write(var[x][0]+\":\"+var[x][1]+\":    \"+ str(0) + \"\\n\")\n\t\t\t\t\t\tbreak\n\t\t\t\t\n\t\t\texcept:\n\t\t\t\tpass\n\tdef CheckProxyFile(self):\n\t\ttry:\n\t\t\tProxiesFile = library.readIT().f(filename=\"proxies.txt\")\n\t\texcept:\n\t\t\traise FileExistsError(\"Hey Dipshit! Try Getting Some Proxies (proxies.txt)\")\n\t\treturn ProxiesFile\n","sub_path":"Mc_Cape.py","file_name":"Mc_Cape.py","file_ext":"py","file_size_in_byte":1118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"578708816","text":"import poisson\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport incidence_matrices\nfrom grid import Grid\nfrom poly_interpolation import PolyBasis\nimport pickle\nimport poisson_dual\n\n\ndef multiple_poisson(n_0, n_f, a, b):\n    \"\"\"Run one element poisson for different p values.\"\"\"\n    m = 151\n    error_history = np.zeros((n_f - n_0))\n    for n in range(n_0, n_f):\n        func = poisson.rhs_sin2\n        f_exact = poisson.original_f_sin2\n        x = np.linspace(a, b, m)\n        phi, c, error_norm = poisson.poisson_homo(func, a, b, n, 151, f_exact=poisson.original_f)\n        poisson.plot_solution(phi, poisson.original_f, x,\n                              'sin2pix', str(n), save=False, show=True)\n        error_history[n - n_0] = error_norm\n    print(error_history)\n    with open('convergence_sin2pix.csv', 'wb') as f_handle:\n        np.savetxt(f_handle, error_history)\n\n\ndef run_hp_poisson(a, b, h, p, m):\n    \"\"\"Run multielement direct formulation of the poisson problem\"\"\"\n    func = poisson.rhs_sin2\n    f_exact = poisson.original_f_sin2\n    phi, x, error = poisson.hp_poisson(h, p, m, func, a, b, f_exact)\n    poisson.plot_solution(phi, f_exact, x,\n                          'sin2pix_h' + str(h), str(p), save=False, show=True)\n\n\ndef run_hp_poisson_mixed(a, b, h, p, m):\n    \"\"\"Run multielement version of the mixed formulation of the problem.\"\"\"\n    func = poisson.rhs_sin2\n    f_exact = poisson.original_f_sin2\n    phi, sigma, error = poisson.mixed_hp_poisson(h, p, a, b, m, func, f_exact)\n    x = np.linspace(a, b, len(phi))\n    poisson.plot_solution(phi, f_exact, x, 'sin2pix_h' + str(h), str(p), save=False, show=True)\n    # poisson.plot_solution(sigma, poisson.d_original_f_sin2_dx, x, 'sin2pix_h' +\n    #   str(h), str(p), save=False, show=True)\n\n\ndef convergence_analysis(hs, ps):\n    \"\"\"Progressive convergence analalsis. (only for direct formulation)\n\n    Args:\n        hs(tuple) = (h_0, h_f) : h_0 number of element to start with and h_f # el. to stop at.\n        ps(tuple) = (p_0, p_f) : initial # of edges, final # of edges\n    \"\"\"\n    error_hystory_h = []\n    error_hystory_p = []\n    func = poisson.rhs_sin2\n    f_exact = poisson.original_f_sin2\n    a, b = -2, 2\n    m = 500\n    if hs is not None:\n        for h in range(hs[0], (hs[1] + 1)):\n            p = 6\n            phi, x, error_h = poisson.hp_poisson(h, p, int(m / h), func, a, b, f_exact)\n            error_hystory_h.append((h, error_h))\n            poisson.plot_solution(phi, f_exact, x,\n                                  'sin2pixh-11',  str(h), str(p), save=True, show=False)\n\n        pickle.dump(error_hystory_h, open(\n            \"data/convergence/convergence_h\" + str(hs[0]) + \"-\" + str(hs[1]) + \"-p\" + str(p) + \".p\", \"wb\"))\n    if ps is not None:\n        for p in range(ps[0], ps[1] + 1):\n            h = 1\n            phi, x, error_p = poisson.hp_poisson(h, p, int(m / h), func, a, b, f_exact)\n            error_hystory_p.append((p, error_p))\n\n        pickle.dump(error_hystory_p, open(\n            \"data/convergence/convergence_p\" + str(ps[0]) + \"-\" + str(ps[1]) + \"-h\" + str(h) + \".p\", \"wb\"))\n\n\ndef convergence_analysis_specific(hs, ps, type='mixed'):\n    \"\"\"Convergence analysis for specific values of h and p.\n\n    Args:\n        hs (np.array/list) = list of h values to be used\n        ps (np.array/list) = list of p values to be used\n    \"\"\"\n    error_hystory_h = []\n    error_hystory_p = []\n\n    # rhs function\n    func = poisson.rhs_sin2\n    f_exact = poisson.original_f_sin2\n    # domain\n    a, b = -1, 1\n    m = 500\n\n    if hs is not None:\n        for h in hs:\n            p = 6\n\n            if type == 'dual_direct':\n                phi, x, error_h = poisson_dual.direct_dual_poisson_hp(\n                    h, p, a, b, int(m / h), func, f_exact)\n            if type == 'mixed':\n                phi, sigma, error_h = poisson.mixed_hp_poisson(\n                    h, p, a, b, int(m / h), func, f_exact)\n                x = np.linspace(a, b, len(phi))\n            else:\n                phi, x, error_h = poisson.hp_poisson(h, p, int(m / h), func, a, b, f_exact)\n            error_hystory_h.append((h, error_h))\n            poisson.plot_solution(phi, f_exact, x,\n                                  'sin2pixh-11',  str(h), str(p), save=False, show=False)\n\n        if type == 'dual_direct':\n            pickle.dump(error_hystory_h, open(\n                \"data/convergence/dual_direct/convergence_h_specific\" + str(hs[0]) + \"-\" + str(hs[-1]) + \"-p\" + str(p) + \".p\", \"wb\"))\n        if type == 'mixed':\n            pickle.dump(error_hystory_h, open(\n                \"data/convergence/mixed/convergence_h_specific\" + str(hs[0]) + \"-\" + str(hs[-1]) + \"-p\" + str(p) + \".p\", \"wb\"))\n        else:\n            pickle.dump(error_hystory_h, open(\n                \"data/convergence/convergence_h_specific\" + str(hs[0]) + \"-\" + str(hs[-1]) + \"-p\" + str(p) + \".p\", \"wb\"))\n\n    if ps is not None:\n        for p in ps:\n            h = 1\n            if type == 'dual_direct':\n                phi, x, error_p = poisson_dual.direct_dual_poisson_hp(\n                    h, p, a, b, int(m / h), func, f_exact)\n\n            if type == 'mixed':\n                phi, sigma, error_p = poisson.mixed_hp_poisson(\n                    h, p, a, b, int(m / h), func, f_exact)\n                x = np.linspace(a, b, len(phi))\n            else:\n                phi, x, error_p = poisson.hp_poisson(h, p, int(m / h), func, a, b, f_exact)\n            error_hystory_p.append((p, error_p))\n            poisson.plot_solution(phi, f_exact, x, r'exact $f(x) = sin(2 \\pi x)$',\n                                  str(h), str(p), save=False, show=False)\n        if type == 'dual_direct':\n            pickle.dump(error_hystory_p, open(\n                \"data/convergence/dual_direct/convergence_p_specific\" + str(ps[0]) + \"-\" + str(ps[-1]) + \"-p\" + str(p) + \".p\", \"wb\"))\n        if type == 'mixed':\n            pickle.dump(error_hystory_p, open(\n                \"data/convergence/mixed/convergence_p_specific\" + str(ps[0]) + \"-\" + str(ps[-1]) + \"-p\" + str(p) + \".p\", \"wb\"))\n        else:\n            pickle.dump(error_hystory_p, open(\n                \"data/convergence/convergence_p_specific\" + str(ps[0]) + \"-\" + str(ps[-1]) + \"-p\" + str(p) + \".p\", \"wb\"))\n\nif __name__ == '__main__':\n\n    a, b = -1, 1\n    # mixed problem\n    # run_hp_poisson_mixed(a, b, h=2, p=8, m=150)\n    # poisson.mixed_hp_poisson(3, 2, a, b, 4, poisson.rhs_sin2, poisson.original_f_sin2)\n\n    #\n    cases = [15, 20, 25, 50, 100, 200]\n    cases_h_specific = list(range(2, 11)) + cases\n    # print(cases_h_specific)\n    cases_p_specific = list(range(2, 15))\n    convergence_analysis_specific(None, cases_p_specific, type='dual_direct')\n    error_h = pickle.load(\n        open('data/convergence/dual_direct/convergence_h_specific2-200-p6.p', \"rb\"))\n    poisson.plot_convergence(error_h, 'h', 6, type='Dual direct')\n    # error_p = pickle.load(open('data/convergence/convergence_p_specific2-4-p4.p', \"rb\"))\n    # error_p_mixed = pickle.load(open('data/convergence/convergence_p_specific2-17-p17.p', \"rb\"))\n    # print(\"error h\", error_h)\n    # # print('error_h ', error_h)\n    # # print('error_h', error_h)\n    # poisson.plot_convergence(error_p, 'p', 6)\n    # poisson.plot_convergence(error_p_mixed, 'p', 6, mixed=False)\n    # run_hp_poisson(a, b, 3, 4, 50)\n    # difference = poisson.reverse_engineer(4, 6, 26, poisson.rhs_sin, a, b, poisson.original_f)\n    # n = 3\n    # m = 150\n    #§ gridi = Grid(a, b, ,in)\n    # grid.gauss_lobatto()\n    # print(\"nodal values : \", grid.nodal_pts)\n    # x = np.linspace(a, b, m)\n    # basis = PolyBasis(x, grid)\n    # basis.edge()\n    # basis.plot()\n    #\n    # multiple_poisson(n_0, n_f, a, b)\n\n    # load convergence_sin2pix\n    # error_conv = np.loadtxt('data/convergence/convergence_sin2pix.csv')\n    # poisson.plot_convergence(error_conv, 2, 'p', save=True)\n\n    # interval domain\n    # a, b = -1, 1\n    # # number of subelements\n    # n = 5\n    # # domain resolution\n    # m = 151\n    # func = poisson.rhs_sin\n    # f_exact = poisson.original_f\n    # x = np.linspace(a, b, m)\n    # phi, c, error_norm = poisson.poisson_homo(func, a, b, n, m, f_exact=poisson.original_f)\n    # poisson.plot_solution(phi, poisson.original_f, x,\n    #                       'sinpix', str(n), save=True, show=False)\n    # # print('Value of FEM solution : \\n', phi)\n    #\n    # plt.plot(x, f_exact(x))\n    # plt.plot(x, phi)\n    # print(\"Error norm for n = \" + str(n) + \": \", error_norm)\n    # plt.show()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"411211767","text":"import numpy\nimport cv2\nimport math\nimport time\nimport vision_definitions\nimport qi\nimport config as cfg\n\nfrom naoqi import ALProxy\nfrom PIL import Image\n\n\nclass ImageProvider:\n    def __init__(self):\n        self.IP = cfg.ip\n        self.PORT = cfg.port\n        self.FPS = cfg.frameRate\n        self.connected = False\n        self.images_counter = 0\n\n    def get_images_counter(self):\n        return self.images_counter\n\n    def reset(self):\n        self.images_counter = 0\n\n    def connect(self):\n        self.cam_proxy = ALProxy('ALVideoDevice', self.IP, self.PORT)\n        self.motion = ALProxy('ALMotion', self.IP, self.PORT)\n        self.memory = ALProxy('ALMemory', self.IP, self.PORT)\n\n        resolution = 2  # VGA\n        color_space = 11 # RGB\n        self.video_client = self.cam_proxy.subscribeCamera('camera_rgb', 0, resolution, color_space, self.FPS)\n\n        color_space_depth = 0 # Depth\n        resolution_depth = 1 # kQVGA\n        self.video_client_depth = self.cam_proxy.subscribeCamera('camera_depth', 2, resolution_depth,\n                                                     color_space_depth, 20)\n        self.connected = True\n\n    def get_cv_image(self):\n        if(not self.connected):\n            raise Exception('ALProxy not initialized.!')\n\n        nao_image = self.cam_proxy.getImageRemote(self.video_client)\n        nao_image_depth = self.cam_proxy.getImageRemote(self.video_client_depth)\n        camInTorsoFrame = self.motion.getPosition('CameraTop', 0 , True)\n        camInRobotFrame = self.motion.getPosition('CameraTop', 1 , True)\n\n        self.images_counter = self.images_counter + 1\n        image_width = nao_image[0]\n        image_height = nao_image[1]\n        array = nao_image[6]\n\n        # Create a PIL Image from our pixel array.\n        im = Image.frombytes('RGB', (image_width, image_height), array)\n        opencv_image = cv2.cvtColor(numpy.array(im), cv2.COLOR_RGB2BGR)\n\n        image_width_depth = nao_image_depth[0]\n        image_height_depth = nao_image_depth[1]\n        array_depth = nao_image_depth[6]\n\n        # Get yaw angle of the head in radians.\n        head_yaw = self.memory.getData('Device/SubDeviceList/HeadYaw/Position/Sensor/Value')\n        head_pitch = self.memory.getData('Device/SubDeviceList/HeadPitch/Position/Sensor/Value')\n\n        # Create a PIL Image from our pixel array.\n        im_depth = Image.frombytes('L', (image_width_depth, image_height_depth), array_depth)\n        opencv_image_depth = numpy.array(im_depth)\n        opencv_image_depth = cv2.resize(opencv_image_depth, (cfg.width, cfg.height))\n\n        return opencv_image, opencv_image_depth, camInRobotFrame[2], head_yaw, head_pitch\n\n    def release_image(self):\n        self.cam_proxy.releaseImage(self.video_client)\n        self.cam_proxy.releaseImage(self.video_client_depth)\n\n    def disconnect(self):\n        self.cam_proxy.unsubscribe(self.video_client)\n        self.cam_proxy.unsubscribe(self.video_client_depth)\n        self.connected = False\n","sub_path":"external_platform/vision/pose_estimation/image_provider/image_provider.py","file_name":"image_provider.py","file_ext":"py","file_size_in_byte":2983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"187521297","text":"\n# coding: utf-8\n\n# In[ ]:\n\n\n\"\"\"This module runs unit tests over functions in diversity_score_module\"\"\"\n\nimport os.path\nimport unittest\nimport pandas as pd\nimport numpy as np\nimport json\nfrom movie_analysis import diversity_score_module as dsm\n\nclass TestDivScore(unittest.TestCase):\n\n\n    \"\"\"This class runs unit tests over functions in the diversity_score_module\"\"\"\n\n\n    # test compute_diversity_score function\n    def test_correcttype(self):\n        \"\"\"This function tests if the values returned by compute_diversity_score \n        are the correct type\n        \"\"\"\n        cast = json.loads(pd.read_csv('../data/raw_data/tmdb_5000_credits.csv')['cast'][5]) #load dataset\n        [ds,cs] = dsm.compute_diversity_score(cast,True)\n        self.assertTrue(type.ds == np.float64 & type.cs == np.float64)\n\n    def test_savefig(self):\n        \"\"\"This function tests that a figure is saved when viz_distribution is run\"\"\"\n        file = 'Figures/Full_Diversity_Dist.pdf'\n        dsm.viz_distribution(np.array([2,3,1,8,3,8]),'diversity score')\n        assert os.path.isfile(file) is True\n\n    def test_subsetrows(self):\n        \"\"\"This function tests if the returned dataframe has the correct number of rows\n        \"\"\"\n        df = pd.read_csv('../data/raw_data/tmdb_5000_credits.csv')\n        subsetno = 70\n        strata = 10\n        subset = dsm.get_subset(df,strata,subsetno)\n        self.assertEqual(len(subset),subsetno)\n\n\n\n","sub_path":"movie_analysis/tests/test_divscore.py","file_name":"test_divscore.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"159431058","text":"class Solution(object):\n    def complexNumberMultiply(self, a, b):\n        \"\"\"\n        :type a: str\n        :type b: str\n        :rtype: str\n        \"\"\"\n        a1,b1 = (int(x) for x in a[:-1].split('+'))\n        a2,b2 = (int(x) for x in b[:-1].split('+'))\n        # a1*a2 + a1*b2i + a2*b1i + b1*b2i*i \n        # = a1*a2 - b1*b2 + (a1*b2+a2*b1)i\n        a0 = a1*a2 - b1*b2\n        b0 = a1*b2 + a2*b1\n        return str(a0) + '+' + str(b0) + 'i'\n","sub_path":"leetcode/537_complex_number_multiplication.py","file_name":"537_complex_number_multiplication.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"609025842","text":"from __future__ import absolute_import\nimport flask\nimport json\nimport os\nfrom textwrap import dedent\nimport itsdangerous\nimport functools\nimport urllib\nimport requests\n\nfrom .auth import Auth\nfrom . import api_requests\n\n\ndef need_request_context(func):\n    @functools.wraps(func)\n    def _wrap(*args, **kwargs):\n        if not flask.has_request_context():\n            raise RuntimeError('`{0}` method needs a flask/dash request'\n                               ' context to run. Make sure to run '\n                               '`{0}` from a callback.'.format(func.__name__))\n        return func(*args, **kwargs)\n    return _wrap\n\n\nclass OAuth(Auth):\n    # Name of the cookie containing the cached permission token\n    AUTH_COOKIE_NAME = 'dash_token'\n    # Name of the cookie containing the OAuth2 access token\n    TOKEN_COOKIE_NAME = 'oauth_token'\n    USERNAME_COOKIE = 'dash_user'\n    USERDATA_COOKIE = 'dash_user_data'\n\n    def __init__(\n            self,\n            app,\n            app_url,\n            client_id=None,\n            secret_key=None,\n            salt=None, authorization_hook=None):\n        Auth.__init__(self, app, authorization_hook)\n\n        self.config = {\n            'permissions_cache_expiry': 5 * 60,\n            'user_cookies_expiry': 604800,  # one week.\n        }\n\n        self._app = app\n        self._app_url = app_url\n        self._oauth_client_id = client_id\n        self._username_cache = {}\n\n        if secret_key is None and app.server.secret_key is None:\n            raise Exception(dedent('''\n                app.server.secret_key is missing.\n                Generate a secret key in your Python session\n                with the following commands:\n\n                >>> import os\n                >>> import base64\n                >>> base64.b64encode(os.urandom(30)).decode('utf-8')\n\n                and assign it to the property app.server.secret_key\n                (where app is your dash app instance).\n                Note that you should not do this dynamically:\n                you should create a key and then assign the value of\n                that key in your code.\n            '''))\n\n        if salt is None:\n            raise Exception(dedent('''\n                salt is missing. The salt parameter needs to a string that\n                is unique to this individual Dash app.\n            '''))\n\n        self._signer = itsdangerous.TimestampSigner(secret_key, salt=salt)\n        self._json_signer = itsdangerous.JSONWebSignatureSerializer(\n            secret_key, salt=salt)\n\n        app.server.add_url_rule(\n            '{}_dash-login'.format(app.config['routes_pathname_prefix']),\n            view_func=self.login_api,\n            methods=['post']\n        )\n\n        app.server.add_url_rule(\n            '{}_oauth-redirect'.format(app.config['routes_pathname_prefix']),\n            view_func=self.serve_oauth_redirect,\n            methods=['get']\n        )\n\n        app.server.add_url_rule(\n            '{}_is-authorized'.format(app.config['routes_pathname_prefix']),\n            view_func=self.check_if_authorized,\n            methods=['get']\n        )\n        _current_path = os.path.dirname(os.path.abspath(__file__))\n\n        # TODO - Dist files\n        with open(os.path.join(_current_path, 'oauth-redirect.js'), 'r') as f:\n            self.oauth_redirect_bundle = f.read()\n\n        with open(os.path.join(_current_path, 'login.js'), 'r') as f:\n            self.login_bundle = f.read()\n\n        @self.app.server.before_request\n        def handle_access_token():\n            urlsplit = urllib.parse.urlsplit(flask.request.url)\n            params = urllib.parse.parse_qs(urlsplit.query)\n\n            if 'logout' in params:\n                return self.logout_api(self.remove_url_query(urlsplit))\n            elif 'access_token' in params or 'code' in params:\n                return self.login_api(self.remove_url_query(urlsplit), params)\n\n            return None\n\n\n    def remove_url_query(self, urlsplit):\n        urlsplit_list = list(urlsplit)\n        urlsplit_list[3] = ''\n        return tuple(urlsplit_list)\n\n\n    def access_token_is_valid(self):\n        if self.AUTH_COOKIE_NAME not in flask.request.cookies:\n            return False\n\n        access_token = flask.request.cookies[self.AUTH_COOKIE_NAME]\n\n        try:\n            self._signer.unsign(\n                access_token,\n                max_age=self.config['permissions_cache_expiry']\n            )\n            return True\n        except itsdangerous.SignatureExpired:\n            # Check access in case the user is valid but the token has expired\n            return False\n        except itsdangerous.BadSignature:\n            # Access tokens in previous versions of `dash-auth`\n            # weren't generated with itsdangerous\n            # and will raise `BadSignature`\n            return False\n\n    def user_data_is_valid(self):\n        user = self.get_user_data()\n        return user and 'services' in user and 'apps' in user\n\n    def is_authorized(self):\n        if self.TOKEN_COOKIE_NAME not in flask.request.cookies:\n            return False\n\n        if not self.user_data_is_valid():\n            return False\n        oauth_token = flask.request.cookies[self.TOKEN_COOKIE_NAME]\n        if not self.access_token_is_valid():\n            return self.check_view_access(oauth_token)\n\n        return True\n\n    def is_service_authorized(self, services=[]):\n        if len(services) == 0:\n            return True\n        if self.is_authorized():\n            user = self.get_user_data()\n            if user and 'services' in user:\n                for service in services:\n                    for user_service in user['services']:\n                        if service == user_service:\n                            return True\n        return False\n\n    def is_app_authorized(self, apps=[]):\n        if len(apps) == 0:\n            return True\n        if self.is_authorized():\n            user = self.get_user_data()\n            if user and 'apps' in user:\n                for app in apps:\n                    for user_app in user['apps']:\n                        if app == user_app:\n                            return True\n        return False\n\n    def check_if_authorized(self):\n        if self.is_authorized():\n            return flask.Response(status=200)\n\n        return flask.Response(status=403)\n\n    def add_access_token_to_response(self, response):\n        \"\"\"\n        Add an access token cookie to a response if it doesn't\n        already have a valid one. (To be called if auth succeeds to make\n        auth \"sticky\" for other requests.)\n\n        :param (flask.Response|str|unicode) response\n        :rtype: (flask.Response)\n        \"\"\"\n        try:\n            # Python 2\n            if isinstance(response, basestring):  # noqa: F821\n                response = flask.Response(response)\n        except Exception:\n            # Python 3\n            if isinstance(response, str):\n                response = flask.Response(response)\n\n        if not self.access_token_is_valid():\n            access_token = self._signer.sign('access')\n            self.set_cookie(\n                response,\n                name=self.AUTH_COOKIE_NAME,\n                value=access_token,\n                max_age=(60 * 60 * 24 * 7),  # 1 week\n            )\n\n            username = self.get_username(validate_max_age=False)\n            userdata = self.get_user_data()\n\n            if username:\n                self.set_username(username)\n            if userdata:\n                self.set_user_data(userdata)\n\n        return response\n\n    def auth_wrapper(self, f):\n        def wrap(*args, **kwargs):\n            if not self.is_authorized():\n                return self.auth_redirect()\n\n            try:\n                response = f(*args, **kwargs)\n            except Exception as err:\n                # Clear the cookie if auth fail\n                if getattr(err, 'status_code', None) in [401, 403]:\n                    return self.auth_redirect()\n                else:\n                    raise\n\n            # TODO - should set secure in this cookie, not exposed in flask\n            # TODO - should set path or domain\n            return self.add_access_token_to_response(response)\n        return wrap\n\n    def auth_redirect(self):\n        auth_base_url = api_requests.config('AGRISTA_AUTH_DOMAIN', 'https://stage-id.agrista.com')\n        auth_endpoint = api_requests.config('AGRISTA_AUTH_ENDPOINT', '/oauth2/authorize')\n        client_id = api_requests.config('AGRISTA_AUTH_CLIENT_ID', '5f59246f-8755-4cb7-8637-147c473acf15')\n        params = {\n            'client_id': client_id,\n            'response_type': 'token',\n            'scope': 'profile',\n            'redirect_uri': flask.request.url\n        }\n\n        return flask.redirect(auth_base_url + auth_endpoint + '?' + urllib.parse.urlencode(params))\n\n    def index_auth_wrapper(self, original_index):\n        def wrap(*args, **kwargs):\n            if self.is_authorized():\n                return original_index(*args, **kwargs)\n            else:\n                return self.login_request()\n        return wrap\n\n    def html(self, script):\n        return ('''\n            <!doctype html>\n            <html lang=\"en\">\n            <head>\n                <meta charset=\"UTF-8\">\n                <title>Log In</title>\n            </head>\n            <body>\n              <div id=\"react-root\"></div>\n            </body>\n            <script id=\"_auth-config\" type=\"application/json\">\n            {}\n            </script>\n            <script type=\"text/javascript\">{}</script>\n            </html>\n        '''.format(\n            json.dumps({\n                'oauth_client_id': self._oauth_client_id,\n                'requests_pathname_prefix':\n                    self._app.config['requests_pathname_prefix']\n            }),\n            script)\n        )\n\n    def login_request(self):\n        return self.html(self.login_bundle)\n\n    def serve_oauth_redirect(self):\n        return self.html(self.oauth_redirect_bundle)\n\n    def set_cookie(self, response, name, value, max_age, httponly=True, samesite='Lax'):\n        response.set_cookie(\n            name,\n            value=value,\n            max_age=max_age,\n            secure=True if 'https:' in self._app_url else False,\n            path=self._app.config['routes_pathname_prefix'],\n            httponly=httponly,\n            samesite=samesite\n        )\n\n    def clear_cookies(self, response):\n        \"\"\"\n        Clear all the auth cookies.\n\n        :param response:\n        :type response: flask.Response\n        :return:\n        \"\"\"\n        for c in (\n                self.AUTH_COOKIE_NAME,\n                self.TOKEN_COOKIE_NAME,\n                self.USERDATA_COOKIE,\n                self.USERNAME_COOKIE):\n            self._clear_cookie(response, c)\n\n    def _clear_cookie(self, response, cookie_name):\n        response.set_cookie(cookie_name,\n                            value='',\n                            expires=0,\n                            secure='https:' in self._app_url)\n\n    def check_view_access(self, oauth_token):\n        \"\"\"Checks the validity of oauth_token.\"\"\"\n        return True\n\n    def login_api(self, split_url, params):\n        if 'code' in params:\n            code = params.get('code')[0]\n            auth_base_url = api_requests.config('AGRISTA_AUTH_DOMAIN', 'https://stage-id.agrista.com')\n            token_endpoint = api_requests.config('AGRISTA_TOKEN_ENDPOINT', '/oauth2/token')\n            client_id = api_requests.config('AGRISTA_AUTH_CLIENT_ID', '5f59246f-8755-4cb7-8637-147c473acf15')\n            client_secret = api_requests.config('AGRISTA_AUTH_CLIENT_SECRET', 'nYmsIJGKbU9ESUXltmOJYTkBnHU7NYBA7xcTU0oi')\n            params = {\n                'client_id': client_id,\n                'client_secret': client_secret,\n                'code': code,\n                'grant_type': 'authorization_code',\n                'redirect_uri': flask.request.url\n            }\n\n            try:\n                res = requests.get(auth_base_url + token_endpoint + '?' + urllib.parse.urlencode(params))\n                data = res.json()\n\n                if 'access_token' in data:\n                    oauth_token = data['access_token']\n                else:\n                    raise Exception('Missing access_token')\n            except Exception as e:\n                print(e)\n                raise e\n        else:\n            oauth_token = params.get('access_token')[0]\n\n        \"\"\"Obtains the access_token from the URL, sets the cookie.\"\"\"\n        userinfo_base_url = api_requests.config('AGRISTA_USERINFO_DOMAIN', 'https://stage-api.agrista.com')\n        headers = {\n            'authorization': 'Bearer ' + oauth_token\n        }\n        try:\n            res = requests.get(userinfo_base_url + '/me', headers=headers)\n        except Exception as e:\n            print(e)\n            raise e\n\n        data = res.json()\n        try:\n            data['apps'] = [app['name'] for app in data['userRole']['apps']]\n            data['services'] = [app['name'] for app in data['organization']['services']]\n            data['organization'].pop('createdAt', None)\n            data['organization'].pop('data', None)\n            data['organization'].pop('hostUrl', None)\n            data['organization'].pop('services', None)\n            data['organization'].pop('status', None)\n            data.pop('accessLevel', None)\n            data.pop('activeDirectory', None)\n            data.pop('isActive', None)\n            data.pop('isBudgetPublisher', None)\n            data.pop('profilePhoto', None)\n            data.pop('status', None)\n            data.pop('teams', None)\n            data.pop('userRole', None)\n        except Exception as e:\n            print(e)\n        response = flask.redirect(urllib.parse.urlunsplit(split_url))\n\n        self.set_username(data.get('email'))\n        self.set_user_data(data)\n        self.set_cookie(\n            response=response,\n            name=self.TOKEN_COOKIE_NAME,\n            value=oauth_token,\n            max_age=None\n        )\n\n        return response\n\n    def logout_api(self, split_url):\n        response = flask.redirect(urllib.parse.urlunsplit(split_url))\n        response.delete_cookie(self.USERNAME_COOKIE)\n        response.delete_cookie(self.USERDATA_COOKIE)\n        response.delete_cookie(self.TOKEN_COOKIE_NAME)\n\n        return response\n\n    @need_request_context\n    def get_username(self, validate_max_age=True):\n        \"\"\"\n        Retrieve the username from the `dash_user` cookie.\n\n        :return: The stored username if any.\n        :rtype: str\n        \"\"\"\n        cached = self._username_cache.get(flask.request.remote_addr)\n        if cached:\n            return cached\n        username = flask.request.cookies.get(self.USERNAME_COOKIE)\n        if username:\n            max_age = None\n            if validate_max_age:\n                max_age = self.config['permissions_cache_expiry']\n            unsigned = self._signer.unsign(username, max_age=max_age)\n            return unsigned.decode('utf-8')\n\n    @need_request_context\n    def get_user_data(self):\n        \"\"\"\n        Retrieve the user data from `dash_user_data` cookie.\n\n        :return: The stored user data if any.\n        :rtype: dict\n        \"\"\"\n\n        user_data = flask.request.cookies.get(self.USERDATA_COOKIE)\n        if user_data:\n            return self._json_signer.loads(user_data)\n\n    @need_request_context\n    def set_username(self, name):\n        \"\"\"\n        Store the username in the `dash_user` cookie.\n\n        :param name: the name of the user.\n        :type name: str\n        :return:\n        \"\"\"\n        self._username_cache[flask.request.remote_addr] = name\n\n        @flask.after_this_request\n        def _set_username(response):\n            self.set_cookie(\n                response,\n                self.USERNAME_COOKIE,\n                self._signer.sign(name),\n                max_age=self.config['user_cookies_expiry'])\n            del self._username_cache[flask.request.remote_addr]\n            return response\n\n    @need_request_context\n    def set_user_data(self, data):\n        \"\"\"\n        Set meta data for a user to store in a cookie.\n\n        :param data: Data to encode and store.\n        :type data: dict, list\n        :return:\n        \"\"\"\n\n        @flask.after_this_request\n        def _set_data(response):\n            self.set_cookie(\n                response,\n                self.USERDATA_COOKIE,\n                self._json_signer.dumps(data),\n                max_age=self.config['user_cookies_expiry'])\n            return response\n","sub_path":"dash_auth/oauth.py","file_name":"oauth.py","file_ext":"py","file_size_in_byte":16516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"407720267","text":"import pickle as pkl\r\n\r\nimport networkx as nx\r\nimport numpy as np\r\nimport scipy.sparse as sp\r\nimport torch\r\nfrom sklearn.metrics import roc_auc_score, average_precision_score\r\n\r\nimport pandas as pd\r\n\r\ndef myload(dataset=\"my\", debug=True):\r\n    df = pd.read_csv(\"data/{}.tsv\".format(dataset), sep=\"\\t\", header=None)\r\n    # print(len(df[0]), len(df[1]))\r\n    # print(list(df[2]))\r\n    myfrom = list(df[0])\r\n    myto = list(df[1])\r\n    if debug:\r\n        myfrom = myfrom[:200]\r\n        myto = myto[:200]\r\n    allx = list(set(myfrom).union(set(myto)))\r\n    n_nodes = len(allx)\r\n    allid = list(range(n_nodes))\r\n    allx_map = dict(zip(allx, allid))\r\n    graph = dict(zip(allid, [[]] * n_nodes))\r\n    for f, t in zip(myfrom, myto):\r\n        fid = allx_map[f]\r\n        tid = allx_map[t]\r\n        graph[fid].append(tid)\r\n    return graph\r\n\r\ndef load_data(dataset, featureless=True):\r\n    # load the data: x, tx, allx, graph\r\n    names = ['x', 'tx', 'allx', 'graph']\r\n    nofeature_dataset = ['my']\r\n    featured_dataset = ['cora', 'citeseer']\r\n    if dataset in featured_dataset:\r\n        objects = []\r\n        for i in range(len(names)):\r\n            '''\r\n            fix Pickle incompatibility of numpy arrays between Python 2 and 3\r\n            https://stackoverflow.com/questions/11305790/pickle-incompatibility-of-numpy-arrays-between-python-2-and-3\r\n            '''\r\n            with open(\"data/ind.{}.{}\".format(dataset, names[i]), 'rb') as rf:\r\n                u = pkl._Unpickler(rf)\r\n                u.encoding = 'latin1'\r\n                cur_data = u.load()\r\n                objects.append(cur_data)\r\n            # objects.append(\r\n            #     pkl.load(open(\"data/ind.{}.{}\".format(dataset, names[i]), 'rb')))\r\n        x, tx, allx, graph = tuple(objects)\r\n        test_idx_reorder = parse_index_file(\r\n            \"data/ind.{}.test.index\".format(dataset))\r\n        test_idx_range = np.sort(test_idx_reorder)\r\n\r\n        if dataset == 'citeseer':\r\n            # Fix citeseer dataset (there are some isolated nodes in the graph)\r\n            # Find isolated nodes, add them as zero-vecs into the right position\r\n            test_idx_range_full = range(\r\n                min(test_idx_reorder), max(test_idx_reorder) + 1)\r\n            tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))\r\n            tx_extended[test_idx_range - min(test_idx_range), :] = tx\r\n            tx = tx_extended\r\n    else:\r\n        graph = myload(dataset)\r\n        print(\"finished loading the graph\")\r\n    # get the features\r\n    if featureless or (dataset in nofeature_dataset):\r\n        # print(test_idx_range) # list of 1000 elements\r\n        # print(len(test_idx_range_full))\r\n        # print(len(test_idx_reorder)) # 1000\r\n        # exit(0)\r\n        n_entities = len(graph.keys())\r\n        edge_indexs = np.array(range(n_entities))\r\n        features = sp.csr_matrix((np.ones(n_entities), (edge_indexs, edge_indexs)), shape=(n_entities, n_entities), dtype=np.float32)\r\n        features = normalize(features)\r\n        features = sparse_mx_to_torch_sparse_tensor(features)\r\n        print(\"finished preparing the features\")\r\n    elif dataset in featured_dataset:\r\n        features = sp.vstack((allx, tx)).tolil()\r\n        features[test_idx_reorder, :] = features[test_idx_range, :]\r\n        features = torch.FloatTensor(np.array(features.todense()))\r\n\r\n    # graph: {2707: [598, 165, 1473, 2706]}\r\n    adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))\r\n    print(\"finished preparing the adjacency matrix\")\r\n    # print(adj.toarray()[-1,598])\r\n\r\n    # print(adj, features)\r\n    # adj:\r\n    #   (id1, id2, 1)\r\n    # features:\r\n    #   2D tensor\r\n    #   scipy.sparse.csr.csr_matrix # https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html\r\n    # print(adj.toarray())\r\n    # \r\n\r\n    return adj, features\r\n\r\ndef normalize(mx):\r\n    \"\"\"Row-normalize sparse matrix\"\"\"\r\n    rowsum = np.array(mx.sum(1))\r\n    r_inv = np.power(rowsum, -1).flatten()\r\n    r_inv[np.isinf(r_inv)] = 0.\r\n    r_mat_inv = sp.diags(r_inv)\r\n    mx = r_mat_inv.dot(mx)\r\n    return mx\r\n\r\n\r\ndef parse_index_file(filename):\r\n    index = []\r\n    for line in open(filename):\r\n        index.append(int(line.strip()))\r\n    return index\r\n\r\n\r\ndef sparse_to_tuple(sparse_mx):\r\n    if not sp.isspmatrix_coo(sparse_mx):\r\n        sparse_mx = sparse_mx.tocoo()\r\n    coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose()\r\n    values = sparse_mx.data\r\n    shape = sparse_mx.shape\r\n    return coords, values, shape\r\n\r\n\r\ndef mask_test_edges(adj, verbose=True):\r\n    # Function to build test set with 10% positive links\r\n    # NOTE: Splits are randomized and results might slightly deviate from reported numbers in the paper.\r\n    # TODO: Clean up.\r\n\r\n    # Remove diagonal elements\r\n    if verbose:\r\n        print(\"Remove diagonal elements\")\r\n    adj = adj - sp.dia_matrix((adj.diagonal()[np.newaxis, :], [0]), shape=adj.shape)\r\n    adj.eliminate_zeros()\r\n    # Check that diag is zero:\r\n    assert np.diag(adj.todense()).sum() == 0\r\n\r\n    if verbose:\r\n        print(\"Remove the testing links\")\r\n    adj_triu = sp.triu(adj)\r\n    adj_tuple = sparse_to_tuple(adj_triu)\r\n    edges = adj_tuple[0]\r\n    edges_all = sparse_to_tuple(adj)[0]\r\n    num_test = int(np.floor(edges.shape[0] / 10.))\r\n    num_val = int(np.floor(edges.shape[0] / 20.))\r\n\r\n    all_edge_idx = list(range(edges.shape[0]))\r\n    np.random.shuffle(all_edge_idx)\r\n    val_edge_idx = all_edge_idx[:num_val]\r\n    test_edge_idx = all_edge_idx[num_val:(num_val + num_test)]\r\n    test_edges = edges[test_edge_idx]\r\n    val_edges = edges[val_edge_idx]\r\n    train_edges = np.delete(edges, np.hstack([test_edge_idx, val_edge_idx]), axis=0)\r\n\r\n    def ismember(a, b, tol=5):\r\n        rows_close = np.all(np.round(a - b[:, None], tol) == 0, axis=-1)\r\n        return np.any(rows_close)\r\n\r\n    if verbose:\r\n        print(\"Do negative sampling of the test links\")\r\n    test_edges_false = []\r\n    while len(test_edges_false) < len(test_edges):\r\n        # if verbose:\r\n        #     print(\"generating from and to\")\r\n        idx_i = np.random.randint(0, adj.shape[0])\r\n        idx_j = np.random.randint(0, adj.shape[0])\r\n        if idx_i == idx_j:\r\n            continue\r\n        # if verbose:\r\n        #     print(\"checking if not really negative\")\r\n        if ismember([idx_i, idx_j], edges_all):\r\n            continue\r\n        if test_edges_false:\r\n            if ismember([idx_j, idx_i], np.array(test_edges_false)):\r\n                continue\r\n            if ismember([idx_i, idx_j], np.array(test_edges_false)):\r\n                continue\r\n        test_edges_false.append([idx_i, idx_j])\r\n\r\n    if verbose:\r\n        print(\"Do negative sampling of the validation links\")\r\n    val_edges_false = []\r\n    while len(val_edges_false) < len(val_edges):\r\n        idx_i = np.random.randint(0, adj.shape[0])\r\n        idx_j = np.random.randint(0, adj.shape[0])\r\n        if idx_i == idx_j:\r\n            continue\r\n        if ismember([idx_i, idx_j], train_edges):\r\n            continue\r\n        if ismember([idx_j, idx_i], train_edges):\r\n            continue\r\n        if ismember([idx_i, idx_j], val_edges):\r\n            continue\r\n        if ismember([idx_j, idx_i], val_edges):\r\n            continue\r\n        if val_edges_false:\r\n            if ismember([idx_j, idx_i], np.array(val_edges_false)):\r\n                continue\r\n            if ismember([idx_i, idx_j], np.array(val_edges_false)):\r\n                continue\r\n        val_edges_false.append([idx_i, idx_j])\r\n\r\n    assert ~ismember(test_edges_false, edges_all)\r\n    assert ~ismember(val_edges_false, edges_all)\r\n    assert ~ismember(val_edges, train_edges)\r\n    assert ~ismember(test_edges, train_edges)\r\n    assert ~ismember(val_edges, test_edges)\r\n\r\n    data = np.ones(train_edges.shape[0])\r\n\r\n    # Re-build adj matrix\r\n    adj_train = sp.csr_matrix((data, (train_edges[:, 0], train_edges[:, 1])), shape=adj.shape)\r\n    adj_train = adj_train + adj_train.T\r\n\r\n    # NOTE: these edge lists only contain single direction of edge!\r\n    return adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false\r\n\r\n\r\ndef preprocess_graph(adj):\r\n    adj = sp.coo_matrix(adj)\r\n    adj_ = adj + sp.eye(adj.shape[0])\r\n    rowsum = np.array(adj_.sum(1))\r\n    degree_mat_inv_sqrt = sp.diags(np.power(rowsum, -0.5).flatten())\r\n    adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt).tocoo()\r\n    # return sparse_to_tuple(adj_normalized)\r\n    return sparse_mx_to_torch_sparse_tensor(adj_normalized)\r\n\r\n\r\ndef sparse_mx_to_torch_sparse_tensor(sparse_mx):\r\n    \"\"\"Convert a scipy sparse matrix to a torch sparse tensor.\"\"\"\r\n    sparse_mx = sparse_mx.tocoo().astype(np.float32)\r\n    indices = torch.from_numpy(\r\n        np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))\r\n    values = torch.from_numpy(sparse_mx.data)\r\n    shape = torch.Size(sparse_mx.shape)\r\n    return torch.sparse.FloatTensor(indices, values, shape)\r\n\r\n\r\ndef get_roc_score(emb, adj_orig, edges_pos, edges_neg):\r\n    def sigmoid(x):\r\n        return 1 / (1 + np.exp(-x))\r\n\r\n    # Predict on test set of edges\r\n    adj_rec = np.dot(emb, emb.T)\r\n    preds = []\r\n    pos = []\r\n    for e in edges_pos:\r\n        preds.append(sigmoid(adj_rec[e[0], e[1]]))\r\n        pos.append(adj_orig[e[0], e[1]])\r\n\r\n    preds_neg = []\r\n    neg = []\r\n    for e in edges_neg:\r\n        preds_neg.append(sigmoid(adj_rec[e[0], e[1]]))\r\n        neg.append(adj_orig[e[0], e[1]])\r\n\r\n    preds_all = np.hstack([preds, preds_neg])\r\n    labels_all = np.hstack([np.ones(len(preds)), np.zeros(len(preds))])\r\n    roc_score = roc_auc_score(labels_all, preds_all)\r\n    ap_score = average_precision_score(labels_all, preds_all)\r\n\r\n    return roc_score, ap_score\r\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":9715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"460330689","text":"#create a list to hold our items\nshopping_list = []\n\n#Instructions for using the app\nprint(\"what should we pick up at the store?\")\nprint(\"enter 'DONE' to stop adding items\")\n\n#ask for new items\nwhile True : \n    newItem = input('> ')\n\n    #be able to quit the app\n    if newItem == 'DONE':\n        break\n    #add new items to our list\n    shopping_list.append(newItem)\n\n#print list items\nprint(\"Here's your list: \")\nfor item in shopping_list:\n    print(item)","sub_path":"shopping_list.py","file_name":"shopping_list.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"379444123","text":"import matplotlib.pyplot as plt\nimport time\nimport os\nimport numpy\nimport matplotlib2tikz\n\n\ndef plot_specific_measure(results: list, algo_name, topology_names: list, measures: list,  measure_index: int,\n                          number_of_samples: int = 1000, plot_type='approach',\n                          defined_color: bool = True, save_tikz: bool = True):\n    \"\"\"\n    Plots the specific measures coming from the results.\n\n    Parameters\n    ----------\n    results : list of tuple containing the results\n        The graph in which we serve the demands according to the global knowledge approach.\n\n    algo_name: str\n        Name of the algorithms used.\n\n    topology_names: list\n        Name of the topologies in which the simulation took place.\n\n    measures: list\n        Name of the measures to be plotted.\n\n    measure_index: int\n        Index of the measure to be plotted.\n\n    number_of_samples: int\n        Number of samples used in the simulation.\n\n    plot_type: str\n        Type of the plot\n\n    defined_color: bool\n        Boolean determining whether or not the pre-defined colors are to be used.\n\n    save_tikz: bool\n        Value defining whether or not to save as a tikz file.\n    \"\"\"\n    timestr = time.strftime(\"%y_%m_%d__%H_%M\")\n\n    if defined_color:\n        colors = ['red', 'green', 'blue', 'purple', 'darkorange', 'magenta']\n    else:\n        colors = numpy.random.rand(len(results), )\n\n    for index in range(len(results)):\n        plt.plot(results[index][measure_index], color=colors[index], label=topology_names[index])\n\n    plt.grid(color='b', linestyle='-', linewidth=0.1)\n    plt.xlabel('Number of source-destination pairs')\n    plt.ylabel(measures[measure_index])\n\n    if plot_type is 'approach':\n        plt.title('Approach: ' + algo_name)\n    elif plot_type is 'model':\n        plt.title('Topology: ' + algo_name)\n\n    plt.legend(loc=0)\n\n    # Create the logs directory, if it is non-existent yet\n    directory = 'plots'\n    if not os.path.exists(directory):\n        os.makedirs(directory)\n    if not save_tikz:\n        plt.savefig('./' + directory + '/' + algo_name + '_' + str(number_of_samples) + '_' +\n                    measures[measure_index] + '_' + timestr + '.png', bbox_inches='tight')\n        plt.show()\n    else:\n        matplotlib2tikz.save('./' + directory + '/' + algo_name + '_' + str(number_of_samples) + '_' +\n                             measures[measure_index] + '_' + timestr + '.tex')\n        plt.clf()\n\n\ndef plot_results(results: list, title: str, topology_names=None,\n                 measure_names=None, plot_type='algo', save_tikz: bool = True):\n    \"\"\"\n    Plot the simulation results.\n\n    Parameters\n    ----------\n    results : list of tuple containing the results\n        The graph in which we serve the demands according to the global knowledge approach.\n\n    title: str\n        Title of the simulation.\n\n    topology_names: list\n        Name of the topologies in which the simulation took place.\n\n    measure_names: list\n        Name of the measures to be plotted.\n\n    plot_type: str\n        Type of the plot\n\n    save_tikz: bool\n        Value defining whether or not to save as a tikz file.\n    \"\"\"\n    if topology_names is None:\n        topology_names = ['dth=1', 'dth=2', 'dth=4', 'dth=8', 'dth=16']\n        # Alternative topology names:\n        # topology_names = ['on-demand', 'dth=1', 'dth=2', 'dth=4']\n    if measure_names is None:\n        measure_names = ['Average waiting times:', 'Number of available links:',\n                         'Number of available edges:', 'Average distances:']\n\n    for measure_index in range(len(measure_names)):\n        plot_specific_measure(results, title, topology_names, measure_names, measure_index,\n                              plot_type=plot_type, save_tikz=save_tikz)\n","sub_path":"lib/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":3783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"321304654","text":"import tensorflow as tf\nfrom keras import Sequential, Input, Model, initializers\nfrom keras.optimizers import RMSprop\nfrom keras.layers import Dense, GRU, Bidirectional, Embedding,\\\n    Reshape, Lambda, Dropout, BatchNormalization, Activation,\\\n    GlobalMaxPooling1D, Masking\nfrom keras.metrics import Accuracy, MeanSquaredError\nfrom keras.preprocessing.sequence import pad_sequences\nimport helpers\nimport numpy as np\nfrom keras.callbacks import LearningRateScheduler, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\nimport data_operations\nimport helpers\nimport os\nimport gc\nimport math\n\n# os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # run on CPU\n\ntf.random.set_seed(3)\ngpus = tf.config.experimental.list_physical_devices('GPU')\ntf.config.experimental.set_memory_growth(gpus[0], True)\n\nmy_init = initializers.glorot_uniform(seed=1)\n\n\ndef add_bidirectional(model, return_sequences=True):\n    model.add(Bidirectional(GRU(128, return_sequences=return_sequences,\n                            kernel_initializer=my_init)))\n    return model\n\n\ndef add_dense(model):\n    model.add(Dense(32, kernel_initializer=my_init))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    return model\n\n\ndef custom_loss(y_true, y_pred):\n    # \"A coefficient to use on the positive examples.\"\n    pos_weight = 5.0 / 1.0\n    # make both y_true and y_pred float32\n    y_true = tf.cast(y_true, tf.float32)\n\n    loss = tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(y_true,\n                                                                   y_pred,\n                                                                   pos_weight))\n    return loss\n\n\ndef custom_generator(num_docs, batch_size, set='tr'):\n    path = helpers.get_path(num_docs, set)\n    counter = 0\n    curr_pos = 0\n    num_batches = num_docs // batch_size\n    while True:\n        x_batch, y_batch, curr_pos = data_operations.read_dumps_in_batches(path, batch_size, curr_pos)\n        avg_doc_len = helpers.find_avg_doc_length_in_batch(x_batch)\n        x_batch = pad_sequences(x_batch, avg_doc_len, padding='post', truncating='post', dtype=np.float32)    # (batch_size, doc_len, sen_len)\n        y_batch = pad_sequences(y_batch, avg_doc_len, padding='post', truncating='post', dtype=np.float32)    # (batch_size, doc_len)\n        print(f'AVG DOC LEN: {avg_doc_len}')\n        counter += 1\n\n        # converting from np.array to tensor because of https://github.com/tensorflow/tensorflow/issues/44705#issuecomment-725803328\n        yield tf.convert_to_tensor(x_batch), tf.convert_to_tensor(np.reshape(y_batch, (y_batch.shape[0], y_batch.shape[1], 1)))\n\n        # restart counter to yield data in the next epoch as well\n        if counter >= num_batches:\n            counter = 0\n            curr_pos = 0\n\n\ndef train_model_generator(batch_size, sent_enc, model, num_epochs, save_model_path, num_docs):\n\n    early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1, mode='min',\n                                   min_delta=0.1)\n    mcp_save = ModelCheckpoint(save_model_path, save_best_only=True, monitor='val_loss',\n                               mode='min')\n    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=2,\n                                       verbose=1, min_delta=0.05, mode='min')\n    # first 0.8 for tr/tst split, second 0.8 for tr/val split\n    len_tr = num_docs * 0.8 * 0.8\n    len_val = num_docs * 0.8 * 0.2\n    num_batches_tr = int(len_tr//batch_size)\n    num_batches_val = int(len_val//(batch_size))\n\n    custom_model = CustomModel(Encoder(batch_size), batch_size)\n    custom_model.compile(optimizer=RMSprop(lr=1e-5),\n                         loss=custom_loss)\n\n    return custom_model.fit(custom_generator(num_docs, batch_size), epochs=num_epochs,\n                            steps_per_epoch=num_batches_tr,\n                            validation_data=custom_generator(num_docs, batch_size, 'val'),\n                            validation_steps=num_batches_val,\n                            callbacks=[early_stopping, mcp_save, reduce_lr])\n\n\nclass Encoder(Model):\n    def __init__(self, batch_size, sen_len=20):\n        super(Encoder, self).__init__()\n        self.masking = Masking(mask_value=0., input_shape=(sen_len, 1))\n        # why batch_input_shape instead of input_shape: https://github.com/tensorflow/tensorflow/issues/37942#issuecomment-604981240\n        self.bidir_first_lower = Bidirectional(GRU(128, return_sequences=True,\n                            kernel_initializer=my_init),\n                            # batch_input_shape=(batch_size, sen_len, 1)\n                            )\n        self.bidir = Bidirectional(GRU(128, return_sequences=True,\n                            kernel_initializer=my_init))\n        self.pooling = GlobalMaxPooling1D()\n\n    def call(self, x):\n        x = self.masking(x)\n        # print(self.masking.input_mask)\n        # print(self.masking.output_mask)\n        x = self.bidir_first_lower(x)\n        x = self.bidir(x)\n        x = self.pooling(x)\n        return x\n\n\nclass CustomModel(Model):\n    def __init__(self, encoder, batch_size, sen_len=20):\n        super(CustomModel, self).__init__()\n        self.metric = MeanSquaredError()\n\n        self.encoder = encoder\n        # why batch_input_shape instead of input_shape: https://github.com/tensorflow/tensorflow/issues/37942#issuecomment-604981240\n        self.bidir_first_upper = Bidirectional(GRU(128, return_sequences=True,\n                            kernel_initializer=my_init))\n                            # batch_input_shape=(batch_size, 1, 256))\n        self.bidir = Bidirectional(GRU(128, return_sequences=True,\n                            kernel_initializer=my_init))\n        self.bidir_last = Bidirectional(GRU(128,\n                            kernel_initializer=my_init))\n        self.dense = Dense(1, activation=\"sigmoid\")\n        self.batch_size = batch_size\n\n    def compile(self, optimizer, loss):\n        super().compile()\n        self.run_eagerly = True\n        self.optimizer = optimizer\n        self.loss = loss\n\n    def call(self, x):  # https://github.com/tensorflow/tensorflow/issues/43173\n        # x.shape = (batch_size, doc_len, sen_len)\n        # x.shape = (batch_size * doc_len, 1, 256)\n        print(f'Shape of input {tf.shape(x)}')\n        out = self.bidir_first_upper(x)\n        out = self.bidir(out)\n        out = self.bidir_last(out)\n        # probability that current sentence starts a new segment\n        out = self.dense(out)\n        return out\n\n    def train_step(self, data):\n        x, y = data\n\n        with tf.GradientTape() as tape:\n            doc_len = tf.shape(x)[1].numpy() # all docs in batch have same len\n            sen_len = tf.shape(x)[2].numpy() # all sentences in corpus have same len\n\n            encodings = tf.TensorArray(tf.float32, size=0, dynamic_size=True, clear_after_read=False,\n                                        element_shape=tf.TensorShape([doc_len, 1, 256]))\n\n            for doc_idx in range(len(x)):\n                curr_doc = tf.TensorArray(tf.float32, size=0, dynamic_size=True, clear_after_read=False,\n                                        element_shape=tf.TensorShape([1, 256]))\n                for sen_idx in range(len(x[doc_idx])):\n                    sen = tf.expand_dims(x[doc_idx][sen_idx], -1)   # (20,) -> (20, 1)\n                    # tf.shape is necessary - https://github.com/tensorflow/models/issues/6245#issuecomment-623877638\n                    sen = tf.reshape(sen, (1, tf.shape(sen)[0], tf.shape(sen)[1]))  # (20, 1) -> (1, 20, 1)\n                    orig_sen = sen\n                    print(f'Suma pre {np.sum(sen)}')\n                    sen = self.encoder(sen)\n                    print(f'Suma posle {np.sum(sen)}')\n                    if math.isnan(np.sum(sen)):\n                        print(f'Orig sen {orig_sen}')\n                        print(f'Sen {sen}')\n                        exit()\n                    curr_doc = curr_doc.write(sen_idx, sen)\n\n                encodings = encodings.write(doc_idx, tf.expand_dims(curr_doc.concat(), 1))\n            encodings = encodings.concat()\n            assert all(tf.shape(encodings) == [self.batch_size * doc_len, 1, 256])\n\n            # calculate predictions\n            y_pred = self(encodings, training=True)\n\n            # the next reshape is so that y and y_pred have the same shape\n            y_pred = tf.reshape(y_pred, (self.batch_size, tf.shape(y_pred)[0] / self.batch_size, 1))\n\n            loss = self.loss(y, y_pred)\n\n        # Gradients\n        training_vars = self.trainable_variables + self.encoder.trainable_variables\n\n        gradients = tape.gradient(loss, training_vars)\n        # print(gradients)\n        print('------------------------------------------')\n        # print(loss)\n        if np.isnan(loss.numpy()):\n            print('U NAN-U SMO')\n            encoder_weights = []\n            for layer in self.encoder.layers:\n                encoder_weights.append(layer.get_weights())\n            with open(\"encoder_weights.txt\", \"w\") as output:\n                output.write(str(encoder_weights))\n            with open(\"bidir_first_upper_weights.txt\", \"w\") as output:\n                output.write(str(self.bidir_first_upper.get_weights()))\n            with open(\"bidir_weights.txt\", \"w\") as output:\n                output.write(str(self.bidir.get_weights()))\n            with open(\"bidir_last_weights.txt\", \"w\") as output:\n                output.write(str(self.bidir_last.get_weights()))\n            with open(\"dense_weights.txt\", \"w\") as output:\n                output.write(str(self.dense.get_weights()))\n            # print(f'x {x}')\n            # for i in range(len(y)):\n            #     for j in range(len(y[i])):\n            #         print(y[i][j], y_pred[i][j])\n            exit()\n\n        # Step with optimizer\n        self.optimizer.apply_gradients(zip(gradients, training_vars))\n        self.metric.update_state(y, y_pred)\n\n        return {\"loss\": loss, \"MSE\": self.metric.result()}\n\n    # def test_step(self, data):\n    #     x, y = data\n\n    #     encodings = encode_sentences(x, self.sen_enc)\n\n    #     # Compute predictions\n    #     y_pred = self.classifier(encodings, training=True)\n\n    #     # Updates the metrics tracking the loss\n    #     loss = self.loss(y, y_pred)\n\n    #     # Update the metrics.\n    #     self.metric.update_state(y, y_pred)\n    #     return {\"loss\": loss, \"MSE\": self.metric.result()}\n\n\nif __name__ == '__main__':\n    num_docs = 10000\n    num_epochs = 1\n    batch_size = 4\n    train_model_generator(batch_size, None, None, num_epochs, f'saved_models/model{num_docs}.h5', num_docs)\n    # generator = custom_generator(None, num_docs, 32)\n    # idx = 0\n    # while idx < 310:\n    #     idx += 1\n    #     x, y = next(generator)\n    #     # print(tf.shape(x), tf.equal(tf.shape(x), (32, 55 ,20)))\n    #     # print(tf.shape(y))\n    #     for doc in x:\n    #         for sen in doc:\n    #             if all(tf.shape(sen) != (20,)):\n    #                 print(f'AAAAA {tf.shape(sen)}')\n\n","sub_path":"nlp/rnn_model.py","file_name":"rnn_model.py","file_ext":"py","file_size_in_byte":11004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"595793999","text":"from flask import url_for\nfrom flask_table import Table, Col, LinkCol\nfrom flask_table.html import element\n\n\nclass BaseTable(Table):\n    classes = [\"table\"]\n    thead_classes = [\"thead-light\"]\n    allow_empty = True\n\n    def __html__(self):\n        tbody = self.tbody()\n        if tbody or self.allow_empty:\n            content = '\\n{thead}\\n{tbody}\\n{tfoot}\\n'.format(\n                thead=self.thead(),\n                tbody=tbody,\n                tfoot=self.tfoot(),\n            )\n            return element(\n                'table',\n                attrs=self.get_html_attrs(),\n                content=content,\n                escape_content=False\n            )\n        else:\n            return element('p', content=self.no_items)\n\n    def tfoot(self):\n        tr_content = (\n            ''.join(self.tfoot_td(i, col_key, col)\n            for i, (col_key, col) in enumerate(self._cols.items())\n            if col.show)\n        )\n        content = element('tr', content=tr_content, escape_content=False)\n        return element('tfoot', content=content, escape_content=False)\n\n    def tfoot_td(self, i, key, col):\n        if key == \"id\":\n            return element(\"td\", content=\"#\")\n\n        if i == 1:\n            url = url_for(\".create_model\")\n            tag = f\"<a href='{url}'>Add new</a>\"\n            return element(\"td\", content=tag, escape_content=False)\n\n        return element(\"td\", content=\"\")\n\n\nclass TestTable(BaseTable):\n    id = Col(\"ID\")\n    name = Col(\"Name\")\n    something = Col(\"Something\")\n    created = Col(\"Created\")\n    edit = LinkCol(\"Edit\", f\".edit_model\", url_kwargs={\"id\": \"id\"})\n","sub_path":"app/tables.py","file_name":"tables.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"533219364","text":"from __future__ import absolute_import, division, print_function, with_statement\n\n\nimport inspect\nimport sys\n\nfrom turbo.util import import_object, camel_to_underscore\nfrom turbo.log import helper_log\n\n\nclass _HelperObjectDict(dict):\n\n    # def __setitem__(self, name, value):\n    #     return super(_HelperObjectDict, self).setdefault(name, value)\n\n    def __getattr__(self, name):\n        try:\n            return self[name]\n        except KeyError:\n            raise\n\n\ndef install_helper(installing_helper_list, package_space):\n    \"\"\" import {m : _HelperObjectDict() } in 'helpers' namespace.\n\n    :param installing_helper_list:\n    :param package_space:\n    :return:\n    \"\"\"\n    for item in installing_helper_list:\n        # db model package\n        package = import_object('.'.join(['helpers', item]), package_space)\n        package_space[item] = _HelperObjectDict()\n        # all py files  included by package\n        all_modules = getattr(package, '__all__', [])\n        for m in all_modules:\n            try:\n                module = import_object('.'.join(['helpers', item, m]), package_space)\n            except Exception as e:\n                helper_log.error('module helpers.%s.%s Import Error'%(item, m), exc_info=True)\n                sys.exit(0)\n\n            for cls_name in getattr(module, 'MODEL_SLOTS', []):\n                cls = getattr(module, cls_name, None)\n                if cls:\n                    camel_name = cls.__name__\n                    underscore_name = camel_to_underscore(camel_name)\n\n                    package_space[item][camel_name] = cls\n                    package_space[item][underscore_name] = cls()\n","sub_path":"turbo/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"289877186","text":"import BackTrade as back\nimport PracTrade as prac\nimport LiveTrade as live\nimport os\n\nif __name__ == '__main__':\n\n\n\n\n    os.system(\"clear\")\n    choice = input(\"Live Trading(l), Practice Trading(p), or Backtesting(b): \")\n    control = input(\"Full Control(f), Assisted(a), or No Control(n): \")\n    gui = input(\"GUI? y or n: \")\n    os.system(\"clear\")\n    if choice == \"l\":\n        lt = live.LiveTrade(control, gui)\n    if choice == \"p\":\n        pt = prac.PracTrade(control, gui)\n    if choice == \"b\":\n        bt = back.BackTrade(control, gui)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"235664237","text":"import os\nfrom subprocess import call\n\nimport click\n\n\nHERE = os.path.abspath(os.path.dirname(__file__))\nPROJECT_ROOT = os.path.join(HERE, os.pardir)\nTEST_PATH = os.path.join(PROJECT_ROOT, 'tests')\n\n\n@click.command()\ndef test():\n    'Runs the tests.'\n    import pytest\n    rv = pytest.main([TEST_PATH, '--verbose'])\n    exit(rv)\n\n\n@click.command()\ndef clean():\n    'Removes *.pyc and *.pyo files recursively starting at current directory.'\n    for dirpath, _, filenames in os.walk('.'):\n        for filename in filenames:\n            if not (\n                filename.endswith('.pyc')\n                or filename.endswith('.pyo')\n            ):\n                continue\n            full_pathname = os.path.join(dirpath, filename)\n            click.echo(f'removing {full_pathname}')\n            os.remove(full_pathname)\n","sub_path":"betanin/commands.py","file_name":"commands.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"215144867","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Write by yskang(kys061@gmail.com)\n\nimport subprocess\nfrom logging.handlers import RotatingFileHandler\nimport logging\nimport sys\nimport re\nimport csv, json\nimport os\nfrom pprint import pprint \nfrom time import sleep, time\nimport pdb\n\n\nLOG_FILENAME=r'/var/log/lcd.log'\nlogger = None\nezio_360_api = '/opt/stm/lcd_drv/bin/ezio_360_api'\n\ndef make_logger():\n    global logger\n    try:\n        logger = logging.getLogger('lcd')\n        fh = RotatingFileHandler(LOG_FILENAME, 'a', 50 * 1024 * 1024, 4)\n        logger.setLevel(logging.INFO)\n        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n        fh.setFormatter(formatter)\n        logger.addHandler(fh)\n    except Exception as e:\n        print('cannot make logger, please check system, {}'.format(e))\n        sys.exit()\n    else:\n        logger.info(\"***** logger starting %s *****\" % (sys.argv[0]))\n\n\nclass Timeout(Exception):\n    def __str__(self):\n        return \"subprocess was timeout, plz check shell command..\"\n\n\ndef subprocess_open(command, timeout):\n    try:\n        p_open = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n    except Exception as e:\n        logger.error(\"subprocess_open() cannot be executed, {}\".format(e))\n        pass\n    else:\n        for t in xrange(timeout):\n            sleep(1)\n            if p_open.poll() is not None:\n                (stdout_data, stderr_data) = p_open.communicate()\n                return stdout_data, stderr_data\n            try:\n                if t == timeout-1:\n                    p_open.kill()\n                    raise Timeout\n            except Timeout:\n                logger.error(\"timout while running subprocess()\")\n\nmake_logger()\n\ndef main():\n    while True:\n        # print(1)\n        cmd = ezio_360_api+\" -d /dev/ttyS1 -s 1 SAISEI  \"\n        print(cmd)\n        p, _ = subprocess_open(cmd, 10)\n\n        sleep(5)\n\n        cmd = ezio_360_api+\" -d /dev/ttyS1 -s 2 FLOWCOMMAND  \"\n        print(cmd)\n        p, _ = subprocess_open(cmd, 10)\n\n        sleep(5)\n\n        cmd = r\"sensors |grep fan2 |egrep '[0-9]{4}' -o\"\n        print(cmd)\n        fan2, _ = subprocess_open(cmd, 10)\n        print(fan2)\n        cmd = ezio_360_api+\" -d /dev/ttyS1 -s 2 fan2:{}  \".format(fan2)\n        print(cmd)\n        p, _ = subprocess_open(cmd, 10)\n\n        sleep(5)\n\n\nif __name__ == \"__main__\":\n    try:        \n        main()\n    except KeyboardInterrupt:\n        logger.info(\"The script is terminated by interrupt!\")\n        # logger.info(\"\\r\\nThe script is terminated by user interrupt!\")\n        logger.info(\"Bye!!\")\n        sys.exit()\n","sub_path":"lcd.py","file_name":"lcd.py","file_ext":"py","file_size_in_byte":2655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"449659192","text":"# Import packageds/modules\nimport argparse\n\n# This function will return all command line arguemnts as an object\ndef get_args():\n\n\t# Initalize an argparser object. Documentation on the argparser module is here:\n\t# https://docs.python.org/3/library/argparse.html\n\targparser = argparse.ArgumentParser(prog='Submit', usage='%(prog)s [options]')\n\n\t# adding argument nargs='+' would make this option an array\n\targparser.add_argument('scardFile', metavar='scard', type=str,  help='Steering card text file')\n\n\t# Add ability for user to specify that they want to use SQLite, instead of MySQL database\n\t# Also, lets user specify the name and path of the SQLite DB file to use\n\targparser.add_argument('--sqlite', help=\"use SQLITE file as DB\", type=str, default=None)\n  \n\t# Boolean arguement of using TEST MySQL DB or the main MySQL DB\n\targparser.add_argument('--test', action='store_true', help='Use table CLAS12TEST instead of default CLAS12OCR', default=False)\n  \n\t# Convert the arguement strings into attributes of the namespace\n\targs = argparser.parse_args()\n\n\treturn args\n","sub_path":"client/options.py","file_name":"options.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"123204422","text":"import os\nimport B3SD_utils as B3SD\nfrom timeit import default_timer as timer\n\n#   Take each 4 second clip and turn it into frames \n#   Save it in /frames/class_statId/\n\ndef make_frames_B3SD():\n\n    start = timer()\n\n    print(\"makeing frames\")\n    # top level entries when loading and saving videos\n    base_in = B3SD.base_path + B3SD.videos_path_clips\n    base_out = B3SD.base_path + B3SD.frames_path\n\n    # retrieve all stat ids, organized in lists per class\n    layup, freethrow, point_2, point_3, no_shot, class_occurrences, urls, all_shot_times = B3SD.organize_shots()\n\n    # urls contain a list of urls.\n    # Each url in urls has a 1-1 correspondance with all_stats_organized\n    all_stats_organized = layup + freethrow + point_2 + point_3 + no_shot\n\n    video_format = \".mp4\"\n\n    # find filename_out_path\n    filename_out_full_paths = []\n    for i in range(0, len(all_stats_organized)):\n        print(\"current stat being processed in make_frames: \" + all_stats_organized[i])\n        frame_base_name = all_stats_organized[i] + video_format\n        try:\n            # may fail soft and continue if the input path is incorrect\n            B3SD.video_to_images(base_in, frame_base_name, base_out)\n        except Exception:\n            # hard failure\n            print(\"failed to make frames for: \" + base_in + frame_base_name +\n                  \" Output path: \" + base_out + \" might be incorrect\")\n            pass\n\n    end = timer()\n    print(\"make_frames.py: Time taken:  \", (end - start)/60, \" minutes\")\n\n    return 0","sub_path":"create_b3sd/make_frames.py","file_name":"make_frames.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"257419690","text":"import os\n\nnumber_of_tests = 41\nexec_file = './lab2'\npath_to_tests = 'Tests/tests/test_'\npath_to_answers = 'Tests/answers/answers_'\npath_to_correct_answers = 'Tests/correct_answers/answers_'\nexp = '.txt'\n\nincorrect_ans = []\n\nfor i in range(number_of_tests):\n    num = i + 1\n    os.system(\n        f'{exec_file} {path_to_tests}{num}{exp} > {path_to_answers}{num}{exp}')\n\n    with open(f'{path_to_correct_answers}{num}{exp}') as corr_ans, \\\n            open(f'{path_to_answers}{num}{exp}') as ans, \\\n            open(f'{path_to_tests}{num}{exp}') as tests:\n        ans_1 = corr_ans.readline().rstrip('\\n')\n        ans_2 = ans.readline().rstrip('\\n')\n        str_test = tests.readline()\n\n    print(f'test_{num}:\\n  string: {str_test}\\n  correct answer: {ans_1}\\n  program answer: {ans_2}\\n  result: ', end='')\n    if ans_1 == ans_2:\n        print(f'correct')\n    else:\n        incorrect_ans.append(num)\n        print(f'incorrect')\n\nif len(incorrect_ans):\n    print(\"\\nIncorrect tests:\", \", \".join(map(str, incorrect_ans)))\nelse:\n    print(\"\\nAll is OK\")\n","sub_path":"Arutyunyan/lab2/testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"484851348","text":"#! /usr/bin/python\n\nimport sys\nimport os\nimport gzip\n\ngdc_gencode_gtf_fn = sys.argv[1]\n\nensid_symbol_outfn = gdc_gencode_gtf_fn.split('.gtf.gz')[0] + '.ensid2symbol.tsv'\noutfn = open(ensid_symbol_outfn, 'w')\noutfn.write('ensembl_id_full\\tensembl_id_short\\tgene_symbol\\n')\nwith gzip.open(gdc_gencode_gtf_fn, 'rb') as f:\n    for line in f:\n        if line.find('##') == 0:\n            continue\n        line = line.strip()\n        linesplit = line.split('\\t')\n        feature_type = linesplit[2]\n        if feature_type != 'gene':\n            continue\n        metadata = linesplit[8].split('; ')\n        gene_values = []\n        for item in metadata:\n            k, v = item.split()\n            if k == \"gene_id\":\n                v = v.lstrip('\"').rstrip('\"')\n                gene_values.append(v)\n                gene_values.append(v.split('.')[0])\n            elif k == \"gene_name\":\n                v = v.lstrip('\"').rstrip('\"')\n                gene_values.append(v)\n                break\n        outfn.write('\\t'.join(gene_values) + '\\n')\noutfn.close()\n    \n","sub_path":"scripts/map_ensid2symbol.py","file_name":"map_ensid2symbol.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"538302481","text":"# -*- coding:utf-8 -*-\n\"\"\"\npandas DataFrame 转换为word表格\n@Time: 2021/9/16 14:44\n@Author: zhangtaoning\n@Email: \n@File: word.py\n@Software: PyCharm\n\n- ps: 这里的word模板一定要用特定自定义的模板，否则可能会出现样式找不到的问题\n      如果需要用自己的模板，就需要更改类 `Style` 中的配置\n\"\"\"\nimport os\nimport docx\nfrom docx import Document\nfrom docx.shared import Cm\nfrom win32com.client.gencache import EnsureDispatch\nfrom win32com.client import constants\n\n\n# 各种样式名\nclass Style:\n    body_text = 'Normal'  # 正文文本格式\n    table_text = 'No Spacing'  # 表格文本格式\n    picture_caption = '图片题注'\n    picture = 'No Spacing'\n    table_caption = '表格题注'\n    table_style = '三线表'  # 表格样式\n\n\nclass Word:\n    \"\"\"\n    word文档对象，在这个对象里进行插入图片、文字、表格的操作\n\n    Parameters\n    ----------\n    template_path : str, default=None\n        模板文档路径，指定后word文档的各种样式都和模板文档一致\n\n    save_path : str, default='tmp.docx'\n        word文档保存路径\n\n    Attributes\n    ----------\n    doc : docx.document.Document\n        文档对象，主要是对其进行操作\n    \"\"\"\n    def __init__(self, template_path=None, save_path='tmp.docx'):\n        self.template_path = template_path\n        self.save_path = save_path\n\n        self.doc = Document(template_path)  # 文档对象\n\n    def save(self):\n        \"\"\" 保存word文档 \"\"\"\n        self.doc.save(self.save_path)\n\n    def load(self, reload=False):\n        \"\"\"\n        加载文档\n        Parameters\n        ----------\n        reload : bool, default=False\n            是否重新加载模板文档，将会丢弃之前添加的内容\n        \"\"\"\n        if reload:\n            self.doc = Document(self.template_path)\n        else:\n            self.doc = Document(self.save_path)\n\n    def add_paragraph(self, text, style=Style.body_text):\n        \"\"\" 增加一段话，默认样式为正文\n\n        Parameters\n        ----------\n        text : str\n            添加的文本内容\n\n        style : str, default=Style.body_text\n            文本样式，默认为正文\n        \"\"\"\n        p = self.doc.add_paragraph(text, style=style)\n        return p\n\n    def add_picture(self, img_path, title=None, max_width=Cm(14.64)):\n        \"\"\"\n        添加图片\n        Parameters\n        ----------\n        img_path : str\n            图片路径\n\n        title : str, default=None\n            图片题注\n\n        max_width : int\n            图片最大宽度，使用docx.shared.Cm() 将厘米转为像素点\n\n        \"\"\"\n        pic = self.doc.add_picture(img_path)\n\n        # 自动缩放\n        if pic.width > max_width:\n            ratio = max_width / pic.width  # 缩放比例\n            pic.width = int(pic.width * ratio)\n            pic.height = int(pic.height * ratio)\n\n        # 居中刚刚插入的图片\n        last_paragraph = self.doc.paragraphs[-1]\n        last_paragraph.alignment = docx.enum.text.WD_ALIGN_PARAGRAPH.CENTER\n        last_paragraph.style = Style.picture\n        # 插入题注\n        if title:\n            self.doc.add_paragraph(title, style=Style.picture_caption)\n\n    def add_table(self, df, title=None):\n        \"\"\"\n\n        Parameters\n        ----------\n        df : DataFrame\n            表格数据\n        title : str, default=None\n            表格题注\n\n        Returns\n        -------\n\n        \"\"\"\n        # 设置表格题注\n        if title:\n            self.doc.add_paragraph(title, style=Style.table_caption)\n\n        table = self.doc.add_table(rows=1, cols=df.shape[1])\n        # 设置表头\n        for header_cell, header in zip(table.rows[0].cells, df.columns):\n            header_cell.text = str(header)\n            header_cell.paragraphs[0].style = Style.table_text\n\n        # 给每个单元格都填充上数据\n        for _, row in df.iterrows():\n            row_cells = table.add_row().cells\n            for i, (_, value) in enumerate(row.iteritems()):\n                if type(value) is float:\n                    row_cells[i].text = \"%.4f\" % value\n                else:\n                    row_cells[i].text = str(value)\n                row_cells[i].paragraphs[0].style = Style.table_text\n\n        # 设置表格样式\n        table.style = Style.table_style\n\n    def add_heading(self, text, level=0):\n        self.doc.add_heading(text, level=level)\n\n    def add_caption(self):\n        def selection_add_caption(s, style, caption_label):\n            s.Find.Style = doc.Styles(style)\n            while s.Find.Execute(\n                    FindText=\"\",\n                    Forward=True,\n                    Wrap=1,\n                    Format=True,\n                    MatchCase=False,\n                    MatchWholeWord=True,\n                    MatchAllWordForms=False,\n                    MatchSoundsLike=False,\n                    MatchWildcards=False, ):\n                s.MoveLeft()\n                s.InsertCaption(Label=caption_label)\n\n        app = EnsureDispatch('Word.Application')\n        doc = app.Documents.Open(os.path.abspath(self.save_path))\n\n        selection_add_caption(app.Selection, '表格题注', '表')\n        selection_add_caption(app.Selection, '图片题注', '图')\n\n        doc.Close(constants.wdSaveChanges)\n\n\n# 将dataframe格式的文件转换为word\ndef df_to_word(df, template_path='tmp', file_path='tmp.docx'):\n    document = Document(template_path)\n    table = document.add_table(rows=1, cols=df.shape[1])\n\n    # 设置表头\n    for header_cell, header in zip(table.rows[0].cells, df.columns):\n        header_cell.text = str(header)\n        header_cell.paragraphs[0].style = Style.table_text\n\n    # 给每个单元格都填充上数据\n    for _, row in df.iterrows():\n        row_cells = table.add_row().cells\n        for i, (_, value) in enumerate(row.iteritems()):\n            if type(value) is float:\n                row_cells[i].text = \"%.4f\" % value\n            else:\n                row_cells[i].text = str(value)\n            row_cells[i].paragraphs[0].style = Style.table_text\n\n    # 设置表格样式\n    table.style = Style.table_style\n    document.save(file_path)\n\n\ndef test_df_to_word():\n    import pandas as pd\n    import config\n    import os\n    df = pd.DataFrame([[1, 2, 3, 4],\n                       [2, 3, 4, 5],\n                       [3, 4, 5, 6]], columns=['apple', 'pear', 'banana', 'orange'])\n\n    df_to_word(df, template_path=config.word_template, file_path='tmp.docx')\n    assert os.path.isfile('tmp.docx')\n    os.remove('tmp.docx')\n\n\ndef test_word():\n    import config\n    word = Word(template_path=config.word_template)\n\n\nif __name__ == '__main__':\n    # test_df_to_word()\n    pass\n","sub_path":"python/python办公自动化/office相关库/word自动化工具/word.py","file_name":"word.py","file_ext":"py","file_size_in_byte":6743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"112230797","text":"from datetime import datetime\n\nfrom dateutil.relativedelta import relativedelta\nfrom django.conf import settings\nfrom django.contrib import messages\nfrom django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin\nfrom django.core.urlresolvers import reverse, reverse_lazy\nfrom django.http.response import HttpResponseRedirect\nfrom django.views.generic import DetailView, FormView, ListView, TemplateView\nfrom rolepermissions.checkers import has_role\n\nfrom core.mixins import AppMixin\n\nfrom .forms import OrderForm\nfrom .models import AssessmentPackage, AssessmentPurchase, Order\n\n\nclass SubscriptionMixin(LoginRequiredMixin, AppMixin):\n    sidebar_section = 'settings'\n    sidebar_item = 'subscription'\n    page_title = 'Subscription & billing'\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        packages = AssessmentPackage.objects.filter(\n            is_active=True\n        ).order_by('number_included')\n        context.update(\n            packages=packages,\n            subscription_defaults=settings.SITE_SUBSCRIPTION,\n            subscription_default_email=settings.DEFAULT_SUBSCRIPTION_FROM_EMAIL,\n        )\n        return context\n\n\nclass SubscriptionListView(SubscriptionMixin, UserPassesTestMixin, TemplateView):\n    template_name = 'subscription.html'\n\n    def test_func(self):\n        return has_role(self.request.user, ['admin'])\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        organisation = self.request.user.organisation\n        pending_assessment_purchase = AssessmentPurchase.objects.get_pending_approval(\n            organisation\n        )\n        context.update(\n            pending_assessment_purchase=pending_assessment_purchase,\n            latest_subscription=organisation.latest_subscription,\n            show_order_history=Order.objects.filter(organisation=organisation).exists()\n        )\n        return context\n\n\nclass OrderView(SubscriptionMixin, UserPassesTestMixin, FormView):\n    template_name = 'order.html'\n    form_class = OrderForm\n    success_url = reverse_lazy('subscription')\n    form_initial_data = None\n\n    def test_func(self):\n        return has_role(self.request.user, ['admin'])\n\n    def get_initial(self):\n        initial = super().get_initial()\n        try:\n            package_id = int(self.request.GET.get('id', 0))\n        except ValueError:\n            package_id = 0\n        organisation = self.request.user.organisation\n        current_subscription = organisation.latest_subscription\n        package = AssessmentPackage.objects.filter(id=package_id).first()\n        initial['subscription'] = current_subscription is None or (\n            current_subscription.get_is_renewal_due()\n        )\n        initial['package'] = package\n        self.form_initial_data = initial\n        return initial\n\n    def get(self, request, *args, **kwargs):\n        context = self.get_context_data(**kwargs)\n        current_subscription = request.user.organisation.latest_subscription\n        if current_subscription and (\n            not current_subscription.order.get_is_approved()\n        ):\n            messages.warning(self.request, (\n                '''\n                You have an order in progress, please complete the order\n                by paying the invoice. Please contact us if you have any\n                questions or you need to make any amends to the order\n                '''\n            ), extra_tags='show-icon')\n            return HttpResponseRedirect(reverse('subscription'))\n        return self.render_to_response(context)\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        current_subscription = self.request.user.organisation.latest_subscription\n        total_amount = 0.0\n        if self.form_initial_data['subscription']:\n            total_amount += context['subscription_defaults']['price']\n        if self.form_initial_data['package']:\n            total_amount += float(self.form_initial_data['package'].price)\n        has_active_subscription = current_subscription and (\n            not current_subscription.get_is_expired()\n        )\n        context.update(\n            valid_until=datetime.now() + relativedelta(years=1),\n            has_active_subscription=has_active_subscription,\n            total_amount=total_amount\n        )\n        return context\n\n    def form_invalid(self, form, **kwargs):\n        messages.warning(\n            self.request, 'Order failed - {}'.format(form.error_message),\n            extra_tags='show-icon'\n        )\n        return super().form_invalid(form)\n\n    def form_valid(self, form, **kwargs):\n        response = super().form_valid(form)\n        instance = form.save()\n        instance.send_order_confirmation()\n        messages.success(\n            self.request, 'You have successfully submitted the order',\n            extra_tags='show-icon'\n        )\n        return response\n\n    def get_form_kwargs(self):\n        kwargs = super().get_form_kwargs()\n        kwargs.update({'organisation': self.request.user.organisation})\n        return kwargs\n\n\nclass OrderHistoryView(SubscriptionMixin, UserPassesTestMixin, ListView):\n    template_name = 'order_history.html'\n    paginate_by = 20\n    page_limit = 5\n    context_object_name = \"orders\"\n\n    def test_func(self):\n        return has_role(self.request.user, ['admin'])\n\n    def get_queryset(self):\n        organisation = self.request.user.organisation\n        return Order.objects.filter(\n            organisation=organisation\n        )\n\n\nclass OrderDetailsView(SubscriptionMixin, UserPassesTestMixin, DetailView):\n    template_name = 'order_details.html'\n    model = Order\n\n    def test_func(self):\n        return has_role(self.request.user, ['admin'])\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        order = context['object']\n        order_items, order_total = order.get_order_details()\n        context.update(\n            order_total=order_total\n        )\n        return context\n","sub_path":"testAutomation/subscriptions/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"147247548","text":"import smtplib, ssl\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\n\ndef sendmail(subject,sender_email, reveiver_email, body):\n    message = MIMEMultipart(\"alternative\")\n    message[\"Subject\"] = subject\n    message[\"From\"] = sender_email\n    message[\"To\"] = reveiver_email\n    try:\n        text = f\"\"\"{body}\"\"\"\n        part1 = MIMEText(text, \"plain\")\n        message.attach(part1)\n        context = ssl.create_default_context()\n        with smtplib.SMTP_SSL(\"smtp.gmail.com\", 465, context=context) as server:\n            server.login(sender_email, cfg.SENDER_MAIL_PASSWORD)\n            server.sendmail(\n                cfg.SENDER_MAIL_ID, reveiver_email, message.as_string()\n            )\n        print(\"Mail Send..\")\n    except:\n        print(\"Error..\")\n        \n        \n","sub_path":".ipynb_checkpoints/mailtrack-checkpoint.py","file_name":"mailtrack-checkpoint.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"343070356","text":"# http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=ITP2_4_B&lang=jp\n# Rotate\n\nfrom collections import deque\nimport sys\ninput = sys.stdin.readline\n\ndef main():\n    n1 = int(input())\n    l1 = list(map(int,input().split()))\n    l2 = []\n    for i in range(int(input())):\n        (b,m,e) = map(int,input().split())   \n        for k in range(b):\n            l2.append(l1[k])\n        for k in range(m,e):\n            l2.append (l1[k]) \n        for k in range(b,m):\n            l2.append(l1[k])\n        for k in range(e,len(l1)):\n            l2.append(l1[k])\n        l1 = l2\n        l2 = []\n    print (\" \".join(map(str,l1)))\n\n\nif __name__ == '__main__':\n    main()","sub_path":"ITP2/ITP2_4_B.py","file_name":"ITP2_4_B.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"584191496","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.utils.timezone\nimport Slides.models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Slide',\n            fields=[\n                ('id', models.AutoField(primary_key=True, verbose_name='ID', auto_created=True, serialize=False)),\n                ('title', models.CharField(max_length=120)),\n                ('image', models.ImageField(upload_to=Slides.models.Slide.upload_location)),\n                ('text_content', models.TextField(null=True, blank=True)),\n                ('tags', models.CharField(max_length=240, null=True, blank=True)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='SlideStack',\n            fields=[\n                ('id', models.AutoField(primary_key=True, verbose_name='ID', auto_created=True, serialize=False)),\n                ('title', models.CharField(max_length=120)),\n                ('slug', models.SlugField(unique=True, blank=True)),\n                ('pub_date', models.DateTimeField(default=django.utils.timezone.now)),\n                ('tags', models.CharField(max_length=240, null=True, blank=True)),\n                ('categories', models.TextField(max_length=500, null=True, blank=True)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n    ]\n","sub_path":"Slides/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"43771699","text":"#%% [markdown]\n## Example on how to use fitted objects\nfrom sklearn.externals import joblib\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\nfrom math import sqrt \nimport os\nimport pandas as pd\nimport numpy as np\n\n#%% [markdown]\n### Loading fitted models\n\n#%%\nalljoblibs = os.listdir('models')\nprint(alljoblibs)\n\n#%%\nsubset = {'datacap':{}, 'timecap':{},'fulldata':{}}\n\nprint('loading files double loop:') \nfor sub in subset:\n    joblibs = [file for file in alljoblibs if sub in file]\n\n    for quantile in ['upper', 'median', 'lower']:\n        path = [f'models/{x}' for x in joblibs if quantile in x][0]\n        loaded = joblib.load(path)\n        subset[sub].update({quantile: loaded})\n\n        print(f'Loaded {sub}-{quantile} --- RMSE: {loaded[\"RMSE\"]:.4f}')\n\nprint(subset.keys())\nprint(subset['datacap'].keys())\n\n\n\n#%% [markdown]\n### Let's say these are the rows that we want to apply model\ndummy_data = subset['datacap']['median']['X_sample']\ndummy_data = dummy_data.head(10)\n\ndummy_data.index = np.arange(0, dummy_data.shape[0])  # resetting index from 0:nrows\ndummy_data\n\n#%%\npredictions = {}\nfor quantile, fit_objs in subset['datacap'].items():\n    model = fit_objs['model']\n\n    predictions.update({f'y_predicted_{quantile}': model.predict(dummy_data)})\n\npredictions = pd.DataFrame(predictions)\npredictions\n\n#%%\n# Combining model predictions and data\npredicted_df = pd.concat([predictions, dummy_data], axis=1)\npredicted_df\n\n#%% [markdown]\n### Inverse label transformation\n# The dummy data I used above has already been label transformed \n# because they are the actual traning data. \n# To return the actual categories, we need to use the label encoder saved\nlab_encoder = subset['datacap']['median']['label_encoders']\n\nfor col, le in lab_encoder.items():\n    inv_transformer = le.inverse_transform\n    predicted_df[col] = predicted_df[col].transform(inv_transformer)\n\npredicted_df\n\n\n#%%\n#%% [markdown]\n### label transformation\n# I'll need to write functions for this, but for now, the following should work\n\n#%%\n# new_df     # insert your own here\n# new_df_encoded = new_df.copy()   # otherwise dataframes are modified by reference\n# lab_encoder = subset['datacap']['median']['label_encoders']\n# \n# for col, le in lab_encoder.items():\n    # transformer = le.inverse_transform\n    # new_df_encoded[col] = new_df_encoded[col].transform(transformer)","sub_path":"python/scripts/3_load_fitted_model.py","file_name":"3_load_fitted_model.py","file_ext":"py","file_size_in_byte":2372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"308769731","text":"#!/usr/bin/env python3\n\nimport telebot\nfrom optparse import OptionParser\n\n\ndef message_for_call():\n    msg = f'<i>Внимание!</i>\\n\\n' \\\n          f'<b>{options.name}</b>\\n' \\\n          f'<i>Заказал обратный звонок</i>\\n\\n' \\\n          f'<i>Его номер телефона:</i> <b>{options.phone}</b>'\n    return msg\n\n\nparser = OptionParser()\nparser.add_option('-n', '--name', dest='name',\n                  help='Name', metavar='NAME')\nparser.add_option('-p', '--phone', dest='phone',\n                  help='phone number', metavar='PHONE')\n\n(options, args) = parser.parse_args()\nprint(options.name)\nprint(options.phone)\n\nwith open('bot_engine/api.txt', 'r') as apifile:\n    bot = telebot.TeleBot(apifile.read())\n\nwith open('bot_engine/chat_id.txt', 'r') as chat_id:\n    if options.name and options.phone:\n        bot.send_message(chat_id=954359151, text=message_for_call(), parse_mode='html')\n    else:\n        print('Use options! Type --help to learn')\n","sub_path":"bot_engine/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"33620078","text":"_E_ix = 0\n_E_jx = 0\n_E_img = None\n\n\ndef get_lb_key(img):\n    global _E_ix, _E_jx, _E_img\n    print (\"> ***** get_lb_key...\")\n    _E_img = img\n    _num_keys = cal_keys()\n    _block_size = cal_keys()\n    # _num_keys = reverse_num(_num_keys)\n    _keys = []\n    for _i in range(0, _num_keys):\n        # _keys.append(reverse_num(cal_keys()))\n        _keys.append(cal_keys())\n    print(\"NK - SB - [Keys] = \", _num_keys, _block_size, _keys)\n    print (\"> ##### get_lb_key...\")\n    return _block_size, _keys\n\n\ndef cal_keys():\n    global _E_ix, _E_jx, _E_img\n    _num, _flag = read_four_pixel()\n    _ns = str(_num)\n    while True:\n        _n, _flag = read_four_pixel()\n        if _n == 15:\n            break\n        _ns += str(_n)\n    return reverse_num(_ns)\n\n\ndef read_four_pixel():\n    global _E_jx, _E_ix, _E_img\n    _zo = \"\"\n    while True:\n        if _E_jx >= _E_img.shape[1]:\n            _E_jx = 0\n            _E_ix += 1\n        if _E_ix >= _E_img.shape[0]:\n            return None, True\n\n        _zo += get_two_lsb(_E_img[_E_ix][_E_jx])\n        _E_jx += 1\n        if _zo.__len__() == 4:\n            break\n\n    return int(_zo, 2), False\n\n\ndef get_two_lsb(pixel):\n    _bp = bin(pixel)\n    return _bp[_bp.__len__() - 2]\n\n\ndef reverse_num(num):\n    _rn = \"\"\n    for _i in range(0, num.__len__()):\n        _rn += num[num.__len__() - 1 - _i]\n    return int(_rn)\n","sub_path":"Decryption.py","file_name":"Decryption.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"409111428","text":"# where you click will appears a red circle,\n# except if where you click exists one or more circles, the color of the circles will change into green\n\nimport simplegui\nimport math\n\nWIDTH = 400\nHEIGHT = 300\nball_list = []\nball_radius = 15\n\ndef distance(p, q):\n    return math.sqrt((p[0] - q[0]) ** 2 + (p[1] - q[1]) ** 2)\n\n# Handler for mouse click\ndef click(pos):\n    changed = False\n    for ball in ball_list:\n        if distance([ball[0],ball[1]], pos) < ball_radius:\n            changed = True\n            ball[2] = \"Green\"\n    if not changed:\n        ball_list.append([pos[0], pos[1],\"Red\"])\n\n# Handler to draw on canvas\ndef draw(canvas):\n    for ball in ball_list:\n        canvas.draw_circle([ball[0],ball[1]], ball_radius, 1, \"Black\", ball[2])\n\n# Create a frame and assign callbacks to event handlers\nframe = simplegui.create_frame(\"Home\", WIDTH, HEIGHT)\nframe.set_canvas_background(\"White\")\n\nframe.set_mouseclick_handler(click)\nframe.set_draw_handler(draw)\n\n# Start the frame animation\nframe.start()\n","sub_path":"An Introduction to Interactive Programming in Python/ex2.py","file_name":"ex2.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"44964395","text":"#!/usr/bin/env python\n\n\"\"\"\nProblem #3, http://projecteuler.net/problem=3\n\nThe prime factors of 13195 are 5, 7, 13 and 29.\n\nWhat is the largest prime factor of the number 600851475143?\n\n\"\"\"\nimport math\nimport toolbox\n\nlargest = 0\nnumber = 600851475143\nsize = int(math.ceil(math.sqrt(number)))\n\nfor n in range(2, size):\n    if toolbox.is_prime(n):\n        if number % n == 0:\n            largest = n\n\nprint(\"Largest prime factor of {number} is {largest}\".format(**locals()))\n","sub_path":"lang-python/Problem0003.py","file_name":"Problem0003.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"639227249","text":"from bs4 import BeautifulSoup\nimport argparse\nimport svgwrite\nimport os\nimport random\nimport colorsys\n\ndef main(name, output):\n    name_to_files_map = get_name_to_files_map(name)\n    name_to_data_map = parse_name_to_files_map(name_to_files_map)\n    bounds = get_universal_bounds(name_to_data_map)\n\n    dwg = svgwrite.Drawing(output.name, profile='tiny')\n    colors = get_n_colors(len(name_to_files_map))\n    color_idx = 0\n\n    for name in name_to_data_map:\n        coords = points_to_coordinates(name_to_data_map[name][\"points\"], bounds)\n        dwg.add(dwg.polyline(coords, stroke=colors[color_idx], stroke_width=\"3\", stroke_opacity=\"0.6\", fill=\"none\"))\n        color_idx += 1\n\n    dwg.save()\n\ndef get_name_to_files_map(input_dir):\n    \"\"\"\n    Gets a map of the name of a given skier to a list of files in a directory with that name. This assumes\n    that the input directory provided to this tool has the following format:\n    dir -\n    | - Person 1\n      | - Day_1.gpx\n      | - Day_2.gpx\n    | - Person 2\n      | - Day_1.gpx\n      | - Day_2.gpx\n\n    \"\"\"\n\n    name_to_files_map = {}\n\n    for dir in os.listdir(input_dir):\n        dir_path = os.path.join(input_dir,dir)\n        name_to_files_map[dir] = map(lambda x: open(os.path.join(dir_path, x), 'r'), os.listdir(dir_path))\n\n    return name_to_files_map\n\ndef parse_name_to_files_map(name_to_files_map):\n    \"\"\"\n    Reads and transforms GPS points for each individual in the name_to_files_map, and figures out the bounds of their\n    entire trip\n    \"\"\"\n    name_to_data_map = {}\n    for input in name_to_files_map:\n        points = []\n        for file in name_to_files_map[input]:\n            points.extend(get_points_from_file(file))\n        bounds = get_bounds(points)\n        name_to_data_map[input] = {\"points\": points, \"bounds\": bounds}\n    return name_to_data_map\n\ndef get_points_from_file(file):\n    \"\"\"\n    Parses a GPX file, and returns an array of all GPS points described by that file\n    \"\"\"\n    parsed = BeautifulSoup(file.read(), 'html.parser')\n    segments = parsed.trk.findAll(\"trkseg\")\n    points = []\n\n    for segment in segments:\n        segment_points = segment.findAll(\"trkpt\")\n        points.extend(map(lambda x: transform_bs4_point(x), segment_points))\n\n    return points\n\ndef transform_bs4_point(bs4_point):\n    \"\"\"\n    Transforms a single BeautifulSoup node representing a node in the GPS coordinate graph\n    into a dictionary representing the elements we care about\n    \"\"\"\n    return {\n        \"lat\": float(bs4_point[\"lat\"]), # Degrees\n        \"lon\": float(bs4_point[\"lon\"]), # Degrees\n        \"ele\": float(bs4_point.ele.string), # Meters?\n        \"time\": bs4_point.time.string # ISO8601\n    }\n\ndef get_universal_bounds(name_to_data_map):\n    \"\"\"\n    Gets the bounds of all recorded segments\n    \"\"\"\n    min_lat = float(\"inf\")\n    max_lat = float(\"-inf\")\n    min_lon = float(\"inf\")\n    max_lon = float(\"-inf\")\n\n    for name in name_to_data_map:\n        item = name_to_data_map[name]\n\n        min_lat = item[\"bounds\"][\"min_lat\"] if item[\"bounds\"][\"min_lat\"] < min_lat else min_lat\n        min_lon = item[\"bounds\"][\"min_lon\"] if item[\"bounds\"][\"min_lon\"] < min_lon else min_lon\n        max_lat = item[\"bounds\"][\"max_lat\"] if item[\"bounds\"][\"max_lat\"] > max_lat else max_lat\n        max_lon = item[\"bounds\"][\"max_lon\"] if item[\"bounds\"][\"max_lon\"] > max_lon else max_lon\n\n    return {\n        \"min_lat\": min_lat,\n        \"min_lon\": min_lon,\n        \"max_lat\": max_lat,\n        \"max_lon\": max_lon\n    }\n\ndef get_bounds(points):\n    \"\"\"\n    Returns the min and max lat and lon within an array of points\n    \"\"\"\n    min_lat = float(\"inf\")\n    max_lat = float(\"-inf\")\n    min_lon = float(\"inf\")\n    max_lon = float(\"-inf\")\n\n    for point in points:\n        min_lat = point[\"lat\"] if point[\"lat\"] < min_lat else min_lat\n        min_lon = point[\"lon\"] if point[\"lon\"] < min_lon else min_lon\n        max_lat = point[\"lat\"] if point[\"lat\"] > max_lat else max_lat\n        max_lon = point[\"lon\"] if point[\"lon\"] > max_lon else max_lon\n\n    return {\n        \"min_lat\": min_lat,\n        \"min_lon\": min_lon,\n        \"max_lat\": max_lat,\n        \"max_lon\": max_lon\n    }\n\ndef points_to_coordinates(points, bounds, desired_width=1000):\n    \"\"\"\n    Transforms GPS points into coordinates for the desired SVG file\n    \"\"\"\n    height = bounds[\"max_lat\"] - bounds[\"min_lat\"]\n    width = bounds[\"max_lon\"] - bounds[\"min_lon\"]\n\n    transform_coefficient = float(desired_width) / width # Make width 1000\n\n    return map(lambda x: point_to_coordinate(x, bounds, transform_coefficient), points)\n\ndef get_n_colors(n):\n    \"\"\"\n    Returns N contrasting RGB fill descriptors\n    \"\"\"\n    transform_constant = 1.0 / n\n\n    rgb_colors = []\n\n    for i in range(0, n):\n        hue = i * transform_constant\n        lightness = 0.5 + 0.1 * random.random()\n        saturation = 0.9 + 0.1 * random.random()\n        rgb = map(lambda x: x * 256.0, colorsys.hls_to_rgb(hue, lightness, saturation))\n        rgb_colors.append(svgwrite.rgb(rgb[0], rgb[1], rgb[2], '%'))\n\n    return rgb_colors\n\n\ndef point_to_coordinate(point, bounds, transform_coefficient):\n    \"\"\"\n    Transforms a single point into a coordinate in an SVG file\n    \"\"\"\n    return ((point[\"lon\"] - bounds[\"min_lon\"]) * transform_coefficient,\n            ((bounds[\"max_lat\"] - bounds[\"min_lat\"]) - (point[\"lat\"] - bounds[\"min_lat\"])) * transform_coefficient)\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description='Turns a gpx file into an art.')\n    parser.add_argument(\"-o\", \"--output\", type=argparse.FileType('w'), required=True, help=\"Directs the output to a name of your choice\")\n    parser.add_argument(\"-i\", \"--input\", required=True, help=\"Specifies the input directory\")\n\n    args = parser.parse_args()\n    main(args.input, args.output)","sub_path":"ski_art/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"566324304","text":"# this is only for portabilization.\n# to simplify the sentence for easy analyzation.\n\nimport nltk, json, pickle \nfrom nltk.corpus import stopwords \nfrom nltk.tokenize import word_tokenize, sent_tokenize \nstop_words = set(stopwords.words('english')) \nion=open(\"README\",\"r\")\n## Dummy text \ntxt=ion.read() \n# sent_tokenize is one of instances of  \n# PunktSentenceTokenizer from the nltk.tokenize.punkt module \n  \ntokenized = sent_tokenize(txt) \n\ntags=[]\n\nfor i in tokenized: \n      \n    # Word tokenizers is used to find the words  \n    # and punctuation in a string \n    wordsList = nltk.word_tokenize(i) \n  \n    # removing stop words from wordList \n#    wordsList = [w for w in wordsList if not w in stop_words]  \n    # you are such a fool.\n\n    # HOW THE FUCK CAN YOU FUCKING REMOVE THE FUCKING STOPWORDS?\n\n    #  Using a Tagger. Which is part-of-speech  \n    # tagger or POS-tagger.  \n    tagged = nltk.pos_tag(wordsList) \n    tags.append(tagged)  \n#    print(tagged) \n#print(tags)\n# ---  [ spliter ]  --- #\n\n#i=open(\"README\",\"r\")\nfulltext=list(filter((lambda x: x!=\"\"),txt.split(\"\\n\")))\nfor x in range(len(fulltext)):\n    fulltext[x]=len(list(filter((lambda x:x!=\"\"),[pos for pos in fulltext[x].split(\".\")])))\n#    this is crap\n    '''for y in range(len(fulltext[x])):\n        fulltext[x][y]=list(filter((lambda x:x!=\"\"),[pos for pos in fulltext[x][y].split(\",\")]))\n        for z in range(len(fulltext[x][y])):\n            fulltext[x][y][z]=list(filter((lambda x:x!=\"\"),[pos for pos in fulltext[x][y][z].split(\" \")]))'''\n\n#print(fulltext)\n# the length should be the same.\n\n#print(len(tags),len(fulltext))\n\nmasochist=[]\nfor k in tags:\n    sadist=list(filter((lambda x:x[0]!=\".\" and x[0]!=\"?\"),k))\n    mark=list(reversed(sorted([pos for pos in range(len(sadist)) if sadist[pos][0]==\",\"])))\n    previous=len(sadist)\n    temp=[]\n    mark.append(-1)\n    for m in mark:\n        # you can filter out the weird things.\n        temp.insert(0,sadist[m+1:previous])\n        previous=m\n    masochist.append(temp)\n#    for d in k:\n\nscavenger=[]\nfor lesbian in range(len(fulltext)):\n    scavenger.append([])\n# lists are the same here.\n# do not use the fucking method again.\n#print(scavenger)\nconsteller=0\n\nfor x0 in range(len(scavenger)):\n    consteller+=fulltext[x0]\n#    print(\"x=\",x0)\n    for y in range(fulltext[x0]):\n#        print(\"y=\",y)\n        nodejs=consteller-fulltext[x0]+y\n#        print(\"nodejs=\",nodejs)\n        gay=masochist[nodejs]\n#        print(gay)\n        scavenger[x0].append(gay)\n#print(masochist)\n# i plan to make a map for the mistyping shit.\n\"\"\"\nfor guido in range(len(masochist)):\n    print(masochist[guido])\nprint(fulltext)\nprint(\"---[fulltext spliter]---\")\nprint(masochist)\nfor vessle in range(len(scavenger)):\n    print(\"---[scavenger spliter]---\")\n    print(scavenger[vessle])\n\"\"\"\nprint(scavenger)\n#this list contains paragraph - sentence - subsentence - word separation with POS data inside.\n# we need to store it into some sort of format for further analysis.\n\nion.close()\n\n# pickle format may contains malicious code.\nwith open('scavenger.pickle', 'wb') as filehandle:\n    # store the data as binary data stream\n    pickle.dump(scavenger, filehandle)\nwith open('scavenger.json','w')as filehandle:\n    # json format is not binary\n    json.dump(scavenger,filehandle)\n\n","sub_path":"multilingual/rockstar/coref/flee.py","file_name":"flee.py","file_ext":"py","file_size_in_byte":3288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"304106692","text":"#!/usr/bin/env python\n\nfrom fshandler import FsHandler\nfrom downloader import Downloader\nimport json\nimport argparse\n\nparser = argparse.ArgumentParser(description='Process some integers.')\nparser.add_argument('folder', help='Foldername in gDrive')\nargs = parser.parse_args()\n\n# read config\nwith open('config.json') as config_file:\n    config = json.load(config_file)\n\n# create file system handler\nfsh = FsHandler()\n\n# get temporary file\nbase_path = fsh.get_path()\n\n# get all files\nDownloader.downloadList(config['files'], base_path)\n\n# upload temp folder to gDrive\nfsh.upload(args.folder, config['drive']['parent_id'])\n\n# clean up the tmp\nif config['options']['removeTemp']:\n    fsh.remove()\n","sub_path":"loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"346761751","text":"#\n# Copyright 2017 Mycroft AI Inc.\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 unittest\nimport datetime\n\nfrom lingua_franca.format import nice_number\nfrom lingua_franca.format import nice_time\nfrom lingua_franca.format import pronounce_number\n\nNUMBERS_FIXTURE_ES = {\n1.435634: '1,436',\n2: '2',\n5.0: '5',\n0.027: '0,027',\n0.5: 'un mig',\n1.333: '1 i 1 terç',\n2.666: '2 i 2 terç',\n0.25: 'un quart',\n1.25: '1 i 1 quart',\n0.75: '3 quarts',\n1.75: '1 i 3 quarts',\n3.4: '3 i 2 cinquens',\n16.8333: '16 i 5 sisens',\n12.5714: '12 i 4 setens',\n9.625: '9 i 5 vuitens',\n6.777: '6 i 7 novens',\n3.1: '3 i 1 dècim',\n2.272: '2 i 3 onzens',\n5.583: '5 i 7 dotzens',\n0.071: 'un catorzè',\n6.466: '6 i 7 quinzens',\n8.312: '8 i 5 setzens',\n0.05: 'un vintè'\n\n}\n\n\nclass TestNiceNumberFormat_es(unittest.TestCase):\n    def test_convert_float_to_nice_number_es(self):\n        for number, number_str in NUMBERS_FIXTURE_ES.items():\n            self.assertEqual(nice_number(number, lang=\"ca-es\"), number_str,\n                             'should format {} as {} and not {}'.format(\n                                 number, number_str, nice_number(\n                                     number, lang=\"ca-es\")))\n\n    def test_specify_denominator_es(self):\n        self.assertEqual(nice_number(5.5, lang=\"ca-es\",\n                                     denominators=[1, 2, 3]),\n                         '5 y medio',\n                         'should format 5.5 as 5 y medio not {}'.format(\n                             nice_number(5.5, lang=\"ca-es\",\n                                         denominators=[1, 2, 3])))\n        self.assertEqual(nice_number(2.333, lang=\"ca-es\",\n                                     denominators=[1, 2]),\n                         '2,333',\n                         'should format 2.333 as 2,333 not {}'.format(\n                             nice_number(2.333, lang=\"ca-es\",\n                                         denominators=[1, 2])))\n\n    def test_no_speech_es(self):\n        self.assertEqual(nice_number(6.777, lang=\"ca-es\", speech=False),\n                         '6 7/9',\n                         'should format 6.777 as 6 7/9 not {}'.format(\n                             nice_number(6.777, lang=\"ca-es\", speech=False)))\n        self.assertEqual(nice_number(6.0, lang=\"ca-es\", speech=False),\n                         '6',\n                         'should format 6.0 as 6 not {}'.format(\n                             nice_number(6.0, lang=\"ca-es\", speech=False)))\n        self.assertEqual(nice_number(1234567890, lang=\"ca-es\", speech=False),\n                         '1 234 567 890',\n                         'should format 1234567890 as'\n                         '1 234 567 890 not {}'.format(\n                             nice_number(1234567890, lang=\"ca-es\",\n                                         speech=False)))\n        self.assertEqual(nice_number(12345.6789, lang=\"ca-es\", speech=False),\n                         '12 345,679',\n                         'should format 12345.6789 as'\n                         '12 345,679 not {}'.format(\n                             nice_number(12345.6789, lang=\"ca-es\",\n                                         speech=False)))\n\n\nclass TestPronounceNumber(unittest.TestCase):\n    def test_convert_int(self):\n        self.assertEqual(pronounce_number(0, lang=\"es\"), \"zero\")\n        self.assertEqual(pronounce_number(1, lang=\"es\"), \"un\")\n        self.assertEqual(pronounce_number(10, lang=\"es\"), \"deu\")\n        self.assertEqual(pronounce_number(15, lang=\"es\"), \"quinze\")\n\n    def test_convert_negative_int(self):\n        self.assertEqual(pronounce_number(-1, lang=\"es\"), \"menys un\")\n        self.assertEqual(pronounce_number(-10, lang=\"es\"), \"menys deu\")\n        self.assertEqual(pronounce_number(-15, lang=\"es\"), \"menos quince\")\n        self.assertEqual(pronounce_number(-99, lang=\"es\"),\n                         \"menys noranta nou\")\n\n    def test_convert_decimals(self):\n        self.assertEqual(pronounce_number(\n            0.05, lang=\"es\"), \"zero coma zero cinc\")\n        self.assertEqual(pronounce_number(\n            -0.05, lang=\"es\"), \"menys zero coma zero cinc\")\n        self.assertEqual(pronounce_number(1.234, lang=\"es\"),\n                         \"un coma dos tres\")\n\nclass TestNiceDateFormat(unittest.TestCase):\n    def test_convert_times(self):\n        dt = datetime.datetime(2017, 1, 31,\n                               13, 22, 3)\n\n        # Verify defaults haven't changed\n        self.assertEqual(nice_time(dt, lang=\"ca-es\"),\n                         nice_time(dt, \"ca-es\", True, False, False))\n    \nif __name__ == \"__main__\":\n    unittest.main()\n","sub_path":"test/test_format_ca.py","file_name":"test_format_ca.py","file_ext":"py","file_size_in_byte":5116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"372899940","text":"#!/usr/bin/env python3\n\"\"\"\nAuthor : qianwenluo\nDate   : 2019-04-25\nPurpose: Rock the Casbah\n\"\"\"\n\nimport argparse\nimport sys\nimport os\nimport logging\nimport re\nfrom tabulate import tabulate\nimport pandas as pd\n\n\n# --------------------------------------------------\ndef get_args():\n    \"\"\"get command-line arguments\"\"\"\n    parser = argparse.ArgumentParser(\n        description='Find common words',\n        formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n    parser.add_argument(\n        'positional', metavar='FILE', help='Input files', nargs=2)\n\n    parser.add_argument(\n        '-m',\n        '--min_len',\n        help='Minimum length of words',\n        metavar='int',\n        type=int,\n        default=0)\n\n    parser.add_argument(\n        '-n',\n        '--hamming_distance',\n        help='Allowed Hamming distance',\n        metavar='int',\n        type=int,\n        default=0)\n\n    parser.add_argument(\n        '-l',\n        '--logfile',\n        help='Logfile name',\n        metavar='str',\n        type=str,\n        default='.log')\n\n    parser.add_argument(\n        '-d', '--debug', help='Debug', action='store_true',default=False)\n\n    parser.add_argument(\n        '-t', '--table', help='Table output', action='store_true',default=False)\n\n    return parser.parse_args()\n\n\n# --------------------------------------------------\ndef warn(msg):\n    \"\"\"Print a message to STDERR\"\"\"\n    print(msg, file=sys.stderr)\n\n\n# --------------------------------------------------\ndef die(msg='Something bad happened'):\n    \"\"\"warn() and exit with error\"\"\"\n    warn(msg)\n    sys.exit(1)\n\n\n# --------------------------------------------------\ndef dist(s1, s2):\n    d = abs(len(s1) - len(s2)) + sum(\n        map(lambda p: 0 if p[0] == p[1] else 1, zip(s1, s2)))\n    logging.debug('s1 = {}, s2 = {}, d = {}'.format(s1, s2, d))\n\n    return d\n# --------------------------------------------------\n\ndef main():\n    \"\"\"Make a jazz noise here\"\"\"\n    args = get_args()\n    files = args.positional\n    min_len = args.min_len\n    h_dist = args.hamming_distance\n    logfile = args.logfile\n    debug = args.debug\n    table = args.table\n\n\n    logging.basicConfig(\n        filename=logfile,\n        filemode='w',\n        level=logging.DEBUG if debug else logging.CRITICAL\n    )\n\n    for file in files:\n        if not os.path.isfile(file):\n            die('\"{}\" is not a file'.format(file))\n\n    if h_dist < 0:\n        die('--distance \"{}\" must be > 0'.format(h_dist))\n\n    fh1 = open(files[0],'r')\n    fh2 = open(files[1],'r')\n    logging.debug('file1 = {}, file2 = {}'.format(files[0],files[1]))\n\n    content1 = []\n    content2 = []\n    for row1 in fh1:\n        for words1 in row1.split():\n            words1 = re.sub('[^a-zA-Z0-9]', '', words1).lower()\n            if len(words1) >= min_len:\n                content1.append(words1)\n\n    for row2 in fh2:\n        for words2 in row2.split():\n            words2 = re.sub('[^a-zA-Z0-9]', '', words2).lower()\n            if len(words2) >= min_len:\n                content2.append(words2)\n\n    count = 0\n    output = {}\n    output['word1'] = []\n    output['word2'] = []\n    output['distance'] = []\n    for i in content1:\n        for j in content2:\n            distance = dist(i,j)\n            if distance <= h_dist:\n                count += 1\n                output['word1'].append(i)\n                output['word2'].append(j)\n                output['distance'].append(distance)\n    if count == 0:\n        print('No words in common.')\n    else:\n        headers = ['word1','word2','distance']\n        df = pd.DataFrame(output)\n        df = df.sort_values(by=['word1','word2'])\n        df = df.drop_duplicates()\n        df = df.set_index('word1')\n        if table:\n            print(tabulate(df, headers, tablefmt='psql'))\n        else:\n            output2 = df.to_csv(sep='\\t', mode='a',line_terminator='\\n')\n            print(output2,end='')\n\n# --------------------------------------------------\nif __name__ == '__main__':\n    main()\n","sub_path":"assignments/15-commoner/commoner.py","file_name":"commoner.py","file_ext":"py","file_size_in_byte":3955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"323071950","text":"# -*- encoding: utf-8 -*-\n##############################################################################\n#\n# Copyright (c) 2007 - 2009 Corvus Latinoamerica, C.A. (http://corvus.com.ve) All Rights Reserved\n#                   \n#\n# WARNING: This program as such is intended to be used by professional\n# programmers who take the whole responsability of assessing all potential\n# consequences resulting from its eventual inadequacies and bugs \n# End users who are looking for a ready-to-use solution with commercial\n# garantees and support are strongly adviced to contract a Free Software\n# Service Company \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., 59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.\n#\n##############################################################################\n\nimport time\nimport datetime\nimport locale\nfrom report import report_sxw\nfrom osv import osv\nimport pooler\n\nclass guide_traspaso(report_sxw.rml_parse):\n    #Variables Globales----------------------------------------------------\n    totalpeso = 0\n    totalcajas = 0\n    #----------------------------------------------------------------------\n    def __init__(self, cr, uid, name, context):\n        super(guide_traspaso, self).__init__(cr, uid, name, context)\n        self.localcontext.update({\n            'time': time,\n            'locale': locale, \n            'get_today': self._get_today,\n            'get_piking_line': self._get_piking_line,\n            'get_totalcajas': self._get_totalcajas,\n            'get_totalpeso': self._get_totalpeso\n        })\n\n    def _get_piking_line(self,guide_id):\n        self.totalpeso = 0\n        self.totalcajas = 0\n        sqlp = \"\"\"\n            SELECT  p.default_code,t.name,p.variants,SUM(m.product_qty) AS cantidad,t.weight_net  \n            FROM\tdelivery_guide_picking_line    \t AS d\n            INNER  JOIN stock_move AS m\t ON d.picking_id=m.picking_id\n            INNER  JOIN product_product \t AS p\t ON m.product_id=p.id\n            INNER  JOIN product_template \t AS t\t ON p.product_tmpl_id=t.id\n            WHERE d.guide_id=%d\n            GROUP BY p.default_code,t.name,p.variants,t.weight_net ;\"\"\"%guide_id\n        self.cr.execute (sqlp)\n        resultSQL = self.cr.fetchall()\n        if not resultSQL:\n            return [{\"codigo\":'',\"producto\":'',\"referencia\":'',\"cajas\":0,\"peso\":0}]\n        rsp = []\n        for inf in resultSQL:\n            peso = 0\t\t\n            if inf[4]: \n                peso += inf[3] * inf[4]\n                self.totalpeso += peso\t\n            rsp.append({\"codigo\":inf[0],\"producto\":inf[1],\"referencia\":inf[2],\"cajas\":inf[3],\"peso\":peso}) \n            self.totalcajas += inf[3]\n        return rsp\n\n    def _get_today(self):\n        today = datetime.datetime.now().strftime(\"%d/%m/%Y\")\n        return today\n\n    def _get_totalcajas(self):\n        return self.totalcajas\t\n\n    def _get_totalpeso(self):\n        return self.totalpeso\n\t\t\t\t\t\t\nreport_sxw.report_sxw('report.delivery_guide_traspaso','delivery.guide','addons/custom_american/delivery_guide/report/report_guide_traspaso.rml',parser=guide_traspaso, header=False)\n","sub_path":"delivery_guide/report/report_guide_traspaso.py","file_name":"report_guide_traspaso.py","file_ext":"py","file_size_in_byte":3687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"303638855","text":"def choosing():\n    print(\"Choose O or X to play the Game:\")\n    while (True):\n        user = input()\n\n        if (user == 'O'):\n            computer = 'X'\n            break\n        elif (user == 'X'):\n            computer = 'O'\n            break\n        else:\n            print(\"You chose the wrong one. Choose again\")\n    return user,computer\n","sub_path":"Tic_tac_toe/choose.py","file_name":"choose.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"480491149","text":"import sys\nsys.path.append(\"/usr/local/cython/\")\n\nimport math\nimport numpy as np\nfrom scipy.optimize import minimize\n\nfrom attrdict import AttrDict\n\nimport torch\nfrom torch.autograd import Variable\nimport collections\nimport pandas as pd\n\nfrom leopy.utils import tf_utils, dir_utils\nfrom leopy.algo.leo_obs_models import TactileModelNetwork\n\nimport logging\nlog = logging.getLogger(__name__)\n\n## factor costs in pytorch ##\n\ndef whiten_factor_error(factor_error, factor_inf):\n    # factor_error: n x 1, factor_inf: n x n\n\n    factor_cost = torch.matmul(torch.matmul(\n        factor_error.permute(1, 0), factor_inf), factor_error)\n    factor_cost = factor_cost.view(-1)\n\n    return factor_cost\n\ndef unary_factor_error(x, key_syms, key_ids, factor_inf, factor_meas, device=None, params=None):\n\n    key_id = key_ids[0]\n    key_id = key_id + int(0.5 * x.shape[0]) if (key_syms[0] == 'e') else key_id\n\n    est_pose = (x[key_id, :]).view(1, -1)\n    meas_pose = factor_meas.view(1, -1)\n\n    err = tf_utils.tf2d_between(est_pose, meas_pose, device=device, requires_grad=True).view(-1, 1) # 3 x 1\n\n    return err\n\ndef binary_odom_factor_error(x, key_syms, key_ids, factor_inf, factor_meas, device=None, params=None):\n    \n    p1 = (x[key_ids[0], :]).view(1, -1) # n x 3\n    p2 = (x[key_ids[1], :]).view(1, -1) # n x 3\n\n    est_val = (tf_utils.tf2d_between(p1, p2, device=device, requires_grad=True)).view(-1)\n\n    # err = (torch.sub(est_val, factor_meas)).view(-1, 1) # 3 x 1\n    est_val = est_val.view(1, -1)\n    factor_meas = factor_meas.view(1, -1)\n    err = (tf_utils.tf2d_between(factor_meas, est_val, device=device, requires_grad=True)).view(-1, 1)\n\n    return err\n\ndef sdf_intersection_factor_error(x, key_syms, key_ids, factor_inf, device=None, params=None):\n\n    err = torch.tensor([[0.]], device=device)\n\n    return err\n\ndef qs_motion_factor_params(obj_shape):\n\n    params = AttrDict()\n\n    if (obj_shape == 'disc'):\n        params.c_sq = math.pow(0.088 / 3, 2)\n    elif (obj_shape == 'rect'):\n        params.c_sq = math.pow(math.sqrt(0.2363**2 + 0.1579**2), 2)\n    elif (obj_shape == 'ellip'):\n        params.c_sq = (0.5 * (0.1638 + 0.2428)) ** 2\n    else:\n        print(\"object shape sdf not found\")\n\n    return params\n\ndef qs_motion_factor_error(x, key_syms, key_ids, factor_inf, device=None, params=None):\n    # keys: o_{t-1}, o_{t}, e_{t-1}, e_{t}\n    \n    offset_e = int(0.5 * x.shape[0])\n\n    obj_pose0 = (x[key_ids[0], :]).view(1, -1) # 1 x 3\n    obj_pose1 = (x[key_ids[1], :]).view(1, -1)\n    ee_pose0 = (x[offset_e + key_ids[2], :]).view(1, -1) # 1 x 3\n    ee_pose1 = (x[offset_e + key_ids[3], :]).view(1, -1)\n\n    obj_ori1 = obj_pose1.clone(); obj_ori1[0, 0] = 0.; obj_ori1[0, 1] = 0.\n    obj_pose_rel__world = tf_utils.tf2d_between(obj_pose0, obj_pose1, device=device, requires_grad=True)\n\n    vel_obj__world = obj_pose1.clone() - obj_pose0.clone(); vel_obj__world[0, 2] = 0.\n    vel_obj__obj = tf_utils.tf2d_between(obj_ori1, vel_obj__world, device=device, requires_grad=True)\n\n    vel_contact__world = ee_pose1.clone() - ee_pose0.clone(); vel_contact__world[0, 2] = 0.\n    vel_contact__obj = tf_utils.tf2d_between(obj_ori1, vel_contact__world, device=device, requires_grad=True)\n    \n    contact_point1 = ee_pose1.clone(); contact_point1[0, 2] = 0.\n    contact_point__obj = tf_utils.tf2d_between(obj_pose1, contact_point1, device=device, requires_grad=True)\n\n    # # D*V = Vp\n    vx = vel_obj__obj[0, 0]\n    vy = vel_obj__obj[0, 1]\n    omega = obj_pose_rel__world[0, 2]\n\n    vpx = vel_contact__obj[0, 0]\n    vpy = vel_contact__obj[0, 1]\n\n    px = contact_point__obj[0, 0]\n    py = contact_point__obj[0, 1]\n\n    D = torch.tensor([[1, 0, -py], [0, 1, px], [-py, px, -params.c_sq]], device=device)\n    V = torch.tensor([vx, vy, omega], device=device)\n    Vp = torch.tensor([vpx, vpy, 0.], device=device)\n\n    err = torch.sub(torch.matmul(D, V), Vp).view(-1, 1) # 3 x 1\n\n    return err\n\ndef tactile_rel_meas_factor_error(x, key_syms, key_ids, factor_inf, factor_meas, device=None, params=None):\n    # keys: o_{t-k}, o_{t}, e_{t-k}, e_{t}\n        \n    offset_e = int(0.5 * x.shape[0])\n\n    obj_pose1 = (x[key_ids[0], :]).view(1, -1) # 1 x 3\n    obj_pose2 = (x[key_ids[1], :]).view(1, -1)\n    ee_pose1 = (x[offset_e + key_ids[2], :]).view(1, -1) # 1 x 3\n    ee_pose2 = (x[offset_e + key_ids[3], :]).view(1, -1)\n\n    ee_pose1__obj = tf_utils.tf2d_between(obj_pose1, ee_pose1, device=device, requires_grad=True) # 1 x 3\n    ee_pose2__obj = tf_utils.tf2d_between(obj_pose2, ee_pose2, device=device, requires_grad=True) # 1 x 3\n    pose_rel_expect = tf_utils.tf2d_between(ee_pose1__obj, ee_pose2__obj, device=device, requires_grad=True) # 1 x 3\n\n    yaw_only_error = True\n    if yaw_only_error:\n        yaw_only_mask = torch.zeros(3, 4, device=device)\n        yaw_only_mask[-1,-1] = 1.\n        pose_rel_meas = torch.asin(torch.matmul(yaw_only_mask, factor_meas)).view(1, -1) # 1 x 3\n        pose_rel_expect = torch.tensor([0., 0., pose_rel_expect[0, 2]], requires_grad=True, device=device).view(1, -1) # 1 x 3\n\n    err = tf_utils.tf2d_between(pose_rel_expect, pose_rel_meas, device=device, requires_grad=True).view(-1, 1) # 3 x 1\n\n    return err\n\ndef tactile_model_output_fixed(tactile_model, img_feats, device=None, params=None):\n    img_feat_i = torch.tensor(img_feats[0], requires_grad=True, device=device).view(1, -1)\n    img_feat_j = torch.tensor(img_feats[1], requires_grad=True, device=device).view(1, -1)\n\n    if (params.norm_img_feat == True):\n        mean_img_feat = torch.tensor(params.mean_img_feat, requires_grad=True, device=device)\n        std_img_feat = torch.tensor(params.std_img_feat, requires_grad=True, device=device)\n\n        img_feat_i = tf_utils.normalize_vector(img_feat_i, mean_img_feat, std_img_feat)\n        img_feat_j = tf_utils.normalize_vector(img_feat_j, mean_img_feat, std_img_feat)\n\n    class_label = torch.tensor(params.class_label, device=device)\n    class_label_vec = torch.nn.functional.one_hot(class_label, params.num_classes)\n    class_label_vec = class_label_vec.view(1, -1)\n\n    tf_pred = tactile_model.forward(img_feat_i, img_feat_j, class_label_vec)\n    tf_pred = tf_pred.view(-1) # [tx, ty, cyaw, syaw]\n\n    return tf_pred\n\ndef tactile_oracle_output(data, key_syms, key_ids, device=None, params=None):\n    # keys: o_{t-k}, o_{t}, e_{t-k}, e_{t}\n\n    obj_pose_gt_i = torch.tensor(data['obj_poses_gt'][key_ids[0]], device=device).view(1, -1) # n x 3\n    obj_pose_gt_j = torch.tensor(data['obj_poses_gt'][key_ids[1]], device=device).view(1, -1)\n    ee_pose_gt_i = torch.tensor(data['ee_poses_gt'][key_ids[2]], device=device).view(1, -1)\n    ee_pose_gt_j = torch.tensor(data['ee_poses_gt'][key_ids[3]], device=device).view(1, -1)\n\n    ee_pose_gt_i__obj = tf_utils.tf2d_between(obj_pose_gt_i, ee_pose_gt_i, device=device) # n x 3\n    ee_pose_gt_j__obj = tf_utils.tf2d_between(obj_pose_gt_j, ee_pose_gt_j, device=device)\n    pose_rel_gt = tf_utils.tf2d_between(ee_pose_gt_i__obj, ee_pose_gt_j__obj, device=device) # n x 3\n\n    yaw_only_error = True\n    if yaw_only_error:\n        tf_pred = torch.tensor([0., 0., torch.cos(pose_rel_gt[0, 2]), torch.sin(pose_rel_gt[0, 2])], device=device).view(-1)\n    \n    return tf_pred\n\ndef init_tactile_model(filename=None, device=None):\n\n    tactile_model = TactileModelNetwork(input_size=2*2*4, output_size=4)\n    tactile_model = tactile_model.to(device)\n    \n    if filename is not None:\n        model_saved = torch.jit.load(filename)\n        state_dict_saved = model_saved.state_dict()\n\n        # fix: saved dict key mismatch\n        state_dict_new = collections.OrderedDict()\n        state_dict_new['fc1.weight'] = state_dict_saved['model.fc1.weight']\n        state_dict_new['fc1.bias'] = state_dict_saved['model.fc1.bias']\n\n        tactile_model.load_state_dict(state_dict_new)\n    \n    return tactile_model\n\ndef update_data_dict(data_dict, factor_name, factor_cost, factor_inf):\n    factor_key = f\"err/factor/{factor_name}\"\n\n    if factor_key not in data_dict:\n        data_dict[factor_key] = [factor_cost.item()]\n    else:\n        (data_dict[factor_key]).append(factor_cost.item())\n\n    return data_dict\n\ndef dict_to_row(data_dict, step):\n\n    for key, val in data_dict.items():\n        data_dict[key] = sum(val) / len(val)\n\n    # data_dict['tstep'] = step\n    data_row = pd.DataFrame(data_dict, index=[step])\n    data_row.index.name = 'tstep'\n\n    return data_row\nclass Cost():\n    def __init__(self, data, theta, params=None, device=None):\n        self.data = data\n        self.theta = theta\n        self.params = params\n        self.device = device\n\n        self.dataframe = None\n                \n    def get_dataframe(self):\n        return self.dataframe\n\n    def costfn(self, x, log=False):\n\n        dataset_type = self.params.dataio.dataset_type\n\n        if (dataset_type == \"push2d\"):\n            nsteps = np.minimum(self.params.optim.nsteps, len(self.data['obj_poses_gt']))\n            tactile_model_filename = \"{0}/local/models/{1}.pt\".format(self.params.BASE_PATH, self.params.tactile_model.name)\n            tactile_model = init_tactile_model(filename=tactile_model_filename, device=self.device)\n        else:\n            nsteps = np.minimum(self.params.optim.nsteps, len(self.data['poses_gt']))\n        \n        if log: self.dataframe = pd.DataFrame()\n\n        fval = torch.tensor([0.], requires_grad=True, device=self.device)\n        num_factors = torch.tensor([0.], requires_grad=True, device=self.device)\n        \n        for tstep in range(1, nsteps):\n\n            # filter out curr step factors\n            factor_idxs = [idxs for idxs, keys in enumerate(\n                self.data['factor_keyids']) if (max(keys) == tstep)]\n\n            factor_keysyms = [self.data['factor_keysyms'][idx] for idx in factor_idxs]\n            factor_keyids = [self.data['factor_keyids'][idx] for idx in factor_idxs]\n            factor_meas = [self.data['factor_meas'][idx] for idx in factor_idxs]\n            factor_names = [self.data['factor_names'][idx] for idx in factor_idxs]\n            \n            # compute factor costs\n            data_dict = {}\n            for idx in range(0, len(factor_idxs)):\n\n                key_syms, key_ids = factor_keysyms[idx], factor_keyids[idx]\n                factor_name = factor_names[idx]\n\n                if (factor_name == 'gps'):\n                    factor_meas_val = torch.tensor(factor_meas[idx], device=self.device)\n                    factor_inf = torch.diag(self.theta.get_sigma_inv(factor_name, z=factor_meas_val))\n                    factor_error = unary_factor_error(x, key_syms, key_ids, factor_inf, factor_meas_val[0:3], device=self.device) if (factor_inf.requires_grad) else None\n                elif (factor_name == 'odom'):\n                    factor_meas_val = torch.tensor(factor_meas[idx], device=self.device)                    \n                    factor_inf = torch.diag(self.theta.get_sigma_inv(factor_name, z=factor_meas_val))\n                    factor_error = binary_odom_factor_error(x, key_syms, key_ids, factor_inf, factor_meas_val[0:3], device=self.device) if (factor_inf.requires_grad) else None\n                elif (factor_name == 'ee_pose_prior'):\n                    factor_meas_val = torch.tensor(factor_meas[idx], device=self.device)                    \n                    factor_inf = torch.diag(self.theta.get_sigma_inv(factor_name, z=factor_meas_val))\n                    factor_error = unary_factor_error(x, key_syms, key_ids, factor_inf, factor_meas_val, device=self.device) if (factor_inf.requires_grad) else None\n                elif (factor_name == 'qs_push_motion'):\n                    params_qs = qs_motion_factor_params(self.params.dataio.obj_shape)\n                    factor_inf = torch.diag(self.theta.get_sigma_inv(factor_name, z=None))\n                    factor_error = qs_motion_factor_error(x, key_syms, key_ids, factor_inf, device=self.device, params=params_qs) if (factor_inf.requires_grad) else None\n                elif (factor_name == 'sdf_motion'):\n                    factor_inf = torch.diag(self.theta.get_sigma_inv(factor_name, z=None))\n                    factor_error = sdf_intersection_factor_error(x, key_syms, key_ids, factor_inf) if (factor_inf.requires_grad) else None\n                elif (factor_name == 'tactile_rel'):\n                    if (self.params.tactile_model.oracle == True):\n                        tf_pred = tactile_oracle_output(self.data, key_syms, key_ids, device=self.device)\n                    else:\n                        if (self.params.dataio.model_type == \"fixed_cov_varying_mean\"):\n                            tf_pred = self.theta.tactile_model_output(img_feats=factor_meas[idx], params=self.params.tactile_model) # learnable model weights\n                        else:\n                            tf_pred = tactile_model_output_fixed(tactile_model, img_feats=factor_meas[idx], device=self.device, params=self.params.tactile_model) # fixed model weights\n\n                    factor_inf = torch.diag(self.theta.get_sigma_inv(factor_name, z=None))\n                    factor_error = tactile_rel_meas_factor_error(x, key_syms, key_ids, factor_inf, tf_pred, device=self.device) # if (factor_inf.requires_grad) else factor_error\n                \n                factor_cost = whiten_factor_error(factor_error, factor_inf) if (factor_error is not None) else torch.tensor([0.], requires_grad=True, device=self.device)\n                fval = fval + 0.5 * factor_cost\n                num_factors = num_factors + 1\n\n                factor_cost_unwhtn = torch.matmul(factor_error.permute(1, 0), factor_error) if (factor_error is not None) else torch.tensor([0.], requires_grad=True, device=self.device)\n                if log: data_dict = update_data_dict(data_dict, factor_name=factor_name, factor_cost=factor_cost_unwhtn, factor_inf=factor_inf)\n            \n            if log: data_row = dict_to_row(data_dict=data_dict, step=tstep)\n            if log: self.dataframe = self.dataframe.append(data_row)\n        \n        fval = fval / num_factors\n\n        return fval\n\n    def grad_theta_costfn(self, x):\n        for name, param in self.theta.named_parameters():\n            if param.data.grad is not None:\n                param.data.grad = None\n        \n        fval = self.costfn(x)\n        fval.backward()\n\n        grad_theta = AttrDict()\n        for name, param in self.theta.named_parameters():\n            grad_theta[name] = param.data.grad\n\n        return grad_theta\n","sub_path":"leopy/src/leopy/optim/cost.py","file_name":"cost.py","file_ext":"py","file_size_in_byte":14391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"499319238","text":"#%%\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom soen_sim import input_signal, synapse, neuron\nfrom _plotting import plot_receiving_loop_current, plot_rate_vs_num_active_synapses, plot_synaptic_integration_loop_current, plot_neuronal_response__single_synaptic_pulse, plot_burst_size_vs_num_active_synapses\n\nplt.close('all')\n\n#%% temporal\nnum_tau_sim = 0\nobservation_duration = 4e-6\ndt = 5e-9\n\nI_sy_vec = np.linspace(35.5e-6,38.5e-6,4)#[36e-6]#\nI_th = 35e-6\ntau_si = 1000e-9\ntau_ref = 25e-9\njitter_params = [0,25e-9]#[gaussian center, gaussian deviation]\nnum_synapses_tot = 9\n\nnum_spikes_out_mat = np.zeros([len(I_sy_vec),num_synapses_tot])\n\n#%% run it\nnum_id = 0   \nfor kk in range(len(I_sy_vec)):\n    print('\\n\\nkk = {} of {} (I_sy)\\n'.format(kk+1,len(I_sy_vec)))\n    I_sy = I_sy_vec[kk]        \n                    \n    for rr in range(num_synapses_tot):\n        print('rr = {} of {} (num_synapses_active)'.format(rr+1,num_synapses_tot))\n        num_synapses_active = rr+1\n        \n        num_id += 1\n        input_synapses = []\n        input_inductances = []\n        for pp in range(num_synapses_tot):\n            \n            # initialize input signals\n            name__i = 'input_signal__{:d}_{:d}'.format(num_id,pp)\n            input_1 = input_signal(name__i, input_temporal_form = 'single_spike', spike_times = np.array([100e-9]),\n            stochasticity = 'gaussian', jitter_params = jitter_params)   \n            # print(input_1.spike_times)\n            \n            # initialize synapses\n            name__s = 'input_synapse__{:d}_{:d}'.format(num_id,pp)\n            if pp+1 <= num_synapses_active:\n                input_signal_name = name__i\n            else:\n                input_signal_name = ''\n            synapse_1 = synapse(name__s, integration_loop_temporal_form = 'exponential', integration_loop_time_constant = tau_si, \n                                integration_loop_self_inductance = 20e-9, integration_loop_output_inductance = 200e-12, \n                                synaptic_bias_current = I_sy_vec[kk], integration_loop_bias_current = 31e-6,\n                                input_signal_name = input_signal_name)\n            \n            input_synapses.append(name__s)\n            coupling_factor = 0.6#-0.0375*num_synapses_tot+0.5375\n            input_inductances.append([10e-12,coupling_factor])\n        \n        #initialize neuron\n        input_inductances.append([20e-12,1])\n        name__n = 'burst_neuron__{:d}'.format(num_id)\n        neuron_1 = neuron(name__n, input_synaptic_connections = input_synapses, input_synaptic_inductances = input_inductances,\n                          thresholding_junction_critical_current = 40e-6, thresholding_junction_bias_current = I_th, \n                          refractory_temporal_form = 'exponential', refractory_time_constant = tau_ref, \n                          refractory_loop_self_inductance = 1e-9, refractory_loop_output_inductance = 200e-12,\n                          refractory_loop_synaptic_bias_current = 39e-6, refractory_loop_saturation_current = 50e-6)\n               \n        # propagate in time                         \n        sim_params = dict()\n        sim_params['dt'] = dt\n        sim_params['pre_observation_duration'] = num_tau_sim*synapse_1.integration_loop_time_constant\n        sim_params['observation_duration'] = observation_duration\n        sim_params['num_tau_sim'] = num_tau_sim\n        neuron_1.sim_params = sim_params\n        neuron_1.run_sim()\n        \n        # plot temporal response\n        plot_save_string = 'I_sy={:2.2f}uA__tau_ref={:04.2f}ns__tau_si={:04.2f}ns__I_th={:2.2f}uA__dt={:04.2f}ns__obs={:04.2f}us__jitter={}ns'.format(1e6*I_sy_vec[kk],1e9*tau_ref,1e9*tau_si,I_th,1e9*dt,observation_duration*1e6,jitter_params[1])\n        plot_receiving_loop_current(neuron_1,plot_save_string)\n        # plt.close('all')\n        \n        # fill observation matrices                \n        num_spikes_out_mat[kk,rr] = neuron_1.num_spikes\n          \n        #save neuron data\n        data_save_string = plot_save_string\n        neuron_1.save_neuron_data(data_save_string)\n        \n        #delete synapses and neurons\n        # del synapse_1, neuron_1\n  \n#%% plot\nneuron_1.num_active_synapses_vec = np.arange(1,num_synapses_tot+1,1)\nneuron_1.tau_si = tau_si\nneuron_1.tau_ref = tau_ref\nneuron_1.I_sy_vec = I_sy_vec\nneuron_1.num_synapses_tot = num_synapses_tot\nneuron_1.num_spikes_out_mat = num_spikes_out_mat\nplot_save_string = 'I_sy={:2.2f}-{:2.2f}uA__tau_ref={:04.2f}ns__tau_si={:04.2f}ns__I_th={:2.2f}uA__dt={:04.2f}ns__obs={:04.2f}us__jitter={}ns'.format(1e6*I_sy_vec[0],1e6*I_sy_vec[-1],1e9*tau_ref,1e9*tau_si,I_th,1e9*dt,observation_duration*1e6,jitter_params[1])\nplot_burst_size_vs_num_active_synapses(neuron_1,plot_save_string)\n# neuron_1.plot_rate_transfer_function__no_lines(plot_save_string = plot_save_string)\n\n\n","sub_path":"nine_pixel/burst/s__num_active_pixels_to_burst.py","file_name":"s__num_active_pixels_to_burst.py","file_ext":"py","file_size_in_byte":4851,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"243428535","text":"#!/usr/bin/python3\n# -*- coding: utf-8\n\n'''\nCompiles a collection of Markdown files into a set of HTML files.\n\nThis script was inspired by rustbook:\nhttps://github.com/steveklabnik/rustbook/\n'''\n\nfrom __future__ import print_function\n\nimport argparse\nimport markdown\nfrom markdown.util import etree\nimport os\nimport re\nimport sys\n\nclass DocException(Exception): pass\n\nDOCUMENT_TEMPLATE = '''\\\n<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n<title>{}</title>\n<link rel=\"stylesheet\" type=\"text/css\" href=\"book.css\">\n<body>\n\n<div id=\"toc\">\n{}\n</div>\n\n<div id=\"content-wrapper\">\n<div id=\"content\">\n{}\n</div>\n</div>\n'''\n\nSTYLESHEET = '''\\\na { color: blue; text-decoration: none; }\nh1 a, h2 a, h3 a, h4 a, h5 a, h6 a { color: black; }\n#toc a { color: black; }\n#toc {\n    position: absolute;\n    left: 0px;\n    top: 0px;\n    bottom: 0px;\n    width: 250px;\n    overflow-y: auto;\n    border-right: 1px solid black;\n    padding: 10px;\n    font-size: 16px;\n}\n#toc ul {\n    list-style: none;\n    padding-left: 0px;\n}\n#toc ul ul {\n    padding-left: 20px;\n}\n#toc li {\n    line-height: 30px;\n}\n#content-wrapper {\n    position: absolute;\n    left: 260px;\n    top: 0px;\n    bottom: 0px;\n    right: 0px;\n    overflow-y: auto;\n}\n#content {\n    margin-left: auto;\n    margin-right: auto;\n    max-width: 750px;\n    padding-bottom: 50px;\n}\n'''\n\nclass DocOptions(argparse.ArgumentParser):\n\n    def __init__(self):\n        super().__init__(usage = '%(prog)s [options] <directory>')\n\n        self.add_argument('-o', '--output', type = str, required = True,\n            metavar = 'DIR', help = 'Output directory')\n\n        self.add_argument('directory', type = str,\n            help = 'Directory containing documents')\n\nclass DocCompiler(object):\n\n    def __init__(self, args):\n        self.base_dir = args.directory\n        self.output_dir = args.output\n        self.file_references = None\n        self.sections = []\n        self.result = 0\n\n    def run(self):\n        self.process_toc('SUMMARY.md')\n        self.process_index('README.md')\n        [self.process_section(s) for s in self.sections]\n        self.write_stylesheet()\n        self.check_references(self.base_dir, '')\n        return self.result\n\n    def check_references(self, path, rel_path):\n        for name in os.listdir(path):\n            abs_name = os.path.join(path, name)\n            rel_name = os.path.join(rel_path, name)\n            if os.path.isdir(name):\n                self.check_references(abs_name, rel_name)\n            elif name.endswith('.md') and rel_name not in self.file_references:\n                print('File not referenced:', rel_name, file = sys.stderr)\n                self.result = 1\n\n    def index_name(self, path):\n        return os.path.join(self.output_dir, 'index.html')\n\n    def output_name(self, path):\n        name, _ = os.path.splitext(path)\n        return os.path.join(self.output_dir, name + '.html')\n\n    def input_name(self, path):\n        return os.path.join(self.base_dir, path)\n\n    def process_toc(self, path):\n        with open(self.input_name(path), 'r') as f:\n            md = markdown.Markdown()\n            p = TocTreeProcessor()\n\n            md.treeprocessors.add('compile_toc', p, '_end')\n            md.convert(f.read())\n            self.sections = p.sections\n            self.file_references = p.references\n\n        self.file_references.add(path)\n\n        md = markdown.Markdown()\n        p = DocProcessor(self.base_dir, self.base_dir)\n        md.treeprocessors.add('compile_doc', p, '_end')\n        self.sidebar = md.convert(self.generate_toc())\n\n    def generate_toc(self):\n        res = []\n\n        for i, section in enumerate(self.sections, 1):\n            res.extend(self.section_toc('{}.'.format(i), 0, section))\n\n        return '\\n'.join(res)\n\n    def section_toc(self, num, indent, section):\n        yield '{}* [**{}** {}]({})'.format('    ' * indent, num,\n            section.title, section.filename)\n\n        for i, sub in enumerate(section.subsections, 1):\n            yield from self.section_toc('{}{}.'.format(num, i), indent + 1, sub)\n\n    def process_doc(self, path):\n        with open(self.input_name(path), 'r') as f:\n            line = f.readline()\n\n            if line.startswith('#'):\n                title = line[1:].strip()\n                f.seek(0)\n            else:\n                raise DocException('File {} does not begin with title line \"# title\"'\n                    .format(path))\n\n            text = f.read()\n\n            md = markdown.Markdown()\n            p = DocProcessor(self.base_dir,\n                os.path.dirname(os.path.join(self.base_dir, path)))\n            md.treeprocessors.add('compile_doc', p, '_end')\n            doc = md.convert(text)\n\n            return DOCUMENT_TEMPLATE.format(title, self.sidebar, doc)\n\n    def process_index(self, path):\n        doc = self.process_doc(path)\n        with open(self.index_name(path), 'w') as f:\n            f.write(doc)\n        self.file_references.add(path)\n\n    def process_section(self, section):\n        if section.filename.endswith('.md'):\n            name = self.output_name(section.filename)\n            doc = self.process_doc(section.filename)\n            with open(name, 'w') as f:\n                f.write(doc)\n\n        [self.process_section(s) for s in section.subsections]\n\n    def write_stylesheet(self):\n        with open(os.path.join(self.output_dir, 'book.css'), 'w') as f:\n            f.write(STYLESHEET)\n\nclass Section(object):\n\n    def __init__(self, title, filename):\n        self.title = title\n        self.filename = filename\n        self.subsections = []\n\nclass TocTreeProcessor(markdown.treeprocessors.Treeprocessor):\n\n    '''\n    Collects entries in the table of contents file\n    '''\n\n    def __init__(self):\n        self.sections = []\n        self.references = set()\n\n    def run(self, root):\n        for child in root:\n            if child.tag == 'ul':\n                for item in child:\n                    a = item.find('a')\n                    if a is None:\n                        continue\n\n                    section = Section(a.text, a.get('href'))\n                    self.sections.append(section)\n                    self.process_section(section, item)\n        return root\n\n    def process_section(self, section, elem):\n        self.references.add(section.filename)\n\n        ul = elem.find('ul')\n\n        if ul is None:\n            return\n\n        for item in elem:\n            a = item.find('a')\n            if a is None:\n                continue\n\n            sub = Section(a.text, a.get('href'))\n            section.subsections.append(sub)\n            self.process_section(sub, item)\n\nclass DocProcessor(markdown.treeprocessors.Treeprocessor):\n\n    '''\n    Adds id attributes and links to headers.\n    Rewrites links to local .md files into .html files.\n    Raises an exception if any linked .md file does not exist.\n    '''\n\n    def __init__(self, abs_path, rel_path):\n        self.abs_path = abs_path\n        self.rel_path = rel_path\n        self.first_header = True\n\n    def create_id(self, text, P = re.compile(r'[^a-zA-Z0-9-_]')):\n        return P.sub('', text.lower().replace(' ', '-'))\n\n    def run(self, elem):\n        for child in elem:\n            if child.tag in { 'h1', 'h2', 'h3', 'h4', 'h5', 'h6' }:\n                if self.first_header:\n                    self.first_header = False\n                    child.set('class', 'title')\n                else:\n                    # Create an <a> which contains all children of this element\n                    a = etree.Element('a')\n                    id_name = self.create_id(''.join(child.itertext()))\n                    a.set('href', '#' + id_name)\n                    child.set('id', id_name)\n                    a.text = child.text\n                    a.tail = child.tail\n                    child.text = None\n                    child.tail = None\n\n                    for e in child:\n                        a.append(e)\n                    for i in reversed(range(len(child))):\n                        del child[i]\n\n                    child.append(a)\n            elif child.tag == 'a':\n                name, frag_ch, fragment = child.get('href').partition('#')\n                if name and '://' not in name:\n                    if name.startswith('/'):\n                        path = os.path.join(self.abs_path, name[1:])\n                    else:\n                        path = os.path.join(self.rel_path, name)\n\n                    if path.endswith('.md'):\n                        if not os.path.exists(path):\n                            raise DocException('File {} does not exist'.format(path))\n\n                        child.set('href', '{}.html{}{}'\n                            .format(name[:-3], frag_ch, fragment))\n            self.run(child)\n        return elem\n\nif __name__ == '__main__':\n    opts = DocOptions()\n    args = opts.parse_args()\n\n    try:\n        os.mkdir(args.output)\n    except OSError as e:\n        print('Failed to create output directory:', e.args[1], file = sys.stderr)\n        sys.exit(2)\n\n    try:\n        sys.exit(DocCompiler(args).run())\n    except DocException as e:\n        print(e, file = sys.stderr)\n        sys.exit(1)\n","sub_path":"compile.py","file_name":"compile.py","file_ext":"py","file_size_in_byte":9130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"33354435","text":"import random\n\nclass Particle():\n    # Particle is a generic particle class\n    # containing all the parameters of itself\n    def __init__(self, pt=0., eta=0., phi=0.):\n        # Each particle has:\n        # - a transverse momentum pt\n        self.pt = pt\n        # - a pseudorapidity eta\n        self.eta = eta\n        # - an azimuthal angle phi\n        self.phi = phi\n    # Method for skipping particles due to efficiency\n    def skip(self, eff=1.0):\n        # Generate random value in (0, 1) range\n        rand = random.uniform(0, 1)\n        # Check if efficiency is lower than generated value\n        skip = False\n        if eff < rand:\n            skip = True\n        # Return the result\n        return skip\n","sub_path":"Particle.py","file_name":"Particle.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"89565616","text":"from fastapi import APIRouter\n\nfrom httprunner.api import HttpRunner\nfrom httprunner.schema import ProjectMeta, TestCase\n\nrouter = APIRouter()\nrunner = HttpRunner()\n\n\n@router.post(\"/hrun/debug/testcase\", tags=[\"debug\"])\nasync def debug_single_testcase(project_meta: ProjectMeta, testcase: TestCase):\n    resp = {\n        \"code\": 0,\n        \"message\": \"success\",\n        \"result\": {}\n    }\n\n    project_meta_json = project_meta.dict(by_alias=True)\n    if project_meta.debugtalk_py:\n        origin_local_keys = list(locals().keys()).copy()\n        exec(project_meta.debugtalk_py, {}, locals())\n        new_local_keys = list(locals().keys()).copy()\n        new_added_keys = set(new_local_keys) - set(origin_local_keys)\n        new_added_keys.remove(\"origin_local_keys\")\n        project_meta_json[\"functions\"] = {}\n        for func_name in new_added_keys:\n            project_meta_json[\"functions\"][func_name] = locals()[func_name]\n\n    testcase_json = testcase.dict(by_alias=True)\n    tests_mapping = {\n        \"project_mapping\": project_meta_json,\n        \"testcases\": [testcase_json]\n    }\n\n    summary = runner.run_tests(tests_mapping)\n    if not summary[\"success\"]:\n        resp[\"code\"] = 1\n        resp[\"message\"] = \"fail\"\n\n    resp[\"result\"] = summary\n    return resp\n\n\n# @router.post(\"/hrun/debug/api\", tags=[\"debug\"])\n# async def debug_single_api():\n#     resp = {\n#         \"code\": 0,\n#         \"message\": \"success\",\n#         \"result\": {}\n#     }\n#\n#     # tests_mapping\n#\n#     # summary = runner.run_tests(tests_mapping)\n#\n#     return resp\n#\n#\n# @router.post(\"/hrun/debug/testcases\", tags=[\"debug\"])\n# async def debug_multiple_testcases(project_meta: ProjectMeta, testcases: TestCases):\n#     tests_mapping = {\n#         \"project_mapping\": project_meta,\n#         \"testcases\": testcases\n#     }\n","sub_path":"httprunner/app/routers/debug.py","file_name":"debug.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"638499926","text":"import paypalrestsdk\nimport logging\n\n\nclass Rest:\n\n    def create_payment(self, headers, json):\n\n        paypalrestsdk.configure({\n            \"mode\": \"sandbox\",  # sandbox or live\n            \"client_id\": \"AT7ca5WFyN6fH-LxieYGw2h7Yy5ekfzh8JbSxloFFJlK73Z5n3-h7sRqyV1EZ2CyC3mykjb0-NAOhiVa\",\n            \"client_secret\": \"EN_u9IcnUkQzjgsWsw1QlJWOVaSq0u3pAhSegwn9qZnTpS_zjMtfgX2Fx-tJinmJ-RkOX6feR8Ke2oLt\"})\n\n        payment = paypalrestsdk.Payment({\n            \"intent\": \"sale\",\n            \"payer\": {\n                \"payment_method\": \"paypal\"},\n            \"redirect_urls\": {\n                \"return_url\": \"http://localhost:8000/payment/execute\",\n                \"cancel_url\": \"http://localhost:3000/\"},\n            \"transactions\": [{\n                \"item_list\": {\n                    \"items\": [{\n                        \"name\": \"item\",\n                        \"sku\": \"item\",\n                        \"price\": \"5.00\",\n                        \"currency\": \"USD\",\n                        \"quantity\": 1}]},\n                \"amount\": {\n                    \"total\": \"5.00\",\n                    \"currency\": \"USD\"},\n                \"description\": \"This is the payment transaction description.\"}]})\n\n        if payment.create():\n            print(\"Payment created successfully\")\n        else:\n            print(payment.error)\n","sub_path":"core/api/paypal.py","file_name":"paypal.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"375859096","text":"import json\nfrom sys import argv\nfrom time import sleep\nfrom urllib import request, parse\n\nBOARD_SYMBOLS = {\n    0: '_',\n    1: 'x',\n    2: 'o'\n}\n\n\ndef parse_response(response):\n    try:\n        res = response.read().decode('utf-8')\n        return json.loads(res)\n    except Exception as e:\n        print(e)\n        return None\n\n\ndef get_games(server):\n    response = request.urlopen(server + '/list')\n    return parse_response(response)\n\n\ndef draw_board(board):\n    result = ''\n    for x in range(0, len(board)):\n        for y in range(0, len(board)):\n                result += BOARD_SYMBOLS[board[x][y]]\n        result += '\\n'\n    print(result)\n\n\ndef create_game(name):\n    response = request.urlopen(server + '/start?name=' + name)\n    id = parse_response(response)['id']\n    response = parse_response(request.urlopen(server + '/status?game=' + str(id)))\n    response['id'] = id\n    return response\n\n\ndef make_move(server, game, id):\n    input_valid = False\n    while not input_valid:\n        inp = input('Your turn (%s): ' % BOARD_SYMBOLS[game['next']])\n        parts = inp.strip().split()\n        if len(parts) != 2:\n            print('invalid input')\n            continue\n        url = '%s/play?game=%d&player=%d&x=%s&y=%s' % (server, id, game['next'], parts[0], parts[1])\n        response = request.urlopen(url)\n        if parse_response(response)['status'] == 'bad':\n            print('invalid input')\n            continue\n        input_valid = True\n\n\ndef play(server, game, this_player):\n    game_status = parse_response(request.urlopen(server + '/status?game=' + str(game['id'])))\n    if game_status['next'] != this_player:\n        print('waiting for the other player')\n    over = False\n    get_status = True\n    while not over:\n        if get_status:\n            game_status = parse_response(request.urlopen(server + '/status?game=' + str(game['id'])))\n        get_status = True\n        if 'winner' in game_status:\n            if game_status['winner'] == this_player:\n                print('you win')\n            elif game_status['winner'] == 0:\n                print('draw')\n            else:\n                print('you lose')\n            over = True\n            continue\n        if 'next' in game_status and game_status['next'] == this_player:\n            draw_board(game_status['board'])\n            make_move(server, game_status, game['id'])\n            game_status = parse_response(request.urlopen(server + '/status?game=' + str(game['id'])))\n            get_status = False\n            if 'winner' not in game_status:\n                draw_board(game_status['board'])\n                print('waiting for the other player')\n        sleep(1)\n\n\ndef run(server):\n    games = get_games(server)\n    for game in games.values():\n        if game['empty']:\n            print('%d %s' % (game['id'], game['name']))\n    inp = input(\"Choose game id or type 'new' and name for a new game:\\n\")\n    if inp.strip().startswith('new'):\n        this_player = 1\n        name = inp.replace('new', '').strip()\n        joined_game = create_game(name)\n    else:\n        joined_game = games[inp]\n        this_player = 2\n    play(server, joined_game, this_player)\n\n\nhost = argv[1]\nport = argv[2]\nserver = 'http://' + host + ':' + port\nrun(server)","sub_path":"12 - Tic Tac Toe/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":3232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"646924717","text":" ## @file DVTestHltDecayTreeTuple-TisTos.py\n #\n #  See DecayTreeTuple documentation\n #\n #  @author P. Koppenburg\n #  @date 2008-03-07\n #\n\nfrom Gaudi.Configuration import *\nfrom GaudiKernel.SystemOfUnits import *\n########################################################################\n#\n# The Decay Tuple\n#\nfrom Configurables import DecayTreeTuple\ntuple = DecayTreeTuple(\"Tuple\")\ntuple.ToolList +=  [\n      \"TupleToolTrigger\"\n    , \"TupleToolGeometry\"\n    , \"TupleToolKinematic\"\n    , \"TupleToolPrimaries\"\n    , \"TupleToolEventInfo\"\n    , \"TupleToolMCTruth\"\n    , \"TupleToolTISTOS\"\n     ]\ntuple.Inputs = [\"Phys/StdLooseJpsi2MuMu\"]\ntuple.Decay = \"J/psi(1S) -> ^mu+ ^mu-\"\n#tuple.OutputLevel = 1 \n########################################################################\n#\n# DaVinci\n#\nfrom Configurables import DaVinci\nDaVinci().EvtMax = 100\nDaVinci().PrintFreq = 1 \nDaVinci().SkipEvents = 0\nDaVinci().DataType = \"MC09\" \nDaVinci().Simulation   = True\nDaVinci().CondDBtag = 'MC09-20100430-vc-md100'\nDaVinci().TupleFile = \"DecayTreeTuple_TisTos.root\"  # Ntuple\nDaVinci().MoniSequence = [ tuple ]\nDaVinci().ReplaceL0BanksWithEmulated = True\nDaVinci().Hlt = True \nDaVinci().HltThresholdSettings = 'Physics_10000Vis_1000L0_40Hlt1_EffectiveHlt2_Jul09'\n\nMessageSvc().Format = \"% F%60W%S%7W%R%T %0W%M\"\n","sub_path":"DaVinci/DaVinciTests/tests/options/DVTestDecayTreeTuple-TisTos.py","file_name":"DVTestDecayTreeTuple-TisTos.py","file_ext":"py","file_size_in_byte":1290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"290526438","text":"from famportal.base.viewset import BaseViewSet,SuperBaseViewSet,\\\n    BASE_STAFF_PERMISSION_CLASS, FCViewSet, AdminReadViewSet\nfrom famportal.auth.models import UserInfo, Institution, UserCapital,\\\n    UserCapitalRecord\nfrom famportal.auth.serializer import UserInfoSerializer, UserSerializer,\\\n    InstitutionSerializer, UserCapitalSerializer,\\\n    UserCapitalRecordSerializerInline,  UserInfoSerializerForFC,\\\n    ClientUserInfoSerializer\nfrom django.contrib.auth.models import User\nfrom rest_framework.viewsets import ReadOnlyModelViewSet\nimport django_filters\nfrom django_filters.filters import DateTimeFilter\nfrom famportal.base.permission import Has_supervisorRight\nfrom rest_framework import mixins\n\nclass UserViewSet(BaseViewSet):\n    emptyInstance = User()\n    queryset = User.objects.all()\n    serializer_class = UserSerializer\n    filter_fields = ('id','is_active',)\n    search_fields = ('code',)  \n    ordering_fields = ('id','lastmodified',)\n    \nclass SuperUserViewSet(UserViewSet,AdminReadViewSet,mixins.ListModelMixin):\n    pass\n    \nclass UserInfoViewSet(BaseViewSet):\n    emptyInstance = UserInfo()\n    queryset = UserInfo.objects.all()\n    serializer_class = UserInfoSerializer\n    filter_fields = ('user__is_active',)\n    search_fields = ()  \n    ordering_fields = ('id','lastmodified',)\n    \nclass UserinfoViweSetSuper(SuperBaseViewSet):\n    permission_classes = BASE_STAFF_PERMISSION_CLASS\n    emptyInstance = UserInfo()\n    queryset = UserInfo.objects.all()\n    serializer_class = UserInfoSerializer\n    filter_fields = ('user__is_active',)\n    search_fields = ('code',)  \n    ordering_fields = ('id','lastmodified',)\n    \nclass InstitutionViewSet(ReadOnlyModelViewSet):\n    emptyInstance = Institution()\n    queryset = Institution.objects.all()\n    serializer_class = InstitutionSerializer\n    filter_fields = ('type','internal','countable',)\n    search_fields = ('name',)  \n    ordering_fields = ('id','lastmodified',)\n    \nclass UserCapitalViewSet(BaseViewSet):\n    emptyInstance = UserCapital()\n    queryset = UserCapital.objects.all()\n    serializer_class = UserCapitalSerializer\n    filter_fields = ('institution__internal','institution__type','institution__countable','is_active')\n    search_fields = ('user__username','user__userinfo__name','institution__name',)\n    ordering_fields = ('id','lastmodified','institution__internal')\n    \nclass Client_CapitalViewSet(mixins.ListModelMixin,UserCapitalViewSet,FCViewSet):\n    filter_fields = ('institution__internal','institution__type','institution__countable','is_active','user__userinfo__id')\n    queryset = UserCapital.objects.all()\n    def get_queryset(self):\n        qs = super(Client_CapitalViewSet,self).get_queryset().select_related('user','user__userinfo')\n        self_userinfo = self.request.user.userinfo;\n        q_ids = [o.id for o in qs if o.user.userinfo.check_supervisor(self_userinfo,[])]\n        q = qs.filter(id__in=q_ids)\n        return q;\n    \nclass DateRangeFilter(django_filters.FilterSet):\n    max = DateTimeFilter(name='date', lookup_type='lte')\n    min = DateTimeFilter(name='date', lookup_type='gte')\n    \nclass recordFilter(DateRangeFilter):\n    class Meta:\n        model = UserCapitalRecord\n        fields = ('id','userCapital__institution_id','userCapital__code','is_active','max','min')\n        \nclass clientRecordFilter(recordFilter):\n    uid = django_filters.CharFilter(name='userCapital__user__userinfo__id')\n    class Meta:\n        model = UserCapitalRecord\n        fields = ('id','userCapital__institution_id','userCapital__code','is_active','max','min','uid',)\n    \nclass UserCapitalRecordViewSet(BaseViewSet):\n    emptyInstance = UserCapitalRecord()\n    queryset = UserCapitalRecord.objects.all()\n    serializer_class = UserCapitalRecordSerializerInline\n    filter_class = recordFilter\n    search_fields = ('userCapital__user__username','userCapital__user__userinfo__name','userCapital__institution__name',)  \n    ordering_fields = ('id','date',)\n    \nclass ClientCapitalRecordViewSet(mixins.ListModelMixin,UserCapitalRecordViewSet,FCViewSet):\n    filter_class = clientRecordFilter\n    search_fields = ('userCapital__user__username','userCapital__user__userinfo__name','userCapital__institution__name')  \n    serializer_class = UserCapitalRecordSerializerInline\n    ordering_fields = ('id','date',)\n    def get_queryset(self):\n        qs = super(ClientCapitalRecordViewSet,self).get_queryset().select_related('userCapital','userCapital__user','userCapital__user__userinfo')\n        self_userinfo = self.request.user.userinfo;\n        q_ids = [o.id for o in qs if o.userCapital.user.userinfo.check_supervisor(self_userinfo,[])]\n        q = qs.filter(id__in=q_ids)\n        return q;\n    \nclass UserInfoViewSetFC(FCViewSet):\n    def get_queryset(self):\n        return UserInfo.objects.filter(pk = self.request.user.userinfo.id)\n    emptyInstance = UserInfo()\n    serializer_class = UserInfoSerializerForFC\n    filter_fields = ('user__is_active',)\n    search_fields = ('code',)  \n    ordering_fields = ('id','lastmodified',)\n    \nclass FCClientInfoViewSet(FCViewSet):\n    permission_classes = FCViewSet.permission_classes+[Has_supervisorRight];\n    queryset = UserInfo.objects.all();\n    emptyInstance = UserInfo()\n    serializer_class = ClientUserInfoSerializer\n    filter_fields = ('user__is_active',)\n    search_fields = ('code',)  \n    ordering_fields = ('id','lastmodified',)\n","sub_path":"famportal/auth/viewset.py","file_name":"viewset.py","file_ext":"py","file_size_in_byte":5393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"649166195","text":"\"\"\"\nAuto-Encoder \nthe same generator architecture of InfoGAN(https://arxiv.org/abs/1606.03657) is used as decoder\nthe encoder is symmetric to the decode.\n\nReplace deconv with upsampling to remove block artifacts.\n\"\"\"\n\nfrom __future__ import print_function\nimport argparse\nimport torch\nimport torch.utils.data\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.autograd import Variable\nfrom torchvision import datasets, transforms\n\nimport os\nimport random\nimport torch.utils.data\nimport torchvision.utils as vutils\nimport torch.backends.cudnn as cudnn\nimport numpy as np\n\ndef initialize_weights(net):\n    for m in net.modules():\n        if isinstance(m, nn.Conv2d):\n            m.weight.data.normal_(0, 0.02)\n            m.bias.data.zero_()\n        elif isinstance(m, nn.ConvTranspose2d):\n            m.weight.data.normal_(0, 0.02)\n            m.bias.data.zero_()\n        elif isinstance(m, nn.Linear):\n            m.weight.data.normal_(0, 0.02)\n            m.bias.data.zero_()\n\n            \nclass ConvAE(nn.Module):\n    def __init__(self, args):\n        super(ConvAE, self).__init__()\n        if args.dataset == 'mnist' or args.dataset == 'Fashion-mnist':\n            self.nc = 1\n        else:\n            self.nc = 3\n        self.args = args\n        self.have_cuda = True\n        self.nz = args.num_latent\n        self.input_size = args.image_size\n        self.conv = nn.Sequential(\n            nn.Conv2d(self.nc, 64, 4, 2, 1),\n            #nn.BatchNorm2d(64),\n            nn.ReLU(True),\n            nn.Conv2d(64, 128, 4, 2, 1),\n            nn.BatchNorm2d(128),\n            nn.ReLU(True),\n        )\n        \n        self.encode_fc = nn.Sequential(\n            nn.Linear(128 * (self.input_size // 4) * (self.input_size // 4), 1024),\n            nn.BatchNorm1d(1024),\n            nn.ReLU(True),\n            nn.Linear(1024, self.nz),\n        )\n        \n        self.decode_fc = nn.Sequential(\n            nn.Linear(self.nz, 1024),\n            nn.BatchNorm1d(1024),\n            nn.ReLU(True),\n            nn.Linear(1024, 128 * (self.input_size // 4) * (self.input_size // 4)),\n            nn.BatchNorm1d(128 * (self.input_size // 4) * (self.input_size // 4)),\n            nn.ReLU(True),\n        )\n        self.deconv = nn.Sequential(\n            nn.UpsamplingBilinear2d(scale_factor=2),\n            #nn.UpsamplingNearest2d(scale_factor=2),\n            nn.Conv2d(128,64,3,padding=1),\n            nn.BatchNorm2d(64),\n            nn.ReLU(True),\n            nn.UpsamplingBilinear2d(scale_factor=2),\n            #nn.UpsamplingNearest2d(scale_factor=2),\n            nn.Conv2d(64,self.nc,3,padding=1),\n            nn.Tanh()\n        )\n        \n        initialize_weights(self)\n\n    def encode(self, x):\n        conv = self.conv(x)\n        h1 = self.encode_fc(conv.view(-1, 128*(self.input_size//4)*(self.input_size//4)))\n        return h1\n    \n    def decode(self, z):\n        deconv_input = self.decode_fc(z)\n        deconv_input = deconv_input.view(-1,128, self.input_size//4, self.input_size//4)\n        output = self.deconv(deconv_input)\n        if self.args.dataset == 'mnist' or self.args.dataset == 'Fashion-mnist':\n            if not self.args.loss_type == 'Perceptual':\n                output = output*0.5 + 0.5\n        return output\n\n    def forward(self, x):\n        z = self.encode(x)\n        decoded = self.decode(z)\n        return decoded, z","sub_path":"GLF/AE/convAE_infoGAN_up.py","file_name":"convAE_infoGAN_up.py","file_ext":"py","file_size_in_byte":3354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"396224478","text":"#!-*-encoding:utf8-*-\nfrom plugin.local_cmd import LocalCMD\n\nclass FetchKernel():\n    \n    def __init__(self,data):\n        self._data = data\n        \n        \n    def run(self,log=None):\n        \n        final_data={}\n        req_data = {\"cmd\":\"rpm -qa |grep kernel\"}\n        if log is not None:\n            log(req_data)\n        (result,code,msg,loop)=LocalCMD(req_data).run()\n        \n        return (result,code,msg,loop,final_data)","sub_path":"rev_server/plugin/fetch_kernel.py","file_name":"fetch_kernel.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"176043902","text":"import argparse\nfrom datetime import datetime\nfrom collections import OrderedDict\nfrom time import time\n\nclass EDGAR:\n\n    def __init__(self, logfile=\"\", duration_file=\"\", outputfile=\"\"):\n        self.logfile = logfile\n        self.duration = self._read_duration(duration_file)\n        self.active_users = OrderedDict()  # key: user ip  value : (min_time, max_time, num_access)\n        self.f_output = open(outputfile, \"w+\")\n        ## helper attributes\n        self.log_header = {}  # Map of header name to index in a row\n        self.maxdict_size = 0\n        self.num_check = 0\n\n    def _parse_line(self, linestr):\n        '''\n        parse the log file string\n        :param linestr: one string for one line in log file\n        :return: parsed ip, dattime obj, cik, accession, extension\n        '''\n        _parsed = linestr.split(\",\")\n        ip = _parsed[self.log_header[\"ip\"]]\n        date = _parsed[self.log_header[\"date\"]]\n        time = _parsed[self.log_header[\"time\"]]\n        datetime_str = \"\".join([date, time])\n        cdatetime = datetime.strptime(datetime_str, \"%Y-%m-%d%H:%M:%S\")\n        cik = _parsed[self.log_header[\"cik\"]]\n        accession = _parsed[self.log_header[\"accession\"]]\n        extention = _parsed[self.log_header[\"extention\"]]\n        return ip, cdatetime, cik, accession, extention\n\n    def _read_duration(self, file):\n        with open(file) as f:\n            duration = f.readline().split()[0]\n        return int(duration)\n\n    def _write_to_output(self, output_dict):\n        for ip, val in output_dict.iteritems():\n            mintime, maxtime, num_access = val\n            mintime_str = mintime.strftime(format=\"%Y-%m-%d %H:%M:%S\")\n            maxtime_str = maxtime.strftime(format=\"%Y-%m-%d %H:%M:%S\")\n            duration = (maxtime - mintime).total_seconds() + 1\n            self.f_output.write(\"%s,%s,%s,%i,%i \\n\"%(ip, mintime_str, maxtime_str, duration, num_access))\n        pass\n\n    def check_expire(self, cdatetime):\n        '''\n        For each datetime update check which  user activity expired, remove it from dict and return\n        :param cdatetime:\n        :return: expired user and the info\n        '''\n        expire_dict = OrderedDict()\n        for ip, val in self.active_users.iteritems():\n            mintime, maxtime, num_access = val\n            self.num_check += 1\n            if (cdatetime - maxtime).total_seconds() > self.duration:\n                del self.active_users[ip]\n                expire_dict[ip] = val\n        return expire_dict\n\n    def parse_log(self, header=1):\n        '''\n        :param header: 0 use predefined header for EDGAR 1: read headers from log file\n        :return:\n        '''\n        if header == 0:\n            self.log_header = {'accession': 5,\n                             'browser\\n': 14,\n                             'cik': 4,\n                             'code': 7,\n                             'crawler': 13,\n                             'date': 1,\n                             'extention': 6,\n                             'find': 12,\n                             'idx': 9,\n                             'ip': 0,\n                             'noagent': 11,\n                             'norefer': 10,\n                             'size': 8,\n                             'time': 2,\n                             'zone': 3}\n        else:\n            with open(self.logfile) as f:\n                headers = f.readline().split(\",\")\n                for i, header in enumerate(headers):\n                    self.log_header[header] = i\n\n                for line in f:\n                    ip, cdatetime, cik, accession, extention = self._parse_line(line)\n                    ## Check expired user and write to output\n                    output_dict = self.check_expire(cdatetime)\n                    if len(output_dict) > 0: self._write_to_output(output_dict)\n                    ## update ip\n                    if len(self.active_users) > self.maxdict_size: self.maxdict_size \\\n                                                                = len(self.active_users)\n                    if ip in self.active_users:\n                        mintime, maxtime, num_access = self.active_users[ip]\n                        self.active_users[ip] = mintime, cdatetime, num_access + 1\n                    else:\n                        self.active_users[ip] = cdatetime, cdatetime, 1\n                ## At the end of log files write rest of active users into the output\n                if len(self.active_users) > 0: self._write_to_output(self.active_users)\n                self.f_output.close() ## Close the output file\n        pass\n\ndef run_time(funcs):\n    start = time()\n    for func in funcs:\n        func()\n    end = time()\n    return end - start\n\ndef main(args):\n    logfile = args.log\n    inactivity_period = args.inactivity_period\n    output = args.output\n    sess = EDGAR(logfile, inactivity_period, output)\n    sess.parse_log()\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-log', '--log', type=str, default='./input/log.csv')\n    parser.add_argument('-inact', '--inactivity_period', type=str, default='./input/inactivity_period.txt')\n    parser.add_argument('-out', '--output', type=str, default='./output/sessionization.txt')\n    args = parser.parse_args()\n    main(args)","sub_path":"src/sessionization_v1.py","file_name":"sessionization_v1.py","file_ext":"py","file_size_in_byte":5308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"66090","text":"import os\r\nimport glob\r\nimport sys\r\nimport numpy as np\r\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\r\nROOT_DIR = os.path.dirname(BASE_DIR)\r\nsys.path.append(BASE_DIR)\r\n\r\ndata_dir = os.path.join(ROOT_DIR, 'data')\r\nmirrored_dir = os.path.join(data_dir, 'mirrored')\r\nif not os.path.exists(mirrored_dir):\r\n    os.mkdir(mirrored_dir)\r\nanno_paths = [line.rstrip() for line in open(os.path.join(BASE_DIR, 'meta/anno_paths.txt'))]\r\n\r\n\r\nfor anno_path in anno_paths:\r\n    print(anno_path)\r\n    elements = anno_path.split('/')\r\n    area = os.path.join(mirrored_dir+'/'+elements[-3])\r\n    if not os.path.exists(area):\r\n        os.mkdir(area)\r\n    part = os.path.join(area+'/'+elements[-2])\r\n    if not os.path.exists(part):\r\n        os.mkdir(part)\r\n    output_dir = os.path.join(part+'/'+'Annotations')\r\n    if not os.path.exists(output_dir):\r\n        os.mkdir(output_dir)\r\n    input = np.loadtxt(data_dir+'/'+'Stanford3dDataset_v1.2_Aligned_Version'+'/'+elements[-3]+'/'+elements[-2]+'/'+elements[-2]+'.txt', dtype=np.float, delimiter=' ')\r\n    num=len(input)\r\n    out = input\r\n    for i in range(num):\r\n        out[i,0] = -input[i,0]\r\n    np.savetxt(part+'/'+elements[-2]+'.txt', out, fmt='%.3f %.3f %.3f %d %d %d')\r\n    for f in glob.glob(os.path.join(data_dir, 'Stanford3dDataset_v1.2_Aligned_Version', anno_path, '*.txt')):\r\n        out_filename = os.path.basename(f)\r\n        input = np.loadtxt(f, dtype=np.float, delimiter=' ')\r\n        print (f)\r\n        num=len(input)\r\n        print (num)\r\n        out = input\r\n        for i in range(num):\r\n            out[i,0] = -input[i,0]\r\n        np.savetxt(output_dir+'/'+out_filename, out, fmt='%.3f %.3f %.3f %d %d %d')\r\n","sub_path":"sem_seg/mirror.py","file_name":"mirror.py","file_ext":"py","file_size_in_byte":1668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"214177408","text":"import os\nimport random\nfrom collections import defaultdict\n\nannot_fps = [\n'/media/dh/DATA4TB/Datasets/SGMaritimeDataset/VIS_Onboard_annot.txt',\n'/media/dh/DATA4TB/Datasets/SGMaritimeDataset/VIS_Onshore_annot.txt',\n'/media/dh/DATA4TB/Datasets/SGMaritimeDataset/NIR_annot.txt',\n            ]\n\nout_train = '/media/dh/DATA4TB/Datasets/SGMaritimeDataset/SMD_train.txt'\nout_val = '/media/dh/DATA4TB/Datasets/SGMaritimeDataset/SMD_val.txt'\ntrain_split = 0.8\n\ntrain_strs = []\nval_strs = []\ntotal_train_vid = 0\ntotal_train_img = 0\ntotal_val_img = 0\ntotal_val_vid = 0\nfor annot_fp in annot_fps:\n    with open(annot_fp,'r') as f:\n        lines = f.readlines()\n\n    frames_dict = defaultdict(list)\n    for line in lines:\n        split = line.strip().replace(';','').split()\n        img_name = split[0]\n        assert '_frame_' in img_name\n        vid_basename = img_name.split('_frame_')[0]\n        frames_dict[vid_basename].append(line)\n\n    vid_names = list(frames_dict.keys())\n    val_target = max(1, round((1-train_split)*len(vid_names))) # at least want 1 vid to be in validation set\n    random.shuffle(vid_names)\n\n    train_vid_count = 0\n    train_img_count = 0\n    val_img_count = 0\n    val_vid_count = 0\n    for vidname in vid_names[:val_target]:\n        val_strs.extend(frames_dict[vidname])\n        val_vid_count += 1\n        val_img_count += len(frames_dict[vidname])\n    for vidname in vid_names[val_target:]:\n        train_strs.extend(frames_dict[vidname])\n        train_vid_count += 1\n        train_img_count += len(frames_dict[vidname])\n    \n    print(os.path.basename(annot_fp))\n    print('\\tTrain: {} frames from {} videos'.format(train_img_count, train_vid_count))\n    print('\\tVal: {} frames from {} videos'.format(val_img_count, val_vid_count))\n    total_train_img += train_img_count\n    total_train_vid += train_vid_count\n    total_val_img += val_img_count\n    total_val_vid += val_vid_count\n\nwith open(out_train,'w') as f:\n    f.writelines(train_strs)\nwith open(out_val,'w') as f:\n    f.writelines(val_strs)\n\nprint('Combined')\nprint('\\tTrain: {} frames from {} videos'.format(total_train_img, total_train_vid))\nprint('\\tVal: {} frames from {} videos'.format(total_val_img, total_val_vid))\n\n        # im = Image.open(img_path)\n        # iw, ih = im.size\n        # bbs = split[1:]\n        # #x_min,y_min,x_max,y_max,class_id\n        # txt_strs = []\n        # is_too_big = False\n        # for bb in bbs:\n        #     x_min, y_min, x_max, y_max, class_id = [int(x) for x in bb.split(',')]\n\n        #     w = x_max - x_min + 1\n        #     h = y_max - y_min + 1\n        #     cx = x_min + w/2 - 1\n        #     cy = y_min + h/2 - 1\n            \n        #     ncx = cx / iw\n        #     ncy = cy / ih\n        #     nw = w / iw\n        #     nh = h / ih\n\n        #     if nh > max_height:\n        #         is_too_big = True\n        #         break\n\n        #     txt_strs.append('{} {} {} {} {}\\n'.format(class_id, ncx, ncy, nw, nh))\n\n        # img_basename = os.path.basename(img_path)\n        # basename = img_basename.split('.')[0]\n\n\n\n","sub_path":"train_val_splitter.py","file_name":"train_val_splitter.py","file_ext":"py","file_size_in_byte":3046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"64140984","text":"from rest_framework import mixins, viewsets\nfrom rest_framework.filters import SearchFilter\nfrom rest_framework.generics import get_object_or_404\nfrom rest_framework.pagination import LimitOffsetPagination\nfrom rest_framework.permissions import (IsAuthenticated,\n                                        IsAuthenticatedOrReadOnly)\n\nfrom api.serializers import (CommentSerializer, FollowSerializer,\n                             GroupSerializer, PostSerializer)\nfrom posts.models import Group, Post\n\nfrom . import permission\n\n\nclass PostViewSet(viewsets.ModelViewSet):\n    queryset = Post.objects.all()\n    serializer_class = PostSerializer\n    permission_classes = [IsAuthenticatedOrReadOnly,\n                          permission.OwnerOrReadOnly]\n    pagination_class = LimitOffsetPagination\n\n    def perform_create(self, serializer):\n        serializer.save(author=self.request.user)\n\n\nclass CommentViewSet(viewsets.ModelViewSet):\n    serializer_class = CommentSerializer\n    permission_classes = [IsAuthenticatedOrReadOnly,\n                          permission.OwnerOrReadOnly]\n\n    def get_queryset(self):\n        post = get_object_or_404(Post, id=self.kwargs.get('post_id'))\n        return post.comments\n\n    def perform_create(self, serializer):\n        post = get_object_or_404(Post, id=self.kwargs.get('post_id'))\n        serializer.save(author=self.request.user, post=post)\n\n\nclass GroupViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin,\n                   viewsets.GenericViewSet):\n    queryset = Group.objects.all()\n    serializer_class = GroupSerializer\n    permission_classes = [IsAuthenticatedOrReadOnly]\n\n\nclass FollowViewSet(mixins.ListModelMixin, mixins.CreateModelMixin,\n                    viewsets.GenericViewSet):\n    serializer_class = FollowSerializer\n    permission_classes = [IsAuthenticated, ]\n    filter_backends = (SearchFilter,)\n    search_fields = ('=following__username', '=user__username')\n\n    def get_queryset(self):\n        return self.request.user.follower\n\n    def perform_create(self, serializer):\n        serializer.save(user=self.request.user)\n","sub_path":"yatube_api/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"433511440","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Feb  1 18:39:43 2016\n\n@author: Glaucia\n\"\"\"\n\"\"\"\nTask 5\n\nCopy the program you wrote for Task 3 and save it as task5.py. \nModify the function you wrote to take π as a default argument. \nInclude in this program the function you wrote to generate e. \nCall the function that produces e, and have it return a value. \nPass that value of e to the function where you made π the default argument.\n\n\"\"\"\n\n#importing pi from the math module\nfrom math import pi\nimport functools\n\ndef messing_with_pi(number=pi):\n    \"\"\"a function that modifies the value of pi to different kinds of classes or round it\"\"\"\n    print(\"number converted to a string:\")\n    print(str(number))\n    print(\"number converted to an integer:\")\n    print(int(number))\n    print(\"number rounded to a single decimal place:\")\n    print(round(number,1))\n    print(\"the unmodified value of number:\")\n    print(number)\n\n    \n    \n\ndef Calculate_e(mn=1,mx=10000):\n    \"\"\"calculates the euler number, and you can specify the range of numbers you will use,\n    instead of going to infinity with a minimum (mn) and maximum (mx) values.\"\"\"\n    #setting an empty list to keep values from while loop\n    values_to_sum=[]\n    #setting one starting value as a denominator in the equation\n    denominator= 1\n    #making a while loop that runs while the \"mn\"value is smaller than the \"mx\" value.\n    while (mn < mx):\n        #calculating the denominators of the function one at a time\n        denominator = denominator * mn\n        #reseting the \"mn\" value, so we can get the next value in a range between the mn and\n        #mx value given as arguments\n        mn = mn + 1\n        #appending the results of multiplication in a list as denominators of 1/\n        values_to_sum.append(1/denominator)\n    #summing all the values in the list made on the previous step\n    eulernumber=1+(functools.reduce(lambda z, y: z+y,values_to_sum))\n    #returning the calculated value\n    return eulernumber\n\n\n\nif __name__ == \"__main__\":\n    e=Calculate_e()\n    messing_with_pi(number=e)","sub_path":"answers/glaudelrio/task5.py","file_name":"task5.py","file_ext":"py","file_size_in_byte":2056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"10082522","text":"# Use experience replay to train Q-learning and EV-SARSA\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\nfrom collections import deque\nimport gym\nfrom qlearning import *\nfrom pandas import DataFrame\n\n\nclass ReplayBuffer(object):\n    def __init__(self, size):\n        \"\"\"\n        Parameters\n        ----------\n        size: int\n            Max number of transitions to store in the buffer. When the buffer\n            overflows the old memories are dropped.\n        \"\"\"\n        self._storage = deque([])\n        self._maxsize = size\n\n\n    def __len__(self):\n        return len(self._storage)\n\n    def add(self, obs_t, action, reward, obs_tp1, done):\n        data = (obs_t, action, reward, obs_tp1, done)\n\n        # add data to storage\n        self._storage.append(data)\n        if self.__len__() > self._maxsize:\n            self._storage.popleft()\n\n    def sample(self, batch_size):\n        \"\"\"Sample a batch of experiences.\n        Parameters\n        ----------\n        batch_size: int\n            How many transitions to sample.\n        Returns\n        -------\n        obs_batch: np.array\n            batch of observations\n        act_batch: np.array\n            batch of actions executed given obs_batch\n        rew_batch: np.array\n            rewards received as results of executing act_batch\n        next_obs_batch: np.array\n            next set of observations seen after executing act_batch\n        done_mask: np.array\n            done_mask[i] = 1 if executing act_batch[i] resulted in\n            the end of an episode and 0 otherwise.\n        \"\"\"\n        idxes = [random.choice(range(self.__len__())) for _ in range(batch_size)]\n\n        # collect <s,a,r,s',done> for each index\n        obs_batch = []\n        act_batch = []\n        rew_batch = []\n        next_obs_batch = []\n        done_mask = []\n\n        for idx in idxes:\n            obs_batch.append(self._storage[idx][0])\n            act_batch.append(self._storage[idx][1])\n            rew_batch.append(self._storage[idx][2])\n            next_obs_batch.append(self._storage[idx][3])\n            done_mask.append(self._storage[idx][4])\n\n        return np.array(obs_batch), np.array(act_batch), np.array(rew_batch), np.array(next_obs_batch), np.array(\n            done_mask)\n\n\ndef play_and_train_with_replay(env, agent, replay=None,\n                               t_max=10 ** 4, replay_batch_size=32):\n    \"\"\"\n    :param replay: ReplayBuffer where agent can store and sample (s,a,r,s',done) tuples.\n        If None, do not use experience replay\n    \"\"\"\n    total_reward = 0.0\n    s = env.reset()\n\n    for t in range(t_max):\n        # get agent to pick action given state s\n        a = agent.get_action(s)\n\n        next_s, r, done, _ = env.step(a)\n\n        # update agent on current transition. Use agent.update\n        agent.update(s, a, r, next_s)\n\n        if replay is not None:\n            # store current <s,a,r,s'> transition in buffer\n            replay.add(s, a, r, next_s, done)\n\n            # sample replay_batch_size random transitions from replay,\n            # then update agent on each of them in a loop\n            s_batch, a_batch, r_batch, next_s_batch, done_batch = replay.sample(replay_batch_size)\n            for i in range(replay_batch_size):\n                agent.update(s_batch[i], a_batch[i], r_batch[i], next_s_batch[i])\n\n        s = next_s\n        total_reward += r\n        if done:\n            break\n\n    return total_reward\n\n\n# OpenAI gym Taxi-v2 example\nprint(\"\\nOpenAI gym Taxi-v2 example\")\nenv = gym.make(\"Taxi-v2\")\nn_actions = env.action_space.n\n\n# Create two agents: first will use experience replay, second will not.\nprint(\"\\nTwo agents: first will use experience replay, second will not.\")\nagent_baseline = QLearningAgent(alpha=0.5, epsilon=0.25, discount=0.99,\n                       get_legal_actions = lambda s: range(n_actions))\n\nagent_replay = QLearningAgent(alpha=0.5, epsilon=0.25, discount=0.99,\n                       get_legal_actions = lambda s: range(n_actions))\n\nprint(\"\\n Replay buffer has size 1000\")\nreplay = ReplayBuffer(1000)\n\nmoving_average = lambda x, span=100: DataFrame({'x': np.asarray(x)}).x.ewm(span=span).mean().values\nrewards_replay, rewards_baseline = [], []\n\nfor i in range(1000):\n    rewards_replay.append(play_and_train_with_replay(env, agent_replay, replay))\n    rewards_baseline.append(play_and_train_with_replay(env, agent_baseline, replay=None))\n\n    agent_replay.epsilon *= 0.99\n    agent_baseline.epsilon *= 0.99\n\n    if i % 100 == 0:\n        print('Baseline : eps =', agent_replay.epsilon, 'mean reward =', np.mean(rewards_baseline[-10:]))\n        print('ExpReplay: eps =', agent_baseline.epsilon, 'mean reward =', np.mean(rewards_replay[-10:]))\n        plt.plot(moving_average(rewards_replay), label='exp. replay')\n        plt.plot(moving_average(rewards_baseline), label='baseline')\n        plt.grid()\n        plt.legend()\n        plt.show()\n","sub_path":"Reinforcement learning/experience_replay.py","file_name":"experience_replay.py","file_ext":"py","file_size_in_byte":4898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"519438611","text":"class Rectangle():\r\n\r\n    def __init__(self, length, breadth):\r\n        self.length = length\r\n        self.breadth = breadth\r\n\r\n    def area(self):\r\n        return self.length * self.breadth\r\n\r\n    def perimeter(self):\r\n        return 2 * (self.length + self.breadth)\r\n\r\n\r\nclass Square(Rectangle):\r\n\r\n    def __init__(self, length):\r\n        super (Square, self).__init__(length, length)\r\n        #or Rectangle.__init_(length, length)\r\n\r\n\r\nprint(\"String formating\")\r\nname = 'JohnPaul'\r\nage = 24\r\nheight = 1.7\r\n\r\nprint('My name is %s and my age is %d but my height is %f' % (name, age, height))\r\nprint()\r\nprint('My name is {} and my age is {} but my height is {}'.format(name, age, height))\r\nprint()\r\nprint(f\"My name is {name} and my age is {age} but my height is {height}\")      \r\n","sub_path":"classRectangle.py","file_name":"classRectangle.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"534736753","text":"def move(direction, coord, path):\n    x = coord['x']\n    y = coord['y']\n    # remove any \\n if it exists\n    dir = direction.rstrip()\n    amt = int(dir[1:len(dir)])\n\n    if dir.startswith(\"R\"):\n        for i in range(x+1, x+amt+1):\n            path.append((i,y))\n        coord['x'] += amt\n    elif dir.startswith(\"L\"):\n        for i in range(x-1, x-amt-1, -1):\n            path.append((i,y))\n        coord['x'] -= amt\n    elif dir.startswith(\"U\"):\n        for i in range(y+1, y+amt+1):\n            path.append((x,i))\n        coord['y'] += amt\n    elif dir.startswith(\"D\"):\n        for i in range(y-1, y-amt-1, -1):\n            path.append((x,i))\n        coord['y'] -= amt\n    else:\n        # Fortunately we don't have any of these\n        print (\"Error! Wasn't expecting that!\")\n    return path\n\ndef findDistancesToCommonPoints(path, intersections):\n    positions = {}\n    previousCoord = (0,0)\n    cumulativeDistance = 0\n    for i in range(0, len(path)):\n        cumulativeDistance += calculateDistance(previousCoord, path[i])\n        if path[i] in intersections and path[i] not in positions:\n            positions[path[i]] = cumulativeDistance\n        # make the current coord the previous one for the next iteration\n        previousCoord = path[i]\n    return positions\n\ndef calculateDistance(point1, point2):\n    return abs(point1[0]-point2[0]) + abs(point1[1]-point2[1])\n\ndef main():\n\n    # Initialize: open file, put both wire directions into lists\n    with open('inputs/input_3.txt') as f:\n        wireOneDirections = f.readline().split(\",\")\n        wireTwoDirections = f.readline().split(\",\")\n\n    # Setup: get the paths that the wires take, put the coordinate points into a trackable list\n    wireCoord = { 'x': 0, 'y': 0}\n    firstpath = []\n    for i in range(0, len(wireOneDirections)):\n        move(wireOneDirections[i], wireCoord, firstpath)\n\n    wireCoord = { 'x': 0, 'y': 0}\n    secondpath = []\n    for i in range(0, len(wireTwoDirections)):\n        move(wireTwoDirections[i], wireCoord, secondpath)\n\n    # Part One: find the intersection point closest to the central port (which is at (0, 0))\n    intersections = set(firstpath).intersection(set(secondpath))\n    # (Initialize with large number...one that will be larger than any of the numbers we might encounter)\n    least = 1000000\n    for tup in intersections:\n        sum = abs(tup[0]) + abs(tup[1])\n        if sum < least:\n            least = sum\n\n    print (\"Part One: %i\" % least)\n\n    # Part Two: find the fewest combined steps the wires must take to reach an intersection\n    distancesOne = findDistancesToCommonPoints(firstpath, intersections)\n    distancesTwo = findDistancesToCommonPoints(secondpath, intersections)\n    combinedDistances = []\n    for key in distancesOne:\n        combinedDistances.append(distancesOne[key]+distancesTwo[key])\n\n    print (\"Part Two: %i\" % min(combinedDistances))\n\nif __name__ == '__main__':\n    main()\n","sub_path":"2019/problem_3_sol.py","file_name":"problem_3_sol.py","file_ext":"py","file_size_in_byte":2911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"219706045","text":"#encoding: utf-8\n\nimport pandas as pd\nimport numpy as np\nimport pymongo\nimport time\nimport sys\n\nsys.path.append(\"D:\\\\Tinysoft\\\\Analyse.NET\\\\\")\n##############################################################################\n##############################################################################\n#########################################################\n\nimport TSLPy3 as ts\n\ndef get_tradingDay_list(start_date, end_date):\n    a = ts.RemoteCallFunc('getTradingDay', [start_date, end_date], {})\n    return [d.decode() for d in a[1]]\n\ndef get_MoneyInFlow_Last30Min_fromTS(date_str):\n    a = ts.RemoteCallFunc('get_MoneyInFlow_Last30Min', [date_str], {})\n    return a[1]\n\ndef get_moneyInflow_by_date(date_str, store_to_csv=True):\n    results = get_MoneyInFlow_Last30Min_fromTS(date_str)\n\n    \n    df = pd.DataFrame(results)\n    df = pd.pivot_table(df, values=[b'x_amount'], \n                            index=[b'stock_id'], columns=[b'date'])\n\n    df_new = pd.DataFrame()\n    df_new[u'超大单'] = (df[df>=1000000].count(axis=1) - \n                      df[df<=-1000000].count(axis=1))\n    df_new[u'大单'] = (df[(df<1000000) & (df>=200000)].count(axis=1) -\n                    df[(df>-1000000) & (df<=-200000)].count(axis=1))\n    df_new[u'中单'] = (df[(df<200000) & (df>=40000)].count(axis=1) -\n                    df[(df>-200000) & (df<=-40000)].count(axis=1))\n    df_new[u'小单'] = (df[(df<40000) & (df>=0)].count(axis=1) -\n                    df[(df>-40000) & (df<=0)].count(axis=1))\n    df_new.index = df_new.index.map(lambda x: x.decode('utf-8'))    \n    df_new.index.name = 'stock_id'\n    \n    if store_to_csv==True:\n        df_new.to_csv(date_str+'.csv', encoding='utf-8', \n                      index_label=df_new.index.name)\n    \n    return df_new\n\n\ndef storeToMongoDB(date_str, df):\n    client = pymongo.MongoClient('mongodb://localhost:27017/')\n    mydb = client['Mesess_Strategy_Monitor']\n    collection = mydb['testing']\n    collection.insert_one({'date_str': date_str, 'df': df.to_json()})   \n\n\ndef getFromMongoDB(date_str):\n    client = pymongo.MongoClient('mongodb://localhost:27017/')\n    mydb = client['Mesess_Strategy_Monitor']\n    collection = mydb['testing']\n    doc= collection.find_one({'date_str': date_str})\n    df = pd.read_json(doc['df'])\n    return df\n\nif __name__=='__main__':\n#    print(get_tradingDay_list('2017-3-12', '2017-4-5'))\n    begin_t = time.time()\n    df1 = get_moneyInflow_by_date('2017-4-5')\n    end_t = time.time()\n    print('cost time1: %.7f sec'%(end_t-begin_t))\n#    storeToMongoDB('2017-4-5', df)\n    \n    begin_t = time.time()\n    df2 = getFromMongoDB('2017-4-5')\n    end_t = time.time()\n    print('cost time2: %.7f sec'%(end_t-begin_t))\n    \n    if df1==df2:\n        print('the same dataframe')\n    else:\n        print('different dataframe')\n#    print(df)\n        \n    \n#    for date_str in get_tradingDay_list('2017-3-12', '2017-4-5'):\n#        start_t = time.time()\n#        df = get_moneyInflow_by_date(date_str)\n#        end_t = time.time()\n#        print('date_str: %s cost time is %.7f sec'%(date_str, end_t-start_t))\n    \n#    df = pd.DataFrame.from_csv('2017-03-31.csv', encoding='utf-8')\n#    print(df)\n    \n","sub_path":"get_data.py","file_name":"get_data.py","file_ext":"py","file_size_in_byte":3177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"283975560","text":"from sklearn.datasets import load_svmlight_file\nfeature_vectors, targets = load_svmlight_file(\"training_data_file.IDF\")\nfrom sklearn import metrics\nfrom sklearn.cluster import KMeans, AgglomerativeClustering\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import chi2\nimport matplotlib.pyplot as plt\nX_new1 = SelectKBest(chi2, k=1000).fit_transform(feature_vectors, targets)\n\nkmeans_silhoutte_scores = []\nkmeans_normalized_mutual_scores = []\nkrange = range(2,25)\nfor n in krange:\n\n    kmeans_model = KMeans(n_clusters=n).fit(X_new1.toarray())\n    clustering_labels = kmeans_model.labels_\n    kmeans_silhoutte_scores.append(metrics.silhouette_score(X_new1.toarray(), clustering_labels\n                                                            , metric='euclidean'))\n    kmeans_normalized_mutual_scores.append(metrics.normalized_mutual_info_score(targets, clustering_labels))\n\nsingle_linkage_silhoutte_scores = []\nsingle_linkage_normalized_mutual_scores = []\n\nfor n in krange:\n    \n    single_linkage_model = AgglomerativeClustering(\n            n_clusters=n, linkage='ward').fit(X_new1.toarray())\n    clustering_labels = single_linkage_model.labels_\n    single_linkage_silhoutte_scores.append(metrics.silhouette_score(X_new1.toarray(), clustering_labels\n                                                                    , metric='euclidean'))\n    single_linkage_normalized_mutual_scores.append(metrics.normalized_mutual_info_score(targets, clustering_labels))\n\nplt.title('Kmeans')\nplt.plot(krange,kmeans_silhoutte_scores)\nplt.plot(krange,single_linkage_silhoutte_scores)\nplt.xlabel('Number of Clusters')\nplt.ylabel('Silhoutte Score')\nplt.legend(['Kmeans','Single Linkage'])\nplt.savefig('KMeans and Single Linkage - Silhoutte.jpg')\nplt.show()\nplt.title('Kmeans')\nplt.plot(krange,kmeans_normalized_mutual_scores)\nplt.plot(krange,single_linkage_normalized_mutual_scores)\nplt.xlabel('Number of Clusters')\nplt.ylabel('Mutual Information Score')\nplt.title('Single Linkage')\nplt.legend(['Kmeans','Single Linkage'])\nplt.savefig('Kmeans and Single Linkage K - Mutual Information.jpg')\nplt.show()","sub_path":"GreeshWorkSpace/Information_retrieval/Information_retrieval_project2/clustering.py","file_name":"clustering.py","file_ext":"py","file_size_in_byte":2122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"396406495","text":"#!/usr/bin/python\n\nimport shutil\nimport os\nimport errno\nimport logging\n\nimport utils_asc as util\n\n\nclass ImageTransporter(object):\n    \"\"\"transports images from a session_id from images_stored to images_processing\"\"\"\n\n    _GITKEEPFILE = \".gitkeep\"\n\n    def __init__(self, session_id):\n        self._target_directory = session_id\n        self._target_dir_with_path_in_stored = util.IMAGES_STORED + \"/\" + self._target_directory\n        self._target_dir_with_path_in_processing = util.IMAGES_PROCESSING + \"/\" + self._target_directory\n\n    def transport_incoming_images_to_processing(self):\n        \"\"\"clears sessionid-dir in processing folder, lists images to move there, moves them\"\"\"\n        self._provide_cleared_processing_classdir()\n        images_to_move = self._make_list_of_incoming_images()\n        self._move_to_processing(images_to_move)\n        return images_to_move\n\n    def _provide_cleared_processing_classdir(self):\n        \"\"\"clears the classdir in processing if this dir already exists,\n        create the dir if it does not. Log errors in creation and removal\"\"\"\n        if os.path.isdir(self._target_dir_with_path_in_processing):\n            for image_name in os.listdir(self._target_dir_with_path_in_processing):\n                self._delete_item_in_images_processing_dir(image_name)\n        else:\n            util.ensure_directory_exists(self._target_dir_with_path_in_processing)\n        log_message = util.construct_log_message(util.LOG_PROVIDED_PROCESSING_DIR,\n                                                 self._target_dir_with_path_in_processing)\n        logging.info(log_message)\n\n    def _delete_item_in_images_processing_dir(self, item_name):\n        \"\"\"deletes an item in the images_processing folder\"\"\"\n        image_with_path = self._target_dir_with_path_in_processing + \"/\" + item_name\n        util.remove_file(image_with_path)\n\n    def _make_list_of_incoming_images(self):\n        \"\"\"store all head_pose_extraction in the folder in a list\"\"\"\n        images_to_transfer = list()\n        for image_name in os.listdir(self._target_dir_with_path_in_stored):\n            image_and_path = self._target_dir_with_path_in_stored + \"/\" + image_name\n            images_to_transfer.append(image_and_path)\n        return images_to_transfer\n\n    def _move_to_processing(self, images_to_move):\n        \"\"\"moves all items in the list to the processing folder (in a class-specific folder)\"\"\"\n        for current_item_path in images_to_move:\n            new_item_path = current_item_path.replace(util.IMAGES_STORED, util.IMAGES_PROCESSING)\n            util.move(current_item_path, new_item_path)\n","sub_path":"src/move_incoming_images_to_processing.py","file_name":"move_incoming_images_to_processing.py","file_ext":"py","file_size_in_byte":2612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"507387369","text":"#!/usr/bin/env python\n\n'''\nCreated by Samvel Khalatyan, Mar 12, 2012\nCopyright 2011, All rights reserved\n'''\n\nfrom __future__ import print_function\n\nimport sys\nimport template.main\n\ndef parser():\n    parser = template.main.parser()\n\n    parser.add_option(\n            \"--systematic\",\n            action = \"store\",\n            help = \"Add systematic suffix to saved plots\")\n\n    parser.add_option(\n            \"--output\",\n            action = \"store\", default = \"theta_input.root\",\n            help = \"output filename\")\n\n    parser.add_option(\n            \"--savechannels\",\n            action = \"store\",\n            help = \"comma separated channels to be saved\")\n\n    parser.add_option(\n            \"--thetaprefix\",\n            action = \"store\", default = \"el\",\n            help = \"specify prefix for Theta plots\")\n\n    return parser\n\ndef main():\n    try:\n        options, args = parser().parse_args()\n\n        # import templates only here otherwise PyROOT inhercepts --help option\n        import templates\n\n        app = templates.Templates(options, args)\n        app.run()\n\n    except Exception as error:\n        # print Exception traceback for debug\n        import traceback\n\n        traceback.print_tb(sys.exc_info()[2])\n\n        print(error, file = sys.stderr)\n\n        return 1\n\n    else:\n        return 0\n\nif \"__main__\" == __name__:\n    sys.exit(main())\n","sub_path":"python/theta/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"490260986","text":"import json\nfrom pathlib import Path\n\nfrom sqlalchemy.schema import Table\n\nfrom .common import ColumnMapping, parse_yaml_file\n\n\nclass InvalidLoaderError(Exception):\n    pass\n\n\nclass BaseLoader:\n    \"\"\"Base class for all Loader classes.\n    \"\"\"\n\n    def __init__(self, data_dir: Path, table: Table, column_map: dict = {}, model_map: dict = {}):\n        self.data_dir = data_dir\n        self.table = table\n        self.column_map = column_map\n        self.model_map = model_map\n\n        self.validate()\n\n    def extract_records(self, model):\n        raise NotImplementedError()\n\n    def validate(self):\n        raise NotImplementedError()\n\n\nclass YAMLSingleFileLoader(BaseLoader):\n    \"\"\"YAMLSingleFileLoader is used to load records from directories which\n    contain a `_all.yml` file.\n\n    Please note that while the existence of this file is a necessary\n    requirement, this loader would still be chosen if the directory contains\n    other files.\n    \"\"\"\n\n    @property\n    def data_path(self):\n        return self.data_dir.joinpath(self.table.name).joinpath('_all.yml')\n\n    def extract_records(self, model):\n        values = parse_yaml_file(self.data_path)\n\n        for value in values:\n            kwargs = {\n                column.name: value.get(column.name)\n                for column in self.table.columns\n            }\n\n            yield model(**kwargs)\n\n    def validate(self):\n        all_ = self.data_path\n\n        if not all_.exists() or not all_.is_file():\n            raise InvalidLoaderError()\n\n\nclass YAMLDirectoryLoader(BaseLoader):\n    \"\"\"YAMLDirectoryLoader is used to load records from directories which\n    contain only YAML-formatted files.\n    \"\"\"\n\n    extensions = ('.yml', '.yaml', '.YML', '.YAML')\n\n    @property\n    def data_path(self):\n        return self.data_dir.joinpath(self.table.name)\n\n    def extract_records(self, model):\n        for entry in self.data_path.glob('**/*'):\n            if not entry.is_file():\n                continue\n\n            values = parse_yaml_file(entry)\n\n            kwargs = {}\n            # iterate default mappings\n            for k, v in self.column_map.items():\n                if v == ColumnMapping.FILE_NAME:\n                    kwargs[k] = entry.stem\n                elif v == ColumnMapping.FOLDER_NAME:\n                    kwargs[k] = entry.parent.name\n                else:\n                    raise ValueError(\"Unknown column mapping '{}'\".format(v))\n            # iterate all mappings in file\n            for k, value in values.items():\n                if k in self.table.columns.keys():\n                    if isinstance(value, list) or isinstance(value, dict):\n                        value = json.dumps(value, indent=4)\n                    kwargs[k] = value\n                # check if we have a table in our schema with this key as name\n                elif self.model_map and k in self.table.metadata.tables.keys() and \\\n                        (isinstance(value, list) or isinstance(value, dict)):\n                    sub_table = self.table.metadata.tables[k]\n                    foreign_kwargs = {}\n                    # if the found table has foreign keys referencing the current table, try to populate these\n                    for const in sub_table.foreign_key_constraints:\n                        for self_key, foreign_key in zip(const.column_keys, const.elements):\n                            if foreign_key.references(self.table):\n                                foreign_key = foreign_key.column.name\n                                foreign_kwargs[self_key] = kwargs[foreign_key]\n                    if isinstance(value, list):\n                        for sub_kwargs in value:\n                            sub_kwargs.update(foreign_kwargs)\n                            yield self.model_map[k][0](**sub_kwargs)\n                    else:\n                        value.update(foreign_kwargs)\n                        yield self.model_map[k][0](**value)\n\n            yield model(**kwargs)\n\n    def validate(self):\n        for entry in self.data_path.glob('**/*'):\n            if not entry.is_file():\n                continue\n\n            if not any(\n                ext for ext in self.extensions if entry.name.endswith(ext)\n            ):\n                raise InvalidLoaderError()\n\n\ndef loader_for(data_dir: Path, table: Table, column_map: dict = {}, model_map: dict = {}):\n    for cls in (YAMLSingleFileLoader, YAMLDirectoryLoader):\n        try:\n            loader = cls(data_dir, table, column_map=column_map, model_map=model_map)\n        except InvalidLoaderError:\n            pass\n        else:\n            return loader\n","sub_path":"flask_filealchemy/loaders.py","file_name":"loaders.py","file_ext":"py","file_size_in_byte":4607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"458345809","text":"from sqlalchemy.orm import joinedload\nfrom twivia import db\nimport difflib\nimport random\n\n\nFUZZY_MATCH_RATIO = 0.8\n\nwith open('data/stopwords.csv') as f:\n    stop_words = set(f.read().split(','))\n\n\nclass Category(db.Model):\n    __tablename__ = 'category'\n\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(100))\n    questions = db.relationship('Question', backref='category', lazy='dynamic')\n\n    def __repr__(self):\n        return '<Category %r>' % self.name\n\n    def get_random_questions(self, num_questions=10):\n        return random.sample(self.questions.all(), num_questions)\n\n\nclass Question(db.Model):\n    __tablename__ = 'question'\n\n    id = db.Column(db.Integer, primary_key=True)\n    question = db.Column(db.String(200))\n    answer = db.Column(db.String(200))\n    category_id = db.Column(db.Integer, db.ForeignKey('category.id'))\n\n    def __repr__(self):\n        return '<Question: %r, Answer: %r>' % (self.question, self.answer)\n\n    def checkAnswer(self, answer):\n        if self.answer == answer:\n            return True\n\n        clean_answer = normalized_answer(self.answer)\n        clean_input_answer = normalized_answer(answer)\n\n        if clean_answer == clean_input_answer:\n            return True\n\n        if difflib.SequenceMatcher(None, clean_answer, clean_input_answer).ratio() >= FUZZY_MATCH_RATIO:\n            return True\n\n        return False\n\n\ndef normalized_answer(answer):\n    return ' '.join(sorted([w for w in answer.lower().split() if w.lower() not in stop_words]))\n\n\ngames_played_table = db.Table('games_played',\n                              db.Column('player_id', db.Integer, db.ForeignKey('player.id')),\n                              db.Column('game_id', db.Integer, db.ForeignKey('game.id'))\n)\n\n\nclass Game(db.Model):\n    CREATED = -1\n    RUNNING = 0\n    FINISHED = 1\n\n    __tablename__ = 'game'\n\n    id = db.Column(db.Integer, primary_key=True)\n    num_players = db.Column(db.Integer)\n    current_round_num = 0\n    rounds = db.relationship('Round', backref='game', lazy='dynamic')\n    players = db.relationship('Player', secondary=games_played_table, backref=db.backref('games', lazy='dynamic'))\n    status = db.Column(db.Integer, default=CREATED)\n\n    def __init__(self, num_players, category, num_rounds=10):\n        self.num_players = num_players\n        self.status = Game.CREATED\n        self.rounds = self.create_rounds(category, num_rounds)\n\n    def create_rounds(self, category, num_rounds):\n        rounds = []\n        questions = category.get_random_questions(num_questions=num_rounds)\n\n        for i in xrange(len(questions)):\n            rounds.append(Round(round_num=i, game=self, question=questions[i]))\n\n        return rounds\n\n    def start(self):\n        self.status = Game.RUNNING\n\n    def end(self):\n        self.status = Game.FINISHED\n\n    def advance_round(self):\n        if self.current_round_num > len(self.rounds.all()):\n            self.end()\n        else:\n            self.current_round_num += 1\n\n    @property\n    def current_round(self):\n        return self.rounds[self.current_round_num]\n\n    @property\n    def started(self):\n        return self.status == Game.RUNNING\n\n    @property\n    def ended(self):\n        return self.status == Game.FINISHED\n\n    def register_player(self, player):\n        # if user.current_game: return False\n        self.players.append(player)\n        #\n        # player = Player(user=user, game=self)\n        # db.session.add(player)\n\n        if (len(self.players) == self.num_players):\n            self.start()\n\n    def submit_answer(self, player, answer):\n        round = self.current_round\n        correct = round.question.checkAnswer(answer)\n        return SubmittedAnswer(player=player, round=round, is_correct=correct, answer=answer)\n\n\nclass Round(db.Model):\n    __tablename__ = 'round'\n\n    id = db.Column(db.Integer, primary_key=True)\n    submitted_answers = db.relationship('SubmittedAnswer', backref='round', lazy='dynamic')\n\n    round_num = db.Column(db.Integer)\n    game_id = db.Column(db.Integer, db.ForeignKey('game.id'))\n\n    question_id = db.Column(db.Integer, db.ForeignKey('question.id'))\n    question = db.relationship(Question)\n\n    def __repr__(self):\n        return '<Id: %s Round_Num: %s Game_Id: %s Question_Id: %s>' % (\n        self.id, self.round_num, self.game_id, self.question_id)\n\n\nclass Player(db.Model):\n    __tablename__ = 'player'\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(100))\n    phone_number = db.Column(db.String(20))\n\n    @property\n    def current_game(self):\n        for game in self.games:\n            if game.started:\n                return game\n                # return Player.query.options(joinedload('game')).filter(\n                #     Player.user_id == self.id and Player.game.status != Game.FINISHED).first()\n\n\nclass SubmittedAnswer(db.Model):\n    __tablename__ = 'submitted_answer'\n\n    id = db.Column(db.Integer, primary_key=True)\n    answer = db.Column(db.String(200))\n\n    round_id = db.Column(db.Integer, db.ForeignKey('round.id'))\n\n    player_id = db.Column(db.Integer, db.ForeignKey('player.id'))\n    player = db.relationship('Player', uselist=False)\n\n    is_correct = db.Column(db.Boolean)\n","sub_path":"twivia/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"517156602","text":"\"\"\"Read ds18b20 and VDD voltage, write to mqtt.\"\"\"\n\nimport time\n\nimport ds18x20\nimport machine\nimport network\nimport onewire\nimport ujson\nfrom machine import ADC\nimport myEspNow\nfrom utils import loadValue\nfrom config import config\n\ntimestamps = {}\n\ndef read_voltage():\n    \"\"\"Read VDD voltage and LiPO battery level.\"\"\"\n    voltage = round( ADC(0).read() * config['ADC2VOLT'], 2 )\n    return {\n        'vcc': voltage\n    }\n\n\ndef read_temps():\n    \"\"\"Read DS18B20's.\"\"\"\n    dat = machine.Pin(config['PIN_1W'])\n    ds = ds18x20.DS18X20(onewire.OneWire(dat))\n\n    ds.convert_temp()\n    time.sleep_ms(750)\n\n    return {'temperature': round(ds.read_temp(rom), 1) for rom in ds.scan()}\n\ndef deepsleep(uptime=0, calibrate=False):\n    \"\"\"Put to sleep for 60 seconds minus uptime.\"\"\"\n    rtc = machine.RTC()\n    rtc.irq(trigger=rtc.ALARM0, wake=machine.DEEPSLEEP)\n    sleeptime = max(config['MIN_INTERVAL'], (config['INTERVAL_SEC'] * 1000) - uptime)\n    rtc.alarm(rtc.ALARM0, sleeptime)\n    machine.deepsleep()\n\n\ndef templog(sleep=False):\n    global timestamps\n    \"\"\"Log voltage and temperature to MQTT.\"\"\"\n    timestamps['start'] = time.ticks_ms()\n\n    espNow = myEspNow.initEspNow()\n    \n\n    # get sensor values\n    values = read_voltage()\n    values.update(read_temps())\n    \n    uptime = loadValue('uptime', -1)\n    values.update(uptime)\n\n    not_connected = loadValue('not_connected', -1)\n    values.update(not_connected)\n\n    retries = loadValue('retries',-1)\n    values.update(retries)\n\n    values.update({'name':config['NAME']})\n\n    print(values)\n    msg = ujson.dumps(values)\n    sent, retries = espNow.send(msg)\n\n    with open('retries','w') as f :\n        f.write(ujson.dumps({'retries': retries}))\n\n    with open('not_connected', 'w') as f:\n        if sent:\n            f.write(ujson.dumps({'not_connected': 0}))\n        else:\n            f.write(ujson.dumps({'not_connected': not_connected['not_connected'] + 1}))\n\n    delta = time.ticks_diff(timestamps['start'], time.ticks_ms())\n    with open('uptime', 'w') as f:\n        f.write(ujson.dumps({'uptime': round(-delta / 1000.0, 1)}))\n    if sleep:\n        deepsleep(delta)\n","sub_path":"templog.py","file_name":"templog.py","file_ext":"py","file_size_in_byte":2131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"155446972","text":"import csv\nimport os\n\nfrom flask import Flask, redirect, render_template, request, url_for\nfrom models2 import *\n\napp = Flask(__name__)\n\napp.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get('DATABASE_URL')\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n\n\ndef main():\n  f = open(\"travellers.csv\")\n  reader = csv.reader(f)\n  for name, flight_id in reader:\n      travellers = Traveller(name=name, flight_id=flight_id)\n      db.session.add(travellers)\n      print(f'{name}, {flight_id}')\n  db.session.commit()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"python /import1.py","file_name":"import1.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"255932459","text":"\"\"\"\n    Notice that this is used as a submodule of groups'a app module\n\"\"\"\nimport logging\nfrom typing import Any, MutableMapping, Optional\n\nfrom pydantic import ValidationError\n\nfrom ._config import SciCrunchSettings\nfrom .service_client import SciCrunch\n\nlogger = logging.getLogger(__name__)\n\n\ndef setup_scicrunch_submodule(\n    app: MutableMapping[str, Any], *, cfg: Optional[SciCrunchSettings] = None\n):\n    try:\n        cfg = SciCrunchSettings()\n        api = SciCrunch.acquire_instance(app, cfg)\n        assert api == SciCrunch.get_instance(app)  # nosec\n\n    except ValidationError as err:\n        logger.warning(\n            \"Failed to setup interface with K-Core. This functionality will not be available: %s\",\n            err,\n        )\n","sub_path":"services/web/server/src/simcore_service_webserver/scicrunch/submodule_setup.py","file_name":"submodule_setup.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"493300238","text":"#  Licensed to the Apache Software Foundation (ASF) under one\n#  or more contributor license agreements.  See the NOTICE file\n#  distributed with this work for additional information\n#  regarding copyright ownership.  The ASF licenses this file\n#  to you under the Apache License, Version 2.0 (the\n#  \"License\"); you may not use this file except in compliance\n#  with the License.  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,\n#  software distributed under the License is distributed on an\n#  \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n#  KIND, either express or implied.  See the License for the\n#  specific language governing permissions and limitations\n#  under the License.\n\n\nimport wx\nimport armid\nfrom EnvironmentListCtrl import EnvironmentListCtrl\nfrom Borg import Borg\n\nclass RiskEnvironmentListCtrl(EnvironmentListCtrl):\n  def __init__(self,parent,winId,dp):\n    EnvironmentListCtrl.__init__(self,parent,winId,dp)\n    self.theCurrentRisk = ''\n\n  def setRisk(self,riskName):\n    self.theCurrentRisk = riskName\n\n  def onAddDimension(self,evt):\n    currentDimensions = self.dimensions()\n    dimensions = self.dbProxy.riskEnvironmentNames(self.theCurrentRisk)\n    remainingDimensions = [x for x in dimensions if x not in currentDimensions]\n    from DimensionNameDialog import DimensionNameDialog\n    dlg = DimensionNameDialog(self,self.theDimensionTable,remainingDimensions,'Add')\n    if (dlg.ShowModal() == armid.DIMNAME_BUTTONACTION_ID):\n      for additionalDimension in dlg.dimensionNames():\n        idx = self.GetItemCount()\n        self.InsertStringItem(idx,additionalDimension)\n\n","sub_path":"cairis/cairis/RiskEnvironmentListCtrl.py","file_name":"RiskEnvironmentListCtrl.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"549052127","text":"# Databricks notebook source\nfrom tweepy import OAuthHandler\nfrom tweepy import API\nfrom tweepy import Cursor\n\nconsumer_key = '' #twitter app’s API Key\nconsumer_secret = '' #twitter app’s API secret Key\naccess_token = '' #twitter app’s Access token\naccess_token_secret = '' #twitter app’s access token secret\n\nauth = OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\nauth_api = API(auth)\n\nIBMacess_tweets = auth_api.user_timeline(screen_name = \"IBMAccess\", count = 1000, include_rts = False, tweet_mode = \"extended\")\nfinal_tweets = [each_tweet.full_text for each_tweet in IBMacess_tweets]\n\nwith open('/dbfs/IBMAccess.txt', 'w') as f:\n  for item in final_tweets:\n    f.write(\"%s\\n\" % item)\n\n# COMMAND ----------\n\nread_tweets = []\nwith open('/dbfs/IBMAccess.txt','r') as f:\n  read_tweets.append(f.read())\n  \nfor x in read_tweets:\n  print(x)\n","sub_path":"SentimentAnalysis/tweet_extraction.py","file_name":"tweet_extraction.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"624981744","text":"import pytest\nimport sys\nsys.path.append(\"./model/\")\nsys.path.append(\"./controller/\")\nsys.path.append(\"./view/\")\nfrom gui_controller import AppWindow\nfrom PyQt5.QtWidgets import QApplication\n\nDATA_FOLDER = 'Testing/Test_Data/'\nEXTRA_INFO = 'Testing/Test_Files/extra_information.xlsx'\n\n\ndef pytest_sessionstart(session):\n    \"\"\" before session.main() is called. \"\"\"\n    for i in range(0, 2):\n        app = QApplication(sys.argv)\n        w = AppWindow()\n        w.showMaximized()\n        aw = (app, w)\n        aw[1].ui.directory.setText(DATA_FOLDER)\n        aw[1].ui.rdb_rachis_yes.setChecked(False)\n        aw[1].ui.btn_load_data.click()\n        aw[1].ui.master_tab.setCurrentIndex(1)\n        aw[1].ui.expinfo.setText(EXTRA_INFO)\n        aw[1].pre_process_controller.load_experimental_data()\n        aw[1].pre_process_controller.clean_data()\n        aw[1].ui.master_tab.setCurrentIndex(2)\n        aw\n        aw[0].quit()\n","sub_path":"Testing/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"79444093","text":"from problems.middle_of_the_linked_list import middle_node, ListNode\nfrom test_utils import compare_list_nodes\n\nDEBUG = False\n\n\ndef test_middle_of_the_linked_list_1():\n    head = ListNode(1, ListNode(2, ListNode(3, ListNode(4, ListNode(5)))))\n    expected = head.next.next\n    actual = middle_node(head)\n    assert compare_list_nodes(expected, actual, DEBUG) is True\n\n\ndef test_middle_of_the_linked_list_2():\n    head = ListNode(1, ListNode(2, ListNode(3, ListNode(4, ListNode(5, ListNode(6))))))\n    expected = head.next.next.next\n    actual = middle_node(head)\n    assert compare_list_nodes(expected, actual, DEBUG) is True\n","sub_path":"tests/test_middle_of_the_linked_list.py","file_name":"test_middle_of_the_linked_list.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"266080522","text":"'''Train CIFAR10 with PyTorch.'''\nfrom __future__ import print_function\nimport sys\nsys.path.append('..')\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.backends.cudnn as cudnn\n\nimport torchvision\nimport torchvision.transforms as transforms\n\nimport os\nimport argparse\n\nfrom models import *\nfrom torch.autograd import Variable\nimport numpy as np\nimport random\n\nparser = argparse.ArgumentParser(description='cSG-MCMC CIFAR10 Ensemble')\nparser.add_argument('--dir', type=str, default=None, required=True, help='path to checkpoints (default: None)')\nparser.add_argument('--data_path', type=str, default=None, required=True, metavar='PATH',\n                    help='path to datasets location (default: None)')\nparser.add_argument('--device_id',type = int, help = 'device id to use')\nparser.add_argument('--seed', type=int, default=1,\n                    help='random seed')\nargs = parser.parse_args()\ndevice_id = args.device_id\nuse_cuda = torch.cuda.is_available()\ntorch.manual_seed(args.seed)\nnp.random.seed(args.seed)\nrandom.seed(args.seed)\n\n# Data\nprint('==> Preparing data..')\ntransform_test = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n])\ntestset = torchvision.datasets.CIFAR10(root=args.data_path, train=False, download=True, transform=transform_test)\ntestloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=0)\n\n# Model\nprint('==> Building model..')\nnet = ResNet18()\nif use_cuda:\n    net.cuda(device_id)\n    cudnn.benchmark = True\n    cudnn.deterministic = True\n\ncriterion = nn.CrossEntropyLoss()\n\ndef get_accuracy(truth, pred):\n    assert len(truth)==len(pred)\n    right = 0\n    for i in range(len(truth)):\n        if truth[i]==pred[i]:\n             right += 1.0\n    return right/len(truth)\n\ndef test():\n    global best_acc\n    net.eval()\n    test_loss = 0\n    correct = 0\n    total = 0\n    pred_list = []\n    truth_res = []\n    with torch.no_grad():\n        for batch_idx, (inputs, targets) in enumerate(testloader):\n            if use_cuda:\n                inputs, targets = inputs.cuda(device_id), targets.cuda(device_id)\n            truth_res += list(targets.data)\n            outputs = net(inputs)\n            pred_list.append(F.softmax(outputs,dim=1))\n            loss = criterion(outputs, targets)\n\n            test_loss += loss.data.item()\n            _, predicted = torch.max(outputs.data, 1)\n            total += targets.size(0)\n            correct += predicted.eq(targets.data).cpu().sum()\n\n    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n        test_loss/len(testloader), correct, total,\n        100. * correct.item() / total))\n    pred_list = torch.cat(pred_list,0)\n    return pred_list,truth_res\n\npred_list = []\nnum_model = 12\nfor m in range(num_model):\n    net.load_state_dict(torch.load(args.dir + '/cifar_model_%i.pt'%(m)))\n    pred, truth_res = test()\n    pred_list.append(pred)\n\nfake = sum(pred_list)/num_model\nvalues, pred_label = torch.max(fake,dim = 1)\npred_res = list(pred_label.data)\nacc = get_accuracy(truth_res, pred_res)\nprint(acc)\n","sub_path":"experiments/cifar_ensemble.py","file_name":"cifar_ensemble.py","file_ext":"py","file_size_in_byte":3177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"132818057","text":"#part of the Missouri Trail game\r\n#by Erik B & David I (davidizzy)\r\n\r\nimport random, outcomes\r\n\r\ndef is_prime(n):\r\n  '''Check to see if a given number is prime'''\r\n\r\n  # Check that n is not a special case (i.e. 0 or 1)\r\n  if n == 0 or n == 1:\r\n    return False\r\n\r\n  # Count number of divisors n has. Anything greater than 2 means\r\n  # n has more than 1 and itself as divisors, therefore not prime\r\n  count = 0\r\n  for i in range(1, n+1):\r\n    if n % i == 0:\r\n      count += 1\r\n    if count > 2:\r\n      return False\r\n  return True\r\n\r\ndef forward(player): #move wagon forward random number of spaces\r\n    spaces = random.randint(0,50)\r\n    player.distanceTraveled += spaces\r\n    if is_prime(spaces):\r\n        return(bad_move(player, spaces))\r\n    else:\r\n        return \"The road is rough, but you're pressing onward!\"\r\n\r\ndef bad_move(player, spaces):\r\n    if spaces <= 13:\r\n        return outcomes.safe_move(player)\r\n    elif spaces > 13 and spaces < 29:\r\n        return outcomes.dysentery(player)\r\n    else:\r\n        return outcomes.wolf_attack(player)\r\n","sub_path":"movements.py","file_name":"movements.py","file_ext":"py","file_size_in_byte":1052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"86872373","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.special import erfinv\n\nfs=20\nlw=5\nxmin=1\nxmax=28\nA=np.arange(xmin,xmax)\nlin=A*(A-1)/2.0\nip=A*(A-1)*(A-2)*(A-3)/8.0\nquad=lin**2-lin\nquadfix=quad-ip\n\nyip=(lin+ip)/lin\nyquad=(lin+quad)/lin\nyquadfix=(lin+quadfix)/lin\n\nplt.ylabel('Number Correlation Terms',fontsize=fs)\nplt.xlabel('A',fontsize=fs)\nplt.xlim([xmin,xmax])\n\n#plt.plot(A,ylin,'b',marker='-',A,yip,'r.-',A,yquad,'g:',linewidth=lw)\nplt.plot(A,yip,'b',A,yquad,'r',A,yquadfix,'g',linewidth=lw)\nplt.figure()\nplt.plot(A,yquad/yip,'r',A,yquadfix/yip,'g')\n\nplt.savefig('scaleing_theory.pdf',format='pdf')\nplt.show()\n","sub_path":"work/prospectus/figures/scaling_theory.py","file_name":"scaling_theory.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"556054918","text":"import pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import cross_val_score, train_test_split\nimport joblib\n\n\ndef train_model(data_to_build_model, prefixes_to_use, clf=None):\n    \"\"\" \n    Function to build a model. The model currently is a sklearn model can be changed\n    to any deep learning model in further iterations\n\n    :type data_to_build_model: str\n    :param data_to_build_model: path to read data for model building\n\n    :type prefixes_to_use: list\n    :param prefixes_to_use: list of column prefixes i.e just pass [\"book\", \"author\"] \n    even though the actual columns being [\"book1\",\"book2\",\"author1\",\"author2\"].\n\n    :type clf: sklearn.model\n    :param clf: Any sklearn model to just fit and dump. If this is provided then no\n    CV is done. Instead just fit and dumped\n\n    \"\"\"\n    train_data = pd.read_csv(data_to_build_model)\n\n    cols_to_keep = list()\n    for i in prefixes_to_use:\n        cols = train_data.columns\n        cols_to_keep.extend(cols[cols.str.startswith(f\"{i}_\")])\n\n    cols_to_keep.append(\"label\")\n    train_data = train_data[cols_to_keep]\n\n    y = train_data[\"label\"]\n    X = train_data.drop([\"label\"], axis=1)\n\n    if clf:\n        print(\"Model provided. Not peforming any CV. Directly fitting and dumping.\")\n    else:\n        clf = RandomForestClassifier()\n        scores = cross_val_score(clf, X, y, scoring=\"f1\", cv=4, n_jobs=-1)\n        print(f\"Cross validation scores: {str(scores)}. Mean: {scores.mean()}\")\n\n    # Finally building the new model\n    clf.fit(X, y)\n    print(\"Model built successfully.\")\n\n    if len(prefixes_to_use)==1:\n        model_path_to_write = f\"models/model_{prefixes_to_use[0]}.pkl\"\n    else:\n        model_path_to_write = f\"models/model.pkl\"\n    with open(model_path_to_write, \"wb\") as f:\n        joblib.dump(clf, f)\n\n    print(f\"Model dumped : {model_path_to_write}\")\n\n    return \"Done\"\n\n\nif __name__ == \"__main__\":\n    # train_model(\"output/prepared_data.csv\",\n    #             [\"book\", \"author\"])\n    train_model(\"output/prepared_data_2.csv\",\n            [\"title\"])\n","sub_path":"dedupe/src/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"18005511","text":"\n\nclass NetAPI(object):\n    # Node Net API facade class for use from within the node net (in node functions)\n\n    @property\n    def uid(self):\n        return self._nodenet.uid\n\n    @property\n    def step(self):\n        \"\"\" The current step of the nodenet \"\"\"\n        return self._nodenet.current_step\n\n    @property\n    def is_running(self):\n        return self._nodenet.is_active\n\n    @property\n    def worldadapter(self):\n        \"\"\" The worldadapter instance \"\"\"\n        return self._nodenet.worldadapter_instance\n\n    @property\n    def logger(self):\n        \"\"\" The nodenet logger \"\"\"\n        return self._nodenet.logger\n\n    @property\n    def statuslogger(self):\n        return self._nodenet.statuslogger\n\n    def __init__(self, nodenet):\n        self._nodenet = nodenet\n\n    def get_nodespace(self, uid):\n        \"\"\"\n        Return the nodespace with the given uid\n        \"\"\"\n        return self._nodenet.get_nodespace(uid)\n\n    def get_nodespaces(self, parent=None):\n        \"\"\"\n        Return a list of all nodespaces inside the given nodespace\n\n        Params\n        ------\n            parent: uid (string), optional\n                consider only nodespaces inside this nodespace\n        \"\"\"\n        return [self._nodenet.get_nodespace(uid) for\n                uid in self._nodenet.get_nodespace(parent).get_known_ids('nodespaces')]\n\n    def get_node(self, uid):\n        \"\"\"\n        Return the node with the given uid\n        \"\"\"\n        return self._nodenet.get_node(uid)\n\n    def get_nodes(self, nodespace=None, node_name_prefix=None, nodetype=None, sortby='ids'):\n        \"\"\"\n        Search for nodes by name, nodespace and nodetype\n\n        Params\n        ------\n            nodespace: uid (string), optional\n                return only nodes from this nodespace\n\n            node_name_prefix: string, optional\n                return only nodes whose name starts with this string\n\n            nodetype: string\n                return only nodes of the given type\n\n            sortby: string 'ids' or 'names'\n                sort returned list of nodes by node.uid or by node.name\n\n        Returns\n        -------\n            List of node instances\n\n        \"\"\"\n        nodes = []\n        all_ids = None\n        if nodespace is not None:\n            all_ids = self._nodenet.get_nodespace(nodespace).get_known_ids('nodes')\n        else:\n            all_ids = self._nodenet.get_node_uids()\n\n        for node_uid in all_ids:\n            node = self._nodenet.get_node(node_uid)\n            if nodetype is not None and nodetype != node.type:\n                continue\n            if node_name_prefix is not None and not node.name.startswith(node_name_prefix):\n                continue\n            nodes.append(node)\n\n        if sortby == 'ids':\n            nodes = sorted(nodes, key=lambda node: node.uid)\n        elif sortby == 'names':\n            nodes = sorted(nodes, key=lambda node: node.name)\n        else:\n            raise ValueError(\"Unknown sortby value %s\" % sortby)\n\n        return nodes\n\n    def get_nodes_in_gate_field(self, node, gate=None, no_links_to=None, nodespace=None):\n        \"\"\"\n        Returns all nodes linked to a given node on the gate, excluding the ones that have\n        links of any of the given types\n        \"\"\"\n        nodes = []\n        if gate is not None:\n            gates = [gate]\n        else:\n            gates = self._nodenet.get_node(node.uid).get_gate_types()\n        for gate in gates:\n            for link in self._nodenet.get_node(node.uid).get_gate(gate).get_links():\n                skip = False\n                if no_links_to is not None:\n                    for g in no_links_to:\n                        g = link.target_node.get_gate(g)\n                        if g and g.get_links():\n                            skip = True\n                            break\n                if skip or (nodespace is not None and nodespace != link.target_node.parent_nodespace):\n                    continue\n                nodes.append(link.target_node)\n        return nodes\n\n    def get_nodes_in_slot_field(self, node, slot=None, no_links_to=None, nodespace=None):\n        \"\"\"\n        Returns all nodes linking to a given node on the given slot, excluding the ones that\n        have links of any of the given types\n        \"\"\"\n        nodes = []\n        if slot is not None:\n            slots = [slot]\n        else:\n            slots = self._nodenet.get_node(node.uid).get_slot_types()\n        for slot in slots:\n            for link in self._nodenet.get_node(node.uid).get_slot(slot).get_links():\n                skip = False\n                if no_links_to is not None:\n                    for g in no_links_to:\n                        g = link.source_node.get_gate(g)\n                        if g and g.get_links():\n                            skip = True\n                            break\n                if skip or (nodespace is not None and nodespace != link.source_node.parent_nodespace):\n                    continue\n                nodes.append(link.source_node)\n        return nodes\n\n    def get_nodes_active(self, nodespace, type=None, min_activation=1, gate=None):\n        \"\"\"\n        Returns all nodes with a min activation, of the given type, active at the given gate, or with node.activation\n        \"\"\"\n        nodes = []\n        for node in self.get_nodes(nodespace):\n            if type is None or node.type == type:\n                if gate is not None:\n                    if gate in node.get_gate_types():\n                        if node.get_gate(gate).activation >= min_activation:\n                            nodes.append(node)\n                else:\n                    if node.activation >= min_activation:\n                        nodes.append(node)\n        return nodes\n\n    def delete_node(self, node):\n        \"\"\"\n        Delete a node and all links connected to it.\n\n        Params\n        ------\n            node: node instance\n        \"\"\"\n        self._nodenet.delete_node(node.uid)\n\n    def delete_nodespace(self, nodespace):\n        \"\"\"\n        Delete a nodesace and all nodes and nodespaces contained within, and all links connected to it.\n\n        Params\n        ------\n            nodespace: nodespace instance\n        \"\"\"\n        self._nodenet.delete_nodespace(nodespace.uid)\n\n    def create_node(self, nodetype, nodespace=None, name=None, **parameters):\n        \"\"\"\n        Creates a new node of the given type\n\n        Params\n        ------\n            nodetype: string\n                the `name` field of the nodetype definition\n\n            nodespace: uid (string)\n                the nodespace that the new node should belong to.\n\n            name: string\n                name of the new node\n\n            **parameters:\n                additional keyword arguments will set the value of the corresponding\n                node parameter.\n\n        Returns\n        -------\n            the new node instance\n\n        \"\"\"\n        if name is None:\n            name = \"\"   # TODO: empty names crash the client right now, but really shouldn't\n        pos = [100, 100, 0]  # default so native modules will not be bothered with positions\n\n        uid = self._nodenet.create_node(nodetype, nodespace, pos, name, parameters=parameters)\n        entity = self._nodenet.get_node(uid)\n        return entity\n\n    def create_nodespace(self, parent_nodespace, name=None, options=None):\n        \"\"\"\n        Create a new nodespace with the given name in the given parent_nodespace\n        Options:\n            new_partition - Whether or not to create a seperate partition for this nodespace\n                            Attention: Experimental Feature, Sensors & Actuators only work in the root-partition\n        \"\"\"\n        if name is None:\n            name = \"\"   # TODO: empty names crash the client right now, but really shouldn't\n\n        uid = self._nodenet.create_nodespace(parent_nodespace, name=name, options=options)\n        entity = self._nodenet.get_nodespace(uid)\n        return entity\n\n    def link(self, source_node, source_gate, target_node, target_slot, weight=1):\n        \"\"\"\n        Create a link between two nodes.\n\n        If the link already exists, it will be updated\n        with the given weight (or the default 1 if not given)\n\n        Params\n        ------\n            source_node: node instance\n\n            source_gate: gate type (string)\n                selects from which of the source node's gates the link should originate\n\n            target_node: node instance\n\n            target_slot: slot type (string)\n                selects in which of the target node's slots the link should land\n\n            weight: numeric\n                the link weight\n\n        \"\"\"\n        self._nodenet.create_link(source_node.uid, source_gate, target_node.uid, target_slot, weight)\n\n    def link_with_reciprocal(self, source_node, target_node, linktype, weight=1):\n        \"\"\"\n        Creates two (reciprocal) links between two nodes, valid linktypes are subsur, porret, catexp and symref\n        \"\"\"\n        target_slot_types = target_node.get_slot_types()\n        source_slot_types = source_node.get_slot_types()\n        if linktype == \"subsur\":\n            subslot = \"sub\" if \"sub\" in target_slot_types else \"gen\"\n            surslot = \"sur\" if \"sur\" in source_slot_types else \"gen\"\n            self._nodenet.create_link(source_node.uid, \"sub\", target_node.uid, subslot, weight)\n            self._nodenet.create_link(target_node.uid, \"sur\", source_node.uid, surslot, weight)\n        elif linktype == \"porret\":\n            porslot = \"por\" if \"por\" in target_slot_types else \"gen\"\n            retslot = \"ret\" if \"ret\" in source_slot_types else \"gen\"\n            self._nodenet.create_link(source_node.uid, \"por\", target_node.uid, porslot, weight)\n            self._nodenet.create_link(target_node.uid, \"ret\", source_node.uid, retslot, weight)\n        elif linktype == \"catexp\":\n            catslot = \"cat\" if \"cat\" in target_slot_types else \"gen\"\n            expslot = \"exp\" if \"exp\" in source_slot_types else \"gen\"\n            self._nodenet.create_link(source_node.uid, \"cat\", target_node.uid, catslot, weight)\n            self._nodenet.create_link(target_node.uid, \"exp\", source_node.uid, expslot, weight)\n        elif linktype == \"symref\":\n            symslot = \"sym\" if \"sym\" in target_slot_types else \"gen\"\n            refslot = \"ref\" if \"ref\" in source_slot_types else \"gen\"\n            self._nodenet.create_link(source_node.uid, \"sym\", target_node.uid, symslot, weight)\n            self._nodenet.create_link(target_node.uid, \"ref\", source_node.uid, refslot, weight)\n\n    def unlink(self, source_node, source_gate=None, target_node=None, target_slot=None):\n        \"\"\"\n        Deletes a link, or links, originating from the given node\n\n        Params\n        ------\n            source_node: node instance\n\n            source_gate: gate type (string)\n                origin of the to-be-deleted link on the source node\n\n            target_node: node instance\n\n            target_slot: slot type (string)\n                endpoint of the to-be-deleted link on the target node\n\n        \"\"\"\n        target_node_uid = target_node.uid if target_node is not None else None\n        source_node.unlink(source_gate, target_node_uid, target_slot)\n\n    def unlink_gate(self, node, gate_name, target_node_uid=None, target_slot_name=None):\n        \"\"\"\n        Deletes all links from the given gate, optionally filtered by target_node_uid or target_slot_name\n        \"\"\"\n        node.unlink(gate_name, target_node_uid=target_node_uid, slot_name=target_slot_name)\n\n    def unlink_slot(self, node, slot_name, source_node_uid=None, source_gate_name=None):\n        \"\"\"\n        Deletes all links to the given slot, optionally filtered by source_node_uid or source_gate_name\n        \"\"\"\n        for l in node.get_slot(slot_name).get_links():\n            if source_node_uid is None or l.source_node.uid == source_node_uid:\n                if source_gate_name is None or l.source_gate.type == source_gate_name:\n                    l.source_node.unlink(l.source_gate.type, target_node_uid=node.uid, slot_name=slot_name)\n\n    def link_actuator(self, node, datatarget, weight=1, gate='sub', slot='sur'):\n        \"\"\"\n        Links a node to an actuator. If no actuator exists in the node's nodespace for the given datatarget,\n        a new actuator will be created, otherwise the first actuator found will be used\n        \"\"\"\n        actuator = None\n        for uid, candidate in self._nodenet.get_actuators(node.parent_nodespace).items():\n            if candidate.get_parameter('datatarget') == datatarget:\n                actuator = candidate\n        if actuator is None:\n            actuator = self.create_node(\"Actuator\", node.parent_nodespace, datatarget)\n            actuator.set_parameter('datatarget', datatarget)\n\n        self.link(node, gate, actuator, 'gen', weight)\n        # self.link(actuator, 'gen', node, slot)\n\n    def link_sensor(self, node, datasource, slot='sur', weight=1):\n        \"\"\"\n        Links a node to a sensor. If no sensor exists in the node's nodespace for the given datasource,\n        a new sensor will be created, otherwise the first sensor found will be used\n        \"\"\"\n        sensor = None\n        for uid, candidate in self._nodenet.get_sensors(node.parent_nodespace).items():\n            if candidate.get_parameter('datasource') == datasource:\n                sensor = candidate\n        if sensor is None:\n            sensor = self.create_node(\"Sensor\", node.parent_nodespace, datasource)\n            sensor.set_parameter('datasource', datasource)\n\n        self.link(sensor, 'gen', node, slot, weight)\n\n    def import_actuators(self, nodespace, datatarget_prefix=None):\n        \"\"\"\n        Makes sure an actuator for all datatargets whose names start with the given prefix, or all datatargets,\n        exists in the given nodespace.\n        \"\"\"\n        all_actuators = []\n        if self.worldadapter is None:\n            return all_actuators\n\n        datatargets = self.worldadapter.get_available_datatargets()\n\n        for datatarget in datatargets:\n            if datatarget_prefix is None or datatarget.startswith(datatarget_prefix):\n                actuator = None\n                for uid, candidate in self._nodenet.get_actuators(nodespace, datatarget).items():\n                    actuator = candidate\n                    break\n                if actuator is None:\n                    actuator = self.create_node(\"Actuator\", nodespace, datatarget)\n                    actuator.set_parameter('datatarget', datatarget)\n                all_actuators.append(actuator)\n        return all_actuators\n\n    def import_sensors(self, nodespace, datasource_prefix=None):\n        \"\"\"\n        Makes sure a sensor for all datasources whose names start with the given prefix, or all datasources,\n        exists in the given nodespace.\n        \"\"\"\n        all_sensors = []\n        if self.worldadapter is None:\n            return all_sensors\n\n        datasources = self.worldadapter.get_available_datasources()\n\n        for datasource in datasources:\n            if datasource_prefix is None or datasource.startswith(datasource_prefix):\n                sensor = None\n                for uid, candidate in self._nodenet.get_sensors(nodespace, datasource).items():\n                    sensor = candidate\n                    break\n                if sensor is None:\n                    sensor = self.create_node(\"Sensor\", nodespace, datasource)\n                    sensor.set_parameter('datasource', datasource)\n                all_sensors.append(sensor)\n        return all_sensors\n\n    def notify_user(self, node, msg):\n        \"\"\"\n        Stops the nodenetrunner for this nodenet and displays some information to the user,\n        who can then choose to continue running or keep it stopped.\n\n        Params\n        ------\n            node: node instance\n                the node object that emits this message\n            msg: a string to display to the user\n        \"\"\"\n        self._nodenet.set_user_prompt(node, None, msg, [])\n\n    def show_user_prompt(self, node, key):\n        \"\"\"\n        Stop the nodenetrunner and display a dialogue that collects user input\n\n        Params\n        ------\n            node: node instance\n                the node that wants to create this dialoge - must have a\n                `user_prompts` field in its nodetype definition\n\n            key: str\n                select a particular prompt from the nodetype's 'user_prompts' field.\n\n        \"\"\"\n        promptinfo = node.get_user_prompt(key)\n        self._nodenet.set_user_prompt(node, key, promptinfo['callback'].__doc__, promptinfo['parameters'])\n\n    def autoalign_nodespace(self, nodespace):\n        \"\"\" Calls the autoalignment on the given nodespace \"\"\"\n        from micropsi_core.nodenet.node_alignment import align\n        if nodespace in self._nodenet.get_nodespace_uids():\n            align(self._nodenet, nodespace)\n\n    def autoalign_entities(self, nodespace, entity_uids):\n        \"\"\" Calls the autoalignment on the given entities in the given nodespace \"\"\"\n        from micropsi_core.nodenet.node_alignment import align\n        if nodespace in self._nodenet.get_nodespace_uids():\n            align(self._nodenet, nodespace, entity_uids)\n\n    def get_modulator(self, modulator):\n        \"\"\"\n        Returns the numeric value of the given global modulator\n        \"\"\"\n        return self._nodenet.get_modulator(modulator)\n\n    def change_modulator(self, modulator, diff):\n        \"\"\"\n        Changes the value of the given global modulator by the value of diff\n        \"\"\"\n        self._nodenet.change_modulator(modulator, diff)\n\n    def set_modulator(self, modulator, value):\n        \"\"\"\n        Changes the value of the given global modulator to the given value\n        \"\"\"\n        self._nodenet.set_modulator(modulator, value)\n\n    def copy_nodes(self, nodes, nodespace_uid):\n        \"\"\"\n        Copys the given nodes into the target nodespace. Also copies the internal linkage\n        between these nodes\n        \"\"\"\n        uids = [node.uid for node in nodes]\n        uidmap = {}\n        for node in nodes:\n            new_uid = self._nodenet.create_node(node.type, nodespace_uid, node.position, name=node.name, parameters=node.clone_parameters(), gate_configuration=node.get_gate_configuration())\n            uidmap[node.uid] = new_uid\n        for node in nodes:\n            for g in node.get_gate_types():\n                for l in node.get_gate(g).get_links():\n                    if l.target_node.uid in uids:\n                        self.link(self.get_node(uidmap[l.source_node.uid]),\n                                  l.source_gate.type,\n                                  self.get_node(uidmap[l.target_node.uid]),\n                                  l.target_slot.type,\n                                  weight=l.weight)\n        mapping = {}\n        for node in nodes:\n            mapping[node] = self.get_node(uidmap[node.uid])\n        return mapping\n\n    def group_nodes_by_names(self, nodespace_uid, node_name_prefix=None, gate=\"gen\", sortby='id', group_name=None):\n        \"\"\"\n        Will group the given set of nodes.\n        Groups can be used in bulk operations.\n        Grouped nodes will have stable sorting accross all bulk operations.\n        If no group name is given, the node_name_prefix will be used as group name.\n        \"\"\"\n        self._nodenet.group_nodes_by_names(nodespace_uid, node_name_prefix, gatetype=gate, sortby=sortby, group_name=group_name)\n\n    def group_nodes_by_ids(self, nodespace_uid, node_uids, group_name, gate=\"gen\", sortby='id'):\n        \"\"\"\n        Will group the given set of nodes.\n        Groups can be used in bulk operations.\n        Grouped nodes will have stable sorting accross all bulk operations.\n        \"\"\"\n        self._nodenet.group_nodes_by_ids(nodespace_uid, node_uids, group_name, gatetype=gate, sortby=sortby)\n\n    def ungroup_nodes(self, nodespace_uid, group):\n        \"\"\"\n        Deletes the given group (not the nodes, just the group assignment)\n        \"\"\"\n        self._nodenet.ungroup_nodes(nodespace_uid, group)\n\n    def get_activations(self, nodespace_uid, group):\n        \"\"\"\n        Returns an array of activations for the given group.\n        \"\"\"\n        return self._nodenet.get_activations(nodespace_uid, group)\n\n    def substitute_activations(self, nodespace_uid, group, new_activations):\n        \"\"\"\n        Sets the activation of the given elements to the given value.\n        Note that this overrides the calculated activations, including all gate mechanics,\n        including gate function, thresholds, min, max, amplification and directional\n        activators - the values passed will be propagated in the next step.\n        \"\"\"\n        return self._nodenet.set_activations(nodespace_uid, group, new_activations)\n\n    def get_gate_configurations(self, nodespace_uid, group, gatefunction_parameter=None):\n        \"\"\"\n        Returns a dictionary containing a list of gatefunction names, and a list of the values\n        of the given gatefunction_parameter (if given)\n        \"\"\"\n        return self._nodenet.get_gate_configurations(nodespace_uid, group, gatefunction_parameter)\n\n    def set_gate_configurations(self, nodespace_uid, group, gatefunction, gatefunction_parameter=None, parameter_values=None):\n        \"\"\"\n        Bulk-sets gatefunctions and a gatefunction_parameter for the given group.\n        Arguments:\n            nodespace_uid (string) - id of the parent nodespace\n            group (string) - name of the group\n            gatefunction (string) - name of the gatefunction to set\n            gatefunction_parameter (optinoal) - name of the gatefunction_paramr to set\n            parameter_values (optional) - values to set for the gatefunction_parameetr\n        \"\"\"\n        self._nodenet.set_gate_configurations(nodespace_uid, group, gatefunction, gatefunction_parameter, parameter_values)\n\n    def get_link_weights(self, nodespace_from_uid, group_from, nodespace_to_uid, group_to):\n        \"\"\"\n        Returns the weights of links between two groups as a matrix.\n        Rows are group_to slots, columns are group_from gates.\n        Non-existing links will be returned as 0-entries in the matrix.\n        \"\"\"\n        return self._nodenet.get_link_weights(nodespace_from_uid, group_from, nodespace_to_uid, group_to)\n\n    def set_link_weights(self, nodespace_from_uid, group_from, nodespace_to_uid, group_to, new_w):\n        \"\"\"\n        Sets the weights of links between two groups from the given matrix new_w.\n        Rows are group_to slots, columns are group_from gates.\n        Note that setting matrix entries to non-0 values will implicitly create links.\n        \"\"\"\n        self._nodenet.set_link_weights(nodespace_from_uid, group_from, nodespace_to_uid, group_to, new_w)\n\n    def get_node_ids(self, nodespace_uid, group):\n        \"\"\"\n        Returns the uids of the nodes in the given group\n        \"\"\"\n        return self._nodenet.get_node_uids(nodespace_uid, group)\n\n    def add_gate_monitor(self, node_uid, gate, name=None, color=None):\n        \"\"\"Adds a continuous monitor to the activation of a gate. The monitor will collect the activation\n        value in every calculation step.\n        Returns the uid of the new monitor.\"\"\"\n        return self._nodenet.add_gate_monitor(node_uid, gate, name=name, color=color)\n\n    def add_slot_monitor(self, node_uid, slot, name=None, color=None):\n        \"\"\"Adds a continuous monitor to the activation of a slot. The monitor will collect the activation\n        value in every calculation step.\n        Returns the uid of the new monitor.\"\"\"\n        return self._nodenet.add_slot_monitor(node_uid, slot, name=name, color=color)\n\n    def add_link_monitor(self, source_node_uid, gate_type, target_node_uid, slot_type, name=None, color=None):\n        \"\"\"Adds a continuous weightmonitor to a link. The monitor will record the linkweight in every calculation step.\n        Returns the uid of the new monitor.\"\"\"\n        return self._nodenet.add_link_monitor(source_node_uid, gate_type, target_node_uid, slot_type, name=name, color=color)\n\n    def add_modulator_monitor(self, modulator, name, color=None):\n        \"\"\"Adds a continuous monitor to a global modulator.\n        The monitor will collect respective value in every calculation step.\n        Returns the uid of the new monitor.\"\"\"\n        return self._nodenet.add_modulator_monitor(modulator, name, color=color)\n\n    def add_custom_monitor(self, function, name, color=None):\n        \"\"\"Adds a continuous monitor, that evaluates the given python-code and collects the\n        return-value for every calculation step.\n        Returns the uid of the new monitor.\"\"\"\n        return self._nodenet.add_custom_monitor(function, name, color=color)\n\n    def add_adhoc_monitor(self, function, name, parameters={}):\n        return self._nodenet.add_adhoc_monitor(function, name, parameters)\n\n    def add_group_monitor(self, nodespace, name, node_name_prefix='', node_uids=[], gate='gen', color=None):\n        \"\"\"Adds a continuous monitor, that tracks the activations of the given group\n        return-value for every calculation step.\n        Returns the uid of the new monitor.\"\"\"\n        return self._nodenet.add_group_monitor(nodespace, name, node_name_prefix=node_name_prefix, node_uids=node_uids, gate=gate, color=color)\n\n    def get_monitor(self, uid):\n        \"\"\"Returns the monitor with the given uid\"\"\"\n        return self._nodenet.get_monitor(uid)\n\n    def remove_monitor(self, uid):\n        \"\"\"Removes the monitor with the given uid\"\"\"\n        return self._nodenet.remove_monitor(uid)\n\n    def set_dashboard_value(self, name, value):\n        \"\"\"Allows the netapi to set values for the statistics and dashboard\"\"\"\n        self._nodenet.dashboard_values[name] = value\n\n    def decay_por_links(self, nodespace_uid):\n        \"\"\" Decayes all por-links in the given nodespace \"\"\"\n        porretdecay = self._nodenet.get_modulator('base_porret_decay_factor')\n        nodes = self.get_nodes(nodespace=nodespace_uid, nodetype=\"Pipe\")\n        decay_factor = (1 - porretdecay)\n        if porretdecay != 0:\n            for node in nodes:\n                porgate = node.get_gate('por')\n                for link in porgate.get_links():\n                    if link.weight > 0:\n                        link._set_weight(max(link.weight * decay_factor, 0))\n\n    def get_nodespace_properties(self, nodespace_uid=None):\n        \"\"\" retrieve the ui properties for the given nodespace\"\"\"\n        return self._nodenet.get_nodespace_properties(nodespace_uid)\n\n    def set_nodespace_properties(self, nodespace_uid, properties):\n        \"\"\" sets the ui properties for the given nodespace\"\"\"\n        self._nodenet.set_nodespace_properties(nodespace_uid, properties)\n\n    def announce_nodes(self, nodespace_uid, numer_of_nodes, average_element_per_node):\n        pass\n","sub_path":"micropsi_core/nodenet/netapi.py","file_name":"netapi.py","file_ext":"py","file_size_in_byte":26836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"256303524","text":"def retry_get(self, url, headers, count):\r\n        try:\r\n            response = requests.get(url, headers=headers, proxies=proxies, timeout=10)\r\n            if response.status_code == 200:\r\n                return response\r\n            raise Exception\r\n        except:\r\n            if count < 10:\r\n                # if self.judge_stop():\r\n                #     return\r\n                response = self.retry_get(url, headers, count + 1)\r\n                return response\r\n            else:\r\n                return None","sub_path":"临时模块/访问重试写法/retry.py","file_name":"retry.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"29564865","text":"import re\nfrom typing import Tuple\n\ndef addr(registers, a, b, c):\n    registers[c] = registers[a] + registers[b]\n\ndef addi(registers, a, b, c):\n    registers[c] = registers[a] + b\n\ndef mulr(registers, a, b, c):\n    registers[c] = registers[a] * registers[b]\n\ndef muli(registers, a, b, c):\n    registers[c] = registers[a] * b\n\ndef banr(registers, a, b, c):\n    registers[c] = registers[a] & registers[b]\n\ndef bani(registers, a, b, c):\n    registers[c] = registers[a] & b\n\ndef borr(registers, a, b, c):\n    registers[c] = registers[a] | registers[b]\n\ndef bori(registers, a, b, c):\n    registers[c] = registers[a] | b\n\ndef setr(registers, a, b, c):\n    registers[c] = registers[a]\n\ndef seti(registers, a, b, c):\n    registers[c] = a\n\ndef gtir(registers, a, b, c):\n    registers[c] = 1 if a > registers[b] else 0\n    \ndef gtri(registers, a, b, c):\n    registers[c] = 1 if registers[a] > b else 0\n    \ndef gtrr(registers, a, b, c):\n    registers[c] = 1 if registers[a] > registers[b] else 0\n\ndef eqir(registers, a, b, c):\n    registers[c] = 1 if a == registers[b] else 0\n    \ndef eqri(registers, a, b, c):\n    registers[c] = 1 if registers[a] == b else 0\n    \ndef eqrr(registers, a, b, c):\n    registers[c] = 1 if registers[a] == registers[b] else 0\n\n\nclass CPU(object):\n    instructions = [\n        addr, addi,\n        mulr, muli,\n        banr, bani,\n        borr, bori,\n        setr, seti,\n        gtir, gtri, gtrr,\n        eqir, eqri, eqrr\n    ]\n    def __init__(self, registers=None, opcode_transform=None):\n        self.registers = registers or [0, 0, 0, 0]\n        self.transform = opcode_transform\n    \n    def execute(self, instruction: Tuple[int, int, int, int]) -> None:\n        opcode, a, b, c = instruction\n        if self.transform:\n            opcode = self.transform[opcode]\n        CPU.instructions[opcode](self.registers, a, b, c)\n\ndef load(filename):\n    samples = []\n    with open(filename) as f:\n        while f:\n            m = re.match(r'Before:\\s+\\[(\\d+), (\\d+), (\\d+), (\\d+)\\]', f.readline())\n            if not m:\n                break\n            before = [int(x) for x in m.groups()]\n            instruction = [int(x) for x in f.readline().split()]\n            m = re.match(r'After:\\s+\\[(\\d+), (\\d+), (\\d+), (\\d+)\\]', f.readline())\n            after = [int(x) for x in m.groups()]\n            samples.append((before, instruction, after))\n            f.readline()\n        \n        program = []\n        for line in f:\n            instruction = tuple(int(x) for x in line.split())\n            if len(instruction) == 4:\n                program.append(instruction)\n\n    return samples, program\n\ndef part1():\n    samples, _ = load('input/16ex')\n    result = []\n    for before, sample, after in samples:\n        print(sample)\n        count = 0\n        for instr in CPU.instructions:\n            registers=list(before)\n            _, a, b, c = sample\n            instr(registers, a, b, c)\n            if registers == after:\n                count += 1\n        result.append(count)\n\n    print(sum(1 for x in result if x >= 3))\n\ndef main():\n    samples, program = load('input/16')\n    possibilities = []\n    for _ in range(len(CPU.instructions)):\n        possibilities.append(set(range(len(CPU.instructions))))\n\n    for before, sample, after in samples:\n        x, a, b, c = sample\n        for opcode in range(len(CPU.instructions)):\n            cpu = CPU(registers=list(before))\n            cpu.execute((opcode, a, b, c))\n            if cpu.registers != after:\n                possibilities[x].discard(opcode)\n    \n    assignments = {}\n    while any(p for p in possibilities):\n        for opcode, ps in enumerate(possibilities):\n            if len(ps) == 0 and opcode not in assignments:\n                raise Exception('Unsolvable!')\n            if len(ps) == 1:\n                i = next(iter(ps))\n                assignments[opcode] = i\n                for s in possibilities:\n                    s.discard(i)\n    \n    cpu = CPU(opcode_transform=assignments)\n    for instruction in program:\n        cpu.execute(instruction)\n    print(cpu.registers)\n\nmain()\n\n","sub_path":"2018/16.py","file_name":"16.py","file_ext":"py","file_size_in_byte":4072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"650679369","text":"import pytest\nfrom woqlclient import (InvalidURIError)\n#import sys\n#sys.path.append('woqlclient')\n\nfrom woqlclient import IDParser\n\ndef test_idParser():\n\tidParser=IDParser()\n\twith pytest.raises(InvalidURIError):\n\t\tidParser.serverURL\n\n\twith pytest.raises(InvalidURIError):\n\t\tidParser.dbID\n\n\tassert idParser.docID==False\n\ndef test_parseServerURL():\n\tservURL=\"http://localhost:6363/\"\n\tidParser=IDParser()\n\tidParser.parseServerURL(servURL)\n\tassert idParser.serverURL == servURL\n\ndef test_parseDBID():\n\tdbName=\"myFirstTerminusDB\"\n\tidParser=IDParser()\n\t#check db as TerminusDB name\n\tidParser.parseDBID(dbName)\n\tassert idParser.dbID==dbName\n\t#check db name as TerminusDB Url\n\ndef test_parseDBURL():\n\tidParser=IDParser()\n\tidParser.parseDBURL(\"http://localhost:6363/myFirstTerminusDB\")\n\tassert idParser.dbID==\"myFirstTerminusDB\"\n\tassert idParser.serverURL== \"http://localhost:6363/\"\n\n\n#@param {string} docURL Terminus Document URL or Terminus Document ID\ndef test_parseDocumentID():\n\tdocumentName=\"chess\"\n\n\tidParser=IDParser()\n\tidParser.parseDocumentID(documentName)\n\tassert idParser.docID==documentName\n\n\ndef test_validURL():\n\tfullURL=\"http://localhost:6363/myFirstTerminusDB\";\n\tidParser=IDParser()\n\tassert idParser.validURL(fullURL)== True;\n\n\tfullURL=\"localhost&899900/myFirstTerminusDB\";\n\tassert idParser.validURL(fullURL)== False;\n\n\ndef test_validIDString():\n\tidString=\"myFirstTerminusDB\";\n\tidParser=IDParser()\n\tassert idParser.validIDString(idString)== True;\n\n\tidString=\"my First:\";\n\tassert idParser.validIDString(idString)== False;\n","sub_path":"tests/test_idParser.py","file_name":"test_idParser.py","file_ext":"py","file_size_in_byte":1529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"450381008","text":"# -*- coding: utf-8 -*-\n# app.urls\n# \n\n# Following few lines is an example urlmapping with an older interface.\n\"\"\"\nfrom werkzeug.routing import EndpointPrefix, Rule\n\ndef make_rules():\n  return [\n    EndpointPrefix('app/', [\n      Rule('/', endpoint='index'),\n    ]),\n  ]\n\nall_views = {\n  'app/index': 'app.views.index',\n}\n\"\"\"\n\nfrom kay.routing import (\n  ViewGroup, Rule\n)\n\nview_groups = [\n  ViewGroup(\n    Rule('/', endpoint='index', view='app.views.index'),\n    Rule('/manage-profile', endpoint='manage-profile', view='app.views.manage_profile'),\n    Rule('/bbs', endpoint='bbs/index', view='app.views.bbs'),\n    Rule('/bbs/<int:id>', endpoint='bbs/thread', view='app.views.bbs_thread'),\n    Rule('/blog/manage', endpoint='blog/manage', view='app.views.blog_manage'),\n    Rule('/blog/create', endpoint='blog/create', view='app.views.blog_create_entry'),\n    Rule('/blog/update/<int:id>', endpoint='blog/update', view='app.views.blog_update_entry'),\n    Rule('/blog/delete/check/<int:id>', endpoint='blog/delete/check', view='app.views.blog_check_delete_entry'),\n    Rule('/blog/delete/<int:id>', endpoint='blog/delete', view='app.views.blog_delete_entry'),\n    Rule('/<user_name>/blog', endpoint='blog/index', view='app.views.blog'),\n    Rule('/<user_name>/blog/<int:id>', endpoint='blog/entry', view='app.views.blog_entry'),\n    Rule('/setting/icon', endpoint='setting/icon', view='app.views.setting_icon'),\n    Rule('/<user_name>/icon_<int:width>x<int:height>.png', endpoint='icon', view='app.views.icon'),\n    Rule('/uploader', endpoint='uploader', view='app.views.uploader'),\n    Rule('/uploader/download/<int:id>/<filename>', endpoint='uploader/download', view='app.views.uploader_download'),\n    Rule('/uploader/delete/check/<int:id>', endpoint='uploader/delete/check', view='app.views.uploader_check_delete'),\n    Rule('/uploader/delete/<int:id>', endpoint='uploader/delete', view='app.views.uploader_delete'),\n  )\n]\n\n","sub_path":"app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"307011083","text":"\"\"\"Python: Programming in Context (2e)\"\"\"\n__author__ = 'ncoop'\n\n\nimport turtle\nimport random\n\n\nclass World:\n    textColor = \"darkblue\"\n\n    def __init__(self, mx, my, initial, res) -> \"Listing 11.1\":\n        self.maxX = mx\n        self.maxY = my\n        self.thingList = []\n        self.grid = []\n        self.res = res\n        self.lifeTime = 0\n        self.lifeCount = {}  # Exercise 11.2\n\n        self.wturtle = turtle.Turtle()\n        self.wscreen = turtle.Screen()\n        self.wscreen.setworldcoordinates(-0.5, -0.5, self.maxX, self.maxY)\n        self.wturtle.hideturtle()\n\n        self.dturtle = turtle.Turtle()\n        self.dturtle.hideturtle()\n\n        self.drawGrid()\n        for arow in range(self.maxY):\n            row = []\n            for acol in range(self.maxX):\n                row.append(None)\n            self.grid.append(row)\n\n        randseq = self.shuffleArray(mx, my)\n        randidx = 0\n        for lifeForm in initial:\n            self.wscreen.addshape(self.getShape(lifeForm))\n            self.lifeCount[lifeForm] = 0\n            for i in range(initial[lifeForm]):\n                newLifeForm = lifeForm(randseq[randidx])\n                self.addThing(newLifeForm)\n                randidx += 1\n\n    @staticmethod\n    def shuffleArray(x, y):\n        randseq = []\n        for i in range(x):\n            for j in range(y):\n                randseq.append((i, j))\n        random.shuffle(randseq)\n        return randseq\n\n    def drawGrid(self) -> \"Listing 11.2\":\n        self.wscreen.tracer(0)\n        self.wturtle.seth(0)\n        for y in range(self.maxY):\n            self.wturtle.pu()\n            self.wturtle.goto(0, y)\n            self.wturtle.pd()\n            self.wturtle.fd(self.maxX-1)\n        self.wturtle.seth(90)\n        for x in range(self.maxX):\n            self.wturtle.pu()\n            self.wturtle.goto(x, 0)\n            self.wturtle.pd()\n            self.wturtle.fd(self.maxY-1)\n\n        self.dturtle.pu()\n        self.dturtle.goto(0, self.maxY-0.5)\n        self.wscreen.tracer(1)\n\n    def freezeWorld(self) -> \"Listing 11.3\":\n        self.wscreen.exitonclick()\n\n    def showCounts(self) -> \"Exercise 11.3\":\n        self.wscreen.tracer(0)\n        self.dturtle.clear()\n        i = 0\n        self.dturtle.color(self.textColor)\n        self.dturtle.setx(i)\n        self.dturtle.write(\"Time: %d\" % self.getLifeTime())\n        for lifeForm in self.lifeCount:\n            i += 4\n            self.dturtle.color(lifeForm.textColor)\n            self.dturtle.setx(i)\n            self.dturtle.write(\"%s: %d\" % (lifeForm.__name__, self.getLifeCount(lifeForm)))\n        self.wscreen.tracer(1)\n\n    def addThing(self, athing) -> \"Listing 11.3\":\n        self.grid[athing.getPos()[1]][athing.getPos()[0]] = athing\n        athing.setWorld(self)\n        self.thingList.append(athing)\n        athing.appear(type(athing))\n        self.showCounts()\n\n    def delThing(self, athing) -> \"Listing 11.3\":\n        athing.hide(type(athing))\n        self.grid[athing.getPos()[1]][athing.getPos()[0]] = None\n        self.thingList.remove(athing)\n        self.showCounts()\n\n    def moveThing(self, oldPos, newPos) -> \"Listing 11.3\":\n        self.grid[newPos[1]][newPos[0]] = self.grid[oldPos[1]][oldPos[0]]\n        self.grid[oldPos[1]][oldPos[0]] = None\n\n    def isInWorld(self, pos):\n        return 0 <= pos[0] < self.getMaxX() and 0 <= pos[1] < self.getMaxY()\n\n    def getMaxX(self) -> \"Listing 11.3\":\n        return self.maxX\n\n    def getMaxY(self) -> \"Listing 11.3\":\n        return self.maxY\n\n    def getShape(self, aclass):\n        return self.res + aclass.__name__ + \".gif\"\n\n    def liveALittle(self) -> \"Listing 11.3\":\n        self.incLifeTime()\n        random.shuffle(self.thingList)\n        for athing in self.thingList:\n            athing.liveALittle()\n\n    def isEmpty(self, pos) -> \"Listing 11.3\":\n        return self.thingAt(pos) is None\n\n    def thingAt(self, pos) -> \"Listing 11.3\":\n        return self.grid[pos[1]][pos[0]]\n\n    def getLifeTime(self):\n        return self.lifeTime\n\n    def incLifeTime(self):\n        self.lifeTime += 1\n\n    def incLifeCount(self, lifeForm) -> \"Exercise 11.2\":\n        self.lifeCount[lifeForm] += 1\n\n    def decLifeCount(self, lifeForm) -> \"Exercise 11.2\":\n        self.lifeCount[lifeForm] -= 1\n\n    def getLifeCount(self, lifeForm) -> \"Exercise 11.2\":\n        return self.lifeCount[lifeForm]\n\n\nclass LifeForm:\n    textColor = \"black\"\n    overcrowding = None\n    starvePeriod = 0\n    breedPeriod = 0\n    world = None\n    prey = None\n    mobile = True\n    offsetList = [(-1, 1), (0, 1), (1, 1), (-1, 0), (1, 0), (-1, -1), (0, -1), (1, -1)]\n\n    def __init__(self, pos):\n        self.pos = pos\n        self.turtle = turtle.Turtle()\n        self.turtle.up()\n        self.turtle.hideturtle()\n        self.turtle.goto(self.getPos())\n\n        self.starveTick = 0\n        self.breedTick = 0\n\n    def setPos(self, newPos):\n        self.pos = newPos\n\n    def getPos(self):\n        return self.pos\n\n    def setOvercrowding(self, n) -> \"Exercise 11.6\":\n        self.overcrowding = n\n\n    def getOvercrowding(self) -> \"Exercise 11.6\":\n        return self.overcrowding\n\n    def setStarvePeriod(self, n):\n        self.starvePeriod = n\n\n    def getStarvePeriod(self):\n        return self.starvePeriod\n\n    def setBreedPeriod(self, n) -> \"Exercise 11.6\":\n        self.breedPeriod = n\n\n    def getBreedPeriod(self) -> \"Exercise 11.6\":\n        return self.breedPeriod\n\n    def setWorld(self, aworld) -> \"Listing 11.5\":\n        self.world = aworld\n        self.turtle.shape(aworld.getShape(type(self)))\n\n    def appear(self, lifeForm) -> \"Listing 11.5\":\n        self.turtle.showturtle()\n        self.world.incLifeCount(lifeForm)\n\n    def hide(self, lifeForm) -> \"Listing 11.5\":\n        self.turtle.hideturtle()\n        self.world.decLifeCount(lifeForm)\n\n    def move(self, newPos) -> \"Listing 11.5\":\n        self.world.moveThing(self.getPos(), newPos)\n        self.setPos(newPos)\n        self.turtle.goto(self.getPos())\n\n    def liveALittle(self):\n        if not self.turtle.isvisible():\n            return\n        if self.isOvercrowded():\n            self.world.delThing(self)\n        else:\n            self.tryToBreed() or self.tryToEat() or self.tryToMove()\n\n    def isOvercrowded(self):\n        neighbors = 0\n        for offset in self.offsetList:\n            pos = self.pos[0] + offset[0], self.pos[1] + offset[1]\n            if self.world.isInWorld(pos) and isinstance(self.world.thingAt(pos), type(self)):\n                neighbors += 1\n\n        return neighbors >= self.getOvercrowding()\n\n    def getEmptyNeighbor(self):\n        empties = []\n        for offset in self.offsetList:\n            pos = self.pos[0] + offset[0], self.pos[1] + offset[1]\n            if self.world.isInWorld(pos) and self.world.isEmpty(pos):\n                empties.append(pos)\n        if len(empties) == 0:\n            return None\n        return random.choice(empties)\n\n    def tryToBreed(self):\n        self.breedTick += 1\n        if self.breedTick < self.getBreedPeriod():\n            return False\n\n        empty = self.getEmptyNeighbor()\n        if not empty:\n            return False\n\n        childThing = type(self)(empty)\n        self.world.addThing(childThing)\n        self.breedTick = 0\n        return True\n\n    def tryToEat(self):\n        if self.prey is None or not self.mobile:\n            return False\n\n        adjprey = []\n        for offset in self.offsetList:\n            pos = self.pos[0] + offset[0], self.pos[1] + offset[1]\n            if self.world.isInWorld(pos) and isinstance(self.world.thingAt(pos), self.prey):\n                adjprey.append(self.world.thingAt(pos))\n\n        if len(adjprey) == 0:\n            self.starveTick += 1\n            return False\n\n        randomprey = adjprey[random.randrange(len(adjprey))]\n\n        self.world.delThing(randomprey)\n        self.move(randomprey.getPos())\n        self.starveTick = 0\n        return True\n\n    def tryToMove(self):\n        if not self.mobile:\n            return False\n\n        empty = self.getEmptyNeighbor()\n        if not empty:\n            return False\n\n        self.move(empty)\n        return True\n\n\nclass Fish(LifeForm):\n    textColor = \"salmon\"\n\n    def __init__(self, pos) -> \"Listing 11.4\":\n        super().__init__(pos)\n        self.prey = Plant\n        self.setOvercrowding(2)  # Exercise 11.5\n        self.setStarvePeriod(10)\n        self.setBreedPeriod(10)\n\n\nclass Bear(LifeForm):\n    textColor = \"brown\"\n\n    def __init__(self, pos) -> \"Listing 11.9\":\n        super().__init__(pos)\n        self.prey = Fish\n        self.setOvercrowding(2)\n        self.setStarvePeriod(10)  # Exercise 11.9\n        self.setBreedPeriod(12)\n\n\nclass Plant(LifeForm):  # Exercise 11.19\n    textColor = \"darkgreen\"\n    mobile = False\n\n    def __init__(self, pos):\n        super().__init__(pos)\n        self.setOvercrowding(3)  # Exercise 11.22\n        self.setBreedPeriod(5)\n\n","sub_path":"chapter11.py","file_name":"chapter11.py","file_ext":"py","file_size_in_byte":8868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"344257609","text":"import numpy as np\r\nimport scipy as sp\r\nimport copy\r\n\r\n# timestep vector (for experimental purposes for now) 48 hours? minutes? seconds? tacos? im hungry for tacos\r\ntime = np.arange(48)\r\n\r\n# I too want tacos\r\n\r\n# begin class code\r\nclass Th17cell:\r\n    # some random sh!t that may come into use later, still tryna figure out the role of variables defined outside of\r\n    # initialization method\r\n    growth_factor = 1\r\n    radius = 1\r\n    # initialize variables, initializtion method, whatever u wanna call it. takes in position of form [x,y,z]\r\n    def __init__(self, pos):\r\n        self.kmil17 = 100 # michaelis menten half concentration to max rate blah blah blah constant (units arbitrary rn)\r\n        self.maxil17 = 200 # michaelis menten max rate. this is a made up value, here to hold up the skeleton of a model\r\n        self.pos = pos # position within the 3d box specified in cellmats 3rd,4th,and 5th arrays. eek!\r\n        self.dil17 = 0 # nitialize rate of il17, hence the d\r\n        self.dgmcsf = 0 # initialize rate of gmcsf\r\n        self.xpos = int(self.pos[0]) # making locations into ints for use in matrix indexing\r\n        self.ypos = int(self.pos[1])\r\n        self.zpos = int(self.pos[2])\r\n        # below is legit work with the 5D array\r\n        # the MAGICAL 6d cell matrix, location is within a 2x2x2 cube space just for shits, irrelevant tbh\r\n        # backbone of the model, everything comes back to this 5dimensional thing which i can barely conceptualize\r\n        # (cytokine, concentration, x, y, z, time). yuh\r\n        self.cellmat = np.zeros((4,len(time),2,2,2,len(time)))\r\n        # this series of for loops assigns the time vector to be the \"dimension of the 6D array. 6d means 6 dimensions\r\n        # thats a lot of dimensions\r\n        for cytokine in range(4):\r\n            for concentration in range(len(time)):\r\n                for xloc in range(2):\r\n                    for yloc in range(2):\r\n                        for zloc in range(2):\r\n                            self.cellmat[cytokine,concentration,xloc,yloc,zloc,:] = time\r\n    # sense cytokines, assign to position in array. for this we assume inital conditions, t=0\r\n    def sense(self, il6, il1b):\r\n        # throughout this code, i refer to the position input, because that is the location within the box thingy\r\n        # where the cell exists and senses and secretes cytokines\r\n        self.il1b = float(il1b)\r\n        self.il6 = float(il6)\r\n        self.cellmat[0, :, self.xpos, self.ypos, self.zpos, 0] = self.il1b * np.ones(len(time))\r\n        self.cellmat[1, :,  self.xpos, self.ypos, self.zpos, 0] = self.il6 * np.ones(len(time))\r\n    # secrete cytokines, assign to values in matrix. matrixes are cool\r\n    # future work: defining net rate of il1b and il6. will require programming of fibrolast class\r\n    # will incorporate a shifting time value based on when the cell senses the cytokines, but not now. It's 2:18am\r\n    # and i need to sleep. Also i just turned 20 like 2 hours ago! yay\r\n    def secrete(self):\r\n        # cytokine rate according to michaelis menten kinetics with switching functions, needs work.\r\n        # currently both cytokines would have the same rate, will look into this later.\r\n        # we are going for a spooky scary skeleton of a model rn\r\n        self.dil17 = self.maxil17*(self.il6/(self.kmil17 + self.il6)) * self.maxil17*(self.il1b/(self.kmil17 + self.il1b))\r\n        self.dgmcsf = self.maxil17*(self.il6/(self.kmil17 + self.il6)) * self.maxil17*(self.il1b/(self.kmil17 + self.il1b))\r\n        # this section exists for graphing purposes, storing the progressive il17/gmcsf  values in their\r\n        # respective vectors. by vectors i mean a vector within the 5D array that changes with the time values\r\n        # python calls them lists. WE the people of matlab will never refer to a VECTOR as a list.\r\n        # it is so much more than a list\r\n        for x in range(len(time)):\r\n            self.cellmat[2, :, self.xpos, self.ypos, self.zpos, x] = self.dil17*x*np.ones(len(time))  # storing il17 values at given times in the matrix\r\n            self.cellmat[3,:, self.xpos, self.ypos, self.zpos, x] = self.dgmcsf*x*np.ones(len(time))   # same, but for gmc-sf\r\n\r\n# our first th17! so cute\r\ncellt = Th17cell([0,0,0])\r\n# cells sense at time 0 and secrete cytokines from time 1-48\r\ncellt.sense(4.0,4.0)\r\ncellt.secrete()\r\n\r\n# for now, cytokine concentrations at a given time are stored in a length 48 vector wtih identical entries.\r\n# Concentration changes wieh time:\r\n# This would print Il17 concentration at timestep 47\r\nprint(cellt.cellmat[2,:,0,0,0,47])\r\n\r\n#csmajor\r\n#pythonfun\r\n#chemewho?\r\n#ihopedaveapproves\r\n#hashtag","sub_path":"Th17cellclass.py","file_name":"Th17cellclass.py","file_ext":"py","file_size_in_byte":4656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"650663591","text":"from app import db\nfrom app.models import DataResult, DataSet\n\ndef save_results(results):\n  if not results['dataset_id']:\n    return False\n  dataset = db.session.query(DataSet).filter_by(id = results['dataset_id']).one()\n  dataresult = DataResult(\n    dataset_id = dataset.id,\n    date       = datetime.utcnow(),\n    accuracy   = results['accuracy'],\n    precision  = results['precision'],\n    recall     = results['recall'],\n    f1score    = results['f1score'])\n  db.session.add(dataresult)\n  db.session.commit()\n  return True\n\n","sub_path":"iaproject/back/app/utils/save_results.py","file_name":"save_results.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"62019815","text":"from django.conf.urls import patterns, include, url\n\n# Uncomment the next two lines to enable the admin:\nimport os\nfrom os.path import dirname\nfrom django.contrib import admin\nadmin.autodiscover()\n\nbasedir = dirname(__file__)\n\nstaticFiles = '%s/web/static/' % basedir\nmedia = '%s/media/' % basedir\n\nurlpatterns = patterns('',\n    # Examples:\n    # url(r'^$', 'maucker.views.home', name='home'),\n    # url(r'^maucker/', include('maucker.foo.urls')),\n    (r'^static/(?P<path>.*)$','django.views.static.serve',{'document_root': staticFiles,'show_indexes': True}),\n    (r'^media/(?P<path>.*)$','django.views.static.serve',{'document_root': media,'show_indexes': True}),\n    # Uncomment the admin/doc line below to enable admin documentation:\n    url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n    #comments\n    (r'^comments/', include('django.contrib.comments.urls')),\n    # Uncomment the next line to enable the admin:\n    url(r'^admin/', include(admin.site.urls)),\n    url(r'^', include('web.urls')),\n    (r'^tinymce/', include('tinymce.urls')),\n)\n","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"85090823","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Users/kghose/.venvs/benten/lib/python3.7/site-packages/benten/langserver/documentsymbol.py\n# Compiled at: 2019-11-13 20:55:00\n# Size of source mod 2**32: 799 bytes\n\"\"\"\ntextDocument/documentSymbol\n\"\"\"\nimport pathlib, json, hashlib\nfrom .base import CWLLangServerBase\nimport logging\nlogger = logging.getLogger(__name__)\n\nclass DocumentSymbol(CWLLangServerBase):\n\n    def serve_textDocument_documentSymbol(self, client_query):\n        params = client_query['params']\n        doc_uri = params['textDocument']['uri']\n        doc = self.open_documents[doc_uri]\n        self._write_out_graph(doc)\n        return doc.symbols\n\n    def _write_out_graph(self, doc):\n        graph_data_file = pathlib.Path(self.config.scratch_path, hashlib.md5(doc.doc_uri.encode()).hexdigest() + '.json')\n        with graph_data_file.open('w') as (f):\n            json.dump((doc.wf_graph), f, indent=2)","sub_path":"pycfiles/benten-2020.3.10.macosx-10.9-x86_64.tar/documentsymbol.cpython-37.py","file_name":"documentsymbol.cpython-37.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"208215235","text":"class Solution:\n    def climbStairs(self, n):\n        \"\"\"\n        :type n: int\n        :rtype: int\n        \"\"\"\n        A = [1] * (n+1)\n        print(A)\n        for i in range(2, n+1):\n            A[i] = A[i-1] + A[i-2]\n\n        return A[-1]\n\nS = Solution()\nprint(S.climbStairs(3))","sub_path":"070 - Climbing Stairs.py","file_name":"070 - Climbing Stairs.py","file_ext":"py","file_size_in_byte":280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"356552357","text":"# vim: tabstop=4 shiftwidth=4 softtabstop=4\n\n#    Copyright (C) 2012 Yahoo! Inc. 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\nimport abc\n\nfrom anvil import colorizer\nfrom anvil import importer\nfrom anvil import log as logging\nfrom anvil import utils\n\nLOG = logging.getLogger(__name__)\n\n\nclass PackageRegistry(object):\n\n    def __init__(self):\n        self.installed = dict()\n        self.removed = dict()\n\n\nclass Packager(object):\n\n    __meta__ = abc.ABCMeta\n\n    def __init__(self, distro, registry):\n        self.distro = distro\n        self.registry = registry\n\n    def install(self, pkg):\n        name = pkg['name']\n        version = pkg.get('version')\n        skip_install = False\n        if name in self.registry.installed:\n            existing_version = self.registry.installed[name]\n            if version == existing_version:\n                LOG.debug(\"Skipping install of %r since it already happened.\", name)\n                skip_install = True\n            else:\n                if existing_version is not None:\n                    if utils.versionize(existing_version) < utils.versionize(version):\n                        LOG.warn((\"A request has come in for a 'potentially' newer version of %s v(%s),\"\n                            \" when v(%s) was previously installed!\"), colorizer.quote(name), version, existing_version)\n                    elif utils.versionize(existing_version) > utils.versionize(version):\n                        LOG.warn((\"A request has come in for a 'potentially' older version of %s v(%s), \"\n                            \"when v(%s) was previously installed!\"), colorizer.quote(name), version, existing_version)\n                else:\n                    LOG.warn((\"A request has come in for a 'potentially' different version of %s v(%s),\"\n                        \" when a unspecified version was previously installed!\"), colorizer.quote(name), version)\n        if not skip_install:\n            self._install(pkg)\n            LOG.debug(\"Noting that %r - v(%s) was installed.\", name, (version or \"??\"))\n            self.registry.installed[name] = version\n            if name in self.registry.removed:\n                del(self.registry.removed[name])\n\n    def remove(self, pkg):\n        removable = pkg.get('removable', True)\n        if not removable:\n            return False\n        name = pkg['name']\n        if name in self.registry.removed:\n            LOG.debug(\"Skipping removal of %r since it already happened.\", name)\n        else:\n            self._remove(pkg)\n            LOG.debug(\"Noting that %r was removed.\", name)\n            self.registry.removed[name] = True\n            if name in self.registry.installed:\n                del(self.registry.installed[name])\n        return True\n\n    def pre_install(self, pkg, params=None):\n        cmds = pkg.get('pre-install')\n        if cmds:\n            LOG.info(\"Running pre-install commands for package %s.\", colorizer.quote(pkg['name']))\n            utils.execute_template(*cmds, params=params)\n\n    def post_install(self, pkg, params=None):\n        cmds = pkg.get('post-install')\n        if cmds:\n            LOG.info(\"Running post-install commands for package %s.\", colorizer.quote(pkg['name']))\n            utils.execute_template(*cmds, params=params)\n\n    @abc.abstractmethod\n    def _remove(self, pkg):\n        pass\n\n    @abc.abstractmethod\n    def _install(self, pkg):\n        pass\n\n\nclass PackagerFactory(object):\n\n    PACKAGER_KEY_NAME = 'packager_name'\n\n    def __init__(self, distro, default_packager_cls):\n        self.default_packager = None\n        self.default_packager_cls = default_packager_cls\n        self.distro = distro\n        self.fetched_packagers = dict()\n        self.registry = PackageRegistry()\n\n    def _construct_pkger(self, cls):\n        return cls(self.distro, self.registry)\n\n    def _get_default_pkgr(self):\n        if not self.default_packager:\n            self.default_packager = self._construct_pkger(self.default_packager_cls)\n            LOG.debug('Loading default package manager %s', self.default_packager_cls)\n        return self.default_packager\n\n    def get_packager_for(self, pkg_info):\n        packager_name = pkg_info.get(self.PACKAGER_KEY_NAME)\n        if not packager_name or not packager_name.strip():\n            packager = self._get_default_pkgr()\n        else:\n            if packager_name in self.fetched_packagers:\n                packager = self.fetched_packagers[packager_name]\n            else:\n                packager_cls = importer.import_entry_point(packager_name)\n                LOG.debug('Loading custom package manager %s for package %r', packager_cls, pkg_info['name'])\n                packager = self._construct_pkger(packager_cls)\n                self.fetched_packagers[packager_name] = packager\n        return packager\n","sub_path":"anvil/packager.py","file_name":"packager.py","file_ext":"py","file_size_in_byte":5331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"392933949","text":"import re\n\n\n#USE THIS SCRIPTS\n\n\n\n#filters\nlistAny=['/Health/','/Science/'] #at least one of these must appear the the category \nlistAll=['Journals'] #all of these must apear\n#     listCategoryPathContains=['Journals']  #!!! filter\n  #!!! filter\n#     listCategoryPathContains=['Health']  #!!! filter\n\n#source\nfilename=\"C:\\Google Drive\\Work\\website category\\dmoz_content.rdf.u8\\content.rdf.u8\"\n#export to csv            \ncsv_finename = filename.split('.')[0] + '.txt'\nof = open(csv_finename, 'w', encoding=\"utf8\")\nof.write('Website Name' + '\\t'+ 'URL' + '\\t' + 'Category' + '\\n')\n\nurls_serial = 0\nread_urls = []\ntopics = {}\n\n\n#extract category to link mapping \nfd = open(filename,'r', encoding=\"utf8\")\nlines = (line.strip() for line in fd)\n\nrgxTopic = re.compile('r:id=\\\"(.*)\\\">')  # <Topic xmlns=\"\" r:id=\"Top/Arts\">\nrgxWebsiteName = re.compile('<d:Title>(.*)</d:Title>')  # <d:Title>Animation World Network</d:Title>\nrgxLink = re.compile('about=\\\"(.*)\\\"')  \n\n\n\n\n\ncategory=''\nfor line in lines:\n\n    if line.startswith('<Topic'):\n        category = rgxTopic.search(line).group(1)\n        category = re.sub('^Top','',category) if category else ''  #remove the  \"TOP/\" prefix\n    \n    elif line.startswith('<ExternalPage'):  \n#    deal with the following structure - url precedes the website name:\n#    <ExternalPage xmlns=\"\" about=\"http://www.awn.com\">\n#    <d:Title>Animation World Network</d:Title>\n        url= rgxLink.search(line).group(1)\n        \n        next_line = next(lines)\n        websiteName = rgxWebsiteName.search(next_line).group(1) \n        \n        #export to file \n        \n        if all(category in category for category in listAll) and any(category in category for category in listAny):\n            line = websiteName + '\\t' + url + '\\t' + category + '\\n'\n            of.write(line)\n\n\n  \n\nfd.close()","sub_path":"xml2csv/parseDmoz2CSV.py","file_name":"parseDmoz2CSV.py","file_ext":"py","file_size_in_byte":1819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"33018096","text":"#! C:\\Python36\\python.exe\n# coding:utf-8\n'''\n·统计招商银行2017年股价的平均数；\n·统计招行2017年股价的中位数；\n·求招行2017年股价的方差和标准差；\n'''\nimport csv\n\n# filePath = \"‪C:\\\\Users\\\\idea\\\\Desktop\\\\600036.csv\"\n# filePath = r\"‪C\\Users\\idea\\Desktop\\600036.csv\"\nfilePath = \"D:/downloads/600036.csv\"\n\nif __name__ == \"__main__\":\n    file = open(filePath, \"r\")\n    csvReader = csv.reader(file)\n\n    totalPrice = 0\n    dayCount = 0\n    priceList = []\n\n    for item in csvReader:\n        # print(item)\n\n        # 只读2017年的数据\n        if item[0].startswith(\"2017\"):\n            # print(item)\n            dayPrice = eval(item[3])\n            priceList.append(dayPrice)\n\n            totalPrice += dayPrice\n            dayCount += 1\n\n    avgPrice = totalPrice / dayCount\n    print(\"2017年的平均股价是%.2f\" % (avgPrice))\n\n    priceList.sort()\n    print(priceList)\n    print(\"2017年股价中位数是%.2f\" % (priceList[dayCount // 2] if len(priceList) % 2 == 1 else (priceList[dayCount // 2] +\n                                                                                         priceList[\n                                                                                             dayCount // 2 - 1]) / 2))\n\n    # 计算股价方差、标准差\n    total = 0\n    for price in priceList:\n        total += (price - avgPrice) ** 2\n    variance = total / len(priceList)\n    print(\"2017年股价方差是%.2f\" % (variance))\n    print(\"2017年股价标准差是%.2f元\" % (variance**(0.5)))\n\n    file.close()\n    print(\"main over\")\n","sub_path":"day1/1025/demos/W6/day3/02StockAnalysis.py","file_name":"02StockAnalysis.py","file_ext":"py","file_size_in_byte":1585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"165258567","text":"import sys\n#\n# >>> Escriba el codigo del mapper a partir de este punto <<<\n#\nif __name__ == \"__main__\":\n\tfor line in sys.stdin:\n\t\tarr = line.split(',')\n\t\tpour = arr[3]\n\t\tval = arr[4]\n\t\tsys.stdout.write(\"{}\\t{}\\n\".format(pour,val))","sub_path":"01-hadoop-50/q02-10/mapper.py","file_name":"mapper.py","file_ext":"py","file_size_in_byte":230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"155232899","text":"# coding: utf-8\n\nfrom __future__ import print_function\n\nimport os\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow.contrib.keras as kr\n\nfrom cnn_model import TCNNConfig, TextCNN\nfrom data.helper import read_file, read_vocab, read_cate, read_encode, batch_iter, process_file, build_vocab\n\nimport sys\n\ntry:\n    bool(type(unicode))\nexcept NameError:\n    unicode = str\n\n# 行业字典路径\nbase_dir = 'data/'\nvocab_dir = os.path.join(base_dir, 'ind_vocab.txt')\n\n# 行业最佳验证结果保存路径\nsave_dir = 'checkpoints/textcnn/ind'\nsave_meta_path1 = os.path.join(save_dir, 'best_validation_ind_34.meta')\nsave_path1 = os.path.join(save_dir, 'best_validation_ind_34')\nsave_meta_path2 = os.path.join(save_dir, 'best_validation_ind_56.meta')\nsave_path2 = os.path.join(save_dir, 'best_validation_ind_56')  \n\n\n\ndef opmap_concat(k1, k2):\n    '''\n    将中间和高位预���结果连接\n    k1:中间预测结果,k2:高位预测结果\n    return:完整预测编码(int)\n    '''\n    uk1 = unicode( (ind_df1.loc[ind_df1['2']==int(k1)])['1'].values[0] )\n    uk2 = unicode( (ind_df2.loc[ind_df2['2']==int(k2)])['1'].values[0] )\n    if u'99' == uk1 and u'99' == uk2:\n        con_encode0 = 999999\n        return con_encode0\n\n    if 1 == len(unicode(uk1)):\n        uk1 = u'0'+unicode(uk1)\n\n    con_encode = uk2+uk1+'00'\n    return int(con_encode)\n\ndef judge_encode(con_encode):\n    '''\n    判断完整预测编码是否存在\n    con_encode:完整预测编码\n    return:bool值\n    '''\n    _, ind_encode = read_encode()\n    if con_encode in ind_encode:\n        return True\n    else:\n        return False\n\n\n\ndef predict(answer):\n    '''\n    根据答案预测编码\n    answer:提供的答案序列\n    return:预测编码(unicode)\n    '''\n\n    # 分别读取行业中位和高位的预测网络模型\n    g1 = tf.Graph()\n    sess1= tf.Session(graph=g1)\n    with g1.as_default():\n        new_saver = tf.train.import_meta_graph(save_meta_path1)\n        new_saver.restore(sess1, save_path1)\n   \n    g2 = tf.Graph()\n    sess2= tf.Session(graph=g2)    \n    with g2.as_default():\n        new_saver = tf.train.import_meta_graph(save_meta_path2)\n        new_saver.restore(sess2, save_path2)\n\n    with sess1.as_default():\n        with g1.as_default():\n            input_x = g1.get_tensor_by_name(\"input_x:0\")\n            keep_prob = g1.get_tensor_by_name(\"keep_prob:0\")\n            argmax1 = g1.get_tensor_by_name(\"score/y_pred_cls:0\")\n            softmax1 = g1.get_tensor_by_name(\"score/Softmax:0\")\n        \n            content = unicode(answer)\n            data = [word_to_id[x] for x in content if x in word_to_id]\n            y_pred_cls1 = np.zeros(shape=1, dtype=np.int32)  # 保存预测结果\n            softtensor1 = np.zeros(shape=(1,ind_df1.shape[0]), dtype=np.float64)\n            topk1 = np.zeros(shape=(1,ind_df1.shape[0]), dtype=np.float64)\n            index1 = np.zeros(shape=(1,ind_df1.shape[0]), dtype=np.int32)\n            feed_dict = {\n                input_x: kr.preprocessing.sequence.pad_sequences([data], config1.seq_length),\n                keep_prob: 1.0\n            }\n            # 预测的类号和logits\n            y_pred_cls1,softtensor1 = sess1.run([argmax1,softmax1], feed_dict=feed_dict)\n            # 概率和索引\n            topk1 = tf.nn.top_k(softtensor1,ind_df1.shape[0])[0].eval(session=sess1)\n            index1 = tf.nn.top_k(softtensor1,ind_df1.shape[0])[1].eval(session=sess1)\n\n    with sess2.as_default():\n        with g2.as_default():\n            input_x = g2.get_tensor_by_name(\"input_x:0\")\n            keep_prob = g2.get_tensor_by_name(\"keep_prob:0\")\n            argmax2 = g2.get_tensor_by_name(\"score/y_pred_cls:0\")\n            softmax2 = g2.get_tensor_by_name(\"score/Softmax:0\")\n        \n            content = unicode(answer)\n            data = [word_to_id[x] for x in content if x in word_to_id]\n            y_pred_cls2 = np.zeros(shape=1, dtype=np.int32)  # 保存预测结果\n            softtensor2 = np.zeros(shape=(1,ind_df2.shape[0]), dtype=np.float64)\n            topk2 = np.zeros(shape=(1,ind_df2.shape[0]), dtype=np.float64)\n            index2 = np.zeros(shape=(1,ind_df2.shape[0]), dtype=np.int32)\n            feed_dict = {\n                input_x: kr.preprocessing.sequence.pad_sequences([data], config2.seq_length),\n                keep_prob: 1.0\n            }\n            # 预测的类号和logits\n            y_pred_cls2,softtensor2 = sess2.run([argmax2,softmax2], feed_dict=feed_dict)\n            # 概率和索引\n            topk2 = tf.nn.top_k(softtensor2,ind_df2.shape[0])[0].eval(session=sess2)\n            index2 = tf.nn.top_k(softtensor2,ind_df2.shape[0])[1].eval(session=sess2)\n\n    # 对预测结果串联,形成完整的行业预测编码\n    k1 = 0\n    k2 = 0\n    pre_code = unicode('0')\n    while True:\n        con_encode = opmap_concat(index1[0][k1], index2[0][k2])\n        if True == judge_encode(con_encode):\n            print ( u'\\'' + answer + u'\\'' + u'的预测编码为：' + unicode (con_encode) )\n            pre_code = unicode(con_encode)\n            break\n        else:\n            if k1+1>=topk1.shape[1] or k2+1>=topk2.shape[1]:\n                print (  u'\\'' + answer + u'\\'' + u'的预测编码为：' + unicode('0') )\n                pre_code = unicode('0')\n                break\n            prob1 = topk1[0][k1+1]*topk2[0][k2]\n            prob2 = topk1[0][k1]*topk2[0][k2+1]\n            prob = [prob1, prob2]\n            indprob = prob.index(max(prob))\n            if 0 == indprob:\n                k1 = k1+1\n            else:\n                k2 = k2+1\n    return pre_code\n\n\n\nif __name__ == '__main__':\n    '''\n    主函数:对合法的字符串进行行业预测编码\n    输入参数:字符串\n    '''\n    occ_df1, occ_df2, occ_df3, ind_df1, ind_df2 = read_cate()\n    words, word_to_id = read_vocab(vocab_dir)\n    vocab_size = len(words)\n    config1 = TCNNConfig(ind_df1.shape[0], vocab_size)  # mid\n    config2 = TCNNConfig(ind_df2.shape[0], vocab_size)  # hig    \n\n    if len(sys.argv) > 1:\n        for i in range(1,len(sys.argv)):\n            if type(sys.argv[i])==str:\n                answer = sys.argv[i].decode('utf8')\n                predict(answer)\n            else:\n                print (\"Please input the valid answer(s).\")\n    else:\n        print (\"Please input the valid answer(s)(need at least 1 answer).\")\n","sub_path":"Python/Python_NLP_Basic/社调行业和职业自动编码/社会经济调查行业和职业自动编码模型代码/基于卷积神经网络的社会经济调查行业和职业自动编码模型/predict_ind.py","file_name":"predict_ind.py","file_ext":"py","file_size_in_byte":6318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"337127372","text":"# -*- coding: utf-8 -*-\nfrom urllib.parse import urlparse, urlencode\nimport time\nimport hashlib\nimport hmac\nfrom bravado.requests_client import Authenticator, RequestsClient\nfrom bravado.client import SwaggerClient\nimport config.settings\n\nHOST = \"https://testnet.bitmex.com\"\nSPEC_URI = HOST + \"/api/explorer/swagger.json\"\napi_config = {'use_models': False, 'validate_responses': False}\n_request_client = None\n\n\nclass APIKeyAuthenticator(Authenticator):\n    \"\"\"?api_key authenticator.\n    This authenticator adds BitMEX API key support via header.\n    :param host: Host to authenticate for.\n    :param api_key: API key.\n    :param api_secret: API secret.\n    \"\"\"\n\n    def __init__(self, host, api_key, api_secret):\n        super(APIKeyAuthenticator, self).__init__(host)\n        self.api_key = api_key\n        self.api_secret = api_secret\n\n    # Forces this to apply to all requests.\n    def matches(self, url):\n        if \"swagger.json\" in url:\n            return False\n        return True\n\n    # Add the proper headers via the `expires` scheme.\n    def apply(self, r):\n        expires = int(round(time.time()) + 5) # 5s grace period in case of clock skew\n        r.headers['api-expires'] = str(expires)\n        r.headers['api-key'] = self.api_key\n        r.headers['api-signature'] = self.generate_signature(self.api_secret, r.method, r.url,\n                                                r.params if hasattr(r, 'params') else '',\n                                                expires, r.data if hasattr(r, 'data') else '')\n        return r\n\n    # Generates an API signature.\n    # A signature is HMAC_SHA256(secret, verb + path + nonce + data), hex encoded.\n    # Verb must be uppercased, url is relative, nonce must be an increasing 64-bit integer\n    # and the data, if present, must be serialized as URL parameters string\n    #\n    # For example, in psuedocode (and in real code below):\n    #\n    # verb = POST\n    # url = /api/v1/order\n    # nonce = 1416993995705\n    # data = symbol=XBTZ14&quantity=1&price=395.01\n    # signature = HEX(HMAC_SHA256(secret, 'POST/api/v1/order1416993995705symbol=XBTZ14&quantity=1&price=395.01'))\n    def generate_signature(self, secret, verb, url, params, nonce, data):\n        \"\"\"Generate a request signature compatible with BitMEX.\"\"\"\n        # Parse the url so we can remove the base and extract just the path.\n        parsedURL = urlparse(url)\n        path = parsedURL.path\n        if parsedURL.query:\n            path = path + '?' + parsedURL.query\n\n        # If GET params are passed in a separate argument, encode them to URL params\n        if params is None or len(params) == 0:\n            params = ''\n        else:\n            params = '?' + urlencode(params)\n\n        # If request data isn't empty, it should be serialized to URL parameters string\n        if data is None or len(data) == 0:\n            data_str = ''\n        else:\n            data_str = urlencode(data)\n        message = bytes((verb + path + params + str(nonce) + data_str).encode('utf-8'))\n        secret_b = bytes(secret.encode('utf-8'))\n        signature = hmac.new(secret_b, message, digestmod=hashlib.sha256).hexdigest()\n        return signature\n\n\ndef get_request_client():\n    global _request_client\n    if not _request_client:\n        rc = RequestsClient()\n        rc.authenticator = APIKeyAuthenticator(HOST, config.settings.bitmex_api_key,\n                                               config.settings.bitmex_secret_key)\n        _request_client = SwaggerClient.from_url(SPEC_URI, config=api_config,\n                                                     http_client=rc)\n    return _request_client\n","sub_path":"core/bitmex_auth.py","file_name":"bitmex_auth.py","file_ext":"py","file_size_in_byte":3625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"4194025","text":"from aiogram import types\nfrom aiogram.dispatcher.storage import FSMContext\nfrom aiogram.dispatcher.filters import Command\n\nfrom loader import dp\nfrom states import UserForm\n\n\n@dp.message_handler(Command('form'))\nasync def get_user_data(message: types.Message):\n    await message.answer(\"Введите <b>имя</b> и\\nотправьте его боту!\")\n    await UserForm.first()\n\n\n@dp.message_handler(state=UserForm.name)\nasync def get_email(message: types.Message, state: FSMContext):\n    name = message.text\n    await state.update_data(\n            {\n                \"name\": name\n            }\n    )\n    await message.answer(\"Введите <b>адрес электронной почты</b>\\n\"\n                         \"и отправьте его буту!\")\n    await UserForm.next()\n\n\n@dp.message_handler(state=UserForm.email)\nasync def get_email(message: types.Message, state: FSMContext):\n    email = message.text\n    await state.update_data(\n            {\n                \"email\": email\n            }\n    )\n    await message.answer(\"Введите <b>номер телефона</b>\\n\"\n                         \"и отправьте его боту!\")\n    await UserForm.next()\n\n\n@dp.message_handler(state=UserForm.phone)\nasync def get_phone(message: types.Message, state: FSMContext):\n    data = await state.get_data()\n    name = data.get('name')\n    email = data.get('email')\n    phone = message.text\n\n    await message.answer(\n            \"Привет!\\n\"\n            \"Ты ввел следующие данные:\\n\"\n            f\"Имя - <b>{name}</b>\\n\"\n            f\"Email - <b>{email}</b>\\n\"\n            f\"Телефон: - <b>{phone}</b>\")\n    await state.reset_state(with_data=True)","sub_path":"handlers/users/user_data.py","file_name":"user_data.py","file_ext":"py","file_size_in_byte":1714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"321747837","text":"\"\"\" module used to create and hold a network model \"\"\"\n#pylint: disable=invalid-name\nfrom keras.models import Model\nfrom keras.layers.core import Dense, Activation, Flatten, Lambda, Dropout\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.pooling import MaxPooling2D\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.layers import Input, concatenate, LSTM, CuDNNLSTM\nfrom keras.layers import TimeDistributed\n\nclass NetworkHandler(object):\n    \"\"\" Class that holds the network in use\"\"\"\n    def __init__(self):\n        self.conv_layers = 0\n        self.pool_layers = 0\n        self.batch_norm_layers = 0\n        self.activation_layers = 0\n        self.dropout_layers = 0\n        self.dense_layers = 0\n        self.lstm_layers = 0\n        self.model = None\n\n    def conv_block(self, x, filters, kernel_size, stride, padding='SAME'):\n        \"\"\" Creates a full conv block \"\"\"\n        x = self.conv(x, filters, kernel_size, stride, padding)\n        x = self.batch_norm(x)\n        x = self.dropout(x, rate=0.0)\n        x = self.activation(x)\n        return x\n\n    def conv(self, x, filters, kernel_size, stride, padding='valid', activation=None):\n        \"\"\" Adds a convolutional layer \"\"\"\n        self.conv_layers += 1\n        x = TimeDistributed(Conv2D(\n            filters=filters,\n            kernel_size=kernel_size,\n            strides=stride,\n            padding=padding,\n            activation=activation,\n            name=\"conv_\" + str(self.conv_layers)))(x)\n        return x\n\n    def max_pool(self, x, pool_size=(2, 2)):\n        \"\"\" Adds a max pooling layer \"\"\"\n        self.pool_layers += 1\n        x = TimeDistributed(\n            MaxPooling2D(pool_size=pool_size, name=\"max_pool_\" + str(self.conv_layers))\n        )(x)\n        return x\n\n    def batch_norm(self, x, chanDim=-1):\n        \"\"\"\n        Adds a batch normalisation layer\n        NB: after a Conv2D layer with data_format=\"channels_first\",\n        set axis=1 in BatchNormalization\n        \"\"\"\n        self.batch_norm_layers += 1\n        x = TimeDistributed(\n            BatchNormalization(axis=chanDim, name=\"batch_norm_\" + str(self.batch_norm_layers))\n        )(x)\n        return x\n\n    def activation(self, x, activation_function=\"relu\"):\n        \"\"\" Adds an activation-layer \"\"\"\n        self.activation_layers += 1\n        x = TimeDistributed(\n            Activation(activation_function, name=\"activation_\" + str(self.activation_layers))\n        )(x)\n        return x\n\n    def dropout(self, x, rate=0.0, name=None, td=True):\n        \"\"\" Adds a dropout layer \"\"\"\n        self.dropout_layers += 1\n        if not td:\n            x = Dropout(rate, name=\"dropout_\" + str(self.dropout_layers))(x)\n        else:\n            x = TimeDistributed(\n                Dropout(rate, name=\"dropout_\" + str(self.dropout_layers)),\n                name=name\n            )(x)\n        return x\n\n    def dense(self, x, output_size, td=True, activation_function=None, name=None):\n        \"\"\" Adds a dense layer \"\"\"\n        if not name:\n            name = \"dense_\" + str(self.dense_layers)\n        self.dense_layers += 1\n        if not td:\n            x = Dense(output_size, activation=activation_function, name=name)(x)\n        else:\n            x = TimeDistributed(Dense(output_size, activation=activation_function), name=name)(x)\n        return x\n\n    def lstm(self, x, output_size, return_sequences=False, activation=\"tanh\", dropout=0):\n        \"\"\" Adds a lstm layer \"\"\"\n        self.lstm_layers += 1\n        x = LSTM(\n            output_size,\n            activation=activation,\n            return_sequences=return_sequences,\n            dropout=dropout\n        )(x)\n        return x\n\n    def cudnnlstm(self, x, output_size, return_sequences=False):\n        \"\"\" Adds a CuDNNLSTM layer \"\"\"\n        self.lstm_layers += 1\n        x = CuDNNLSTM(output_size, return_sequences=return_sequences)(x)\n        return x\n\n\ndef hsv_convert(x):\n    \"\"\" Converts input from rgb to hsv in the range 0-1 \"\"\"\n    import tensorflow as tf\n    return tf.image.rgb_to_hsv(x)\n\n\ndef load_network(conf):\n    \"\"\" Creates and returns a model using the class network handler \"\"\"\n    input_measures = [key for key in conf.available_columns if conf.input_data[key]]\n    output_measures = [key for key in conf.available_columns if conf.output_data[key]]\n    input_size_data = conf.input_size_data\n    seq_len = input_size_data[\"Sequence_length\"]\n    inputs = []\n    x = Input(shape=[seq_len] + input_size_data[\"Image\"], name=\"input_Image\")\n    inputs.append(x)\n    net = NetworkHandler()\n\n    # RGB TO HSV\n    x = TimeDistributed(Lambda(hsv_convert))(x)\n\n    # CONV 1\n    x = net.conv(x, 24, 5, 2, activation=\"relu\")\n    #x = net.batch_norm(x)\n    # CONV 2\n    x = net.conv(x, 36, 5, 2, activation=\"relu\")\n    #x = net.batch_norm(x)\n\n    # CONV 3\n    x = net.conv(x, 48, 5, 2, activation=\"relu\")\n    #x = net.batch_norm(x)\n    #x = net.dropout(x, 0.5)\n\n    # CONV 4\n    x = net.conv(x, 64, 3, 1, activation=\"relu\")\n    #x = net.batch_norm(x)\n\n    # CONV 5\n    x = net.conv(x, 64, 3, 1, activation=\"relu\")\n\n    # FLATTEN\n    x = TimeDistributed(Flatten(), name=\"time_flatten\")(x)\n     #x = net.batch_norm(x)\n\n\n    #######     INPUT DATA     #######\n    for measure in input_measures:\n        input_layer = Input([seq_len] + input_size_data[measure], name=\"input_\" + measure)\n        inputs.append(input_layer)\n        x = concatenate([x, input_layer])\n\n    #x = net.dense(x, 100, activation_function=\"relu\")\n    x = net.lstm(x, 128, return_sequences=False)\n    x = net.dense(x, 64, td=False, activation_function=\"relu\")\n    x = net.dense(x, 16, td=False)\n\n    outputs = []\n    for measure in output_measures:\n        output_layer = net.dense(\n            x,\n            conf.output_size_data[measure],\n            td=False,\n            activation_function=conf.activation_functions[\"output_\" + measure],\n            name=\"output_\" + measure\n        )\n        outputs.append(output_layer)\n\n    net.model = Model(inputs=inputs, outputs=outputs)\n\n    return net\n","sub_path":"Training/Temporal/Networks/network_temporal.py","file_name":"network_temporal.py","file_ext":"py","file_size_in_byte":6011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"52370676","text":"import load_data\r\nimport pysex\r\n\r\nimport numpy as np\r\n\r\nimport multiprocessing as mp\r\nimport pickle as pickle\r\n\r\n\r\n\"\"\"\r\nExtract a bunch of extra info to get a better idea of the size of objects\r\n\"\"\"\r\n\r\n\r\nSUBSETS = ['train', 'test']\r\nTARGET_PATTERN = \"data/pysex_params_gen2_%s.npy.gz\"\r\nSIGMA2 = 5000 # 5000 # std of the centrality weighting (Gaussian)\r\nDETECT_THRESH = 10.0 # detection threshold for sextractor\r\nNUM_PROCESSES = 8\r\n\r\n\r\ndef estimate_params(img):\r\n    img_green = img[..., 1] # supposedly using the green channel is a good idea. alternatively we could use luma.\r\n    # this seems to work well enough.\r\n\r\n    out = pysex.run(img_green, params=[\r\n            'X_IMAGE', 'Y_IMAGE', # barycenter\r\n            # 'XMIN_IMAGE', 'XMAX_IMAGE', 'YMIN_IMAGE', 'YMAX_IMAGE', # enclosing rectangle\r\n            # 'XPEAK_IMAGE', 'YPEAK_IMAGE', # location of maximal intensity\r\n            'A_IMAGE', 'B_IMAGE', 'THETA_IMAGE', # ellipse parameters\r\n            # 'PETRO_RADIUS',\r\n            'KRON_RADIUS', 'PETRO_RADIUS', 'FLUX_RADIUS', 'FWHM_IMAGE', # various radii\r\n        ], conf_args={ 'DETECT_THRESH': DETECT_THRESH })\r\n\r\n    # x and y are flipped for some reason.\r\n    # theta should be 90 - theta.\r\n    # we convert these here so we can plot stuff with matplotlib easily.\r\n    try:\r\n        ys = out['X_IMAGE'].tonumpy()\r\n        xs = out['Y_IMAGE'].tonumpy()\r\n        as_ = out['A_IMAGE'].tonumpy()\r\n        bs = out['B_IMAGE'].tonumpy()\r\n        thetas = 90 - out['THETA_IMAGE'].tonumpy()\r\n        # kron_radii = out['KRON_RADIUS'].tonumpy()\r\n        petro_radii = out['PETRO_RADIUS'].tonumpy()\r\n        # flux_radii = out['FLUX_RADIUS'].tonumpy()\r\n        # fwhms = out['FWHM_IMAGE'].tonumpy()\r\n\r\n        # detect the most salient galaxy\r\n        # take in account size and centrality\r\n        surface_areas = np.pi * (as_ * bs)\r\n        centralities = np.exp(-((xs - 211.5)**2 + (ys - 211.5)**2)/SIGMA2) # 211.5, 211.5 is the center of the image\r\n\r\n        # salience is proportional to surface area, with a gaussian prior on the distance to the center.\r\n        saliences = surface_areas * centralities\r\n        most_salient_idx = np.argmax(saliences)\r\n\r\n        x = xs[most_salient_idx]\r\n        y = ys[most_salient_idx]\r\n        a = as_[most_salient_idx]\r\n        b = bs[most_salient_idx]\r\n        theta = thetas[most_salient_idx]\r\n        # kron_radius = kron_radii[most_salient_idx]\r\n        petro_radius = petro_radii[most_salient_idx]\r\n        # flux_radius = flux_radii[most_salient_idx]\r\n        # fwhm = fwhms[most_salient_idx]\r\n\r\n    except TypeError: # sometimes these are empty (no objects found), use defaults in that case\r\n        x = 211.5\r\n        y = 211.5\r\n        a = np.nan # dunno what this has to be, deal with it later\r\n        b = np.nan # same\r\n        theta = np.nan # same\r\n        # kron_radius = np.nan\r\n        petro_radius = np.nan\r\n        # flux_radius = np.nan\r\n        # fwhm = np.nan\r\n\r\n\r\n    # return (x, y, a, b, theta, flux_radius, kron_radius, petro_radius, fwhm)\r\n    return (x, y, a, b, theta, petro_radius)\r\n\r\n\r\n\r\nfor subset in SUBSETS:\r\n    print(\"SUBSET: %s\" % subset)\r\n    print()\r\n\r\n    if subset == 'train':\r\n        num_images = load_data.num_train\r\n        ids = load_data.train_ids\r\n    elif subset == 'test':\r\n        num_images = load_data.num_test\r\n        ids = load_data.test_ids\r\n    \r\n\r\n    def process(k):\r\n        print(\"image %d/%d (%s)\" % (k + 1, num_images, subset))\r\n        img_id = ids[k]\r\n        img = load_data.load_image(img_id, from_ram=True, subset=subset)\r\n        return estimate_params(img)\r\n\r\n    pool = mp.Pool(NUM_PROCESSES)\r\n\r\n    estimated_params = pool.map(process, range(num_images), chunksize=100)\r\n    pool.close()\r\n    pool.join()\r\n\r\n    # estimated_params = map(process, xrange(num_images)) # no mp for debugging\r\n\r\n    params_array = np.array(estimated_params)\r\n\r\n    target_path = TARGET_PATTERN % subset\r\n    print(\"Saving to %s...\" % target_path)\r\n    load_data.save_gz(target_path, params_array)\r\n","sub_path":"data/external/repositories_2to3/70362/kaggle-galaxies-master/extract_pysex_params_extra.py","file_name":"extract_pysex_params_extra.py","file_ext":"py","file_size_in_byte":4001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"499055815","text":"n = int(input(\"Podaj n: \"))\nprint(\"Liczby pierwsze: \", end=\"\")\nk=0\ni=2\nwhile k < n:\n    isFirst = True\n    for j in range(2,i-1):\n        if(i % j == 0):\n            isFirst = False\n    if(isFirst == True):\n        print(i, end=\" \")\n        k+=1\n    i+=1\n\n    ","sub_path":"02-ControlStructures/45.py","file_name":"45.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"682705","text":"import socket\nimport os\nfrom requests import get\n\nclear = lambda: os.system('cls')\nconsole_color = lambda: os.system('color a')\nconsole_color()\nclear()\n\nstart_program = input(\"---> What's your IP <---\\n\\nDo you wish to start the program? [y/n]\\n\")\n\nq1 = 0\nq2 = 0\n\nwhile q1 == 0:\n    if start_program == \"y\":\n        clear()\n        break\n    elif start_program == \"n\":\n        clear()\n        q2 += 1\n        return_to_loader = input(\"Do you wish to return to the main loader? [y/n]\\n\")\n        break\n    else:\n        clear()\n        print(\"This is not a valid input, please try again\\n\")\n        start_program = input(\"---> What's your IP <---\\n\\nDo you wish to start the program? [y/n]\\n\")\n\nwhile q2 == 1:\n    if return_to_loader == \"y\":\n        clear()\n        exec(open(\"loader.py\").read())\n        break\n    elif return_to_loader == \"n\":\n        clear()\n        print(\"Exiting..\")\n        exit()\n    else:\n        clear()\n        print(\"This is not a valid input, please try again\")\n        return_to_loader = input(\"Do you wish to return to the main loader? [y/n]\\n\")\n\nhostname = socket.gethostname()\nlocal_ip = socket.gethostbyname(hostname)\npublic_ip = get(\"https://api.ipify.org\").text\n\nprint(f\"Hostname: {hostname}\")\nprint(f\"Local IP: {local_ip}\")\nprint(f\"Public IP: {public_ip}\")\n\nreturn_or_leave = input(\"\\nDo you wish to return to the main loader? [y/n]\\n\")\nq3 = 1\n\nwhile q3 == 1:\n    if return_or_leave == \"y\":\n        clear()\n        exec(open(\"loader.py\").read())\n        break\n    elif return_or_leave == \"n\":\n        clear()\n        print(\"Exiting..\")\n        exit()\n","sub_path":"Hack_app/apps/whatsYourIP_app.py","file_name":"whatsYourIP_app.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"554690835","text":"import pymysql\n\n\nclass DataBase:\n    def __init__(self, host, username, password, database):\n        self.conn = pymysql.connect(host, username, password, database, charset='utf8', cursorclass=pymysql.cursors.DictCursor)\n        self.table_columns = {\n            table: [r['Field'] for r in self.execute(f'describe {table}', res=True)] \\\n            for table in [r[0] for r in self.execute('show tables', res='tuple')]\n        }\n        \n        self._decorate()\n    \n    def create(self, table, **columns):\n        self.execute(f'''\n            create table {table} ({','.join([f\"{column_name} {column_type}\" for column_name, column_type in columns.items()])})\n        ''')\n    \n    def drop(self, table):\n        self.execute(f'drop table {table}')\n    \n    @staticmethod\n    def _placeholder_expression(concat_word, constraints, formatter=0):\n        assert concat_word in ['and', 'or', ',']\n        if constraints:\n            if concat_word in ['and', 'or']:\n                return f' {concat_word} '.join([f'{key} {\"in\" if isinstance(value, list) else \"=\"} {f\"%({key})s\"}' for key, value in constraints.items()])\n            else:\n                if formatter == 0:\n                    return f','.join([key for key, _ in constraints.items()])\n                elif formatter == 1:\n                    return f','.join([f'%({key})s' for key, _ in constraints.items()])\n                elif formatter == 2:\n                    return f','.join([f'{key.strip(\"-\")}=%({key})s' for key, _ in constraints.items()])\n        return ''\n\n    def select(self, options, *columns, **constraints):\n        col_exp = '*' if not columns else ','.join(columns)\n        return self.execute((\n            f\"select {col_exp} from {options['table']} \"\n            f\"{'where ' if constraints else ' '}\"\n            f\"{self._placeholder_expression(options['concat'], constraints)}\"\n            f\"{options['extra']}\"\n        ), constraints, res=options['res'])\n\n    def insert(self, options, **data):\n        lastrowid = self.execute((\n            f\"insert into {options['table']} \"\n            f\"({self._placeholder_expression(',', data)}) values \"\n            f\"({self._placeholder_expression(',', data, 1)})\"\n        ), data, commit=options['commit'])\n        try:\n            return lastrowid\n            # primary_key = options['primary_key']\n            # return max(self.select(options['table'], primary_key), key=lambda r: r[primary_key])[primary_key]\n        except:\n            return -1\n\n    def update(self, options, **data):\n        data = {f'-{key}': value for key, value in data.items()}\n        sql = (\n            f\"update {options['table']} set {self._placeholder_expression(',', data, 2)} \"\n            f\"{'where ' if options['constraints'] else ' '} \"\n            f\"{self._placeholder_expression(options['concat'], options['constraints'])}\"\n        )\n        data.update(options['constraints'])\n        self.execute(sql, data, commit=options['commit'])\n\n    def delete(self, options, **constraints):\n        self.execute((\n            f\"delete from {options['table']} \"\n            f\"{'where ' if constraints else ' '} \"\n            f\"{self._placeholder_expression(options['concat'], constraints)}\"\n        ), constraints, commit=options['commit'])\n\n    def random(self, table, num, *columns):\n        # return self.select(table, *columns, id=[i for i in random.sample([r['id'] for r in self.select(table, 'id')], num)])\n        col_exp = ','.join(columns or self.columns.keys())\n        return [self.execute(f'''\n           select {col_exp} from `{table}` as t1\n           join (\n               select round (\n                   rand() * (\n                       (select max(id) from `{table}`) - (select min(id) from `{table}`)\n                   ) + (select min(id) from `{table}`)\n               ) as id_\n           ) as t2\n           where t1.id >= t2.id_ order by t1.id limit 1\n        ''', res=True) for _ in range(num)]\n\n    def execute(self, sql, data=None, res=False, commit=False):\n        self.conn.connect()\n        if res == 'tuple':\n            cursor = self.conn.cursor(cursor=pymysql.cursors.Cursor)\n        elif res == 'dict':\n            cursor = self.conn.cursor(cursor=pymysql.cursors.DictCursor)\n        else:\n            cursor = self.conn.cursor()\n        try:\n            cursor.execute(sql, data)\n            if commit:\n                self.commit()\n            if res:\n                return cursor.fetchall()\n        except Exception as e:\n            raise e\n        finally:\n            cursor.close()\n        return cursor.lastrowid\n    \n    def commit(self):\n        self.conn.commit()\n    \n    def close(self):\n        self.conn.close()\n    \n    def __enter__(self):\n        return self\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        try:\n            self.close()\n        except:\n            pass\n\n    def __del__(self):\n        try:\n            self.close()\n        except:\n            pass\n\n    class Check:\n        def __init__(self, func, valid_table_columns):\n            self.func = func\n            self.valid_table_columns = valid_table_columns\n            self.default_options = {\n                'select': {\n                    'extra': '',\n                    'concat': 'and',\n                    'res': True,\n                },\n                'insert': {\n                    'res': True,\n                    'commit': True,\n                    'primary_key': 'id'\n                },\n                'update': {\n                    'concat': 'and',\n                    'commit': True,\n                    'contraints': {}\n                },\n                'delete': {\n                    'concat': 'and',\n                    'commit': True,\n                    'contraints': {}\n                }\n            }\n        def __call__(self, options, *columns, **constraints):\n            \"\"\"\n            options: {\n                'table': table_name,\n                'res': False or True or 'tuple' or 'dict', \n                'extra': extra query constraints appended at end, *** ONLY USE `extra` FOR YOURSELF, SUCH AS `asc by id`, NEVER ACCEPT `extra` FROM USER ***,\n                'concat': 'and' or 'or',\n                'commit': False or True\n            }\n            *columns: columns to query\n            *constraints: string or list(tuple), string for `=` and list for `in`\n            \"\"\"\n            if type(options) is str:\n                options = {'table': options}\n            options_ = self.default_options[self.func.__name__].copy()\n            options_.update(options)\n            table = options_.get('table')\n            # if self.valid_table_columns.get(table) and set(columns) <= set(self.valid_table_columns[table]):\n            return self.func(options_, *columns, **constraints)\n            # raise ValueError('table or columns are not invalid')\n    \n    def _decorate(self):\n        # use `Check` class to decorate the `Database` instance method and pass the instance as parameter to `Check`\n        # if use syntactic sugar, call the decorated function from the `Check` instance will not get the `Database` instance\n        # or we can use __get__ to get the `Database` instance\n        self.select = self.Check(self.select, self.table_columns)\n        self.insert = self.Check(self.insert, self.table_columns)\n        self.update = self.Check(self.update, self.table_columns)\n        self.delete = self.Check(self.delete, self.table_columns)\n","sub_path":"lib/utils/mysql.py","file_name":"mysql.py","file_ext":"py","file_size_in_byte":7439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"225612693","text":"\r\nwith open(\"advent.txt\", \"r\") as file:\r\n    base = file.readlines()\r\n\r\n\r\ndef logic(x):\r\n    return int(x) // 3 - 2\r\n\r\n\r\ndef fuel_calc(list_of_modules):\r\n    amended_lom = []\r\n    for i in list_of_modules:\r\n        while logic(i) > 0:\r\n            i_temp = int(i)\r\n            amended_lom.append(logic(i_temp))\r\n            i = i_temp // 3 - 2\r\n    return sum(amended_lom)\r\n\r\n\r\nprint(fuel_calc(base))\r\n","sub_path":"Advent.py","file_name":"Advent.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"359451286","text":"# Your code here\n\n\n\ndef finder(files, queries):\n    \"\"\"\n    YOUR CODE HERE\n    \"\"\"\n    # Your code here\n    cache = {}\n    for file in files:\n        f = file[1:].split('/')\n        if len(f) == 2:\n            cache[f[1]] = f[0]\n        if len(f) > 2:\n            for i in range(len(f)-1):\n                cache[f[i+1]] = file[1:].split('/')[i]\n    result = []\n    for q in queries:\n        \n        if q in cache:\n            answer = q\n            parentfile = cache[q]\n            answer = cache[q]+'/'+answer\n            while parentfile in cache:\n                answer = cache[parentfile]+'/'+answer\n                parentfile = cache[parentfile]\n            result.append(\"/\"+answer)\n    return result\n\n\nif __name__ == \"__main__\":\n    files = [\n        '/bin/foo',\n        '/bin/bar',\n        '/usr/bin/baz'\n    ]\n    queries = [\n        \"foo\",\n        \"qux\",\n        \"baz\"\n    ]\n    print(finder(files, queries))\n","sub_path":"hashtables/ex5/ex5.py","file_name":"ex5.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"509882674","text":"\nfrom k3.utils import *\nfrom k3.drafts.karguments.parse_utils.classifying import *\n\nparameter_types = ['int_','float_','bool_','name_','path_','list_']\n\ndef process_defaults(Defaults):\n\n    W = {'value':{},'doc':{},'type':{}}\n\n    D = Defaults\n\n    for k in D:\n        doc = ''\n        if type(k) is tuple:\n            assert len(k) == 2\n            parameter_name = k[0]\n            doc = k[1]\n            assert doc\n        else:\n            assert type(k) is str\n            parameter_name = k\n        assert parameter_name\n        #print(parameter_name,doc)\n\n        q = D[k]\n        if type(q) is tuple:\n            if len(q) > 2:\n                cE('type tuple',q,'must long be longer than 2 in length.')\n            assert len(q) <= 2\n            parameter_type = q[0]\n            if parameter_type not in parameter_types:\n                cE(parameter_type,'not in',parameter_types)\n            if len(q) == 1:\n                val = '<required>'\n            else:\n                val = q[1]\n        else:\n            val = q\n            parameter_type,val = classify_token(q)\n        W['value'][parameter_name] = val #cast_parameter(parameter_type,val)\n        W['type'][parameter_name] = parameter_type\n        W['doc' ][parameter_name] = doc\n\n    return W\n\n\"\"\"\ndef cast_parameter(parameter_type,val):\n    cm(parameter_type,val)\n    if parameter_type in ['name','path']:\n        return str(val)\n    elif parameter_type == 'bool':\n        cr(val)\n        if val in ['True',True]:\n            return True\n        elif val in ['False',False]:\n            return False\n        else:\n            assert False\n    elif parameter_type == 'int':\n        return int(val)\n    elif parameter_type == 'float':\n        return float(val)\n    elif parameter_type == 'list':\n        return list(val)\n    else:\n        cE('Error, parameter_type',parameter_type,'not understood.')\n        assert(False)\n\"\"\"\n\nif __name__ == '__main__':\n    D = {\n        'm'             :   'a/a',\n        ('mm','mmmm!')  :   [1,2,3],\n        'c'             :   ('name_',),\n        'd'             :   ('path_','a/b')\n    }\n    #zprint(D,'D')\n    W = process_defaults(D)\n    zprint(W,'W')\n\n\n\n#EOF\n","sub_path":"drafts/karguments/defaults.py","file_name":"defaults.py","file_ext":"py","file_size_in_byte":2170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"279711172","text":"def quantize_nodes_recursively(self, current_node):\n    'The entry point for quantizing nodes to eight bit and back.'\n    self.already_visited[current_node.name] = True\n    for input_node_name in current_node.input:\n        input_node_name = node_name_from_input(input_node_name)\n        if (input_node_name in self.already_visited):\n            continue\n        input_node = self.nodes_map[input_node_name]\n        self.quantize_nodes_recursively(input_node)\n    nodes_to_quantize = ['Conv2D', 'BiasAdd', 'MatMul']\n    if any(((current_node.op in s) for s in nodes_to_quantize)):\n        for input_name in current_node.input:\n            input_name = node_name_from_input(input_name)\n            input_node = self.nodes_map[input_name]\n            self.quantize_node(input_node)\n        self.quantize_node(current_node)\n    else:\n        new_node = tf.NodeDef()\n        new_node.CopyFrom(current_node)\n        self.add_output_graph_node(new_node)","sub_path":"Data Set/bug-fixing-4/da7eb65ba523c0ca5a807b7bbb2331b217b01dae-<quantize_nodes_recursively>-fix.py","file_name":"da7eb65ba523c0ca5a807b7bbb2331b217b01dae-<quantize_nodes_recursively>-fix.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"223391167","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n#  rekurencja_cw1_Sierant.py\n\n#  \n\ndef ciąg(n):\n    if n ==1 : \n        return 2\n    return ciag(n-1) * ciag(n *n) +1\n    \ndef main(args):\n    n = int(input('podaj wyraz ciagu:'))\n    print (ciag(n))\n    return 0\n\nif __name__ == '__main__':\n    import sys\n    sys.exit(main(sys.argv))\n","sub_path":"python/rekurencja_cw1_Sierant.py","file_name":"rekurencja_cw1_Sierant.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"234779359","text":"#!/user/bin/env python3\n# _*_ coding: utf-8 _*_\n\n\nimport os\nimport collections\nimport requests\nfrom bs4 import BeautifulSoup\n\n\nclass HotQiushi:\n    soup = None\n    url = None\n    def __init__(self, url):\n        if url != None:\n            self.url = url\n\n    def get_html(self):\n        r = requests.get(self.url)\n        self.soup = BeautifulSoup(r.content, 'lxml')\n\n    def get_auth(self):\n        if self.soup == None:\n            self.get_html()\n        soup = self.soup\n        auth = soup.find('div', class_='author clearfix').h2.get_text()\n        return auth\n\n    def get_content(self):\n        if self.soup == None:\n            self.get_html()\n        soup = self.soup\n        content = soup.find('div', class_='content').get_text()\n        return content\n\n    def get_hot_contents(self):\n        if self.soup == None:\n            self.get_html()\n        soup = self.soup\n        s = soup.find_all('div', class_='article block untagged mb15')\n        if s is not None:\n            for j in range(len(s)):\n                vote = s[j].find('span', class_='stats-vote').i.get_text()\n                contents = s[j].find('div', class_='content').get_text()\n                yield (vote, contents)\n\n    def to_txt(self):\n        if os.path.exists(os.path.join(os.getcwd(), \"Qiushi.txt\")):\n            with open(os.path.join(os.getcwd(), \"Qiushi.txt\"), 'a') as f:\n                f.write('\\n\\n')\n        else:\n            for vote, item in self.get_hot_contents():\n                with open(os.path.join(os.getcwd(), \"Qiushi.txt\"), 'a') as f:\n                    f.write(item)\n\n    def get_stats(self):\n        if self.soup == None:\n            self.get_html()\n        soup = self.soup\n        vote = soup.find('span', class_='stats-vote').i.get_text()\n        return vote\n\n\nclass User:\n    soup = None\n    url = None\n    home_url = 'http://www.qiushibaike.com/'\n\n    def __init__(self, url):\n        if url != None:\n            self.url = url\n\n    def parser(self):\n        r = requests.get(self.url)\n        self.soup = BeautifulSoup(r.content, 'lxml')\n\n    def user_info(self):\n        alist = []\n        if self.soup == None:\n            self.parser()\n        soup = self.soup\n\n        user_name = soup.find('div', class_='user-header-cover').h2.get_text()\n        alist.append(user_name)\n        s = soup.find('div', class_='user-col-left').find_all('li')\n        for j in range(11):\n            value = s[j].get_text()\n            alist.append(value)\n        return alist\n\n    def get_atricle_url(self):\n        article_url = []\n        if self.soup == None:\n            self.parser()\n        soup = self.soup\n        s = soup.find_all('div', class_='user-block user-feed')\n        if s is not None:\n            for j in range(len(s)):\n                article_url.append(s[j].find('li', class_='user-article-text').a['href'])\n        return article_url\n\n    def scandal(self):\n        if self.soup == None:\n            self.parser()\n        soup = self.soup\n        s = soup.find_all('div', class_='user-block user-feed')\n        if s is not None:\n            for j in range(len(s)):\n                contents = s[j].find('li', class_='user-article-text').a.get_text()\n                yield contents\n\n    def get_top_i_scandal(self, n):\n        adict = {}\n        url_list = self.get_atricle_url()\n        for i in range(len(url_list)):\n            article_url = self.home_url + url_list[i]\n\n            article = HotQiushi(article_url)\n            adict[article_url] = article.get_stats()\n        # lambda d: d[0]以key排序，lambda d: d[1]以value排序\n        adict = collections.OrderedDict(sorted(adict.iteritems(), key=lambda d: d[1]))\n\n        print('+-^-'*20)\n        print(adict)\n        print('+-^-'*20)\n\n        alist = []\n        for key in adict:\n            alist.append(key)\n            if len(alist) == n:\n                break\n\n        for url in alist:\n            article = HotQiushi(url)\n            contents = article.get_content()\n            vote = article.get_stats()\n            yield (vote, contents)","sub_path":"qshi.py","file_name":"qshi.py","file_ext":"py","file_size_in_byte":4037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"109893584","text":"###\n# Lovenote : A simple abstract message queue system\n# \n# @author Marcus Povey <marcus@marcus-povey.co.uk>\n# @copyright Marcus Povey 2012\n# @link http://www.marcus-povey.co.uk\n# @link https://github.com/mapkyca/lovenote\n# \n# Copyright (c) 2011-12 Marcus Povey\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of\n# this software and associated documentation files (the \"Software\"), to deal in\n# the Software without restriction, including without limitation the rights to\n# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of\n# the Software, and to permit persons to whom the Software is furnished to do so,\n# subject to the following conditions:\n# \n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n# \n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS\n# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR\n# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER\n# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN\n# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n###\nimport threading\nfrom threading import Thread\nimport time\nimport urlparse\nimport httplib\nimport random\nimport Queue # TODO: Extend queue to only show messages older than now, add persistent storage and have a populate thread\nimport json\n\nqueue = Queue.Queue() # Create a lifo queue\n\n\nclass LovenoteDispatcherException(Exception):\n\tpass\n\t\n\t\n# Message dispatcher, gets a message off the queue and delivers it \nclass LovenoteDispatcher(Thread):\n\tdef __init__(self, queue):\n\t\tThread.__init__(self)\n\t\tself.queue = queue\n\t\t\n\tdef sendNote(self, note):\n\t\tprint(threading.currentThread().getName() + \" Sending note\")\n\t\t\n\t\t# Get available servers\n\t\tservers = note.servers\n\t\t\n\t\tserver_responses = {}\n\t\t\n\t\t# Randomise servers\n\t\tprint(threading.currentThread().getName() + \" Shuffling the deck\")\n\t\trandom.shuffle(servers)\n\t\t\n\t\t# Iterate through servers\n\t\tfor server in servers:\n\t\t\tprint(threading.currentThread().getName() + \" Sending note payload to \" + server)\n\t\t\t\n\t\t\tping = {}\n\t\t\t\n\t\t\ttry:\n\t\t\t\t# Split parts\n\t\t\t\turl_parts = urlparse.urlparse(server)\n\t\t\t\tprint(threading.currentThread().getName() + \" Url parts \", url_parts)\n\t\t\t\t\n\t\t\t\t# Create connection\n\t\t\t\tconn = httplib.HTTPConnection(url_parts.netloc)\n\t\t\t\tif url_parts.scheme == 'https':\n\t\t\t\t\tconn = httplib.HTTPSConnection(url_parts.netloc)\n\t\t\t\t\n\t\t\t\t# Make request\n\t\t\t\tconn.request('POST', url_parts.path, note.payload)\n\t\t\t\t\n\t\t\t\t# Get response\n\t\t\t\tresponse = conn.getresponse()\n\t\t\t\t\n\t\t\t\t# Construct endpoint ping\n\t\t\t\tping['http_response_code'] = response.status\n\t\t\t\tping['response'] = response.read()\n\t\t\t\t\n\t\t\t\tconn.close()\n\t\t\t\t\n\t\t\t\tprint(threading.currentThread().getName() + \" Remote server pinged, response code was \"+ str(ping[\"http_response_code\"]))\n\t\t\t\t\n\t\t\t\t# Message details\n\t\t\t\tserver_responses[server] = ping\n\t\t\t\n\t\t\t\t# Got here without an exception, so we can assume it was ok\n\t\t\t\tbreak;\n\t\t\t\n\t\t\t\t\n\t\t\texcept Exception as error:\n\t\t\t\tprint(threading.currentThread().getName() + \" \", error)\n\t\t\t\tping['message'] = str(error)\n\n\t\t\t# Message details\n\t\t\tserver_responses[server] = ping\n\t\t\t\n\t\t# Now send ping to callback\n\t\ttry:\n\t\t\tif note.callback != \"\":\n\t\t\t\tprint(threading.currentThread().getName() + \" Pinging callback \" + note.callback)\n\t\t\t\t\n\t\t\t\tcb_url_parts = urlparse.urlparse(note.callback)\n\t\t\t\t\n\t\t\t\tcb_conn = httplib.HTTPConnection(cb_url_parts.netloc)\n\t\t\t\tif cb_url_parts.scheme == 'https':\n\t\t\t\t\tcb_conn = httplib.HTTPSConnection(cb_url_parts.netloc)\n\t\t\t\t\t\n\t\t\t\tcb_conn.request('POST', cb_url_parts.path, json.dumps(server_responses))\n\t\t\t\t\n\t\t\t\tcb_response = cb_conn.getresponse()\n\t\t\t\tprint(threading.currentThread().getName() + \" Callback pinged, response code was \"+ str(cb_response.status))\n\t\t\t\t\n\t\t\t\tcb_conn.close()\n\t\t\t\t\t\n\t\texcept Exception as error:\n\t\t\tprint(threading.currentThread().getName() + \" \", error)\n\n\tdef run(self):\n\t\twhile True:\n\t\t\t\n\t\t\t# Wait for a message off queue\n\t\t\tnote = self.queue.get(True)\n\t\t\t\n\t\t\tprint(threading.currentThread().getName() + \" Processing note\")\n\t\t\t\n\t\t\t# See if we can process it, if so, process and have done, otherwise push back on queue\n\t\t\tif note.send_ts < int(time.time()):\n\t\t\t\t# Can send\n\t\t\t\tself.sendNote(note)\n\t\t\telse:\n\t\t\t\tprint(threading.currentThread().getName() + \" Note is in the future, pushing back on the queue\")\n\t\t\t\t\n\t\t\t\t# Can't send at this time, push to the back of the queue\n\t\t\t\tself.queue.put(note, False)\n\t\t\t\t\n\t\t\t\ttime.sleep(10) # Avoid tight loops\n\t\t\t\n\t\t\t# We have handled this, so say so\n\t\t\tself.queue.task_done()\n\t\t\n","sub_path":"lovenote/dispatcher.py","file_name":"dispatcher.py","file_ext":"py","file_size_in_byte":4750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"625628812","text":"# 单元测试\n'''\n    如果你听说过“测试驱动开发”（TDD：Test-Driven Development），单元测试就不陌生。单元测试是用来对一个模块、\n    一个函数或者一个类来进行正确性检验的测试工作。\n比如对函数abs()，我们可以编写出以下几个测试用例：\n\n    1、输入正数，比如1、1.2、0.99，期待返回值与输入相同；\n    2、输入负数，比如-1、-1.2、-0.99，期待返回值与输入相反；\n    3、输入0，期待返回0；\n    4、输入非数值类型，比如None、[]、{}，期待抛出TypeError。\n\n把上面的测试用例放到一个测试模块里，就是一个完整的单元测试。\n    如果单元测试通过，说明我们测试的这个函数能够正常工作。如果单元测试不通过，要么函数有bug，要么测试条件输入\n    不正确，总之，需要修复使单元测试能够通过。\n    单元测试通过后有什么意义呢？如果我们对abs()函数代码做了修改，只需要再跑一遍单元测试，如果通过，说明我们的\n    修改不会对abs()函数原有的行为造成影响，如果测试不通过，说明我们的修改与原有行为不一致，要么修改代码，要么\n    修改测试。\n    这种以测试为驱动的开发模式最大的好处就是确保一个程序模块的行为符合我们设计的测试用例。在将来修改的时候，可\n    以极大程度地保证该模块行为仍然是正确的。\n'''\n# 示��\n'''\n    我们来编写一个Dict类，这个类的行为和dict一致，但是可以通过属性来访问。\n    为了编写单元测试，我们需要引入Python自带的unittest模块，编写单元测试时，我们需要编写一个测试类，\n    从unittest.TestCase继承。以test开头的方法就是测试方法，不以test开头的方法不被认为是测试方法，\n    测试的时候就不会被执行。\n    对每一个类，测试都需要编写一个test_xxx()方法。由于unittest.TestCase提供了很多内置的条件判断，我们只需要\n    调用这些方法就可以断言输出是否是我们所期望的。最常用的断言就是assertEqual()。\n    另外一种中岛的断言就是期待抛出指定类型的Error，比如通过d['empty']访问不存在的key时，断言会抛出KeyError。\n    而通过d.empty访问不存在的key时，我们期待抛出AttributeError。\n'''\nimport unittest\n\nfrom mydict import Dict\n\nclass TestDict(unittest.TestCase):\n    def setUp(self):\n        print('setUp...')\n\n    def test_init(self):\n        d = Dict(a=1,b='test')\n        self.assertEqual(d.a,1)\n        self.assertEqual(d.b,'test')\n        self.assertTrue(isinstance(d,dict))\n    \n    def test_key(self):\n        d = Dict()\n        d['key'] = 'value'\n        self.assertEqual(d.key,'value')\n    \n    def test_attr(self):\n        d = Dict()\n        d.key = 'value'\n        self.assertTrue('key' in d)\n        self.assertEqual(d['key'],'value')\n\n    def test_keyerror(self):\n        d = Dict()\n        with self.assertRaises(KeyError):\n            value = d['empty']\n    \n    def test_attrerror(self):\n        d = Dict()\n        with self.assertRaises(AttributeError):\n            value = d.empty\n\n    def tearDown(self):\n        print('tearDown...')\n    \nif __name__ == '__main__':\n    unittest.main()\n    \n# 运行单元测试\n'''\n    一旦编写好单元测试，我们就可以运行单元测试。最简单的运行方式是在mydict_test.py的最后加上两行代码：\n----------------\nif __name__ == '__main__':\n    unittest.main()\n----------------\n    这是推荐的做法，因为这样可以一次批量运行很多单元测试，并且，有很多工具可以自动来运行这些单元测试。\n'''\n# setUp 与 tearDown\n'''\n    可以在单元测试中编写两个特殊的setUp()和tearDown()方法。这两个方法会分别在每调用一个测试方法的前后分别被执行。\n    setUp()和tearDown()方法有什么用呢？设想你的测试需要启动一个数据库，这时，就可以在setUp()方法中连接数据库，\n    在tearDown()方法中关闭数据库，这样，不必在每个测试方法中重复相同的代码：\n'''\n# -*- coding: utf-8 -*-\nimport unittest\n\nclass Student(object):\n    def __init__(self, name, score):\n        self.name = name\n        self.score = score\n\n    def get_grade(self):\n        if self.score > 100 or self.score <0:\n            raise ValueError(r\"'score' is not right value\")\n        if self.score >= 80:\n            return 'A'\n        if self.score >= 60:\n            return 'B'\n        return 'C'\n\nclass TestStudent(unittest.TestCase):\n\n    def test_80_to_100(self):\n        s1 = Student('Bart', 80)\n        s2 = Student('Lisa', 100)\n        self.assertEqual(s1.get_grade(), 'A')\n        self.assertEqual(s2.get_grade(), 'A')\n\n    def test_60_to_80(self):\n        s1 = Student('Bart', 60)\n        s2 = Student('Lisa', 79)\n        self.assertEqual(s1.get_grade(), 'B')\n        self.assertEqual(s2.get_grade(), 'B')\n\n    def test_0_to_60(self):\n        s1 = Student('Bart', 0)\n        s2 = Student('Lisa', 59)\n        self.assertEqual(s1.get_grade(), 'C')\n        self.assertEqual(s2.get_grade(), 'C')\n\n    def test_invalid(self):\n        s1 = Student('Bart', -1)\n        s2 = Student('Lisa', 101)\n        with self.assertRaises(ValueError):\n            s1.get_grade()\n        with self.assertRaises(ValueError):\n            s2.get_grade()\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"TestingandDebug/Testing.py","file_name":"Testing.py","file_ext":"py","file_size_in_byte":5495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"153173358","text":"#! /usr/bin/env python3\n#\n# Author:        Chunhua Shen [http://cs.adelaide.edu.au/~chhshen/], The University of Adelaide\n# Creation:      Tuesday 08/01/2013 19:15.\n# Last Revision: Monday 11/02/2022 11:43.\n# CS, 11 Feb 2013, add link to the title\n# CS, 2020 July, now using Python3\n\nfrom __future__ import print_function\nimport pkg_resources\npkg_resources.require(\"pybtex==0.18\")\n\n\n\ntry:\n    from pybtex.database.input.bibtex import Parser\nexcept ImportError:\n    print(\"You need to install pybtex first!\")\n\nimport re\n# from datetime import datetime\n# import os.path\n# from IPython.core.debugger import Tracer\n# debug_here = Tracer()\n\nfrom functools import cmp_to_key\n\ndef load_citekey(citekey):\n# Get the info corresponding to specific citekey\n    entry = bib_data.entries[citekey]\n    return entry\n\n\ndef gen_citefields(citekey):\n# Rearrange the bib entries into something more useful.\n    entry = load_citekey(citekey)\n    D = entry.fields\n\n    authors = [\" \".join( p.first()  + p.prelast() + p.last()  ) for p in entry.persons['author']]\n    D['authors'] = \", \".join(authors)\n    return D\n\n\ndef sort_list_cmp(y, x):\n    yearcmp = int(x[1].fields['year']) - int(y[1].fields['year'])\n    if yearcmp != 0:\n        return yearcmp\n\n    if 'journal' in x[1].fields and 'journal' not in y[1].fields:\n        return 1\n    if 'journal' not in x[1].fields and 'journal' in y[1].fields:\n        return -1\n\n    if 'booktitle' in x[1].fields and 'booktitle' not in y[1].fields:\n        return 1\n    if 'booktitle' not in x[1].fields and 'booktitle' in y[1].fields:\n        return -1\n\n    if x[1].fields['venue'] > y[1].fields['venue']:\n        return -1\n    else:\n        return 1\n\n    if x[1].fields['title'] > y[1].fields['title']:\n        return 1\n    else:\n        return -1\n\n\ndef initialize_name(x):\n# x is in the format of \"Chunhua Shen, X XX ...\"\n# return \"C. Shen, X. XX\"\n    change = 1\n    s = ''\n    for i in range(len(x)):\n\n        if change == -1 and i < len(x) and x[i] == \" \":\n            change = 0\n\n        if change == 0:\n            s = s + x[i]\n\n        if change == 1 and x[i] != \" \" :\n            s = s + x[i]\n            s = s + \".\"\n            change = -1 # ignore the rest of the given name\n\n        if x[i] == \",\" and x[i+1] == \" \":\n            change = 1\n            s = s + \" \"\n    return s\n\n\n# highlight = \"C. +Shen\"\ndef highlight_name( strx,  highlight  ):\n\n    x = highlight.replace('+', '')\n    x = r\"\\\\highlight{\" + x + \"}\"\n    s = re.sub( highlight, x, strx )\n\n    return s\n\n\n\ndef capitalize_string(x):\n\n    # x = x.decode('ascii', 'ignore')\n\n    y=''\n    transform = 1\n    for i in range(len(x)):\n        if x[ i ] == \"{\":\n            transform = 0\n            continue\n\n        if x[ i ] == \"}\":\n            transform = 1\n            continue\n\n        if transform:\n            y = y + x[ i ].lower()\n        else:\n            y = y + x[ i ]\n\n        z =''\n        z = z + y[0].upper()\n        z = z + y[1:len(y)]\n    return z\n\n\ndef remove_curly_bracket(x):\n    x = x.replace('{', '')\n    x = x.replace('}', '')\n    return x\n\n\ndef single_dash(x):\n    for i in range(10):\n        x = x.replace('--', '-')\n\n    x = x.replace('-', '$-$')\n    return x\n\n\ndef instance_fields( x, fields ):\n    if x in fields and len( fields[x]) > 0:\n        return fields[x]\n    else:\n        return \"\"\n\n\nBIBFILE = 'cs.bib'\n\n\njfile = open('journal.text', 'w')\ncfile = open('conf.text', 'w')\n\ncfile_selected = open('conf_selected.text', 'w')\ncfile_selected2 = open('conf_selected2.text', 'w')\n\ncfile_other    = open('conf_other.text', 'w')\n\n\nparser = Parser()\nbib_data = parser.parse_file( BIBFILE )\nbib_sorted = sorted(bib_data.entries.items(), key=cmp_to_key( sort_list_cmp))\n\njnumber = 0\njyear   = 9999\nprint_jyear = 0\n\ncnumber = 0\ncyear   = 9999\nprint_cyear = 0\n\ncnumber_select = 0\ncyear_select   = 9999\nprint_cyear_select = 0\n\ncnumber_select2 = 0\ncyear_select2   = 9999\nprint_cyear_select2 = 0\n\n\n\ncnumber_other = 0\ncyear_other   = 9999\nprint_cyear_other = 0\n\n\nSelected_Conf_List=[\"icml\", \"neurips\", \"cvpr\", \"iccv\", \"eccv\"]\n\n# 2019 March\nSelected_Conf_List2=[\"aaai\", \"ijcai\", \"icra\", \"bmvc\", \"kdd\", \"miccai\", \"acmmm\"]\n\n\n\n\n\nfor key, value in bib_sorted:\n    citef   = gen_citefields( key )\n    author_ = remove_curly_bracket( instance_fields('authors', citef) )\n    author_ = initialize_name( author_ )\n    author_ = highlight_name( author_, \"C. +Shen\" )\n\n    year_   = instance_fields('year', citef)\n\n    title_  = capitalize_string( instance_fields('title', citef) )\n\n    # Google search\n    pre_str = \"http://www.google.com/search?lr=&ie=UTF-8&oe=UTF-8&q=\"\n    t1 = remove_curly_bracket(value.fields['title']) + \"+\" + remove_curly_bracket(value.fields['author'])\n    t2 = pre_str + re.sub(r' +', '+', t1.strip() )\n    title_ = \"\\href{\" + t2 + \"}{\" + title_ + \"}\"\n\n\n    pages_  = instance_fields('pages', citef)\n    pages_  = single_dash( pages_ )\n\n    vol_    = instance_fields('volume', citef)\n\n    # if (len(pages_) > 0 ):\n    #   pages_ = \": \" + pages_\n\n    if 'article' == bib_data.entries[ key ].type:\n        jnumber = jnumber + 1\n\n        y = int( citef['year'] )\n        if y != jyear:\n            jyear = y\n            print_jyear = 1\n        else:\n            print_jyear = 0\n\n        journal_  = instance_fields('journal', citef)\n\n        print(\"%%\", file=jfile )\n        print(\"%% \" + key, file=jfile )\n\n\n        if print_jyear == 1:\n            print(\"\\id{\" + str(jnumber) + \"~~\" + str( y ) + \"}\", file=jfile, end='')\n        else:\n            print(\"\\id{\" + str(jnumber) + \"}\",  file=jfile, end='')\n\n        print(\"\\decoauthor{\" + author_ +  \"}\",  file=jfile )\n        print(\"\\decoyear{\" + year_ +  \"}\",  file=jfile, end='')\n        print(\"\\decotitle{\" + title_ +  \"}\",  file=jfile)\n        print(\"\\decojournal{\" + journal_ + \"}\",  file=jfile, end='')\n        print(\"\\decovolume{\" + vol_ +   \"}\",  file=jfile, end='')\n        print(\"\\decopage{\" + pages_ +   \"}\",  file=jfile, end='')\n        print(\"\\\\\\\\[.125cm]\",  file=jfile)\n\n    elif 'inproceedings' == bib_data.entries[ key ].type:\n\n        cnumber = cnumber + 1\n        y = int( citef['year'] )\n        if y != cyear:\n            cyear = y\n            print_cyear = 1\n        else:\n            print_cyear = 0\n\n        conf_  = instance_fields('booktitle', citef)\n\n        print(\"%%\", file=cfile )\n        print(\"%% \" + key, file=cfile )\n\n        if print_cyear == 1:\n            print(\"\\id{\" + str(cnumber) + \"~~\" + str( y ) + \"}\", file=cfile, end='')\n        else:\n            print(\"\\id{\" + str(cnumber) + \"}\",  file=cfile, end='')\n\n        print(\"\\decoauthor{\" + author_ +  \"}\",  file=cfile )\n        print(\"\\decoyear{\" + year_ +  \"}\",  file=cfile, end='')\n        print(\"\\decotitle{\" + title_ +  \"}\",  file=cfile)\n        print(\"\\decoconf{\" + conf_ + \"}\",  file=cfile, end='')\n        # print(\"\\decovolume{\" + vol_ + \"}\",  file=cfile, end='')\n        print(\"\\decopage{\" + pages_ +   \"}\",  file=cfile, end='')\n        print(\"\\\\\\\\[.125cm]\",  file=cfile)\n\n        if any( citef['venue'].lower() == s for s in Selected_Conf_List ):\n            print(\"%%\", file=cfile_selected )\n            print(\"%% \" + key, file=cfile_selected )\n\n            cnumber_select = cnumber_select + 1\n\n            if y != cyear_select:\n                cyear_select = y\n                print_cyear_select = 1\n            else:\n                print_cyear_select = 0\n\n            if print_cyear_select == 1:\n                print(\"\\id{\" + str(cnumber_select) + \"~~\" + str( y ) + \"}\",\n                        file=cfile_selected, end='')\n            else:\n                print(\"\\id{\" + str(cnumber_select) + \"}\",   file=cfile_selected, end='')\n\n            print(\"\\decoauthor{\" + author_ +  \"}\",  file=cfile_selected )\n            print(\"\\decoyear{\" + year_ +  \"}\",  file=cfile_selected, end='')\n            print(\"\\decotitle{\" + title_ +  \"}\",  file=cfile_selected)\n            print(\"\\decoconf{\" + conf_ + \"}\",  file=cfile_selected, end='')\n            # print(\"\\decovolume{\" + vol_ + \"}\",  file=cfile_selected, end='')\n            print(\"\\decopage{\" + pages_ +   \"}\",  file=cfile_selected, end='')\n            print(\"\\\\\\\\[.125cm]\",  file=cfile_selected)\n\n\n\n\n\n# Part 2 Conf.  AAAI, IJCAI, ICRA\n\n\n        elif any( citef['venue'].lower() == s for s in Selected_Conf_List2  ):\n            print(\"%%\", file=cfile_selected2 )\n            print(\"%% \" + key, file=cfile_selected2 )\n\n            cnumber_select2 = cnumber_select2 + 1\n\n            if y != cyear_select2:\n                cyear_select2 = y\n                print_cyear_select2 = 1\n            else:\n                print_cyear_select2 = 0\n\n            if print_cyear_select2 == 1:\n                print(\"\\id{\" + str(cnumber_select2) + \"~~\" + str( y ) + \"}\",\n                        file=cfile_selected2, end='')\n            else:\n                print(\"\\id{\" + str(cnumber_select2) + \"}\",   file=cfile_selected2, end='')\n\n            print(\"\\decoauthor{\" + author_ +  \"}\",  file=cfile_selected2 )\n            print(\"\\decoyear{\" + year_ +  \"}\",  file=cfile_selected2, end='')\n            print(\"\\decotitle{\" + title_ +  \"}\",  file=cfile_selected2)\n            print(\"\\decoconf{\" + conf_ + \"}\",  file=cfile_selected2, end='')\n            # print(\"\\decovolume{\" + vol_ + \"}\",  file=cfile_selected2, end='')\n            print(\"\\decopage{\" + pages_ +   \"}\",  file=cfile_selected2, end='')\n            print(\"\\\\\\\\[.125cm]\",  file=cfile_selected2)\n\n\n\n        else:\n\n            print(\"%%\", file=cfile_other )\n            print(\"%% \" + key, file=cfile_other )\n\n            cnumber_other = cnumber_other + 1\n\n            if y != cyear_other:\n                cyear_other = y\n                print_cyear_other = 1\n            else:\n                print_cyear_other = 0\n\n            if print_cyear_other == 1:\n                print(\"\\id{\" + str(cnumber_other) + \"~~\" + str( y ) + \"}\",\n                        file=cfile_other, end='')\n            else:\n                print(\"\\id{\" + str(cnumber_other) + \"}\",    file=cfile_other, end='')\n\n            print(\"\\decoauthor{\" + author_ +  \"}\",  file=cfile_other )\n            print(\"\\decoyear{\" + year_ +  \"}\",  file=cfile_other, end='')\n            print(\"\\decotitle{\" + title_ +  \"}\",  file=cfile_other)\n            print(\"\\decoconf{\" + conf_ + \"}\",  file=cfile_other, end='')\n            # print(\"\\decovolume{\" + vol_ + \"}\",  file=cfile_other, end='')\n            print(\"\\decopage{\" + pages_ +   \"}\",  file=cfile_other, end='')\n            print(\"\\\\\\\\[.125cm]\",  file=cfile_other)\n\n\n\nfn_journals = open('num_journals.text', 'w')\nprint(jnumber, file=fn_journals)\n\nfn_confs = open('num_confs.text', 'w')\nprint(cnumber, file=fn_confs)\n\nfn_confs_s = open('num_confs_select.text', 'w')\nprint(cnumber_select, file=fn_confs_s)\n\n\nfn_confs_s2 = open('num_confs_select2.text', 'w')\nprint(cnumber_select2, file=fn_confs_s2)\n\n\n\nfn_confs_o = open('num_confs_other.text', 'w')\nprint(cnumber_other, file=fn_confs_o)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# vim: ft=python ts=4 sw=4 :\n# EoF\n\n","sub_path":"list_pdf/bin/bin/bibparse.py","file_name":"bibparse.py","file_ext":"py","file_size_in_byte":10917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"279464811","text":"#   Copyright 2016, 2017 California Institute of Technology\n#   Users must agree to abide by the restrictions listed in the\n#   file \"LegalStuff.txt\" in the PROPER library directory.\n#\n#   PROPER developed at Jet Propulsion Laboratory/California Inst. Technology\n#   Original IDL version by John Krist\n#   Python translation by Navtej Saini, with Luis Marchen and Nikta Amiri\n\n\nimport os\nimport proper\n\n\ndef prop_use_fftw(**kwargs):\n    \"\"\"Instructs PROPER to use the FFTW FFT routines rather than the built-in\n    numpy FFT.\n\n    See the manual for how to use FFTW.  FFTW will be used by PROPER for all\n    future runs (including after exiting python shell), unless changed by using\n    the DISABLE switch.\n\n\n    Parameters\n    ----------\n        None\n\n\n    Returns\n    -------\n        None\n\n\n    Other Parameters\n    ----------------\n    DISABLE : bool\n        If set, the Numpy FFT routine will be used by pyPROPER routines in\n        future.\n    \"\"\"\n    fftw_dummy_file = os.path.join(proper.lib_dir, '.use_fftw')\n\n    # Check if pyFFTW is available on user's machine\n    try:\n        import pyfftw\n    except ImportError:\n        print('pyFFTW not found, using Numpy FT.')\n        fftw_flag = False\n    else:\n        fftw_flag = True\n\n    if \"DISABLE\" in kwargs or not fftw_flag:\n        if os.path.isfile(fftw_dummy_file):\n            os.remove(fftw_dummy_file)\n        proper.use_fftw = False\n    else:\n        with open(fftw_dummy_file, 'w') as fd:\n            fd.write('Using FFTW routines\\n')\n        proper.use_fftw = True\n\n    return\n","sub_path":"Proper/build/lib.macosx-10.7-x86_64-3.6/proper/prop_use_fftw.py","file_name":"prop_use_fftw.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"580709673","text":"import pandas as pd\r\nimport string\r\nfrom nltk.corpus import stopwords\r\n\r\ndef lower_tokens(dev):\r\n    dev.token = dev.token.str.lower()\r\n    return dev\r\ndef remove_stopwords(dev):\r\n    stopword_list = stopwords.words(\"english\")\r\n\r\n    possible_negation_cues = [\"not\", \"no\", \"never\", \"nor\", \"than\", \"nothing\", \"never\", \"cannot\"]\r\n\r\n    new_stopword_list = []\r\n\r\n    for words in stopword_list:\r\n        if (words not in possible_negation_cues) & (\"n't\" not in words):\r\n            new_stopword_list.append(words)\r\n\r\n    for words in new_stopword_list:\r\n        dev.token = dev.token.replace(words, \"\")\r\n\r\n    return dev\r\n\r\ndef remove_numbers(dev):\r\n    dev.token = dev.token.str.replace(\"\\d+\", \"\")\r\n    return dev\r\n\r\ndef remove_punctuation(dev):\r\n    punctuations = string.punctuation\r\n\r\n    for punct in punctuations:\r\n        dev.token = dev.token.replace(punct, \"\")\r\n    dev = dev.replace('``', \"\")\r\n    dev = dev.replace(\"''\", \"\")\r\n    dev = dev.replace(\"--\", \"\")\r\n    return dev\r\n\r\ndef main():\r\n    path = \"SEM-2012-SharedTask-CD-SCO-training-simple.v2.txt\"\r\n    dev = pd.read_csv(\"../data/\"+path, sep=\"\\t\", header=None)\r\n    dev.columns = ['chapter', 'sentence_id', 'token_id', 'token', 'target']\r\n\r\n    dev = lower_tokens(dev)\r\n    dev = remove_stopwords(dev)\r\n    dev = remove_numbers(dev)\r\n    dev = remove_punctuation(dev)\r\n\r\n    # remove empty cell rows\r\n    new_dev = dev[dev.token != \"\"]\r\n\r\n    # save preprocessed dataset\r\n    new_dev.to_csv(\"../data/preprocessed_\" + path, sep=\"\\t\", header=True, index=False)\r\n\r\nif __name__ == '__main__':\r\n    main()","sub_path":"code/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":1563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"479671962","text":"import copy\nimport json\nimport re\nimport urllib.request, urllib.parse, urllib.error\nimport urllib.parse\nfrom mako.lookup import TemplateLookup\nfrom oic.oauth2 import rndstr\nfrom oic.oic import RegistrationResponse\nfrom oic.oic import AccessTokenResponse\nfrom oic.oic import AuthorizationResponse\nfrom oic.oic import AccessTokenRequest\nfrom oic.utils.keyio import KeyBundle\n\nfrom oidctest.mode import extract_mode\nfrom oidctest.mode import setup_op\nfrom src.oidctest.common import Trace\n\n__author__ = 'roland'\n\nROOT = './'\n\nLOOKUP = TemplateLookup(directories=[ROOT + 'templates', ROOT + 'htdocs'],\n                        module_directory=ROOT + 'modules',\n                        input_encoding='utf-8', output_encoding='utf-8')\n\nimport argparse\n\nfrom setup import main_setup\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-v', dest='verbose', action='store_true')\nparser.add_argument('-d', dest='debug', action='store_true')\nparser.add_argument('-p', dest='port', default=80, type=int)\nparser.add_argument('-k', dest='insecure', action='store_true')\nparser.add_argument('-l', dest='logfile')\nparser.add_argument('-t', dest='timestamp')\nparser.add_argument(dest=\"config\")\nargs = parser.parse_args()\n\nCOM_ARGS, OP_ARG, config = main_setup(args, LOOKUP)\n\nmode, endpoint = extract_mode(OP_ARG[\"baseurl\"])\n\nENVIRON = {}\n\nPROVIDER = setup_op(mode, COM_ARGS, OP_ARG, Trace())\n\n\ndef get_response(line):\n    parts = urllib.parse.urlparse(line)\n    if parts.query:\n        return parts.query\n\n    return parts.fragment\n\n\ndef get_request(line):\n    if re.match(r'^[A-Z]*:', line):\n        return \"\"\n\n    return line\n\n\nop_map = {\n    \"registration\": [\n        (\" <-- \", json.loads, \"resp\"),\n        (\" Client JWKS: \", eval, \"jwks\")\n    ],\n    \"authorization\": {\n        (' --> METHOD: ', copy.copy, \"method\"),\n        (' --> ', get_request, \"req\"),\n        (\" Client JWKS: \", eval, \"jwks\"),\n        (\" <-- \", get_response, \"resp\"),\n    },\n    \"token\": {\n        ('--> HTTP_AUTHORIZATION: ', copy.copy, \"authz\"),\n        (\" <-- \", json.loads, \"resp\"),\n    },\n    \"userinfo\": []\n}\n\n\ndef parse_lines(lines, spec):\n    res = {}\n    for line in lines:\n        for pattern, func, key in spec:\n            if pattern in line:\n                time, info = line.split(pattern)\n                _val = func(info)\n                if _val:\n                    res[key] = _val\n                break\n    return res\n\n\ndef client_registration(lines, state):\n    info = parse_lines(lines, op_map[\"registration\"])\n\n    _resp = info[\"resp\"]\n    ruris = []\n    for ruri in _resp[\"redirect_uris\"]:\n        base, query = urllib.parse.splitquery(ruri)\n        if query:\n            ruris.append((base, urllib.parse.parse_qs(query)))\n        else:\n            ruris.append((base, query))\n\n    rr = RegistrationResponse(**_resp)\n    drr = rr.to_dict()\n    drr[\"redirect_uris\"] = ruris\n    drr[\"client_salt\"] = rndstr(8)\n\n    _cid = str(_resp[\"client_id\"])\n    PROVIDER.cdb[_cid] = drr\n    PROVIDER.keyjar[_cid] = KeyBundle(keys=info[\"jwks\"][\"keys\"])\n    PROVIDER.keyjar.add_symmetric(issuer=_cid, key=_resp[\"client_secret\"],\n                                  usage=[\"sig\"])\n\n    return 'client_info', rr\n\n\ndef authorization(lines, state):\n    info = parse_lines(lines, op_map[\"authorization\"])\n\n    if \"jwks\" in info:\n        _cid = [c for c in list(PROVIDER.keyjar.issuer_keys.keys()) if c != ''][\n            0]\n        PROVIDER.keyjar[_cid].append(KeyBundle(keys=info[\"jwks\"][\"keys\"]))\n\n    resp = PROVIDER.authorization_endpoint(info[\"req\"])\n\n    parts = urllib.parse.urlparse(resp.message)\n\n    if parts.query:\n        aresp = AuthorizationResponse().from_urlencoded(parts.query)\n    else:\n        aresp = AuthorizationResponse().from_urlencoded(parts.fragment)\n\n    return 'authorization', aresp\n\n\ndef token(lines, state):\n    info = parse_lines(lines, op_map[\"token\"])\n    _authz = state[\"authorization\"]\n    _cinfo = state[\"client_info\"]\n\n    treq = AccessTokenRequest(\n        client_id=_cinfo[\"client_id\"],\n        code=_authz[\"code\"],\n        redirect_uri=\"\",\n        grant_type=\"authorization_code\",\n        client_secret=_cinfo[\"client_secret\"]\n    )\n\n    _resp = PROVIDER.token_endpoint(treq)\n\n    tresp = AccessTokenResponse().from_json(_resp.message)\n\n    return \"token\", tresp\n\n\ndef userinfo(lines, state):\n    _ = parse_lines(lines, op_map[\"userinfo\"])\n    tresp = state[\"token\"]\n    authn = \"Bearer {}\".format(tresp[\"access_token\"])\n\n    return \"user_info\", PROVIDER.userinfo_endpoint(\"\", authn=authn)\n\n\nOP = {\n    \"registration\": client_registration,\n    \"authorization\": authorization,\n    \"token\": token\n}\n# every request response log begins with a line with \"Trace init\"\n\nexchange = []\nlines = []\nop = \"\"\nstart = False\nfor l in open(args.logfile).readlines():\n    if not start:\n        if args.timestamp in l:\n            start = True\n        else:\n            continue\n    l = l.strip(\"\\r\\n\")\n    if l.startswith(\"Trace init:\"):\n        exchange.append((op, lines))\n        lines = []\n    else:\n        if \"PATH:\" in l:\n            _, op = l.split(' PATH: ')\n        lines.append(l)\n\n# end of file\nexchange.append((op, lines))\n\nstate = {}\nfor op, lin in exchange:\n    if not op:\n        continue\n    print(op)\n\n    try:\n        key, val = OP[op](lin, state)\n    except KeyError as err:\n        if '.well-known/openid-configuration' in op:\n            mode, _ = extract_mode(op)\n            # PROVIDER.behavior_type = mode[\"behavior\"]\n        continue\n\n    if key:\n        try:\n            state[key].append(val)\n        except KeyError:\n            state[key] = [val]\n","sub_path":"test_tool/test_rp/op/rerun.py","file_name":"rerun.py","file_ext":"py","file_size_in_byte":5566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"627651381","text":"# Gabriel Lemyre\n# Simon Bernier St-Pierre\n# utf8, LF\n\nfrom multiprocessing import Process\nimport exemplaires\n\n\ndef is_perm(p):\n    \"\"\"\n    S'assure que la permuation est valide. Chaque valeur doit être unique et inférieure ou égale aux nombre d'éléments.\n    \"\"\"\n    return all(x <= len(p) for x in p) and len(p) == len(set(p))\n\n\ndef check_perms(**kwargs):\n    \"\"\"\n    S'assure que toute les permutations sont de même taille et qu'elle sont valides.\n    \"\"\"\n    if len(set(len(p) for p in kwargs.values())) > 1:\n        raise ValueError(\"Les permutations ne sont pas de même longeur\")\n    for name, val in kwargs.items():\n        if not is_perm(val):\n            raise ValueError(\"Erreur, {}={} n'est pas une permutation\".format(name, val))\n\n\ndef prod(p1, p2):\n    \"\"\"\n    Effectue le produit de permutations.\n    \"\"\"\n    r = [0] * len(p1)\n    for idx, val in enumerate(p1):\n        r[idx] = p2[val - 1]\n    return tuple(r)\n\n\ndef perm2(k, p, p1, p2):\n    \"\"\"\n    Utilise une méthode bête récursive/exponentielle pour tester chaque combinaison de permutations.\n    Si on atteint la limite k, on arrête avec False.\n    Si on obtient un produit qui fonctionne, on arrête avec True\n    Sinon on continue à chercher avec k-1 et l'option p1/p2\n    \"\"\"\n    check_perms(p=p, p1=p1, p2=p2)\n\n    def rec(k, c):\n        if k < 0:\n            return False\n        if p == c:\n            return True\n        return rec(k - 1, prod(c, p1)) or rec(k - 1, prod(c, p2))\n    return rec(k - 1, p1) or rec(k - 1, p2)\n\n\n# test with pytest\n\ndef test_prod():\n    p = (2, 4, 1, 3, 5)\n    q = (5, 4, 3, 2, 1)\n    assert prod(p, q) == (4, 2, 5, 3, 1)\n\n\ndef test_is_perm():\n    assert is_perm((1, 3, 2))\n    assert not is_perm((3, 1, 1))\n    assert not is_perm((3, 2))\n\n\nTIMEOUT = 60  # in seconds\n\ninput_set = vars(exemplaires)\nfor i in range(1, 8):\n    # little hack to grab all the input sets\n    k = input_set['k%d' % i]\n    p = input_set['p%d' % i]\n    p1 = input_set['p1%d' % i]\n    p2 = input_set['p2%d' % i]\n\n    def runner(k, p, p1, p2):\n        try:\n            v = perm2(k, p, p1, p2)\n            print(i, v)\n        except ValueError as e:\n            print(i, str(e))\n\n    # use a process to timeout on sets that take too much time\n    t = Process(target=runner, args=(k, p, p1, p2))\n    t.start()\n    t.join(TIMEOUT)\n    if t.is_alive():\n        print(i, 'Timed out')\n        t.terminate()\n","sub_path":"dev1/dev1_no4.py","file_name":"dev1_no4.py","file_ext":"py","file_size_in_byte":2391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"528665728","text":"class A:\n    def __init__(self):\n        self.first = 1\n        self.second = 2\n        self.price =  00.0\n\nclass B:\n    def __init__(self):\n        self.first = \"B\"\n        self.second = A()\n    \n    #def price(self):\n    #   print(self.second.price)\n\n        # create a list as object\n        '''\n        self.second = [A(),A()]\n        '''\n    #nomal inheritance\n    def display(self):\n        print(self.first, self.second.first, self.second.second,self.second.price)\n'''\n    #list inheritance\n    def display(self):\n        print(self.second[0].first, self.second[1].second,self.second[1].first, self.second[1].second)\n'''\nif __name__ == \"__main__\":\n    object = B()\n    print(\"first display everyone\")\n    object.display()\n    print(\"accessing B first object\")\n    print(object.first)\n\n    print(\"accessing A first, and second object\")\n    print(object.second.first, object.second.second)    \n    \n    print(\"accessing B first list\")\n    #append to a pre list made on object\n    \n    '''list'''\n    objects = []\n    objects.append(object)\n    print(objects[0].first)\n    print(\"accessing A first list\")    \n    print(objects[0].second.first)\n    print(\"accessing A second list\")\n    print(objects[0].second.second)\n    \n    '''\n    #working with list inside methods of the objects\n    print(\"accessing A second list .appended\")\n    object.second[1].second = 24214\n    object.display()\n    '''\n    \n\n    object.first = \"B appended\"\n    object.second.first = \"A first Appended\"\n    object.second.second = \"A second Appended\"\n    print(\"Accessing appended objects\")\n    print(objects[0].first, objects[0].second.first, objects[0].second.second)\n\n    ''' using for in - yeah'''\n\n    objects.append(object)\n    print(\"for list of item in the same object\")\n    for thelist in objects:\n        thelist.display()\n\n    ''' using for in - wiht a different object'''\n    object = B()\n    objects = []\n    objects.append(object)\n    object.first = \"first appended in a different object\"\n    object.second.first = \"second - first appended in a different object\"\n    object.second.second = \"second - second appended in a different object\"\n    object.second.price = 45\n    print(\"for list of item in a different object\")\n    count = 0\n    for thelist in objects:\n        count += 1\n        print(count)\n        thelist.display()\n\n    ''' using for in - with  various different object'''\n\n    objectA = B()\n    objects = []\n    objects.append(objectA)    \n    objectA.first = \"AA first appended in a different object\"\n    objectA.second.first = \"AA second - first appended in a different object\"\n    objectA.second.second = \"AA second - second appended in a different object\"\n    objectA.second.price = 55\n    objectB = B()\n    objects.append(objectB)\n    objectB.first = \"BB first appended in a different object\"\n    objectB.second.first = \"BB second - first appended in a different object\"\n    objectB.second.second = \"BB second - second appended in a different object\"\n    objectB.second.price = 66\n    objectC = B()\n    objects.append(objectC)    \n    objectC.second.price = 67\n\n    objectD = B()\n    objects.append(objectD)    \n    objectC.second.price = 52\n\n\n\n    print(\"for list of item in a different object\")\n    count = 0\n    for thelist in objects:\n        count += 1\n        print(count)\n        thelist.display()\nprint(\"price\")\nprint(objects[1].second.price)\n\nprint(\"sum elements in a list\")\ncount_product = 0\nsuma = 0\nfor thelist in objects:\n    new_product = objects[count_product].second.price\n    print(new_product, \"+ \")\n    suma += new_product\n    count_product += 1\nprint(suma)\n    #suma = 0\n    #suma += ","sub_path":"python/cs241/w04/teacher_class A - By.py","file_name":"teacher_class A - By.py","file_ext":"py","file_size_in_byte":3615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"266902423","text":"from pandas import DataFrame\nfrom typing import List, Tuple, Dict\nfrom models.team_stats import TeamSkeletonStats\n\n\ndef get_opponents_in_given_fixture_list(team_id: int, fixtures: DataFrame) -> List[int]:\n    home_opponents = fixtures.loc[fixtures['team_a'] == team_id, 'team_h']\n    away_opps = fixtures.loc[fixtures['team_h'] == team_id, 'team_a']\n\n    return list(home_opponents.values) + list(away_opps.values)\n\n\ndef get_prev_and_next_opponents(\n        team_id: int,\n        number_of_opponents_to_get: int,\n        fixtures: DataFrame\n) -> Tuple[List[int], List[int]]:\n\n    team_fixtures = fixtures[(fixtures['team_a'] == team_id) | (fixtures['team_h'] == team_id)]\n\n    completed_fixtures = team_fixtures[team_fixtures['finished'] == True]\n    upcoming_fixtures = team_fixtures[team_fixtures['finished'] == False]\n\n    # all_previous_opponents = get_opponents_in_given_fixture_list(team_id, completed_fixtures)\n    previous_x_opponents = get_opponents_in_given_fixture_list(team_id, completed_fixtures.tail(number_of_opponents_to_get))\n    next_x_opponents = get_opponents_in_given_fixture_list(team_id, upcoming_fixtures.head(number_of_opponents_to_get))\n\n    return previous_x_opponents, next_x_opponents\n\n\ndef get_current_team_stats(team: str, team_id: int, teams_data: Dict[str, DataFrame]) -> TeamSkeletonStats:\n    relevant_team_data = teams_data[team]\n    xg_total = relevant_team_data.xG.sum()\n    xga_total = relevant_team_data.xGA.sum()\n    npxg_total = relevant_team_data.npxG.sum()\n    npxga_total = relevant_team_data.npxGA.sum()\n    goals_scored_total = relevant_team_data.scored.sum()\n    goals_conceded_total = relevant_team_data.missed.sum()\n    games_played = len(relevant_team_data.index)\n\n    xg_avg = xg_total / games_played\n    xga_avg = xga_total / games_played\n    goals_scored_avg = goals_scored_total / games_played\n    goals_conceded_avg = goals_conceded_total / games_played\n\n    return TeamSkeletonStats(\n        xg_total=xg_total,\n        xga_total=xga_total,\n        npxg_total=npxg_total,\n        npxga_total=npxga_total,\n        g_total=goals_scored_total,\n        ga_total=goals_conceded_total,\n        games_played=games_played,\n        xg_avg=xg_avg,\n        xga_avg=xga_avg,\n        goals_scored_avg=goals_scored_avg,\n        goals_conceded_avg=goals_conceded_avg,\n        name=team,\n        team_id=team_id,\n    )","sub_path":"myfpl/methods/opponent_info.py","file_name":"opponent_info.py","file_ext":"py","file_size_in_byte":2358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"29529992","text":"\"\"\".. Ignore pydocstyle D400.\n\n==================\nList Docker images\n==================\n\n\"\"\"\n\nimport functools\nimport operator\nimport shlex\nimport subprocess\n\nimport yaml\n\nfrom django.conf import settings\nfrom django.core.management.base import BaseCommand, CommandError\n\nfrom resolwe.flow.models import Process\n\n\nclass Command(BaseCommand):\n    \"\"\"List Docker images used by processes.  Optionally also pull them.\"\"\"\n\n    help = \"List Docker images used by processes in either plain text or YAML (for Ansible)\"\n\n    def add_arguments(self, parser):\n        \"\"\"Add an argument to specify output format.\"\"\"\n        parser.add_argument('--format', dest='format', default='plain',\n                            help=\"Set output format ('plain' [default] or 'yaml')\")\n        parser.add_argument('--pull', dest='pull', default=False, action='store_true',\n                            help=\"Pull all images with Docker\")\n        parser.add_argument('--ignore-pull-errors', dest='ignore_pull_errors',\n                            default=getattr(settings, 'FLOW_DOCKER_IGNORE_PULL_ERRORS', False),\n                            action='store_true',\n                            help=\"Don't fail whenever a Docker image can't be pulled\")\n\n    def handle(self, *args, **options):\n        \"\"\"Handle command list_docker_images.\"\"\"\n        # Check that the specified output format is valid\n        if options['format'] != 'plain' and options['format'] != 'yaml':\n            raise CommandError(\"Unknown output format: %s\" % options['format'])\n\n        # Gather only unique latest custom Docker requirements that the processes are using\n        # The 'image' field is optional, so be careful about that as well\n        unique_docker_images = set(\n            p.requirements['executor']['docker']['image']\n            for p in Process.objects.order_by(\n                'slug', '-version'\n            ).distinct(\n                'slug'\n            ).only(\n                'requirements'\n            ).filter(\n                requirements__icontains='docker'\n            )\n            if 'image' in p.requirements.get('executor', {}).get('docker', {})\n        )\n\n        # Add the default image if it exists\n        if 'CONTAINER_IMAGE' in settings.FLOW_EXECUTOR:\n            # This Docker image is used if a process doesn't specify its own\n            default_docker_image = settings.FLOW_EXECUTOR['CONTAINER_IMAGE']\n\n            unique_docker_images.add(default_docker_image)\n\n        # Sort the set of unique Docker images for nicer output\n        unique_docker_images = sorted(unique_docker_images)\n\n        # Pull images if requested or just output the list in specified format\n        if options['pull']:\n            # Get the desired 'docker' command from settings or use the default\n            docker = getattr(settings, 'FLOW_DOCKER_COMMAND', 'docker')\n\n            # Pull each image\n            for img in unique_docker_images:\n                ret = subprocess.call(shlex.split('{} pull {}'.format(docker, img)))\n\n                if ret != 0:\n                    errmsg = \"Failed to pull Docker image '{}'!\".format(img)\n\n                    if not options['ignore_pull_errors']:\n                        # Print error and stop execution\n                        raise CommandError(errmsg)\n                    else:\n                        # Print error, but keep going\n                        self.stderr.write(errmsg)\n                else:\n                    self.stdout.write(\"Docker image '{}' pulled successfully!\".format(img))\n        else:\n            # Convert the set of unique Docker images into a list of dicts for easier output\n            imgs = [\n                dict(name=s[0], tag=s[1] if len(s) == 2 else 'latest')\n                for s in (img.split(':') for img in unique_docker_images)\n            ]\n\n            # Output in YAML or plaintext (one image per line), as requested\n            if options['format'] == 'yaml':\n                out = yaml.safe_dump(imgs, default_flow_style=True, default_style=\"'\")\n            else:\n                out = functools.reduce(operator.add,\n                                       ('{name}:{tag}\\n'.format(**i) for i in imgs), '')\n\n            self.stdout.write(out, ending='')\n","sub_path":"resolwe/flow/management/commands/list_docker_images.py","file_name":"list_docker_images.py","file_ext":"py","file_size_in_byte":4223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"581444928","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport sys\nimport re\nimport requests as req\nimport json\nfrom string import ascii_letters\nfrom random import choice\nimport hashlib\nimport socket\n\n\nFLAG_FORMAT = r\"(^[a-z0-9]{8}-[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{12}$)\"\n\n\ndef post(site, data, s):\n    try:\n        data = s.post(site, data=data)\n        return data.text\n    except req.exceptions.ConnectTimeout:\n        return None\n    except req.exceptions.ReadTimeout:\n        return None\n    except req.exceptions.ConnectionError:\n        return None\n\n\ndef get(site, s, headers={}):\n    try:\n        data = s.get(site, headers=headers)\n        return data\n    except req.exceptions.ConnectTimeout:\n        return None\n    except req.exceptions.ReadTimeout:\n        return None\n    except req.exceptions.ConnectionError:\n        return None\n\n\ndef find_names(data):\n    return re.findall(r'<td>([a-zA-Z]+)</td>', data) or []\n\n\ndef find_flags(data):\n    return re.findall(FLAG_FORMAT, data) or []\n\n\ndef exploit(url):\n    print(url)\n    flags = []\n    s = req.Session()\n    data = {\n        'inputLogin': 'A'*900,\n        'inputPassword': 'asd',\n        'inputName': 1,\n        'register': True\n    }\n    data = post('http://10.218.13.3/registration.php', data, s)\n    print(data)\n    # flags.extend(find_flags(data))\n    # return flags\n\n\nif __name__ == \"__main__\":\n    # На вход получаем адрес команды\n    host = '10.218.13.3'\n    # print(url)\n    # Возвращаем ответ в консоль, чтобы наш основной процесс перехватил сообщение\n    print(exploit(host), flush=True)\n","sub_path":"client/s3.py","file_name":"s3.py","file_ext":"py","file_size_in_byte":1659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"434571490","text":"# -*- coding:utf-8 -*-\n\nimport requests\nimport re\nimport pymysql\n\ndb = pymysql.connect(\n    host='localhost',\n    port=3306,\n    user='root',\n    passwd='123456',\n    db='doutu',\n    charset='utf8'\n)\ncursor = db.cursor()  #  游标，相当于指针\n\n\n\ndef getImageList(page=1):\n    response = requests.get('http://www.doutula.com/photo/list/?page={}'.format(page))\n    html = response.text\n    reg = r' data-original=\"(.+?)\".*?alt=\"(.*?)\"'  # 禁止转义\n    img_list = re.findall(reg,html,re.S)  # re.S 正则表达式具备匹配多行的能力\n    return img_list\n\nfor page in range(1,1122):\n    for url,title in getImageList(page):\n        print('正在插入数据库----{}'.format(title))\n        cursor.execute(\"insert into images(name,imageUrl)VALUES ('{}','{}')\".format(title,url)) # 执行sql语句 orm引擎\n        db.commit()\n\n\ndb.close()","sub_path":"斗图/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"182468306","text":"\"\"\"Miscellaneous transformations to do on data, from grouping frequencies and\nproducts to performing the m-mode transform.\n\nTasks\n=====\n\n.. autosummary::\n    :toctree:\n\n    FrequencyRebin\n    SelectFreq\n    CollateProducts\n    MModeTransform\n\"\"\"\n\nimport numpy as np\nfrom caput import mpiarray, config\nfrom caput import mpiutil\n\nfrom ..core import containers, task, io\nfrom ..util import tools\nfrom ..util import regrid\n\n\nclass FrequencyRebin(task.SingleTask):\n    \"\"\"Rebin neighbouring frequency channels.\n\n    Parameters\n    ----------\n    channel_bin : int\n        Number of channels to in together.\n    \"\"\"\n\n    channel_bin = config.Property(proptype=int, default=1)\n\n    def process(self, ss):\n        \"\"\"Take the input dataset and rebin the frequencies.\n\n        Parameters\n        ----------\n        ss : containers.SiderealStream or containers.TimeStream\n            Input data to rebin. Can also be an `andata.CorrData` instance,\n            however the output will be a `containers.TimeStream` instance.\n\n        Returns\n        -------\n        sb : containers.SiderealStream or containers.TimeStream\n            Rebinned data. Type should match the input.\n        \"\"\"\n\n        if \"freq\" not in ss.index_map:\n            raise RuntimeError(\"Data does not have a frequency axis.\")\n\n        if len(ss.freq) % self.channel_bin != 0:\n            raise RuntimeError(\"Binning must exactly divide the number of channels.\")\n\n        # Get all frequencies onto same node\n        ss.redistribute([\"time\", \"ra\"])\n\n        # Calculate the new frequency centres and widths\n        fc = ss.index_map[\"freq\"][\"centre\"].reshape(-1, self.channel_bin).mean(axis=-1)\n        fw = ss.index_map[\"freq\"][\"width\"].reshape(-1, self.channel_bin).sum(axis=-1)\n\n        freq_map = np.empty(fc.shape[0], dtype=ss.index_map[\"freq\"].dtype)\n        freq_map[\"centre\"] = fc\n        freq_map[\"width\"] = fw\n\n        # Create new container for rebinned stream\n        sb = containers.empty_like(ss, freq=freq_map)\n\n        # Get all frequencies onto same node\n        sb.redistribute([\"time\", \"ra\"])\n\n        # Rebin the arrays, do this with a loop to save memory\n        for fi in range(len(ss.freq)):\n\n            # Calculate rebinned index\n            ri = fi // self.channel_bin\n\n            sb.vis[ri] += ss.vis[fi] * ss.weight[fi]\n            sb.gain[ri] += (\n                ss.gain[fi] / self.channel_bin\n            )  # Don't do weighted average for the moment\n\n            sb.weight[ri] += ss.weight[fi]\n\n            # If we are on the final sub-channel then divide the arrays through\n            if (fi + 1) % self.channel_bin == 0:\n                sb.vis[ri] *= tools.invert_no_zero(sb.weight[ri])\n\n        sb.redistribute(\"freq\")\n\n        return sb\n\n\nclass CollateProducts(task.SingleTask):\n    \"\"\"Extract and order the correlation products for map-making.\n\n    The task will take a sidereal task and format the products that are needed\n    or the map-making. It uses a BeamTransfer instance to figure out what these\n    products are, and how they should be ordered. It similarly selects only the\n    required frequencies.\n\n    It is important to note that while the input\n    :class:`~containers.SiderealStream` can contain more feeds and frequencies\n    than are contained in the BeamTransfers, the converse is not true. That is,\n    all the frequencies and feeds that are in the BeamTransfers must be found in\n    the timestream object.\n\n    Parameters\n    ----------\n    weight : string ('natural', 'uniform', or 'inverse_variance')\n        How to weight the redundant baselines when stacking:\n            'natural' - each baseline weighted by its redundancy (default)\n            'uniform' - each baseline given equal weight\n            'inverse_variance' - each baseline weighted by the weight attribute\n    \"\"\"\n\n    weight = config.Property(proptype=str, default=\"natural\")\n\n    def setup(self, tel):\n        \"\"\"Set the Telescope instance to use.\n\n        Parameters\n        ----------\n        tel : TransitTelescope\n        \"\"\"\n\n        if self.weight not in [\"natural\", \"uniform\", \"inverse_variance\"]:\n            KeyError(\"Do not recognize weight = %s\" % self.weight)\n\n        self.telescope = io.get_telescope(tel)\n\n        # Precalculate the stack properties\n        self.bt_stack = np.array(\n            [\n                (tools.cmap(upp[0], upp[1], self.telescope.nfeed), 0)\n                if upp[0] <= upp[1]\n                else (tools.cmap(upp[1], upp[0], self.telescope.nfeed), 1)\n                for upp in self.telescope.uniquepairs\n            ],\n            dtype=[(\"prod\", \"<u4\"), (\"conjugate\", \"u1\")],\n        )\n\n        # Construct the equivalent prod and stack index_map for the telescope instance\n        triu = np.triu_indices(self.telescope.nfeed)\n        dt_prod = np.dtype([(\"input_a\", \"<u2\"), (\"input_b\", \"<u2\")])\n        self.bt_prod = np.array(triu).astype(\"<u2\").T.copy().view(dt_prod).reshape(-1)\n\n        # Construct the equivalent reverse_map stack for the telescope instance.\n        # Note that we identify invalid products here using an index that is the\n        # size of the stack axis.\n        feedmask = self.telescope.feedmask[triu]\n\n        self.bt_rev = np.empty(\n            feedmask.size, dtype=[(\"stack\", \"<u4\"), (\"conjugate\", \"u1\")]\n        )\n        self.bt_rev[\"stack\"] = np.where(\n            feedmask, self.telescope.feedmap[triu], self.telescope.npairs\n        )\n        self.bt_rev[\"conjugate\"] = np.where(feedmask, self.telescope.feedconj[triu], 0)\n\n    def process(self, ss):\n        \"\"\"Select and reorder the products.\n\n        Parameters\n        ----------\n        ss : SiderealStream\n\n        Returns\n        -------\n        sp : SiderealStream\n            Dataset containing only the required products.\n        \"\"\"\n        # For each input in the file, find the corresponding index in the telescope instance\n        input_ind = tools.find_inputs(\n            self.telescope.input_index, ss.input, require_match=False\n        )\n\n        # Figure out the reverse mapping (i.e., for each input in the telescope instance,\n        # find the corresponding index in file)\n        rev_input_ind = tools.find_inputs(\n            ss.input, self.telescope.input_index, require_match=True\n        )\n\n        # Figure out mapping between the frequencies\n        freq_ind = tools.find_keys(\n            ss.freq[:], self.telescope.frequencies, require_match=True\n        )\n\n        bt_freq = ss.index_map[\"freq\"][freq_ind]\n\n        # Determine the input product map and conjugation.\n        # If the input timestream is already stacked, then attempt to redefine\n        # its representative products so that they contain only feeds that exist\n        # and are not masked in the telescope instance.\n        if ss.is_stacked:\n\n            stack_new, stack_flag = tools.redefine_stack_index_map(\n                self.telescope, ss.input, ss.prod, ss.stack, ss.reverse_map[\"stack\"]\n            )\n\n            if not np.all(stack_flag):\n                self.log.warning(\n                    \"There are %d stacked baselines that are masked \"\n                    \"in the telescope instance.\" % np.sum(~stack_flag)\n                )\n\n            ss_prod = ss.prod[stack_new[\"prod\"]]\n            ss_conj = stack_new[\"conjugate\"]\n\n        else:\n            ss_prod = ss.prod\n            ss_conj = np.zeros(ss_prod.size, dtype=np.bool)\n\n        # Create output container\n        if isinstance(ss, containers.SiderealStream):\n            OutputContainer = containers.SiderealStream\n            output_kwargs = {\"ra\": ss.ra[:]}\n        else:\n            OutputContainer = containers.TimeStream\n            output_kwargs = {\"time\": ss.time[:]}\n\n        sp = OutputContainer(\n            freq=bt_freq,\n            input=self.telescope.input_index,\n            prod=self.bt_prod,\n            stack=self.bt_stack,\n            reverse_map_stack=self.bt_rev,\n            axes_from=ss,\n            attrs_from=ss,\n            distributed=True,\n            comm=ss.comm,\n            **output_kwargs\n        )\n\n        # Add gain dataset.\n        # if 'gain' in ss.datasets:\n        #     sp.add_dataset('gain')\n\n        # Ensure all frequencies and products are on each node\n        ss.redistribute([\"ra\", \"time\"])\n        sp.redistribute([\"ra\", \"time\"])\n\n        # Initialize datasets in output container\n        sp.vis[:] = 0.0\n        sp.weight[:] = 0.0\n        sp.input_flags[:] = ss.input_flags[rev_input_ind, :]\n\n        # The gain transfer below fails when distributed over multiple nodes,\n        # have to debug.\n        # if 'gain' in ss.datasets:\n        #     sp.gain[:] = ss.gain[freq_ind][:, rev_input_ind, :]\n\n        # Infer number of products that went into each stack\n        if self.weight != \"inverse_variance\":\n\n            ssi = ss.input_flags[:]\n            ssp = ss.index_map[\"prod\"][:]\n            sss = ss.reverse_map[\"stack\"][\"stack\"][:]\n            nstack = ss.vis.shape[1]\n\n            nprod_in_stack = tools.calculate_redundancy(ssi, ssp, sss, nstack)\n\n            if self.weight == \"uniform\":\n                nprod_in_stack = (nprod_in_stack > 0).astype(np.float32)\n\n        # Find the local times (necessary because nprod_in_stack is not distributed)\n        ntt = ss.vis.local_shape[-1]\n        stt = ss.vis.local_offset[-1]\n        ett = stt + ntt\n\n        # Create counter to increment during the stacking.\n        # This will be used to normalize at the end.\n        counter = np.zeros_like(sp.weight[:])\n\n        # Dereference the global slices now, there's a hidden MPI call in the [:] operation.\n        spv = sp.vis[:]\n        ssv = ss.vis[:]\n        spw = sp.weight[:]\n        ssw = ss.weight[:]\n\n        # Iterate over products (stacked) in the sidereal stream\n        for ss_pi, ((ii, ij), conj) in enumerate(zip(ss_prod, ss_conj)):\n\n            # Map the feed indices into ones for the Telescope class\n            bi, bj = input_ind[ii], input_ind[ij]\n\n            # If either feed is not in the telescope class, skip it.\n            if bi is None or bj is None:\n                continue\n\n            sp_pi = self.telescope.feedmap[bi, bj]\n            feedconj = self.telescope.feedconj[bi, bj]\n\n            # Skip if product index is not valid\n            if sp_pi < 0:\n                continue\n\n            # Generate weight\n            if self.weight == \"inverse_variance\":\n                wss = ssw[freq_ind, ss_pi]\n\n            else:\n                wss = (ssw[freq_ind, ss_pi] > 0.0).astype(np.float32)\n                wss *= nprod_in_stack[np.newaxis, ss_pi, stt:ett]\n\n            # Accumulate visibilities, conjugating if required\n            if feedconj == conj:\n                spv[:, sp_pi] += wss * ssv[freq_ind, ss_pi]\n            else:\n                spv[:, sp_pi] += wss * ssv[freq_ind, ss_pi].conj()\n\n            # Accumulate variances in quadrature.  Save in the weight dataset.\n            spw[:, sp_pi] += wss ** 2 * tools.invert_no_zero(ssw[freq_ind, ss_pi])\n\n            # Increment counter\n            counter[:, sp_pi] += wss\n\n        # Divide through by counter to get properly weighted visibility average\n        sp.vis[:] *= tools.invert_no_zero(counter)\n        sp.weight[:] = counter ** 2 * tools.invert_no_zero(sp.weight[:])\n\n        # Switch back to frequency distribution\n        ss.redistribute(\"freq\")\n        sp.redistribute(\"freq\")\n\n        return sp\n\n\nclass SelectFreq(task.SingleTask):\n    \"\"\"Select a subset of frequencies from a container.\n\n    Attributes\n    ----------\n    freq_physical : list\n        List of physical frequencies in MHz.\n        Given first priority.\n    channel_range : list\n        Range of frequency channel indices, either\n        [start, stop, step], [start, stop], or [stop]\n        is acceptable.  Given second priority.\n    channel_index : list\n        List of frequency channel indices.\n        Given third priority.\n    freq_physical_range : list\n        Range of physical frequencies to include given as (low_freq, high_freq).\n        Given fourth priority.\n    \"\"\"\n\n    freq_physical = config.Property(proptype=list, default=[])\n    freq_physical_range = config.Property(proptype=list, default=[])\n    channel_range = config.Property(proptype=list, default=[])\n    channel_index = config.Property(proptype=list, default=[])\n\n    def process(self, data):\n        \"\"\"Selet a subset of the frequencies.\n\n        Parameters\n        ----------\n        data : containers.ContainerBase\n            A data container with a frequency axis.\n\n        Returns\n        -------\n        newdata : containers.ContainerBase\n            New container with trimmed frequencies.\n        \"\"\"\n\n        # Set up frequency selection.\n        freq_map = data.index_map[\"freq\"]\n\n        # Construct the frequency channel selection\n        if self.freq_physical:\n            newindex = sorted(\n                set(\n                    [\n                        np.argmin(np.abs(freq_map[\"centre\"] - freq))\n                        for freq in self.freq_physical\n                    ]\n                )\n            )\n\n        elif self.channel_range and (len(self.channel_range) <= 3):\n            newindex = slice(*self.channel_range)\n\n        elif self.channel_index:\n            newindex = self.channel_index\n\n        elif self.freq_physical_range:\n            low, high = sorted(self.freq_physical_range)\n            newindex = np.where(\n                (freq_map[\"centre\"] >= low) & (freq_map[\"centre\"] < high)\n            )[0]\n\n        else:\n            ValueError(\n                \"Must specify either freq_physical, channel_range, or channel_index.\"\n            )\n\n        freq_map = freq_map[newindex]\n\n        # Destribute input container over ra or time.\n        data.redistribute([\"ra\", \"time\", \"pixel\"])\n\n        # Create new container with subset of frequencies.\n        newdata = containers.empty_like(data, freq=freq_map)\n\n        # Make sure all datasets are initialised\n        for name in data.datasets.keys():\n            if name not in newdata.datasets:\n                newdata.add_dataset(name)\n\n        # Redistribute new container over ra or time.\n        newdata.redistribute([\"ra\", \"time\", \"pixel\"])\n\n        # Copy over datasets. If the dataset has a frequency axis,\n        # then we only copy over the subset.\n        if isinstance(data, containers.ContainerBase):\n\n            for name, dset in data.datasets.items():\n\n                if \"freq\" in dset.attrs[\"axis\"]:\n                    slc = [slice(None)] * len(dset.shape)\n                    slc[list(dset.attrs[\"axis\"]).index(\"freq\")] = newindex\n                    newdata.datasets[name][:] = dset[slc]\n                else:\n                    newdata.datasets[name][:] = dset[:]\n\n        else:\n            newdata.vis[:] = data.vis[newindex]\n            newdata.weight[:] = data.weight[newindex]\n            newdata.gain[:] = data.gain[newindex]\n\n            newdata.input_flags[:] = data.input_flags[:]\n\n        # Switch back to frequency distribution\n        data.redistribute(\"freq\")\n        newdata.redistribute(\"freq\")\n\n        return newdata\n\n\nclass MModeTransform(task.SingleTask):\n    \"\"\"Transform a sidereal stream to m-modes.\n\n    Currently ignores any noise weighting.\n\n    The maximum m used in the container is derived from the number of\n    time samples, or if a manager is supplied `telescope.mmax` is used.\n    \"\"\"\n\n    def setup(self, manager=None):\n        \"\"\"Set the telescope instance if a manager object is given.\n\n        This is used to set the `mmax` used in the transform.\n\n        Parameters\n        ----------\n        manager : manager.ProductManager, optional\n            The telescope/manager used to set the `mmax`. If not set, `mmax`\n            is derived from the timestream.\n        \"\"\"\n        if manager is not None:\n            self.telescope = io.get_telescope(manager)\n        else:\n            self.telescope = None\n\n    def process(self, sstream):\n        \"\"\"Perform the m-mode transform.\n\n        Parameters\n        ----------\n        sstream : containers.SiderealStream\n            The input sidereal stream.\n\n        Returns\n        -------\n        mmodes : containers.MModes\n        \"\"\"\n\n        sstream.redistribute(\"freq\")\n\n        # Sum the noise variance over time samples, this will become the noise\n        # variance for the m-modes\n        weight_sum = sstream.weight[:].sum(axis=-1)\n\n        if self.telescope is not None:\n            mmax = self.telescope.mmax\n        else:\n            mmax = None\n\n        # Construct the array of m-modes\n        marray = _make_marray(sstream.vis[:], mmax)\n        marray = mpiarray.MPIArray.wrap(marray[:], axis=2, comm=sstream.comm)\n\n        # Create the container to store the modes in\n        mmax = marray.shape[0] - 1\n        ma = containers.MModes(mmax=mmax, axes_from=sstream, comm=sstream.comm)\n        ma.redistribute(\"freq\")\n\n        # Assign the visibilities and weights into the container\n        ma.vis[:] = marray\n        ma.weight[:] = weight_sum[np.newaxis, np.newaxis, :, :]\n\n        ma.redistribute(\"m\")\n\n        return ma\n\n\ndef _make_marray(ts, mmax):\n    # Construct an array of m-modes from a sidereal time stream\n    mmodes = np.fft.fft(ts, axis=-1) / ts.shape[-1]\n    marray = _pack_marray(mmodes, mmax)\n\n    return marray\n\n\ndef _pack_marray(mmodes, mmax=None):\n    # Pack an FFT into the correct format for the m-modes (i.e. [m, freq, +/-,\n    # baseline])\n\n    N = mmodes.shape[-1]  # Total number of modes\n    N_pos_mmodes = N // 2  # Total number of positive modes\n    if mmax is None:\n        mmax = mlim = N_pos_mmodes\n        mlim_neg = mlim - 1 + N % 2  # Index for negative modes\n    elif mmax >= N_pos_mmodes:  # Modes larger than N_pos_mmodes set to 0.\n        mlim = N_pos_mmodes\n        mlim_neg = mlim - 1 + N % 2\n    else:\n        mlim = mmax\n        mlim_neg = mlim\n\n    shape = mmodes.shape[:-1]\n\n    marray = np.zeros((mmax + 1, 2) + shape, dtype=np.complex128)\n\n    # Non-negative modes\n    marray[: mlim + 1, 0] = np.rollaxis(mmodes[..., : mlim + 1], -1, 0)\n    # Negative modes\n    marray[1 : mlim_neg + 1, 1] = np.rollaxis(\n        mmodes[..., -1 : -(mlim_neg + 1) : -1].conj(), -1, 0\n    )\n\n    return marray\n\n\nclass MModeInverseTransform(task.SingleTask):\n    \"\"\"Transform m-modes to sidereal stream.\n\n    Currently ignores any noise weighting.\n\n    Attributes\n    ----------\n    n_time : int\n        Number of time bins in the output. Note that if\n        the number of samples does not Nyquist sample the\n        maximum m, information may be lost.\n    \"\"\"\n\n    n_time = config.Property(proptype=int, default=None)\n\n    def process(self, mmodes):\n        \"\"\"Perform the m-mode inverse transform.\n\n        Parameters\n        ----------\n        mmodes : containers.MModes\n            The input m-modes.\n\n        Returns\n        -------\n        sstream : containers.SiderealStream\n            The output sidereal stream.\n        \"\"\"\n        # NOTE: If n_time is smaller than Nyquist sampling the m-mode axis then\n        # the m-modes get clipped. If it is larger, they get zero padded. This\n        # is NOT passed directly as parameter 'n' to `numpy.fft.ifft`, as this\n        # would give unwanted behaviour (https://github.com/numpy/numpy/pull/7593).\n\n        # Ensure m-modes are distributed in frequency\n        mmodes.redistribute(\"freq\")\n\n        # Re-construct array of S-streams\n        ssarray = _make_ssarray(mmodes.vis[:], n=self.n_time)\n        ntime = ssarray.shape[-1]\n        ssarray = mpiarray.MPIArray.wrap(ssarray[:], axis=0, comm=mmodes.comm)\n\n        # Construct container and set visibility data\n        sstream = containers.SiderealStream(\n            ra=ntime, axes_from=mmodes, distributed=True, comm=mmodes.comm\n        )\n        sstream.redistribute(\"freq\")\n\n        # Assign the visibilities and weights into the container\n        sstream.vis[:] = ssarray\n        # There is no way to recover time information for the weights.\n        # Just assign the time average to each baseline and frequency.\n        sstream.weight[:] = mmodes.weight[0, 0, :, :][:, :, np.newaxis] / ntime\n\n        return sstream\n\n\ndef _make_ssarray(mmodes, n=None):\n    # Construct an array of sidereal time streams from m-modes\n    marray = _unpack_marray(mmodes, n=n)\n    ssarray = np.fft.ifft(marray * marray.shape[-1], axis=-1)\n\n    return ssarray\n\n\ndef _unpack_marray(mmodes, n=None):\n    # Unpack m-modes into the correct format for an FFT\n    # (i.e. from [m, +/-, freq, baseline] to [freq, baseline, time-FFT])\n\n    shape = mmodes.shape[2:]\n    mmax_plus = mmodes.shape[0] - 1\n    if (mmodes[mmax_plus, 1, ...].flatten() == 0).all():\n        mmax_minus = mmax_plus - 1\n    else:\n        mmax_minus = mmax_plus\n\n    if n is None:\n        ntimes = mmax_plus + mmax_minus + 1\n    else:\n        ntimes = n\n        mmax_plus = np.amin((ntimes // 2, mmax_plus))\n        mmax_minus = np.amin(((ntimes - 1) // 2, mmax_minus))\n\n    # Create array to contain mmodes\n    marray = np.zeros(shape + (ntimes,), dtype=np.complex128)\n    # Add the DC bin\n    marray[..., 0] = mmodes[0, 0]\n    # Add all m-modes up to mmax_minus\n    for mi in range(1, mmax_minus + 1):\n        marray[..., mi] = mmodes[mi, 0]\n        marray[..., -mi] = mmodes[mi, 1].conj()\n\n    if mmax_plus != mmax_minus:\n        # In case of even number of samples. Add the Nyquist frequency.\n        marray[..., mmax_plus] = mmodes[mmax_plus, 0]\n\n    return marray\n\n\nclass Regridder(task.SingleTask):\n    \"\"\"Interpolate time-ordered data onto a regular grid.\n\n    Uses a maximum-likelihood inverse of a Lanczos interpolation to do the\n    regridding. This gives a reasonably local regridding, that is pretty well\n    behaved in m-space.\n\n    Attributes\n    ----------\n    samples : int\n        Number of samples to interpolate onto.\n    start: float\n        Start of the interpolated samples.\n    end: float\n        End of the interpolated samples.\n    lanczos_width : int\n        Width of the Lanczos interpolation kernel.\n    snr_cov: float\n        Ratio of signal covariance to noise covariance (used for Wiener filter).\n    mask_zero_weight: bool\n        Mask the output noise weights at frequencies where the weights were\n        zero for all time samples.\n    \"\"\"\n\n    samples = config.Property(proptype=int, default=1024)\n    start = config.Property(proptype=float)\n    end = config.Property(proptype=float)\n    lanczos_width = config.Property(proptype=int, default=5)\n    snr_cov = config.Property(proptype=float, default=1e-8)\n    mask_zero_weight = config.Property(proptype=bool, default=False)\n\n    def setup(self, observer):\n        \"\"\"Set the local observers position.\n\n        Parameters\n        ----------\n        observer : :class:`~caput.time.Observer`\n            An Observer object holding the geographic location of the telescope.\n            Note that :class:`~drift.core.TransitTelescope` instances are also\n            Observers.\n        \"\"\"\n        self.observer = observer\n\n    def process(self, data):\n        \"\"\"Regrid visibility data in the time direction.\n\n        Parameters\n        ----------\n        data : containers.TODContainer\n            Time-ordered data.\n\n        Returns\n        -------\n        new_data : containers.TODContainer\n            The regularly gridded interpolated timestream.\n        \"\"\"\n\n        # Redistribute if needed\n        data.redistribute(\"freq\")\n\n        # View of data\n        weight = data.weight[:].view(np.ndarray)\n        vis_data = data.vis[:].view(np.ndarray)\n\n        # Get input time grid\n        timelike_axis = data.vis.attrs[\"axis\"][-1]\n        times = data.index_map[timelike_axis][:]\n\n        # check bounds\n        if self.start is None:\n            self.start = times[0]\n        if self.end is None:\n            self.end = times[-1]\n        if self.start < times[0] or self.end > times[-1]:\n            msg = \"Start or end points for regridder fall outside bounds of input data.\"\n            self.log.error(msg)\n            raise RuntimeError(msg)\n\n        # perform regridding\n        new_grid, new_vis, ni = self._regrid(vis_data, weight, times)\n\n        # Wrap to produce MPIArray\n        new_vis = mpiarray.MPIArray.wrap(new_vis, axis=data.vis.distributed_axis)\n        ni = mpiarray.MPIArray.wrap(ni, axis=data.vis.distributed_axis)\n\n        # Create new container for output\n        cont_type = data.__class__\n        new_data = cont_type(axes_from=data, **{timelike_axis: new_grid})\n        new_data.redistribute(\"freq\")\n        new_data.vis[:] = new_vis\n        new_data.weight[:] = ni\n\n        return new_data\n\n    def _regrid(self, vis_data, weight, times):\n\n        # Create a regular grid, padded at either end to supress interpolation issues\n        pad = 5 * self.lanczos_width\n        interp_grid = (\n            np.arange(-pad, self.samples + pad, dtype=np.float64) / self.samples\n        )\n        # scale to specified range\n        interp_grid = interp_grid * (self.end - self.start) + self.start\n\n        # Construct regridding matrix for reverse problem\n        lzf = regrid.lanczos_forward_matrix(\n            interp_grid, times, self.lanczos_width\n        ).T.copy()\n\n        # Reshape data\n        vr = vis_data.reshape(-1, vis_data.shape[-1])\n        nr = weight.reshape(-1, vis_data.shape[-1])\n\n        # Construct a signal 'covariance'\n        Si = np.ones_like(interp_grid) * self.snr_cov\n\n        # Calculate the interpolated data and a noise weight at the points in the padded grid\n        sts, ni = regrid.band_wiener(lzf, nr, Si, vr, 2 * self.lanczos_width - 1)\n\n        # Throw away the padded ends\n        sts = sts[:, pad:-pad].copy()\n        ni = ni[:, pad:-pad].copy()\n        interp_grid = interp_grid[pad:-pad].copy()\n\n        # Reshape to the correct shape\n        sts = sts.reshape(vis_data.shape[:-1] + (self.samples,))\n        ni = ni.reshape(vis_data.shape[:-1] + (self.samples,))\n\n        if self.mask_zero_weight:\n            # set weights to zero where there is no data\n            w_mask = weight.sum(axis=-1) != 0.0\n            ni *= w_mask[..., np.newaxis]\n\n        return interp_grid, sts, ni\n","sub_path":"draco/analysis/transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":26016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"154746342","text":"import json\nimport re\n\nfrom django.http import JsonResponse\nfrom django.shortcuts import render\n\n# Create your views here.\nfrom job_info_app.models import Hoteljob\nfrom job_info_app.my_log import MyLog\nfrom job_info_app.my_utils import sortData\nfrom job_info_app.paging import MyPaginator\nfrom job_info_app.my_connect import getMyConn\n\n#\ncode2city = {\n    '1': '北京',\n    '2': '上海',\n    '3': '广州',\n    '4': '深圳',\n}\n\ncode2job = {\n    '1': 'Web',\n    '2': '爬虫',\n    '3': '大数据',\n    '4': 'AI',\n}\n\n\ndef main_page(request):\n    return render(request, 'job_pages/main.html')\n\n\ndef introduce_page(request):\n    return render(request, 'job_pages/introduce.html')\n\n\ndef menu_page(request):\n    \"\"\"\n    数据菜单页\n    :param request:\n    :return:\n    \"\"\"\n    # 1.获取请求参数\n    # 城市\n    city = code2city.get(request.GET.get('city') or '1')\n    # 职业\n    key = code2job.get(request.GET.get('key') or '3')\n    # 页号\n    pn = int(request.GET.get('pn') or 1)\n    # 搜索类型\n    sk = request.GET.get('sk')\n    # 搜索内容\n    sc = request.GET.get('sc')\n\n    # 是否是异步请求\n    isasy = request.GET.get('isasy')\n    print('params: ', city, key, pn, sk, sc, isasy)\n\n    # request.session['sortdict'] = {'sa': '', 'exp': '',}\n    login_user = request.session.get('login_user')\n    return deal_menu_page(request, city, key, pn, sk, sc, isasy, login_user)\n\n\n@MyLog\ndef deal_menu_page(request, city, key, pn, sk, sc, isasy, login_user):\n    \"\"\"\n\n    :param request:\n    :param city:\n    :param key:\n    :param pn:\n    :param sk:\n    :param sc:\n    :param isasy:\n    :param login_user:\n    :return:\n    \"\"\"\n    # sk=='1'('2') 表示搜索类型为城市(职位)，搜索内容为sc\n    if sk == '1':\n        city = sc\n        key = ''\n    elif sk == '2':\n        key = sc\n        city = ''\n\n    # 2.查询数据\n    sf, sd = request.session.get('sort_rule', ('0', '0'))\n    dataName = '%s:%s:%s:%s'%(city, key, sf, sd)\n    # 获取缓存数据\n    # rdc = getMyConn().getRedisConn()\n    # # pagesBytes = b'' and rdc.get(dataName)\n    # byteDatas = rdc.get(dataName)\n    datas = getMyConn().getRedisData(dataName)\n    print('xxxxx',datas)\n    # pages = request.session.get(pageName)\n    # 如果没有缓存\n    if not datas:\n\n        # datas = getDatas(city, key, login_user)\n        # request.session['datas'] = datas\n\n        datas = sortData(getDatas(city, key, login_user), sf, sd)\n        # rdc.set(dataName, json.dumps(datas, default=None))\n        getMyConn().save2Redis(dataName, datas)\n        request.session['data_name'] = dataName\n\n    # 3. 分页 封装数据 字典格式\n    pages = MyPaginator(datas)\n    # request.session[pageName] = pages\n    # request.session['total_count'] = len(datas)\n\n    request.session['pn'] = pn\n    resp = {\n        'result': 1,\n        'page': pages.pageDict(pn),\n        'params': {'city': city, 'key': key},\n    }\n    print(resp)\n\n    # 4. 响应\n    if isasy:\n        return JsonResponse(resp)\n    else:\n        return render(request, 'job_pages/menu.html', resp)\n\n\n\ndef dataSort(request):\n    \"\"\"\n\n    :param request:\n    :return:\n    \"\"\"\n    # 排序字段 1：salary 2：experience\n    sf = request.GET.get('sf') or '0'\n    # 排序方向 1：asc 2：desc\n    sd = request.GET.get('sd') or '0'\n    print('dataSort: ', sf, sd)\n    dataName = request.session.get('data_name')\n    # rdc = getMyConn().getRedisConn()\n    # datas = rdc.get(dataName)\n    datas = getMyConn().getRedisData(dataName)\n    pn = request.session.get('pn', 1)\n    request.session['sort_rule'] = (sf, sd)\n    resp = {\n        'result': 1,\n        'page': MyPaginator(sortData(datas, sf, sd)).pageDict(pn),\n    }\n\n    return JsonResponse(resp)\n\n\ndef getDatas(city, key, login_user='1'):\n    \"\"\"\n    查询数据\n    :param city:\n    :param key:\n    :param login_state:\n    :return:\n    \"\"\"\n    # 如果未登录，只能看10页, 从mysql获取数据\n    datas = list(getDatasFromMysql(city, key))\n    hbasedatas = []\n    # 登录了，全查, 从hbase 获取数据\n    if login_user:\n        hbasedatas = getDatasFromHbase(city, key)\n        # hbasedatas = [{k.replace('show:', ''): v for k, v in d.items()} for d in hbasedatas if hbasedatas]\n\n    print('mysqldatas: ', datas)\n    print('hbasedatas: ', hbasedatas)\n    # for d in alldatas:\n    #     for k,v in d.items():\n    #         print(k, v)\n    datas.extend(hbasedatas)\n\n    return datas\n\n\ndef getDatasFromMysql(city, key):\n    \"\"\"\n    从mysql中查询数据\n    :param city:\n    :param key:\n    :return:\n    \"\"\"\n    return Hoteljob.objects.filter(city__icontains=city, job_type__icontains=key).values('job_title', 'company_name', 'salary', 'job_description', 'experience', 'degree', 'company_address', 'format_salary', 'format_experience')\n\n\ndef getDatasFromHbase(city, key):\n    \"\"\"\n    从hbase中查询数据\n    :param city:\n    :param key:\n    :return:\n    \"\"\"\n    # 'wnm1'是域名，需要在计算机中配置\n    # 获取连接\n    conn = getMyConn().getHbaseConn()\n    # 获取表对象\n    table = conn.table('jobs:t_hoteljob1')\n    # 查询数据\n    try:\n        return [{k1.decode('utf-8').replace('show:', ''): v1.decode('utf-8') for k1, v1 in v.items()} for k, v in\n            table.scan(filter=\"RowFilter(=,'regexstring:\\.*%s.*%s.*/')\" % (city, key), columns=('show',), limit=1)]\n    except:\n        return []\n\n\n# 城市匹配数据库\nsuggests_city = \"\"\"\n北京\n上海\n广州\n深圳\nbeijing\nshanghai\nguangzhou\nshenzhen\"\"\"\n# 职位匹配数据库\nsuggests_key = \"\"\"\nai\npython web\n大数据\ndashuju\n爬虫\npachong\"\"\"\n# 全部匹配\nsuggests = \"\"\"\n北京\n上海\n广州\n深圳\nbeijing\nshanghai\nguangzhou\nshenzhen\nai\npython web\n大数据\ndashuju\n爬虫\npachong\"\"\"\n\ndef suggest_ajax(request):\n    \"\"\"\n    ajax异步接收搜索框传来的数据，并进行正则匹配，将匹配的数据用json返回\n    :param request:\n    :return:\n    \"\"\"\n    # 下拉表单的值：0：未选择；1：城市；2：职位；\n    area = request.POST.get(\"type\")\n    # 搜索框中的内容\n    words = request.POST.get(\"message\")\n    # 将搜索框中的字母转为小写\n    words = words.lower()\n\n    # 匹配城市\n    if area == \"1\":\n        rule = re.compile('.*' + words + '.*', re.M)\n        result = rule.findall(suggests_city)\n    # 匹配职业\n    elif area == \"2\":\n        rule = re.compile('.*' + words + '.*', re.M)\n        result = rule.findall(suggests_key)\n    # 全部匹配\n    else:\n        rule = re.compile('.*' + words + '.*', re.M)\n        result = rule.findall(suggests)\n    print(words, result, area)\n    return JsonResponse({\"result\": result})\n","sub_path":"scrapy_django/job_info_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"582616671","text":"from credentials.models import Credential\n\nfrom .bybit import BybitExchangeProxy\n\n\ndef exchange_proxy_factory(\n    *, exchange_code: str, pair: str = \"\", test_net: bool = False\n):\n    exchange_proxy_map = {\n        \"bybit\": BybitExchangeProxy,\n    }\n\n    proxy_class = exchange_proxy_map[exchange_code]\n    proxy = proxy_class(pair=pair)\n    if test_net:\n        proxy.enable_test_net()\n\n    return proxy\n","sub_path":"yufu/core/proxies/factory.py","file_name":"factory.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"450541504","text":"import gdb\n\nfrom undodb.debugger_extensions import udb\n\n\ndef count_calls(func_name):\n    \"\"\"\n    Counts how many times func_name is hit during the replay of the currently\n    loaded recording and returns the hit count.\n    \"\"\"\n    # Set a breakpoint for the specified function.\n    bp = gdb.Breakpoint(func_name)\n\n    # Do \"continue\" until we have gone through the whole recording, potentially\n    # hitting the breakpoint several times.\n    end_of_time = udb.get_event_log_extent().max_bbcount\n    while udb.time.get().bbcount < end_of_time:\n        gdb.execute(\"continue\")\n\n    return bp.hit_count\n\n\n# UDB will automatically load the modules passed to UdbLauncher.add_extension\n# and, if present, automatically execute any function (with no arguments) called\n# \"run\".\ndef run():\n    # The function where to stop is passed to us from the outer script in the\n    # run_data dictionary.\n    func_name = udb.run_data[\"func_name\"]\n\n    hit_count = count_calls(func_name)\n\n    # Pass the number of time we hit the breakpoint back to the outer script.\n    udb.result_data[\"hit-count\"] = hit_count\n","sub_path":"count_calls/count_calls_extension.py","file_name":"count_calls_extension.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"260651725","text":"from pygame import mixer\r\nimport pygame\r\nfrom SubLib import py_ffmpeg\r\nfrom SubLib.GUI import gui\r\n\r\nclock = pygame.time.Clock()\r\nclass Mp3Player:\r\n    # define global variable for the class\r\n    btn_play = pygame.K_1\r\n    btn_pause = pygame.K_2\r\n    btn_unpause = pygame.K_3\r\n    btn_stop = pygame.K_4\r\n    btn_repeat_A = pygame.K_5\r\n    btn_repeat_B = pygame.K_6\r\n    # pasue is 1\r\n    # unpause is 2\r\n    pause_status = 0\r\n    # incorrect button is pressed\r\n    wrg_btn = 0\r\n    # position\r\n    pos = 0\r\n    path_name = ''\r\n    repeat_A = 0\r\n    repeat_B = 0\r\n    display = 0\r\n\r\n    # initialization\r\n    def __init__(self):\r\n        # Initialize pygame below mixer to play mp3\r\n        mixer.init()\r\n        pygame.init()\r\n\r\n    # function to keyboard to control player\r\n    def control_player_by_keyboard(self):\r\n        # get event\r\n        self.wrg_btn = 0\r\n\r\n        events = pygame.event.get()\r\n        for event in events:\r\n            # if press keyboard event, then check which keyboard\r\n            if event.type == pygame.KEYDOWN:\r\n                if event.key == self.btn_stop:\r\n                    mixer.music.stop()\r\n                elif event.key == self.btn_pause:\r\n                    mixer.music.pause()\r\n                    self.pause_status = 1\r\n                elif event.key == self.btn_unpause:\r\n                    mixer.music.unpause()\r\n                    self.pause_status = 2\r\n                elif event.key == self.btn_play:\r\n                    mixer.music.play()\r\n                    self.pause_status = 0\r\n                elif event.key == self.btn_repeat_A:\r\n                    print('btn')\r\n                    if self.playing_mp3() == 1:\r\n                        self.repeat_A = self.get_position()\r\n                        self.repeat()\r\n                elif event.key == self.btn_repeat_B:\r\n                    if self.playing_mp3() == 1:\r\n                        self.repeat_B = self.get_position()\r\n                else:\r\n                    self.wrg_btn = 1\r\n                    self.pause_status = 0\r\n            elif event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n\r\n    def play_mp3(self, path_name):\r\n        mixer.music.load(path_name)\r\n        mixer.music.play()\r\n        self.path_name = path_name\r\n\r\n    def play_mp3_button(self, path_name, loop_1):\r\n        if loop_1:\r\n            mixer.music.load(path_name)\r\n            mixer.music.play()\r\n            self.path_name = path_name\r\n\r\n    def load(self, path_name):\r\n        mixer.music.load(path_name)\r\n\r\n    def stop_mp3(self):\r\n        mixer.music.stop()\r\n\r\n    def display_to_get_event(self, width, height):\r\n        # Add display to get keyboard event\r\n        self.display = pygame.display.set_mode((width, height))\r\n\r\n    def playing_mp3(self):\r\n        return mixer.music.get_busy()\r\n\r\n    # get millisecond from playback mp3\r\n    def get_position(self):\r\n        if self.playing_mp3():\r\n            # plus pos in case using set_position\r\n            return mixer.music.get_pos() + self.pos\r\n        else:\r\n            return 0\r\n\r\n    def set_position(self, position):\r\n        if self.playing_mp3():\r\n            # divide 1000 to set position\r\n            mixer.music.set_pos(position/1000)\r\n            # save position to update get_position\r\n            self.pos = position\r\n            return True\r\n        else:\r\n            return False\r\n\r\n    def change_speed(self, speed):\r\n        py_ffmpeg.py_ffmpeg_change_speed_mp3(speed, self.path_name)\r\n\r\n    def get_init_mp3(self):\r\n        return mixer.get_init()\r\n\r\n    def repeat(self):\r\n        # print('repeat')\r\n        # in case, start of repeat is set, then repeat at start\r\n        # when playback is stop\r\n        if self.repeat_A != 0 and self.repeat_B == 0:\r\n            if self.playing_mp3() == 0:\r\n                print('repeat end')\r\n                self.play_mp3(self.path_name)\r\n                self.set_position(self.repeat_A)\r\n        # in other cases, stop of repeat is set,\r\n        # then repeat at start point immediately\r\n        else:\r\n            if self.get_position() == self.repeat_B:\r\n                self.play_mp3(self.path_name)\r\n                self.set_position(self.repeat_A)\r\n# Test\r\n# g = gui()\r\n# mp3 = Mp3Player()\r\n# mp3.display_to_get_event(800, 600)\r\n# loop = 1\r\n# # mp3.set_position(140000)\r\n# while True:\r\n#\r\n#    # mp3.play_mp3('file.mp3')\r\n#     #print('start')\r\n#     # mp3.change_speed('0.5')\r\n#     # t = 0\r\n#     #print(mp3.get_position())\r\n#     mp3.control_player_by_keyboard()\r\n#     if g.button(mp3.display, 'play', 150, 450, 100, 50, g.green, g.blue):\r\n#         mp3.play_mp3('Kalimba.mp3', loop)\r\n#     if mp3.playing_mp3():\r\n#         loop = 0\r\n#     else:\r\n#         loop = 1\r\n#     if g.button(mp3.display, 'stop', 350, 450, 100, 50, g.blue, g.red):\r\n#        mp3.stop_mp3()\r\n#\r\n#     pygame.display.update()\r\n#     #clock.tick(15)\r\n#     #mp3.repeat()\r\n#\r\n# print('End')\r\n","sub_path":"SubLib/mp3player.py","file_name":"mp3player.py","file_ext":"py","file_size_in_byte":4926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"103460129","text":"# -*- coding: utf-8 -*-\n\n\"\"\"Test fixtures.\"\"\"\n\n#------------------------------------------------------------------------------\n# Imports\n#------------------------------------------------------------------------------\n\nimport logging\nimport shutil\n\nimport numpy as np\nfrom pytest import fixture\n\nfrom phylib.utils._misc import _read_python, _write_text\nfrom ..model import TemplateModel\nfrom phylib.io.datasets import download_test_file\n\nlogger = logging.getLogger(__name__)\n\n\n#------------------------------------------------------------------------------\n# Fixtures\n#------------------------------------------------------------------------------\n\n_FILES = ['template/params.py',\n          'template/sim_binary.dat',\n          'template/spike_times.npy',\n          'template/spike_templates.npy',\n          'template/spike_clusters.npy',\n          'template/amplitudes.npy',\n\n          'template/cluster_group.tsv',\n\n          'template/channel_map.npy',\n          'template/channel_positions.npy',\n\n          'template/similar_templates.npy',\n          'template/whitening_mat.npy',\n\n          'template/templates.npy',\n          'template/template_ind.npy',\n\n          'template/pc_features.npy',\n          'template/pc_feature_ind.npy',\n          'template/pc_feature_spike_ids.npy',\n\n          'template/template_features.npy',\n          'template/template_feature_ind.npy',\n          'template/template_feature_spike_ids.npy',\n\n          ]\n\n\ndef _remove(path):\n    if path.exists():\n        path.unlink()\n        logger.debug(\"Removed %s.\", path)\n\n\n@fixture(scope='function', params=('dense', 'sparse', 'misc'))\ndef template_path(tempdir, request):\n    # Download the dataset.\n    paths = list(map(download_test_file, _FILES))\n    # Copy the dataset to a temporary directory.\n    for path in paths:\n        to_path = tempdir / path.name\n        # Skip sparse arrays if is_sparse is False.\n        if request.param == 'sparse' and ('_ind.' in str(to_path) or 'spike_ids.' in str(to_path)):\n            continue\n        logger.debug(\"Copying file to %s.\", to_path)\n        shutil.copy(path, to_path)\n\n    # Some changes to files if 'misc' fixture parameter.\n    if request.param == 'misc':\n        # Remove spike_clusters and recreate it from spike_templates.\n        _remove(tempdir / 'spike_clusters.npy')\n        # Replace spike_times.npy, in samples, by spikes.times.npy, in seconds.\n        if (tempdir / 'spike_times.npy').exists():\n            st = np.load(tempdir / 'spike_times.npy')\n            np.save(tempdir / 'spikes.times.npy', st / 25000.)  # sample rate\n            _remove(tempdir / 'spike_times.npy')\n        # Buggy TSV file should not cause a crash.\n        _write_text(tempdir / 'error.tsv', '')\n        # Remove some non-necessary files.\n        _remove(tempdir / 'template_features.npy')\n        _remove(tempdir / 'pc_features.npy')\n        _remove(tempdir / 'sim_binary.dat')\n\n    template_path = tempdir / paths[0].name\n    return template_path\n\n\n@fixture\ndef template_model(template_path):\n    params = _read_python(template_path)\n    params['dat_path'] = template_path.parent / params['dat_path']\n    params['dir_path'] = template_path.parent\n    model = TemplateModel(**params)\n    return model\n","sub_path":"phylib/io/tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":3227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"606977686","text":"import urllib\nimport urllib.parse\nimport random\nimport string\nimport json\nimport re\nfrom django.core.serializers.json import DjangoJSONEncoder\nfrom copy import deepcopy\n\nfrom django.shortcuts import redirect\nfrom django_recurly.conf import SUBDOMAIN\nimport recurly\nimport logging\n\n\nlogger = logging.getLogger(__name__)\n\n\n# FIXME - broken\nclass RecurlyJsonEncoder(DjangoJSONEncoder):\n    def __init__(self, js=False, *args, **kwargs):\n        super(RecurlyJsonEncoder, self).__init__(*args, **kwargs)\n        self.js = js\n\n    def default(self, obj):\n        if isinstance(obj, recurly.resource.Money):\n            return str(obj)\n\n        # Resolve 'relatiator' attributes\n        if callable(obj):\n            result = obj()\n            try:\n                return result.to_dict(js=self.js)\n            except:\n                return result\n\n        if isinstance(obj, recurly.Resource):\n            return obj.to_dict(js=self.js)\n\n        try:\n            if issubclass(obj, dict) or issubclass(obj, list):\n                return list(obj)\n        except:\n            pass\n\n        return DjangoJSONEncoder.default(self, obj)\n\n\ndef dump(obj, encoder=RecurlyJsonEncoder, js=False):\n    data = obj\n    try:\n        data = data.to_dict(js=js)\n    except AttributeError:\n        pass\n\n    data = to_camel(data, js=js)\n\n    return json.dumps(\n        data,\n        sort_keys=True,\n        indent=2,\n        cls=encoder,\n        js=js)\n\n\ndef hosted_login_url(hosted_login_token):\n    return 'https://%s.recurly.com/account/%s' % (\n        SUBDOMAIN,\n        hosted_login_token,\n    )\n\n\ndef hosted_payment_page_url(plan_code, account_code, data=None):\n    if data is None:\n        data = {}\n\n    return 'https://%s.recurly.com/subscribe/%s/%d?%s' % (\n        SUBDOMAIN,\n        plan_code,\n        account_code,\n        urllib.urlencode(data),\n    )\n\n\ndef safe_redirect(request, url, fallback=\"/\"):\n    netloc = urlparse.urlparse(url or \"\")[1]\n\n    if not url:\n        safe_url = fallback\n    # Don't redirect the user to a different host\n    elif netloc and netloc != request.get_host():\n        safe_url = fallback\n    else:\n        safe_url = url\n\n    return redirect(safe_url)\n\n\ndef random_string(length=32):\n    return ''.join(random.choice(string.letters + string.digits) for i in range(length))\n\n\ndef dict_merge(target, *args):\n    '''Recursively merge one or more dict's into `target` and return the result.\n    Righter args will override existing non-dict values if already set. If you\n    don't want to modify an existing object, pass an empty dict as the first\n    argument and store the returned value. This is similar to\n    [jQuery.extend()](http://api.jquery.com/jQuery.extend/).'''\n\n    # Merge multiple dicts\n    if len(args) > 1:\n        for obj in args:\n            dict_merge(target, obj)\n        return target\n\n    # Recursively merge dict values and set or override non-dict values\n    obj = args[0]\n    if not isinstance(obj, dict):\n        return obj\n    for k, v in obj.items():\n        if k in target and isinstance(target[k], dict):\n            dict_merge(target[k], v)\n        else:\n            target[k] = deepcopy(v)\n    return target\n\n\ndef to_camel(data, js=False):\n    def underscore_to_camel(match):\n        return match.group()[0] + match.group()[2].upper()\n\n    def camelize(data):\n        try:\n            data = data.to_dict(js=js)\n        except:\n            data = data\n\n        if type(data) == type({}):\n            new_dict = {}\n            for key, value in data.items():\n                new_key = re.sub(r\"[a-z]_[a-z]\", underscore_to_camel, key)\n                new_dict[new_key] = camelize(value)\n            return new_dict\n        if type(data) in (type([]), type(())):\n            for i in range(len(data)):\n                data[i] = camelize(data[i])\n            return data\n        return data\n\n    return camelize(data)\n\n\ndef from_camel(content):\n    # Changes camelCase json names to object containing underscore_separated names\n    data = json.loads(content)\n\n    def camel_to_underscore(match):\n        return match.group()[0] + \"_\" + match.group()[1].lower()\n\n    def underscorize(data):\n        if type(data) == type({}):\n            new_dict = {}\n            for key, value in data.items():\n                new_key = re.sub(r\"[a-z][A-Z]\", camel_to_underscore, key)\n                new_dict[new_key] = underscorize(value)\n            return new_dict\n        if type(data) in (type([]), type(())):\n            for i in range(len(data)):\n                data[i] = underscorize(data[i])\n            return data\n        return data\n\n    underscored_data = underscorize(data)\n\n    return underscored_data\n","sub_path":"django_recurly/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"240770589","text":"import numpy as np\n\nclass LogisticRegression:\n    \n    def __init__(self, weight):\n        self.weight = weight\n#%%\n# Logistic Regression:\n# 1. Use gradient descent to find weights for log-odds ratio\n# 2. Compute predicted probability (sigmoid function)\n# 3 prob > 0.5, y = 1;   prob < 0.5, y = 0;   prob = 0.5, use random decider\n    def logistic(self,input1, input2):\n        z = np.dot(input1, input2)\n        \n        pos_num = np.where(z>=0)[0]\n        neg_num = np.where(z<0)[0]\n        \n        ans = np.empty(z.shape[0])\n        \n        ans[pos_num] = 1/(1+np.exp(-z[pos_num]))\n        ans[neg_num] = np.exp(z[neg_num])/(1+np.exp(z[neg_num]))\n    \n        return ans\n    \n    def gradient(self,X,y,w):\n        N,m = X.shape\n    #    th = 1/(1+np.exp(-np.dot(w.T, X)))\n        th = self.logistic(X, w.T)\n#        print(\"sigmoid: \", th)\n    #    th = logistic(np.dot(X,w))\n        grad = (1/N) * np.dot(X.T, th-y)\n        return grad\n    \n    \n    # using epsilon as stopping criteria\n    def gradientDescent(self, X, y, a, end_cond, lamda):\n\n        N,m = X.shape\n        w = np.zeros(m)\n        \n        g = np.inf\n        \n        while np.linalg.norm(g) > end_cond:\n            g = self.gradient(X,y,w)\n#            w = w - a*g\n            w = w - a*(g + (1/N)*lamda*w)\n        return w\n    \n    # using num. of iterations as stopping criteria\n    def gradientDescent_iter(self, X, y, a, num_iter, lamda):\n\n        N,m = X.shape\n        w = np.zeros(m)\n        \n        g = np.inf\n        i = 0\n        while i < num_iter:\n            g = self.gradient(X,y,w)\n\n            w = w - a*(g + (1/N)*lamda*w)\n\n            i = i+1\n        return w\n    \n\n    # fit using epsilon as stopping criteria for GD\n    def fit(self, X, y, a, end_cond, lamda):\n        # N = number of instances, m = number of features\n        N,m = X.shape\n        \n        w = self.gradientDescent(X, y, a, end_cond, lamda)\n        self.weight = w\n    \n    \n    # fit using number of iterations as stopping criteria for gradient descent\n    def fit_iter(self, X, y, a, num_iter, lamda):\n        N,m = X.shape\n        \n        w = self.gradientDescent_iter(X, y, a, num_iter, lamda)\n        self.weight = w\n        \n    def predict(self, X):\n        #CODE\n        N,m = X.shape\n        w = self.weight\n        prob = self.logistic(X, w.T)\n        \n#        class_1_idx = np.where(prob > 0.5)[0]\n#        class_0_idx = np.where(prob < 0.5)[0]\n#        equal_prob_idx = np.where(prob == 0.5)[0]\n#        \n#        pred_arr = np.empty(N)\n#        \n#        pred_arr[class_1_idx] = 1\n#        pred_arr[class_0_idx] = 0\n#        pred_arr[equal_prob_idx] = np.random.choice(2,1)\n            \n        return prob\n        \n\n    def evaluate(self, y_truth, y_pred):\n        comparison = np.equal(y_truth, y_pred)\n        accuracy = (np.where(comparison == True)[0].size)/comparison.size\n        return accuracy","sub_path":"Project3/LogisticRegression.py","file_name":"LogisticRegression.py","file_ext":"py","file_size_in_byte":2879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"87287675","text":"import __init__\nimport time, datetime\nimport tensorflow as tf\nimport ocvqa\nimport dataset as data_util\nimport utils\nimport train\n\nfrom absl import app\nfrom absl import flags\nfrom absl import logging\n\nFLAGS = flags.FLAGS\n# flags.DEFINE_string(\"model_dir\", \"./checkpoints/\",\n#                     \"Where to save the checkpoints.\")\n# flags.DEFINE_integer(\"seed\", 0, \"Random seed.\")\n# flags.DEFINE_integer(\"batch_size\", 4, \"Batch size for the model.\")\n# flags.DEFINE_integer(\"num_slots\", 7, \"Number of slots in Slot Attention.\")\n# flags.DEFINE_integer(\"num_iterations\", 3, \"Number of attention iterations.\")\n# flags.DEFINE_float(\"learning_rate\", 0.0004, \"Learning rate.\")\n# flags.DEFINE_integer(\"num_train_steps\", 500000, \"Number of training steps.\")\n# flags.DEFINE_integer(\"warmup_steps\", 10000,\n#                      \"Number of warmup steps for the learning rate.\")\n# flags.DEFINE_float(\"decay_rate\", 0.5, \"Rate for the learning rate decay.\")\n# flags.DEFINE_integer(\"decay_steps\", 100000,\n#                      \"Number of steps for the learning rate decay.\")\n# flags.DEFINE_bool(\"full_eval\", False,\n#                   \"If True, use full evaluation set, otherwise a single batch.\")\n# flags.DEFINE_string(\"job_number\", \"0\", \"number of the job runnning\")\n\n\n\ndef main(argv):\n    del argv\n    # Hyperparameters of the model.\n    batch_size = FLAGS.batch_size\n    num_slots = FLAGS.num_slots\n    num_iterations = FLAGS.num_iterations\n    base_learning_rate = FLAGS.learning_rate\n    num_train_steps = FLAGS.num_train_steps\n    decay_rate = FLAGS.decay_rate\n    decay_steps = FLAGS.decay_steps\n    job_number =  FLAGS.job_number\n    tf.random.set_seed(FLAGS.seed)\n    resolution = (128, 128)\n\n    # load dataset. batch must be a dictionary with keys {\"images\", \"question\", \"answer\"}\n\n    data_iterator, tokenizers = data_util.build_clevr_iterator(batch_size=batch_size, split='train[:30%]')\n    test_data_iterator, _ = data_util.build_clevr_iterator(batch_size=batch_size, split='validation', tokenizer=tokenizers)\n    vocab_size = len(tokenizers[0].get_vocabulary())\n    answer_vocab_size = len(tokenizers[1].get_vocabulary())\n    logging.info(\"vocabulary\", tokenizers[1].get_vocabulary())\n\n    optimizer = tf.keras.optimizers.Adam(base_learning_rate, epsilon=1e-08)\n\n    model = ocvqa.build_ocvqa_model(vocab_size, answer_vocab_size, batch_size=batch_size)\n    print(model.summary())\n    \n    # Prepare checkpoint manager.\n    checkpoint_dir = FLAGS.model_dir + job_number + \"/\"\n    global_step = tf.Variable(\n        0, trainable=False, name=\"global_step\", dtype=tf.int64)\n    ckpt = tf.train.Checkpoint(\n        network=model, optimizer=optimizer, global_step=global_step)\n    ckpt_manager = tf.train.CheckpointManager(\n        checkpoint=ckpt, directory=checkpoint_dir, max_to_keep=5)\n    ckpt.restore(ckpt_manager.latest_checkpoint).expect_partial()\n    \n    if ckpt_manager.latest_checkpoint:\n        logging.info(\"Restored from %s\", ckpt_manager.latest_checkpoint)\n    else:\n        logging.info(\"Initializing from scratch.\")\n    loss , accuracy = train.evaluate(data_iterator, model)\n    logging.info(\"Train Loss:\", loss, \"accuracy\", accuracy)\n    loss , accuracy = train.evaluate(test_data_iterator, model)\n    logging.info(\"Validation Loss:\", loss, \"accuracy\", accuracy)\n    \nif __name__ == \"__main__\":\n    app.run(main)","sub_path":"src/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"465387969","text":"#!/usr/bin/python\nimport cgi\nimport sys\nimport os\nimport pickle\nform = cgi.FieldStorage()\n\ndirname = os.path.dirname(__file__)\n\nprint(\"Content-type: text/html\")\nprint(\"\")\n\ndef make_form():\n    sform = ['latstart', 'latend', 'lonstart', 'lonend', 'tsulat', 'tsulon', 'tsuamp', 'tsutype', 'tsuwx', 'tsuwy', 'theta', 'simtime']\n    dc = {}\n    for i in sform:\n       tmp = form.getfirst(i, 'empty')\n       dc[i] = cgi.escape(tmp)\n    return dc\n\nf = make_form()\n\ntry:\n    qplace = 10\n    trypath = os.path.join(dirname, '../data/simreq'+str(qplace)+'.dict')\n    if os.path.isfile(trypath):\n        print('the queue is currently full. please try again later.')\n        exit()\n    while qplace > 0 and not os.path.isfile(trypath):\n        qplace -= 1\n        trypath = os.path.join(dirname, '../data/simreq'+str(qplace)+'.dict')\n    srpath =  os.path.join(dirname, '../data/simreq'+str(qplace+1)+'.dict')\n    with open(srpath, 'wb') as pklfile:\n        pickle.dump(f, pklfile)\n    if (qplace > 0): print('you are place %d in the queue.<br>'%(qplace,))\n    print('<a href=\"../output.html\">click here to view output</a><br>')\nexcept Exception as e:\n    print('the sever encountered an error in processing your request.')\n    print(e)\n#\n# cargs = []\n#\n# for k,v in f.items():\n#     cargs.append(k)\n#     cargs.append(v)\n#\n#\n# import subprocess\n#\n# subprocess.Popen([sys.executable, './controll.py'] + cargs) # 'python3',\n#\n","sub_path":"cgi-bin/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"187028785","text":"#Print a hashtag pyramid\nfrom cs50 import get_int\n\n#Prompt user for the pyramid height\nwhile True:\n    height = get_int(\"Height: \")\n    if height >= 0 and height < 24:\n        break\n\nwidth = height + 1\n\n#iterate through the rows of the pyramid and print spaces and hashtags\nfor i in range(height):\n    hashes = i + 2\n    spaces = width - hashes\n\n    print(\" \" * spaces, end=\"\")\n    print(\"#\" * hashes)\n\n#Another implementation\n\n#    for j in range(height + 1):\n#        if i + j >= height - 1:\n#            print(\"#\", end=\"\")\n#        else:\n#            print(\" \", end=\"\")\n#    print()","sub_path":"pset6/mario/less/mario.py","file_name":"mario.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"620331281","text":"# Learning Matplotlib from Corey Schafer - https://www.youtube.com/watch?v=UO98lJQ3QGI\n# Chapter 1: Simple line charts\n\nimport pandas as pd\nfrom pathlib import Path\nfrom matplotlib import pyplot as plt\n\nfrom pandas.plotting import register_matplotlib_converters\nregister_matplotlib_converters()\n\n# get data from _df_ohlc.pkl\np = str(Path(__file__).parents[2])\ndf_ohlc = pd.read_pickle(p+r\"\\data\\nse\\_df_ohlc.pkl\")\n\n# print(plt.style.available)\nplt.style.use('fivethirtyeight')  # to use the styles\n# plt.xkcd()  # comic style!\n\n# reset index to remove (symbol, dte) tuple\ndf = df_ohlc.reset_index()\n\n# filter out one symbol\ndf = df[df.symbol == 'AXISBANK']\n\n# set the x-axis data\nx = df.date\n\n# print(df_ohlc)\n\n# plt.plot_date(x, df.close, color='black',\n#               linestyle='-', marker='', label=\"close\", linewidth=3)\n# plt.plot_date(x, df.average, color='blue',\n#               linestyle=':', marker='', label=\"average\")\n\nplt.plot(x, df.close, marker='o', label=\"close\")\n# plt.plot(x, df.average,\n#          label=\"average\")\n\nplt.xlabel('date')\nplt.ylabel('prices')\nplt.title('AXISBANK')\nplt.legend()\nplt.tight_layout()  # to add some padding\n# plt.grid(True) # no need for grid when a style is used\n\n# save the plots\n# plt.savefig(p+r'\\ref\\learn\\plot.png')\n# plt.savefig(p+r'\\ref\\learn\\plot.svg', format='svg')\n\nplt.show()\n","sub_path":"matplotlib/corey_mpl01.py","file_name":"corey_mpl01.py","file_ext":"py","file_size_in_byte":1332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"590505309","text":"#!/usr/bin/env python3\n\nimport json\nimport urllib.request\n\nOUTPUT_FILE=\"weather.txt\"\nAPIKEY='dac76178c27f507675918ff8d9bc2b95'\n\ndef get_city():\n    with urllib.request.urlopen(' http://ip-api.com/json') as response:\n        data = json.loads(response.read())\n        return(data[\"city\"],data[\"countryCode\"])\n\n\ndef get_weather(location,output_filename):\n    url='http://api.openweathermap.org/data/2.5/weather?q=' + location[0] + ',' + location[1] + '&appid=' + APIKEY\n    with urllib.request.urlopen(url) as response:\n        data = json.loads(response.read())\n    with open(output_filename,'a') as filename:\n        filename.write('The weather at ' + location[0] + ' is ' + data[\"weather\"][0][\"description\"] + \"\\n\")\n\ndef get_weather_for_multiple_cities(city_list):\n    for city in city_list:\n        location = [city,\"IL\"]\n        get_weather(location,OUTPUT_FILE)\n\ndef main():\n    location = get_city()\n    get_weather(location,OUTPUT_FILE)\n    get_weather_for_multiple_cities([\"Ashkelon\",\"Jerusalem\",\"Tel Aviv\",\"Haifa\",\"Raanana\",\"Eilat\",\"Carmiel\",\"Holon\",\"Ashdod\",\"Ramat Gan\"])\n\n\n\nif __name__ == \"__main__\":\n    # execute only if run as a script\n    main()\n","sub_path":"home-assignments/session1/exercise2.py","file_name":"exercise2.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"601099310","text":"from SimpleEngine import *\nfrom pygame.locals import *\n\npygame.init()\npygame.mixer.init()\nFPS = pygame.time.Clock()\n\nLayer1Compose()\n\ndebugmode = False\nmain = True\nwhile main:\n    Layer0Display()\n    Layer1Display()\n    Layer2Display()\n    Player.move()\n\n    if debugmode == True:\n        playerxcoord = HudElement(str(-Player.x), \"Text\", 0, 0)\n        playerycoord = HudElement(str(-Player.y), \"Text\", 0, 50)\n        \n    playerscore = HudElement(\"$\"+str(Player.money), \"Text\", 0, 0)\n    playerscore.x = width-(playerscore.rect.right-playerscore.rect.left)\n    \n    Layer3Display()\n    for event in pygame.event.get():\n        if event.type == QUIT:\n            main = False\n        if event.type == KEYDOWN:\n            if event.key == K_ESCAPE:\n                if debugmode == False:\n                    debugmode = True\n                else:\n                    debugmode = False\n    pygame.display.update()\n    FPS.tick(60)\npygame.quit()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"553446832","text":"import pygame\nimport tkinter as tkr\nfrom tkinter.filedialog import askdirectory\nimport os\n\nmusicplayer = tkr.Tk()\nmusicplayer.title(\"Music player\")\nmusicplayer.geometry(\"450x350\")\n\nlist1 = askdirectory()\nos.chdir(list1)\nsonglist = os.listdir()\nplaylist = tkr.Listbox(musicplayer,font=\"Helventica 12 bold\",bg=\"yellow\",selectmode=tkr.SINGLE)\n\nfont_effect = (\"Helventica 12 bold\")\n\nfor item in songlist:\n    pos = 0\n    playlist.insert(pos, item)\n    pos = pos + 1\n\npygame.init()\npygame.mixer.init()\n\ndef play():\n    pygame.mixer.music.load(playlist.get(tkr.ACTIVE))\n    var.set(playlist.get(tkr.ACTIVE))\n    pygame.mixer.music.play()\n\ndef stop():\n    pygame.mixer.music.stop()\n\ndef pause():\n    pygame.mixer.music.pause()\n\ndef unpause():\n    pygame.mixer.music.unpause()\n\nButton1 = tkr.Button(musicplayer,font=font_effect,bg=\"red\",fg=\"white\",text=\"PLAY\",command=play)\nButton2 = tkr.Button(musicplayer,font=font_effect,bg=\"blue\",fg=\"white\",text=\"STOP\",command=stop)\nButton3 = tkr.Button(musicplayer,font=font_effect,bg=\"orange\",fg=\"white\",text=\"PAUSE\",command=pause)\nButton4 = tkr.Button(musicplayer,font=font_effect,bg=\"green\",fg=\"white\",text=\"UNPAUSE\",command=unpause)\n\nvar = tkr.StringVar()\nsongtitle = tkr.Label(musicplayer,textvariable=var,font=font_effect)\n\nsongtitle.pack()\nButton1.pack(fill=\"x\")\nButton2.pack(fill=\"x\")\nButton3.pack(fill=\"x\")\nButton4.pack(fill=\"x\")\nplaylist.pack(fill=\"both\",expand=\"yes\")\n\nmusicplayer.mainloop()\n","sub_path":"Music_Player/musicplayer.py","file_name":"musicplayer.py","file_ext":"py","file_size_in_byte":1434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"563658321","text":"from django.contrib.auth.models import User\nfrom rest_framework import serializers\nfrom .models import *\nfrom rest_framework.fields import CurrentUserDefault\n\n\nclass UserSerializer(serializers.ModelSerializer):\n    \"\"\"docstring forUserSerializer.\"\"\"\n    class Meta:\n        model = User\n        fields = ('id','username','email')\n\nclass TagSerializers(serializers.Serializer):\n    \"\"\"Serializers class for serialize Tags\"\"\"\n    name = serializers.CharField(max_length=100)\n\nclass SeasonSerializers(serializers.Serializer):\n    \"\"\"Serializers class for serialize Season\"\"\"\n    name = serializers.CharField(max_length=100)\n\n\nclass BenefitsSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Benefit\n        fields = '__all__'\n\nclass ImagesSerializer(serializers.Serializer):\n    # class Meta:\n    #     model = Images\n    #     fields =('id','image')\n    pk = serializers.IntegerField()\n    image =serializers.CharField(max_length=250)\n\nclass ProfileSerializer(serializers.Serializer):\n    \"\"\"\n    get serialize profile data.\n    \"\"\"\n    id = serializers.IntegerField(required=False)\n    userType = serializers.CharField(max_length=250,required=False,read_only=True)\n    user = UserSerializer(many=False, read_only=True)\n    mobile = serializers.CharField(max_length=10,required=False,allow_blank=True)\n    date_of_birth = serializers.DateField(required=False)\n    name = serializers.CharField(allow_blank=True,max_length=200,required=False)\n    address_line1 = serializers.CharField(allow_blank=True,max_length=250,required=False)\n    address_line2 = serializers.CharField(allow_blank=True,max_length=250,required=False)\n    state = serializers.CharField(allow_blank=True,max_length=100,required=False)\n    city = serializers.CharField(allow_blank=True,max_length=100,required=False)\n    nearest_landmark = serializers.CharField(allow_blank=True,max_length=250,required=False)\n    pincode = serializers.IntegerField(required=False)\n    latitude = serializers.FloatField(required=False)\n    longitude = serializers.FloatField(required=False)\n\n    def update(self, instance, validated_data):\n        \"\"\"\n        Update and return an existing `Snippet` instance, given the validated data.\n        \"\"\"\n        instance.mobile = validated_data.get('mobile', instance.mobile)\n        instance.date_of_birth = validated_data.get('date_of_birth', instance.date_of_birth)\n        instance.name = validated_data.get('name', instance.name)\n        instance.address_line1 = validated_data.get('address_line1', instance.address_line1)\n        instance.address_line2 = validated_data.get('address_line2', instance.address_line2)\n        instance.state = validated_data.get('state', instance.state)\n        instance.city = validated_data.get('city', instance.city)\n        instance.nearest_landmark = validated_data.get('nearest_landmark', instance.nearest_landmark)\n        instance.pincode = validated_data.get('pincode', instance.pincode)\n        instance.latitude = validated_data.get('latitude', instance.latitude)\n        instance.longitude = validated_data.get('longitude', instance.longitude)\n        instance.save()\n        return instance\n\n\n\nclass VendorSerializer(serializers.ModelSerializer):\n    \"\"\"docstring forUserSerializer.\"\"\"\n    userprofile = ProfileSerializer(many=False, read_only=True)\n    class Meta:\n        model = User\n        fields = ('email','userprofile')\n\n\nclass CategorySerializer(serializers.ModelSerializer):\n    benefits = BenefitsSerializer(many=True, read_only=True)\n    banner_images = ImagesSerializer(many=True, read_only=True)\n    class Meta:\n        model = Category\n        fields = '__all__'\n\nclass PlantsSerializer(serializers.ModelSerializer):\n    # category = CategorySerializer(many=False, read_only=True)\n    # benefits = BenefitsSerializer(many=True, read_only=True)\n    images = ImagesSerializer(many=True, read_only=True)\n    class Meta:\n        model = Plants\n        fields = ('id','name','images')\n\nclass PlantDetailsSerializer(serializers.ModelSerializer):\n    category = CategorySerializer(many=True, read_only=True)\n    # benefits = BenefitsSerializer(many=True, read_only=True)\n    images = ImagesSerializer(many=True, read_only=True)\n    tags = TagSerializers(many=True, read_only=True)\n    season = SeasonSerializers(many=True, read_only=True)\n    class Meta:\n        model = Plants\n        fields = '__all__'\n\nclass PlantStockSerializer(serializers.ModelSerializer):\n    \"\"\"docstring for PlantStockSerializer.\"\"\"\n    plant = PlantsSerializer(many=False,read_only=True)\n    class Meta:\n        model = PlantStock\n        fields = ('id','plant','quantity')\n\nclass PlantStockDetailsSerializer(serializers.ModelSerializer):\n    \"\"\"docstring for PlantStockSerializer.\"\"\"\n    plant = PlantDetailsSerializer(many=False,read_only=True)\n    class Meta:\n        model = PlantStock\n        fields = ('id','plant','quantity')\n\n\nclass OrderSerializers(serializers.ModelSerializer):\n    \"\"\"docstring for OrderSerializers.\"\"\"\n    plant = PlantsSerializer(many=False,read_only=True)\n    images = ImagesSerializer(many=True, read_only=True)\n    class Meta:\n        model = OrderDetails\n        fields = ('id','order','plant','images','quantity','status')\n\nclass DeliveryOrderSerializers(serializers.ModelSerializer):\n    \"\"\"docstring for OrderSerializers.\"\"\"\n    plant = PlantsSerializer(many=False,read_only=True)\n    vendor = VendorSerializer(many=False,read_only=True)\n    class Meta:\n        model = OrderDetails\n        fields = ('id','vendor','plant','quantity','status')\n\n\nclass DeliveryBoyBooking(serializers.Serializer):\n    \"\"\"\n    docstring forDeliveryBoyBooking.\n    \"\"\"\n    id = serializers.IntegerField(required=False)\n    orderdetails = serializers.SerializerMethodField('order_details')\n    status = serializers.CharField(max_length=100)\n    customer = serializers.SerializerMethodField('customer_details')\n    shipping_name = serializers.CharField(max_length=150)\n    shipping_phone = serializers.CharField(max_length=150)\n    shipping_address1 = serializers.CharField(max_length=150)\n    shipping_address2 = serializers.CharField(max_length=150)\n    shipping_state = serializers.CharField(max_length=150)\n    shipping_city = serializers.CharField(max_length=150)\n    shipping_pincode = serializers.CharField(max_length=150)\n    shipping_latitude = serializers.FloatField()\n    shipping_longitude = serializers.FloatField()\n    delivery_date = serializers.DateField()\n\n    def customer_details(self,obj):\n        \"\"\"\n        get customer details\n        \"\"\"\n        return VendorSerializer(obj.user).data\n\n    def order_details(self,obj):\n        \"\"\"\n        function for getting order details\n        \"\"\"\n        order_details = OrderDetails.objects.filter(order = obj)\n        orders = []\n        for od in order_details:\n            n= DeliveryOrderSerializers(od).data\n            orders.append(n)\n        return orders\n","sub_path":"core/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":6891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"274694734","text":"# Copyright 2017 TensorHub, Inc.\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\nfrom __future__ import absolute_import\nfrom __future__ import division\n\nimport os\n\nimport guild.cli\nimport guild.cmd_support\nimport guild.project\n\nDEFAULT_PROJECT_LOCATION = \".\"\n\ndef main(args, ctx):\n    project = guild.cmd_support.project_for_location(\n        args.project_location, ctx)\n    guild.cli.table(\n        [_format_op(op) for op in _iter_ops(project, args)],\n        cols=[\"full_name\", \"description\"],\n        detail=([\"cmd\"] if args.verbose else []))\n\ndef _iter_ops(project, args):\n    for model in _project_models(project, args):\n        for op in model.operations:\n            yield op\n\ndef _project_models(project, args):\n    if args.model:\n        return [_try_model(args.model, project)]\n    else:\n        return list(project)\n\ndef _try_model(name, project):\n    try:\n        return project[name]\n    except KeyError:\n        _no_such_model_error(name, project)\n\ndef _no_such_model_error(name, project):\n    guild.cli.error(\n        \"model '%s' is not defined in %s\\n\"\n        \"Try 'guild models%s' for a list of models.\"\n        % (name, os.path.relpath(project.src),\n           _project_opt(project.src)))\n\ndef _project_opt(project_src):\n    relpath = os.path.relpath(project_src)\n    if relpath == \"MODEL\" or relpath == \"MODELS\":\n        return \"\"\n    else:\n        return \" -p %s\" % relpath\n\ndef _format_op(op):\n    return {\n        \"name\": op.name,\n        \"model\": op.model.name,\n        \"full_name\": \"%s:%s\" % (op.model.name, op.name),\n        \"description\": op.description or \"\",\n        \"cmd\": op.cmd,\n    }\n","sub_path":"guild/commands/operations_impl.py","file_name":"operations_impl.py","file_ext":"py","file_size_in_byte":2112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"236224852","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 23 14:25:50 2014\n\nThis script reads a set of microstructures designated by the set-ID and saves\nthe microstructure function in real and frequency space.\n\n@author: nhpnp3\n\"\"\"\n\nimport time\nimport numpy as np\nimport functions_composite as rr\nimport scipy.io as sio\n\ndef msf(el,ns,H,set_id,wrt_file):\n    \n    start = time.time()    \n    \n    ## import microstructures\n    pre_micr = np.zeros([el**3,ns,H])\n    microstructure = sio.loadmat('M_%s%s.mat' %(ns,set_id))['M']\n    \n    for h in xrange(H):\n        pre_micr[...,h] = (microstructure == h).astype(int)\n    \n    del microstructure\n\n    micr = np.swapaxes(pre_micr[::-1,...].reshape([el,el,el,ns,H]),1,2)    \n    \n    del pre_micr    \n\n    np.save('msf_%s%s' %(ns,set_id),micr)\n    \n    end = time.time()\n    timeE = np.round((end - start),3)\n    msg = \"generate real-space microstructure function from GSH-coefficients: %s seconds\" %timeE\n    rr.WP(msg,wrt_file)\n    \n    ## Microstructure functions in frequency space\n    start = time.time()\n    \n    M = np.fft.fftn(micr, axes = [0,1,2])    \n    del micr\n    size = M.nbytes\n    np.save('M_%s%s' %(ns,set_id),M)\n    \n    end = time.time()\n    timeE = np.round((end - start),3)\n    \n    msg = \"FFT3 conversion of micr to M_%s%s: %s seconds\" %(ns,set_id,timeE)\n    rr.WP(msg,wrt_file)\n    msg = 'Size of M_%s%s: %s bytes' %(ns,set_id,size)\n    rr.WP(msg,wrt_file)","sub_path":"fip_collab/2014_10_03_composite_test/microstructure_function.py","file_name":"microstructure_function.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"409499056","text":"from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n    url(r'^$',views.index,name='index'),\n    url(r'^/add/$',views.add,name='add'),\n    url(r'^userpage/$',views.user_page,name='userpage'),\n    url(r'^reloaddata/$',views.reload_data,name='reloaddata'),\n    url(r'^search/$', views.search, name='search'),\n    url(r'^regitw/$', views.register_from_twitter, name='regitw'),\n    url(r'^deleterecord/$',views.delete,name='deleterecord'),\n    url(r'^gridview/$',views.gridview,name='gridview'),\n    ]\n","sub_path":"manager/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"646622418","text":"from django.urls import path\nfrom todo_app import views\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('create/', views.create_template, name='create_template'),\n    path('create1/', views.create, name='create'),\n    path('edit/<int:pk>', views.edit, name='edit'),\n    path('delete/<int:pk>', views.delete, name='delete'),\n    path('mark-done/<int:pk>', views.mark_todo_done, name='mark todo done')\n]\n","sub_path":"python_web_basics/todo_project/todo_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"551466825","text":"import functools\r\nfrom typing import Optional, Iterable, Union, List, Mapping, Tuple\r\nimport numpy as np\r\nimport pathlib\r\nimport json\r\n\r\nimport torch\r\nfrom torch import nn\r\nfrom torch import Tensor, FloatTensor, LongTensor\r\n\r\nfrom GANsynth_pytorch.loader import make_masked_phase_transform\r\nfrom GANsynth_pytorch.normalizer import (DataNormalizer,\r\n                                         DataNormalizerStatistics)\r\n\r\nfrom .encoder_decoder import RosinalityEncoder, RosinalityDecoder\r\nfrom .bottleneck import (QuantizedBottleneck, QuantizedBottleneckWithRestarts,\r\n                         UnquantizedBottleneck)\r\n\r\n\r\nclass BiasedNonLinearity(nn.Module):\r\n    def __init__(self, activation: nn.Module, bias: float,\r\n                 channel_index: int = 0):\r\n        super().__init__()\r\n        self.activation = activation\r\n        self.bias = bias\r\n        self.channel_index = channel_index\r\n\r\n    def forward(self, input: torch.Tensor) -> torch.Tensor:\r\n        # retrieve channel\r\n        channel = input[:, self.channel_index]\r\n        channel = self.bias + self.non_linearity(channel)\r\n        input[:, self.channel_index] = channel\r\n        return input\r\n\r\n\r\nclass VQVAE(nn.Module):\r\n    n_embed_t: int\r\n    n_embed_b: int\r\n\r\n    \"\"\"Implementation of the VQ-VAE model\r\n\r\n    Arguments:\r\n        in_channel (int):\r\n            number of channels in the input images\r\n        channel (int):\r\n            number of channels in the encoder/decoder\r\n        n_res_block (int):\r\n            number of residual blocks in the encoder/decoder\r\n        n_res_channel (int):\r\n            number of channels in the residual blocks\r\n        embed_dim (int or Iterable[int]):\r\n            dimension of the latent codes\r\n        n_embed (int):\r\n            size of the latent book (number of different latent codes to learn)\r\n        decay (float, 0 =< decay =< 1):\r\n            decay rate of the latent codes\r\n        resolution_factors (Mapping[str, int]):\r\n            associates each code layer to its down/up-sampling factor.\r\n            NOTE: the factors are meant respective to the previous\r\n            code layer, e.g. for a two-level VQ-VAE, with top obtained\r\n            by further downsampling bottom, the resolution_factors['top']\r\n            is the down/up-sampling factor from the bottom layer to the top\r\n            layer.\r\n    \"\"\"\r\n    def __init__(\r\n        self,\r\n        encoders: Optional[Mapping[str, nn.Module]] = None,\r\n        decoders: Optional[Mapping[str, nn.Module]] = None,\r\n        in_channel: int = 3,\r\n        num_hidden_channels: int = 128,\r\n        n_res_block: int = 2,\r\n        num_residual_channels: int = 32,\r\n        embed_dim: int = 64,\r\n        num_embeddings: Union[int, Iterable[int]] = 512,\r\n        decay: float = 0.99,\r\n        groups: int = 1,\r\n        use_local_kernels: bool = False,\r\n        output_activation_type: Optional[str] = None,\r\n        output_spectrogram_min_magnitude: Optional[float] = None,\r\n        resolution_factors: Mapping[str, int] = {\r\n            'bottom': 4,\r\n            'top': 2,\r\n        },\r\n        embeddings_initial_variance: float = 1,\r\n        # TODO(theis): remove this parameter, hard to serialize and save\r\n        decoder_output_activation: Optional[nn.Module] = None,\r\n        normalizer_statistics: Optional[\r\n            Union[DataNormalizerStatistics, Mapping[str, float]]] = None,\r\n        corruption_weights: Mapping[str, Optional[List[float]]] = {'top': None,\r\n                                                                   'bottom': None},\r\n        adapt_quantized_durations: bool = True,\r\n        disable_quantization: bool = False,\r\n        restarts_usage_threshold: float = 1.\r\n    ):\r\n        if decoder_output_activation is not None:\r\n            raise NotImplementedError(\"TODO\")\r\n\r\n        # store instantiation parameters\r\n        self.in_channel = in_channel\r\n        self.num_hidden_channels = num_hidden_channels\r\n        self.n_res_block = n_res_block\r\n        self.num_residual_channels = num_residual_channels\r\n        self.embed_dim = embed_dim\r\n        self.use_local_kernels = use_local_kernels\r\n        self.num_embeddings = num_embeddings\r\n        self.decay = decay\r\n        self.groups = groups\r\n        self.resolution_factors = resolution_factors\r\n        self.embeddings_initial_variance = embeddings_initial_variance\r\n        self.output_activation_type = output_activation_type\r\n        self.decoder_output_activation = decoder_output_activation\r\n        self.corruption_weights = corruption_weights\r\n        self.output_spectrogram_min_magnitude = (\r\n            output_spectrogram_min_magnitude)\r\n        self.restarts_usage_threshold = restarts_usage_threshold\r\n\r\n        if isinstance(normalizer_statistics, DataNormalizerStatistics):\r\n            self.normalizer_statistics = normalizer_statistics.__dict__\r\n        else:\r\n            self.normalizer_statistics = normalizer_statistics\r\n\r\n        self._instantiation_parameters = self.__dict__.copy()\r\n\r\n        super().__init__()\r\n\r\n        if encoders is None:\r\n            self.enc_b = RosinalityEncoder(\r\n                self.in_channel, self.num_hidden_channels, self.n_res_block,\r\n                self.num_residual_channels,\r\n                resolution_factor=self.resolution_factors['bottom'],\r\n                groups=self.groups,\r\n                use_local_kernels=self.use_local_kernels)\r\n            self.enc_t = RosinalityEncoder(\r\n                self.num_hidden_channels, self.num_hidden_channels,\r\n                self.n_res_block, self.num_residual_channels,\r\n                resolution_factor=self.resolution_factors['top'],\r\n                groups=self.groups,\r\n                use_local_kernels=self.use_local_kernels)\r\n        else:\r\n            self.enc_t = encoders['top']\r\n            self.enc_b = encoders['bottom']\r\n\r\n        if isinstance(self.num_embeddings, int):\r\n            # use single value for both code layers\r\n            self.n_embed_t, self.n_embed_b = [self.num_embeddings] * 2\r\n        else:\r\n            self.n_embed_t, self.n_embed_b = self.num_embeddings\r\n\r\n        self.quantize_conv_t = nn.Conv2d(self.num_hidden_channels,\r\n                                         self.embed_dim, 1)\r\n\r\n        if not disable_quantization:\r\n            if self.restarts_usage_threshold == 1.:\r\n                bottleneck = QuantizedBottleneck\r\n            else:\r\n                bottleneck = functools.partial(\r\n                    QuantizedBottleneckWithRestarts,\r\n                    restart_threshold=self.restarts_usage_threshold)\r\n        else:\r\n            bottleneck = UnquantizedBottleneck\r\n\r\n        self.quantize_t = bottleneck(\r\n            self.embed_dim, self.n_embed_t, decay=self.decay,\r\n            corruption_weights=self.corruption_weights['top'],\r\n            embeddings_initial_variance=self.embeddings_initial_variance)\r\n        if decoders is None:\r\n            self.dec_t = RosinalityDecoder(\r\n                self.embed_dim, self.embed_dim, self.num_hidden_channels,\r\n                self.n_res_block, self.num_residual_channels, groups=self.groups,\r\n                resolution_factor=self.resolution_factors['top'],\r\n                use_local_kernels=self.use_local_kernels,\r\n            )\r\n        else:\r\n            self.dec_t = decoders['top']\r\n        self.quantize_conv_b = nn.Conv2d(\r\n            self.embed_dim + self.num_hidden_channels,\r\n            self.embed_dim, 1)\r\n        self.quantize_b = bottleneck(\r\n            self.embed_dim, self.n_embed_b, decay=self.decay,\r\n            corruption_weights=self.corruption_weights['bottom'],\r\n            embeddings_initial_variance=self.embeddings_initial_variance)\r\n\r\n        # upsample from 'top' layer back to 'bottom' layer resolution\r\n        upsampling_layers = []\r\n        num_upsampling_layers = int(np.log2(self.resolution_factors['top']))\r\n\r\n        upsampling_stride = 2\r\n        if not self.use_local_kernels:\r\n            upsampling_kernel_size = upsampling_stride * 2\r\n        else:\r\n            upsampling_kernel_size = upsampling_stride\r\n\r\n        for i in range(num_upsampling_layers):\r\n            upsampling_layers.append(\r\n                nn.ConvTranspose2d(\r\n                    self.embed_dim, self.embed_dim,\r\n                    kernel_size=upsampling_kernel_size,\r\n                    stride=upsampling_stride,\r\n                    padding=1)\r\n            )\r\n        self.upsample_top_to_bottom = nn.Sequential(*upsampling_layers)\r\n\r\n        if decoders is None:\r\n            self.dec = RosinalityDecoder(\r\n                self.embed_dim + self.embed_dim,\r\n                self.in_channel,\r\n                self.num_hidden_channels,\r\n                self.n_res_block,\r\n                self.num_residual_channels,\r\n                resolution_factor=self.resolution_factors['bottom'],\r\n                groups=self.groups,\r\n                use_local_kernels=self.use_local_kernels,\r\n                output_activation=self.decoder_output_activation,\r\n            )\r\n        else:\r\n            self.dec = decoders['bottom']\r\n\r\n        self.use_gansynth_normalization = (self.normalizer_statistics\r\n                                           is not None)\r\n\r\n        if self.use_gansynth_normalization:\r\n            data_normalizer_statistics = DataNormalizerStatistics(\r\n                **self.normalizer_statistics)\r\n            self.data_normalizer = DataNormalizer(data_normalizer_statistics)\r\n        else:\r\n            self.data_normalizer = None\r\n\r\n        if self.output_activation_type == 'threshold_gelu':\r\n            assert self.output_spectrogram_min_magnitude is not None\r\n            self.output_activation = BiasedNonLinearity(\r\n                nn.GELU(), self.output_spectrogram_min_magnitude,\r\n                channel_index=0\r\n            )\r\n        else:\r\n            assert self.output_activation_type is None, (\r\n                \"Unexpected output activation type\")\r\n\r\n        self.output_transform = None\r\n        if self.output_spectrogram_min_magnitude is not None:\r\n            self.output_transform = make_masked_phase_transform(\r\n                self.output_spectrogram_min_magnitude)\r\n\r\n        self.adapt_quantized_durations = adapt_quantized_durations\r\n\r\n    def forward(self, input):\r\n        (quant_t, quant_b, diff, id_t, id_b,\r\n         perplexity_t, perplexity_b) = self.encode(input)\r\n        dec = self.decode(quant_t, quant_b)\r\n        return dec, diff, perplexity_t, perplexity_b, id_t, id_b\r\n\r\n    def encode(self, input: Tensor) -> Tuple[\r\n            FloatTensor, FloatTensor, FloatTensor,\r\n            LongTensor, LongTensor, FloatTensor, FloatTensor]:\r\n        if self.data_normalizer is not None:\r\n            input = self.data_normalizer.normalize(input)\r\n\r\n        enc_b = self.enc_b(input)\r\n        enc_t = self.enc_t(enc_b)\r\n\r\n        quant_t = self.quantize_conv_t(enc_t).permute(0, 2, 3, 1)\r\n        quant_t, diff_t, id_t, perplexity_t = self.quantize_t(quant_t)\r\n        quant_t = quant_t.permute(0, 3, 1, 2)\r\n        diff_t = diff_t.unsqueeze(0)\r\n\r\n        dec_t = self.dec_t(quant_t)\r\n        if self.adapt_quantized_durations:\r\n            quantized_duration = min(dec_t.shape[-1], enc_b.shape[-1])\r\n            dec_t = dec_t[..., :quantized_duration]\r\n            enc_b = enc_b[..., :quantized_duration]\r\n        enc_b = torch.cat([dec_t, enc_b], 1)\r\n\r\n        quant_b = self.quantize_conv_b(enc_b).permute(0, 2, 3, 1)\r\n        quant_b, diff_b, id_b, perplexity_b = self.quantize_b(quant_b)\r\n        quant_b = quant_b.permute(0, 3, 1, 2)\r\n        diff_b = diff_b.unsqueeze(0)\r\n\r\n        return (quant_t, quant_b, diff_t + diff_b, id_t, id_b,\r\n                perplexity_t, perplexity_b)\r\n\r\n    def decode(self, quant_t: Tensor, quant_b: Tensor):\r\n        upsample_top_to_bottom = self.upsample_top_to_bottom(quant_t)\r\n        quant = torch.cat([upsample_top_to_bottom, quant_b], 1)\r\n        dec = self.dec(quant)\r\n\r\n        dec = self.post_process(dec)\r\n        return dec\r\n\r\n    def decode_code(self, code_t: LongTensor, code_b: LongTensor):\r\n        quant_t = self.quantize_t.embed_code(code_t)\r\n        quant_t = quant_t.permute(0, 3, 1, 2)\r\n        quant_b = self.quantize_b.embed_code(code_b)\r\n        quant_b = quant_b.permute(0, 3, 1, 2)\r\n\r\n        dec = self.decode(quant_t, quant_b)\r\n        return dec\r\n\r\n    def post_process(self, dec: Tensor) -> Tensor:\r\n        if self.use_gansynth_normalization:\r\n            dec = self.data_normalizer.denormalize(dec)\r\n        if self.output_transform is not None:\r\n            dec = self.output_transform(dec)\r\n        return dec\r\n\r\n    @classmethod\r\n    def from_parameters_and_weights(\r\n            cls, parameters_json_path: pathlib.Path,\r\n            model_weights_checkpoint_path: pathlib.Path,\r\n            device: Union[str, torch.device] = 'cpu',\r\n            encoders: Optional[Mapping[str, nn.Module]] = None,\r\n            decoders: Optional[Mapping[str, nn.Module]] = None,\r\n            ) -> 'VQVAE':\r\n        \"\"\"Re-instantiate a stored model using init parameters and weights\r\n\r\n        Arguments:\r\n            parameters_json_path (pathlib.Path)\r\n                Path to the a json file containing the keyword arguments used\r\n                to initialize the object\r\n            model_weights_checkpoint_path (pathlib.Path)\r\n                Path to a model weights checkpoint file as created by\r\n                torch.save\r\n            device (str or torch.device, default 'cpu')\r\n                Device on which to load the stored weights\r\n        \"\"\"\r\n        with open(parameters_json_path, 'r') as f:\r\n            parameters = json.load(f)\r\n        vqvae = cls(**parameters, encoders=encoders,\r\n                    decoders=decoders)\r\n\r\n        model_state_dict = torch.load(model_weights_checkpoint_path,\r\n                                      map_location=device)\r\n        if 'model' in model_state_dict.keys():\r\n            model_state_dict = model_state_dict['model']\r\n        # HACK(theis): uses strict=False to allow loading old models where\r\n        # DataNormalizer was not yet a submodule\r\n        # TODO(theis): remove this eventually\r\n        vqvae.load_state_dict(model_state_dict, strict=False)\r\n        return vqvae\r\n\r\n    def store_instantiation_parameters(self, path: pathlib.Path) -> None:\r\n        \"\"\"Store the parameters used to create this instance as JSON\"\"\"\r\n        with open(path, 'w') as f:\r\n            json.dump(self._instantiation_parameters, f, indent=4)\r\n","sub_path":"interactive_spectrogram_inpainting/vqvae/vqvae.py","file_name":"vqvae.py","file_ext":"py","file_size_in_byte":14414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"229059891","text":"import random,json,sys,copy,pylab,math,imageio,random,warnings\nfrom skimage import io,data\nimport numpy as np\nfrom PIL import Image\nfrom math import sin,cos\nimport matplotlib.pyplot as plt\nfrom skimage import img_as_ubyte\n\n# below are all global variables\nglobal mass,time_step,friction_factor,ball_radius,max_velocity,bounded,vel_scale, pos_scale,force_scale\n\nmass=1.0\ntime_step = 1000.0/1000     \nfriction_factor = 0      # friction factor on the ball\nball_radius = 4         # radius of the ball\nmax_velocity = 5.0      # maximum velocity\nbounded=1    # max magnitude for force in each direction\n# scale\nvel_scale=5.0\npos_scale=10.0\nforce_scale=10.0\n\ndef generate_(data_range):\n    '''\n    generate a data in form [Fx,Fy], chosen randomly from @data_range\n    '''\n    data = []\n    x = np.random.choice(data_range, 1)\n    x = x[0]\n    y = np.random.choice(data_range, 1)\n    y = y[0]\n    # y=-x\n    data.append(float(x))\n    data.append(float(y))\n    return data\n\n\ndef create_gif(frames, gif_name):\n    '''\n    create gif from a sequence of pictures,save in @gif_name\n    @frames:list of image arrays\n    '''\n    imageio.mimsave(gif_name, frames, 'GIF', duration = 0.1)\n    return\n\ndef create_gif_byreading(path,epoch,total_step, gif_name):\n    '''\n    create gif from a sequence of pictures,save in @gif_name\n    @path: directory of pictures\n    @epoch: the number of epoch\n    @total_step:  the number of pictures in a gif\n    '''\n\n    image_list=[path+str(epoch)+'_'+str(i)+'.jpg' for i in range(total_step)]\n\n    frames = []\n    for image_name in image_list:\n        frames.append(imageio.imread(image_name))\n    # Save them as frames into a gif \n    imageio.mimsave(gif_name, frames, 'GIF', duration = 0.1)\n    return\n\ndef drawplot(y_1,y_2,path,name,issave=True):\n    plt.cla()\n    plt.axis('on') \n    fig = plt.figure() \n    \n    plt.title(name,fontsize='large',fontweight='bold')\n    ax=plt.plot(np.arange(0,y_1.shape[0]),y_1,color='r',label=\"actor\")\n    ax=plt.plot(np.arange(0,y_2.shape[0]),y_2,color='b',label=\"imitator\")\n    ax = plt.gca()\n    plt.legend(bbox_to_anchor=(0, 0), loc=3, borderaxespad=0)\n\n    ax.locator_params(nbins=8)\n    \n    if(issave):\n        plt.savefig(path, format='png')\n    plt.close\ndef error(message):\n    \"\"\"\n    Return an error message and exit the program.\n    @param message: Error message to display.\n    \"\"\"\n    sys.stderr.write(\"Error: \" + message)\n    sys.exit(1)\n\ndef rect_to_polar(x, y):\n        \"\"\"\n        Convert a rectangular coordinate system vector to its polar equivalent.\n        @param x: X value\n        @param y: Y value\n        @return: Dict with magnitude and direction parameters.\n        \"\"\"\n        # Calculate the magnitude\n        magnitude = math.sqrt(x**2 + y**2)\n\n        # Calculate the direction\n        direction = 0\n        if x == 0:\n            if y > 0:\n                direction = 90\n            elif y < 0:\n                direction = 270\n        else:\n            raa = math.degrees(math.atan(y / x))\n            if x > 0:\n                direction = 360 + raa\n            else:\n                direction = 180 + raa\n\n        # Normalize the direction value\n        if direction >= 360:\n            direction -= 360\n\n        return {'magnitude': magnitude, 'direction': direction}\n\ndef get_final_json_blob(ball_config,balls):\n    \"\"\"\n    Get the string version of the final positions in JSON format.\n    @param balls: List of balls in resting position.\n    @return: String of JSON with final resting position information.\n    \"\"\"\n    output_balls = []\n    for ball in balls:\n        output_ball = {}\n        output_ball['name'] = ball.name\n        output_ball['position'] = [ball.position['x'], ball.position['y']]\n        output_ball['velocity'] = [ball.velocity['x'], ball.velocity['y']]\n        output_ball['acceleration'] = [ball.acceleration['x'], ball.acceleration['y']]\n        output_balls.append(output_ball)\n    output = {}\n    output['balls'] = output_balls\n    output['width'] = ball_config['width']\n    output['height'] = ball_config['height']\n\n    return json.dumps(output, indent=4)\n\n\ndef load_from_file(input_file_name):\n    \"\"\"\n    Load the JSON blob from the given input file.\n    Save the results into a global object named 'ball_config'\n    \"\"\"\n    # Load the input file.\n    try:\n        input_file = open(input_file_name)\n        input_file_contents = input_file.read()\n    except Exception as e:\n        error(e)\n\n    # Parse the input file to a ball_configuration file structure.\n    # global ball_config\n    ball_config = json.loads(input_file_contents)\n\n    return ball_config\n\n\ndef draw(info,force,ball_config,path,isdraw=False):\n    '''\n    # draw the picture, with white background and black ball\n    @isdraw: save the picture if true\n    '''\n    width=ball_config['width']#128\n    height=ball_config['height']#128\n    img=np.ones(width*height*3)\n    img=img.reshape(width,height,3)\n    # img[:,:,2]=0 #yellow background\n    ball=info[0]\n    ball_x=int(ball.position['x'])\n    ball_y=int(ball.position['y'])\n    for i in range(-ball_radius,ball_radius+1):\n        for j in range(-ball_radius,ball_radius+1):\n            if ball_x+i>=0 and ball_x+i<height and ball_y+j>=0 and ball_y+j<width:\n                img[ball_x+i,ball_y+j]=[0.0,0.0,0.0]#[-1,-1,-1]#\n    if(isdraw==True):\n        io.imsave(path+'.jpg',img)\n    return np.array(img)\n\ndef img_concat(img1,img2,path,pos1,pos2,ball_config,count=0,force_real=[0,0],force_fake=[0,0],is_force_line=False,issave=False):\n    # input: two images\n    # output: contactenate two images\n\n    # is_force_line: draw the force line on the ball if True. @issave must be True now\n    # issave: save the concatenated images if true.\n    # pos1,pos2: the postion of balls\n    # count: Number of step in the process\n\n    # concatenated image\n    img_concat0=np.concatenate((img1,img2),axis=1)\n    \n    if(is_force_line==False):\n        if issave:\n             with warnings.catch_warnings():\n                    warnings.simplefilter(\"ignore\")\n                    io.imsave(path+str(count)+'.jpg',img_as_ubyte(img_concat0))\n        return img_concat0\n    else:\n        ball_x=int(pos1[0])\n        ball_y=int(pos1[1])\n\n        ball_x_1=int(pos2[0])\n        ball_y_1=int(pos2[1])   \n        # draw the force line\n        width=ball_config['width']#64\n        height=ball_config['height']#64\n\n        # scale to [-1,1]\n        force_real=[i*ball_radius/force_scale for i in force_real]\n        force_fake=[i*ball_radius/force_scale for i in force_fake]\n\n        force_real_polar=rect_to_polar(force_real[0],force_real[1])\n        force_real_dir_rad = math.radians(force_real_polar['direction'])\n\n        force_fake_polar=rect_to_polar(force_fake[0],force_fake[1])\n        force_fake_dir_rad = math.radians(force_fake_polar['direction'])\n\n        # 0: force 1: origin 2: velocity\n        draw_x=[int(ball_x+32*force_real_polar['magnitude']*cos(force_real_dir_rad)),int(ball_x)]#int(ball_x+32*vel_polar['magnitude']*cos(vel_dir_rad))]\n        draw_y=[int(ball_y+32*force_real_polar['magnitude']*sin(force_real_dir_rad)),int(ball_y)]#int(ball_y+32*vel_polar['magnitude']*sin(vel_dir_rad))]\n        \n        draw_x_1=[int(ball_x_1+32*force_fake_polar['magnitude']*cos(force_fake_dir_rad)),int(ball_x_1)]\n        draw_y_1=[height+int(ball_y_1+32*force_fake_polar['magnitude']*sin(force_fake_dir_rad)),height+int(ball_y_1)]\n        \n        for i in [0]:#,2:\n            draw_x[i]=max(draw_x[i],0)\n            draw_x[i]=min(draw_x[i],width-1)\n            draw_y[i]=max(draw_y[i],0)\n            draw_y[i]=min(draw_y[i],height-1)\n\n            draw_x_1[i]=max(draw_x_1[i],0)\n            draw_x_1[i]=min(draw_x_1[i],width-1)\n            draw_y_1[i]=max(draw_y_1[i],0)\n            draw_y_1[i]=min(draw_y_1[i],2*height-1)\n        \n        # print(img_concat0)\n        img_concat0.dtype='float'\n        ##save with ancillary force line\n        plt.clf()\n        # im = plt.array(img_concat0)\n        plt.imshow(img_concat0)\n\n        plt.axis('off') # close y axis if 'off\n\n        plt.plot(draw_y[1],draw_x[1], 'r*')        \n        plt.plot(draw_y[0:2],draw_x[0:2], color='r')#draw the force line\n\n        plt.plot(draw_y_1[1],draw_x_1[1], 'b*')\n        plt.plot(draw_y_1[0:2],draw_x_1[0:2], color='b')#draw the new force line\n        # print(draw_y_1[0:2],draw_x_1[0:2])\n\n        plt.savefig(path+str(count)+'.jpg')\n\n    return img_concat0\n    \n\n","sub_path":"ball_simulation/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"372793622","text":"import unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\nfrom torch.autograd import gradcheck\n\nfrom monai.config.deviceconfig import USE_COMPILED\nfrom monai.networks.blocks.warp import Warp\nfrom monai.utils import GridSampleMode, GridSamplePadMode\n\nLOW_POWER_TEST_CASES = [  # run with BUILD_MONAI=1 to test csrc/resample, BUILD_MONAI=0 to test native grid_sample\n    [\n        {\"mode\": \"nearest\", \"padding_mode\": \"zeros\"},\n        {\"image\": torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), \"ddf\": torch.zeros(1, 2, 2, 2)},\n        torch.arange(4).reshape((1, 1, 2, 2)),\n    ],\n    [\n        {\"mode\": \"bilinear\", \"padding_mode\": \"zeros\"},\n        {\"image\": torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), \"ddf\": torch.ones(1, 2, 2, 2)},\n        torch.tensor([[[[3, 0], [0, 0]]]]),\n    ],\n    [\n        {\"mode\": \"bilinear\", \"padding_mode\": \"border\"},\n        {\n            \"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),\n            \"ddf\": torch.ones(1, 3, 2, 2, 2) * -1,\n        },\n        torch.tensor([[[[[0, 0], [0, 0]], [[0, 0], [0, 0]]]]]),\n    ],\n    [\n        {\"mode\": \"bilinear\", \"padding_mode\": \"reflection\"},\n        {\n            \"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),\n            \"ddf\": torch.ones(1, 3, 2, 2, 2) * -1,\n        },\n        torch.tensor([[[[[7.0, 6.0], [5.0, 4.0]], [[3.0, 2.0], [1.0, 0.0]]]]]),\n    ],\n]\n\nCPP_TEST_CASES = [  # high order, BUILD_MONAI=1 to test csrc/resample\n    [\n        {\"mode\": 2, \"padding_mode\": \"border\"},\n        {\n            \"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),\n            \"ddf\": torch.ones(1, 3, 2, 2, 2) * -1,\n        },\n        torch.tensor([[[[[0.0000, 0.1250], [0.2500, 0.3750]], [[0.5000, 0.6250], [0.7500, 0.8750]]]]]),\n    ],\n    [\n        {\"mode\": 2, \"padding_mode\": \"reflection\"},\n        {\n            \"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),\n            \"ddf\": torch.ones(1, 3, 2, 2, 2) * -1,\n        },\n        torch.tensor([[[[[5.2500, 4.7500], [4.2500, 3.7500]], [[3.2500, 2.7500], [2.2500, 1.7500]]]]]),\n    ],\n    [\n        {\"mode\": 2, \"padding_mode\": \"zeros\"},\n        {\n            \"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),\n            \"ddf\": torch.ones(1, 3, 2, 2, 2) * -1,\n        },\n        torch.tensor([[[[[0.0000, 0.0020], [0.0039, 0.0410]], [[0.0078, 0.0684], [0.0820, 0.6699]]]]]),\n    ],\n    [\n        {\"mode\": 2, \"padding_mode\": 7},\n        {\n            \"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),\n            \"ddf\": torch.ones(1, 3, 2, 2, 2) * -1,\n        },\n        torch.tensor([[[[[0.0000, 0.0020], [0.0039, 0.0410]], [[0.0078, 0.0684], [0.0820, 0.6699]]]]]),\n    ],\n    [\n        {\"mode\": 3, \"padding_mode\": \"reflection\"},\n        {\"image\": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float), \"ddf\": torch.ones(1, 3, 2, 2, 2)},\n        torch.tensor([[[[[4.6667, 4.3333], [4.0000, 3.6667]], [[3.3333, 3.0000], [2.6667, 2.3333]]]]]),\n    ],\n]\n\nTEST_CASES = LOW_POWER_TEST_CASES\nif USE_COMPILED:\n    TEST_CASES += CPP_TEST_CASES\n\n\nclass TestWarp(unittest.TestCase):\n    @parameterized.expand(TEST_CASES, skip_on_empty=True)\n    def test_resample(self, input_param, input_data, expected_val):\n        warp_layer = Warp(**input_param)\n        result = warp_layer(**input_data)\n        np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4)\n\n    def test_ill_shape(self):\n        warp_layer = Warp()\n        with self.assertRaisesRegex(ValueError, \"\"):\n            warp_layer(\n                image=torch.arange(4).reshape((1, 1, 1, 2, 2)).to(dtype=torch.float), ddf=torch.zeros(1, 2, 2, 2)\n            )\n        with self.assertRaisesRegex(ValueError, \"\"):\n            warp_layer(\n                image=torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), ddf=torch.zeros(1, 2, 1, 2, 2)\n            )\n        with self.assertRaisesRegex(ValueError, \"\"):\n            warp_layer(image=torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), ddf=torch.zeros(1, 2, 3, 3))\n\n    def test_grad(self):\n        for b in GridSampleMode:\n            for p in GridSamplePadMode:\n                warp_layer = Warp(mode=b.value, padding_mode=p.value)\n                input_image = torch.rand((2, 3, 20, 20), dtype=torch.float64) * 10.0\n                ddf = torch.rand((2, 2, 20, 20), dtype=torch.float64) * 2.0\n                input_image.requires_grad = True\n                ddf.requires_grad = False  # Jacobian mismatch for output 0 with respect to input 1\n                gradcheck(warp_layer, (input_image, ddf), atol=1e-2, eps=1e-2)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","sub_path":"tests/test_warp.py","file_name":"test_warp.py","file_ext":"py","file_size_in_byte":4771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"624406282","text":"\"\"\"\nSome Text\n\n\n\"\"\"\nimport os\nimport sys\nimport json\nimport numpy as np\nimport data_helpers\nimport timeit\nfrom keras.models import Sequential, model_from_json\nfrom keras.layers import Dense\nfrom keras.datasets import imdb\nfrom keras.preprocessing import sequence, text\nfrom os.path import join, exists, split\nfrom gensim.models import word2vec\nfrom gensim.models.keyedvectors import KeyedVectors\nimport pickle as pickle\nimport tensorflow as tf\n\n# ---------------------- Parameters section -------------------\n#\n\n# Log Level\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n# print(\"we coool\")\n# Prepossessing parameters\nsequence_length = 400\nmax_words = 5000\n\nw2v = \"\"\n\n\n#\n# ---------------------- Parameters end -----------------------\n\ndef load_data(data_source):\n    # global sequence_length\n    assert data_source in [\"keras_data_set\", \"local_dir\", \"pickle\"], \"Unknown data source\"\n    if data_source == \"keras_data_set\":\n        (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_words, start_char=None, oov_char=None,\n                                                              index_from=None)\n        x_train = sequence.pad_sequences(x_train, maxlen=sequence_length, padding=\"post\", truncating=\"post\")\n        x_test = sequence.pad_sequences(x_test, maxlen=sequence_length, padding=\"post\", truncating=\"post\")\n\n        vocabulary = imdb.get_word_index()\n        vocabulary_inv = dict((v, k) for k, v in vocabulary.items())\n        vocabulary_inv[0] = \"<PAD/>\"\n\n\n    # elif data_source == \"pickle\":\n    #     vocabulary_inv = pickle.load(open(\".models/vocabulary.p\",\"rb\"))\n    #     return \"\",\"\",\"\",\"\",vocabulary_inv\n    else:\n        x, y, vocabulary, vocabulary_inv_list = data_helpers.load_data()\n        vocabulary_inv = {key: value for key, value in enumerate(vocabulary_inv_list)}\n        y = y.argmax(axis=1)\n\n        # Shuffle data\n        shuffle_indices = np.random.permutation(np.arange(len(y)))\n        x = x[shuffle_indices]\n        y = y[shuffle_indices]\n        train_len = int(len(x) * 0.9)\n        x_train = x[:train_len]\n        y_train = y[:train_len]\n        x_test = x[train_len:]\n        y_test = y[train_len:]\n\n    return x_train, y_train, x_test, y_test, vocabulary_inv\n\n\n# Data Preparation\ndef load_dict(data_source):\n    print(\"Load data...\")\n    x_train, y_train, x_test, y_test, vocabulary_inv = load_data(data_source)\n    vocabulary = dict((v, k) for k, v in vocabulary_inv.items())\n    return vocabulary\n\n\ndef load_model():\n    print(\"Load model...\")\n\n    graph = tf.get_default_graph()\n    json_file = open('./models/model.json', 'r')\n    loaded_model_json = json_file.read()\n    json_file.close()\n    loaded_model = model_from_json(loaded_model_json)\n    loaded_model.load_weights(\"./models/model.h5\")\n    # evaluate loaded model on test data\n    loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n    print(\"Model loaded...\")\n    loaded_model._make_predict_function()\n    return loaded_model\n\n\ndef load_w2v():\n    model_dir = '../data'\n    # model_name = \"{:d}features_{:d}minwords_{:d}context\".format(num_features, min_word_count, context)\n    model_name = \"GoogleNews-vectors-negative300.bin\"\n    # model_name = \"Test.bin\"\n    model_name = join(model_dir, model_name)\n    global w2v\n    if exists(model_name):\n        # embedding_model = word2vec.Word2Vec.load(model_name)\n        w2v = KeyedVectors.load_word2vec_format(model_name, binary=True)\n        print('Load existing Word2Vec model \\'%s\\'' % split(model_name)[-1])\n    return\n\n\ndef predict_Problem(loaded_model, vocabulary, sentence):\n    # print(\"sentence Before\", sentence)\n    sentence = sentence.split(\" \")\n    # print(json.dumps(vocabulary , indent=4))\n    sentence = map(lambda x: data_helpers.clean_str(x), sentence)\n    # print(\"sentence after\", str(sentence))\n    sequence_length = 49\n    # Schauen ob word in dictionary vorhanden ist. Falls ja dessen Index im dict einer Liste hinzufuegen\n\n    startT = timeit.default_timer()\n    pred = []\n    for word in sentence:\n        # print(\"Predecting\", word)\n        if not word:\n            continue\n        x = vocabulary.get(word)\n        if x != None:\n            pred.append(x)\n        else:  # Testen ob in w2v ein aehnliches wort gefunden werden kann und ob dieses im Dictionary ist\n            print('Word {} is not in dict'.format(word))\n            # w2vList=w2v.wv.most_similar(word)\n            try:\n                w2vList = w2v.most_similar(word)\n            except Exception:\n                pred.append(0)\n                continue\n            for w in w2vList:\n                w = w[0]\n                print(w)\n                print('similarity: {} in dict: {}'.format(w, vocabulary.get(w)))\n                if vocabulary.get(w) != None:\n                    pred.append(vocabulary.get(w))\n                    break\n    stopT = timeit.default_timer()\n    print('Prepare duration: {}'.format(stopT - startT), \"Result:\\n\", pred)\n    # <PAD/> einfuegen falls word nicht im dict?\n    #\telse:\n    #\t\tpred.append(0)\n    #\t\tprint(str(word) + \" \" + str(x))\n    # print(\"test1\")\n    # print(json.dumps(pred, indent=4))\n    # Die eben erzeugte Liste mit 0 --> <PAD/> auf die Laenge der sequence_length (400) auffuellen\n\n    empty_spaces = sequence_length - len(pred)\n    for x in range(0, empty_spaces):\n        pred.append(0)\n\n    # print(\"test2\")\n    # print(json.dumps(pred, indent=4))\n    # Liste zu Numpy array konvertieren\n    pred = np.array(pred)\n    pred = pred[None, :]\n    # print(\"Liste zu Numpy\",json.dumps(pred, indent=4))\n    pred.T\n\n    # print(\"test3\")\n    # print(pred)\n    # print(json.dumps(pred, indent=4))\n\n    print(\"Pred before: \")\n    startT = timeit.default_timer()\n    prediction = loaded_model.predict(pred, verbose=1)\n    stopT = timeit.default_timer()\n    print('Prediction duration: {}'.format(stopT - startT))\n    # print(\"Pred#: \")\n    # print(prediction)\n    return [prediction, sequence_length, pred]\n\n\ndef predict_Problem_List(loaded_model, vocabulary, sentence_list):\n    ret_p = []\n    ret_c = []\n    sequence_length = 49\n    for line in sentence_list:\n        sentence = line.split(\" \")\n        pred = []\n        # print(\"Len: %d \\n\" %len(sentence))\n        if 1 < len(sentence):\n            for word in sentence:\n                x = vocabulary.get(word)\n                if x != None:\n                    pred.append(x)\n\n            iter = sequence_length - len(pred)\n            for x in range(0, iter):\n                pred.append(0)\n\n            pred = np.array(pred)\n            pred = pred[None, :]\n            pred.T\n            prediction = loaded_model.predict(pred, verbose=0)\n            prediction = prediction.tolist()[0][0]\n            prediction = prediction * 100 if prediction else None\n            if prediction <= 40:\n                form_class = 'bg-danger'\n            elif prediction > 40 and prediction < 60:\n                form_class = 'bg-warning'\n            else:\n                form_class = 'bg-success'\n            ret_c.append(form_class)\n            ret_p.append(round(prediction, 2))\n\n    return [ret_c, ret_p, sequence_length, pred]\n\n\nfrom textgenrnn import textgenrnn\n\nTEXT_GEN_CS = textgenrnn()\nTEXT_GEN_VP = textgenrnn()\nTEXT_GEN_CH = textgenrnn()\nTEXT_GEN_CR = textgenrnn()\nTEXT_GEN_RS = textgenrnn()\nTEXT_GEN_KR = textgenrnn()\nTEXT_GEN_KA = textgenrnn()\nTEXT_GEN_KP = textgenrnn()\nTEXT_GEN_CS = textgenrnn()\nTEXT_GEN_Ch = textgenrnn()\nTEXT_GEN_Cu = textgenrnn()\nTEXT_GEN_Cm = textgenrnn()\nTEXT_GEN_Kr = textgenrnn()\nTEXT_GEN_Pr = textgenrnn()\nTEXT_GEN_Re = textgenrnn()\nTEXT_GEN_Sol = textgenrnn()\nTEXT_GEN_Ua = textgenrnn()\nTEXT_GEN_UVP = textgenrnn()\nGenerators = {\n    \"BMC Customer Segments\": TEXT_GEN_CS,\n    \"BMC Value Proposition\": TEXT_GEN_VP,\n    \"BMC Channels\": TEXT_GEN_CH,\n    \"BMC Customer Relationship\": TEXT_GEN_CR,\n    \"BMC Revenue Streams\": TEXT_GEN_RS,\n    \"BMC Key Ressouces\": TEXT_GEN_KR,\n    \"BMC Key Activities\": TEXT_GEN_KA,\n    \"BMC Key Partnerships\": TEXT_GEN_KP,\n    \"BMC Cost Structure\": TEXT_GEN_CS,\n    \"Channels\": TEXT_GEN_Ch,\n    \"Cost Structure\": TEXT_GEN_Cu,\n    \"Customer Segments\": TEXT_GEN_Cm,\n    \"Key Metrics\": TEXT_GEN_Kr,\n    \"Problem\": TEXT_GEN_Pr,\n    \"Revenue Stream\": TEXT_GEN_Re,\n    \"Solution\": TEXT_GEN_Sol,\n    \"Unfair Advantage\": TEXT_GEN_Ua,\n    \"Unique Value Proposition\": TEXT_GEN_UVP\n}\nfor smart_field in Generators.keys():\n    try:\n        dirname = os.path.dirname(__file__)\n        filename = os.path.join(dirname, \"textgeneration/exported/\" + smart_field + \".txt\")\n        weightfile = os.path.join(dirname, \"textgeneration/weights/\" + smart_field + \".hdf5\")\n        try:\n            Generators[smart_field].load(weightfile)\n            Generators[smart_field].model._make_predict_function()\n        except Exception as e:\n            Generators[smart_field].train_from_file(filename, num_epochs=1)\n            Generators[smart_field].save(weights_path=weightfile)\n    except:\n        pass\n\ndef text_optimizer(smart_field=\"BMC Key Partners\"):\n    try:\n        return Generators[smart_field].generate(1, True, \"Ai_Suggests: \", 0.7, 100)[0]\n    except:\n        return \"No Data Could be provided.\"\n","sub_path":"canvas-maker/Prototype/WebApp/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":9139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"560798916","text":"miTexto = \"'Master'\" #Dependiendo de como  se inicia si es con comillas dobles o simple la otra funciona para dejarla como string\r\nmiTexto2 = \"En \\\"Python\\\"\" #Con \\\" sirve para agregar una comilla entre el textoy solo funciona en comillas dobles no en comillas simple\r\n\r\n\"\"\"\r\n\\n sirve para salto de linea\r\n\\t sirve para tabulacion\r\n\\r borra las variables previas a este comando\r\n\"\"\"\r\ntextoUnido = miTexto + \" \\n \" + miTexto2\r\n\r\nprint(textoUnido)","sub_path":"Master-python/02-Variables-y-Tipos/Curiosidades.py","file_name":"Curiosidades.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"633840561","text":"import os\nimport os.path\nimport time\nimport utils.config as config\nimport utils.dataset as dataset\nimport models.ddpm as ddpm\nimport utils.visualizer as visualizer\nimport utils.utils as utils\n\n\ndef modify_commandline_options(parser):\n    '''\n        Options only for training\n    '''\n    parser.add_argument('--phase', type=str, default='train', help='training Phase')\n    parser.add_argument('--resume', action='store_true', help='resume training')\n    parser.add_argument('--dataroot', required=True, help='path to images')\n    parser.add_argument('--log_dir', type=str, default='outputs/log', help='log Directory')\n    parser.add_argument('--display_freq', type=int, default=400, help='frequency of showing training results on screen')\n    parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results')\n    parser.add_argument('--save_epoch_freq', type=int, default=50, help='frequency of saving checkpoints at the end of epochs')\n    parser.add_argument('--save_by_iter', action='store_true', help='save models by each iter')\n    parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>, ...')\n    parser.add_argument('--niter', type=int, default=1000, help='# of iter at starting learning rate')\n    parser.add_argument('--niter_decay', type=int, default=1000, help='# of iter to linearly decay learning rate to zero')\n    parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')\n    parser.add_argument('--lr', type=float, default=0.00002, help='initial learning rate for adam')\n    parser.add_argument('--lr_policy', type=str, default='linear', help='learning rate policy. [linear | step | plateau | cosine]')\n    parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations')\n\n\nif __name__ == '__main__':\n    # option setup\n    cfg = config.Config()\n    modify_commandline_options(cfg.parser)\n    opt = cfg.parse()\n\n    # dataset setup\n    dataset = dataset.DatasetLoader(opt)\n    dataset_size = len(dataset)\n    print('The number of training images = %d' % dataset_size)\n\n    # model setup\n    model = ddpm.DDPModel(opt)\n    model.setup(opt)\n\n    # save options in the checkpoints directory\n    cfg.save()\n\n    # traiing\n\n    # create a logging file to store training losses\n    log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')\n    with open(log_name, \"a\") as log_file:\n        now = time.strftime(\"%c\")\n        log_file.write('================ Training Loss (%s) ================\\n' % now)\n\n    total_iters = 0                # the total number of training iterations\n\n    for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):    # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>\n        epoch_start_time = time.time()  # timer for entire epoch\n        iter_data_time = time.time()    # timer for data loading per iteration\n        epoch_iter = 0                  # the number of training iterations in current epoch, reset to 0 every epoch\n\n        for i, data in enumerate(dataset):  # inner loop within one epoch\n            iter_start_time = time.time()  # timer for computation per iteration\n            if total_iters % opt.print_freq == 0:\n                t_data = iter_start_time - iter_data_time\n\n            total_iters += opt.batch_size\n            epoch_iter += opt.batch_size\n            model.set_input(data)         # unpack data from dataset and apply preprocessing\n            model.train()   # calculate loss functions, get gradients, update network weights\n\n            if total_iters % opt.display_freq == 0:   # display current result on tensorboard\n                model.compute_visuals(total_iters)\n\n            if total_iters % opt.print_freq == 0:    # print training losses and save logging information to the disk\n                losses = model.get_current_losses()\n                t_comp = (time.time() - iter_start_time) / opt.batch_size\n                utils.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data, log_name)\n\n            if total_iters % opt.save_latest_freq == 0:   # cache our latest model every <save_latest_freq> iterations\n                print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters))\n                save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'\n                model.save_denoise_model(save_suffix)\n\n            iter_data_time = time.time()\n\n        if epoch % opt.save_epoch_freq == 0:              # cache our model every <save_epoch_freq> epochs\n            print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))\n            model.save_denoise_model('latest')\n            model.save_denoise_model(epoch)\n\n        print('End of epoch %d / %d \\t Time Taken: %d sec' % (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))\n        model.update_learning_rate()                     # update learning rates at the end of every epoch.\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"616882414","text":"# encoding:utf-8\n\nfrom __future__ import unicode_literals\nfrom django.utils.timezone import datetime\nfrom .models import OutBlockWarning\ndef get_global():\n    return globals()\n\ndef get_warning_last_pk():\n    warn = OutBlockWarning.objects.last()\n    if warn:\n        return warn.pk\n    else:\n        return -1","sub_path":"dianzi_weilan/ajax.py","file_name":"ajax.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"275701009","text":"# -*- coding: utf-8 -*-\nimport pandas as pd\nimport config\nimport threading\nimport json\nimport io\nimport geo\nimport requests\nimport threading \nfrom datetime import datetime\nfrom sqlalchemy import create_engine, Column, MetaData, Table, DateTime, String, Integer, ForeignKey, BIGINT,TEXT,FLOAT,inspect, event\n\nresult = []\n\ndef call_product(offset, stores_len_in):\n    for i in range(0, stores_len_in):\n        get_product(int(result_stores[i]['store_id']), str(result_stores[i]['category_id']), offset)\n    return\n\ndef get_product(store_id, aisle_id, offset):\n    \n    #{\"state\": {\"aisle_id\": \"21111\", \"parent_id\": \"21111\"}, \"limit\": 10, \"offset\": 0, \"context\": \"sub_aisles\", \"stores\": [store]}\n    parameters = {\"state\": {\"aisle_id\": aisle_id}, \"limit\": 10000, \"offset\": offset, \"context\": \"aisle_detail\", \"stores\": [int(store_id)]}\n    \n    #parameters = {\"state\": {\"lat\": lat, \"lng\": lng, \"parent_store_type\": \"market\"}, \"context\": \"store_home\", \"limit\": 100,\"stores\": [int(store_id)]}\n    print(parameters)\n    url = 'https://services.mxgrability.rappi.com/api/dynamic/context/content'\n    y = requests.post(url, json=parameters)\n            \n    if y.status_code == 200:\n        #print('ok')\n        data = json.loads(y.text)\n        #print(data)\n        \n        components_len = len(data['data']['components'])\n        for i in range(0, components_len):\n            products_len = len(data['data']['components'][i]['resource']['products'])\n            for j in range(0, products_len):\n                product_id = data['data']['components'][i]['resource']['products'][j]['product_id']\n                \n                ean = data['data']['components'][i]['resource']['products'][j]['ean']\n                name = data['data']['components'][i]['resource']['products'][j]['name']\n                name = name.replace('\\n', ' ')\n                name = name.replace('\\r', ' ')\n                name = name.replace('\\t', ' ')\n                print(name)\n                \n                price = str(data['data']['components'][i]['resource']['products'][j]['price'])\n                \n                product_type = data['data']['components'][i]['resource']['products'][j]['product_type']\n                product_type = product_type.replace('\\n', ' ')\n                product_type = product_type.replace('\\r', ' ')\n                product_type = product_type.replace('\\t', ' ')\n                \n                presentation = data['data']['components'][i]['resource']['products'][j]['presentation']\n                presentation = presentation.replace('\\n', ' ')\n                presentation = presentation.replace('\\r', ' ')\n                presentation = presentation.replace('\\t', ' ')\n                \n                retail_id = data['data']['components'][i]['resource']['products'][j]['retail_id']\n                \n                trademark = data['data']['components'][i]['resource']['products'][j]['trademark']\n                trademark = trademark.replace('\\n', ' ')\n                trademark = trademark.replace('\\r', ' ')\n                trademark = trademark.replace('\\t', ' ')\n                \n                status = data['data']['components'][i]['resource']['products'][j]['in_stock']\n                if status == False:\n                    print(name)\n                date = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n                \n                data_in = {'product_id': product_id, 'store_id': store_id, 'ean': ean, 'product_name': name, 'price': price, \n                           'product_type': product_type, 'presentation': presentation, 'retail_id': retail_id, 'brand': trademark,'creation_date': date, 'status': status}\n                result.append(data_in)\n                \n            \n    else:\n        print('error')\n    \n    return \n    \n    \nengine = create_engine('postgresql://'+config.DATABASE_CONFIG['user']+':'+config.DATABASE_CONFIG['password']+'@'+config.DATABASE_CONFIG['host']+':'+config.DATABASE_CONFIG['port']+'/'+config.DATABASE_CONFIG['dbname']\n         , connect_args={'options': '-csearch_path={}'.format(config.DATABASE_CONFIG['schema_mx'])})\n\nconnection = engine.connect()\n\nresult_stores = connection.execute(config.RAPPI_MARKET_PRODUCT['query_aisle_mx']).fetchall()\nstores_len = len(result_stores)\n'''\nfor i in range(0, stores_len):\n    print(result_stores[i])\n    get_product(int(result_stores[i]['store_id']), str(result_stores[i]['category_id']), 0)\n    get_product(int(result_stores[i]['store_id']), str(result_stores[i]['category_id']), 10)\n    get_product(int(result_stores[i]['store_id']), str(result_stores[i]['category_id']), 20)\n '''\n#get_product(990002972, '99716')\n\n\n\n\nt1 = threading.Thread(name=\"hilo1\", target=call_product, args=(0, stores_len))\nt2 = threading.Thread(name=\"hilo2\", target=call_product, args=(10, stores_len))\nt3 = threading.Thread(name=\"hilo3\", target=call_product, args=(20, stores_len))\n\n\n#ejecuta los hilos\nt1.start()\nt2.start()\nt3.start()\n\n#espera a que terminen los hilos, para continuar\nt1.join()\nt2.join()\nt3.join()\n\n\ndf = pd.DataFrame(result)    \n\ndf.head(0).to_sql(config.RAPPI_MARKET_PRODUCT['table_spot_mx'], engine, if_exists='append',index=False)\n                    \nconn = engine.raw_connection()\ncur = conn.cursor()\noutput = io.StringIO()\ndf.to_csv(output, sep='\\t', header=False, index=False)\noutput.seek(0)\ncur.copy_from(output, config.RAPPI_MARKET_PRODUCT['table_spot_mx'], null=\"\") # null values become ''\nconn.commit()\n\nconnection.close()\n\n","sub_path":"Rappi/rappi_market_product_mx.py","file_name":"rappi_market_product_mx.py","file_ext":"py","file_size_in_byte":5441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"571123928","text":"# fibonati -------------------------------------\n\ndef fib(n):\n    if (n == 1 or n == 2):\n        return 1\n    return fib(n - 1) + fib(n - 2)\n\n\nfib_lb = lambda n: 1 if (n == 1 or n == 2) else fib(n - 1) + fib(n - 2)\n\ndef fib_iterativo(n):\n    tot=0\n    while(n > 1):\n        tot += n+n-1\n        n -= 1\n\n    return tot\n\n# print(fib_iterativo(6))\n# print(fib(6))\n\ndef explosao_da_stack(N):\n    cont=1\n    try:\n        while(cont < N):\n            print(\"cont {}\".format(cont))\n            fib(cont)\n            cont += 1\n    except:\n        return cont\n\n# explosao = explosao_da_stack(500)\n# print(explosao)\n\n\n# exp -------------------------------------\n\"\"\"\n    base, exp\n    base = 2\n    exp=  10\n    2 ** 10 = 1024\n\"\"\"\n\ndef pot(base, exp):\n\n    if(exp == 0):\n        return 1\n    return base * pot(base, exp-1)\n\nprint(pot(2, 10))\n\n\n","sub_path":"estrutura_dados_udemy/recursividade.py","file_name":"recursividade.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"503024836","text":"import pytest\nimport requests\nimport os\n\n\n@pytest.fixture(scope='session')\ndef host():\n    return os.getenv(\"TEST_HOST\", 'frontend')\n\n\n@pytest.fixture(scope='session')\ndef port():\n    return os.getenv(\"TEST_PORT\", 5000)\n\n\ndef _send_request(host, port, action, ident):\n    return requests.get(\n        \"http://{}:{}/{}/{}\".format(\n            host, port, action, ident)\n    )\n\n\n@pytest.mark.parametrize(\n    \"ident,code,expected\", [\n        ('doesnotexist', 404, 'Not found'),\n        ('999', 404, 'Not found'),\n        ('2', 200,\n         {\"id\": \"2\", \"name\": \"Ivysaur\",\n          \"type\": [\"Grass\", \"Poison\"],\n          \"stats\": {\"total\": 405, \"hp\": 60,\n                    \"attack\": 62, \"defence\": 63,\n                    \"sp.atk\": 80, \"sp.def\": 80,\n                    \"speed\": 60},\n          \"gen\": 1, \"legendary\": False})\n    ]\n)\ndef test_query_by_id(\n    host, port,\n    ident, code, expected\n):\n    response = _send_request(host, port, 'id', ident)\n\n    assert response.status_code == code\n    assert response.json() == expected\n\n\n@pytest.mark.parametrize(\n    \"ident,code,expected\", [\n        ('doesnotexist', 404, 'Not found'),\n        ('Bulbasaur', 200,\n         [{\"id\": \"1\", \"name\": \"Bulbasaur\", \"type\": [\"Grass\", \"Poison\"],\n           \"stats\": {\"total\": 318, \"hp\": 45, \"attack\": 49, \"defence\": 49,\n                     \"sp.atk\": 65, \"sp.def\": 65, \"speed\": 45},\n           \"gen\": 1, \"legendary\": False}]),\n        ('Bulba*', 200,\n         [{\"id\": \"1\", \"name\": \"Bulbasaur\", \"type\": [\"Grass\", \"Poison\"],\n           \"stats\": {\"total\": 318, \"hp\": 45, \"attack\": 49, \"defence\": 49,\n                     \"sp.atk\": 65, \"sp.def\": 65, \"speed\": 45},\n           \"gen\": 1, \"legendary\": False}]),\n        ('*chu', 200,\n         [{\"id\": \"26\", \"name\": \"Raichu\", \"type\": [\"Electric\"],\n           \"stats\": {\"total\": 485, \"hp\": 60, \"attack\": 90, \"defence\": 55,\n                     \"sp.atk\": 90, \"sp.def\": 80, \"speed\": 110},\n           \"gen\": 1, \"legendary\": False},\n          {\"id\": \"172\", \"name\": \"Pichu\", \"type\": [\"Electric\"],\n           \"stats\": {\"total\": 205, \"hp\": 20, \"attack\": 40, \"defence\": 15,\n                     \"sp.atk\": 35, \"sp.def\": 35, \"speed\": 60},\n           \"gen\": 2, \"legendary\": False},\n          {\"id\": \"25\", \"name\": \"Pikachu\", \"type\": [\"Electric\"],\n           \"stats\": {\"total\": 320, \"hp\": 35, \"attack\": 55, \"defence\": 40,\n                     \"sp.atk\": 50, \"sp.def\": 50, \"speed\": 90},\n           \"gen\": 1, \"legendary\": False}])\n    ]\n)\ndef test_query_by_name(\n    host, port,\n    ident, code, expected\n):\n    response = _send_request(host, port, 'name', ident)\n\n    assert response.status_code == code\n    actual = response.json()\n    if code == 200:\n        assert len(actual) == len(expected)\n        for expected_p in expected:\n            assert expected_p in actual\n    else:\n        assert actual == expected\n","sub_path":"testing/test_queries.py","file_name":"test_queries.py","file_ext":"py","file_size_in_byte":2830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"393303441","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 28 11:46:28 2020\n\n@author: leo\n\"\"\"\nimport time, json\nimport pandas as pd\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras.utils import to_categorical\n\nfrom tensorflow.python.lib.io.tf_record import TFRecordWriter\n\ntrain = pd.read_csv('Train.csv')\n\ntest = pd.read_csv('Test.csv')\n\n\ncodes = {code for seq in train['SEQUENCE'] for code in seq}\n\n\ndef create_dict(codes):\n  char_dict = {}\n  for index, val in enumerate(codes):\n    char_dict[val] = index+1\n\n  return char_dict\n\nchar_dict = create_dict(codes)\n\nprint(char_dict)\nprint(\"Dict Length:\", len(char_dict))\n\n\n\ndef integer_encoding(data):\n  \"\"\"\n  - Encodes code sequence to integer values.\n  - 20 common amino acids are taken into consideration\n    and rest 4 are categorized as 0.\n  \"\"\"\n  \n  encode_list = []\n  for row in data['SEQUENCE'].values:\n    row_encode = []\n    for code in row:\n      row_encode.append(char_dict.get(code, 0))\n    encode_list.append(np.array(row_encode))\n  \n  return encode_list\n  \n\ndef clean_train(csv):\n    csv['LABEL'] = csv['LABEL'].str.replace('[A-Za-z]', '').astype(int)\n    csv['SEQUENCE'] = csv['SEQUENCE'].apply(lambda x: str(x)[:400])\n    train_encode = integer_encoding(csv) \n    train_pad = pad_sequences(train_encode, maxlen=400, padding='post', truncating='post')\n    csv['SEQUENCE'] = [list(i) for i in train_pad]\n    return csv\n\n\ndef clean_test(csv):\n    #csv['LABEL'] = csv['LABEL'].str.replace('[A-Za-z]', '').astype(int)\n    csv['SEQUENCE'] = csv['SEQUENCE'].apply(lambda x: str(x)[:400])\n    test_encode = integer_encoding(csv) \n    test_pad = pad_sequences(test_encode, maxlen=400, padding='post', truncating='post')\n    test_pad = pad_sequences(test_encode, maxlen=400, padding='post', truncating='post')\n    csv['SEQUENCE'] = [list(i) for i in test_pad]\n    return csv\n\n\ntrain = clean_train(train)\ntest = clean_test(test)\n\n\n# @tf.function(jit_compile=True)\ntf.compat.v1.enable_eager_execution()\ndef create_tf_example(idx, features, label):\n    # Examples maps string keys to either ints, floats or bytes\n    tf_example = tf.train.Example(features=tf.train.Features(feature={\n        'idx': tf.train.Feature(int64_list=tf.train.Int64List(value=[idx])),\n        # Sequence is stored in UTF-8 bytes\n        'sequence': tf.train.Feature(bytes_list=tf.train.BytesList(value=[features[1].encode('utf-8')])),\n        # Group is the creature the sample came from and is stored in UTF-8 bytes\n        'group': tf.train.Feature(bytes_list=tf.train.BytesList(value=[features[2].encode('utf-8')])),\n        # Label is the class we are trying to predict\n        'label': tf.train.Feature(int64_list=tf.train.Int64List(value=[label]))\n    }))\n\n    # Encoded example can be extracted strings, \n    return tf_example\n\n\ndef convert_train_to_tfrecord(csv, file_name):\n    start_time = time.time()\n    writer = TFRecordWriter(file_name)\n    for index, row in csv.iterrows():\n        try:\n            if row is None:\n                raise Exception('Row Missing')\n            if row[0] is None or row[1] is None or row[2] is None:\n                raise Exception('Value Missing')\n            if row[1].strip() == \"\":\n                raise Exception('Sequence is empty')\n            features, label = row[:-1], row[-1]\n            example = create_tf_example(index, features, label)\n            writer.write(example.SerializeToString())\n        except Exception as inst:\n            print(type(inst))\n            print(inst.args)\n            print(inst)\n    writer.close()\n    print(f\"{file_name}: --- {(time.time() - start_time)} seconds ---\")\n    \n\ndef convert_test_to_tfrecord(csv, file_name):\n    start_time = time.time()\n    writer = TFRecordWriter(file_name)\n    for index, row in csv.iterrows():\n        try:\n            if row is None:\n                raise Exception('Row Missing')\n            if row[0] is None or row[1] is None or row[2] is None:\n                raise Exception('Value Missing')\n            if row[1].strip() == \"\":\n                raise Exception('Sequence is empty')\n            features, label = row, \"\"\n            example = create_tf_example(index, features, label)\n            writer.write(example.SerializeToString())\n        except Exception as inst:\n            print(type(inst))\n            print(inst.args)\n            print(inst)\n    writer.close()\n    print(f\"{file_name}: --- {(time.time() - start_time)} seconds ---\")\n    \n    \ndef generate_json_info(local_file_name):\n    info = {\"train_length\": len(train),\n    # , \"validation_length\": len(validate_df),\n             \"test_length\": len(test)}\n\n    with open(local_file_name, 'w') as outfile:\n        json.dump(info, outfile)\n    \n    \nconvert_train_to_tfrecord(train, \"data/enzyme_train.tfrecord\")\n\nconvert_test_to_tfrecord(test, \"data/enzyme_test.tfrecord\")\n\ngenerate_json_info(\"data/enzyme.json\")\n    \n\n\n    \n   \n# tr_ds = tf.data.TFRecordDataset(\"data/enzyme.tfrecord\")        \n# # The dataset for train information\n\n# feature_spec = {\n#     'idx': tf.io.FixedLenFeature([], tf.int64),\n#     'sequence': tf.io.FixedLenFeature([], tf.string),\n#     'group': tf.io.FixedLenFeature([], tf.string),\n#     'label': tf.io.FixedLenFeature([], tf.int64)\n# }\n\n# def parse_example(example_proto):\n#   # Parse the input tf.Example proto using the dictionary above.\n#   return tf.io.parse_single_example(example_proto, feature_spec)\n\n\n# tr_parse_ds = tr_ds.map(parse_example)\n\n# dataset_iterator = iter(tr_parse_ds)\n\n# dataset_iterator.get_next()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n    \n","sub_path":"dataset_iterator.py","file_name":"dataset_iterator.py","file_ext":"py","file_size_in_byte":5603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"128425009","text":"import logging\nimport os\nimport pprint\n\nimport gspread\nimport pandas\nfrom oauth2client.service_account import ServiceAccountCredentials\n\n# Remove all handlers associated with the root logger object.\nfor handler in logging.root.handlers[:]:\n    logging.root.removeHandler(handler)\nlogging.basicConfig(\n    level=logging.DEBUG,\n    format=\"%(levelname)s:%(asctime)s:%(module)s:%(lineno)d %(message)s\"\n)\nlogging.getLogger('gspread').setLevel(logging.INFO)\nlogging.getLogger('oauth2client').setLevel(logging.INFO)\n\n\ndef get_sheet(spreadhsheet_id, worksheet_name, google_key):\n    \"\"\"\n\n    :param spreadhsheet_id: \n    :param worksheet_name: \n    :param google_key: Path to a json file which can be obtained from https://console.cloud.google.com/iam-admin/serviceaccounts (create a project, generate a key via \"Actions\" column.).\n\n    :return: \n    \"\"\"\n    scopes = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']\n    creds = ServiceAccountCredentials.from_json_keyfile_name(google_key, scopes)\n\n    client = gspread.authorize(creds)\n    logging.debug(pprint.pformat(client.list_spreadsheet_files()))\n    sheet_book = client.open_by_key(spreadhsheet_id)\n    logging.debug(sheet_book.worksheets())\n    return sheet_book.worksheet(worksheet_name)\n\n\nclass IndexSheet(object):\n    def __init__(self, spreadhsheet_id, worksheet_name, google_key,\n                 id_column):\n        self.data_sheet = get_sheet(spreadhsheet_id=spreadhsheet_id, worksheet_name=worksheet_name, google_key=google_key)\n        self.id_column = id_column\n        episode_sheet_values = self.data_sheet.get_all_values()\n        df = pandas.DataFrame(episode_sheet_values[1:], columns=episode_sheet_values.pop(0))\n        df = df.set_index(self.id_column)\n        self._df = df\n\n    def get_value(self, id, column_name):\n        try:\n            return self._df.loc[id, column_name]\n        except KeyError:\n            logging.debug(\"Could not find %s in the sheet\", id)\n            return None\n\n\nclass FileUrlSheet(IndexSheet):\n    \"\"\"\n    Represents image data stored in a Google spreadsheet.\n    \"\"\"\n    def __init__(self, spreadhsheet_id, worksheet_name, google_key,\n                 url_column, file_name_column=None):\n        \"\"\"\n    \n        :return: \n        \"\"\"\n        # noinspection PyPep8Naming\n        super(FileUrlSheet, self).__init__(spreadhsheet_id=spreadhsheet_id, worksheet_name=worksheet_name, google_key=google_key, id_column=url_column)\n        self.file_name_column = file_name_column\n\n    def get_file_name(self, url):\n        \"\"\"\n        Read the name of the person who recorded this episode.\n    \n        :param url: \n        :return: \n        \"\"\"\n        file_name = self._df.loc[url, self.file_name_column]\n        file_name = file_name.strip()\n        import re\n        file_name = re.sub(\"[^a-zA-Z0-9]+\", \"-\", file_name)\n        return file_name\n\n    def download_all(self, destination_dir, skip_existing=True):\n        import wget\n        os.makedirs(destination_dir, exist_ok=True)\n        for url in self._df.index:\n            extension = os.path.splitext(url)[1]\n            out_file = os.path.join(destination_dir, \"%s%s\" % (self.get_file_name(url=url), extension))\n            if not skip_existing or not os.path.exists(out_file):\n                logging.info(\"Getting %s as %s\", url, out_file)\n                wget.download(url=url, out=out_file)","sub_path":"curation_utils/google/sheets.py","file_name":"sheets.py","file_ext":"py","file_size_in_byte":3386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"317526308","text":"import numpy as np\nimport cv2\nfrom matplotlib import pyplot as plt\nimport urllib.request as ur\nimport time\n\nurl='http://192.168.43.56:8080/shot.jpg'\nprint(\"entered loop\")\n\n\n#rec=cv2.VideoCapture(0)\n\nwhile(1):\n    print(\"in loop\")\n    # Use urllib to get the image from the IP camera\n    imgResp = ur.urlopen(url)\n    print(\"hi\")\n    \n    # Numpy to convert into a array\n    imgNp = np.array(bytearray(imgResp.read()),dtype=np.uint8)\n    \n    # Finally decode the array to OpenCV usable format ;)\n    img2 = cv2.imdecode(imgNp,-1)\n    MIN_MATCH_COUNT = 10\n    #ret,img2=rec.read()\n    img1 = cv2.imread('QR.jpeg',0)          # queryImage\n    #img2 = cv2.imread('vraj2.jpeg',0) # trainImage\n    #   img1=cv2.imread(\"QR.jpeg\",0)\n    # Initiate SIFT detector\n    \n    #img1=cv2.imread(\"QR.jpeg\",0)\n    sift = cv2.xfeatures2d.SIFT_create()\n    \n    # find the keypoints and descriptors with SIFT\n    kp1, des1 = sift.detectAndCompute(img1,None)\n    kp2, des2 = sift.detectAndCompute(img2,None)\n    \n    FLANN_INDEX_KDTREE = 0\n    index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)\n    search_params = dict(checks = 50)\n    \n    flann = cv2.FlannBasedMatcher(index_params, search_params)\n    \n    matches = flann.knnMatch(des1,des2,k=2)\n    \n    # store all the good matches as per Lowe's ratio test.\n    good = []\n    for m,n in matches:\n        if m.distance < 0.7*n.distance:\n            good.append(m)\n    if len(good)>MIN_MATCH_COUNT:\n        src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)\n        dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)\n        \n        M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)\n        matchesMask = mask.ravel().tolist()\n        \n        h,w = img1.shape\n        pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)\n        \n        if M is None:\n            print('no')\n        elif(len(M)>2):\n            dst=cv2.perspectiveTransform(pts,M)\n            dst=np.int32(dst)\n            img2=cv2.polylines(img2,[np.int32(dst)],True,255,3,cv2.LINE_AA)\n            a0=dst[0][0][0]\n            a1=dst[0][0][1]\n            a2=dst[1][0][0]\n            a3=dst[1][0][1]\n            a4=dst[2][0][0]\n            a5=dst[2][0][1]\n            a6=dst[3][0][0]\n            a7=dst[3][0][1]\n            \n            \n            d0=np.array([[a0,a1],[a2,a3]])\n            d1=np.array([[a2,a3],[a4,a5]])\n            d2=np.array([[a4,a5],[a6,a7]])\n            d3=np.array([[a6,a7],[a0,a1]])\n            \n            \n            \n            \n            det0=np.linalg.det(d0)\n            det1=np.linalg.det(d1)\n            det2=np.linalg.det(d2)\n            det3=np.linalg.det(d3)\n            \n            area=0.5*abs(det0+det1+det2+det3)\n            #print(d0)\n            #print(d0.shape)\n            print(area)\n    else:\n     print (\"Not enough matches are found - %d/%d\" % (len(good),MIN_MATCH_COUNT))\n     matchesMask = None\n     draw_params = dict(matchColor = (0,255,0), # draw matches in green color\n     singlePointColor = None,\n     matchesMask = matchesMask, # draw only inliers\n                       flags = 2)\n        \n    img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)\n    cv2.imshow('matches',img3)\n\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\n\n\ncv2.destroyAllWindows()\n\n","sub_path":"Code with Cam/homo1.py","file_name":"homo1.py","file_ext":"py","file_size_in_byte":3335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"116320581","text":"\"\"\"\r\nAuthor      :Birhan Tesfaye\r\nLast Edit   :May 23\r\n\"\"\"\r\n\r\nENCODING = \"UTF-8\"\r\n\r\ndef flags_decomposer(flags):\r\n    \"\"\"Make font flags human readable.\"\"\"\r\n    l = []\r\n    if flags & 2 ** 0:\r\n        l. append(\"superscript\")\r\n    if flags & 2 ** 1:\r\n        l. append(\"italic\")\r\n    if flags & 2 ** 2:\r\n        l. append(\"serifed\")\r\n    else:\r\n        l. append(\"sans\")\r\n    if flags & 2 ** 3:\r\n        l. append(\"monospaced\")\r\n    else:\r\n        l. append(\"proportional\")\r\n    if flags & 2 ** 4:\r\n        l. append(\"bold\")\r\n    style=l\r\n    return style\r\n\r\n\r\n\r\n\r\n","sub_path":"PDF Summarizer/PDF Summarizer (user edition)/pdf/font.py","file_name":"font.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"160109182","text":"import random\nimport fastrand\nimport sys\nimport numpy as np\n\n# Input:\n# Input String, Count\n# Output\n# No Action, Push, Pop, Dequeue\n\nclass SeqRecall:\n    def __init__(self, count = 10, seq = 5):\n        # Set init input\n        self.count_n = count\n        self.seq_n = seq\n        self.reset()\n\n    def seed(self, num):\n        print(\"Seed\")\n        print(num)\n        fastrand.pcg32_seed(num)\n        random.seed(num)\n\n    def restart(self):\n        self.count = self.initCount\n        self.curr_step = 1\n        return np.array([self.input[0], 0])\n\n    def reset(self):\n        self.initCount = fastrand.pcg32bounded(self.count_n)+1\n        self.count = self.initCount\n        seq_len = fastrand.pcg32bounded(self.seq_n)+1\n        self.input = []\n        for i in range(seq_len):\n            # https://stackoverflow.com/a/46820635\n            self.input += [1 if random.random() < 0.5 else -1]\n        self.input_len = len(self.input)\n        self.num_steps = self.seq_n * 2 + self.count\n        self.curr_step = 1\n        return np.array([self.input[0], 0])\n\n    def decode_action(self, action):\n        if (np.zeros(action)): # Do nothing\n            return 0 # Do nothing\n        elif (action[0] and (not (action[1] or action[2]))):\n            return 1 # Pop\n        elif (action[1] and (not (action[0] or action[2]))):\n            return 2 # Push\n        elif (action[2] and (not (action[0] or action[1]))):\n            return 3 # Dequeue\n        else:\n            return 4 # Invalid Setting\n\n    def step(self, action):\n        # Push action[0]\n        # Pop action[1]\n        # Dequeue action[2]\n        score = 0\n        done = 0\n        value = 0\n        count = 0\n        selected_action = self.decode_action(action)\n\n        # Phases\n        if (self.curr_step < self.input_len and selected_action == 1):\n            score += 1 # Input\n        elif (self.curr_step < (self.input_len + self.count) and selected_action == 0):\n            score += 1 # Hold while count down\n            count = self.count\n            self.count -= 1\n        elif (self.curr_step > (self.input_len + self.count) and selected_action == 3):\n            score += 1 # Dequeue \n        else:\n            score -= 1 # Invalid Setting\n\n        if self.curr_step == self.num_steps:\n            done = 1 # End\n        elif self.curr_step < self.input_len:\n            value = self.input[self.curr_step] # Give Input\n            self.curr_step += 1\n        else:\n            self.curr_step += 1\n\n        return np.array([value, count]), score/self.num_steps, done, \"N/A\"\n","sub_path":"SeqRecall.py","file_name":"SeqRecall.py","file_ext":"py","file_size_in_byte":2555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"63535307","text":"\"\"\"\r\nthis is a project related to used cars. In this project, I will analyze the top ten cars that maintain their\r\nvalues.\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataframe_original = pd.read_csv(\"C:/used car project/vehicles.csv\")\r\n\r\n\r\n\"\"\"\r\nwe only need the price, year, manufacturer, model of the cars, and to clean up the dataset, we are going to\r\ndrop all the na values.\r\n\"\"\"\r\n\r\ndf = dataframe_original[['price','year','manufacturer','model','condition']].dropna()\r\nprint(df.size)\r\nprint(df.head(10))\r\nnew_model_name = df[\"manufacturer\"] + df[\"model\"]\r\ndf[\"model\"] = new_model_name\r\n\r\n\"\"\"\r\nnow we can take a look at our dataset. We have 1198390 rows of data, and we have the price, year, manufacturer, model\r\nand condition of the cars for our columns. The next step is to exclude cars that are in bad conditions. As a consumer, \r\nwe do not want to buy cars in bad condition because that will cost us a lot of extra money to fix it. \r\n\"\"\"\r\n\r\nprint(df.groupby('condition').size())\r\n# as we can see, there are 532 cars in our dataframe that has the \"salvage\" title, meaning that it has been\r\n# damaged before and the is declared a total loss by the insurance company. we definitely want to exclude that\r\n# from our dataset. And we want to exclude \"fair\" from our dataset, too, because most sellers have a tendency to\r\n# prettify their car's condition so that they could sell it in a higher price. A car that has fair condition usually\r\n# indicates that the condition is bad.\r\n\r\n# Besides, we are only choosing cars that are newer than 2015 and more than 10000 dollars and less than 50000\r\n# because I do not want to buy a car before 2015 and my budget is between 10000 to 50000\r\n\r\n\r\ndf = df[df.condition != \"salvage\"]\r\ndf = df[df.condition != \"fair\"]\r\ndf = df[df.year >= 2015.0]\r\ndf = df[df[\"price\"].between(10000, 50000)]\r\nprint(df.head(10))\r\nprint(df.size)\r\n\r\n\"\"\"\r\nthe mean of the price of different models\r\n\"\"\"\r\nman_model_df = df.groupby([\"manufacturer\", \"model\",\"year\"]).mean()\r\nprint(man_model_df)\r\n\r\n\"\"\"\r\nlets look at the years\r\n\"\"\"\r\nprint(df.sort_values(by = [\"year\"], ascending=False))\r\n# we could see that the latest year is 2022\r\n\r\n\"\"\"\r\nWe now want to only keep the models that have more than 1 data because we cannot calculate the depreciation rate\r\nbased on one data\r\n\"\"\"\r\ndf = df[df.duplicated(subset=[\"model\"], keep=False)]\r\nprint(df.head(10))\r\ngroup_by_data = df.groupby([\"model\",\"year\"], as_index=False).mean()\r\n\r\nprint(group_by_data)\r\n\r\n# delete all the rows that only has car from a single year.\r\ndf_clean = group_by_data[group_by_data.duplicated(subset=[\"model\"], keep=False)]\r\nprint(df_clean)\r\n\r\n# Now we complete all the data cleaning and selection\r\n# process. We have 7145 data. And because we used the group by statement, the year is already in ascending order.\r\ndf_clean.reset_index()\r\n\r\n\r\nrow_num = df_clean.shape[0]  # number of rows in the clean dataframe\r\ndepreciation_ratio_lst = [];\r\ni = 0\r\nmodel_new_price = 0\r\nnewer_year = 0\r\ntemp_lst_model_year = []\r\ntemp_lst_model_price = []\r\n\r\nwhile i < row_num:\r\n    model_name = df_clean.iat[i, 0]\r\n    try:\r\n        if df_clean.iat[i + 1, 0] == model_name:\r\n            temp_lst_model_year.append(df_clean.iat[i, 1])\r\n            temp_lst_model_price.append(df_clean.iat[i, 2])\r\n            i += 1\r\n            if df_clean.iat[i + 1, 0] != model_name:\r\n                newer_year = df_clean.iat[i, 1]\r\n                model_new_price = df_clean.iat[i, 2]\r\n                depreciation_rate = (model_new_price - temp_lst_model_price[0]) / temp_lst_model_price[0] / \\\r\n                                    (newer_year - temp_lst_model_year[0])\r\n                depreciation_ratio_lst.append([model_name, depreciation_rate])\r\n                temp_lst_model_year = []\r\n                temp_lst_model_price = []\r\n                i += 1\r\n                continue\r\n            else:\r\n                newer_year = df_clean.iat[i, 1]\r\n                model_new_price = df_clean.iat[i, 2]\r\n                i += 1\r\n                continue\r\n        else:\r\n            # we use the straight line formula depreciation formula here to calculate the depreciation rate,\r\n            newer_year = df_clean.iat[i, 1]\r\n            model_new_price = df_clean.iat[i, 2]\r\n            depreciation_rate = (model_new_price - temp_lst_model_price[0]) / temp_lst_model_price[0] / \\\r\n                                (newer_year - temp_lst_model_year[0])\r\n            depreciation_ratio_lst.append([model_name, depreciation_rate])\r\n            temp_lst_model_year = []\r\n            temp_lst_model_price = []\r\n            i += 1\r\n            continue\r\n    # this is used to handle the last element that could be out of index bound because we should i + 1 before\r\n    except IndexError as error:\r\n        break\r\n\r\ndepreciation_ratio_lst.sort(key=lambda x: x[1])\r\nfor i in range(50):\r\n    print(depreciation_ratio_lst[i])\r\n\r\ntop_value_model_name = []\r\nfor i in range(50):\r\n    top_value_model_name.append(depreciation_ratio_lst[i])\r\n\r\nresult = df[np.isin(df, top_value_model_name).any(axis=1)]\r\nprint(result)\r\ngroup_by_result = result.groupby([\"model\"]).count()\r\ngroup_by_result.reset_index()\r\ntop_ten_group_by = group_by_result[(group_by_result > 10).any(axis=1)]\r\n\r\nprint(top_ten_group_by)\r\n\r\n\r\ntop_ten_list = list(top_ten_group_by.index)\r\n\r\nfinal_df = df_clean[np.isin(df_clean, top_ten_list).any(axis=1)]\r\nprint(final_df)\r\nprint(final_df.groupby([\"model\", \"year\"]).mean())\r\n# we found out there are some duplicated values. So we decide to just delete all duplicate values\r\n# because it is highly unlikely that two cars have the exactly same depreciation rate\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"53172221","text":"#coding: utf-8\nimport requests, re, yaml, json, time, datetime\nfrom urllib import unquote\nfrom dateutil.relativedelta import relativedelta\n\ntoday = datetime.date.today()\nyesterDay = (today - datetime.timedelta(days = 1)).strftime('%Y%m%d')\nbeforeDay = (today - datetime.timedelta(days = 2)).strftime('%Y%m%d')\nsevenDaysAgo = (today - datetime.timedelta(days = 7)).strftime('%Y%m%d')\nsevenWeeksAgo = (today - datetime.timedelta(days = 49)).strftime('%Y%m%d')\nthis_month_first_day = datetime.date(day = 1, month = today.month, year = today.year)\nlast_month_last_day = (this_month_first_day - datetime.timedelta(days = 1)).strftime('%Y%m%d')\nsix_month_fist_day = (this_month_first_day - relativedelta(months = 6)).strftime('%Y%m%d')\n\ndef dayInterval(day_one, day_two):\n    day_one_timeStamp = time.mktime(time.strptime(day_one, '%Y%m%d'))\n    day_two_timeStamp = time.mktime(time.strptime(day_two, '%Y%m%d'))\n    interval = int((day_one_timeStamp - day_two_timeStamp) / 86400.0)\n    return interval\n\ndef getAppData():\n    AppData = {}\n    with open('app.txt', 'r') as f:\n        content = f.read()\n    content += '\\n'\n    urlList = re.compile(r\"keydata_(\\w.*?) HTTP\").findall(content)\n    dataList = re.compile(r'{\\\".*?}\\n').findall(content)\n\n    for i in range(len(urlList)):           \n        if \"operation\" in urlList[i]:          # 数组时间顺序\n            temp = json.loads(dataList[i])['data']['login']['data']\n            if abs(dayInterval(temp[0][0], temp[1][0])) == 1:\n                AppData['operation_day_loginData'] = temp\n                AppData['operation_day_payData'] = json.loads(dataList[i])['data']['prepaid']['data']\n            elif abs(dayInterval(temp[0][0], temp[1][0])) == 7:\n                AppData['operation_week_loginData'] = temp\n                AppData['operation_week_payData'] = json.loads(dataList[i])['data']['prepaid']['data']\n            elif abs(dayInterval(temp[0][0], temp[1][0])) >= 28:\n                AppData['operation_month_loginData'] = temp\n                AppData['operation_month_payData'] = json.loads(dataList[i])['data']['prepaid']['data']\n\n        elif \"cu\" in urlList[i]:               # 数组时间顺序\n            AppData['cu_data'] = json.loads(dataList[i])['data']\n\n        elif \"pay\" in urlList[i]:              # 数组时间顺序\n            temp = json.loads(dataList[i])['thead']\n            if len(temp) == 2:\n                AppData['pay_cash'] = json.loads(dataList[i])['data']\n            elif u'\\u4ed8\\u8d39\\u7387' in temp[1]:\n                AppData['pay_rate'] = json.loads(dataList[i])['data']\n            elif u'\\u4ed8\\u8d39' in temp[1]:\n                AppData['pay_role'] = json.loads(dataList[i])['data']\n            elif 'ARPU' in temp[1]:\n                AppData['pay_arpu'] = json.loads(dataList[i])['data']\n            elif 'VIP' in temp[2]:\n                AppData['pay_vip'] = json.loads(dataList[i])['data']\n\n        elif \"ltv\" in urlList[i]:              # 数组时间倒序\n            if not AppData.has_key('ltv_account_data'):\n                AppData['ltv_account_data'] = json.loads(dataList[i])['data']\n            else:\n                AppData['ltv_role_data'] = json.loads(dataList[i])['data']\n\n        elif \"retention\" in urlList[i]:        # 数组时间顺序\n            if not AppData.has_key('retention_account_data'):\n                AppData['retention_account_data'] = json.loads(dataList[i])['data']\n            elif not AppData.has_key('retention_role_data'):\n                AppData['retention_role_data'] = json.loads(dataList[i])['data']\n            else:\n                AppData['retention_device_data'] = json.loads(dataList[i])['data']\n\n        elif \"channel\" in urlList[i]:          # 字典,时间为键,值为列表\n            temp = json.loads(dataList[i])['thead']\n            if u'\\u767b\\u5f55' in temp[1]:\n                AppData['channel_login_data'] = json.loads(dataList[i])['data']\n            elif u'\\u4ed8\\u8d39\\u7387' in temp[1]:\n                AppData['channel_pay_data'] = json.loads(dataList[i])['data']\n            elif 'ARPU' in temp[1]:\n                AppData['channel_arpu_data'] = json.loads(dataList[i])['data']\n            elif u'\\u6b21\\u65e5' in temp[1]:\n                AppData['channel_retention_data'] = json.loads(dataList[i])['data']\n\n        elif \"player\" in urlList[i]:           # 字典,时间为键,值为列表\n            temp = json.loads(dataList[i])['thead']\n            if u'\\u7b49\\u7ea7' in temp[0]:\n                AppData['player_level_data'] = json.loads(dataList[i])['data']\n            elif u'\\u670d\\u52a1\\u5668' in temp[0]:\n                AppData['player_server_data'] = json.loads(dataList[i])['data']\n            elif u'\\u7701\\u4efd' in temp[0]:\n                AppData['player_map_data'] = json.loads(dataList[i])['data']\n    return AppData\n\ndef getResponse(index, type = None):\n    headers = {}\n    cookies = {}\n    data = []\n    with open('api.yaml', 'r') as f:\n        content = yaml.load(f)\n    content = content[index]['cURL']\n    url = re.compile(r'curl \\\"(.*?)\\\"').findall(content)[0].replace('^', '')\n    if index == 2:\n        if type:\n            url = url.replace('login', type)\n            if type == 'rete':\n                url = url.replace(yesterDay, beforeDay)\n\n    headers_key = re.compile(r'-H \\\"(.*?):').findall(content)\n    headers_value = re.compile(r'-H \\\".*?: (.*?)\\\" ').findall(content)\n    for i in range(len(headers_key)):\n        if headers_key[i] != 'Cookie':\n            headers[headers_key[i]] = headers_value[i].replace('^', '').replace('\\\\', '')\n        else:\n            cookie_temp = headers_value[i].replace('^', '').replace('\\\\', '').split('; ')\n            for c in range(len(cookie_temp)):\n                cookie_content = cookie_temp[c].replace('\\\"', '').split('=')\n                cookies[cookie_content[0]] = cookie_content[1]\n\n    data_temp = re.compile(r'--data \\\"(.*?)\\\" ').findall(content)\n    if data_temp:\n        data_temp = data_temp[0]\n        if index == 0:\n            if type == 'week':\n                data_temp = data_temp.replace('type=day', 'type=week').replace(sevenDaysAgo, sevenWeeksAgo)\n            elif type == 'month':\n                data_temp = data_temp.replace('type=day', 'type=month').replace(sevenDaysAgo, six_month_fist_day).replace(yesterDay, last_month_last_day)\n        elif index == 1:\n            if type:\n                data_temp = data_temp.replace('account', type)\n        elif index == 3:\n            if 'level' in type:\n                data_temp = data_temp.replace('dataDesc=7', 'dataDesc=6').replace('^&mapType=china', '')\n            elif 'server' in type:\n                data_temp = data_temp.replace('dataDesc=7', 'dataDesc=8').replace('^&mapType=china', '')\n        elif index == 4:\n            if 'role' in type:\n                data_temp = data_temp.replace('dataDesc=7', 'dataDesc=8')\n            elif 'rate' in type:\n                data_temp = data_temp.replace('dataDesc=7', 'dataDesc=9').replace('os=-127', 'os=-127^&idx=account')\n            elif 'arpu' in type:\n                data_temp = data_temp.replace('dataDesc=7', 'dataDesc=10').replace('os=-127', 'os=-127^&idx=account')\n            elif 'vip' in type:\n                start_temp = re.compile(r'startDate=(.*?)\\^&').findall(data_temp)[0]\n                data_temp = data_temp.replace('dataDesc=7', 'dataDesc=11').replace(start_temp, yesterDay)\n\n        data_temp = data_temp.replace('^', '').split('&')\n        for i in range(len(data_temp)):\n            data_value = data_temp[i].split('=')\n            if not '%' in data_value[1]:\n                data.append((data_value[0], data_value[1]))\n            else:\n                data_value[1] = unquote(data_value[1])\n                data.append((data_value[0], data_value[1]))\n    if re.compile(r'--insecure').findall(content):\n        verify = False\n    else:\n        verify = True\n\n    try:\n        if data:\n            response = requests.post(url, headers = headers, cookies = cookies, data = data, verify = verify)\n        else:\n            response = requests.get(url, headers = headers, cookies = cookies, verify = verify)\n        return response.json()\n    except:\n        raise IOError('Something were wrong with cURL config.')","sub_path":"tools/Fetch.py","file_name":"Fetch.py","file_ext":"py","file_size_in_byte":8215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"10787753","text":"# encoding=utf-8\n\nfrom django.conf.urls import url\nfrom views import (\n    ProfileAvatarView\n)\n\nurlpatterns = [\n    url(\n        r\"^profile/?$\",\n        ProfileAvatarView.as_view()\n    ),\n    url(\n        r\"^profile/(?P<label>[a-zA-Z]+)/?$\",\n        ProfileAvatarView.as_view()\n    ),\n]\n","sub_path":"avatars/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"408877120","text":"from fastapi import APIRouter, HTTPException\nfrom fastapi.responses import StreamingResponse\nfrom app.sql_query_function import fetch_query\nfrom typing import Optional\n\nimport io\nimport pandas as pd\nimport plotly.express as px\nimport plotly.graph_objects as go\n\nrouter = APIRouter()\n\n\n@router.get('/rent_viz/{city}_{statecode}')\nasync def rental_price_visualization(city: str, statecode: str,\n              city2: Optional[str]=None, statecode2: Optional[str]=None,\n              city3: Optional[str]=None, statecode3: Optional[str]=None, view: Optional[str]=None):\n        \"\"\"\n        Visualize city-level rental price estimates from\n        [Apartment List](https://www.apartmentlist.com/research/category/data-rent-estimates) 📈\n\n        ## Path Parameters\n        `city`: The name of a U.S. city; e.g. `Atlanta` or `Los Angeles`\n\n        `statecode`: The [USPS 2 letter abbreviation](https://en.wikipedia.org/wiki/List_of_U.S._state_and_territory_abbreviations#Table)\n        (case insensitive) for any of the 50 states or the District of Columbia.\n\n        ## Query Parameters (Optional)\n        `city2`: The name of a second US city to make rental comparison.\n\n        `statecode2`: The statecode for second US city to make rental comparison.\n\n        `city3`: The name of a third US city to make rental comparison.\n\n        `statecode3`: The statecode for third US city to make rental comparison.\n\n        `view`: If 'True' (string), returns a PNG instead of JSON.\n\n        ## Response\n        - Plotly bar chart of `city`'s rental price estimates\n        \"\"\"\n\n        # Get the city's rental price from database\n        query = \"\"\"\n        SELECT\n            city,\n            \"state\",\n            bedroom_size,\n            price_2020_08\n        FROM rp_clean1\n        \"\"\"\n\n        columns = [\"city\", \"state\", \"bedroom_size\", \"price_2020_08\"]\n\n        # Re-formatting bedroom values. Data type/graph population issue.\n        bedrooms = {'Studio': 'Studio',\n                    '1br': 'One',\n                    '2br': 'Two',\n                    '3br': 'Three',\n                    '4br': 'Four'}\n\n        df = pd.read_json(fetch_query(query, columns))\n\n        df['bedroom_size'] = df['bedroom_size'].replace(bedrooms)\n\n        # Make a set of cities in the rental price data\n        citynames = set(df.city.to_list())\n        statecodes = set(df.state.to_list())\n\n        # Input sanitization\n        city = city.title()\n        statecode = statecode.lower().upper()\n\n        # Handle Edge Cases:\n        # saint\n        if city[0:5] == \"Saint\":\n            city = city.replace(\"Saint\", \"St.\")\n        elif city[0:3] == \"St \":\n            city = city.replace(\"St\", \"St.\")\n        # fort\n        elif city[0:3] == \"Ft \":\n            city = city.replace(\"Ft\", \"Fort\")\n        elif city[0:3] == \"Ft.\":\n            city = city.replace(\"Ft.\", \"Fort\")\n        # multiple caps\n        elif city[0:2] == 'Mc':\n            city = city[:2] + city[2:].capitalize()\n\n        if city2 and statecode2:\n            city2 = city2.title()\n            statecode2 = statecode2.lower().upper()\n            # saint\n            if city2[0:5] == \"Saint\":\n                city = city.replace(\"Saint\", \"St.\")\n            elif city2[0:3] == \"St \":\n                city2 = city2.replace(\"St\", \"St.\")\n            # fort\n            elif city2[0:3] == \"Ft \":\n                city2 = city2.replace(\"Ft\", \"Fort\")\n            elif city2[0:3] == \"Ft.\":\n                city2 = city2.replace(\"Ft.\", \"Fort\")\n            # multiple caps\n            elif city2[0:2] == 'Mc':\n                city2 = city2[:2] + city2[2:].capitalize()\n\n        if city3 and statecode3:\n            city3 = city3.title()\n            statecode3 = statecode3.lower().upper()\n            # saint\n            if city3[0:5] == \"Saint\":\n                city = city.replace(\"Saint\", \"St.\")\n            elif city3[0:3] == \"St \":\n                city3 = city3.replace(\"St\", \"St.\")\n            # fort\n            elif city3[0:3] == \"Ft \":\n                city3 = city3.replace(\"Ft\", \"Fort\")\n            elif city3[0:3] == \"Ft.\":\n                city3 = city3.replace(\"Ft.\", \"Fort\")\n            # multiple caps\n            elif city3[0:2] == 'Mc':\n                city3 = city3[:2] + city3[2:].capitalize()\n                \n        # Raise HTTPException for unknown inputs\n        if city not in citynames:\n            raise HTTPException(\n                status_code=404,\n                detail=f'City name \"{city}\" not found!'\n            )\n        if city2 not in citynames and city2:\n            raise HTTPException(\n                status_code=404,\n                detail=f'City name \"{city2}\" not found!'\n            )\n        if city3 not in citynames and city3:\n            raise HTTPException(\n                status_code=404,\n                detail=f'City name \"{city3}\" not found!'\n            )\n\n        if statecode not in statecodes:\n            raise HTTPException(\n                status_code=404,\n                detail=f'Statecode \"{statecode}\" not found!'\n            )\n        if statecode2 not in statecodes and statecode2:\n            raise HTTPException(\n                status_code=404,\n                detail=f'Statecode \"{statecode2}\" not found!'\n            )\n        if statecode3 not in statecodes and statecode3:\n            raise HTTPException(\n                status_code=404,\n                detail=f'Statecode \"{statecode3}\" not found!'\n            )\n\n        city1_df = df[df.city == city]\n\n        # CASE: All are the same.\n        if ((city == city2 == city3) and\n           (statecode == statecode2 == statecode3)):\n            city2, statecode2 = None, None\n            city3, statecode3 = None, None\n\n        # CASE: 2nd and 3rd city, state are the same.\n        if city3 == city2 and statecode3 == statecode2:\n            city3, statecode3 = None, None\n\n        # CASE: 1st and 3rd city, state are the same.\n        if city == city3 and statecode == statecode3:\n            city3, statecode3 = None, None\n\n        # CASE: 1st and 2nd city, state are the same.\n        if city2 == city and statecode2 == statecode:\n            if city3 and statecode3:\n                city2, statecode2 = city3, statecode3\n                city3, statecode3 = None, None\n            elif not city3 and not statecode3:\n                city2, statecode2 = None, None\n\n        if city and not city2 and not city3:\n            if view and view.title() == 'True':\n                return single(city1_df, city, statecode, 'True')\n            else:\n                return single(city1_df, city, statecode)\n\n        if city and city2 and not city3:\n            city2_df = df[df.city == city2]\n            if view and view.title() == 'True':\n                return two(city1_df, city2_df, city, statecode, city2, statecode2, 'True')\n            else:\n                return two(city1_df, city2_df, city, statecode, city2, statecode2)\n\n        if city and city2 and city3:\n            city2_df = df[df.city == city2]\n            city3_df = df[df.city == city3]\n            if view and view.title() == 'True':\n                return three(city1_df, city2_df, city3_df,\n                            city, statecode, city2, statecode2,\n                            city3, statecode3, 'True')\n            else:\n                return three(city1_df, city2_df, city3_df,\n                             city, statecode, city2, statecode2, city3, statecode3)\n\n\ndef single(city1_df, city, statecode, view=None):\n        \"\"\"Used to create and style a visualization with only one city.\"\"\"\n\n        styling = dict()\n        styling['city1color'] = ['lightgreen', '#4BB543', 'darkcyan',\n                                 '#663399', '#CC0000']\n        styling['title'] = f'Rental Price Estimates for {city}, {statecode}'\n\n        layout = go.Layout(\n            paper_bgcolor='rgba(0,0,0,0)',\n            plot_bgcolor='rgba(0,0,0,0)'\n        )\n\n        # Instantiate figure.\n        fig = go.Figure(data=go.Bar(name=f'{city}, {statecode}',\n                        x=city1_df['bedroom_size'],\n                        y=city1_df['price_2020_08'],\n                        marker_color=styling.get('city1color')),\n                        layout=layout)\n        fig.update_layout(barmode='group',\n                          title_text=styling.get('title'),\n                          xaxis_title='Number of Bedrooms',\n                          yaxis_title='Monthly Rental Estimate',\n                          font=dict(family='Open Sans, extra bold',\n                                      size=10),\n                          legend_title='Cities')\n\n        if view:\n            img = fig.to_image(format=\"png\")\n            return StreamingResponse(io.BytesIO(img), media_type=\"image/png\")\n        else:\n            return fig.to_json()\n\n\n\ndef two(city1_df, city2_df, city, statecode, city2, statecode2, view=None):\n        \"\"\"Used to create and style a visualization with two cities.\"\"\"\n\n        styling = dict()\n        city1max = min(city1_df['price_2020_08'].values)\n        city2max = min(city2_df['price_2020_08'].values)\n        # If there's only 2 cities, make comparison / style.\n        if city1max > city2max:\n                styling['city1color'] = '#CC0000'  # red\n                styling['city2color'] = '#4BB543'  # green\n                styling['title'] = (f'{city}, {statecode} has higher rental ' +\n                                    f'rates than {city2}, {statecode2}.')\n        # Make comparison / style (opposite order).\n        elif city1max < city2max:\n                styling['city1color'] = '#4BB543'  # green\n                styling['city2color'] = '#CC0000'  # red\n                styling['title'] = (f'{city}, {statecode} has lower rental ' +\n                                    f'rates than {city2}, {statecode2}.')\n\n        # Set background to be transparent.\n        layout = go.Layout(\n            paper_bgcolor='rgba(0,0,0,0)',\n            plot_bgcolor='rgba(0,0,0,0)'\n        )\n\n        # Instantiate figure.\n        fig = go.Figure(data=go.Bar(name=f'{city}, {statecode}',\n                        x=city1_df['bedroom_size'],\n                        y=city1_df['price_2020_08'],\n                        marker_color=styling.get('city1color')),\n                        layout=layout)\n        # Add bar for city 2.\n        fig.add_bar(name=f'{city2}, {statecode2}',\n                    x=city2_df['bedroom_size'],\n                    y=city2_df['price_2020_08'],\n                    marker_color=styling.get('city2color'))\n        # Update layout.\n        fig.update_layout(barmode='group',\n                          title_text=styling.get('title'),\n                          xaxis_title='Number of Bedrooms',\n                          yaxis_title='Monthly Rental Estimate',\n                          font=dict(family='Open Sans, extra bold',\n                                    size=10),\n                          legend_title='Cities')\n\n        if view:\n            img = fig.to_image(format=\"png\")\n            return StreamingResponse(io.BytesIO(img), media_type=\"image/png\")\n        else:\n            return fig.to_json()\n\n\n\ndef three(city1_df, city2_df, city3_df,\n          city, statecode, city2, statecode2, city3, statecode3, view=None):\n        \"\"\"Used to create and style a visualization with three cities.\"\"\"\n\n        styling = dict()\n\n        city1max = min(city1_df['price_2020_08'].values)\n        city2max = min(city2_df['price_2020_08'].values)\n        city3max = min(city3_df['price_2020_08'].values)\n\n        # POSSIBLE CONDITIONS:\n        # 1 > 2 > 3\n        # 1 > 3 > 2\n        # 2 > 1 > 3\n        # 2 > 3 > 1\n        # 3 > 1 > 2\n        # 3 > 2 > 1\n\n        # Make comparison / style.\n        if (city1max > city2max) and (city2max > city3max):\n            # 1 > 2 > 3\n                styling['city1color'] = '#CC0000'  # red\n                styling['city2color'] = 'darkcyan'\n                styling['city3color'] = '#4BB543'  # green\n                styling['title'] = (f'{city}, {statecode} has higher rental rates ' +\n                                    f'than {city2}, {statecode2} and {city3}, {statecode3}.')\n        # Make comparison / style.\n        elif (city1max > city3max) and (city3max > city2max):\n            # 1 > 3 > 2\n                styling['city1color'] = '#CC0000'  # red\n                styling['city2color'] = '#4BB543'  # green\n                styling['city3color'] = 'darkcyan'\n                styling['title'] = (f'{city}, {statecode} has higher rental rates ' +\n                                    f'than {city2}, {statecode2} and {city3}, {statecode3}.')\n        # Make comparison / style.\n        elif (city2max > city1max) and (city1max > city3max):\n            # 2 > 1 > 3\n                styling['city1color'] = 'darkcyan'\n                styling['city2color'] = '#CC0000'  # red\n                styling['city3color'] = '#4BB543'  # green\n                styling['title'] = (f'{city}, {statecode} has higher rental rates ' +\n                                    f'than {city3}, {statecode3}, but lower than {city2}, {statecode2}.')\n        # Make comparison / style.\n        elif (city2max > city3max) and (city3max > city1max):\n            # 2 > 3 > 1\n                styling['city1color'] = '#4BB543'  # green\n                styling['city2color'] = '#CC0000'  # red\n                styling['city3color'] = 'darkcyan'\n                styling['title'] = (f'{city}, {statecode} has lower rental rates ' +\n                                    f'than {city2}, {statecode2} and {city3}, {statecode3}.')\n        # Make comparison / style.\n        elif (city3max > city1max) and (city1max > city2max):\n            # 3 > 1 > 2\n                styling['city1color'] = 'darkcyan'\n                styling['city2color'] = '#4BB543'  # green\n                styling['city3color'] = '#CC0000'  # red\n                styling['title'] = (f'{city}, {statecode} has higher rental rates ' +\n                                    f'than {city2}, {statecode2}, but lower than {city3}, {statecode3}.')\n        # Make comparison / style.\n        elif (city3max > city2max) and (city2max > city1max):\n            # 3 > 2 > 1\n                styling['city1color'] = '#4BB543'  # green\n                styling['city2color'] = 'darkcyan'\n                styling['city3color'] = '#CC0000'  # red\n                styling['title'] = (f'{city}, {statecode} has lower rental rates ' +\n                                    f'than {city2}, {statecode2} and {city3}, {statecode3}.')\n\n        # Set background to be transparent.\n        layout = go.Layout(\n            paper_bgcolor='rgba(0,0,0,0)',\n            plot_bgcolor='rgba(0,0,0,0)'\n        )\n\n        # Instantiate figure.\n        fig = go.Figure(data=go.Bar(name=f'{city}, {statecode}',\n                        x=city1_df['bedroom_size'],\n                        y=city1_df['price_2020_08'],\n                        marker_color=styling.get('city1color')),\n                        layout=layout)\n        # Add bar for city 2.\n        fig.add_bar(name=f'{city2}, {statecode2}',\n                    x=city2_df['bedroom_size'],\n                    y=city2_df['price_2020_08'],\n                    marker_color=styling.get('city2color'))\n\n        # Add bar for city 3.\n        fig.add_bar(name=f'{city3}, {statecode3}',\n                    x=city3_df['bedroom_size'],\n                    y=city3_df['price_2020_08'],\n                    marker_color=styling.get('city3color'))\n\n        fig.update_layout(barmode='group',\n                          title_text=styling.get('title'),\n                          xaxis_title='Number of Bedrooms',\n                          yaxis_title='Monthly Rental Estimate',\n                          font=dict(family='Open Sans, extra bold',\n                                    size=10),\n                          legend_title='Cities',\n                          height=412,\n                          width=640)\n        if view:\n            img = fig.to_image(format=\"png\")\n            return StreamingResponse(io.BytesIO(img), media_type=\"image/png\")\n        else:\n            return fig.to_json()\n","sub_path":"project/app/api/rentviz2.py","file_name":"rentviz2.py","file_ext":"py","file_size_in_byte":16065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"47657665","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n- ``__getitem__(self, index)`` 实现了 x[i] 的行为\n- ``__setitem__(self, index, value)`` 实现了 x[i] = j 的行为\n\"\"\"\n\n\nclass MyListClass(object):\n    \"\"\"A custom list class, which support set value by index, even the index\n    is out of range. (automatically pad None value)\n\n    **中文文档**\n\n    本例实现了一个自定义的list对象。提供了在 ``x[i] = j`` 赋值时当index超出范围时, \n    自动填充None值的功能。 \n    \"\"\"\n\n    def __init__(self):\n        self.data = list()\n\n    def append(self, value):\n        self.data.append(value)\n\n    def __setitem__(self, index, value):\n        if isinstance(index, int) and index > -1:\n            if (index + 1) <= len(self.data):  # index is in range\n                self.data[index] = value\n            else:  # index is out of range, pad None value\n                for i in range(index - len(self.data)):\n                    self.data.append(None)\n                self.data.append(value)\n\n    def __getitem__(self, index):\n        return self.data[index]\n\n    def __str__(self):\n        return str(self.data)\n\n\nif __name__ == \"__main__\":\n    my_list = MyListClass()\n    my_list.append(0)\n    assert my_list[0] == 0\n\n    my_list[1] = 1\n    assert my_list[1] == 1\n\n    my_list[1] = 10\n    assert my_list[1] == 10\n\n    my_list[3] = 3\n    assert my_list[2] is None\n    assert my_list[3] == 3\n","sub_path":"pyclopedia/p02_ref/p03_objective_oriented/p04_magic_method/p04__getitem__and__setitem__.py","file_name":"p04__getitem__and__setitem__.py","file_ext":"py","file_size_in_byte":1427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"75856346","text":"# -*- coding: utf-8 -*-\nfrom luckyapi.model import BaseModel\n\n\nclass Template(BaseModel):\n    structure = {\n        'name': basestring,\n        'desc': basestring,\n        'images': basestring,\n        'graphics': basestring,\n        'goods_id': int,\n        'target_amount': int,\n        'price': int,\n        'unit': int,\n        'country': basestring\n    }\n\n\nclass CalcRecord(BaseModel):\n    structure = {\n        'lucky_number': basestring,  # 幸运号码\n        'calc': {\n            'url': basestring,  # 计算详情页面,不为null则跳转打开，否则使用native方式呈现下面的数据\n            'result_a': long,\n            'result_b': long,\n            'a_list': [\n                {\n                    'time': basestring,  # 夺宝时间\n                    'code': basestring,  # 组合数值\n                    'name': basestring,  # 用户nick name\n                }\n            ]\n        },\n        'reveal_time': basestring,  # 揭晓时间\n    }\n\n\nclass Revealed(BaseModel):\n    structure = {\n        'term': int,  # 期号\n        'name': basestring,  # 活动名称\n        'activity_id': basestring,\n        'lucky_number': basestring,  # 幸运号码\n        'reveal_time': basestring,  # 揭晓时间\n        'winner': {\n            'uid': basestring,  # user_id\n            'avatar': basestring,  # avar url\n            'nick_name': basestring,  # nick name\n            'ip': basestring,  # ip\n            'addr': basestring,  # address\n            'num_count': int,  # 获奖者的号码个数\n            'time': basestring,  # 夺宝时间\n        }\n    }\n\n\nclass ActivityDetail(BaseModel):\n    structure = {\n        'id': basestring,\n        'gid': int,\n        'term': int,  # 当前期号\n        'created_at': basestring,\n        'latest_term': int,  # 最新一期的期号\n        'latest_id': basestring,  # 最新一期的id\n        'status': int,  # activity::status 1:进行中  2:即将揭晓  3:已经揭晓\n        'goods': {\n            'name': basestring,\n            'desc': basestring,  # activity::desc\n            'images': basestring,  # 组图，activity:images\n            'cover': basestring,  # 封面 activity::images[0]\n            'detail_url': basestring,  # 商品详情页面，如果为null，则使用graphics数据呈现\n            'graphics': basestring,  # 图文详情，activity:graphics\n        },\n        'price': int,  # 购买一次的单价，默认为1个夺宝币，基本不用\n        'buy_limit': int,  # 购买限制，0元购即为1\n        'unit': int,  # 购买的最小次数，10元区即为10\n        'target_amount': int,  # 目标次数\n        'current_amount': int,  # 当前筹集次数\n        'my': {\n            'numbers': list,  # 我的号码\n            'order_id': long,  # 我的中奖订单号\n            'status': int,  # 订单状态\n        },\n        'revealing': {  # 即将揭晓的数据\n            'remain_ms': long,\n            'calc': {\n                'url': basestring,  # 计算详情页面,不为null则跳转打开，否则使用native方式呈现下面的数据\n                'result_a': long,\n                'result_b': None\n            }\n        },\n        'revealed': {  # 已经揭晓的数据\n            'lucky_number': basestring,  # 幸运号码\n            'calc': {\n                'url': basestring,  # 计算详情页面,不为null则跳转打开，否则使用native方式呈现下面的数据\n                'result_a': long,\n                'result_b': long\n            },\n            'reveal_time': basestring,  # 揭晓时间\n            'winner': {\n                'uid': basestring,  # user_id\n                'avatar': basestring,  # avar url\n                'nick_name': basestring,  # nick name\n                'ip': basestring,  # ip\n                'addr': basestring,  # address\n                'numbers': list,  # 获奖者的号码列表\n                'num_count': int,\n                'time': basestring,  # 夺宝时间\n            }\n        },\n        'start_time': basestring,  # 夺宝开始的事件，或者第一次购买的时间\n        'is_star': int,\n        'star_desc': basestring,\n        'is_resell': int,\n        'resell_price_h': int,\n        'resell_price_l': int,\n    }\n\n\nclass ActivityLite(BaseModel):\n    structure = {\n        'id': basestring,\n        'gid': int,  # activity::template_id\n        'term': int,  # activity::term_id\n        'created_at': basestring,\n        'goods': {  # 商品相关数据\n            'name': basestring,  # activity::name\n            'desc': basestring,  # activity::desc\n            'cover': basestring,  # 封面 activity::images[0]\n        },\n        'unit': int,  # 购买的最小次数，10元区即为10\n        'price': int,  # 购买一次的单价，默认为1个夺宝币，基本不用\n        'buy_limit': int,  # 购买限制，0元购即为1\n        'status': int,  # activity::status\n        'target_amount': int,  # activity::target_amount\n        'current_amount': int,  # activity:current_amount\n        'remain_ms': long,  # 离开奖还剩xxx毫秒\n        'lucky_number': basestring,  # 幸运号码\n        'winner': {\n            'uid': basestring,  # user_id\n            'avatar': basestring,  # avar url\n            'nick_name': basestring,  # nick name\n        },\n        'is_star': int,\n        'is_resell': int,\n        'resell_price': int,\n    }\n","sub_path":"luckyapi/model/activity.py","file_name":"activity.py","file_ext":"py","file_size_in_byte":5395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"184406041","text":"import cv2\nimport numpy as np\n\n\ndef euclidean_distance(p0, p1):\n    \"\"\"Gets the distance between two (x,y) points\n\n    Args:\n        p0 (tuple): Point 1.\n        p1 (tuple): Point 2.\n\n    Return:\n        float: The distance between points\n    \"\"\"\n\n    return np.sqrt((p1[0] - p0[0])**2 + (p1[1]-p0[1])**2)\n\n\ndef get_corners_list(image):\n    \"\"\"Returns a ist of image corner coordinates used in warping.\n\n    These coordinates represent four corner points that will be projected to\n    a target image.\n\n    Args:\n        image (numpy.array): image array of float64.\n\n    Returns:\n        list: List of four (x, y) tuples\n            in the order [top-left, bottom-left, top-right, bottom-right].\n    \"\"\"\n\n    height, width = image.shape[0], image.shape[1]\n    return [(0, 0), (0, height-1), (width-1, 0), (width-1, height-1)]\n\n\ndef is_duplicate(mid_point, point_list):\n    for point in point_list:\n        if euclidean_distance(point, mid_point) <= 10:\n            return True\n    return False\n\n\ndef find_markers(image, template=None):\n    \"\"\"Finds four corner markers.\n\n    Use a combination of circle finding, corner detection and convolution to\n    find the four markers in the image.\n\n    Args:\n        image (numpy.array): image array of uint8 values.\n        template (numpy.array): template image of the markers.\n\n    Returns:\n        list: List of four (x, y) tuples\n            in the order [top-left, bottom-left, top-right, bottom-right].\n    \"\"\"\n\n    image = add_noise_to_image(image)\n    corners = get_harris_corners(image)\n    _, _, centroids = cv2.kmeans(np.asarray(corners).astype(np.float32), 4, None,\n               (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0), 10, cv2.KMEANS_RANDOM_CENTERS)\n\n    centroids = sorted([tuple(l) for l in np.array(centroids).astype(int)], key = lambda x:x[0])\n    top_left, bottom_left = sorted(centroids[:2], key = lambda x:x[1])\n    top_right, bottom_right = sorted(centroids[2:], key = lambda x:x[1])\n    return top_left, bottom_left, top_right, bottom_right\n\n\ndef get_harris_corners(image):\n    corners = []\n    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    gray = cv2.GaussianBlur(gray, (13, 13), 25)\n    gray = np.float32(gray)\n    dst = cv2.cornerHarris(gray, 7, 3, 0.1)\n    dst = cv2.dilate(dst, None)\n    locs = np.where(dst > 0.15 * dst.max())\n    for (i, j) in zip(*locs):\n        corners.append((j, i))\n    return corners\n\n\ndef draw_box(image, markers, thickness=1):\n    \"\"\"Draws lines connecting box markers.\n\n    Use your find_markers method to find the corners.\n    Use cv2.line, leave the default \"lineType\" and Pass the thickness\n    parameter from this function.\n\n    Args:\n        image (numpy.array): image array of uint8 values.\n        markers(list): the points where the markers were located.\n        thickness(int): thickness of line used to draw the boxes edges.\n\n    Returns:\n        numpy.array: image with lines drawn.\n    \"\"\"\n\n    img_out = image.copy()\n    top_left, bottom_left, top_right, bottom_right = markers\n    cv2.line(img_out, top_left, bottom_left, (0, 255, 0), thickness = thickness)\n    cv2.line(img_out, top_left, top_right, (0, 255, 0), thickness = thickness)\n    cv2.line(img_out, top_right, bottom_right, (0, 255, 0), thickness = thickness)\n    cv2.line(img_out, bottom_left, bottom_right, (0, 255, 0), thickness = thickness)\n    return img_out\n\n\ndef project_imageA_onto_imageB(imageA, imageB, homography):\n    \"\"\"Projects image A into the marked area in imageB.\n\n    Using the four markers in imageB, project imageA into the marked area.\n\n    Use your find_markers method to find the corners.\n\n    Args:\n        imageA (numpy.array): image array of uint8 values.\n        imageB (numpy.array: image array of uint8 values.\n        homography (numpy.array): Transformation matrix, 3 x 3.\n\n    Returns:\n        numpy.array: combined image\n    \"\"\"\n    height, width, _ = imageA.shape\n    dst_height, dst_width, _ = imageB.shape\n    inv_homography = np.linalg.inv(homography)\n    for x in range(0, dst_width):\n        for y in range(0, dst_height):\n            projected_point = np.dot(inv_homography, np.array([x, y, 1]))\n            x_p, y_p = int(projected_point[0]/projected_point[2]), int(projected_point[1]/projected_point[2])\n            if 0 < x_p < width and 0 < y_p < height:\n                intensity_values = imageA[y_p, x_p]\n                imageB[y, x] = intensity_values\n    return imageB\n\n\ndef find_four_point_transform(src_points, dst_points):\n    \"\"\"Solves for and returns a perspective transform.\n\n    Each source and corresponding destination point must be at the\n    same index in the lists.\n\n    Do not use the following functions (you will implement this yourself):\n        cv2.findHomography\n        cv2.getPerspectiveTransform\n\n    Hint: You will probably need to use least squares to solve this.\n\n    Args:\n        src_points (list): List of four (x,y) source points.\n        dst_points (list): List of four (x,y) destination points.\n\n    Returns:\n        numpy.array: 3 by 3 homography matrix of floating point values.\n    \"\"\"\n    matrix = []\n    for (sx,sy), (dstx, dsty) in zip(src_points, dst_points):\n        matrix.extend([[sx, sy, 1, 0, 0, 0, -dstx * sx, -dstx * sy],\n                        [0, 0, 0, sx, sy, 1, -dsty * sx, -dsty * sy]])\n    matrix = np.matrix(matrix).astype(np.float32)\n    solution_matrix, _, _, _ = np.linalg.lstsq(matrix, np.reshape(dst_points, 8), rcond=-1)\n    return np.append(solution_matrix, [1]).reshape((3, 3))\n\n\ndef video_frame_generator(filename):\n    \"\"\"A generator function that returns a frame on each 'next()' call.\n\n    Will return 'None' when there are no frames left.\n\n    Args:\n        filename (string): Filename.\n\n    Returns:\n        None.\n    \"\"\"\n    # Todo: Open file with VideoCapture and set result to 'video'. Replace None\n    video = cv2.VideoCapture(filename)\n    # Do not edit this while loop\n    while video.isOpened():\n        ret, frame = video.read()\n\n        if ret:\n            yield frame\n        else:\n            break\n\n    # Todo: Close video (release) and yield a 'None' value. (add 2 lines)\n    video.release()\n    yield None\n","sub_path":"ps3.py","file_name":"ps3.py","file_ext":"py","file_size_in_byte":6132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"164211591","text":"#MCCA (Multiview Canonical Correlation Analysis)\n\nimport numpy as np \nfrom scipy import linalg as lin \nfrom sklearn.preprocessing import StandardScaler\n\nclass MCCA:\n    \n    def _init_(self,n_components=2,reg_param=0.01):\n        self.n_components = n_components\n        self.reg_param = reg_param\n        \n        self.views = 0 #number of views\n        self.C = [[]]  #covariance matix \n        self.w_list = []  # list of projections\n        self.score_list = []\n        self.sum_dimen = []\n        \n    #To normalize data so that mean=0 and std dev=1\n    def normalize(self,X):\n        return StandardScaler().fit_transform(X)\n    \n    #To find the covariance matrix containing\n    #both within view and between view covariance\n    def cov_mat(self,X_list):\n        \n        views = len(X_list)\n        X_list_stacked = np.vstack(X.T for X in X_list)\n        cov = np.cov(X_list_stacked)\n        \n        #dimention of views\n        dimen = [0]*views\n        for i in range(views):\n            dimen[i] = len(X_list[i].T)\n            \n        #sum of dimention till individual view\n        sum_dimen = [0]*(views+1)\n        for i in range(1,views):\n            sum_dimen[i] = sum([x for x in dimen][:i+1])\n        self.sum_dimen=sum_dimen\n            \n        #cov_mat containing both within view and between view covariance\n        C = [[np.array([]) for i in range(views)] for j in range(views)]\n        \n        #for calculating C11 C12...C21 C22 ....Cm1,Cm2....Cmm\n        for i in range(views):\n            for j in range(views):\n                C[i][j] = cov[sum_dimen[i]:sum_dimen[i+1],sum_dimen[j]:sum_dimen[j+1]]\n                \n        return C\n    \n    def add_reg_param(self,C):\n        \n        for i in range(views):\n            C[i][i] += self.reg_param * np.average(np.diag(cov_mat[i][i])) * np.eye(cov_mat[i][i].shape[0])\n            \n        return C\n    \n    def fit(self,*X_list):\n        \n        views = len(X_list)\n        x_normalize = [self.normalize(x) for x in X_list]\n        C = self.cov_mat(x_normalize)\n        #C=add_reg_param(C)\n        \n        #Constructing A(left) and B(right) matrix of GEP(generalized eigen value problem) \n        A_rows = [np.hstack([np.zeros_like(C[i][j]) if i == j else C[i][j] for j in range(views)])for i in range(views)]\n        A = np.vstack(A_rows)\n        B_rows = [np.hstack([np.zeros_like(C[i][j]) if i != j else C[i][j] for j in range(views)])for i in range(views)]\n        B = np.vstack(B_rows)\n        \n        #calculating eigen value and eigen vector \n        eig_vals,eig_vecs = lin.eig(A,B)\n        w_list = [eig_vecs[start:end] for start, end in zip(self.sum_dimen[0:-1], self.sum_dimen[1:])]\n        self.w_list = w_list\n        self.views = views\n        self.C = C\n        \n    def transform(self,*X_list):\n        views = len(X_list)\n        X_normalize = [self.normalize(x) for x in X_list]\n        i=0\n        for X,W in zip(X_normalize,self.w_list):\n            score_list[i]=np.dot(X,W)\n            i=i+1\n        self.score_list=score_list\n        return score_list\n                       \n    def fit_transform(self,X_list):\n        self.fit(X_list)\n        self.tranform(X_list)\n                       \n                       \n\na = np.random.rand(5,5)\nb = np.random.rand(5,6)\nc = np.random.rand(5,7)\nd = np.random.rand(5,8)\nmcca = MCCA()\nmcca.fit(a,b,c,d)\nres=mcca.transform(a,b,c,d)\nprint(res)  \n","sub_path":"Groups/Group_ID_19/MCCA/mcca.py","file_name":"mcca.py","file_ext":"py","file_size_in_byte":3387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"291399738","text":"# -*- coding: utf-8 -*-\n\nimport os\n\nfrom resources.lib.commonmethods import Common as C\n\n\nclass Common(C):\n\n    # LINE notifyの通知用エンドポイント\n    ENDPOINT = 'https://notify-api.line.me/api/notify'\n\n    # ディレクトリパス\n    RESOURCES_PATH = os.path.join(C.PLUGIN_PATH, 'resources')\n    DATA_PATH = os.path.join(RESOURCES_PATH, 'data')\n\n    # キャッシュパス\n    CACHE_PATH = os.path.join(C.PROFILE_PATH, 'cache')\n    if not os.path.isdir(CACHE_PATH):\n        os.makedirs(CACHE_PATH)\n\n    # ファイルパス\n    TOKEN_FILE = os.path.join(C.PROFILE_PATH, 'tokens.json')\n    TEMPLATE_FILE = os.path.join(DATA_PATH, 'settings.xml')\n    SETTINGS_FILE = os.path.join(RESOURCES_PATH, 'settings.xml')\n","sub_path":"resources/lib/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"69195826","text":"#! /usr/bin/env ipython\n\n'''\nResearch code\n\nJason Yosinski\n'''\n\nimport ipdb as pdb\nimport os, sys, time\nfrom numpy import *\nfrom PIL import Image\nfrom GitResultsManager import resman\n#from IPython.parallel import Client\n\nfrom tica import TICA\nfrom visualize import plotImageData, plotCov, plotImageRicaWW, plotRicaActivations, plotRicaReconstructions\nfrom util.dataLoaders import loadAtariData, loadUpsonData, loadUpsonData3, loadRandomData, saveToFile\nfrom util.dataPrep import PCAWhiteningDataNormalizer, printDataStats\nfrom util.misc import pt, pc\nfrom paramSearchTica import runTest\n\n\n\ndef main():\n    resman.start('junk', diary = True)\n\n    params = {}\n    randomParams = False\n    if randomParams:\n        params['hiddenISize'] = random.choice((2, 4, 6, 8, 10, 15, 20))\n        params['hiddenJSize'] = params['hiddenISize']\n        params['neighborhoodSize'] = random.choice((.1, .3, .5, .7, 1.0, 1.5, 2.0, 2.5, 3.5, 5.0))\n        lambd = exp(random.uniform(log(.0001), log(10)))   # Uniform in log space\n        params['lambd'] = round(lambd, 1-int(floor(log10(lambd))))  # Just keep two significant figures\n        params['randSeed'] = int(random.uniform(0,9999))\n        #params['dataWidth'] = random.choice((2, 4))   # just quick\n        #params['dataWidth'] = random.choice((2, 3, 4, 6, 10, 15, 20, 25, 28))\n        params['dataWidth'] = random.choice((2, 3, 4, 6, 10, 15, 20))  # 25 and 28 are incomplete\n        params['nColors'] = random.choice((1, 3))\n    else:\n        params['hiddenISize'] = 15\n        params['hiddenJSize'] = params['hiddenISize']\n        params['neighborhoodSize'] = 1.0\n        params['lambd'] = .026\n        params['randSeed'] = 22\n        #params['dataWidth'] = random.choice((2, 4))   # just quick\n        #params['dataWidth'] = random.choice((2, 3, 4, 6, 10, 15, 20, 25, 28))\n        params['dataWidth'] = 10\n        params['nColors'] = 3\n    params['isColor'] = (params['nColors'] == 3)\n    params['imgShape'] = ((params['dataWidth'], params['dataWidth'], 3)\n                          if params['isColor'] else\n                          (params['dataWidth'], params['dataWidth']))\n    params['maxFuncCalls'] = 300\n    params['whiten'] = True    # Just false for Space Invaders dataset...\n    params['dataCrop'] = None       # Set to None to not crop data...\n\n    paramsRand = params.copy()\n    paramsRand['dataLoader'] = 'loadRandomData'\n    paramsRand['dataPath'] = ('../data/random/randomu01_train_%02d_50000_%dc.pkl.gz'\n                              % (paramsRand['dataWidth'], paramsRand['nColors']))\n\n    paramsData = params.copy()\n    #paramsData['dataLoader'] = 'loadAtariData'\n    #paramsData['dataPath'] = ('../data/atari/space_invaders_train_%02d_50000_%dc.pkl.gz'\n    #                          % (paramsData['dataWidth'], paramsData['nColors']))\n    paramsData['dataLoader'] = 'loadUpsonData3'\n    paramsData['dataPath'] = ('../data/upson_rovio_3/train_%02d_50000_%dc.pkl.gz'\n                              % (paramsData['dataWidth'], paramsData['nColors']))\n        \n\n    doRand = False\n    if doRand:\n        #resultsRand = reliablyRunTest((0, resman.rundir, '00000_rand', paramsRand, os.getcwd(), os.getenv('DISPLAY','')))\n        randResultsDir = os.makedirs(resman.rundir, 'rand')\n        runTest(randResultsDir, paramsRand)\n    #resultsData = reliablyRunTest((0, resman.rundir, '00000_data', paramsData, os.getcwd(), os.getenv('DISPLAY','')))\n\n    runTest(resman.rundir, paramsData)\n    \n    resman.stop()\n    \n\nif __name__ == '__main__':\n    main()\n","sub_path":"ica/localUpsonTica.py","file_name":"localUpsonTica.py","file_ext":"py","file_size_in_byte":3509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"359707280","text":"import sqlite3\nimport pandas as pd\n\ndef toxic(jerk):\n    return [[jerk[1][a],jerk[2][a]] for a in range(len(jerk[1]))]\n\ndef to_tables(cer):\n    db = sqlite3.connect(cer)\n    cursor = db.cursor()\n    cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n    tables = cursor.fetchall()\n    cursor.close()\n    db.close()\n    return tables\n\ndef to_columns(per,cer):\n    db=sqlite3.connect(cer)\n    db.text_factory = str\n    cur = db.cursor()\n    der=[]\n    for jer in per:\n        result = cur.execute(\"PRAGMA table_info('%s')\" % jer).fetchall()\n        her=toxic(list(zip(*result)))\n        der.append(her)\n    cur.close()\n    db.close()\n    return der\n\ndef to_sample(cer):\n    db = sqlite3.connect(cer)\n    cursor = db.cursor()\n    cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n    tables = cursor.fetchall()\n    for table_name in tables:\n        table_name = table_name[0]\n        table = pd.read_sql_query(\"SELECT * FROM %s LIMIT 1\" % table_name, db)\n        print(table)\n#        table.to_csv(dir0+\"/\"+table_name + '.csv', index_label='index')\n    cursor.close()\n    db.close()\n","sub_path":"multilingual/rockstar/newdawn/info_gather-v0/wizard/Module/verifyIfWorks.py","file_name":"verifyIfWorks.py","file_ext":"py","file_size_in_byte":1117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"645438137","text":"from typing import List\n\n\nclass AlgorithmModel_Model(object):\n\n    def __init__(self, Model: str,\n                       FractionCorrect: float,\n                       NumberTested: int,\n                       HashValue: str,\n                       StandardDev: float,\n                       DateCreated: str,\n                       Accessions: List[int]):\n\n        self.Model = Model\n        self.FractionCorrect = FractionCorrect\n        self.NumberTested = NumberTested\n        self.DateCreated = DateCreated\n        self.HashValue = HashValue\n        self.StandardDev = StandardDev\n        self.Accessions = Accessions\n\n    def create_simplified_run(self):\n        \"\"\"Generate a new Training Run Summary with much more succinct data for a human readable result set\"\"\"\n\n        return AlgorithmModel_Model(None, self.FractionCorrect, self.NumberTested,\n                                    None, self.StandardDev, self.DateCreated, None)","sub_path":"algorithm development/algorithm_run/algorithm_models/algorithm_model.py","file_name":"algorithm_model.py","file_ext":"py","file_size_in_byte":939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"69432125","text":"import logging\nimport logging.config\nfrom logging.handlers import TimedRotatingFileHandler\nimport conf\n\nDEBUG_MODE = conf.DEBUG_MODE\nPATH = conf.LOGS\nFAST_LOOP = conf.FAST_LOOP \nFORMATTER = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n\nfile_logger = logging.getLogger('Report')\nif FAST_LOOP:\n    #Minute logger \n    minute_handler = TimedRotatingFileHandler(f'{PATH}/Report.log', when=\"m\", interval=1)\n    minute_handler.setLevel(logging.INFO)\n    minute_handler.prefix = \"%H%M\"\n    f_format = logging.Formatter(FORMATTER)\n    minute_handler.setFormatter(f_format)\n    file_logger.addHandler(minute_handler)\n\n    #6 minutes logger\n    montly_handler = TimedRotatingFileHandler(f'{PATH}/Montly_report.log', when=\"m\", interval=6)\n    montly_handler.setLevel(logging.INFO)\n    montly_handler.prefix = \"%Y%m\"\n    f_format = logging.Formatter(FORMATTER)\n    montly_handler.setFormatter(f_format)\n    file_logger.addHandler(montly_handler)\nelse:\n    #Daily logger\n    daily_handler = TimedRotatingFileHandler(f'{PATH}/Report.log', when=\"midnight\", interval=1)\n    daily_handler.setLevel(logging.INFO)\n    daily_handler.prefix = \"%Y%m%d\"\n    f_format = logging.Formatter(FORMATTER)\n    daily_handler.setFormatter(f_format)\n    file_logger.addHandler(daily_handler)\n\n    #Monthtly logger\n    montly_handler = TimedRotatingFileHandler(f'{PATH}/Montly_report.log', when=\"D\", interval=31)\n    montly_handler.setLevel(logging.INFO)\n    montly_handler.prefix = \"%Y%m\"\n    f_format = logging.Formatter(FORMATTER)\n    montly_handler.setFormatter(f_format)\n    file_logger.addHandler(montly_handler)\n\n\ndef warning(msg):\n    file_logger.warning(msg)\n\ndef info(msg):\n    file_logger.info(msg)","sub_path":"server/custom_logger.py","file_name":"custom_logger.py","file_ext":"py","file_size_in_byte":1684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"136528924","text":"#/usr/bin/python\nimport sys\nsys.path.append(\"/opt/Mantid/bin\")\nfrom mantid.simpleapi import *\nworkingdir=\"/SNS/users/rwp/Li2-xNaxIrO4/smooth_van/\"\nruns=range(7421,7449)\n\n\nfor r in runs:\n    datai=LoadMD(workingdir+\"15_25_15/TOPAZ_\"+str(r)+\"_MDE.nxs\")\n    datai_rebinned=BinMD(InputWorkspace=datai,AxisAligned='0',\n\t    BasisVector0='[H,0,0],in 1.178 A^-1,1,0,0',BasisVector1='[0,K,0],in 1.178 A^-1,0,1,0',\n\t    BasisVector2='[0,0,L],in 1.644 A^-1,0,0,1',OutputExtents='-10.05,10.05,-25.125,25.125,-10.05,10.05',\n\t    OutputBins='201,201,201',Parallel='1')\n    if mtd.doesExist('data_histo'):\n\t    PlusMD(LHSWorkspace=\"data_histo\",RHSWorkspace=datai_rebinned,OutputWorkspace=\"data_histo\")\n    else:\n\t    CloneMDWorkspace(datai_rebinned,OutputWorkspace=\"data_histo\")\n    vani=LoadMD(workingdir+\"15_25_15/VAN_\"+str(r)+\"_MDE.nxs\")\n    vani_rebinned=BinMD(InputWorkspace=vani,AxisAligned='0',\n\t    BasisVector0='[H,0,0],in 1.178 A^-1,1,0,0',BasisVector1='[0,K,0],in 1.178 A^-1,0,1,0',\n\t    BasisVector2='[0,0,L],in 1.644 A^-1,0,0,1',OutputExtents='-10.05,10.05,-25.125,25.125,-10.05,10.05',\n\t    OutputBins='201,201,201',Parallel='1')\n    if mtd.doesExist('van_histo'):\n\t    PlusMD(LHSWorkspace=\"van_histo\",RHSWorkspace=vani_rebinned,OutputWorkspace=\"van_histo\")\n    else:\n\t    CloneMDWorkspace(vani_rebinned,OutputWorkspace=\"van_histo\")\n\nSaveMD(\"van_histo\",workingdir+\"10_25_10/vanHisto.nxs\")\nSaveMD(\"data_histo\",workingdir+\"10_25_10/dataHisto.nxs\") \n#symmetryzation\n#data_histo=mtd[\"data_histo\"]\n#van_histo=mtd[\"van_histo\"]\n#data_array=data_histo.getSignalArray()\n#van_array=van_histo.getSignalArray()\n#data_array=data_array[:,:,:]+data_array[::1,::-1,::-1]#+data_array[::-1,::1,::-1]+data_array[::-1,::-1,::1]\n#van_array=van_array[:,:,:]+van_array[::1,::-1,::-1]#+van_array[::-1,::1,::-1]+van_array[::-1,::-1,::1]\n#data_histo.setSignalArray(data_array)\n#van_histo.setSignalArray(van_array)\nDivideMD(LHSWorkspace=\"data_histo\",RHSWorkspace=\"van_histo\",OutputWorkspace=\"out\")\nSaveMD(\"out\",workingdir+\"10_25_10/out.nxs\")\n","sub_path":"Li2-xNaxIrO4/rebin_10_25_10.py","file_name":"rebin_10_25_10.py","file_ext":"py","file_size_in_byte":2015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"407449780","text":"import datetime\nimport time\nfrom multiprocessing import Pool\nimport sys\nimport os\n\nimport bandit_problems.custom_helpers\nimport bandit_problems.slot_machines\nimport bandit_problems.bayesian\nimport bandit_problems.directed_networks\nimport bandit_problems.dummy_networks\n\n\nthetas = [.5, .55]\n\nnet_type = bandit_problems.dummy_networks.CompleteDummyNetwork\nif len(sys.argv) > 1:\n    if sys.argv[1] in ('star'):\n        net_type = bandit_problems.dummy_networks.StarDummyNetwork\n    elif sys.argv[1] in ('complete-complete', 'cc', 'c-c'):\n        net_type = bandit_problems.dummy_networks.CompleteCompleteDummyNetwork\n    elif sys.argv[1] in ('complete-star', 'cs', 'c-s'):\n        net_type = bandit_problems.dummy_networks.CompleteStarDummyNetwork\n\nagent_type = bandit_problems.bayesian.BetaAgentRandomPriors\n\nnum_agents_list = range(5, 21)\n\nruns = 100000\nsteps = 1000\n\n\ndef batch(num_agents):\n    agents = [bandit_problems.bayesian.BetaAgentRandomPriors([1, 1], [1, 1]) for i in range(num_agents)]\n\n    machines = [bandit_problems.slot_machines.BernoulliMachine(t) for t in thetas]\n\n    net = net_type(machines, agents)\n\n    counter = 0\n\n    for r in range(runs):\n        target, thetas_scrambled = bandit_problems.custom_helpers.scramble(thetas)\n        machines = [bandit_problems.slot_machines.BernoulliMachine(t) for t in thetas_scrambled]\n        net.machines = machines\n\n        net.reset_agents()\n\n        for s in range(steps):\n            net.step()\n\n        counter += 1 if net.has_dummy_learned(target) else 0\n\n    print(num_agents, counter)\n    return counter\n\nt0 = time.time()\n\np = Pool(4)\n\nresults = list(p.map(batch, num_agents_list))\n\n\ndName = 'beta_dummy_agents'\ntimeString = datetime.datetime.now().strftime(\"%Y-%m-%d-%H:%M:%S\")\nfName = dName+'/'+net_type.str_structure()+'-scrambled-'+timeString+'.txt'\ndirName = os.path.dirname(os.path.abspath(__file__))+\"/\"+dName\nif not os.path.exists(dirName):\n    os.makedirs(dirName)\n\nwith open(fName, 'w') as f:\n    f.write('{}\\n'.format(agent_type.str_type()))\n    f.write('thetas = {}, runs = {}, steps = {}\\n'.format(thetas, runs, steps))\n    f.write('elapsed time: {} hours\\n'.format((time.time()-t0)/60/60))\n    f.write('numAgents,dummy\\n')\n    for n, r in zip(num_agents_list, results):\n        f.write('{},{}\\n'.format(n, r))\n\nprint(\"Elapsed time: {} hours\".format((time.time()-t0)/60/60))\n","sub_path":"betadummy_agents.py","file_name":"betadummy_agents.py","file_ext":"py","file_size_in_byte":2354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"112524133","text":"from tkinter import *\r\nclass App:\r\n    def __init__(self, master):\r\n        self.master = master\r\n        self.initWidgets()\r\n        self.hi = None\r\n    def initWidgets(self):\r\n        self.show = Label(relief= SUNKEN, font = ('Courier New', 24),\\\r\n         width=23, bg='white', anchor=W)\r\n        self.show.pack(side=TOP, pady=10)\r\n        p = Frame(self.master)\r\n        p.pack(side=TOP)\r\n        names = (\"+\", \"1\" , \"2\" , \"3\" , \"return\"\r\n         ,\"-\", \"4\" , \"5\" , \"6\" , \"**\" , \"*\", \"7\" , \"8\"\r\n         , \"9\", \"//\", \"/\" , \".\" , \"0\" , \"%\", \"=\")\r\n        for i in range(len(names)):\r\n         b = Button( p , text=names[i], font=('Verdana', 20), width=5)\r\n         b.grid(row=i // 5, column=i % 5)\r\n         b.bind('<Button-1>', self.click)\r\n         if b['text'] == 'return': b.bind('<Button-1>', self.clean)\r\n\r\n        self.i = 0\r\n\r\n    def click(self, event):\r\n        if(event.widget['text'] in ('0', '1', '2', '3',\\\r\n        '4', '5', '6', '7', '8', '9', '.')):\r\n            if self.i == 0:\r\n                self.show['text']= ''\r\n                self.show['text'] = self.show['text'] + event.widget['text']\r\n                self.i = self.i + 1\r\n                print(self.i)\r\n            else:\r\n                self.show['text'] = self.show['text'] + event.widget['text']\r\n                self.i= self.i + 1\r\n                print(self.i)\r\n\r\n        elif(event.widget['text'] in ('+', '-', '*', '/', '%', '**', '//')):\r\n            self.show['text'] = self.show['text'] + event.widget['text']\r\n        elif(event.widget['text']=='=' and self.show['text'] is not None):\r\n            self.hi = self.show['text']\r\n            self.show['text'] = str(eval(self.hi))\r\n            print(self.hi)\r\n            self.show['text'] = str(eval(self.hi))\r\n            self.hi = None\r\n            self.i = 0\r\n\r\n    def clean(self, event):\r\n        self.hi = None\r\n        self.show['text'] = ''\r\nroot= Tk()\r\nroot.title(\"calculater\")\r\nApp(root)\r\nroot.mainloop()\r\n","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"249044225","text":"a=13\nb=24\nc=a | b\nd=a&b\n#print(c) #或\n#print(d) #与\n#print(~a) #非  符号位\n#print(a^b) #异或\n#print(a<<1) #左移1位\n#print(a>>1) #右移一位\n'''\n不用四则运算两个数相加\n1.异或运算\n2.与运算\n3.前两个相加\n'''\n\ndef fun(a,b):\n\tsum=a^b\n\tnum=(a&b)<<1\n\twhile num!=0:\n\t\ttemp=sum\n\t\tqwer=num\n\t\tsum=temp^num\n\t\tnum=(temp&qwer)<<1\n\n\treturn sum\n\nprint(fun(12,14))","sub_path":"算法/list65.py","file_name":"list65.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"336888940","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Sep 10 14:51:04 2018\n\n@author: han\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\n\nclass Net(torch.nn.Module):\n    def __init__(self, n_feature, n_hidden, n_output):\n        super(Net, self).__init__()\n        self.hidden = torch.nn.Linear(n_feature, n_hidden)\n        self.predict = torch.nn.Linear(n_hidden, n_output)\n    \n    # 激励函数在 forward() 功能中才被调用的    \n    def forward(self, x):\n        x = F.relu(self.hidden(x))\n        x = self.predict(x)\n        return x\n    \n\ntorch.manual_seed(1)    # reproducible\n\n# exp data\nx = torch.unsqueeze(torch.linspace(-1,1,100), dim=1) \ny = x.pow(2) + 0.2*torch.rand(x.size())\n\n# train\nnet1 = Net(1,10,1)\noptimizer = torch.optim.SGD(net1.parameters(), lr = 0.2)\nloss_func = torch.nn.MSELoss() \nfor t in range(100):\n    prediction = net1(x)\n    loss = loss_func(prediction, y)\n    optimizer.zero_grad()\n    loss.backward()\n    optimizer.step()\n       \n# save\ntorch.save(net1, 'net.pkl') # save entrie net\ntorch.save(net1.state_dict(), 'net_params.pkl') # save params\n    \n# restore():\nnet2 = torch.load('net.pkl')\nnet3 = Net(1,10,1)\nnet3.load_state_dict(torch.load('net_params.pkl'))\n    \n# plot \nplt.figure(figsize=(9,3))\nplt.subplot(131)\nplt.title('net1')\nplt.scatter(x.data.numpy(), y.data.numpy(), color = 'k')\nplt.plot(x.data.numpy(), net1(x).data.numpy(),  'r-', lw = 3)\n\nplt.subplot(132)\nplt.title('net2')\nplt.scatter(x.data.numpy(), y.data.numpy(), color = 'k')\nplt.plot(x.data.numpy(), net2(x).data.numpy(),  'g-', lw = 3)\n\nplt.subplot(133)\nplt.title('net3')\nplt.scatter(x.data.numpy(), y.data.numpy(), color = 'k')\nplt.plot(x.data.numpy(), net3(x).data.numpy(),  'b-', lw = 3)\n\nplt.show()\n\n\n\n        \n        ","sub_path":"neuralNetwork/04_save_reload.py","file_name":"04_save_reload.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"495091978","text":"from flask import Flask,request,abort\nimport os\n\nfrom linebot import (\n    LineBotApi,WebhookHandler\n)\nfrom linebot.exceptions import (\n    InvalidSignatureError\n)\nfrom linebot.models import (\n    MessageEvent,TextMessage,TextSendMessage\n)\n\n\napp = Flask(__name__)\n\n\n\nCHANNEL_ACCESS_TOKEN = os.environ[\"CHANNEL_ACCESS_TOKEN\"]\nCHANNEL_SECRET = os.environ[\"CHANNEL_SECRET\"]\n\nline_bot_api = LineBotApi(CHANNEL_ACCESS_TOKEN)\nhandler = WebhookHandler(CHANNEL_SECRET)\n\n@app.route('/')\ndef hello():\n    return \"HELLO\"\n\n@app.route('/callback',methods=['POST'])\ndef callback():\n    signature = request.headers['X-Line-Signature']\n\n    body = request.get_data(as_text=True)\n    app.logger.info(\"Request body :\" + body)\n\n    try:\n        handler.handle(body,signature)\n    except InvalidSignatureError:\n        abort(400)\n    \n    return \"OK\"\n\n@handler.add(MessageEvent,message=TextMessage)\ndef handle_message(event):\n    line_bot_api.reply_message(\n        event.reply_token,\n        TextSendMessage(text=event.message.text))\n\nif __name__==\"__main__\":\n    app.run(debug=True,port=3000)","sub_path":"todaysholobot/weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"639922856","text":"import numpy as np\nimport autode.values as val\nfrom copy import deepcopy\nfrom datetime import date\nfrom typing import Optional, Union, List, Sequence\nfrom scipy.spatial import distance_matrix\nfrom autode.log import logger\nfrom autode.atoms import Atom, Atoms, AtomCollection\nfrom autode.exceptions import CalculationException\nfrom autode.geom import calc_rmsd, get_rot_mat_euler\nfrom autode.constraints import Constraints\nfrom autode.log.methods import methods\nfrom autode.conformers.conformers import Conformers\nfrom autode.solvent.solvents import ExplicitSolvent, get_solvent\nfrom autode.calculation import Calculation\nfrom autode.wrappers.keywords import Keywords\nfrom autode.config import Config\nfrom autode.input_output import atoms_to_xyz_file\nfrom autode.mol_graphs import is_isomorphic, reorder_nodes\nfrom autode.methods import get_lmethod, get_hmethod, ElectronicStructureMethod\nfrom autode.mol_graphs import make_graph\nfrom autode.hessians import Hessian\nfrom autode.units import ha_per_ang_sq, ha_per_ang\nfrom autode.thermochemistry.symmetry import symmetry_number\nfrom autode.thermochemistry.igm import calculate_thermo_cont, LFMethod\nfrom autode.utils import (requires_atoms,\n                          work_in,\n                          requires_conformers)\n\n\nclass Species(AtomCollection):\n\n    def __str__(self):\n        \"\"\"Unique species identifier\"\"\"\n\n        if self.atoms is None:\n            atoms_str = ''\n\n        else:\n            # Only use the first 100 atoms\n            atoms_str = ''.join([atom.label for atom in self.atoms[:100]])\n\n        solv_str = self.solvent.name if self.solvent is not None else 'none'\n\n        return f'{self.name}_{self.charge}_{self.mult}_{atoms_str}_{solv_str}'\n\n    def _repr(self, prefix: str):\n        \"\"\"Base representation of a Species/Molecule/Complex etc.\"\"\"\n\n        string = (f'{prefix}('\n                  f'{self.name}, '\n                  f'n_atoms={self.n_atoms}, '\n                  f'charge={self.charge}, '\n                  f'mult={self.mult})')\n\n        return string\n\n    def __repr__(self):\n        \"\"\"Brief representation of this species\"\"\"\n        return self._repr(prefix='Species')\n\n    def copy(self) -> 'Species':\n        \"\"\"Copy this whole species\"\"\"\n        return deepcopy(self)\n\n    def new_species(self, name='species') -> 'Species':\n        \"\"\"\n        A new version of this species, identical properties without any\n        energies, gradients, hessian, conformers or constraints.\n\n        Keyword Arguments:\n            name (str): Name of the new species\n\n        Returns:\n            (autode.species.Species):\n        \"\"\"\n        species = Species(name, deepcopy(self.atoms), self.charge, self.mult)\n        species.graph = None if self.graph is None else self.graph.copy()\n        species.solvent = None if self.solvent is None else self.solvent.copy()\n\n        return species\n\n    @property\n    def charge(self) -> int:\n        \"\"\"Total charge on this species\"\"\"\n        return self._charge\n\n    @charge.setter\n    def charge(self, value) -> None:\n        self._charge = int(value)\n\n    @property\n    def mult(self) -> int:\n        \"\"\"Total spin multiplicity on this species (2S + 1)\"\"\"\n        return self._mult\n\n    @mult.setter\n    def mult(self, value) -> None:\n        self._mult = int(value)\n\n    @AtomCollection.atoms.setter\n    def atoms(self,\n              value: Union[List[Atom], Atoms, None]):\n        \"\"\"\n        Set the atoms for this species, and reset the energies\n\n        Arguments:\n            value (list(autode.atoms.Atom) | None):\n        \"\"\"\n\n        if value is None:\n            self._atoms = None\n            return\n\n        # If the geometry is identical up to rotations/translations then\n        # energies do not need to be changed\n        if (self.n_atoms == len(value)\n            and all(a.label == v.label for a, v in zip(self.atoms, value))):\n\n            rmsd = calc_rmsd(coords1=np.array([v.coord for v in value]),\n                             coords2=np.array([a.coord for a in self.atoms]))\n        else:\n            rmsd = None\n\n        if rmsd is None or rmsd > 1E-8:\n            logger.info(f'Geometry changed- resetting energies of {self.name}')\n            self.energies.clear()\n            self.gradient = None\n            self.hessian = None\n\n        self._atoms = Atoms(value)\n        return\n\n    @property\n    def formula(self) -> str:\n        \"\"\"\n        Molecular formula of this species. Example:\n\n        .. code-block:: Python\n\n            >>> import autode as ade\n            >>> blank_mol = ade.Molecule()\n            >>> blank_mol.formula\n            ''\n            >>> h2 = ade.Molecule(smiles='[H][H]')\n            >>> h2.formula\n            'H2'\n\n        Returns:\n            (str): Formula\n        \"\"\"\n\n        if self.atoms is None:\n            return \"\"\n\n        symbols = [atom.label for atom in self.atoms]\n\n        formula_str = ''\n        for symbol in sorted(set(symbols)):\n            num = symbols.count(symbol)\n            formula_str += f'{symbol}{num if num > 1 else \"\"}'\n\n        return formula_str\n\n    @property\n    def hessian(self) -> Optional[Hessian]:\n        \"\"\"\n        Hessian (d^2E/dx^2) at this geometry (autode.values.Hessian | None)\n        shape = (3*n_atoms, 3*n_atoms)\n        \"\"\"\n        return self._hess\n\n    @hessian.setter\n    def hessian(self,\n                value: Union[Hessian, np.ndarray, None]):\n        \"\"\"Set the Hessian matrix as a Hessian value\"\"\"\n\n        req_shape = (3*self.n_atoms, 3*self.n_atoms)\n        if hasattr(value, 'shape') and value.shape != req_shape:\n            raise ValueError('Could not set the Hessian. Incorrect shape: '\n                             f'{value.shape} != {req_shape}')\n\n        if value is None:\n            self._hess = None\n\n        elif isinstance(value, Hessian):\n            self._hess = value\n\n            if self._hess.atoms is None:\n                self._hess.atoms = self.atoms\n\n        elif isinstance(value, np.ndarray):\n            logger.warning('Setting the Hessian from a numpy array - assuming '\n                           'units of Ha Å^-2')\n            self._hess = Hessian(value, atoms=self.atoms, units=ha_per_ang_sq)\n\n        else:\n            raise ValueError(f'Could not set Hessian with {value}, Must be '\n                             f'a numpy array or a Hessian.')\n\n    @property\n    def gradient(self) -> Optional[val.Gradient]:\n        \"\"\"\n        Gradient (dE/dx) at this geometry.\n\n        Returns:\n            (autode.values.Gradients | None): Gradient with shape = (n_atoms, 3)\n        \"\"\"\n        return self._grad\n\n    @gradient.setter\n    def gradient(self,\n                 value: Union[val.Gradient, np.ndarray, None]):\n        \"\"\"Set the gradient matrix\"\"\"\n\n        if hasattr(value, 'shape') and value.shape != (self.n_atoms, 3):\n            raise ValueError('Could not set the gradient. Incorrect shape: '\n                             f'{value.shape} != {(self.n_atoms, 3)}')\n\n        if value is None:\n            self._grad = None\n\n        elif isinstance(value, val.Gradient):\n            self._grad = value\n\n        elif isinstance(value, np.ndarray):\n            logger.warning('Setting the gradients from a numpy array - '\n                           'assuming Ha / Å units')\n            self._grad = val.Gradient(value, units=ha_per_ang)\n\n        else:\n            raise ValueError(f'Could not set the gradient with {value}, Must '\n                             f'be a numpy array or a Hessian.')\n\n    @property\n    def frequencies(self) -> Optional[List[val.Frequency]]:\n        \"\"\"\n        Frequencies from Hessian diagonalisation, in cm-1 by default and\n        are projected from rotation and translation\n\n        Returns:\n            (list(autode.values.Frequency) | None):\n        \"\"\"\n        if self.hessian is None:\n            logger.warning('No Hessian has been calculated - no frequencies')\n            return None\n\n        return self.hessian.frequencies_proj\n\n    @property\n    def vib_frequencies(self) -> Optional[List[val.Frequency]]:\n        \"\"\"\n        Vibrational frequencies, which are all but the lowest 6 for a\n        non-linear molecule and all but the lowest 5 for a linear one\n\n        Returns:\n            (list(autode.values.Frequency) | None): Vibrational frequencies\n        \"\"\"\n        n = 6 if not self.is_linear() else 5\n\n        return self.frequencies[n:] if self.frequencies is not None else None\n\n    @property\n    def imaginary_frequencies(self) -> Optional[List[val.Frequency]]:\n        \"\"\"\n        Imaginary frequencies of a molecule\n\n        Returns:\n            (list(autode.values.Frequency) | None): Imaginary frequencies, or\n                                                    None if there are none\n        \"\"\"\n        if self.frequencies is None:\n            logger.warning('Had no frequencies - could not find any imaginary')\n            return None\n\n        imag_freqs = [freq for freq in self.frequencies if freq.is_imaginary]\n\n        if len(imag_freqs) == 0:\n            logger.warning('No imaginary frequencies')\n            return None\n\n        return imag_freqs\n\n    def normal_mode(self, mode_number: int) -> Optional[val.Coordinates]:\n        \"\"\"\n        Vibrational normal mode indexed from 0, the first 6 are translation\n        and rotation and have zero displacements. The first vibrational mode\n        has mode_number = 6.\n\n        Arguments:\n            mode_number (int):\n\n        Returns:\n            (autode.values.Coordinates):\n        \"\"\"\n        if self.hessian is None:\n            logger.warning('Could not calculate a normal mode displacement')\n            return None\n\n        return self.hessian.normal_modes_proj[mode_number]\n\n    @property\n    @requires_atoms\n    def bond_matrix(self) -> np.ndarray:\n        \"\"\"\n        Numpy boolean array containing which atoms are bonded, also known as\n        an adjacency matrix.\n\n        Returns:\n            (np.ndarray): Adjacency matrix. shape = (n_atoms, n_atoms)\n\n        Raises:\n             (ValueError): If the molecular graph is not set\n        \"\"\"\n\n        matrix = np.zeros(shape=(self.n_atoms, self.n_atoms), dtype=bool)\n\n        if self.graph is None:\n            raise ValueError('No molecular graph set. Bonds are not defined')\n\n        for bond in self.graph.edges:\n            matrix[tuple(bond)] = matrix[tuple(reversed(bond))] = True\n\n        return matrix\n\n    @property\n    def radius(self) -> val.Distance:\n        \"\"\"Calculate an approximate radius of this species\"\"\"\n        if self.n_atoms == 0:\n            return val.Distance(0.0)\n\n        coords = self.coordinates\n        return val.Distance(np.max(distance_matrix(coords, coords)) / 2.0)\n\n    @property\n    def sn(self) -> int:\n        \"\"\"\n        Calculate the symmetry number (σ_R) of the atoms. Only implemented for\n        'small' molecules <50 atoms\n\n\n        References:\n        [1] Theor Chem Account (2007) 118:813\n        [2] . Phys. Chem. B (2010) 114:16304\n\n        Returns:\n            (int): σ_R\n        \"\"\"\n        if self.n_atoms == 0:\n            return 1\n\n        if self.n_atoms > 50:\n            logger.warning('Symmetry number calculations are not implemented '\n                           'for large molecules. Assuming C1 -> σ_R=1')\n            return 1\n\n        return symmetry_number(self)\n\n    @property\n    def is_explicitly_solvated(self) -> bool:\n        return isinstance(self.solvent, ExplicitSolvent)\n\n    @property\n    def energy(self) -> Optional[val.PotentialEnergy]:\n        \"\"\"\n        Last computed potential energy. Setting with a float assumes electornic\n        Hartree units. Example:\n\n        .. code-block:: Python\n\n            >>> import autode as ade\n            >>> species = ade.Species(name='H', atoms=[ade.Atom('H')], charge=0, mult=1)\n            >>> species.energy is None\n            True\n            >>> species.energy = -0.5\n            >>> species.energy\n            Energy(-0.5 Ha)\n            >>> species.single_point(method=ade.methods.ORCA())\n            >>> species.energy\n            Energy(-0.50104 Ha)\n\n        Energies are instances of autode.values.Energy so can be converted\n        to different units simply:\n\n        .. code-block:: Python\n\n            >>> species.energy.to('kcal mol-1')\n            Energy(-314.40567 kcal mol-1)\n            >>> species.energy.to('eV')\n            Energy(-13.63394 eV)\n\n        All previsouly calculated energies of a species are availble with the\n        energies attribute:\n\n        .. code-block:: Python\n\n            >>> species.energies\n            [Energy(-0.5 Ha), Energy(-0.50104 Ha)]\n\n        Returns:\n            (autode.values.PotentialEnergy): Energy\n        \"\"\"\n        return self.energies.last(val.PotentialEnergy)\n\n    @energy.setter\n    def energy(self, value: Union[val.Energy, float, None]):\n        \"\"\"\n        Add an energy to the list of energies at this geometry\n\n        Arguments:\n            value (float | autode.values.Energy | None):\n        \"\"\"\n\n        if value is None:\n            # No change required\n            pass\n\n        elif isinstance(value, val.PotentialEnergy):\n            self.energies.append(value)\n\n        else:\n            # Attempt to cast the value to Potential energy\n            self.energies.append(val.PotentialEnergy(value))\n\n    @property\n    def h_cont(self) -> Optional[val.EnthalpyCont]:\n        \"\"\"\n        Return the enthalpic contribution to the energy\n\n        Returns:\n             (autode.values.Energy | None): H - E_elec\n        \"\"\"\n        return self.energies.last(val.EnthalpyCont)\n\n    @property\n    def g_cont(self) -> Optional[val.FreeEnergyCont]:\n        \"\"\"\n        Return the Gibbs (free) contribution to the energy\n\n        Returns:\n             (autode.values.Energy | None): G - E_elec\n        \"\"\"\n        return self.energies.last(val.FreeEnergyCont)\n\n    @property\n    def free_energy(self) -> Optional[val.FreeEnergy]:\n        \"\"\"\n        Free energy (G or A) of this species, calculated using the last energy\n        and free energy contribution\n\n        Returns:\n            (autode.values.FreeEnergy | None): 'Gibbs' free energy\n        \"\"\"\n        try:\n            return val.FreeEnergy(self.energy + self.g_cont)\n\n        except TypeError:\n            logger.warning('Could not calculate G - an energy was None')\n            return None\n\n    @property\n    def enthalpy(self) -> Optional[val.Enthalpy]:\n        \"\"\"\n        Enthalpy (H) of this species, calculated using the last energy and\n        enthalpy contribution. Example:\n\n        .. code-block:: Python\n\n            >>> import autode as ade\n            >>> h2 = ade.Molecule(smiles='[H][H]')\n            >>> orca = ade.methods.ORCA()\n            >>>\n            >>> h2.optimise(method=orca)\n            >>> h2.calc_h_cont(method=orca)\n            >>> h2.enthalpy\n            Enthalpy(-1.15069 Ha)\n\n        The enthalpy contribution is seperated, so performing a single point\n        provides a new enthalpy using the electronic energy at the single-point\n        level of theory:\n\n        .. code-block:: Python\n\n            >>> h2.single_point(method=orca)\n            >>> h2.enthalpy\n            Enthalpy(-1.15497 Ha)\n\n\n        Returns:\n            (autode.values.Enthalpy | None): Enthalpy\n        \"\"\"\n        try:\n            return val.Enthalpy(self.energy + self.h_cont)\n\n        except TypeError:\n            logger.warning('Could not calculate H - an energy was None')\n            return None\n\n    @property\n    def n_conformers(self) -> int:\n        \"\"\"\n        Number of conformers of this species\n\n        Returns:\n            (int):\n        \"\"\"\n        return 0 if self.conformers is None else len(self.conformers)\n\n    @property\n    def conformers(self) -> 'autode.conformers.Conformers':\n        \"\"\"Conformers of this species\"\"\"\n        return self._conformers\n\n    @conformers.setter\n    def conformers(self,\n                   value: Union[List['autode.conformers.Conformer'],\n                                'autode.conformers.Conformers',\n                                None]\n                   ) -> None:\n        \"\"\"\n        Set conformers of this species\n\n        Arguments:\n            value (list(autode.conformers.Conformer) | None):\n        \"\"\"\n        if value is None:\n            self._conformers.clear()\n            return\n\n        self._conformers = Conformers([conf for conf in value])\n\n    def _generate_conformers(self, *args, **kwargs):\n        raise NotImplementedError('Could not generate conformers. '\n                                  'generate_conformers() not implemented')\n\n    def _run_hess_calculation(self, method):\n        \"\"\"Run a Hessian calculation on this species\"\"\"\n\n        if self.n_atoms < 2:\n            logger.warning(f'Not running a Hessian calculation on only '\n                           f'{self.n_atoms} atom(s). Cannot have frequencies')\n            return None\n\n        method = method if method is not None else get_hmethod()\n\n        calc = Calculation(name=f'{self.name}_hess',\n                           molecule=self,\n                           method=method,\n                           keywords=method.keywords.hess,\n                           n_cores=Config.n_cores)\n        calc.run()\n        self.energy = calc.get_energy()\n        self.hessian = calc.get_hessian()\n\n        return None\n\n    @requires_conformers\n    def _set_lowest_energy_conformer(self):\n        \"\"\"Set the species energy and atoms as those of the lowest energy\n        conformer\"\"\"\n        lowest_energy = None\n\n        for conformer in self.conformers:\n            if conformer.energy is None or conformer.atoms is None:\n                continue\n\n            # Conformers don't have a molecular graph, so make it\n            make_graph(conformer)\n\n            if not is_isomorphic(conformer.graph, self.graph,\n                                 ignore_active_bonds=True):\n                logger.warning('Conformer had a different graph. Ignoring')\n                continue\n\n            # If the conformer retains the same connectivity, up the the active\n            # atoms in the species graph\n\n            if lowest_energy is None:\n                lowest_energy = conformer.energy\n\n            if conformer.energy <= lowest_energy:\n                self.atoms = conformer.atoms\n                self.energy = conformer.energy\n                lowest_energy = conformer.energy\n\n        if lowest_energy is None:\n            raise RuntimeError(\"Failed to set the lowest energy conformer as \"\n                               \"no suitable conformers were present\")\n\n        return None\n\n    def populate_conformers(self, *args, **kwargs):\n        \"\"\"Populate self.conformers\"\"\"\n        return self._generate_conformers(*args, **kwargs)\n\n    @requires_atoms\n    def reorder_atoms(self, mapping: dict) -> None:\n        \"\"\"\n        Reorder the atoms in this species (in place) using a mapping. For\n        example, to reorder the atoms in a HF molecule:\n\n        .. code-block:: Python\n\n            >>> import autode as ade\n            >>> hf = ade.Species(name='HF', charge=0, mult=1,\n            ...                   atoms=[ade.Atom('H'), ade.Atom('F', x=1)])\n            >>> hf.atoms\n            Atoms([Atom(H, 0.0 0.0 0.0), Atom(F, 1.0, 0.0, 0.0)])\n            >>> hf.reorder_atoms(mapping={0: 1, 1: 0})\n\n\n        Arguments:\n            mapping (dict): Dictionary keyed with current atom indexes with\n                            the values as the required indexing\n\n        Raises:\n            (ValueError): If the mapping is invalid\n        \"\"\"\n        if not (set(mapping.keys()) == set(mapping.values()) == set(list(range(self.n_atoms)))):\n            raise ValueError('Invalid mapping. Must be 1-1 for all atoms')\n\n        self._atoms = Atoms([self.atoms[i] for i in\n                             sorted(mapping, key=mapping.get)])\n\n        if self.graph is None:\n            return  # No need to re-order a graph that is not set\n\n        self.graph = reorder_nodes(graph=self.graph,\n                                   mapping={u: v for v, u in mapping.items()})\n        return\n\n    @requires_atoms\n    def is_linear(self,\n                  tol:       Optional[float] = None,\n                  angle_tol: val.Angle = val.Angle(1.0, units='deg')) -> bool:\n        \"\"\"\n        Determine if a species is linear i.e all atoms are colinear\n\n        Keyword Arguments:\n            tol (float | None): Tolerance on |cos(θ)| - 1 where θ is the angle\n                                between the vector from atom 0 to 1 and from\n                                0 to n (n > 1). Present for compatibility and\n                                overrides angle_tol if not None\n\n            angle_tol (autode.values.Angle): Tolerance on the angle considered\n                                             to be linear\n        \"\"\"\n        if tol is not None:\n            angle_tol = val.Angle(np.arccos(1.0 - tol), units='rad')\n\n        return self.atoms.are_linear(angle_tol=angle_tol)\n\n    @requires_atoms\n    def translate(self, vec: Sequence[float]) -> None:\n        \"\"\"\n        Translate the molecule by vector\n\n        Arguments:\n            vec (np.ndarray | list(float)): Vector to translate by shape = (3,)\n        \"\"\"\n        for atom in self.atoms:\n            atom.translate(vec)\n\n        return None\n\n    @requires_atoms\n    def rotate(self,\n               axis:   Union[np.ndarray, Sequence],\n               theta:  Union[val.Angle, float],\n               origin: Union[np.ndarray, Sequence, None] = None) -> None:\n        \"\"\"\n        Rotate the molecule by around an axis\n\n        Arguments:\n            axis (np.ndarray | list(float)): Axis to rotate around. len(axis)=3\n\n            theta (Angle | float): Angle to rotate anticlockwise by if float\n                                   then assume radian units\n\n        Keyword Arguments:\n            origin (np.ndarray | list(float) | None): Origin of the rotation\n        \"\"\"\n\n        # NOTE: Requires copy as the origin may be one of the coordinates\n        origin = np.zeros(3) if origin is None else np.array(origin, copy=True)\n\n        coords = self.coordinates\n        coords -= origin\n        coords = np.dot(coords, get_rot_mat_euler(axis=axis, theta=theta).T)\n        coords += origin\n\n        # Set the new coordinates of each atom\n        for atom, new_coord in zip(self.atoms, coords):\n            atom.coord = new_coord\n\n        return None\n\n    @requires_atoms\n    def centre(self) -> None:\n        \"\"\"Translate this molecule so the centroid (~COM) is at the origin\"\"\"\n        self.translate(vec=-np.average(self.coordinates, axis=0))\n        return None\n\n    @requires_atoms\n    def print_xyz_file(self,\n                       title_line:            Optional[str] = None,\n                       filename:              Optional[str] = None,\n                       additional_title_line: Optional[str] = None) -> None:\n        \"\"\"\n        Print a standard xyz file from this Molecule's atoms\n\n        Keyword Arguments:\n            title_line (str | None): String to add as the second line of the\n                                     .xyz file\n\n            filename (str | None): Filename ending with .xyz. If None then will\n                                   use the name of this molecule\n\n            additional_title_line (str | None): Additional elements to add to\n                                                then title line\n        \"\"\"\n\n        if filename is None:\n            filename = f'{self.name}.xyz'\n\n        # Default generated title line\n        if title_line is None:\n            title_line = f'Generated by autodE on: {date.today()}. '\n            if self.energy is not None:\n                title_line += f'E = {self.energy:.6f} Ha'\n\n        if additional_title_line is not None:\n            title_line += additional_title_line\n\n        return atoms_to_xyz_file(self.atoms, filename, title_line=title_line)\n\n    @requires_atoms\n    def optimise(self,\n                 method:      Optional[ElectronicStructureMethod] = None,\n                 reset_graph: bool = False,\n                 calc:        Optional[Calculation] = None,\n                 keywords:    Optional[Keywords] = None,\n                 n_cores:     Optional[int] = None) -> None:\n        \"\"\"\n        Optimise the geometry using a method\n\n        Arguments:\n            method (autode.wrappers.base.ElectronicStructureMethod):\n\n        Keyword Arguments:\n            reset_graph (bool): Reset the molecular graph\n\n            calc (autode.calculation.Calculation): Different e.g. constrained\n                                                   optimisation calculation\n\n            keywords (autode.wrappers.keywords.Keywords):\n\n            n_cores (int | None): Number of cores to use for the calculation,\n                                  if None then will default to\n                                  autode.Config.n_cores\n        Raises:\n            (autode.exceptions.CalculationException):\n        \"\"\"\n        logger.info(f'Running optimisation of {self.name}')\n\n        if calc is None and method is None:\n            raise ValueError('Optimisation cannot be performed without '\n                             'a specified method or calculation.')\n\n        if calc is None:\n            calc = Calculation(name=f'{self.name}_opt',\n                               molecule=self,\n                               method=method,\n                               keywords=method.keywords.opt if keywords is None else keywords,\n                               n_cores=Config.n_cores if n_cores is None else n_cores)\n\n        calc.run()\n        self.atoms = calc.get_final_atoms()\n        self.energy = calc.get_energy()\n\n        method_name = '' if method is None else method.name\n        self.print_xyz_file(filename=f'{self.name}_optimised_{method_name}.xyz')\n\n        if reset_graph:\n            make_graph(self)\n\n        return None\n\n    @requires_atoms\n    def calc_thermo(self,\n                    method:     Optional[ElectronicStructureMethod] = None,\n                    calc:       Optional[Calculation] = None,\n                    temp:       float = 298.15,\n                    lfm_method: Union[LFMethod, str, None] = None,\n                    ss:         Optional[str] = None,\n                    **kwargs) -> None:\n        \"\"\"\n        Calculate the free energy and enthalpy contributions using the\n        ideal gas approximation\n\n        Keyword Arguments:\n            method (autode.wrappers.base.ElectronicStructureMethod):\n\n            calc (autode.calculation.Calculation):\n\n            temp (float): Temperature in K\n\n            lfm_method (LFMethod | str | None): Method to treat low freqency\n                                           modes. {'igm', 'truhlar', 'grimme'}.\n                                           Defaults to Config.lfm_method\n\n            ss (str | None): Standard state to use.\n                             Defaults to Config.standard_state\n\n        Raises:\n            (autode.exceptions.CalculationException | KeyError):\n\n        See Also:\n            autode.thermochemistry.igm.calculate_thermo_cont for additional kwargs\n        \"\"\"\n        if lfm_method is not None:\n            kwargs['lfm_method'] = lfm_method\n\n        if type(lfm_method) is str:\n            try:\n                kwargs['lfm_method'] = LFMethod[lfm_method.lower()]\n            except KeyError:\n                raise ValueError(f'{lfm_method} is not valid. Must be on of '\n                                 f'{[m for m in LFMethod]}')\n\n        if ss is not None:\n            if ss.lower() not in ('1m', '1atm'):\n                raise ValueError(f'{ss} is not a valid standard state. Must be'\n                                 ' either \"1m\" or \"1atm\"')\n            kwargs['ss'] = ss.lower()\n\n        if calc is not None and calc.output.exists:\n            self.atoms = calc.get_final_atoms()\n            self.energy = calc.get_energy()\n            self.hessian = calc.get_hessian()\n\n        elif (calc is not None and not calc.output.exists\n              or self.hessian is None):\n            logger.info('Calculation did not exist or Hessian was None - '\n                        'calculating the Hessian')\n            self._run_hess_calculation(method=method)\n\n        calculate_thermo_cont(self, temp=temp, **kwargs)\n        return None\n\n    @requires_atoms\n    def calc_g_cont(self, *args, **kwargs) -> None:\n        \"\"\"Calculate the Gibbs free (G) contribution for this species  using\n        Species.calc_thermo()\"\"\"\n        return self.calc_thermo(*args, **kwargs)\n\n    @requires_atoms\n    def calc_h_cont(self, *args, **kwargs) -> None:\n        \"\"\"Calculate the enthalpic (H) contribution for this species using\n        Species.calc_thermo()\"\"\"\n        return self.calc_thermo(*args, **kwargs)\n\n    @requires_atoms\n    def single_point(self,\n                     method:   ElectronicStructureMethod,\n                     keywords: Optional[Keywords] = None,\n                     n_cores:  Optional[int] = None) -> None:\n        \"\"\"Calculate the single point energy of the species with a\n        autode.wrappers.base.ElectronicStructureMethod\"\"\"\n        logger.info(f'Running single point energy evaluation of {self.name}')\n\n        sp = Calculation(name=f'{self.name}_sp',\n                         molecule=self,\n                         method=method,\n                         keywords=method.keywords.sp if keywords is None else keywords,\n                         n_cores=Config.n_cores if n_cores is None else n_cores)\n        sp.run()\n        energy = sp.get_energy()\n\n        if energy is None:\n            raise CalculationException(\"Failed to calculate a single point \"\n                                       f\"energy for {self}\")\n        self.energy = energy\n\n        return None\n\n    @work_in('conformers')\n    def find_lowest_energy_conformer(self,\n                                     lmethod: Optional[ElectronicStructureMethod] = None,\n                                     hmethod: Optional[ElectronicStructureMethod] = None) -> None:\n        \"\"\"\n        For a molecule object find the lowest conformer in energy and set the\n        molecule.atoms and molecule.energy\n\n        Arguments:\n            lmethod (autode.wrappers.ElectronicStructureMethod):\n            hmethod (autode.wrappers.ElectronicStructureMethod):\n        \"\"\"\n        logger.info('Finding lowest energy conformer')\n\n        if self.n_atoms <= 2:\n            logger.warning('Cannot have conformers of a species with 2 atoms '\n                           'or fewer')\n            return None\n\n        if lmethod is None:\n            logger.info('Getting the default low level method')\n            lmethod = get_lmethod()\n\n        methods.add('Low energy conformers located with the')\n        self._generate_conformers()\n\n        # For all generated conformers optimise with the low level of theory\n        method_string = f'and optimised using {lmethod.name}'\n        if hmethod is not None:\n            method_string += f' then with {hmethod.name}'\n        methods.add(f'{method_string}.')\n\n        self.conformers.optimise(method=lmethod)\n        self.conformers.prune(remove_no_energy=True)\n\n        if hmethod is not None:\n\n            if Config.hmethod_sp_conformers:\n                # Use only single point energies on lmethod geometries\n                assert hmethod.keywords.low_sp is not None\n                self.conformers.single_point(method=hmethod,\n                                             keywords=hmethod.keywords.low_sp)\n            else:\n                # Otherwise run a full optimisation\n                self.conformers.optimise(hmethod)\n\n        self._set_lowest_energy_conformer()\n\n        logger.info(f'Lowest energy conformer found. E = {self.energy}')\n        return None\n\n    # --- Method aliases ---\n    symmetry_number = sn\n\n    def __init__(self,\n                 name:         str,\n                 atoms:        Union[List[Atom], Atoms, None],\n                 charge:       Union[float, int],\n                 mult:         Union[float, int],\n                 solvent_name: Optional[str] = None):\n        \"\"\"\n        A molecular species. A collection of atoms with a charge and spin\n        multiplicity in a solvent (None is gas phase)\n\n        ----------------------------------------------------------------------\n        Arguments:\n            name (str): Name of the species\n\n            atoms (list(autode.atoms.Atom) | None): List of atoms in the\n                                                    species, or None\n\n            charge (int): Charge on the species\n\n            mult (int): Spin multiplicity of the species. 2S+1, where S is the\n                        number of unpaired electrons\n\n        Keyword Arguments:\n            solvent_name (str | None): Name of the solvent, or None for a\n                                       species  in the gas phase\n        \"\"\"\n        super().__init__(atoms=atoms)\n\n        self.name = name\n\n        self._charge = int(charge)\n        self._mult = int(mult)\n\n        self.solvent = get_solvent(solvent_name=solvent_name)\n\n        #: All energies calculated at a geometry (autode.values.Energies)\n        self.energies = val.Energies()\n\n        self._grad = None\n        self._hess = None\n\n        #: Molecular graph with atoms(V) and bonds(E) (NetworkX.Graph | None)\n        self.graph = None\n\n        self._conformers = Conformers()\n\n        self.constraints = Constraints()\n","sub_path":"autode/species/species.py","file_name":"species.py","file_ext":"py","file_size_in_byte":33379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"136137187","text":"import logging\nimport os\nimport sys\n\nfrom flask import Flask, Blueprint, redirect\n\nfrom iter8_analytics.api.restplus import api\nfrom iter8_analytics.api.health.endpoints.health import health_namespace\nfrom iter8_analytics.api.analytics.endpoints.analytics \\\n    import analytics_namespace\nfrom iter8_analytics.api.analytics.endpoints.analytics \\\n    import experiment_namespace\nimport iter8_analytics.constants as constants\n\n#  Create a Flask application\napp = Flask(__name__)\n\n# Make sure we can serve requests to endpoints with or without trailing slashes\napp.url_map.strict_slashes = False\n\n# Disable Flask-Restplus X-Fields header used for partial object fetching\napp.config['RESTPLUS_MASK_SWAGGER'] = False\n\n\n@app.after_request\ndef modify_headers(response):\n    '''Sets the server HTTP header returned to the clients for all requests\n    to hide the runtime information'''\n    response.headers['server'] = 'iter8 Analytics'\n    return response\n\n\ndef config_logger():\n    '''Configures the global logger'''\n    logger = logging.getLogger('')\n    handler = logging.StreamHandler()\n    debug_mode = os.getenv(constants.ITER8_ANALYTICS_DEBUG_ENV, 'false')\n    if debug_mode == '1' or str.lower(debug_mode) == 'true':\n        logger.setLevel(logging.DEBUG)\n        handler.setLevel(logging.DEBUG)\n    else:\n        logger.setLevel(logging.INFO)\n        handler.setLevel(logging.INFO)\n    formatter = logging.Formatter(\n        fmt='%(asctime)s - %(name)s - %(levelname)s'\n            ' - %(filename)s:%(lineno)d - %(message)s')\n    handler.setFormatter(formatter)\n    logger.addHandler(handler)\n    logging.getLogger(__name__).info(\"Configured logger\")\n\n\ndef config_env():\n    '''Reads the environment variables that control the server behavior and\n    populates the config dictionary'''\n    if not os.getenv(constants.ITER8_ANALYTICS_METRICS_BACKEND_URL_ENV):\n        logging.getLogger(__name__).critical(\n            u'The environment variable {0} was not set. '\n            'Example of a valid value: \"http://localhost:9090\". '\n            'Aborting!'.format(\n                constants.ITER8_ANALYTICS_METRICS_BACKEND_URL_ENV))\n        sys.exit(1)\n\n    logging.getLogger(__name__).info('Configuring iter8 analytics server')\n\n    app.config[constants.ITER8_ANALYTICS_SERVER_PORT_ENV] = \\\n        os.getenv(constants.ITER8_ANALYTICS_SERVER_PORT_ENV, 5555)\n\n    logging.getLogger(__name__).info(\n        u'The iter8 analytics server will listen on port {0}. '\n        'This value can be set by the environment variable {1}'.format(\n            app.config[constants.ITER8_ANALYTICS_SERVER_PORT_ENV],\n            constants.ITER8_ANALYTICS_SERVER_PORT_ENV))\n\n    debug_mode = os.getenv(constants.ITER8_ANALYTICS_DEBUG_ENV, 'false')\n    if debug_mode == '1' or str.lower(debug_mode) == 'true':\n        app.config[constants.ITER8_ANALYTICS_DEBUG_ENV] = True\n    else:\n        app.config[constants.ITER8_ANALYTICS_DEBUG_ENV] = False\n    logging.getLogger(__name__).info(u'Debug mode: {0}'.format(debug_mode))\n\n\ndef initialize(flask_app):\n    '''Initializes the Flask application'''\n    blueprint = Blueprint('api_v1', __name__, url_prefix='/api/v1')\n    api.init_app(blueprint)\n    api.add_namespace(health_namespace)\n    api.add_namespace(analytics_namespace)\n    api.add_namespace(experiment_namespace)\n    flask_app.register_blueprint(blueprint)\n    config_env()\n\n\n#######\n# main function\n#######\nif __name__ == '__main__':\n    config_logger()\n    initialize(app)\n    logging.getLogger(__name__).info('Starting iter8 analytics server')\n    app.run(\n        host='0.0.0.0', debug=app.config\n        [constants.ITER8_ANALYTICS_DEBUG_ENV],\n        port=int(app.config[constants.ITER8_ANALYTICS_SERVER_PORT_ENV]))\n","sub_path":"iter8_analytics/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"428813193","text":"# lambda-function :-\r\n\"\"\"\r\nLambda function is also called as one-liner function. It is as same as user defined function. It does not need multiple\r\nlines to initialize. It could be called as a shortcut to initiate lengthy functions quickly rather than creating user defined\r\nfunctions every-time.\r\n\"\"\"\r\n\r\n# lamder = lambda a, b: a - b\r\n\r\n# print(lamder(1, -1))\r\n\"\"\"\r\nThe above expression is the normal syntax for lambda function. For lambda function, you must have a variable to assign \r\nlambda function and desired number of arguments in the actual function. \r\nAfter declaring arguments, you must provide a condition after the \"colon( : )\" symbol. You can give a condition like a + b,\r\na - b, a /b or anything else.\r\n\r\nFor Ex:-   \r\n\"\"\"\r\n\r\n# def getlamder(a, b):\r\n#    c = a + b\r\n#    return c\r\n\r\n#           OR\r\n\r\n\r\n# def getlamdder():\r\n#    c = int(input(\"Enter first number:-\"))\r\n#    d = int(input(\"Enter second number:-\"))\r\n#    F = c / d\r\n#    print(F)\r\n\r\n\r\n# print(getlamder(23, 45))\r\n# getlamdder()\r\n\r\n\"\"\"\r\nwhile a user defined function required 2 or more lines, lambda function used only a single line to explain the condition\r\nto respective function. Both produce same result but lambda function acquires very much small space compared to normal \r\nuser defined functions.\r\n\r\nLambda function is useful when the user wants to perform small tasks like addition, subtraction, Multiplication, squaring,\r\ncubing much quickly. Lambda function do not appear more functional when it comes to huge code and complexity. \r\n\"\"\"\r\n\r\n# Sort-function in lambda expressions:-\r\n\"\"\"\r\nSort function takes a function as an input with a \"key\" as an argument. 'key' is a part of sort function which takes user \r\ndefined functions as an input.\r\n\r\nnote:- Sort function only works for lists and not for other collections.\r\n\"\"\"\r\n\r\n# a = [[1, 4], [32, 2], [5, 34]]\r\n#\r\n#\r\n# def adder(a):\r\n#     return a[int(input(\"Enter the index:-\"))]\r\n#\r\n#\r\n# a.sort(key=adder)\r\n# print(a)\r\n\r\n\"\"\"\r\nHere, first of all, list 'a' was created in which group of two numbers were stored in three lists respectively. Later, A\r\nfunction named adder is created with 'a' as an argument and then a statement is added into it regarding to present the numbers \r\nin ascending order. It is ordered to be in ascending order because of the sort function. In function, return [1] or [0] \r\nmeans the list will display the character present at index 1 or 0. In list 'a', there are 3 lists inside the list. So, \r\nthe function will present each character present on position 1 or 0 from each list and arrange the lists in ascending \r\norder according to the lower to greater order.\r\n\r\nIn above example, a = [[1, 4], [32, 2], [5, 34]]. After execution of program, the list will call the function. As the \r\nstatement asks list to return [0] means 0th position element of all lists, the list will compare the 0th position of \r\neach list with each other and will represent the result like this:- [[1, 4], [5, 34], [32, 2]].\r\n\r\nAs you can see, the list with lower 0'th element is brought forward while greater elements than the previous one has \r\nappeared later.\r\n\r\nBut when it comes to lambda functions, user do not need lengthy text . In lambda-functions, you just need to enter the \r\nwhole lambda equation as an argument to the \"key\". \r\nFor Ex :- \r\n\"\"\"\r\n\r\nb = [[1, 5], [5, 61], [66, 23], [22, 34]]\r\n# b.sort(key=lambda x: x[0])\r\n# print(b)\r\n\r\n\r\n\"\"\"\r\nIn the above example, We tested lambda function with sort() to observe the conclusion. To proceed with a lambda equation \r\nwe must define a variable and then the index which we want to sort. Thus the final equation is \"lambda x: x[1]\".\r\n\r\nThe reason for inserting a lambda equation at a time is that , the sort function's key needs a function as an input. As \r\nwe were using lambda function which was not callable, we directly assigned the complete equation to key. In the equation, \r\nwe also assigned wanted index as variable 'a' was separately assigned with the value and then was executed into sort(). \r\n\r\nBesides entering the whole equation into 'key', You can try assigning the equation to a variable and then assigning the \r\nvariable to 'key'.\r\nFor Ex:-         \r\n\"\"\"\r\n\r\nEquator = lambda x: x[1]\r\nb.sort(key=Equator)\r\nprint(b)\r\n","sub_path":"lambda-functn.py","file_name":"lambda-functn.py","file_ext":"py","file_size_in_byte":4226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"167826541","text":"#spark-submit --master=yarn-cluster euler.py 3000000000 output/96\nfrom pyspark import SparkConf, SparkContext\nimport sys, operator\nimport re, string\nimport math\nimport unicodedata\nimport json\nimport operator,random\n\n\ninputs = sys.argv[1]\noutput = sys.argv[2]\n\nconf = SparkConf().setAppName('euler')\nsc = SparkContext(conf=conf)\n\niteration = int(inputs)\npartition=100\neach_iteration=iteration/partition\n\ndef do_things(n):\n    #rand =random.Random()\n    each_count=0\n    for i in range(1,each_iteration):\n        sum=0.0\n        while(sum<1):\n            sum=sum+random.uniform(0,1)\n            each_count=each_count+1\n\n    return each_count\n\n\nblank=[1] * partition\ncount=sc.parallelize(blank,100).map(do_things).reduce(operator.add)\n\n#count.saveAsTextFile(output)\n#print(\"count=%s\" %(count))\n#print(\"iterations=%s\" %(iteration))\n#print(float(count)/iteration)\ne=sc.parallelize([count,iteration,float(count)/iteration]).coalesce(1)\n\ne.saveAsTextFile(output)\n","sub_path":"CMPT732/euler.py","file_name":"euler.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"269062307","text":"from src.NodeNormalization import NodeNormalization\n\n\nif __name__ == '__main__':\n    # instantiate the class that does all the work\n    nn = NodeNormalization()\n\n    # call to load redis instances with normalized node data\n    success: bool = nn.load(500000)\n\n    # check the return\n    if not success:\n        nn.print_debug_msg(f'Failed to load node normalization data.', True)\n    else:\n        nn.print_debug_msg(f'Success', True)\n","sub_path":"load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"187065043","text":"#!/usr/bin/env python\n\"\"\"\nHOD - Heap On Disk\n~~~~~~~~~~~~~~~~~~\n\n\"\"\"\n\nimport os as _os\n\nfrom distutils import core as _core\nfrom distutils.command import build_ext as _build_ext\nimport setuptools as _setuptools\n\n# pylint: disable = invalid-name\n\n\ndef _doc(filename):\n    \"\"\" Read docs file \"\"\"\n    try:\n        with open(_os.path.join('docs', filename)) as fp:\n            return fp.read()\n    except IOError:\n        return None\n\n\ndef _lines(multiline):\n    \"\"\" Split multiline string into single line % empty and comments \"\"\"\n    return [line for line in (\n        line.strip() for line in multiline.splitlines(False)\n    ) if line and not line.startswith('#')]\n\n\npackage = dict(\n    name='hod',\n    top='hod',\n    pathname='hod',\n    provides=_doc('PROVIDES'),\n    desc=_doc('SUMMARY'),\n    longdesc=_doc('DESCRIPTION'),\n    author=u'Andr\\xe9 Malo',\n    email='nd@perlig.de',\n    url='http://opensource.perlig.de/hod/',\n    classifiers=_lines(_doc('CLASSIFIERS') or ''),\n    install_requires=_lines(\"\"\"\n        six >= 1.10.0\n    \"\"\"),\n)\n\n\nclass build_ext(_build_ext.build_ext):  # pylint: disable = no-init\n    \"\"\" Improved extension building code \"\"\"\n\n    def build_extension(self, ext):\n        \"\"\"\n        Build C extension - with extended functionality\n\n        The following features are added here:\n\n        - The macros ``EXT_PACKAGE`` and ``EXT_MODULE`` will be filled (or\n          unset) depending on the extensions name, but only if they are not\n          already defined.\n\n        - \".\" is added to the include directories (for cext.h)\n\n        :Parameters:\n          `ext` : `Extension`\n            The extension to build\n\n        :Return: whatever ``distutils.command.build_ext.build_ext`` returns\n        :Rtype: any\n        \"\"\"\n        # handle name macros\n        macros = dict(ext.define_macros or ())\n        tup = ext.name.split('.')\n        if len(tup) == 1:\n            pkg, mod = None, tup[0]\n        else:\n            pkg, mod = '.'.join(tup[:-1]), tup[-1]\n        if pkg is not None and 'EXT_PACKAGE' not in macros:\n            ext.define_macros.append(('EXT_PACKAGE', pkg))\n        if 'EXT_MODULE' not in macros:\n            ext.define_macros.append(('EXT_MODULE', mod))\n        if pkg is None:\n            macros = dict(ext.undef_macros or ())\n            if 'EXT_PACKAGE' not in macros:\n                ext.undef_macros.append('EXT_PACKAGE')\n\n        if not ext.include_dirs:\n            ext.include_dirs = ['.']\n        elif '.' not in ext.include_dirs:\n            ext.include_dirs.insert(0, '.')\n\n        return _build_ext.build_ext.build_extension(self, ext)\n\n\nEXTENSIONS = [\n]\n\n\ndef setup():\n    \"\"\" Main \"\"\"\n    with open('%(pathname)s/__init__.py' % package) as fp:\n        for line in fp:  # pylint: disable = redefined-outer-name\n            if line.startswith('__version__'):\n                version = line.split('=', 1)[1].strip()\n                if version.startswith((\"'\", '\"')):\n                    version = version[1:-1].strip()\n                break\n        else:\n            raise RuntimeError(\"Version not found\")\n\n    packages = [package['top']] + [\n        '%s.%s' % (package['top'], item)\n        for item in\n        _setuptools.find_packages(package['pathname'])\n    ]\n\n    _core.setup(\n        cmdclass={'build_ext': build_ext},\n        name=package['name'],\n        author=package['author'],\n        author_email=package['email'],\n        classifiers=package['classifiers'],\n        description=package['desc'],\n        long_description=package['longdesc'],\n        url=package['url'],\n        ext_modules=EXTENSIONS,\n        install_requires=package['install_requires'],\n        packages=packages,\n        version=version,\n        zip_safe=False,\n    )\n\n\nif __name__ == '__main__':\n    setup()\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":3751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"286118388","text":"# Copyright 2008-2020 CDPedistas (see AUTHORS.txt)\n#\n# This program is free software: you can redistribute it and/or modify it\n# under the terms of the GNU General Public License version 3, as published\n# by the Free Software Foundation.\n#\n# This program is distributed in the hope that it will be useful, but\n# WITHOUT ANY WARRANTY; without even the implied warranties of\n# MERCHANTABILITY, SATISFACTORY QUALITY, or FITNESS FOR A PARTICULAR\n# PURPOSE.  See the GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License along\n# with this program.  If not, see <http://www.gnu.org/licenses/>.\n#\n# For further info, check  https://github.com/PyAr/CDPedia/\n\nfrom __future__ import print_function\n\nimport codecs\nimport sys\nimport xml.sax\n\nusage = \"\"\"Use: %s [-x] filename title outfile\n\n-x means \"exact\"\n\"\"\"\n\n\nclass Handler(xml.sax.handler.ContentHandler):\n    in_page = False\n    in_title = False\n    this_page = False\n    in_this_text = False\n\n    def __init__(self, exacto, wanted, out):\n        self.wanted = wanted\n        self.accum_title = \"\"\n        self.out = out\n        self.exacto = exacto\n\n    def startElement(self, name, attrs):\n        if name == 'page':\n            self.in_page = True\n        if self.in_page and name == 'title':\n            self.in_title = True\n        if self.this_page and name == \"text\":\n            self.in_this_text = True\n\n    def endElement(self, name):\n        if name == 'page':\n            self.in_page = False\n            self.this_page = False\n        if self.in_page and name == 'title':\n            self.in_title = False\n            if self.exacto:\n                if self.wanted == self.accum_title:\n                    print(self.accum_title)\n                    self.this_page = True\n            else:\n                if self.wanted in self.accum_title:\n                    print(self.accum_title)\n                    self.this_page = True\n            self.accum_title = \"\"\n        if name == \"text\":\n            self.in_this_text = False\n\n    def characters(self, content):\n        if self.in_title:\n            self.accum_title += content\n            return\n        if self.in_this_text:\n            self.out.write(content)\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) < 4:\n        print(usage % sys.argv[0])\n        sys.exit(1)\n\n    exacto = False\n    if sys.argv[1] == \"-x\":\n        exacto = True\n        del sys.argv[1]\n\n    filename = sys.argv[1]\n    wanted = sys.argv[2].decode(\"utf8\")\n    arch = codecs.open(sys.argv[3], \"w\", \"utf8\")\n    h = Handler(exacto, wanted, arch)\n    xml.sax.parse(filename, h)\n","sub_path":"utilities/xml_vs_html/articleExtractor.py","file_name":"articleExtractor.py","file_ext":"py","file_size_in_byte":2622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"552763068","text":"import math\nimport random\nfrom enum import Enum\n\nfrom erika.image_converter import WrappedImage, NotAnImageException\n\n\nclass Direction(Enum):\n    NORTHEAST = 1\n    NORTHWEST = 2\n    SOUTHEAST = 3\n    SOUTHWEST = 4\n\n\nclass ErikaImageRenderer:\n    def __init__(self, some_erika, rendering_strategy_string):\n        self.erika = some_erika\n        self.strategy_string = rendering_strategy_string\n\n    def render_file(self, file_path):\n        strategy = self.create_strategy()\n        self.render_file_for_fixed_strategy(file_path, strategy)\n\n    def render_file_for_fixed_strategy(self, file_path, strategy):\n        erika_image_abstraction = ErikaAndInputFacadeFactory.create_for_path(self.erika, file_path)\n        strategy.render(erika_image_abstraction)\n\n    def render_lines(self, lines):\n        strategy = self.create_strategy()\n        self.render_lines_for_fixed_strategy(lines, strategy)\n\n    def render_lines_for_fixed_strategy(self, lines, strategy):\n        erika_image_abstraction = ErikaAndInputFacadeFactory.create_for_lines(self.erika, lines)\n        strategy.render(erika_image_abstraction)\n\n    def create_strategy(self):\n        strategies = {\n            'LineByLine': LineByLineErikaImageRenderingStrategy,\n            'Interlaced': InterlacedErikaImageRenderingStrategy,\n            'PerpendicularSpiralInward': PerpendicularSpiralInwardErikaImageRenderingStrategy,\n            'RandomDotFill': RandomDotFillErikaImageRenderingStrategy,\n            'ArchimedeanSpiralOutward': ArchimedeanSpiralOutwardErikaImageRenderingStrategy\n        }\n        return strategies[self.strategy_string]()\n\n\nclass ErikaImageRenderingStrategy:\n\n    def __init__(self):\n        pass\n\n    def render(self, erika_image_abstraction):\n        raise Exception('Not implemented')\n\n    @staticmethod\n    def _generate_coordinates(range_x_upper, range_y_upper, range_x_lower=0, range_y_lower=0):\n        for x in range(range_x_lower, range_x_upper):\n            for y in range(range_y_lower, range_y_upper):\n                yield (x, y)\n\n    def _initialize_printed_characters_map(self, max_line_length, line_count):\n        self.printed = []\n        for y in range(line_count):\n            new_row = []\n            for x in range(max_line_length):\n                new_row.append(False)\n            self.printed.append(new_row)\n\n\nclass LineByLineErikaImageRenderingStrategy(ErikaImageRenderingStrategy):\n\n    def __init__(self):\n        ErikaImageRenderingStrategy.__init__(self)\n\n    def render(self, erika_image_abstraction):\n        for y in range(0, erika_image_abstraction.height()):\n            erika_image_abstraction.print_line_at(y)\n            erika_image_abstraction.crlf()\n\n\nclass InterlacedErikaImageRenderingStrategy(ErikaImageRenderingStrategy):\n\n    def __init__(self):\n        ErikaImageRenderingStrategy.__init__(self)\n\n    def render(self, erika_image_abstraction):\n        line_count = erika_image_abstraction.height()\n        moved = 0\n        for even in range(0, line_count, 2):\n            erika_image_abstraction.print_line_at(even)\n            erika_image_abstraction.crlf()\n            erika_image_abstraction.crlf()\n            moved += 2\n\n        # reset cursor to start of line\n        erika_image_abstraction.crlf()\n\n        # do not compensate the line that this adds - the extra line will position the cursor right where we want it\n        # erika_image_abstraction.move_up()\n\n        for lines_to_move_up in range(0, moved):\n            erika_image_abstraction.move_up()\n\n        for odd in range(1, line_count, 2):\n            erika_image_abstraction.print_line_at(odd)\n            erika_image_abstraction.crlf()\n            erika_image_abstraction.crlf()\n\n\nclass PerpendicularSpiralInwardErikaImageRenderingStrategy(ErikaImageRenderingStrategy):\n\n    def __init__(self):\n        ErikaImageRenderingStrategy.__init__(self)\n\n    def render(self, erika_image_abstraction):\n        line_count = erika_image_abstraction.height()\n        max_line_length = erika_image_abstraction.width()\n        self._print_spiral_recursively(erika_image_abstraction, 0, 0, max_line_length - 1, line_count - 1)\n\n    def _print_spiral_recursively(self, erika_image_abstraction,\n                                  upper_left_x, upper_left_y,\n                                  lower_right_x, lower_right_y):\n        \"\"\"ASSUMPTION: current position is at (upper_left_x, upper_left_y)\"\"\"\n        if upper_left_x > lower_right_x or upper_left_y > lower_right_y:\n            return\n\n        # render one round of the spiral, clockwise\n\n        #\n        # =====>\n        #\n        for x in range(upper_left_x, lower_right_x + 1):\n            erika_image_abstraction.print_at(x, upper_left_y)\n\n        # edge case: this was the only last row\n        if upper_left_y == lower_right_y:\n            return\n\n        # reset to last cursor position\n        erika_image_abstraction.move_left()\n\n        # go down - do not print a character twice\n        erika_image_abstraction.move_down()\n\n        # ||\n        # ||\n        # \\/\n        for y in range(upper_left_y + 1, lower_right_y):\n            erika_image_abstraction.print_at(lower_right_x, y)\n            erika_image_abstraction.move_down()\n            erika_image_abstraction.move_left()\n\n        # special handling of last character - smoothen transition\n        erika_image_abstraction.print_at(lower_right_x, lower_right_y)\n\n        # edge case: this was the only last column\n        if upper_left_x == lower_right_x:\n            return\n\n        # back to last printed character\n        erika_image_abstraction.move_left()\n        erika_image_abstraction.move_left()\n\n        #\n        # <=====\n        #\n        for x in range(lower_right_x - 1, upper_left_x, -1):\n            erika_image_abstraction.print_at(x, lower_right_y)\n            erika_image_abstraction.move_left()\n            erika_image_abstraction.move_left()\n\n        # make this a separate step - curses will fail if this would go off screen\n        for x in range(upper_left_x, upper_left_x - 1, -1):\n            erika_image_abstraction.print_at(x, lower_right_y)\n            erika_image_abstraction.move_left()\n\n        erika_image_abstraction.move_up()\n\n        # /\\\n        # ||\n        # ||\n        for y in range(lower_right_y - 1, upper_left_y + 1, -1):\n            erika_image_abstraction.print_at(upper_left_x, y)\n            erika_image_abstraction.move_up()\n            erika_image_abstraction.move_left()\n\n        # special handling of last character - smoothen transition\n        if lower_right_y - 1 >= upper_left_y + 1:\n            erika_image_abstraction.print_at(upper_left_x, upper_left_y + 1)\n\n        # continue with smaller spiral - restart recursion\n        self._print_spiral_recursively(erika_image_abstraction,\n                                       upper_left_x + 1, upper_left_y + 1,\n                                       lower_right_x - 1, lower_right_y - 1)\n\n\nclass RandomDotFillErikaImageRenderingStrategy(ErikaImageRenderingStrategy):\n\n    def __init__(self):\n        ErikaImageRenderingStrategy.__init__(self)\n        self.current_x = 0\n        self.current_y = 0\n\n    def render(self, erika_image_abstraction):\n        self.current_x = 0\n        self.current_y = 0\n\n        line_count = erika_image_abstraction.height()\n        max_line_length = erika_image_abstraction.width()\n        self._initialize_printed_characters_map(max_line_length, line_count)\n        self._render_random_dots_naive(line_count, max_line_length, erika_image_abstraction)\n\n    # naive approach: generate random position, render if not yet printed there\n    # * this results in about 100 additional re-generation attempts for random numbers for the small test images\n    # * improvement idea:\n    # ** define a cut-off point (e.g. \"if 100 letters left\")\n    # ** add remaining dots to a list + use random.choice(list) on those instead of continuing to guess blindly\n    def _render_random_dots_naive(self, line_count, max_line_length, erika_image_abstraction):\n        # once more, assuming a square image\n        number_of_characters = line_count * max_line_length\n\n        for i in range(0, number_of_characters):\n            # generate random position\n            # look up if already printed - re-generate until empty space was found\n            position_x, position_y = self._generate_random_position(max_line_length, line_count)\n            while self.printed[position_y][position_x]:\n                position_x, position_y = self._generate_random_position(max_line_length, line_count)\n\n            # move there + print\n            self._move_to(erika_image_abstraction, position_x, position_y)\n\n            erika_image_abstraction.print_at(position_x, position_y)\n            self.current_x += 1\n            self.printed[position_y][position_x] = True\n            # repeat\n\n    @staticmethod\n    def _generate_random_position(exclusive_max_x, exclusive_max_y):\n        random1 = random.randint(0, exclusive_max_x - 1)\n        random2 = random.randint(0, exclusive_max_y - 1)\n        return random1, random2\n\n    def _move_to(self, erika_image_abstraction, position_x, position_y):\n        # naive: adjust X position first, then Y position\n        while self.current_x < position_x:\n            erika_image_abstraction.move_right()\n            self.current_x += 1\n\n        while position_x < self.current_x:\n            erika_image_abstraction.move_left()\n            self.current_x -= 1\n\n        while self.current_y < position_y:\n            erika_image_abstraction.move_down()\n            self.current_y += 1\n\n        while position_y < self.current_y:\n            erika_image_abstraction.move_up()\n            self.current_y -= 1\n\n\nclass ArchimedeanSpiralOutwardErikaImageRenderingStrategy(ErikaImageRenderingStrategy):\n\n    # TODO mention \"This is a reST style.\" in commit message + link to https://stackoverflow.com/a/24385103/1143126\n    def __init__(self, spiral_param_a=1, spiral_param_b=0.35, spiral_step_size=0.01, render_remaining_characters=True):\n        \"\"\"\n        \"...with real numbers a and b.\n\n        Changing the parameter a turns the spiral,\n\n        while b controls the distance between successive turnings.\"\n\n        https://en.wikipedia.org/wiki/Archimedean_spiral\n\n        :param spiral_param_a: turns the spiral\n        :param spiral_param_b: controls distance between spiral turns\n        :param spiral_step_size: step size between successive datapoints\n        :param render_remaining_characters: if False, will only render the spiral, not fill remaining gaps later\n        \"\"\"\n        ErikaImageRenderingStrategy.__init__(self)\n\n        self.spiral_param_a = spiral_param_a\n        self.spiral_param_b = spiral_param_b\n\n        # round spiral:\n        # 1 / 20 * pi\n        # 1 / 10\n        #\n        # jagged spiral:\n        # 1\n        self.spiral_step_size = spiral_step_size\n\n        # tolerance around 45° angle (in all directions) to mark as area for cut-off\n        self.CUTOFF_ANGLE_TOLERANCE = 15\n\n        self.render_remaining_characters = render_remaining_characters\n\n        # init - only used later\n        self.current_x = 0\n        self.current_y = 0\n        self.spiral_offset_x = 0\n        self.spiral_offset_y = 0\n\n    def render(self, erika_image_abstraction):\n        self.current_x = 0\n        self.current_y = 0\n\n        line_count = erika_image_abstraction.height()\n        max_line_length = erika_image_abstraction.width()\n        self._initialize_printed_characters_map(max_line_length, line_count)\n\n        self._render_spiral(erika_image_abstraction, max_line_length, line_count)\n\n        if self.render_remaining_characters:\n            self._render_remaining(erika_image_abstraction, max_line_length, line_count)\n\n        self._reset_to_upper_left(erika_image_abstraction)\n\n    # Formula for Archimedean spiral:\n    # https://en.wikipedia.org/wiki/Archimedean_spiral\n    # r = a + b * phi\n    # (with phi replaced later by t = 0, 1, ...)\n    #\n    # from polar coordinates to cartesian coordinates:\n    # x = r * cos(phi)\n    # y = r * sin(phi)\n    #\n    # follows:\n    # r = x / cos(phi)\n    # x / cos(phi) = a + b * phi\n    # x = (a + b * phi) * cos(phi)\n    #\n    # r = y / sin(phi)\n    # y / sin(phi) = a + b * phi\n    # y = (a + b * phi) * sin(phi)\n    #\n    def _render_spiral(self, erika_image_abstraction, max_line_length, line_count):\n\n        max_x = max_line_length - 1\n        max_y = line_count - 1\n\n        i = 1\n        directions_out_of_bounds = {}\n        self.spiral_offset_x = math.floor(max_x / 2)\n        self.spiral_offset_y = math.floor(max_y / 2)\n        self._move_to(erika_image_abstraction, self.spiral_offset_x, self.spiral_offset_y)\n        while True:\n            t = i * self.spiral_step_size\n\n            x = (self.spiral_param_a + self.spiral_param_b * t) * math.cos(t) + self.spiral_offset_x\n            y = (self.spiral_param_a + self.spiral_param_b * t) * math.sin(t) + self.spiral_offset_y\n\n            # cut off: cut off if turtle is in the corner (close to 45 degree angle in all directions) + out of bounds\n            current_angle = (math.degrees(t) % 360)\n            if ((45 - self.CUTOFF_ANGLE_TOLERANCE <= current_angle)\n                    and (current_angle <= 45 + self.CUTOFF_ANGLE_TOLERANCE)):\n                self._note_if_out_of_bounds(directions_out_of_bounds, Direction.NORTHEAST, max_x, max_y, x, y)\n            if ((135 - self.CUTOFF_ANGLE_TOLERANCE <= current_angle)\n                    and (current_angle <= 135 + self.CUTOFF_ANGLE_TOLERANCE)):\n                self._note_if_out_of_bounds(directions_out_of_bounds, Direction.NORTHWEST, max_x, max_y, x, y)\n            if ((225 - self.CUTOFF_ANGLE_TOLERANCE <= current_angle)\n                    and (current_angle <= 225 + self.CUTOFF_ANGLE_TOLERANCE)):\n                self._note_if_out_of_bounds(directions_out_of_bounds, Direction.SOUTHEAST, max_x, max_y, x, y)\n            if ((315 - self.CUTOFF_ANGLE_TOLERANCE <= current_angle)\n                    and (current_angle <= 315 + self.CUTOFF_ANGLE_TOLERANCE)):\n                self._note_if_out_of_bounds(directions_out_of_bounds, Direction.SOUTHWEST, max_x, max_y, x, y)\n\n            # all 4 directions are out of bounds now\n            if len(directions_out_of_bounds) > 3:\n                break\n\n            rounded_x = math.floor(x)\n            rounded_y = math.floor(y)\n            # move to calculated next point + print\n            self._goto_and_print_if_in_bounds(erika_image_abstraction, max_x, max_y, rounded_x, rounded_y)\n\n            i += 1\n\n    @staticmethod\n    def _note_if_out_of_bounds(directions_out_of_bounds, direction, max_x, max_y, x, y):\n        if (x < 0) or (y < 0) or (max_x < x) or (max_y < y):\n            directions_out_of_bounds[direction] = 1\n\n    def _goto_and_print_if_in_bounds(self, erika_image_abstraction, max_x, max_y, x, y):\n        if (x < 0) or (y < 0) or (max_x < x) or (max_y < y):\n            return\n        self._goto_and_print(erika_image_abstraction, x, y)\n\n    def _goto_and_print(self, erika_image_abstraction, x, y):\n        if self.printed[y][x]:\n            return\n\n        self.printed[y][x] = True\n        self._move_to(erika_image_abstraction, x, y)\n        erika_image_abstraction.print_at(x, y)\n        self.current_x += 1\n\n    # TODO dry\n    def _move_to(self, erika_image_abstraction, position_x, position_y):\n        # naive: adjust X position first, then Y position\n        while self.current_x < position_x:\n            erika_image_abstraction.move_right()\n            self.current_x += 1\n\n        while position_x < self.current_x:\n            erika_image_abstraction.move_left()\n            self.current_x -= 1\n\n        while self.current_y < position_y:\n            erika_image_abstraction.move_down()\n            self.current_y += 1\n\n        while position_y < self.current_y:\n            erika_image_abstraction.move_up()\n            self.current_y -= 1\n\n    def _render_remaining(self, erika_image_abstraction, max_line_length, line_count):\n        # render remaining letters in ascending order of distance to middle point\n        found = True\n        while found:\n            found = False\n            min_distance = float(\"inf\")\n            min_position_x = 0\n            min_position_y = 0\n\n            for x, y in self._generate_coordinates(max_line_length, line_count):\n                is_printed = self.printed[y][x]\n                is_new_min_distance = self._distance_to_spiral_center(x, y) < min_distance\n                if is_new_min_distance and not is_printed:\n                    found = True\n                    min_position_x = x\n                    min_position_y = y\n                    min_distance = self._distance_to_spiral_center(x, y)\n            if found:\n                self._goto_and_print(erika_image_abstraction, min_position_x, min_position_y)\n\n    def _distance_to_spiral_center(self, x, y):\n        delta_x = math.fabs(self.spiral_offset_x - x)\n        delta_y = math.fabs(self.spiral_offset_y - y)\n        return math.sqrt(delta_x * delta_x + delta_y * delta_y)\n\n    def _reset_to_upper_left(self, erika_image_abstraction):\n        self._move_to(erika_image_abstraction, 0, 0)\n\n\nclass ErikaAndInputFacadeFactory:\n\n    @classmethod\n    def create_for_path(cls, erika, file_path):\n        try:\n            image = WrappedImage(file_path)\n            return ErikaAndImageInputFacade(erika, image)\n        except NotAnImageException:\n            lines = _read_lines_without_trailing_newlines(file_path)\n            return ErikaAndAsciiArtInputFacade(erika, lines)\n\n    @classmethod\n    def create_for_lines(cls, erika, lines):\n        return ErikaAndAsciiArtInputFacade(erika, lines)\n\n\nclass ErikaAndInputFacade:\n\n    def move_left(self):\n        pass\n\n    def move_right(self):\n        pass\n\n    def move_up(self):\n        pass\n\n    def move_down(self):\n        pass\n\n    def crlf(self):\n        pass\n\n    def print_at(self, x, y):\n        pass\n\n    def print_line_at(self, y):\n        pass\n\n    def print_whole_line(self, line):\n        pass\n\n\nclass ErikaAndAsciiArtInputFacade(ErikaAndInputFacade):\n\n    def __init__(self, erika, lines):\n        \"\"\"\n        Indirection for rendering ASCII art images - hiding the concrete type of image from the rendering strategy.\n        :param erika an Erika instance (or test double)\n        :param lines ASCII art image's text lines\n        \"\"\"\n        self.erika = erika\n        self.lines = lines\n\n    def move_left(self):\n        self.erika.move_left()\n\n    def move_right(self):\n        self.erika.move_right()\n\n    def move_up(self):\n        self.erika.move_up()\n\n    def move_down(self):\n        self.erika.move_down()\n\n    def crlf(self):\n        self.erika.crlf()\n\n    def print_at(self, x, y):\n        self.erika.print_ascii(self.lines[y][x])\n\n    def print_line_at(self, y):\n        self.erika.print_ascii(self.lines[y])\n\n    def height(self):\n        return len(self.lines)\n\n    def width(self):\n        return self._calculate_max_line_length(self.lines)\n\n    @staticmethod\n    def _calculate_max_line_length(lines):\n        max_length = 0\n        for line in lines:\n            if len(line) > max_length:\n                max_length = len(line)\n        return max_length\n\n\nclass ErikaAndImageInputFacade(ErikaAndInputFacade):\n\n    def __init__(self, erika, wrapped_image):\n        \"\"\"\n        Indirection for rendering ASCII art images - hiding the concrete type of image from the rendering strategy.\n        :param erika an Erika instance (or test double)\n        :param wrapped_image a WrappedImage abstracting from the image data\n        \"\"\"\n        self.erika = erika\n        self.wrapped_image = wrapped_image\n\n        # because we can't rely on the crlf command, we need to keep track of\n        # the position along the X axis for supporting \"newline\" for image output\n        self.position_x = 0\n\n    def move_left(self):\n        self.erika.move_left_microsteps(1)\n        self.position_x -= 1\n\n    def move_right(self):\n        self.erika.move_right_microsteps(1)\n        self.position_x += 1\n\n    def move_up(self):\n        self.erika.move_up_microstep()\n\n    def move_down(self):\n        self.erika.move_down_microstep()\n\n    def crlf(self):\n        self.erika.move_left_microsteps(self.position_x)\n        self.position_x = 0\n        self.erika.move_down_microstep()\n\n    def print_at(self, x, y):\n        if self.wrapped_image.is_pixel_set(x, y):\n            self.erika.print_pixel()\n        else:\n            self.erika.move_right_microsteps(1)\n        self.position_x += 1\n\n    def print_line_at(self, y):\n        for x in range(0, self.wrapped_image.width()):\n            self.print_at(x, y)\n\n    def height(self):\n        return self.wrapped_image.height()\n\n    def width(self):\n        return self.wrapped_image.width()\n\n\ndef _read_lines_without_trailing_newlines(file_path):\n    with open(file_path, \"r\") as open_file:\n        lines = open_file.readlines()\n    return _remove_trailing_newlines(lines)\n\n\ndef _remove_trailing_newlines(lines):\n    lines_without_newlines = []\n    for line in lines:\n        lines_without_newlines.append(_remove_trailing_newline(line))\n    return lines_without_newlines\n\n\ndef _remove_trailing_newline(line):\n    return line.replace('\\n', \"\").replace('\\r', \"\")\n","sub_path":"erika/erika_image_renderer.py","file_name":"erika_image_renderer.py","file_ext":"py","file_size_in_byte":21218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"329874117","text":"import warnings\nimport pandas as pd\nimport numpy as np\nimport random\n\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import roc_curve\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import classification_report\n\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.model_selection import StratifiedShuffleSplit\n\nfrom load_data import sc2_phenotype, sc2_outcome,sc2_cnv_data\nfrom feature_selection import feature_selection_wrapper\nfrom stack_ensemble import stack_ensemble_model\n\n\ndef sc2_model(cnv_data, phenotype, outcome):\n    \"\"\"\n    Parameters\n    ----------\n    cnv_data:\n        pandas DataFrame, DNA copy number and rows are samples, \n        columns are genomic cytobands.Values are reported as chromosome \n        instability values (CIN Index)\n    phenotype:\n        pandas DataFrame, One-Hot encoding to represent clinical phenotype\n        [SEX, WHO_GRADING, CANCER_TYPE],rows are samples.\n    outcome:\n        pandas DataFrame, survival status outcome,A value of 0 means \n        the patient was alive or censoring at last follow up.A value \n        of 1 means patient died before the last scheduled follow up.\n    Returns\n    -------\n    y_test:\n        array, last time y_test.\n    y_pred:\n        array, last time y_pred.\n    auc:\n        float, last time cross validation auc\n    model:\n        Logistic Regression model train by all data\n    feature:\n        list, feature list\n    \"\"\"\n\n    target = 'SURVIVAL_STATUS'\n    data = pd.merge(outcome,\n                    cnv_data,\n                    left_index=True,\n                    right_index=True)\n    data = pd.merge(data,\n                    phenotype,\n                    left_index=True,\n                    right_index=True)\n    cnv_feature = list(cnv_data.var().sort_values(ascending=False)[:50].index)\n    cnv_feature = feature_selection_wrapper(data[cnv_feature],\n                                            data[target], \n                                            target, \n                                            10, \n                                            feature_return='log')\n    feature = cnv_feature+list(phenotype.columns)\n    \n    x, y = data[feature].values.astype(float), data[target].values.astype(float)\n    # Stratified ShuffleSplit cross-validator\n    n = 20\n    sp = StratifiedShuffleSplit(n_splits=n, test_size=0.5, random_state=0)\n    tmp_auc = []\n    # Stratified ShuffleSplit cross-validator\n    for train_sample, test_sample in sp.split(x, y):\n        print(len(tmp_auc))\n        x_train, y_train = x[train_sample], y[train_sample]\n        x_test, y_test = x[test_sample], y[test_sample]\n        with warnings.catch_warnings():\n            warnings.simplefilter('ignore')\n            clf = stack_ensemble_model(x_train, y_train)\n\n        print('test AUC',roc_auc_score(y_test,clf.predict_proba(x_test)[:,1]))\n        y_pred = clf.predict(x_test)\n        tmp_auc.append(roc_auc_score(y_test,clf.predict_proba(x_test)[:,1]))\n        \n    with warnings.catch_warnings():\n        warnings.simplefilter('ignore')\n        model = stack_ensemble_model(x, y)\n    print('average AUC',np.mean(tmp_auc))\n    return y_test, y_pred, tmp_auc[-1], model, feature\n\n\nif __name__ == '__main__':\n    y_test, y_pred, auc, model, feature = sc2_model(sc2_cnv_data, sc2_phenotype, sc2_outcome)\n    # Model Performance\n    [[tn,fp],[fn,tp]] = confusion_matrix(y_test, \n                                         y_pred)\n    #print confusion matrix\n    print('T\\\\P\\tAlive\\tDied\\tSum\\nAlive\\t{}\\t{}\\t{}\\nDied\\t{}\\t{}\\t{}\\nSum\\t{}\\t{}\\t{}\\n'.format(tn,fp,tn+fp,\n                                                                                        fn,tp,fn+tp,\n                                                                                        tn+fn,fp+tp,tn+fp+fn+tp))\n    print(classification_report(y_test, y_pred))\n    print('Auc score',auc)\n    print('Overall accuracy:',accuracy_score(y_test, y_pred,normalize=True))\n    print('Feature list:{}'.format(feature))","sub_path":"src/sc2_model.py","file_name":"sc2_model.py","file_ext":"py","file_size_in_byte":4042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"296866134","text":"# Name:    Kevin Khomiakov\n# Section: 1\n# Represents a shopping list\n# containing items and quantities\nclass ShoppingList:\n\n    # DO NOT CHANGE\n    # Constructor\n    # input: name of the list,\n    #        dictionary {item name:quantity} (optional)\n    def __init__(self, listname, starter={}):\n        self.listname = listname\n        self.items = {}\n\n        for k,v in starter.items():\n            self.items[k] = v\n\n    # DO NOT CHANGE\n    # Two lists are considered equal if they have the same\n    # name and the same set of items/quantities\n    def __eq__(self, other):\n        return (isinstance(other, ShoppingList) and\n                self.listname == other.listname and\n                self.items == other.items)\n\n    # TODO\n    # String representation of the list\n    # output: == List Name ==\n    #         item: quantity\n    def __str__(self):\n        str_d = \"\"\n        for i in sorted(self.items.items()):\n            str_d += (str(i[0]) + ': ')\n            str_d += (str(i[1]) + '\\n')\n        s = '== {0} =='.format(self.get_name())\n        if not self.items.items():\n            return s + str_d.rstrip()\n        else:\n            return s + '\\n' + str_d.rstrip()\n\n    # TODO\n    # Produces a new list that sums the two input lists\n    # input: list 1, list 2\n    # output: combined list with name as Name1+Name2\n    #         and items being the union of the two\n    #         (where overlapping items have quantities added)\n    def __add__(self, other):\n\n        item_names = other.get_item_names()\n\n        for i in range(len(item_names)):\n            self.add_item(item_names[i], other.items.get(item_names[i]))\n\n        return ShoppingList(self.get_name() + '+' + other.get_name(), starter=self.items)\n\n    # DO NOT CHANGE\n    # Gets the list name\n    # output: name fo the list\n    def get_name(self):\n        return self.listname\n\n    # TODO\n    # Add an item to this list\n    # input: item name,\n    #        item quantity\n    # output: quantity of the item in the list\n    def add_item(self, name, quantity):\n        if name in self.items:\n            quantity += quantity\n        self.items.update({name: quantity})\n        return quantity\n\n    # TODO\n    # Gets the current quantity of the item\n    # in this list\n    # input: item name\n    # output: quantity (or 0 if not in the list)\n    def get_quantity(self, name):\n        if name in self.items:\n            return self.items.get(name)\n        else:\n            return 0\n\n    # TODO\n    # Removes some quantity of an item from the list\n    # input: item name,\n    #        quantity to remove (optional; all if not supplied)\n    # output: quantity of the item in this list after removal\n    def remove_item(self, name, quantity=None):\n\n        q = self.get_quantity(name)\n\n        if name in self.items:\n            if quantity == None or quantity > q:\n                self.items.pop(name)\n                return 0\n\n            elif quantity < q:\n                q = q - quantity\n                self.items.update({name: q})\n                return q\n\n        else:\n            return 0\n\n    # TODO\n    # Gets a sorted list of the items in the list\n    # output: names of items in the list (sorted)\n    def get_item_names(self):\n        return sorted(self.items.keys())\n\n    # TODO\n    # Gets the cost of the list given a price dictionary\n    # input: {item:unit price}\n    # output: total cost of the list,\n    #         sorted list of missing item names\n    def cost(self, price_dict):\n        c = 0\n        arr =[]\n\n        for item in self.items.keys():\n            quantity = self.get_quantity(item)\n\n            if (item in price_dict):\n                c += quantity* price_dict.get(item)\n\n            else:\n                arr.append(item)\n\n        return (c, sorted(arr))\n# TODO\n# Reads a dictionary from a file where each\n# line is item [whitespace] value\n# input: path to file,\n#        function to apply to each value\n# output: {item:preprocess_func(value)}\ndef read_dict_from_file(filepath, preprocess_func):\n    dict = {}\n\n    with open(filepath, 'r') as f:\n        array = [line for line in f.readlines()]\n        answer = [line.split() for line in array]\n\n    for i in answer:\n        if i != []:\n            dict.update({i[0]: preprocess_func(i[1])})\n\n    return dict\n\n# TODO\n# Reads a shopping list from a file\n# input: path to file,\n#        list name\n# output: ShoppingList\ndef shopping_list_from_file(filepath, name):\n    return ShoppingList(name, starter=read_dict_from_file(filepath, float))\n\n# TODO\n# Creates a dictionary of store prices\n# from a dictionary of store files\n# input: {name:file path}\n# output: {name:{item:price}}\ndef files_to_prices(files):\n    keys, values = files.keys(), files.values()\n    arr1, arr2 = [], []\n    answer = {}\n\n    for i in keys:\n        arr1.append(i)\n\n    for i in values:\n        arr2.append(i)\n\n    for i in range(len(values)):\n        answer.update({arr1[i]: read_dict_from_file(arr2[i], float)})\n\n    return answer\n\n# TODO\n# Given a dictionary of prices, gets the\n# store name with the best deal\n# input: {store name:{item:price}},\n#        item\n# output: store name (None if item is not in any store)\ndef best_deal(prices, item):\n    min = 100000000000\n    s = ''\n    j = 0\n\n    for i in prices.values():\n        for k in i.keys():\n            if k == item and i.get(k) < min:\n                j += 1\n                min = i.get(k)\n                s = i\n\n    for i in prices:\n        for k in prices.keys():\n            if s == prices.get(k):\n                answer = k\n\n    if j == 0:\n        return None\n\n    else:\n        return answer\n\n# TODO\n# Given a shopping list file and a dictionary mapping\n# store names to store prices, produce an optimal list of store\n# shopping lists and a leftover list of items that can't\n# be purchased\n# input: file for shopping list,\n#        {store:file}\n# output: [list of shopping lists sorted by name],\n#         shopping list of items not possible to purchase\ndef need_and_prices_to_lists(need, prices):\n    stores = []\n    not_found = ShoppingList(\"leftover\", starter = {})\n    for store in prices.keys():\n        prices.update({store: read_dict_from_file(prices.get(store), float)})\n    need = read_dict_from_file(need, float)\n    for needed in need.keys():\n        if (best_deal(prices, needed) == None):\n            not_found.add_item(needed, need.get(needed))\n        else:\n            store = ShoppingList(best_deal(prices, needed), starter = {needed: need.get(needed)})\n            idx = -1\n            for str in stores:\n                if store.get_name() == str.get_name():\n                    idx = stores.index(str)\n            if idx == -1:\n                stores.append(store)\n            else:\n                stores[idx].add_item(needed, need.get(needed))\n    names = []\n    sorted_stores = []\n\n    for store in stores:\n        names.append(store.listname)\n\n    names = sorted(names)\n\n    for name in names:\n        for store in stores:\n            if store.listname == name:\n                sorted_stores.append(store)\n\n    return sorted_stores, not_found\n","sub_path":"HW08/shoppinglist.py","file_name":"shoppinglist.py","file_ext":"py","file_size_in_byte":7059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"330161","text":"from django.shortcuts import render, redirect\n\nfrom CMS.enums import enums\nfrom core.utils import get_page_for_language, get_new_landing_page_for_language\nfrom institutions.models import InstitutionDetailPage, Institution\nfrom site_search.models import SearchLandingPage\n\n\ndef institution_detail(request, institution_id, language=enums.languages.ENGLISH):\n    institution, error = Institution.find(institution_id, language)\n\n    if error:\n        redirect_page = get_new_landing_page_for_language(language)\n        #redirect_page = get_page_for_language(language, SearchLandingPage.objects.all()).url\n        return redirect(redirect_page + '?load_error=true&error_type=0')\n\n    page = get_page_for_language(language, InstitutionDetailPage.objects.all())\n\n    if not page:\n        return render(request, '404.html')\n\n    page.tef_report_link = page.tef_report_link.replace('{{institution_name}}', institution.pub_ukprn_name)\n    path = request.path.replace(\"/en/\", \"/\")\n    if language == enums.languages.ENGLISH:\n        translated_url = '/cy' + path if language == enums.languages.ENGLISH else path\n    else:\n        translated_url = path.replace('/cy/', '/')\n\n    context = page.get_context(request)\n    context.update({\n        'page': page,\n        'institution': institution,\n        'translated_url': translated_url,\n        'cookies_accepted': request.COOKIES.get('discoverUniCookies')\n    })\n\n    return render(request, 'institutions/institution_detail_page.html', context)\n","sub_path":"institutions/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"166709379","text":"#!/usr/bin/env python\n\"\"\"\nCopyright 2008-2022 VMware, Inc.  All rights reserved.\n-- VMware Confidential\n\nThis module is the vmomi client stub for talking to PyVmomiServer managed object\ndirectly\n\"\"\"\n__author__ = \"VMware, Inc\"\n\nfrom pyVmomi import SoapAdapter, VmomiSupport\nfrom pyVmomi.VmomiSupport import newestVersions\nfrom .PyVmomiServer import _ImportTypesAndManagedObjects\nfrom . import SoapHandler\nimport logging\n\n\n# SOAP stub adapter object for PyVmomiServer SoapHandler direct execution\nclass LocalClientStubAdapter(SoapAdapter.SoapStubAdapter):\n    # Constructor\n    #\n    # The endpoint can be specified individually as either a host/port\n    # combination, or with a URL (using a url= keyword).\n    #\n    # @param self self\n    # @param version API version\n    # @param printRequest print SOAP request\n    # @param printResponse print SOAP response\n    def __init__(self,\n                 version=newestVersions.GetName('vim'),\n                 printRequest=False,\n                 printResponse=False):\n        SoapAdapter.SoapStubAdapter.__init__(self, version=version)\n        self.ns = VmomiSupport.GetVersionNamespace(version)\n        self.soapHandler = SoapHandler.SoapHandler()\n        self.soapDeserializer = SoapAdapter.SoapResponseDeserializer(self)\n        self.printRequest = printRequest\n        self.printResponse = printResponse\n        _ImportTypesAndManagedObjects()\n\n    # Invoke a managed method\n    #\n    # @param self self\n    # @param mo the \"this\"\n    # @param info method info\n    # @param args arguments\n    def InvokeMethod(self, mo, info, args):\n        # Serialize\n        request = self.SerializeRequest(mo, info, args)\n        if self.printRequest:\n            logging.info(\"*\" * 60)\n            logging.info(request)\n            logging.info(\"*\" * 60)\n\n        # Send request\n        isFault, response = self.soapHandler.HandleRequest(request, self.ns)\n        if self.printResponse:\n            logging.info(\"=\" * 60)\n            logging.info(response)\n            logging.info(\"=\" * 60)\n\n        # Deserialize\n        obj = self.soapDeserializer.Deserialize(str(response), info.result)\n\n        if not isFault:\n            return obj\n        else:\n            raise obj\n","sub_path":"rvc_rvc/lib/rvc/modules/vsantest/automation/lib/parse-support-bundle/pyVpx/pyVmomi/server/LocalClientStub.py","file_name":"LocalClientStub.py","file_ext":"py","file_size_in_byte":2214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}
+{"seq_id":"378418439","text":"\n# 求一个字符串的最长的回文子串。\n\nclass Solution:\n    def longestPalindrome(self, s: str) -> str:\n\n    \tdef spread(s,left,right):\n    \t\twhile left>=0 and right<len(s) and s[left]==s[right]:\n    \t\t\tleft-=1\n    \t\t\tright+=1\n    \t\treturn s[left+1:right]\n\n    \tif s==s[::-1]:\n    \t\treturn s\n\n        # 默认的条件下是一个长度的值。\n        # 如果所有的值都不相等的化，就是单一的一个字符的长度的值。\n    \tres = s[:1]\n\n    \tfor i in range(len(s)):\n\n            # 判断奇数的回文字符串结果值。\n    \t\tpalindrome_odd = spread(s,i,i)\n\n            # 判断偶数的回文字符串的结果值。\n    \t\tpalindrome_even = spread(s,i,i+1)\n    \t\tres = max(palindrome_odd, palindrome_even,res, key=len)\n    \treturn res\n\n\nif __name__ == \"__main__\":\n\ts = Solution()\n\tstr2 =\"babad\"\n\tprint(s.longestPalindrome(str2))\n\n\n","sub_path":"PartC/py最长的回文子串3.py","file_name":"py最长的回文子串3.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"15"}