diff --git "a/1228.jsonl" "b/1228.jsonl" new file mode 100644--- /dev/null +++ "b/1228.jsonl" @@ -0,0 +1,363 @@ +{"seq_id": "423106259", "text": "import matplotlib.pyplot as plt\nimport socket\n\nimport h5_storage\nimport analysis\n\nplt.close('all')\n\nhostname = socket.gethostname()\nif 'psi' in hostname or 'lc6a' in hostname or 'lc7a' in hostname:\n default_dir = '/sf/data/measurements/2021/04/25/'\n archiver_dir = '/afs/psi.ch/intranet/SF/Beamdynamics/Philipp/data/archiver_api_data/'\nelif hostname == 'desktop':\n default_dir = '/storage/data_2021-04-25/'\n archiver_dir = '/storage/Philipp_data_folder/archiver_api_data/'\nelif hostname == 'pubuntu':\n default_dir = '/home/work/data_2021-04-25/'\n archiver_dir = '/home/work/archiver_api_data/'\n\nstreaker_calib_file = default_dir + '2021_04_25-16_55_25_Calibration_SARUN18-UDCP020.h5'\ndata_dict = h5_storage.loadH5Recursive(streaker_calib_file)\nanalysis.analyze_streaker_calibration(data_dict['raw_data'])\n\n\n\nplt.show()\n", "sub_path": "050_improve_offset_scan.py", "file_name": "050_improve_offset_scan.py", "file_ext": "py", "file_size_in_byte": 837, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.close", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "socket.gethostname", "line_number": 9, "usage_type": "call"}, {"api_name": "h5_storage.loadH5Recursive", "line_number": 21, "usage_type": "call"}, {"api_name": "analysis.analyze_streaker_calibration", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "191353790", "text": "# coding=UTF-8\nimport requests\nfrom prices_us import PricesUS\nfrom prices_eu import PricesEU\nfrom geopy.geocoders import Nominatim\n\n\nclass GeoData:\n def __init__(self, address):\n self.address = address\n geolocator = Nominatim()\n location = geolocator.geocode(address)\n self.lat = location.latitude\n self.lon = location.longitude\n\n payload = {'key': '6TMPLBB3OFXI', 'format': 'json', 'by': 'position', 'lat': self.lat, 'lng': self.lon}\n self._country_detail = requests.get('http://api.timezonedb.com/v2/get-time-zone', params=payload).json()\n\n self.set_timezone()\n self.set_price()\n\n @property\n def country_name(self):\n return self._country_detail['countryName']\n\n @property\n def country_code(self):\n return self._country_detail['countryCode']\n\n\n def set_timezone(self):\n self.timezone_name = self._country_detail['abbreviation']\n self.timezone_time = self._country_detail['formatted']\n self.timezone_region = self._country_detail['zoneName']\n self.timezone_gmt_offset = self._country_detail['gmtOffset']\n\n\n def set_price(self):\n # responsible for determining the price per kwh of electricity in the country that the given address is within\n if self.country_code == 'GB':\n self.price = 0.12\n self.currency = \"£\"\n elif self.country_code == 'US':\n self.price = PricesUS.get_latest_elec_cost(self.lat, self.lon)\n self.currency = PricesUS.currency_str\n else:\n self.price = PricesEU.get_latest_elec_cost(self.country_code)\n self.currency = PricesEU.currency_str\n", "sub_path": "geo_calc/geo_data.py", "file_name": "geo_data.py", "file_ext": "py", "file_size_in_byte": 1677, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "geopy.geocoders.Nominatim", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "prices_us.PricesUS.get_latest_elec_cost", "line_number": 44, "usage_type": "call"}, {"api_name": "prices_us.PricesUS", "line_number": 44, "usage_type": "name"}, {"api_name": "prices_us.PricesUS.currency_str", "line_number": 45, "usage_type": "attribute"}, {"api_name": "prices_us.PricesUS", "line_number": 45, "usage_type": "name"}, {"api_name": "prices_eu.PricesEU.get_latest_elec_cost", "line_number": 47, "usage_type": "call"}, {"api_name": "prices_eu.PricesEU", "line_number": 47, "usage_type": "name"}, {"api_name": "prices_eu.PricesEU.currency_str", "line_number": 48, "usage_type": "attribute"}, {"api_name": "prices_eu.PricesEU", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "473970139", "text": "import json\nimport mock\n\nfrom django.test import TestCase\n\nfrom billing_proxy.api.middleware.bssapi_middleware import \\\n AdminAuthenticationMiddleware\n\nfrom billing_proxy.client import cloud_guard_client\nfrom billing_proxy.client import common_req_client\nfrom billing_proxy import models\n\nfrom django.conf import settings\n\n\nclass ResourceStatisticTestcase(TestCase):\n def setUp(self):\n super(ResourceStatisticTestcase, self).setUp()\n self.httpclient = common_req_client.construct_http_client(\n username=settings.BILLING_PROXY_USER,\n password=settings.BILLING_PROXY_PASSWORD,\n tenant_name=settings.BILLING_PROXY_TENANT,\n auth_url=settings.IDENTITY_URI + 'v2.0')\n self.cg_client = cloud_guard_client.Client(\n username=settings.BILLING_PROXY_USER,\n password=settings.BILLING_PROXY_PASSWORD,\n tenant_name=settings.BILLING_PROXY_TENANT,\n auth_url=settings.IDENTITY_URI + 'v2.0')\n\n def test_client_authenticate(self):\n self.assertEqual(\"keystone\", self.httpclient.auth_strategy)\n self.httpclient.authenticate_and_fetch_endpoint_url()\n auth_info = self.httpclient.get_auth_info()\n self.assertIn(\"auth_token\", auth_info)\n self.assertIn(\"auth_tenant_id\", auth_info)\n self.assertIn(\"auth_user_id\", auth_info)\n self.assertIn(\"endpoint_url\", auth_info)\n\n def test_client_get_antiddos_data(self):\n AdminAuthenticationMiddleware.process_request = mock.Mock()\n\n models.get_order_by_resource_id = mock.Mock(\n return_value=\"a4cc02f6-bb23-4aac-be47-96d9133edb9f\")\n body = {\n \"startTime\": \"2017-02-01\",\n \"endTime\": \"2017-02-11\",\n \"usage\": [\n {\"resourceType\": \"Anti-DDoS\",\n \"resourceId\": \"ac5bf3d3-421c-4e89-b161-4c5f2bdb1f05\"}\n ]\n }\n body = json.dumps(body)\n resp = self.client.post('/bss_api/ResourceUsage', data=body,\n content_type=\"json\")\n self.assertIn(\"data\", resp)\n self.assertIn(\"bill\", resp[\"data\"])\n self.assertIn(\"DomainNameCount\", resp[\"data\"][\"bill\"])\n self.assertIn(\"NormalFixedBandwidth\", resp[\"data\"][\"bill\"])\n self.assertIn(\"CCPeakRequest\", resp[\"data\"][\"bill\"])\n self.assertIn(\"DDosPeakFlow\", resp[\"data\"][\"bill\"])\n", "sub_path": "billing_proxy/tests/units/test_cloudguard_interface.py", "file_name": "test_cloudguard_interface.py", "file_ext": "py", "file_size_in_byte": 2384, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.test.TestCase", "line_number": 16, "usage_type": "name"}, {"api_name": "billing_proxy.client.common_req_client.construct_http_client", "line_number": 19, "usage_type": "call"}, {"api_name": "billing_proxy.client.common_req_client", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.settings.BILLING_PROXY_USER", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.settings.BILLING_PROXY_PASSWORD", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.settings.BILLING_PROXY_TENANT", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 22, "usage_type": "name"}, {"api_name": "django.conf.settings.IDENTITY_URI", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 23, "usage_type": "name"}, {"api_name": "billing_proxy.client.cloud_guard_client.Client", "line_number": 24, "usage_type": "call"}, {"api_name": "billing_proxy.client.cloud_guard_client", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.settings.BILLING_PROXY_USER", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.settings.BILLING_PROXY_PASSWORD", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.settings.BILLING_PROXY_TENANT", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.settings.IDENTITY_URI", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 28, "usage_type": "name"}, {"api_name": "billing_proxy.api.middleware.bssapi_middleware.AdminAuthenticationMiddleware.process_request", "line_number": 40, "usage_type": "attribute"}, {"api_name": "billing_proxy.api.middleware.bssapi_middleware.AdminAuthenticationMiddleware", "line_number": 40, "usage_type": "name"}, {"api_name": "mock.Mock", "line_number": 40, "usage_type": "call"}, {"api_name": "billing_proxy.models.get_order_by_resource_id", "line_number": 42, "usage_type": "attribute"}, {"api_name": "billing_proxy.models", "line_number": 42, "usage_type": "name"}, {"api_name": "mock.Mock", "line_number": 42, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "496830517", "text": "# Copyright 2021 Huawei Technologies Co., Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ============================================================================\n\n\"\"\"post process for 310 inference\"\"\"\nimport os\nimport argparse\nimport pickle\nimport numpy as np\n\nparser = argparse.ArgumentParser(description=\"ecolite inference\")\nparser.add_argument(\"--result_path\", type=str, required=True, help=\"result files path.\")\nparser.add_argument(\"--label_path\", type=str, required=True, help=\"image file path.\")\nparser.add_argument('--batch_size', default=256, type=int,\n metavar='N', help='mini-batch size (default: 256)')\nargs = parser.parse_args()\nbatch_size = args.batch_size\nnum_classes = 101\n\n\ndef get_result(result_path, label_path):\n \"\"\"Get final accuracy result\"\"\"\n files = os.listdir(result_path)\n top1 = 0\n top5 = 0\n total_data = batch_size * len(files)\n for i in range(len(files)):\n video_label_id = i\n file = 'eval_predict_' + str(video_label_id) + '_.bin'\n data_path = os.path.join(result_path, file)\n result = np.fromfile(data_path, dtype=np.float32).reshape(batch_size, num_classes)\n predict = np.argsort(result, axis=-1)[:, -5:]\n\n label_file_path = label_path + 'eval_label_'\n label_file_path += str(video_label_id)\n label_file_path += '.pkl'\n pkllabelfile = open(label_file_path, 'rb')\n label = pickle.load(pkllabelfile)\n for batch in range(batch_size):\n if predict[batch][-1] == label[batch]:\n top1 += 1\n top5 += 1\n elif label[batch] in predict[batch][-5:]:\n top5 += 1\n print(f\"Total data: {total_data}, top1 accuracy: {top1 / total_data}, top5 accuracy: {top5 / total_data}.\")\n\n\nif __name__ == '__main__':\n get_result(args.result_path, args.label_path)\n", "sub_path": "research/cv/ecolite/postprocess.py", "file_name": "postprocess.py", "file_ext": "py", "file_size_in_byte": 2354, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.fromfile", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.argsort", "line_number": 43, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "344263715", "text": "#!/usr/bin/env python\nimport tornado.httpserver\nimport tornado.ioloop\nimport tornado.web\nimport tornado.auth\nimport tornado.httpclient\nimport auth9\nimport json\n\n# this base handler defines how you get your user info, cookie or session\nclass BaseHandler(tornado.web.RequestHandler):\n def get_current_user(self):\n user = self.get_secure_cookie(\"user\")\n if user:\n return json.loads(user)\n return None\n\n# this handler handles login and saves user info\nclass LoginHandler(auth9.LoginHandler):\n def on_login(self, user, redirect_url):\n if not user:\n raise tornado.web.HTTPError(500, \"net9 auth failed\")\n self.set_secure_cookie(\"user\", json.dumps(user))\n self.redirect(redirect_url)\n\n# normal handler\nclass MainHandler(BaseHandler):\n @tornado.web.authenticated\n def get(self):\n self.write(\"Hello\" + json.dumps(self.current_user))\n\nsettings = {\n \"cookie_secret\": \"just a test\",\n \"login_url\": \"/login\",\n \"client_id\": \"WlNIuDqhEIKIxEZd-Rjj8QycscQ\",\n \"client_secret\": \"asd\"\n}\n\napplication = tornado.web.Application([\n (r\"/\", MainHandler),\n (r\"/login\", LoginHandler)\n], **settings)\n\nif __name__ == \"__main__\":\n http_server = tornado.httpserver.HTTPServer(application)\n http_server.listen(8888)\n tornado.ioloop.IOLoop.instance().start()\n\n", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1351, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tornado.httpserver.web", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 11, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 15, "usage_type": "call"}, {"api_name": "auth9.LoginHandler", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tornado.httpserver.web.HTTPError", "line_number": 22, "usage_type": "call"}, {"api_name": "tornado.httpserver.web", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 22, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 23, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 30, "usage_type": "call"}, {"api_name": "tornado.httpserver.web", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 28, "usage_type": "name"}, {"api_name": "tornado.httpserver.web.Application", "line_number": 39, "usage_type": "call"}, {"api_name": "tornado.httpserver.web", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 39, "usage_type": "name"}, {"api_name": "tornado.httpserver.httpserver.HTTPServer", "line_number": 45, "usage_type": "call"}, {"api_name": "tornado.httpserver.httpserver", "line_number": 45, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 45, "usage_type": "name"}, {"api_name": "tornado.httpserver.ioloop.IOLoop.instance", "line_number": 47, "usage_type": "call"}, {"api_name": "tornado.httpserver.ioloop", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 47, "usage_type": "name"}]} +{"seq_id": "117803502", "text": "import asyncio\n\nimport asyncpg\n\nfrom data import config\n\n\nclass Database:\n def __init__(self, loop: asyncio.AbstractEventLoop):\n self.pool: asyncio.pool.Pool = loop.run_until_complete(\n asyncpg.create_pool(\n user = config.PGUSER,\n password = config.PGPASSWORD,\n host = config.ip,\n\n )\n )\n\n async def create_table_users(self):\n sql = \"\"\"\n CREATE TABLE IF NOT EXISTS Users (\n id INT NOT NULL,\n Name VARCHAR(255) NOT NULL,\n email VARCHAR(255),\n PRIMARY KEY (id))\n \"\"\"\n await self.pool.execute(sql)\n\n @staticmethod\n def format_args(sql, parameters: dict):\n sql += \"AND \".join([\n f\"{item} = ${num}\" for num, item in enumerate(parameters, start=1)\n ])\n return sql, tuple(parameters.values())\n\n async def add_user(self, id: int, name: str, email: str = None):\n sql = \"INSERT INTO Users (id, name, email) VALUES ($1, $2, $3)\"\n try:\n await self.pool.execute(sql, id, name, email)\n except asyncpg.exceptions.UniqueViolationError:\n pass\n self.pool.execute(sql, id, name, email)\n\n async def select_all_users(self):\n sql = \"SELECT * FROM Users\"\n return self.pool.fetch(sql)\n\n async def select_user(self, **kwargs):\n sql = \"SELECT * FROM Users WHERE \"\n sql, parameters = self.format_args(sql, kwargs)\n return await self.pool.fetchrow(sql, *parameters)\n\n async def count_users(self):\n return await self.pool.fetchval(\"SELECT COUNT(*) FROM Users\")\n\n async def update_user_email(self, email, id):\n sql = \"UPDATE Users SET email = $1 WHERE id = $2\"\n return await self.pool.execute(sql, email, id)\n\n async def delete_users(self):\n await self.pool.execute(\"DELETE FROM Users WHERE True\")\n", "sub_path": "TelegramBot/telegram_bot/utils/db_api/postgresql.py", "file_name": "postgresql.py", "file_ext": "py", "file_size_in_byte": 1879, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "asyncio.AbstractEventLoop", "line_number": 9, "usage_type": "attribute"}, {"api_name": "asyncio.pool", "line_number": 10, "usage_type": "attribute"}, {"api_name": "asyncpg.create_pool", "line_number": 11, "usage_type": "call"}, {"api_name": "data.config.PGUSER", "line_number": 12, "usage_type": "attribute"}, {"api_name": "data.config", "line_number": 12, "usage_type": "name"}, {"api_name": "data.config.PGPASSWORD", "line_number": 13, "usage_type": "attribute"}, {"api_name": "data.config", "line_number": 13, "usage_type": "name"}, {"api_name": "data.config.ip", "line_number": 14, "usage_type": "attribute"}, {"api_name": "data.config", "line_number": 14, "usage_type": "name"}, {"api_name": "asyncpg.exceptions", "line_number": 40, "usage_type": "attribute"}]} +{"seq_id": "283007015", "text": "def f(x, t):\n return exp(-(x - 3*t)**2)*sin(3*pi*(x - t))\n\nimport matplotlib.pyplot as plt\nfrom numpy import sin, exp, pi, linspace\nx = linspace(-4, 4, 501) #x values\nt = 0\ny = f(x, t)\n\nplt.plot(x, y, 'r-') #plot\nplt.ylabel('f(x, t)')\nplt.xlabel('x')\nplt.title('Wavepacket')\nplt.savefig('wave1.eps')\nplt.show()\n", "sub_path": "plot_wavepacket.py", "file_name": "plot_wavepacket.py", "file_ext": "py", "file_size_in_byte": 320, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.linspace", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "293036794", "text": "#!/usr/bin/env python\n\"\"\"Test Mapchete main module and processing.\"\"\"\n\nimport os\nimport shutil\nimport rasterio\nimport numpy.ma as ma\nfrom cPickle import dumps\nfrom functools import partial\nfrom multiprocessing import Pool\n\nfrom mapchete import Mapchete\nfrom mapchete.config import MapcheteConfig\nfrom mapchete.tile import BufferedTile\nfrom mapchete.io.raster import create_mosaic\n\nscriptdir = os.path.dirname(os.path.realpath(__file__))\nout_dir = os.path.join(scriptdir, \"testdata/tmp\")\n\n\ndef test_processing():\n \"\"\"Test correct processing (read and write) outputs.\"\"\"\n for cleantopo_process in [\n \"testdata/cleantopo_tl.mapchete\", \"testdata/cleantopo_br.mapchete\"\n ]:\n process = Mapchete(\n MapcheteConfig(os.path.join(scriptdir, cleantopo_process)))\n for zoom in range(6):\n tiles = []\n for tile in process.get_process_tiles(zoom):\n output = process.execute(tile)\n tiles.append(output)\n assert isinstance(output, BufferedTile)\n assert isinstance(output.data, ma.MaskedArray)\n assert output.data.shape == output.shape\n assert not ma.all(output.data.mask)\n process.write(output)\n mosaic, mosaic_affine = create_mosaic(tiles)\n try:\n temp_vrt = os.path.join(out_dir, str(zoom)+\".vrt\")\n gdalbuildvrt = \"gdalbuildvrt %s %s/%s/*/*.tif > /dev/null\" % (\n temp_vrt, out_dir, zoom)\n os.system(gdalbuildvrt)\n with rasterio.open(temp_vrt, \"r\") as testfile:\n for file_item, mosaic_item in zip(\n testfile.meta[\"transform\"], mosaic_affine\n ):\n assert file_item == mosaic_item\n band = testfile.read(1, masked=True)\n assert band.shape == mosaic.shape\n assert ma.allclose(band, mosaic)\n assert ma.allclose(band.mask, mosaic.mask)\n except:\n raise\n finally:\n try:\n os.remove(temp_vrt)\n shutil.rmtree(out_dir)\n except:\n pass\n\n\ndef test_multiprocessing():\n \"\"\"Test parallel tile processing.\"\"\"\n process = Mapchete(\n MapcheteConfig(os.path.join(\n scriptdir, \"testdata/cleantopo_tl.mapchete\")))\n assert dumps(process)\n assert dumps(process.config)\n assert dumps(process.config.output)\n for tile in process.get_process_tiles():\n assert dumps(tile)\n f = partial(_worker, process)\n try:\n pool = Pool()\n for zoom in reversed(process.config.zoom_levels):\n for raw_output in pool.imap_unordered(\n f, process.get_process_tiles(zoom), chunksize=8\n ):\n process.write(raw_output)\n except KeyboardInterrupt:\n pool.terminate()\n except:\n raise\n finally:\n pool.close()\n pool.join()\n try:\n shutil.rmtree(out_dir)\n except:\n pass\n\n\ndef _worker(process, process_tile):\n \"\"\"Multiprocessing worker processing a tile.\"\"\"\n return process.execute(process_tile)\n", "sub_path": "test/test_mapchete.py", "file_name": "test_mapchete.py", "file_ext": "py", "file_size_in_byte": 3260, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "mapchete.Mapchete", "line_number": 26, "usage_type": "call"}, {"api_name": "mapchete.config.MapcheteConfig", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "mapchete.tile.BufferedTile", "line_number": 33, "usage_type": "argument"}, {"api_name": "numpy.ma.MaskedArray", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.ma", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.ma.all", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 36, "usage_type": "name"}, {"api_name": "mapchete.io.raster.create_mosaic", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 43, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.ma.allclose", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 51, "usage_type": "name"}, {"api_name": "numpy.ma.allclose", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 52, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 57, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 58, "usage_type": "call"}, {"api_name": "mapchete.Mapchete", "line_number": 65, "usage_type": "call"}, {"api_name": "mapchete.config.MapcheteConfig", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "cPickle.dumps", "line_number": 68, "usage_type": "call"}, {"api_name": "cPickle.dumps", "line_number": 69, "usage_type": "call"}, {"api_name": "cPickle.dumps", "line_number": 70, "usage_type": "call"}, {"api_name": "cPickle.dumps", "line_number": 72, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 73, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 75, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 89, "usage_type": "call"}]} +{"seq_id": "49889472", "text": "from importlib import import_module\n\n__author__ = 'dur'\nclass ModulesLoader:\n\n\tdef loadModules(self, filePath):\n\t\tself.modules={}\n\t\twith open(filePath, 'r') as f:\n\t\t\tfor singleLine in f:\n\t\t\t\tif( singleLine == '\\n' or singleLine.startswith(\"#\")):\n\t\t\t\t\tcontinue\n\t\t\t\tsingleLine = singleLine.replace('\\n','')\n\t\t\t\tsplitedLine = singleLine.split(':')\n\t\t\t\tif splitedLine[0] in self.modules:\n\t\t\t\t\tself.modules[splitedLine[0]].append(import_module('UserConf.ExtendedModules.'+splitedLine[1]))\n\t\t\t\telse:\n\t\t\t\t\tself.modules[splitedLine[0]]=[import_module('UserConf.ExtendedModules.'+splitedLine[1])]\n\t\treturn self.modules", "sub_path": "ServerSide/utils/ModulesLoader.py", "file_name": "ModulesLoader.py", "file_ext": "py", "file_size_in_byte": 609, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "importlib.import_module", "line_number": 15, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "422174092", "text": "import pickle # To store processed data to save time\nimport os # To access directories\nfrom csv import reader # To read label data\nfrom sklearn import svm # To create the SVM model\nimport dlib # For facial recognition\nimport cv2 # To read images\nimport numpy as np # For data processing\nfrom matplotlib import pyplot as plt # To generate figures\nfrom sklearn.metrics import plot_confusion_matrix # To generate confusion matrices\nfrom sklearn import model_selection # To use the train test split function\n\n# Main function called from the main file\ndef main(sel, model, cfm):\n if sel==0: # User input arg 1 - Selects which mode to use: Arg1==0 creates the model to use\n print(\"Task A1: Gender Detection\")\n model = create_model() # Create model function called to create model object with desired parameters\n\n return model # Returns model object to main file\n elif sel==1: # User input arg 1 - Arg1==1 trains the model and validates it\n directory = 'celeba/' # Directory of the dataset for training\n y_name, y_gen, y_smile, face_list, points = import_data(directory) # Imports data from csv file as well as pickle file if found. If not, then the data is generated by this function.\n print(\"No. of faces detected in this set: \", len(face_list)) # face_list contains an index of all detected faces\n datnew = procnew(points, face_list) # This regularises the x data\n x_use, y_use = process(points, face_list, y_gen) # Process function flattens the data\n x_train, x_vald, y_train, y_vald = model_selection.train_test_split(datnew, y_use, train_size=0.8, random_state=0) # x,y data is split with a gives percentage and seed\n print(\"Number of training samples: \", len(x_train))\n model = train_model(model, x_train, y_train) # Model is trained using x and y training data\n print(\"Number of validation samples: \", len(x_vald))\n accuracy = test_model(model, x_vald, y_vald) # Model is validated using x and y validation data\n print(\"Accuracy of validation set: \", str(accuracy), \"\\n\")\n\n if cfm==1: # Use input arg 3 - Arg1==1 plots the confusion matrix\n disp = plot_confusion_matrix(model, x_vald, y_vald, cmap=plt.cm.Blues) # Generate confusion matrix\n print(disp.confusion_matrix)\n plt.show()\n\n return accuracy # Returns the accuracy of the model using validation data to main file\n elif sel==2: # User input arg 1 - Arg1==2 tests the model using a test set of data\n directory = 'celeba_test/' # Directory of dataset for testing\n y_name, y_gen, y_smile, face_list, points = import_data(directory) # Imports data from csv file as well as pickle file if found. If not, then the data is generated by this function.\n print(\"No. of faces detected in this set: \", len(face_list)) # face_list contains an index of all detected faces\n datnew = procnew(points, face_list) # This regularises the x data\n x_use, y_use = process(points, face_list, y_gen) # Process function flattens the data\n print(\"Number of test samples: \", len(x_use))\n accuracy = test_model(model, datnew, y_use) # Model is tested using x and y test data\n print(\"Accuracy of unseen test set: \", str(accuracy), \"\\n\")\n if cfm==1: # Use input arg 3 - Arg1==1 plots the confusion matrix\n disp = plot_confusion_matrix(model, x_use, y_use, cmap=plt.cm.Blues) # Generate confusion matrix\n print(disp.confusion_matrix)\n plt.show()\n\n return accuracy # Returns the accuracy of the model using test data to main file\n\n# Regularises X data\ndef procnew(points, face_list):\n use_pics = len(face_list) # List of usable faces\n temp = []\n for i in range(0, use_pics):\n idx = face_list[i]\n temp.append(points[idx]) # Extracts usable faces from main array\n new = np.array(temp, dtype='float32') # Turns extracted list into numpy array\n datmean = new.mean(axis=1) # Mean of data\n datstd = new.std(axis=1) # Standard deviation of data\n normed = [] # Empty list to store new data in\n for i in range(0, use_pics):\n new[i] = new[i]-datmean[i]\n new[i] = new[i]/datstd[i] # Regularisation\n normed.append(new[i].flatten()) # Flattens coordinates and appends to the list\n\n return normed # Return normalised and flattened x data\n\n# Creates SVM\ndef create_model():\n print(\"Creating model...\")\n clf = svm.SVC(kernel='linear', probability=True, C=10, gamma=1e-4)\n \n return clf # The model object is passed back to the main function\n\n# Train SVM\ndef train_model(model, x_train, y_train):\n print(\"Training model...\")\n model = model.fit(x_train, y_train) # Model is trained using x and y train data\n print(\"Model training finished\")\n\n return model # The trained model is passed back to the main function\n\n# Testing mdoel\ndef test_model(model, x_t, y_t):\n print(\"Testing model...\")\n accuracy = model.score(x_t, y_t) # Model is trained using x and y test data\n print(\"Model testing finished\")\n\n return accuracy # The model accuracy is passed back to the main function\n\n# Flatten raw coordinates\ndef process(data, face_list, y_use):\n use_pic = len(face_list) # Number of usable images\n dat_pr = [] # Create an empty list to append data to\n y_proc = [] # Create an empty list to append data to\n for i in range(0, use_pic): # For each usable picture\n idx = face_list[i] # References the face_list to generate index to access correct entries in the label data\n temp = [] # Empty list to append coordinates to\n for m in range(0, 68): # For each landmark\n for n in range(0, 2): # For x and y\n temp.append(data[idx][m][n]) # The temporary list append each x and y coordinate for each landmark\n dat_pr.append(temp) # The empty data list appends each temporary list containing flattened landmark coordinates for each image\n y_proc.append(y_use[idx]) # Empty list appends correct label from position generated by face_list\n\n return dat_pr, y_proc # Returns the flattened coordinates and y labels for each usable image\n\n# Imports csv data. If pickle file is available, imports data, else generates it.\ndef import_data(directory):\n print(\"Acquiring labels and landmark data...\")\n # Directories for each file is generated as each image is in a sub folder of celeba\n full_dir = str(os.path.dirname(__file__)[:-2])+'/Datasets/'+directory\n csv_src = os.path.join(full_dir, \"labels.csv\")\n img_src = os.path.join(full_dir, \"img/\")\n\n y_name = [] # Stores the file name for each image\n y_gen = [] # Stores gender data for each image\n y_smile = [] # Stores smile data for each image\n face_list = [] # Stores a list of all images where a face is detected\n with open(csv_src) as file: # Opens the labels.csv file\n dat_read = reader(file, delimiter='\\t') # Data is tab spaced\n temp = list(dat_read)[1:] # Remove first element of list (headers)\n for n in range(0, len(temp)): # Go through each row\n y_name.append(temp[n][1]) # Second element is the name\n y_gen.append(temp[n][2]) # Third element is gender\n y_smile.append(temp[n][3]) # Fourth element is smiling or not\n no_pics = len(y_name) # Total number of images (NOT total number of faces)\n\n if not os.path.isfile(full_dir+'facial_landmarks.pickle'): # If pickle data file containing facial landmarks doesnt exist\n print(\"Landmark data not found - generating...\")\n detector = dlib.get_frontal_face_detector() # Generate detector object using dlib's face detector\n predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat') # Generate landmark predictor using dlib's 68 landmarks model\n points = np.array([[[None] * 2] * 68] * no_pics) # 68 landmark coordinates stored here as generated\n\n for i in range(0, no_pics): # Open image and find face\n src = img_src + y_name[i] # Generating path for each image\n gray = cv2.imread(src, 0) # Image is read (using path) in grayscale - easier to process\n faces = detector(gray) # Run the frontal face detector on the grayscale image\n\n if list(faces): # If a face is detected i.e. faces object contains data\n face_list.append(i) # Keep track of iteration number in a list of detected faces\n face = list(faces)[0] # Takes data from first element of faces (the detector can detect multiple faces)\n landmarks = predictor(gray, face) # Run the dlib 68 landmarks predictor on gray image in face region\n for n in range(0, 68): # For each of the 68 landmarks\n points[i][n][0] = landmarks.part(n).x # Save the x coordinate of the nth landmark\n points[i][n][1] = landmarks.part(n).y # Save the y coordinate of the nth landmark\n\n points = list(points) # Converts the points into a list - this means pickle data can be read by a script without numpy\n face_list = list(face_list) # Convert the face_list into a list\n points.append(face_list) # Combines the points and face_list array into a single variable to store in a single pickle file\n with open(full_dir+'facial_landmarks.pickle', 'wb') as f1: # A pickle file is opened (created if it doesnt exist)\n pickle.dump(points, f1) # Dump points data to pickle file\n\n with open(full_dir+'facial_landmarks.pickle', 'rb') as f2: # Open the pickle file containing the landmark data\n temp1 = pickle.load(f2) # Load the file into a temporary variable\n points = temp1 # Turn the temporary file object into a list\n face_list = points[-1] # Extract the face_list from combined matrix (as it was appended)\n points = points[0:-1] # Extract landmark data into one list (as it was joined with face_list)\n\n return y_name, y_gen, y_smile, face_list, points # The labels, face_list and landmark data is passed back to the main function\n\n\n\n", "sub_path": "AMLS_20_21_17010729/A1/A1.py", "file_name": "A1.py", "file_ext": "py", "file_size_in_byte": 10245, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.model_selection.train_test_split", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 25, "usage_type": "name"}, {"api_name": "sklearn.metrics.plot_confusion_matrix", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 33, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "sklearn.metrics.plot_confusion_matrix", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 48, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 61, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 75, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 75, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "dlib.get_frontal_face_detector", "line_number": 134, "usage_type": "call"}, {"api_name": "dlib.shape_predictor", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 136, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 140, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 155, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 158, "usage_type": "call"}]} +{"seq_id": "193068310", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom pytransform3d import rotations as pr\nfrom pytransform3d import transformations as pt\nfrom pytransform3d.transform_manager import TransformManager\n\n# https://dfki-ric.github.io/pytransform3d/_auto_examples/plots/plot_transform_manager.html#sphx-glr-auto-examples-plots-plot-transform-manager-py\n# https://dfki-ric.github.io/pytransform3d/_auto_examples/index.html\nt = np.array([[0],\n [0],\n [1]])\n\nR = np.array([[-1, 0, 0],\n [0, 1, 0],\n [0, 0, -1]])\n\nprint(R.shape)\nprint(t.shape)\n\ncam0_in_world = pt.transform_from(R, t.ravel())\ncam1_in_cam0 = pt.transform_from(np.eye(3), t.ravel())\n\ntm = TransformManager()\n\ntm.add_transform(\"world\", \"cam0\", cam0_in_world)\ntm.add_transform(\"cam0\", \"cam1\", cam1_in_cam0)\n\ncam1_in_world = tm.get_transform(\"cam1\", \"world\")\nax = tm.plot_frames_in(\"world\", s=0.1)\n\n\nax.set_xlim((-0.25, 0.75))\nax.set_ylim((-0.5, 0.5))\nax.set_zlim((0.0, 1.0))\nplt.show()\n\n\nT_cam_new_in_cam_previous = np.zeros([4, 4])\nT_cam_new_in_cam_previous[:3, :3] = R\nT_cam_new_in_cam_previous[:3, 3] = t.ravel()\nT_cam_new_in_cam_previous[3, 3] = 1\nprint(T_cam_new_in_cam_previous[:3, :3])\nprint(T_cam_new_in_cam_previous)\n\n\n# Monocular Vision and OpenCV\n# https://www.youtube.com/watch?v=wwpKvGfNwIc&t\n# https://www.youtube.com/watch?v=N451VeA8XRA&t\n\n# Visual Odometry with RGBD Images in Open3D\n# https://www.youtube.com/watch?v=_6JHjY6MwrU\n\n# Visual Odometry with a Stereo Camera\n# https://www.youtube.com/watch?v=WV3ZiPqd2G4\n", "sub_path": "scripts/transformations3d_example.py", "file_name": "transformations3d_example.py", "file_ext": "py", "file_size_in_byte": 1538, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.array", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "pytransform3d.transformations.transform_from", "line_number": 20, "usage_type": "call"}, {"api_name": "pytransform3d.transformations", "line_number": 20, "usage_type": "name"}, {"api_name": "pytransform3d.transformations.transform_from", "line_number": 21, "usage_type": "call"}, {"api_name": "pytransform3d.transformations", "line_number": 21, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 21, "usage_type": "call"}, {"api_name": "pytransform3d.transform_manager.TransformManager", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "139615930", "text": "import json\nimport logging\n\nimport os\nfrom joblib import Parallel, delayed\n\nimport matrixcomponent.matrix as matrix\nfrom matrixcomponent import ODGI_VERSION\nfrom itertools import islice\n\nimport numpy as np\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef process_path(line=None):\n pangenome_length = -1\n bin_width = -1\n p = None\n\n path = json.loads(line)\n\n if \"odgi_version\" in path:\n # this is the header\n assert path[\"odgi_version\"] == ODGI_VERSION, f\"Expecting version {ODGI_VERSION}.\" \\\n f\"This version added the header with pangenome nucleotide count.\"\n print(f\"Found file with {path['pangenome_length']} nucleotides in the pangenome and\"\n f\" a {path['bin_width']}bp bin width.\", flush=True)\n pangenome_length = path['pangenome_length']\n bin_width = path['bin_width']\n\n if \"path_name\" in path:\n LOGGER.info(\"reading \" + path['path_name'])\n\n p = matrix.Path(path['path_name'])\n\n for b in path['bins']:\n p.bins.append(p.Bin(b[0], b[1], b[2], b[4], b[5]))\n p.finalize_bins()\n\n p.links = np.array(path['links'])\n\n return [pangenome_length, bin_width, p]\n\n\ndef parse(file, parallel_cores):\n paths = []\n pangenome_length = 0\n bin_width = 0\n\n if parallel_cores > 0:\n chunk_size = parallel_cores\n else:\n chunk_size = os.cpu_count()\n\n with open(file) as f, Parallel(n_jobs=chunk_size, prefer=\"processes\") as parallel:\n while True:\n lines = list(islice(f, chunk_size))\n if len(lines) == 0:\n break\n\n results = parallel(delayed(process_path)(line) for line in lines)\n for res in results:\n plen, bwidth, p = res[0], res[1], res[2]\n if plen > -1:\n pangenome_length = plen\n if bwidth > -1:\n bin_width = bwidth\n if p is not None:\n paths.append(p)\n\n return (paths, pangenome_length, bin_width)\n", "sub_path": "matrixcomponent/JSONparser.py", "file_name": "JSONparser.py", "file_ext": "py", "file_size_in_byte": 2061, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "matrixcomponent.ODGI_VERSION", "line_number": 25, "usage_type": "name"}, {"api_name": "matrixcomponent.matrix.Path", "line_number": 35, "usage_type": "call"}, {"api_name": "matrixcomponent.matrix", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "os.cpu_count", "line_number": 54, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 56, "usage_type": "call"}, {"api_name": "itertools.islice", "line_number": 58, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "254757928", "text": "from django.views.decorators.csrf import csrf_exempt\nfrom django.shortcuts import render, redirect\nfrom profesor.forms import *\nfrom profesor.models import *\nfrom alumno.models import Cita\n# Create your views here.\n\nPROFESOR = 'PROFESOR'\n\n\ndef home(request):\n user_id = request.session.get('user', None)\n type = request.session.get('type', None)\n if type == PROFESOR:\n user = Profesor.objects.filter(pk=user_id).first()\n if user != None:\n return render(request,'profesor/Tony/home.html', {\"usuario\": user})\n return redirect('/profesor/login')\n\n\ndef login(request):\n if request.method == 'POST':\n form = LoginForm(request.POST)\n if form.is_valid():\n codigoUlima = form.cleaned_data['codigoUlima']\n password = form.cleaned_data['password']\n boo, profesor = Profesor.verify_user(codigoUlima, password)\n if boo:\n request.session['user'] = profesor.pk\n request.session['type'] = PROFESOR\n return redirect('/profesor/home')\n\n form = LoginForm()\n args = {'form': form}\n return render(request,'profesor/login.html', args)\n\ndef historial(request):\n user_id = request.session.get('user', None)\n user = Profesor.objects.filter(pk=user_id).first()\n if user != None:\n args = {\"citas\": citas, \"usuario\": user}\n return render(request, 'profesor/Tony/historial.html', args)\n return redirect('/profesor/login')\n\ndef citas(request):\n user_id = request.session.get('user', None)\n user = Profesor.objects.filter(pk=user_id).first()\n if user != None:\n asesorias = user.asesoria_set.all()\n args = {\"asesorias\": asesorias, \"usuario\": user}\n return render(request, 'profesor/Tony/citas.html', args)\n return redirect('/profesor/login')\n\ndef eliminar_cita(request, cita):\n Cita.objects.get(pk=cita).delete()\n return redirect('/profesor/citas')\n\n@csrf_exempt\ndef change_state(request):\n state = request.POST['state']\n user_id = request.session.get('user', None)\n user = Profesor.objects.filter(pk=user_id).first()\n if user != None:\n user.state = state\n user.save()\n return ''\n\ndef calendar(request):\n user_id = request.session.get('user', None)\n user = Profesor.objects.filter(pk=user_id).first()\n if user != None:\n asesorias = user.asesoria_set.all()\n semana = {\n 'Horario': ['7-8', \"8-9\", '9-10', '10-11', '11-12', '12-13', '13-14', '14-15', '15-16', '16-17', '17-18', '18-19', '19-20', '20-21', '21-22'],\n 'Lunes': ['']*15,\n 'Martes': ['']*15,\n 'Miércoles': ['']*15,\n 'Jueves': ['']*15,\n 'Viernes': ['']*15,\n }\n for asesoria in asesorias:\n semana[asesoria.fechaAsesoria][int(asesoria.horaInicio.hour) - 7] = \"Favorito: \" + asesoria.codCurso.nombreCurso + \" - \" + user.nombrepProfesor + \" - \" + asesoria.lugar.lugar\n\n args = {\"semana\": semana, \"usuario\": user}\n return render(request, 'profesor/Tony/calendar.html', args)\n return redirect('/profesor/login')\n\n\ndef perfil_profesor(request):\n comentarios = []\n total_rate = 0\n num_rate = 0\n\n user_id = request.session.get('user', None)\n user = Profesor.objects.filter(pk=user_id).first()\n if user != None:\n for asesoria in user.asesoria_set.all():\n for cita in asesoria.cita_set.all():\n for comentario in cita.comentario_set.all():\n comentarios.append(comentario)\n total_rate += comentario.rate\n num_rate +=1\n rate = total_rate / num_rate if num_rate > 0 else \" ** No hay Puntuación **\"\n args = {\"profesor\": user, \"comentarios\": comentarios, \"rate\": rate}\n return render(request, 'profesor/Tony/perfil_profesor.html', args)\n return redirect('/profesor/login')\n\n\ndef log_out(request):\n try:\n del request.session['user']\n del request.session['type']\n except Exception as e:\n pass\n return redirect('/profesor/login')\n", "sub_path": "profesor/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4079, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.shortcuts.render", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 18, "usage_type": "call"}, {"api_name": "profesor.forms", "line_number": 27, "usage_type": "name"}, {"api_name": "profesor.forms.pk", "line_number": 29, "usage_type": "attribute"}, {"api_name": "profesor.forms", "line_number": 29, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 51, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 52, "usage_type": "call"}, {"api_name": "alumno.models.Cita.objects.get", "line_number": 55, "usage_type": "call"}, {"api_name": "alumno.models.Cita.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "alumno.models.Cita", "line_number": 55, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 56, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 58, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 85, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 86, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 105, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 106, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "531096718", "text": "from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.index, name='teams'),\n url(r'^detail/(?P[0-9]+)/$', views.team_detail, name='team_detail'),\n url(r'^detail/(?P[0-9]+)/delete/$', views.team_delete, name='team_delete'),\n url(r'^create_new_team/$', views.create_new_team, name='create_new_team'),\n url(r'^detail/(?P[0-9]+)/roles/$', views.roles, name='team_roles'),\n url(r'^detail/(?P[0-9]+)/invitepeople/$', views.invite_people, name='team_invite_people'),\n url(r'^invites/(?P[0-9]+)/accept/$', views.invite_accept, name='invite_accept'),\n url(r'^invites/(?P[0-9]+)/reject/$', views.invite_reject, name='invite_reject'),\n url(r'^detail/(?P[0-9]+)/requestjoin/$', views.request_join, name='team_request_join'),\n url(r'^joinrequests/(?P[0-9]+)/accept/$', views.joinrequest_accept, name='joinrequest_accept'),\n url(r'^joinrequests/(?P[0-9]+)/reject/$', views.joinrequest_reject, name='joinrequest_reject'),\n]\n", "sub_path": "teams/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1068, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "70671310", "text": "import tkinter as tk\r\nfrom tkinter import filedialog\r\nimport csv\r\n#from mpl_toolkits.mplot3d import axes3d\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom matplotlib.animation import FuncAnimation\r\nfrom numpy import genfromtxt\r\n\r\n###############################################################\r\n# open file\r\n###############################################################\r\nroot = tk.Tk()\r\nroot.withdraw()\r\nfile_path = filedialog.askopenfilename()\r\n\r\nif file_path is None:\r\n print('File not chosen.')\r\n quit()\r\nelse:\r\n dadostranspostos = genfromtxt(file_path, delimiter='\\t')\r\n #with open(file_path, newline='') as csvfile:\r\n # dados = csv.DictReader(csvfile)\r\n #data = list(csv.reader(csvfile))\r\n # for row in dados:\r\n # print(row)\r\n # print(row['time'], row['Va'], row['Vb'], row['Vc'])\r\n dados = np.transpose(dadostranspostos)\r\n\r\n#print(dados[0])\r\n\r\n##################################################\r\n# Constants and figures\r\n##################################################\r\nfig = plt.figure(figsize=plt.figaspect(0.45))\r\nabcscalars1 = fig.add_subplot(1,2,1)\r\nabcspace = fig.add_subplot(1,2,2, projection='3d')\r\n\r\n##################################################\r\n# Plotting mains voltages\r\n##################################################\r\nabcscalars1.plot(dados[0], dados[1], color='red', label='a')\r\nabcscalars1.plot(dados[0], dados[2], color='darkgreen', label='b')\r\nabcscalars1.plot(dados[0], dados[3], color='blue', label='c')\r\n\r\n##################################################\r\n# Settings of abc scalars chart\r\n##################################################\r\n#abcscalars1.set_xlim([0.00, 2*np.pi])\r\nabcscalars1.set_xlim([0.00, 0.04])\r\nabcscalars1.set_ylim([-1.2, 1.2])\r\n#abcscalars1.set_xlabel('angle')\r\n#abcscalars1.set_xlabel('time (rad)')\r\nabcscalars1.set_xlabel('time (s)')\r\nabcscalars1.set_ylabel('mains')\r\nabcscalars1.grid(False)\r\n#abcscalars1.set_xticks([])\r\n#abcscalars1.set_yticks([])\r\n#abcscalars1.set_zticks([])\r\n\r\n##################################################\r\n# Settings of abc space chart\r\n##################################################\r\n#abcspace.view_init(azim=-45, elev=20)\r\nabcspace.view_init(azim=45, elev=35.26) #top view from zero sequence line\r\nabcspace.set_proj_type('ortho')\r\nabcspace.set_xlim([-1,1])\r\nabcspace.set_ylim([-1,1])\r\nabcspace.set_zlim([-1,1])\r\n#abcspace.set_xlabel('a')\r\n#abcspace.set_ylabel('b')\r\n#abcspace.set_zlabel('c')\r\nabcspace.set_xticks([])\r\nabcspace.set_yticks([])\r\nabcspace.set_zticks([])\r\nabcspace.set_axis_off()\r\n#abcspace.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))#use if only the background is to be white\r\n#abcspace.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\r\n#abcspace.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\r\n##################################################\r\n# Plotting Balanced Plane\r\n##################################################\r\nbalancedplaneamplitude = 0\r\nnsampbalplane = 361\r\nanglesoneturn = np.linspace(start=0, stop=2*np.pi, num=nsampbalplane, endpoint=True)\r\nif balancedplaneamplitude > 0:\r\n balancedplanecircle = np.zeros((3, nsampbalplane))\r\n balancedplanecirclephaseshift = np.array([0, -2 * np.pi / 3, 2 * np.pi / 3])\r\n for numphase in range (0, 3):\r\n balancedplanecircle[numphase, :] = (balancedplaneamplitude\r\n * np.cos(anglesoneturn + balancedplanecirclephaseshift[numphase]))\r\n abcspace.plot(balancedplanecircle[0, :],\r\n balancedplanecircle[1, :],\r\n balancedplanecircle[2, :],\r\n color='blue',\r\n linewidth=0.5)\r\n\r\n balancedplanelines = np.ones((3, 50))\r\n anglestep = 10\r\n for i in range(0,10,1):\r\n for numphase in range(0, 3, 1):\r\n balancedplanelines[numphase, i * 5] = balancedplanecircle[numphase, anglestep * i]\r\n for numphase in range(0, 3, 1):\r\n balancedplanelines[numphase, i * 5 + 1] = balancedplanecircle[numphase, 180 - anglestep * i]\r\n for numphase in range(0, 3, 1):\r\n balancedplanelines[numphase, i * 5 + 2] = balancedplanecircle[numphase, 180 + anglestep * i]\r\n for numphase in range(0, 3, 1):\r\n balancedplanelines[numphase, i * 5 + 3] = balancedplanecircle[numphase, 360 - anglestep * i]\r\n for numphase in range(0, 3, 1):\r\n balancedplanelines[numphase, i * 5 + 4] = balancedplanecircle[numphase, anglestep * i]\r\n abcspace.plot(balancedplanelines[0, :],\r\n balancedplanelines[1, :],\r\n balancedplanelines[2, :],\r\n color='blue', label='a+b+c=0 plane',\r\n linewidth=0.5)\r\n\r\n##################################################\r\n# Plotting a b c base vectors\r\n##################################################\r\nshowabcbases = True\r\nbaselength = 1\r\n#quiverpivot = 'middle'\r\nquiverpivot = 'tail'\r\nif showabcbases:\r\n abcspace.quiver(0, 0, 0, 1, 0, 0, length=baselength,\r\n pivot=quiverpivot, arrow_length_ratio=0.025,\r\n linewidth=1, linestyle='dotted',\r\n color='black')\r\n abcspace.quiver(0, 0, 0, 0, 1, 0, length=baselength,\r\n pivot=quiverpivot, arrow_length_ratio=0.025,\r\n linewidth=1, linestyle='dotted',\r\n color='black')\r\n abcspace.quiver(0, 0, 0, 0, 0, 1, length=baselength,\r\n pivot=quiverpivot, arrow_length_ratio=0.025,\r\n linewidth=1, linestyle='dotted',\r\n color='black')\r\n abcspace.text(baselength/2, 0, 0, 'a', (0, 1, 0))\r\n abcspace.text(0, baselength/2, 0, 'b', (0, 1, 0))\r\n abcspace.text(0, 0, baselength/2, 'c', (0, 1, 0))\r\n\r\n##################################################\r\n# Plotting a b c in scalargraph + abcspace + timeslide\r\n##################################################\r\nmainsquiver = abcspace.quiver([0], [0], [0], dados[1, 100], dados[2, 100], dados[3, 100], arrow_length_ratio=0.1,\r\n color='black', label='mains')\r\n\r\naquiver = abcspace.quiver(0, 0, 0, 1, 0, 0, length=1,\r\n pivot='tail', arrow_length_ratio=0.2, color='red')\r\nbquiver = abcspace.quiver(0, 0, 0, 0, 1, 0, length=-0.5,\r\n pivot='tail', arrow_length_ratio=0.2, color='green')\r\ncquiver = abcspace.quiver(0, 0, 0, 0, 0, 1, length=-0.5,\r\n pivot='tail', arrow_length_ratio=0.2,\r\n color='blue')\r\n\r\ntimeslide, = abcscalars1.plot([0,0], [-2,2], color='brown', linestyle='dashed')\r\n\r\n##################################################\r\n# Plotting a b c path\r\n##################################################\r\n#pathquiver = abcspace.plot(dados[1, 0:10], dados[2, 0:10], dados[3, 0:10],\r\n# color='black', linestyle='dotted', linewidth=0.5)\r\npathquiver = abcspace.plot(dados[1,:], dados[2,:], dados[3,:],\r\n color='black', linestyle='dashed', linewidth=1)\r\n\r\n\r\n##################################################\r\n# Plotting a b c legends\r\n##################################################\r\nabcspace.legend()\r\nabcscalars1.legend()\r\n\r\nrotate3dspace = False\r\ncadaumporsi = 2\r\nshowmainsquiver = True\r\n\r\ndef update(i):\r\n global mainsquiver\r\n global timeslide\r\n global pathquiver\r\n global aquiver, bquiver, cquiver\r\n\r\n timeslide.remove()\r\n\r\n if showmainsquiver:\r\n mainsquiver.remove()\r\n mainsquiver = abcspace.quiver([0], [0], [0], dados[1, i], dados[2, i], dados[3, i], arrow_length_ratio=0.1,\r\n color='black')\r\n\r\n if cadaumporsi == 1:\r\n aquiver.remove()\r\n bquiver.remove()\r\n cquiver.remove()\r\n aquiver = abcspace.quiver(0, 0, 0, 1, 0, 0, length=dados[1, i],\r\n pivot='tail', arrow_length_ratio=0.2, color='red')\r\n bquiver = abcspace.quiver(0, 0, 0, 0, 1, 0, length=dados[2, i],\r\n pivot='tail', arrow_length_ratio=0.2, color='green')\r\n cquiver = abcspace.quiver(0, 0, 0, 0, 0, 1, length=dados[3, i],\r\n pivot='tail', arrow_length_ratio=0.2,\r\n color='blue')\r\n if cadaumporsi == 2:\r\n aquiver.remove()\r\n bquiver.remove()\r\n cquiver.remove()\r\n aquiver = abcspace.quiver(0, 0, 0, 1, 0, 0, length=dados[1, i],\r\n pivot='tail', arrow_length_ratio=0.2, color='red')\r\n bquiver = abcspace.quiver(dados[1, i], 0, 0, 0, 1, 0, length=dados[2, i],\r\n pivot='tail', arrow_length_ratio=0.2, color='green')\r\n cquiver = abcspace.quiver(dados[1, i], dados[2, i], 0, 0, 0, 1, length=dados[3, i],\r\n pivot='tail', arrow_length_ratio=0.2,\r\n color='blue')\r\n\r\n timeslide, = abcscalars1.plot([dados[0, i], dados[0, i]],\r\n [-2, 2],\r\n color='black', linestyle='dashed')\r\n #if i>2:\r\n # pathquiver = abcspace.plot(dados[1, 0:i], dados[2, 0:i], dados[3, 0:i],\r\n # color='black', linewidth=1)\r\n\r\n if rotate3dspace:\r\n deltaelevconstant = -45\r\n deltaelevdangle = -2*deltaelevconstant/180\r\n if i < 180:\r\n abcspace.view_init(azim=45, elev=deltaelevdangle * i + deltaelevconstant)\r\n else:\r\n abcspace.view_init(azim=45, elev=-deltaelevdangle * (i - 180) - deltaelevconstant)\r\n\r\nanimacao = FuncAnimation(fig, update, frames=len(dados[0]), interval=10)\r\nplt.show()", "sub_path": "csv_one3phase.py", "file_name": "csv_one3phase.py", "file_ext": "py", "file_size_in_byte": 9640, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tkinter.Tk", "line_number": 13, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 15, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.genfromtxt", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figaspect", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 85, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}]} +{"seq_id": "459792315", "text": "import requests\ngm_user = \"admin\"\ngm_pwd = \"infoblox\"\n\n# This is the \"find\" API call for Policy Rules \nurl = \"https://netmri/api/3.4/policy_rules/find\"\n\n# rlike allows us use regex with the \"val_c_name\"\n# this will return all the rules that start with Sif_\npayload = \"{\\n \\\"op_name\\\":\\\"rlike\\\",\\n \\\"val_c_name\\\":\\\"Sif_\\\"\\n}\"\nheaders = {\n 'Content-Type': 'application/json',\n }\nresponse = requests.request(\"GET\", url, headers=headers, data = payload, verify=False, auth=(gm_user, gm_pwd))\nprint(response.text.encode('utf8'))", "sub_path": "python/get_rules_with_regex.py", "file_name": "get_rules_with_regex.py", "file_ext": "py", "file_size_in_byte": 531, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.request", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "35217768", "text": "\nimport torch\n\n# create a tensor\nx = torch.Tensor([1, 2, 3])\n\nprint(x)\n\nx = torch.Tensor([[1, 2],\n [5, 3]])\n\nprint(x)\n\nx = torch.Tensor([[[1, 2],\n [5, 3]],\n [[5,3],\n [6,7]]])\n\n#print(x)\nprint(x[0][1])\n\n# layer, row, column\n\n# we are indexing tensors\n# indexing tensor: layer row, column\n\nprint(x[0][1])\n\nprint(x[0][1][0])\n\n\n\n# a variable is different from tensors\n\n# tensors is values\n# variables has values that change\n\nfrom torch.autograd import Variable\n\n# n is the number of features\nn = 2\n\n# m is the number of training points\nm = 300\n\n# m is the number of training samples, m>>n\n\n# randn(.,.) is Gaussian with zero-mean and unit variance\n\n# we create a matrix of depth n and breadth m\nx = torch.randn(n, m)\n\nX = Variable(x)\n\n# we create a fake data set, we create Y\n\n# we use: X.data[0,:], where this is the first row of the matrix X\n# we use: X.data[1,:], where this is the second row of the matrix X\n\nY = Variable(2*X.data[0,:] + 6*X.data[1,:] + 10)\n\nw = Variable(torch.randn(1,2), requires_grad=True)\nb = Variable(torch.randn([1]), requires_grad=True)\n\n\n\ncosts = []\n\n\n\n#import matplotlib\nimport matplotlib.pyplot as plt\n\n\n\nfrom mpl_toolkits.mplot3d import Axes3D\n\nplt.ion()\n\n#fig = plt.figure()\nfig = plt.figure()\n\nax1 = fig.add_subplot(111, projection=\"3d\")\nax1.scatter(X[0,:].data, X[1,:].data, Y.data)\n\nplt.show()\n#matplotlib.pyplot.show()\n\n#plt.pause(9999999999)\n#plt.pause(2)\n\nplt.pause(1)\n\n\n\n#epochs = 500\n#epochs = 100\n\nepochs = 10\n\n# learning rate lr\n#lr = 0.5\n\nlr = 0.1\n\n#import numpy as np\n\n#x1 = np.arange(-2, 10)\n#x1 = np.arange(100)\nx1 = np.arange(-2, 4)\n\n#x2 = np.arange(-2, 10)\n#x2 = np.arange(100)\nx2 = np.arange(-2, 4)\n\nx1, x2 = np.meshgrid(x1, x2)\n\nfor epoch in range(epochs):\n h = torch.mm(w, X) + b\n cost = torch.sum(0.5*(h-Y)**2)/m\n # to the power of 2, we use: ** 2\n cost.backward()\n w.data -= lr * w.grad.data\n b.data -= lr * b.grad.data\n w.grad.data.zero_()\n # the underscore _ means replace it with zero\n b.grad.data.zero_()\n # the underscore _ means replace it with zero\n costs.append(cost.data)\n y = b.data[0] + x1*w.data[0][0] + x2*w.data[0][1]\n s = ax1.plot_surface(x1, x2, y)\n fig.canvas.draw()\n s.remove()\n plt.pause(1)\n\n\n\n", "sub_path": "program1_Torch.py", "file_name": "program1_Torch.py", "file_ext": "py", "file_size_in_byte": 2274, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.Tensor", "line_number": 5, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.mm", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}]} +{"seq_id": "492498844", "text": "#!/usr/bin/python3\n'''\nReads line of the form\nNNN Useragent string\nand outputs a CSV with the read useragents resolved into browser and OS info.\n'''\nimport argparse\nfrom collections import defaultdict\nimport csv\nfrom functools import reduce\nimport requests\nimport urllib.parse\nimport sqlite3\nimport sys\n\n\nDB_NAME = \"useragents.sqlite\"\n\nINFO_HEADERS = (\"ua_type\", \"ua_brand\", \"ua_name\", \"ua_version\", \"ua_url\",\n \"os_name\", \"os_version\",\n \"browser_name\", \"browser_version\",\n \"engine_name\", \"engine_version\")\nDB_COLUMNS = (\"useragent\", ) + INFO_HEADERS\nCSV_HEADERS = (\"count\", \"error message\") + INFO_HEADERS + (\"original\",)\n\n\ndef flatten(l):\n return reduce(\n lambda xs, x: (\n xs + list(flatten(x)) if isinstance(x, (list, tuple))\n else xs + [x]),\n l, [])\n\n\ndef dict_factory(cursor, row):\n return {col[0]: row[idx] for idx, col in enumerate(cursor.description)}\n\n\ndef get_useragent_info_from_api(useragent, apikey):\n headers = {\n \"Accept\": \"application/json\"\n }\n url = \"https://useragentapi.com/api/v4/json/{apikey}/{agent}\".format(\n apikey=apikey,\n agent=urllib.parse.quote(useragent, safe=''))\n response = requests.get(url, headers=headers, verify=True)\n if response.status_code != 200:\n raise RuntimeError(\"Http error {}: {}\".format(\n response.status_code, response.content))\n return response.json()\n\n\ndef get_useragent_info_from_db(cursor, useragent):\n cursor.execute('SELECT * from useragents WHERE useragent=?', (useragent, ))\n rows = cursor.fetchall()\n assert len(rows) <= 1, useragent\n if len(rows) == 0:\n return None\n return rows[0]\n\n\nINSERT_STMT = '''INSERT INTO useragents({}) VALUES ({})'''.format(\n ','.join(DB_COLUMNS), ','.join([':' + col for col in DB_COLUMNS]))\n\n\ndef update_useragent_info(cursor, useragent, info):\n insertdict = defaultdict(str, info)\n insertdict['useragent'] = useragent\n cursor.execute(INSERT_STMT, insertdict)\n\n\ndef get_info(cursor, useragent, args):\n\n def _error(error):\n return error, [\"\"] * len(INFO_HEADERS)\n\n info = get_useragent_info_from_db(cursor, useragent)\n if info:\n return \"\", [info.get(field, \"\") for field in INFO_HEADERS]\n elif args.db_only:\n return _error(\"Useragent not in DB\")\n else:\n info = get_useragent_info_from_api(useragent, args.apikey)\n if \"data\" in info:\n info_row = [\n info[\"data\"].get(field, \"\") for field in INFO_HEADERS]\n if not args.no_db_update:\n update_useragent_info(cursor, useragent, info[\"data\"])\n return \"\", info_row\n else:\n return _error(info[\"error\"][\"message\"])\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--input\", help=\"input file\")\n parser.add_argument(\n \"--output\", help=\"output CSV name\")\n parser.add_argument(\n \"--db-only\", default=False, action=\"store_true\",\n help=\"never query external service\")\n parser.add_argument(\n \"--no-db-update\", default=False, action=\"store_true\",\n help=\"never query external service\")\n args = parser.parse_args()\n\n conn = sqlite3.connect(DB_NAME)\n conn.row_factory = dict_factory\n if args.output:\n output_file = args.output\n elif args.input:\n output_file = args.input + \".csv\"\n else:\n parser.error(\n \"Need to specify an output file when reading standard input\")\n\n if not args.db_only:\n with open('.apikey', 'rt') as f:\n args.apikey = f.readline().strip()\n with conn,\\\n open(args.input, \"rt\") if args.input else sys.stdin as f_in,\\\n open(output_file, \"wt\") as csvfile:\n cursor = conn.cursor()\n writer = csv.writer(csvfile)\n writer.writerow(CSV_HEADERS)\n for line in f_in:\n count, agent = line.strip().split(' ', 1)\n row = (count, get_info(cursor, agent, args), agent)\n writer.writerow(flatten(row))\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n", "sub_path": "py/useragent/useragents.py", "file_name": "useragents.py", "file_ext": "py", "file_size_in_byte": 4112, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "functools.reduce", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib.parse.parse.quote", "line_number": 45, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 45, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 45, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 46, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 67, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 95, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 108, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 122, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 125, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 134, "usage_type": "call"}]} +{"seq_id": "622886023", "text": "from aiosmb.dcerpc.v5.transport.smbtransport import SMBTransport\nfrom aiosmb.dcerpc.v5 import srvs\nfrom aiosmb.dcerpc.v5.dtypes import RPC_SID\nfrom aiosmb.wintypes.ntstatus import NTStatus\nfrom aiosmb import logger\nfrom aiosmb.dcerpc.v5.dtypes import NULL\n\t\t\nclass SMBSRVS:\n\tdef __init__(self, connection):\n\t\tself.connection = connection\n\t\tself.service_manager = None\n\t\t\n\t\tself.dce = None\n\t\tself.handle = None\n\t\t\n\t\tself.domain_ids = {} #sid to RPC_SID\n\t\tself.domain_handles = {} #handle to sid\n\t\t\n\tasync def __aenter__(self):\n\t\treturn self\n\t\t\n\tasync def __aexit__(self, exc_type, exc, traceback):\n\t\tawait self.close()\n\t\t\n\tasync def connect(self, open = True):\n\t\trpctransport = SMBTransport(self.connection, filename=r'\\srvsvc')\n\t\tself.dce = rpctransport.get_dce_rpc()\n\t\tawait self.dce.connect()\n\t\tawait self.dce.bind(srvs.MSRPC_UUID_SRVS)\n\t\t\n\tasync def close(self):\n\t\tif self.dce:\n\t\t\ttry:\n\t\t\t\tawait self.dce.disconnect()\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\treturn\n\t\n\tasync def list_shares(self, level = 1):\n\t\tlevel_name = 'Level%s' % level\n\t\tstatus = NTStatus.MORE_ENTRIES\n\t\tresumeHandle = 0\n\t\twhile status == NTStatus.MORE_ENTRIES:\n\t\t\ttry:\n\t\t\t\tresp = await srvs.hNetrShareEnum(self.dce, level, resumeHandle = resumeHandle)\n\t\t\texcept Exception as e:\n\t\t\t\tprint(str(e))\n\t\t\t\tif str(e).find('STATUS_MORE_ENTRIES') < 0:\n\t\t\t\t\traise\n\t\t\t\tresp = e.get_packet()\n\t\t\t\n\t\t\tfor entry in resp['InfoStruct']['ShareInfo'][level_name]['Buffer']:\n\t\t\t\tyield entry['shi1_netname'][:-1], entry['shi1_type'], entry['shi1_remark']\n\t\t\t\n\t\t\tresumeHandle = resp['ResumeHandle'] \n\t\t\tstatus = NTStatus(resp['ErrorCode'])\t\n\t\n\tasync def list_sessions(self, level = 10):\n\t\tif level not in [1, 10]:\n\t\t\traise Exception('Only levels 1 and 10 implemented!')\n\t\tlevel_name = 'Level%s' % level\n\t\tstatus = NTStatus.MORE_ENTRIES\n\t\tresumeHandle = 0\n\t\twhile status == NTStatus.MORE_ENTRIES:\n\t\t\ttry:\n\t\t\t\tresp = await srvs.hNetrSessionEnum(self.dce, '\\x00', NULL, level, resumeHandle = resumeHandle)\n\t\t\texcept Exception as e:\n\t\t\t\tprint(str(e))\n\t\t\t\tif str(e).find('STATUS_MORE_ENTRIES') < 0:\n\t\t\t\t\traise\n\t\t\t\tresp = e.get_packet()\n\n\t\t\tif level == 1:\n\t\t\t\tfor entry in resp['InfoStruct']['SessionInfo'][level_name]['Buffer']:\n\t\t\t\t\tusername = entry['sesi1_username'][:-1]\n\t\t\t\t\tip_addr = entry['sesi1_cname'][:-1]\t\t\t\t\t\n\t\t\t\t\tyield username, ip_addr\n\n\t\t\telif level == 10:\n\t\t\t\tfor entry in resp['InfoStruct']['SessionInfo'][level_name]['Buffer']:\n\t\t\t\t\tusername = entry['sesi10_username'][:-1]\n\t\t\t\t\tip_addr = entry['sesi10_cname'][:-1]\n\t\t\t\t\t\n\t\t\t\t\tyield username, ip_addr\n\t\t\t\n\t\t\tresumeHandle = resp['ResumeHandle'] \n\t\t\tstatus = NTStatus(resp['ErrorCode'])\t", "sub_path": "aiosmb/dcerpc/v5/interfaces/srvsmgr.py", "file_name": "srvsmgr.py", "file_ext": "py", "file_size_in_byte": 2584, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "aiosmb.dcerpc.v5.transport.smbtransport.SMBTransport", "line_number": 26, "usage_type": "call"}, {"api_name": "aiosmb.dcerpc.v5.srvs.MSRPC_UUID_SRVS", "line_number": 29, "usage_type": "attribute"}, {"api_name": "aiosmb.dcerpc.v5.srvs", "line_number": 29, "usage_type": "name"}, {"api_name": "aiosmb.wintypes.ntstatus.NTStatus.MORE_ENTRIES", "line_number": 41, "usage_type": "attribute"}, {"api_name": "aiosmb.wintypes.ntstatus.NTStatus", "line_number": 41, "usage_type": "name"}, {"api_name": "aiosmb.wintypes.ntstatus.NTStatus.MORE_ENTRIES", "line_number": 43, "usage_type": "attribute"}, {"api_name": "aiosmb.wintypes.ntstatus.NTStatus", "line_number": 43, "usage_type": "name"}, {"api_name": "aiosmb.dcerpc.v5.srvs.hNetrShareEnum", "line_number": 45, "usage_type": "call"}, {"api_name": "aiosmb.dcerpc.v5.srvs", "line_number": 45, "usage_type": "name"}, {"api_name": "aiosmb.wintypes.ntstatus.NTStatus", "line_number": 56, "usage_type": "call"}, {"api_name": "aiosmb.wintypes.ntstatus.NTStatus.MORE_ENTRIES", "line_number": 62, "usage_type": "attribute"}, {"api_name": "aiosmb.wintypes.ntstatus.NTStatus", "line_number": 62, "usage_type": "name"}, {"api_name": "aiosmb.wintypes.ntstatus.NTStatus.MORE_ENTRIES", "line_number": 64, "usage_type": "attribute"}, {"api_name": "aiosmb.wintypes.ntstatus.NTStatus", "line_number": 64, "usage_type": "name"}, {"api_name": "aiosmb.dcerpc.v5.srvs.hNetrSessionEnum", "line_number": 66, "usage_type": "call"}, {"api_name": "aiosmb.dcerpc.v5.dtypes.NULL", "line_number": 66, "usage_type": "argument"}, {"api_name": "aiosmb.dcerpc.v5.srvs", "line_number": 66, "usage_type": "name"}, {"api_name": "aiosmb.wintypes.ntstatus.NTStatus", "line_number": 87, "usage_type": "call"}]} +{"seq_id": "58460646", "text": "import multiprocessing\nfrom mpdaemon import DaemonWrapper\nimport time\n\n\ndef sender(conn):\n \"\"\"\n function to send messages to other end of pipe\n \"\"\"\n while 1:\n time.sleep(1)\n conn.send('Hello')\n conn.close()\n\n\ndef receiver(conn, daemon):\n \"\"\"\n function to print the messages received from other\n end of pipe\n \"\"\"\n while 1:\n msg = conn.recv()\n daemon.logger.info(msg)\n\n\ndef main(daemon):\n # creating a pipe\n parent_conn, child_conn = multiprocessing.Pipe()\n\n # creating new processes\n p1 = multiprocessing.Process(target=sender, args=(parent_conn,))\n p2 = multiprocessing.Process(target=receiver, args=(child_conn, daemon))\n\n # running processes\n p1.start()\n p2.start()\n\n import signal\n import sys\n\n def signal_handler(signal, frame):\n daemon.logger.info('MAIN GOT KILLED')\n p1.terminate()\n p2.terminate()\n sys.exit(0)\n\n signal.signal(signal.SIGTERM, signal_handler)\n\n # wait until processes finish\n p1.join()\n p2.join()\n\n\nif __name__ == '__main__':\n daemon = DaemonWrapper('daemon')\n daemon.run(main, daemon)\n", "sub_path": "_PYTHON_/_problems_/multiprocessing_with_mpdaemon/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1145, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.sleep", "line_number": 11, "usage_type": "call"}, {"api_name": "multiprocessing.Pipe", "line_number": 28, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 31, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 32, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 45, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 47, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 47, "usage_type": "attribute"}, {"api_name": "mpdaemon.DaemonWrapper", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "260573035", "text": "from django.conf.urls import patterns, include, url\nfrom .views import *\nurlpatterns = (\n url(r'^$', indexPage),\n url(r'^addstudent', addStudentPage),\n url(r'^idcheck', idCheckPage),\n url(r'^login', loginPage),\n url(r'^register', registerPage),\n url(r'^search', searchPage),\n url(r'^setstudent', setStudentPage),\n url(r'^accesslist', accessListPage),\n url(r'^setteacher', setTeacherPage),\n url(r'^attendancecheck', attendanceCheckingPage),\n url(r'^attendancestatus', attendanceStatusPage),\n url(r'^getclassname', getClassNamePage),\n url(r'^classlist', classListPage),\n url(r'^imageupload', imageUploadPage),\n)", "sub_path": "server/academyMGS/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 652, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.url", "line_number": 4, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "570059850", "text": "# Used for MS & Config reading/writing\n# Reads from screen and harddrive\nfrom ctypes import windll, Structure, c_long, byref\nimport sys, os, json, subprocess, psutil, pyperclip\n\n\n\nclass vhWindows:\n cursor = {\"x\":0,\"y\":0}\n server = \"vibhub.io\"\n deviceID = \"TestDevice\"\n appName = \"VH-WoW-Python\"\n dc = windll.user32.GetDC(0)\n r = 0\n g = 0\n b = 0\n wowPid = 0\n # Max intensity of output\n maxIntensity = 255\n minIntensity = 30\n # Percent of max intensity to add from taking damage\n hpRatio = 5\n\n # Event raised when WoW is started or stopped\n # Takes 1 argument which is true/false\n onWowStatus = None\n\n\n def init(self):\n self.getConfig()\n\n # Screen reader\n def updatePixelColor(self):\n parse = windll.gdi32.GetPixel(self.dc,self.cursor[\"x\"],self.cursor[\"y\"])\n self.r = parse & 0xFF\n self.g = (parse >> 8) & 0xFF\n self.b = (parse >> 16) & 0xFF\n #print(\"Parse at \", self.cursor[\"x\"], self.cursor[\"y\"], \"=\", self.r, self.g, self.b)\n\n # Checks if WoW is running or not\n def processScan(self):\n #Scan for WoW\n if not self.wowPid:\n cmd = 'WMIC PROCESS where \"name=\\'Wow-64.exe\\' or name=\\'Wow.exe\\'\" get Caption,Processid'\n proc = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)\n for line in proc.stdout:\n spl = line.split()\n if len(spl) > 1 and (spl[0] == b'Wow-64.exe' or spl[0] == b'Wow.exe'):\n self.wowPid = int(spl[1])\n if self.onWowStatus:\n self.onWowStatus(True)\n\n # Make sure WoW process still exists\n elif not psutil.pid_exists(self.wowPid):\n self.wowPid = 0\n if self.onWowStatus:\n self.onWowStatus(False)\n\n def saveConfig(self):\n confFile = open(\"conf.json\", \"w\")\n confFile.write(json.dumps({\n \"cursor\" : [self.cursor[\"x\"],self.cursor[\"y\"]],\n \"server\" : self.server,\n \"deviceID\" : self.deviceID,\n \"maxIntensity\" : self.maxIntensity,\n \"hpRatio\" : self.hpRatio,\n \"minIntensity\" : self.minIntensity\n }))\n confFile.close()\n\n def getConfig(self):\n try:\n confFile = open(\"conf.json\", \"r\")\n js = json.loads(confFile.read())\n confFile.close()\n if \"cursor\" in js and isinstance(js[\"cursor\"], list) and len(js[\"cursor\"]):\n self.cursor[\"x\"] = js[\"cursor\"][0]\n if \"cursor\" in js and isinstance(js[\"cursor\"], list) and len(js[\"cursor\"]) > 1:\n self.cursor[\"y\"] = js[\"cursor\"][1]\n if \"server\" in js:\n self.server = js[\"server\"]\n if \"deviceID\" in js:\n self.deviceID = js[\"deviceID\"]\n if \"maxIntensity\" in js:\n self.maxIntensity = js[\"maxIntensity\"]\n if \"hpRatio\" in js:\n self.hpRatio = js[\"hpRatio\"]\n if \"minIntensity\" in js:\n self.minIntensity = min(js[\"minIntensity\"], self.maxIntensity)\n \n print(\"Loaded settings:\")\n print(\" DeviceID: \", self.deviceID)\n print(\" Server: \", self.server)\n print(\" Max Intens: \", self.maxIntensity)\n print(\" Min Intens: \", self.minIntensity)\n print(\" Cursor: \", self.cursor[\"x\"], self.cursor[\"y\"])\n \n print(\"Start the program with reset as an argument to reconfigure\")\n except FileNotFoundError:\n pass\n\n def copyWeakaura(self):\n try:\n confFile = open(\"weakaura.txt\", \"r\")\n data = confFile.read()\n confFile.close()\n pyperclip.copy(data)\n except FileNotFoundError:\n pass\n", "sub_path": "Python/lib/vhWindows.py", "file_name": "vhWindows.py", "file_ext": "py", "file_size_in_byte": 3810, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ctypes.windll.user32.GetDC", "line_number": 13, "usage_type": "call"}, {"api_name": "ctypes.windll.user32", "line_number": 13, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 13, "usage_type": "name"}, {"api_name": "ctypes.windll.gdi32.GetPixel", "line_number": 34, "usage_type": "call"}, {"api_name": "ctypes.windll.gdi32", "line_number": 34, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 34, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 45, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "psutil.pid_exists", "line_number": 54, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 61, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 74, "usage_type": "call"}, {"api_name": "pyperclip.copy", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "447289428", "text": "from datetime import datetime\nfrom django.http import HttpResponseRedirect\nimport sys\n\nclass SessionExpiredMiddleware:\n def process_request(self,request):\n now = datetime.now()\n #request.session['last_activity'] = now\n last_activity = now\n try:\n last_activity = request.session['last_activity']\n except:\n pass\n #print >> sys.stdout, last_activity\n #print >> sys.stdout, (now - last_activity).seconds/60\n if (now - last_activity).seconds/60 > 30:\n # Do logout / expire session\n # and then...\n from Core.UserManagement import logout\n from Core.Backend.PrivilegeManagement.sites import get_client_from_url\n logout(request)\n request.session['last_activity'] = now\n return HttpResponseRedirect(\"/%s/\" %get_client_from_url(request.get_full_path()))\n\n if not request.is_ajax():\n # don't set this for ajax requests or else your\n # expired session checks will keep the session from\n # expiring :)\n request.session['last_activity'] = now\n\n\nclass APIExceptionMiddleware:\n def process_response(self,request, response):\n #print >> sys.stdout,response\n return response\n def process_exception(self,request,exception):\n if \"/api/v1\" in request.get_full_path():\n print >> sys.stdout, \"exception happen\"\n \n", "sub_path": "Core/Backend/SessionManagement/middleware.py", "file_name": "middleware.py", "file_ext": "py", "file_size_in_byte": 1442, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.now", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 7, "usage_type": "name"}, {"api_name": "Core.UserManagement.logout", "line_number": 21, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 23, "usage_type": "call"}, {"api_name": "Core.Backend.PrivilegeManagement.sites.get_client_from_url", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "600059209", "text": "from django.db import IntegrityError\nfrom django.contrib.auth.models import User\nfrom django.db import models\nfrom django.db.models import UniqueConstraint\nfrom decimal import getcontext\n\n\n# Create your models here.\nclass Book(models.Model):\n \"\"\"\n Model represents Book.\n \"\"\"\n\n class Meta:\n verbose_name = \"Book\"\n verbose_name_plural = \"Books\"\n\n objects = models.Manager()\n title = models.CharField(max_length=50,\n verbose_name=\"Title\", # Display in Admin site.\n help_text=\"Enter a title of the book.\"\n )\n date = models.DateTimeField(verbose_name=\"Date\",\n auto_now_add=True,\n null=True\n )\n summary = models.TextField(max_length=1000,\n verbose_name=\"Summary\",\n help_text=\"Enter a short description of the book.\",\n null=True,\n blank=True)\n authors = models.ManyToManyField(User, blank=True, related_name=\"books\")\n users_rate_score = models.DecimalField(verbose_name=\"rate\",\n max_digits=2,\n decimal_places=1,\n default=0.0)\n users_rate_count = models.PositiveIntegerField(default=0,\n null=True,\n verbose_name=\"Number of users, who rated.\")\n rate_stars_num = models.PositiveIntegerField(default=0,\n null=True,\n verbose_name=\"Number of checked stars\")\n\n def __str__(self):\n \"\"\"Display Book model\"\"\"\n return self.title\n\n\nclass RateBookUser(models.Model):\n \"\"\"\n Model represents Rate through the ManyToMany links between Book and User models.\n \"\"\"\n\n class Meta:\n constraints = [\n UniqueConstraint(fields=['book', 'user'], name='rate_unique_book_user')\n ]\n\n objects = models.Manager()\n book = models.ForeignKey(Book, on_delete=models.CASCADE, related_name=\"rated_user_table\")\n user = models.ForeignKey(User, on_delete=models.CASCADE, related_name=\"rated_book_table\")\n user_rate_score = models.DecimalField(max_digits=2,\n decimal_places=1,\n verbose_name=\"user_book_rate\",\n default=0.0)\n\n def save(self, **kwargs):\n getcontext().prec = 2 # Decimal has a precious = 2 number.\n try:\n super(RateBookUser, self).save(kwargs)\n except IntegrityError:\n exist_rate_entry = RateBookUser.objects.get(user=self.user, book=self.book)\n new_users_rate_score = self.book.users_rate_score * self.book.users_rate_count\n new_users_rate_score += self.user_rate_score\n new_users_rate_score -= exist_rate_entry.user_rate_score\n new_users_rate_score /= self.book.users_rate_count\n exist_rate_entry.user_rate_score = new_users_rate_score\n RateBookUser.objects.filter(user=self.user, book=self.book).update(user_rate_score=self.user_rate_score)\n\n else:\n new_users_rate_score = self.book.users_rate_score * self.book.users_rate_count\n self.book.users_rate_count += 1\n new_users_rate_score += self.user_rate_score\n new_users_rate_score /= self.book.users_rate_count\n\n self.book.rate_stars_num = int(new_users_rate_score)\n self.book.users_rate_score = new_users_rate_score\n self.book.save()\n\n\n# class LikeBookUser(models.Model):\n# \"\"\"\n# Model represents Likes through the ManyToMany links between Book and User models.\n# \"\"\"\n#\n# class Meta:\n# constraints = [\n# UniqueConstraint(fields=['book', 'user'], name='like_unique_book_user')\n# ]\n#\n# objects = models.Manager()\n# book = models.ForeignKey(Book, on_delete=models.CASCADE, related_name=\"liked_user_table\")\n# user = models.ForeignKey(User, on_delete=models.CASCADE, related_name=\"liked_book_table\")\n#\n# def save(self, **kwargs):\n# try:\n# super(LikeBookUser, self).save(kwargs)\n# except IntegrityError:\n# LikeBookUser.objects.get(user=self.user, book=self.book).delete()\n# self.book.likes -= 1\n# else:\n# self.book.likes += 1\n# self.book.save()\n\n\nclass Comment(models.Model):\n \"\"\"\n Model represents user's comment for the book.\n \"\"\"\n\n class Meta:\n verbose_name = \"Comment\"\n verbose_name_plural = \"Comments\"\n\n objects = models.Manager()\n text = models.TextField(max_length=1000,\n verbose_name=\"Comment\",\n blank=True,\n null=True)\n date = models.DateTimeField(auto_now_add=True)\n book = models.ForeignKey(Book, on_delete=models.CASCADE, related_name=\"comments\")\n author = models.ForeignKey(User,\n on_delete=models.CASCADE,\n null=True,\n blank=True,\n related_name=\"comments\")\n likes = models.PositiveIntegerField(default=0)\n user_likes = models.ManyToManyField(User,\n through=\"manager.LikeCommentUser\",\n blank=True, related_name=\"liked_comments\")\n\n def __str__(self):\n \"\"\"Display Comment model\"\"\"\n return f\"Comment: id={self.id} author={self.author}\"\n\n\nclass LikeCommentUser(models.Model):\n \"\"\"\n Model represents Likes through the ManyToMany link between Comment and User models.\n \"\"\"\n\n class Meta:\n constraints = [\n UniqueConstraint(fields=[\"comment\", \"user\"], name=\"like_unique_comment_user\")\n ]\n\n objects = models.Manager()\n comment = models.ForeignKey(Comment, on_delete=models.CASCADE, related_name=\"liked_user_table\")\n user = models.ForeignKey(User, on_delete=models.CASCADE, related_name=\"liked_comment_table\")\n\n def save(self, **kwargs):\n try:\n super(LikeCommentUser, self).save(kwargs)\n except IntegrityError:\n LikeCommentUser.objects.get(user=self.user, comment_id=self.comment).delete()\n self.comment.likes -= 1\n else:\n self.comment.likes += 1\n self.comment.save()\n", "sub_path": "book_shop/manager/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 6653, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.db.models.Model", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.Manager", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 32, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 49, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 49, "usage_type": "name"}, {"api_name": "django.db.models.UniqueConstraint", "line_number": 56, "usage_type": "call"}, {"api_name": "django.db.models.Manager", "line_number": 59, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 59, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 60, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 60, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 61, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 61, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 61, "usage_type": "attribute"}, {"api_name": "django.db.models.DecimalField", "line_number": 62, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 62, "usage_type": "name"}, {"api_name": "decimal.getcontext", "line_number": 68, "usage_type": "call"}, {"api_name": "django.db.IntegrityError", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 116, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 116, "usage_type": "name"}, {"api_name": "django.db.models.Manager", "line_number": 125, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 125, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 126, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 126, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 130, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 130, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 131, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 131, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 131, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 132, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 132, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 132, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 133, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 133, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 137, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 137, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 138, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 138, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 138, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 147, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 147, "usage_type": "name"}, {"api_name": "django.db.models.UniqueConstraint", "line_number": 154, "usage_type": "call"}, {"api_name": "django.db.models.Manager", "line_number": 157, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 157, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 158, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 158, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 158, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 159, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 159, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 159, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 159, "usage_type": "attribute"}, {"api_name": "django.db.IntegrityError", "line_number": 164, "usage_type": "name"}]} +{"seq_id": "94373416", "text": "from flask import render_template, json\nfrom . import streams\n\n@streams.route('/')\ndef index():\n # Get the streams information from the twitch api\n # twitch_api = 'https://api.twitch.tv/kraken/streams'\n # Save the data to a variable, use .decode('utf-8')\n # to convert to string, and save to raw_data.dat\n lol_channels = []\n\n # Save the raw data to a file, then use json\n # to convert from str to object.\n with open('/tmp/raw_data.dat', 'r') as raw_data:\n twitch_api_raw_data = json.loads(raw_data.read())\n streams_data = twitch_api_raw_data['streams']\n\n for stream in streams_data:\n if 'League of Legends' in stream['channel']['game']:\n lol_channels.append(stream)\n\n return render_template('index.html', stream_list=lol_channels)\n\n", "sub_path": "app/streams/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 794, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.json.loads", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "96877423", "text": "# -*- coding: utf-8 -*-\n\nimport logging\n\nimport db\nimport model\nimport const\n\nlogging.basicConfig(level=logging.INFO,\n format='%(asctime)s %(filename)s %(levelname)s %(message)s',\n filename='/data/applogs/wuzhao/app.log')\nlogger = logging.getLogger(__name__)\n\ndef getLatestMsgs(meId,otherId,limit=20):\n if meId and otherId:\n dbMessages = db.getLatestMsgs(meId,otherId,limit)\n msgList = [model.Message(dbMessage) for dbMessage in dbMessages]\n msgList.reverse()\n simpleUser1,simpleUser2 = None,None\n dbUsersMe = db.get_simple_user(meId)\n dbUsersOther = db.get_simple_user(otherId)\n if len(dbUsersMe):\n simpleUser1 = model.simpleuser(dbUsersMe[0],const.AVATAR_M)\n if len(dbUsersOther):\n simpleUser2 = model.simpleuser(dbUsersOther[0],const.AVATAR_M)\n if simpleUser1 and simpleUser2:\n return msgList,simpleUser1,simpleUser2\n return None,None,None\n\ndef getMsgPersons(userId):\n '''userId需要先转成long类型'''\n if userId:\n msgPersons = []\n dbMsgPersonIds = db.getMsgPersonIds(userId)\n personIdSet = set()\n for each in dbMsgPersonIds:\n fromId = each.from_id\n toId = each.to_id\n otherId = toId if fromId==userId else fromId\n personIdSet.add(otherId)\n for otherId in personIdSet:\n dbUser = None\n dbUsers = db.get_simple_user(otherId)\n if len(dbUsers):\n dbUser = dbUsers[0]\n dbMsgs = db.getLatestMsgs(userId,otherId,10)\n unReadNum = db.countUnreadMsgs(userId, otherId)\n msgPerson = model.MsgPerson(dbUser,dbMsgs,unReadNum)\n msgPersons.append(msgPerson)\n return msgPersons\n return []\n\ndef getAllUnreadMsgs(userId):\n '''统计用户的所有未读消息数量'''\n if userId:\n return db.countAllUnreadMsgs(userId)\n return 0\n\ndef markMsgRead(meId,otherId):\n if meId and otherId:\n db.markMsgRead(meId, otherId)\n\ndef sendMessage(fromId,toId,content):\n if fromId and toId and content:\n messageId = db.insertMessage(fromId, toId, content)\n return True if messageId>0 else False\n return False\n \ndef deleteMessages(meId,otherId):\n if meId and otherId:\n db.deleteMessages(meId, otherId)\n return True\n return False\n \n", "sub_path": "controller/messagectr.py", "file_name": "messagectr.py", "file_ext": "py", "file_size_in_byte": 2368, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "db.getLatestMsgs", "line_number": 16, "usage_type": "call"}, {"api_name": "model.Message", "line_number": 17, "usage_type": "call"}, {"api_name": "db.get_simple_user", "line_number": 20, "usage_type": "call"}, {"api_name": "db.get_simple_user", "line_number": 21, "usage_type": "call"}, {"api_name": "model.simpleuser", "line_number": 23, "usage_type": "call"}, {"api_name": "const.AVATAR_M", "line_number": 23, "usage_type": "attribute"}, {"api_name": "model.simpleuser", "line_number": 25, "usage_type": "call"}, {"api_name": "const.AVATAR_M", "line_number": 25, "usage_type": "attribute"}, {"api_name": "db.getMsgPersonIds", "line_number": 34, "usage_type": "call"}, {"api_name": "db.get_simple_user", "line_number": 43, "usage_type": "call"}, {"api_name": "db.getLatestMsgs", "line_number": 46, "usage_type": "call"}, {"api_name": "db.countUnreadMsgs", "line_number": 47, "usage_type": "call"}, {"api_name": "model.MsgPerson", "line_number": 48, "usage_type": "call"}, {"api_name": "db.countAllUnreadMsgs", "line_number": 56, "usage_type": "call"}, {"api_name": "db.markMsgRead", "line_number": 61, "usage_type": "call"}, {"api_name": "db.insertMessage", "line_number": 65, "usage_type": "call"}, {"api_name": "db.deleteMessages", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "77682594", "text": "#!/usr/bin/python3.6\nimport vk\nimport json\n\nsession = vk.Session('95b281c395b281c395b281c3ed95d425dd995b295b281c3ce799373392c3bcf1673cdbb')\napi = vk.API(session, v='5.35', lang='ru', timeout=1000000)\n\nf = open('cit.json', 'r')\nout = list(set(json.loads(f.read())))\nf.close()\nprint(len(out))\n\n\ndef parseG(url, n):\n group = api.wall.get(domain=url, count=n)\n\n out = []\n reclam = ['😇', 'заказ', 'менеджер', 'коиплектущие', 'магазин', 'vk.cc', 'vk', '👉 vk.cc', \"DOMINIK\", 'цена пугала',\n 'акции', 'акция', 'iPhone 7', 'www', 'визитки', 'буклеты', 'брошюры', 'http']\n\n mat = list(set(json.loads(open('mat.json').read())))\n\n for i in group['items']:\n i = i['text']\n flag = False\n for j in reclam:\n if (i.find(j) != -1):\n flag = True\n for j in mat:\n if (i.find(j) != -1):\n flag = True\n\n if (not flag and i != \"\"):\n out.append(i)\n\n return out\n\n\npabl = ['skinnycatclub' ,'dimastoff132903768']\n\ni = 0\nfor i in pabl:\n n = parseG(i, 10000)\n for j in n:\n out.append(j)\n\nprint(len(out))\nf = open('cit.json', 'w')\nf.write(json.dumps(out))\nf.close()\n", "sub_path": "modules_alice/data/quotes/parse.py", "file_name": "parse.py", "file_ext": "py", "file_size_in_byte": 1255, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "vk.Session", "line_number": 5, "usage_type": "call"}, {"api_name": "vk.API", "line_number": 6, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 9, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "652757654", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 2.4 (62061)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/ice/adverlet/metaconfigure.py\n# Compiled at: 2008-12-22 07:00:12\n__license__ = 'GPL v.3'\nfrom zope.component import provideUtility\nfrom interfaces import IAdverlet\nfrom adverlet import Adverlet\n\ndef registerAdverlet(_context, name, description=None, default=None, wysiwyg=True):\n adverlet = Adverlet()\n adverlet.__name__ = name\n adverlet.description = description\n adverlet.default = default\n adverlet.wysiwyg = wysiwyg\n adverlet.newlines = not wysiwyg\n provideUtility(adverlet, IAdverlet, name=name)", "sub_path": "pycfiles/ice.adverlet-0.2.3-py2.4/metaconfigure.py", "file_name": "metaconfigure.py", "file_ext": "py", "file_size_in_byte": 709, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "adverlet.Adverlet", "line_number": 13, "usage_type": "call"}, {"api_name": "adverlet.__name__", "line_number": 14, "usage_type": "attribute"}, {"api_name": "adverlet.description", "line_number": 15, "usage_type": "attribute"}, {"api_name": "adverlet.default", "line_number": 16, "usage_type": "attribute"}, {"api_name": "adverlet.wysiwyg", "line_number": 17, "usage_type": "attribute"}, {"api_name": "adverlet.newlines", "line_number": 18, "usage_type": "attribute"}, {"api_name": "zope.component.provideUtility", "line_number": 19, "usage_type": "call"}, {"api_name": "interfaces.IAdverlet", "line_number": 19, "usage_type": "argument"}]} +{"seq_id": "377502577", "text": "\n\nfrom django.urls import path\nfrom .views import import_xls,ReportesMenuView,FeligresesReporteView,FeligresesFiltroEdadView,FeligresesFiltroEdadView,FeligresesFiltroMatrimonioView,FiltroFeligresesView\nfrom .views import FiltroSeleccionFeligresesView,FiltroPastoralesFeligresesView,FeligresesFiltroNacimientoView,FeligresesFiltroMatrimonioView,ImpresionFeligresEdadesView\nfrom .views import ImpresionFeligresMatrimonioView,ImpresionFeligresMatrimonioView,PastoralIngresoFeligresView,PastoralFeligresesReporteView,PastoralAsistenciaView\nfrom .views import crea_lista_mail_edad,crea_lista_mail_anios,crea_lista_mail_pastoral,crea_lista_todos_mail_edad,crea_lista_todos_mail_anios,ImpresionFeligresesFiltroView\nfrom .views import crea_lista_todos_mail_pastoral,crea_lista_todos_filtro_feligreses,FeligresesEnvioMailView,FeligresesEnvioWathsappView,FeligresesEnvioSmSView\nfrom .views import CrmHomeView,FeligresesMenuView,PastoralesMenuView,FeligresesReporteView,successView,crea_lista_todos_filtro_pastorales,InscripcionActividadView,MessengerMenuView\nfrom django.conf.urls import include\nfrom django.contrib.auth.decorators import login_required \n\n\"\"\" from messenger.urls import messenger_patterns\nfrom profiles.urls import profiles_patterns\nfrom registration.urls import registration_patterns \"\"\"\n\ncrm_patterns = ([\n\n #path('messenger/',include(messenger_patterns),name='messenger'),\n #path('profiles/',include(profiles_patterns),name='profiles'),\n #path('registration/',include(registration_patterns),name='registration'),\n\n #path('accounts/', include('django.contrib.auth.urls')),\n #path('registration/', include(registration_patterns)),\n # Paths de profiles\n #path('profiles/', include(profiles_patterns)),\n # Paths de Messenger\n #path('messenger/', include(messenger_patterns)),\n \n path('importar_xls/', import_xls, name=\"importar_xls\"),\n path('menu_reportes/', ReportesMenuView.as_view(),name='menu_reportes'),\n \n path('reporte_feligreses/',FeligresesReporteView,name='reporte_feligreses/'),\n #path('menu_reservas/reporte_recursos/',RecursosReporteView,name='reporte_recursos/'),\n #path('menu_reservas/reporte_reservas/',ReservasReporteView,name='reporte_reservas/'),\n \n path('filtro_edad_feligreses/',FeligresesFiltroEdadView,name='filtro_edad_feligreses'),\n path('filtro_fecha_matrimonio_feligreses/',FeligresesFiltroMatrimonioView,name='filtro_fecha_matrimonio_feligreses'),\n path('filtro_feligreses/',FiltroFeligresesView,name='filtro_feligreses'),\n path('filtro_seleccion_feligreses//',FiltroSeleccionFeligresesView,name='filtro_seleccion_feligreses'),\n\n path('filtro_feligreses_pastorales/',FiltroPastoralesFeligresesView,name='filtro_feligreses_pastorales'),\n #path('filtro_reservas/',FiltroReservasView,name='filtro_reservas'),\n\n path('filtro_fecha_nacimiento_feligreses/',FeligresesFiltroNacimientoView,name='filtro_fecha_nacimiento_feligreses'),\n path('filtro_fecha_matrimonio_feligreses/',FeligresesFiltroMatrimonioView,name='filtro_fecha_matrimonio_feligreses'),\n\n path('success/', successView, name='success'),\n\n path('imprimir_feligreses_edades/',ImpresionFeligresEdadesView,name='imprimir_feligreses_edades'),\n path('imprimir_feligreses_matrimonio/',ImpresionFeligresMatrimonioView,name='imprimir_feligreses_matrimonio'),\n path('imprimir_feligreses_filtro/',ImpresionFeligresesFiltroView,name='imprimir_feligreses_filtro'),\n #path('imprimir_filtro_reservas/',ImpresionReservasFiltroView,name='imprimir_filtro_reservas'),\n\n\n path('ingreso_feligreses_pastorales/',PastoralIngresoFeligresView.as_view(),name='ingreso_feligreses_pastorales'),\n path('reporte_feligreses_pastorales/',PastoralFeligresesReporteView,name='reporte_feligreses_pastorales'),\n #path('reporte_pastorales/',PastoralesReporteView,name='reporte_pastorales'),\n #path('ingreso_feligreses_pastorales/',FeligresesFiltroEdadView,name='ingreso_feligreses_pastorales'),\n path('ingreso_asistencia_pastorales/',PastoralAsistenciaView.as_view(),name='ingreso_asistencia_pastorales'),\n\n path('crea_lista_mail_edad///',crea_lista_mail_edad,name='crea_lista_mail_edad'),\n path('crea_lista_mail_anios///',crea_lista_mail_anios,name='crea_lista_mail_anios'),\n path('crea_lista_mail_pastoral///',crea_lista_mail_pastoral,name='crea_lista_mail_pastoral'),\n\n path('adicionar_todos_lista_mail_edad/',crea_lista_todos_mail_edad,name='adicionar_todos_lista_mail_edad'),\n path('adicionar_todos_lista_mail_anios/',crea_lista_todos_mail_anios,name='adicionar_todos_lista_mail_anios'),\n path('adicionar_todos_lista_mail_pastoral/',crea_lista_todos_mail_pastoral,name='adicionar_todos_lista_mail_pastoral'),\n path('adicionar_todos_lista_filtro_feligreses/',crea_lista_todos_filtro_feligreses,name='adicionar_todos_lista_filtro_feligreses'),\n path('adicionar_todos_lista_filtro_pastorales/',crea_lista_todos_filtro_pastorales,name='adicionar_todos_lista_filtro_pastorales'),\n\n path('envio_mail/',FeligresesEnvioMailView,name='envio_mail'),\n path('envio_mensaje_wathsapp/',FeligresesEnvioWathsappView,name='envio_mensaje_wathsapp'),\n path('envio_mensaje_sms/',FeligresesEnvioSmSView,name='envio_mensaje_sms'),\n #path('envio_mensaje_wathsapp/'\n\n\n #path('filtro_edad_feligreses1///',FeligresesFiltroEdadView2,name='filtro_edad_feligreses1'),\n\n path('home_crm/', CrmHomeView.as_view(),name='home_crm'),\n path('menu_feligreses/', FeligresesMenuView.as_view(),name='menu_feligreses'),\n path('menu_pastorales/', PastoralesMenuView.as_view(),name='menu_pastorales'),\n path('menu_messenger/', MessengerMenuView.as_view(),name='menu_messenger'),\n path('inscripcion_actividad/', InscripcionActividadView.as_view(),name='inscripcion_actividad'),\n #path('crm:home_crm/', FeligresesMenuView.as_view(),name='crm:home_crm'),\n #path('menu_reservas/', ReservasMenuView.as_view(),name='menu_reservas'),\n #path('menu_tareas/', TareasMenuView.as_view(),name='menu_tareas'),\n #path('menu_activos/', ActivosMenuView.as_view(),name='menu_activos'),\n\n path('reporte_feligreses/',FeligresesReporteView,name='reporte_feligreses'),\n #path('reporte_pastorales/', PastoralesReporteView,name='reporte_pastorales'),\n #path('reporte_tareas/', TareasReporteView,name='reporte_tareas'),\n #path('reporte_activos/', ActivosReporteView,name='reporte_activos'),\n #path('eventos/', EventosView,name='eventos'),\n\n #path('imprimir/',FeligresesFiltroEdadView,name='imprimir'),\n \n #path('envia_lista_mail/',envia_lista_mail.as_view(),name='envia_lista_mail'),\n \n path('mensaje_feligres/',FeligresesFiltroEdadView,name='mensaje_feligres'),\n \n \n #path('filtro_edad_feligreses///',FeligresesFiltroEdadView,name='filtro_edad_feligreses'),\n #path('filtro_fecha_nacimiento_feligreses/',FeligresesFiltroFechaNacimientoView,name='filtro_fecha_nacimiento_feligreses'),\n #path('filtro_fecha_matrimonio_feligreses/',FeligresesFiltroFechaMatrimonioView,name='filtro_fecha_matrimonio_feligreses'), \n\n], 'crm')", "sub_path": "erp_iglesia/crm/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 7105, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "views.import_xls", "line_number": 30, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "views.ReportesMenuView.as_view", "line_number": 31, "usage_type": "call"}, {"api_name": "views.ReportesMenuView", "line_number": 31, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "views.FeligresesReporteView", "line_number": 33, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "views.FeligresesFiltroEdadView", "line_number": 37, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "views.FeligresesFiltroMatrimonioView", "line_number": 38, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "views.FiltroFeligresesView", "line_number": 39, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "views.FiltroSeleccionFeligresesView", "line_number": 40, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "views.FiltroPastoralesFeligresesView", "line_number": 42, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 45, "usage_type": "call"}, {"api_name": "views.FeligresesFiltroNacimientoView", "line_number": 45, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "views.FeligresesFiltroMatrimonioView", "line_number": 46, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 48, "usage_type": "call"}, {"api_name": "views.successView", "line_number": 48, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 50, "usage_type": "call"}, {"api_name": "views.ImpresionFeligresEdadesView", "line_number": 50, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 51, "usage_type": "call"}, {"api_name": "views.ImpresionFeligresMatrimonioView", "line_number": 51, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 52, "usage_type": "call"}, {"api_name": "views.ImpresionFeligresesFiltroView", "line_number": 52, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 56, "usage_type": "call"}, {"api_name": "views.PastoralIngresoFeligresView.as_view", "line_number": 56, "usage_type": "call"}, {"api_name": "views.PastoralIngresoFeligresView", "line_number": 56, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 57, "usage_type": "call"}, {"api_name": "views.PastoralFeligresesReporteView", "line_number": 57, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 60, "usage_type": "call"}, {"api_name": "views.PastoralAsistenciaView.as_view", "line_number": 60, "usage_type": "call"}, {"api_name": "views.PastoralAsistenciaView", "line_number": 60, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 62, "usage_type": "call"}, {"api_name": "views.crea_lista_mail_edad", "line_number": 62, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 63, "usage_type": "call"}, {"api_name": "views.crea_lista_mail_anios", "line_number": 63, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 64, "usage_type": "call"}, {"api_name": "views.crea_lista_mail_pastoral", "line_number": 64, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 66, "usage_type": "call"}, {"api_name": "views.crea_lista_todos_mail_edad", "line_number": 66, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 67, "usage_type": "call"}, {"api_name": "views.crea_lista_todos_mail_anios", "line_number": 67, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 68, "usage_type": "call"}, {"api_name": "views.crea_lista_todos_mail_pastoral", "line_number": 68, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 69, "usage_type": "call"}, {"api_name": "views.crea_lista_todos_filtro_feligreses", "line_number": 69, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 70, "usage_type": "call"}, {"api_name": "views.crea_lista_todos_filtro_pastorales", "line_number": 70, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 72, "usage_type": "call"}, {"api_name": "views.FeligresesEnvioMailView", "line_number": 72, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 73, "usage_type": "call"}, {"api_name": "views.FeligresesEnvioWathsappView", "line_number": 73, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 74, "usage_type": "call"}, {"api_name": "views.FeligresesEnvioSmSView", "line_number": 74, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 80, "usage_type": "call"}, {"api_name": "views.CrmHomeView.as_view", "line_number": 80, "usage_type": "call"}, {"api_name": "views.CrmHomeView", "line_number": 80, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 81, "usage_type": "call"}, {"api_name": "views.FeligresesMenuView.as_view", "line_number": 81, "usage_type": "call"}, {"api_name": "views.FeligresesMenuView", "line_number": 81, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 82, "usage_type": "call"}, {"api_name": "views.PastoralesMenuView.as_view", "line_number": 82, "usage_type": "call"}, {"api_name": "views.PastoralesMenuView", "line_number": 82, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 83, "usage_type": "call"}, {"api_name": "views.MessengerMenuView.as_view", "line_number": 83, "usage_type": "call"}, {"api_name": "views.MessengerMenuView", "line_number": 83, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 84, "usage_type": "call"}, {"api_name": "views.InscripcionActividadView.as_view", "line_number": 84, "usage_type": "call"}, {"api_name": "views.InscripcionActividadView", "line_number": 84, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 90, "usage_type": "call"}, {"api_name": "views.FeligresesReporteView", "line_number": 90, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 100, "usage_type": "call"}, {"api_name": "views.FeligresesFiltroEdadView", "line_number": 100, "usage_type": "argument"}]} +{"seq_id": "71172176", "text": "#!/usr/bin/env python3.6\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import NoSuchElementException, TimeoutException\nfrom selenium import webdriver\nimport json\nimport logging\nimport platform\nimport configparser\nimport datetime\nimport sys\nimport os\n\n\nlogging.basicConfig(format=\"%(levelname)s - %(module)s - %(asctime)s - %(message)s\",\n datefmt=\"%d/%m/%Y %H:%M:%S\",\n level=logging.INFO)\nCFG = configparser.ConfigParser()\nCFG.read(\"/home/ernie/cfg.ini\")\nEMPLOYEE_NO = CFG[\"Work\"][\"EMP_NO\"]\nSTORE_NO = CFG[\"Work\"][\"STORE_NO\"]\nPIN = CFG[\"Work\"][\"PIN\"]\nPAGE = CFG[\"Work\"][\"PAGE\"]\nIMG_FOLDER = \"/home/ernie/pics/rota-images\"\n\n\nclass RotaScraper:\n\n weekly_rota = []\n c_weekly_rota = {\"Shifts\": []}\n\n def __init__(self, emp_no=None, store_no=None, pin=None):\n self.init_time = datetime.datetime.now()\n\n self.emp_no = emp_no\n self.store_no = store_no\n self.pin = pin\n\n options = webdriver.ChromeOptions()\n options.add_argument(\"--headless\")\n options.add_argument(\"--window-size=1920, 1080\")\n\n if platform.system() == \"Linux\":\n self.browser = webdriver.Chrome(executable_path='/usr/local/bin/chromedriver',\n chrome_options=options)\n elif platform.system() == \"Windows\":\n self.browser = webdriver.Chrome(options=options)\n else:\n logging.error(\"OS not recognized\")\n sys.exit()\n\n self._login()\n\n def _login(self):\n try:\n self.browser.get(PAGE)\n self.browser.find_element_by_name(\"storeNumber\").send_keys(self.store_no)\n self.browser.find_element_by_name(\"employeeNumber\").send_keys(self.emp_no,\n Keys.RETURN)\n pin_element = WebDriverWait(self.browser, 5).until(\n EC.presence_of_element_located((By.NAME, \"PIN\"))\n )\n pin_element.send_keys(self.pin, Keys.RETURN)\n logging.info(\" Logged in...\")\n\n # Paginating to first weekly rota\n self.browser.find_element_by_link_text(\"Rota\").click()\n self._scraping()\n\n except NoSuchElementException:\n logging.error(\" Elements not found\")\n self.browser.quit()\n except TimeoutException:\n logging.error(\" Timed out\")\n self.browser.quit()\n\n\n def _scraping(self):\n rota_list = self._process_rota()\n\n if rota_list:\n try:\n week_no_elements = self.browser.find_elements_by_css_selector(\".navbar-link\")\n week_no = week_no_elements[1].text[3:]\n self.weekly_rota += rota_list\n self._screenshot()\n logging.info(\" Scraped Week \" + week_no + \" Rota\")\n week_no_elements[2].click() # Next page link\n self._scraping()\n\n except NoSuchElementException:\n logging.error(\" Couldn't find elements!\")\n self.browser.quit()\n\n else:\n logging.info(\" All rotas successfully scraped\")\n self.browser.quit()\n self._write_json()\n finish_time = datetime.datetime.now() - self.init_time\n logging.info(\" Time taken: \" + str(finish_time))\n\n def _process_rota(self):\n try:\n week_no = self.browser.find_elements_by_css_selector(\".navbar-link\")[1].text[3:]\n rota_days = self.browser.find_elements_by_css_selector(\"p:first-of-type\")[2:9]\n rota_dates = self.browser.find_elements_by_css_selector(\"p:last-of-type\")[2:9]\n rota_shifts = self.browser.find_elements_by_css_selector(\".details\")[0:7]\n\n weeks = [week_no for _ in range(7)] # Need 7 copies for the c_list\n\n new_dates = ['0' + date.text if len(date.text) == 3 else date.text for date in rota_dates]\n\n edited_dates = [day.text + ' ' + date[:-2] for day, date in zip(rota_days, new_dates)]\n\n string_shifts = [time.text for time in rota_shifts]\n\n rota_list = [[date, shift] for (date, shift) in zip(edited_dates, string_shifts)]\n\n c_rota_list = [(date, time, week) for (\n date, time, week) in zip(edited_dates, string_shifts, weeks)]\n\n c_rota_list = [element for sublist in c_rota_list for element in sublist] # Flattening\n\n self.c_weekly_rota[\"Shifts\"] += c_rota_list\n\n return rota_list\n\n except NoSuchElementException:\n logging.error(\" Elements not found\")\n self.browser.quit()\n except TimeoutException:\n logging.error(\" Timed out\")\n self.browser.quit()\n\n def _screenshot(self):\n week_no_text = self.browser.find_elements_by_class_name(\"navbar-link\")[1].text\n image_path = IMG_FOLDER + \"/\" + week_no_text + \".png\"\n\n if not os.path.isdir(IMG_FOLDER):\n logging.info(\" Screenshots folder not found, creating it...\")\n os.makedirs(img_folder)\n\n # Could overwrite, but want to preserve metadata to\n # observe potential changes in rota over time,\n # so skip if week image already exists\n if os.path.isfile(image_path):\n logging.info(week_no_text + \" screenshot already exists\")\n return\n\n self.browser.save_screenshot(image_path)\n\n def _write_json(self):\n if not os.path.isdir(\"/home/ernie/json\"):\n logging.info(\"'json' directory not found, creating it...\")\n os.makedirs(\"json\")\n\n try:\n with open(\"/home/ernie/json/jsonrota.json\", \"w\") as f1, open(\"/home/ernie/json/cjsonrota.json\", \"w\") as f2:\n json.dump(self.weekly_rota, f1, indent=4)\n json.dump(self.c_weekly_rota, f2, indent=4)\n\n logging.info(\" Rota successfully written to files ['json/jsonrota.json']\"\n \" and ['json/cjsonrota.json']\")\n except:\n logging.error(\"Error writing json file\")\n\n\nr = RotaScraper(EMPLOYEE_NO, STORE_NO, PIN)\n\n", "sub_path": "scrape_rotas.py", "file_name": "scrape_rotas.py", "file_ext": "py", "file_size_in_byte": 6308, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 19, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 41, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 41, "usage_type": "name"}, {"api_name": "platform.system", "line_number": 45, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 46, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 46, "usage_type": "name"}, {"api_name": "platform.system", "line_number": 48, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 49, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 49, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 52, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.RETURN", "line_number": 61, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 61, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 62, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 63, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 63, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.NAME", "line_number": 63, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 63, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.RETURN", "line_number": 65, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 65, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 66, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 72, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 73, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 75, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 76, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 89, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 93, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 94, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 98, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 101, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 101, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 102, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 130, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 131, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 133, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 142, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 156, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 157, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 161, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 162, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 164, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 167, "usage_type": "call"}]} +{"seq_id": "73291172", "text": "\"\"\"Test doit_tasks/packaging.py.\"\"\"\n\nimport json\nimport re\nfrom collections import defaultdict\n\nimport loguru\nimport pendulum\nimport pytest\n\nfrom calcipy.doit_tasks.packaging import (\n _PATH_PACK_LOCK, _check_for_stale_packages, _get_release_date, _HostedPythonPackage, _read_packages,\n find_stale_packages, task_check_for_stale_packages, task_publish, task_publish_test_pypi,\n)\n\nfrom ..configuration import PATH_TEST_PROJECT\n\n\nclass MockLogger: # noqa: H601, D101, D102\n # FIXME: Replace MockLogger with a more generic alternative. See:\n # https://pawamoy.github.io/posts/unify-logging-for-a-gunicorn-uvicorn-app/\n\n logs = defaultdict(list)\n\n def warning(self, message: str, **kwargs):\n self.logs['warnings'].append({'message': message, 'kwargs': kwargs})\n\n\ndef test_task_publish():\n \"\"\"Test task_publish.\"\"\"\n result = task_publish()\n\n actions = result['actions']\n assert len(actions) == 2\n assert 'poetry run nox --session build_dist build_check' in str(actions[0])\n assert 'poetry publish' in str(actions[1])\n\n\ndef test_task_publish_test_pypi():\n \"\"\"Test task_publish_test_pypi.\"\"\"\n result = task_publish_test_pypi()\n\n actions = result['actions']\n assert len(actions) == 2\n assert 'poetry run nox --session build_dist build_check' in str(actions[0])\n assert 'poetry publish --repository testpypi' in str(actions[1])\n\n\ndef test_read_packages():\n \"\"\"Test _read_packages.\"\"\"\n path_lock = PATH_TEST_PROJECT / 'poetry.lock'\n\n result = _read_packages(path_lock)\n\n assert len(result) > 20\n assert all(isinstance(pkg, _HostedPythonPackage) for pkg in result)\n assert len(result[0].name) > 3\n assert '.' in result[0].version\n assert result[0].datetime is None\n assert result[0].latest_version == ''\n assert result[0].latest_datetime is None\n\n\n@pytest.mark.vcr()\ndef test_get_release_date():\n \"\"\"Test _get_release_date.\"\"\"\n package = _HostedPythonPackage(\n domain='https://test.pypi.org/pypi/{name}/{version}/json',\n name='twine', version='1.11.0rc1',\n )\n\n result = _get_release_date(package)\n\n assert isinstance(result, _HostedPythonPackage)\n assert result.domain == package.domain\n assert result.name == package.name\n assert result.version == package.version\n assert result.latest_version != package.version\n assert result.datetime.year == 2018\n assert result.latest_datetime.year >= 2018\n\n\n@pytest.mark.vcr()\ndef test_find_stale_packages(fix_test_cache, monkeypatch):\n \"\"\"Test find_stale_packages.\"\"\"\n fake_lock = \"\"\"\n[[package]]\nname = \"twine\"\nversion = \"2.0.0\"\n\n[package.dependencies]\ntokenize-rt = \">=3.0.1\"\n\n[[package]]\nname = \"z_package\"\nversion = \"1.2.3\"\n\"\"\"\n fake_pack_lock = {\n 'z_package': {\n 'name': 'z_package', 'version': '1.2.3',\n 'datetime': pendulum.now().to_iso8601_string(),\n },\n }\n path_lock = fix_test_cache / 'poetry.lock'\n path_lock.write_text(fake_lock)\n path_pack_lock = fix_test_cache / _PATH_PACK_LOCK.name\n path_pack_lock.write_text(json.dumps(fake_pack_lock))\n expected_err = r'Found stale packages that may be a dependency risk:\\s+- \\d+ months ago: twine 2\\.0\\.0[^\\n]+'\n mock_logger = MockLogger()\n monkeypatch.setattr(loguru.logger, 'warning', mock_logger.warning)\n\n find_stale_packages(path_lock, path_pack_lock, stale_months=18) # act\n\n assert len(mock_logger.logs['warnings']) == 1\n assert re.match(expected_err, mock_logger.logs['warnings'][-1]['message'])\n assert mock_logger.logs['warnings'][-1]['kwargs'] == {}\n assert [*json.loads(path_pack_lock.read_text()).keys()] == ['twine', 'z_package']\n\n\ndef test_check_for_stale_packages():\n \"\"\"Test check_for_stale_packages.\"\"\"\n packages = [\n _HostedPythonPackage(\n name='twine',\n datetime=pendulum.now(), version='1.11.0rc1',\n latest_datetime=pendulum.now(), latest_version='1.11.0rc1',\n ),\n ]\n\n result = _check_for_stale_packages(packages, stale_months=1)\n\n # TODO: Capture logging output and check...\n assert result is None\n # Also check if not stale\n assert _check_for_stale_packages(packages, stale_months=999) is None\n\n\ndef test_task_check_for_stale_packages():\n \"\"\"Test task_check_for_stale_packages.\"\"\"\n result = task_check_for_stale_packages()\n\n actions = result['actions']\n assert len(actions) == 2\n assert isinstance(actions[0][0], type(find_stale_packages))\n assert len(actions[0][1]) == 2\n assert actions[0][1][0].name == 'poetry.lock'\n assert actions[0][1][1].name == _PATH_PACK_LOCK.name\n assert actions[0][2] == {'stale_months': 48}\n assert 'poetry run pip list --outdated' in str(actions[1])\n", "sub_path": "tests/test_doit_tasks/test_packaging.py", "file_name": "test_packaging.py", "file_ext": "py", "file_size_in_byte": 4711, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.defaultdict", "line_number": 23, "usage_type": "call"}, {"api_name": "calcipy.doit_tasks.packaging.task_publish", "line_number": 31, "usage_type": "call"}, {"api_name": "calcipy.doit_tasks.packaging.task_publish_test_pypi", "line_number": 41, "usage_type": "call"}, {"api_name": "configuration.PATH_TEST_PROJECT", "line_number": 51, "usage_type": "name"}, {"api_name": "calcipy.doit_tasks.packaging._read_packages", "line_number": 53, "usage_type": "call"}, {"api_name": "calcipy.doit_tasks.packaging._HostedPythonPackage", "line_number": 56, "usage_type": "argument"}, {"api_name": "calcipy.doit_tasks.packaging._HostedPythonPackage", "line_number": 67, "usage_type": "call"}, {"api_name": "calcipy.doit_tasks.packaging._get_release_date", "line_number": 72, "usage_type": "call"}, {"api_name": "calcipy.doit_tasks.packaging._HostedPythonPackage", "line_number": 74, "usage_type": "argument"}, {"api_name": "pytest.mark.vcr", "line_number": 64, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pendulum.now", "line_number": 101, "usage_type": "call"}, {"api_name": "calcipy.doit_tasks.packaging._PATH_PACK_LOCK.name", "line_number": 106, "usage_type": "attribute"}, {"api_name": "calcipy.doit_tasks.packaging._PATH_PACK_LOCK", "line_number": 106, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 107, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 110, "usage_type": "attribute"}, {"api_name": "calcipy.doit_tasks.packaging.find_stale_packages", "line_number": 112, "usage_type": "call"}, {"api_name": "re.match", "line_number": 115, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 117, "usage_type": "call"}, {"api_name": "pytest.mark.vcr", "line_number": 83, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 83, "usage_type": "attribute"}, {"api_name": "calcipy.doit_tasks.packaging._HostedPythonPackage", "line_number": 123, "usage_type": "call"}, {"api_name": "pendulum.now", "line_number": 125, "usage_type": "call"}, {"api_name": "pendulum.now", "line_number": 126, "usage_type": "call"}, {"api_name": "calcipy.doit_tasks.packaging._check_for_stale_packages", "line_number": 130, "usage_type": "call"}, {"api_name": "calcipy.doit_tasks.packaging._check_for_stale_packages", "line_number": 135, "usage_type": "call"}, {"api_name": "calcipy.doit_tasks.packaging.task_check_for_stale_packages", "line_number": 140, "usage_type": "call"}, {"api_name": "calcipy.doit_tasks.packaging.find_stale_packages", "line_number": 144, "usage_type": "argument"}, {"api_name": "calcipy.doit_tasks.packaging._PATH_PACK_LOCK.name", "line_number": 147, "usage_type": "attribute"}, {"api_name": "calcipy.doit_tasks.packaging._PATH_PACK_LOCK", "line_number": 147, "usage_type": "name"}]} +{"seq_id": "577570298", "text": "import sys\nimport shutil\nimport zipfile\nfrom pathlib import Path\n\nclass ZipReplace:\n\tdef __init__(self, zipname):\n\t\tself.zipname = zipname\n\t\tself.temp_directory = Path(\"unzipped-{}\".format(\n\t\t\tzipname[:-4]))\n\n\tdef process_zip(self):\n\t\tself.unzip_files()\n\t\tself.process_files()\n\t\tself.zip_files()\n\n\tdef unzip_files(self):\n\t\tself.temp_directory.mkdir()\n\t\twith zipfile.ZipFile(self.zipfile) as zip:\n\t\t\tzip.extractall(str(self.temp_directory))\n\n\tdef zip_files(self):\n\t\twith zipfile.ZipFile(self.zipname, 'w') as file:\n\t\t\tfor filename in self.temp_directory.iterdir():\n\t\t\t\tfile.write(str(filename), filename.name)\n\t\tshutil.rmtree(str(self.temp_directory))\n\n\nif __name__ == \"__main__\":\n\tZipReplace(*sys.argv[1:4]).zip_find_replace()", "sub_path": "Python/Practice/OOP/chapter_5/zip_processor.py", "file_name": "zip_processor.py", "file_ext": "py", "file_size_in_byte": 726, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pathlib.Path", "line_number": 9, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 19, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 23, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 30, "usage_type": "attribute"}]} +{"seq_id": "391458339", "text": "\"\"\"\nCreated on Wed Mar 09 16:34:30 2016\n\n@author: ps09og\n\"\"\"\n\n\nimport pandas as pd\nimport seaborn as sns\nimport numpy as np\nimport matplotlib.pylab as plt\nimport pdb\nimport scipy.stats as sts\nimport patsy, pickle\nimport itertools as it\nfrom pymc.utils import hpd\nimport matplotlib.patches as mpatches\nimport matplotlib.ticker as ticker \nfrom mpl_toolkits.mplot3d import axes3d\nimport stanplotting as sp\nimport matplotlib\n\n\nsamples = pd.read_csv(\"..//MCMC_samples//maths_samples2.csv\", index_col = 0) #Get the data\n\nbeta_names = [x for x in samples.columns if 'beta' in x]\nbeta = samples[beta_names]\nsigma = samples['sigma']\n\nx_names = [r'$\\beta_0$', 'Age', 'InT', 'Ckat:\\nTracing', 'Ckat:\\nAiming', \n 'Ckat:\\nTracking', 'Balance:\\nOpen', 'Balance:\\nClosed']\nx_names2 = [r'$\\beta_0$', 'Age', 'InT', 'Ckat: Tracing', 'Ckat: Aiming', \n 'Ckat: Tracking', 'Balance: Open', 'Balance: Closed']\n#sp.errorplot(x_names, beta.values, ls = \"None\")\n\nbeta_colors = [(0.8941176470588236, 0.10196078431372549, 0.10980392156862745),\n (0.21568627450980393, 0.49411764705882355, 0.7215686274509804),\n (0.30196078431372547, 0.6862745098039216, 0.2901960784313726),\n (0.596078431372549, 0.3058823529411765, 0.6392156862745098),\n (0.596078431372549, 0.3058823529411765, 0.6392156862745098),\n (0.596078431372549, 0.3058823529411765, 0.6392156862745098),\n (1.0, 0.4980392156862745, 0.0),\n (1.0, 0.4980392156862745, 0.0)]\n\n\nsns.set(context = \"paper\", style = \"white\", \n rc= {'axes.labelsize': 10, \n 'axes.titlesize': 12,\n 'xtick.labelsize': 10,\n 'ytick.labelsize':10,\n 'savefig.dpi' : 1000}, \n font = 'sans-serif')\n\n\n\n###PPC and data plotting\nrdata = pd.read_csv(\"..//Raw_data//master_concat_data.csv\")\nrdata = rdata.dropna()\n \n\n\n\n############################################\n###################PPC######################\n############################################\n############################################\n############################################\n\n\ny_true = rdata['Attainment_Maths'].values\ny_rep = samples[[i for i in samples.columns if 'y_rep' in i]].values\n\n\n#PPC test statistics\nfig_pp, ax_pp = plt.subplots(1, 2, figsize = (5, 2), sharey = True)\nsns.kdeplot(y_rep.mean(axis = 1), shade = True, alpha = 0.4, color = beta_colors[1], ax = ax_pp[0])\nsns.kdeplot(y_rep.std(axis = 1), shade = True, alpha = 0.4, color = beta_colors[1],ax = ax_pp[1])\n\n\nax_pp[0].set_xlabel(r\"Mean ($y^{rep}$)\")\nax_pp[1].set_xlabel(r\"Std ($y^{rep}$)\")\n[ax_pp[i].get_yaxis().set_visible(False) for i in range(2)]\nax_pp[0].axvline(y_true.mean(), color = '.3', linestyle = '--')\nax_pp[1].axvline(y_true.std(), color = '.3', linestyle = '--')\n\nax_pp[0].set_ylim([0, 3.6])\nax_pp[0].set_xlabel(\"Mean\")\nax_pp[1].set_xlabel(\"SD\")\n\nsns.despine()\n\n\n[ax_pp[i].text(-0.1, 1.22, label, transform=ax_pp[i].transAxes,va='top', ha='right', fontsize = 18) for i, label in enumerate(['a', 'b'])]\n\nax_pp[0].xaxis.set_major_locator(ticker.MultipleLocator(.25))\nax_pp[1].xaxis.set_major_locator(ticker.MultipleLocator(.25))\n\nplt.subplots_adjust(top=0.81,\n bottom=0.225,\n left=0.09,\n right=0.94,\n hspace=0.2,\n wspace=0.2)\n\n\n\n\nplt.figure(figsize = (3.5, 3.5))\nplt.plot(rdata['interception'], y_rep.mean(axis = 0), 'or', color = beta_colors[0], label = r\"$\\mathbb{E}(y^{rep})$\")\nplt.plot(rdata['interception'], y_true, 'ob', color = beta_colors[1], label = r\"$y$\" )\nplt.xlabel(\"IntT\")\nplt.ylabel(\"Mathematics Attainment\")\nplt.legend(title = \"Data\", frameon = True, bbox_to_anchor = (0.35, 1))\nsns.despine()\nplt.tight_layout()\n\n\n", "sub_path": "Plotting/FIGURE_S3_S4.py", "file_name": "FIGURE_S3_S4.py", "file_ext": "py", "file_size_in_byte": 3658, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pylab.subplots", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 75, "usage_type": "name"}, {"api_name": "seaborn.kdeplot", "line_number": 76, "usage_type": "call"}, {"api_name": "seaborn.kdeplot", "line_number": 77, "usage_type": "call"}, {"api_name": "seaborn.despine", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pylab.subplots_adjust", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pylab.figure", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pylab.plot", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pylab.plot", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pylab.xlabel", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pylab.ylabel", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pylab.legend", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 113, "usage_type": "name"}, {"api_name": "seaborn.despine", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pylab.tight_layout", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 115, "usage_type": "name"}]} +{"seq_id": "496774298", "text": "from car import Car \nimport colorama\nfrom colorama import Fore, Back, Style\n\ndef createObject(make, model, year, color, originalPrice, totalPrice, bonus, additionalDiscount):\n car = Car()\n car.setMake(make)\n car.setModel(model)\n car.setYear(year)\n car.setColor(color)\n car.setOriginalPrice(originalPrice)\n car.setTotalPrice(totalPrice)\n car.setBonus(bonus)\n car.setAdditionalDiscount(additionalDiscount)\n return car \n\n\ndef calculateTotalPrice(price, color, warVetOrDisabled):\n bonus = 0\n totalPrice = 0\n additionalDiscount = 0\n subtotal = 0\n taxes = .07\n if(str.upper(color) == \"BLACK\" and warVetOrDisabled == False):\n additionalDiscount = price*.25\n subtotal = price-additionalDiscount\n totalPrice = subtotal + (subtotal * taxes)\n print(\n f\"As you chose {color}, you have a discount of 25% which is ${additionalDiscount} \")\n\n elif(str.upper(color) == \"BLACK\" and warVetOrDisabled):\n bonus = 500\n additionalDiscount = price*.25\n subtotal = price - bonus - additionalDiscount\n totalPrice = subtotal + (subtotal * taxes)\n print(Fore.YELLOW, f'''\n As you chose {color}, you have a discount of 25% which is ${additionalDiscount} \n and as you are a veteran or disabled you get an extra bonus of ${bonus}''')\n\n elif(str.upper(color) == \"WHITE\" and warVetOrDisabled == False):\n bonus = 400\n subtotal = price - 400\n totalPrice = subtotal + (subtotal * taxes)\n print(Fore.YELLOW, f\"As you chose {color}, you have a discount of ${bonus}\")\n print(Style.RESET_ALL)\n\n elif(str.upper(color) == \"WHITE\" and warVetOrDisabled):\n bonus = 500\n additionalDiscount = price * .25\n subtotal= price - bonus - additionalDiscount\n totalPrice = subtotal + (subtotal * taxes)\n print(Fore.YELLOW, f'''\n As you chose {color}, you have a discount of ${bonus} \n and as you are a veteran or disabled you get an extra 25% off which is ${additionalDiscount}''')\n print(Style.RESET_ALL)\n else:\n totalPrice = price + (price * taxes)\n return totalPrice, bonus, additionalDiscount\n\n\ndef checkInventory(value, collection):\n field = str(input(f'What is the {value}? >> '))\n while True:\n if(field.upper() not in collection):\n print(f'{value} is not found in our records. Please try again')\n field = str(input('What is the make? >> ')) \n else:\n break \n return field", "sub_path": "Dealership Challenge Solution/functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 2542, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "car.Car", "line_number": 6, "usage_type": "call"}, {"api_name": "car.setMake", "line_number": 7, "usage_type": "call"}, {"api_name": "car.setModel", "line_number": 8, "usage_type": "call"}, {"api_name": "car.setYear", "line_number": 9, "usage_type": "call"}, {"api_name": "car.setColor", "line_number": 10, "usage_type": "call"}, {"api_name": "car.setOriginalPrice", "line_number": 11, "usage_type": "call"}, {"api_name": "car.setTotalPrice", "line_number": 12, "usage_type": "call"}, {"api_name": "car.setBonus", "line_number": 13, "usage_type": "call"}, {"api_name": "car.setAdditionalDiscount", "line_number": 14, "usage_type": "call"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 36, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 36, "usage_type": "name"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 44, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 44, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 45, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 45, "usage_type": "name"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 52, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 52, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 55, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "152952333", "text": "# -*- coding: utf-8 -*-\nimport os\nimport getpass\nimport json\nimport logging\nfrom collections import OrderedDict\n\nimport scrapy\nfrom lxml import html\n\nclass NotDoneSpider(scrapy.Spider):\n name = \"notdone\"\n allowed_domains = [\"crs.upd.edu.ph\"]\n start_urls = ['http://crs.upd.edu.ph/']\n\n # TODO: make setting variable instead\n SID_FILE = \"notdone.txt\"\n\n def __init__(self, *args, **kwargs):\n logger = logging.getLogger('scrapy.downloadermiddlewares.redirect')\n logger.setLevel(logging.WARNING)\n super().__init__(*args, **kwargs)\n\n def parse(self, response):\n return scrapy.FormRequest.from_response(\n response,\n formdata={'txt_login': self.settings.get('USERNAME'), 'pwd_password': self.settings.get('PASSWORD')},\n callback=self.after_login,\n )\n\n\n def after_login(self, response):\n if b\"Login Error\" in response.body:\n self.logger.error(\"Login failed\")\n return\n \n return scrapy.Request(\n \"https://crs.upd.edu.ph/user/switch_role/175328\",\n callback=self.switch_role,\n dont_filter=True,\n )\n\n\n def switch_role(self, response):\n return scrapy.Request(\n \"https://crs.upd.edu.ph/validation\",\n callback=self.notdone_start,\n )\n\n def notdone_start(self, response):\n with open(self.SID_FILE) as f:\n lines = [i.strip() for i in f.readlines()]\n\n self.status = OrderedDict.fromkeys(lines)\n self.units = OrderedDict.fromkeys(lines)\n self.error = []\n self.current = 0\n self.total = len(lines)\n\n for sid in lines:\n request = scrapy.Request(\n \"https://crs.upd.edu.ph/validation\",\n callback=self.indiv_not_done,\n dont_filter=True,\n )\n request.meta['sid'] = sid\n\n yield request\n\n def indiv_not_done(self, response):\n sid = response.meta['sid']\n\n self.current += 1\n print(sid, self.current, \"/\", self.total, str(self.current / self.total * 100.0) + \"%\")\n\n crs_internal_aytermid = response.xpath(\"//input[@id='hid_aytermid']/@value\").extract_first()\n\n request = scrapy.FormRequest.from_response(\n response,\n formdata={\n 'studentno': sid,\n 'aytermid': crs_internal_aytermid,\n },\n callback=self.done,\n dont_filter=True,\n formnumber=0,\n )\n request.meta['sid'] = sid\n\n yield request\n\n def done(self, response):\n sid = response.meta['sid']\n\n if response.url == \"https://crs.upd.edu.ph/validation/manage\":\n self.status[sid] = \"N\"\n elif response.url == \"https://crs.upd.edu.ph/validation\":\n body = response.body.decode('utf8')\n\n if \"Student has already been assessed!\" in body:\n self.status[sid] = \"Y\"\n elif \"Student is ineligible!\" in body:\n self.status[sid] = \"I\"\n elif \"No Student Profile\" in body:\n self.status[sid] = \"X\"\n elif \"Student not within scope\" in body:\n self.error.append(sid)\n del self.status[sid]\n else:\n self.error.append(sid)\n del self.status[sid]\n else:\n input(\"ERROR FOR \" + sid + \": \" + response.url)\n\n if sid in self.status and self.status[sid]:\n request = scrapy.Request(\n \"https://crs.upd.edu.ph/online_advising/advise/120172/\" + sid,\n callback=self.check_enlisted_units,\n )\n\n request.meta['sid'] = sid\n\n return request\n\n def check_enlisted_units(self, response):\n sid = response.meta['sid']\n body = response.body.decode('utf8')\n\n if \"Total Units\" not in body:\n self.units[sid] = 0\n else:\n doc = html.fromstring(body)\n units = int(doc.xpath('//b[text()=\"Total Units:\"]/following-sibling::span[1]/text()')[0].replace(\".0\", \"\"))\n print(sid, \"has\", units, \"units\")\n\n self.units[sid] = units\n\n def closed(self, reason):\n if reason == \"finished\":\n output = []\n\n with open(\"notdone_output.txt\", \"w\") as f:\n for key, value in self.status.items():\n output.append(\"\\t\".join((key, value, str(self.units[key]) + \"\\n\")))\n \n f.writelines(output)\n\n print(\"Not in scope:\", len(self.error), self.error)\n print(\"DONE\")\n", "sub_path": "scraping/crscraper/crscraper/spiders/notdone.py", "file_name": "notdone.py", "file_ext": "py", "file_size_in_byte": 4622, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "scrapy.Spider", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 21, "usage_type": "attribute"}, {"api_name": "scrapy.FormRequest.from_response", "line_number": 25, "usage_type": "call"}, {"api_name": "scrapy.FormRequest", "line_number": 25, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 37, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 45, "usage_type": "call"}, {"api_name": "collections.OrderedDict.fromkeys", "line_number": 54, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 54, "usage_type": "name"}, {"api_name": "collections.OrderedDict.fromkeys", "line_number": 55, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 55, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 61, "usage_type": "call"}, {"api_name": "scrapy.FormRequest.from_response", "line_number": 78, "usage_type": "call"}, {"api_name": "scrapy.FormRequest", "line_number": 78, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 116, "usage_type": "call"}, {"api_name": "lxml.html.fromstring", "line_number": 132, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 132, "usage_type": "name"}]} +{"seq_id": "492708279", "text": "import numpy as np\nfrom functools import partial\nfrom collections import namedtuple\nimport matplotlib.pyplot as plt\n\nAnabolicModel = namedtuple(\"AnabolicModel\", [\"S\", \"rate_funcs\"])\n\ndef calc_current_rates(rate_funcs, state):\n return list(rf(state) for rf in rate_funcs)\n \ndef simulate_model(model, n_steps):\n SimResults = namedtuple('SimResults', ['trajectories', 'wait_times'])\n n_species = np.shape(model.S)[0]\n state_out = np.zeros((n_steps+1, n_species))\n w_times = np.zeros(n_steps+1)\n state = np.zeros(n_species)\n state_out[0, :] = state\n w_times[0] = 0.0\n \n for i in xrange(1, n_steps+1):\n curr_rates = calc_current_rates(model.rate_funcs, state)\n dt = np.random.exponential(1/np.sum(curr_rates))\n mres = np.random.multinomial(1, pvals=curr_rates/np.sum(curr_rates))\n ri = np.nonzero(mres)[0][0]\n state = state + model.S[:, ri]\n state_out[i, :] = state\n w_times[i] = dt\n\n return SimResults._make([state_out, w_times])\n\ndef get_rate_funcs(la, beta, C):\n # create rate functions for anabolic process\n # based on give params\n prod = partial(lambda x, la: la, la=la)\n deg1 = partial(lambda x, beta: beta*x[0], beta=beta)\n deg2 = partial(lambda x, beta: beta*x[1], beta=beta)\n complex_form = partial(lambda x, C: C*x[0]*x[1], C=C)\n\n return [prod, deg1, prod, deg2, complex_form]\n\ndef init_model(la, beta, C):\n # S : Stoichiometric matrix species x reactions\n S = np.array([[1, -1, 0, 0, -1],\n [0, 0, 1, -1, -1]])\n rate_funcs = get_rate_funcs(la, beta, C)\n mod = AnabolicModel._make([S, rate_funcs])\n\n return mod\n\ndef get_res_dist(sim_res):\n # Given a specific realisaition of the process\n # return the average and variance of the species in\n # two tuples.\n n_species = np.shape(sim_res.trajectories)[1]\n traj = sim_res.trajectories[500:, :]\n w_times = sim_res.wait_times[500:]\n species_var = []\n species_avg = []\n \n for i in xrange(n_species):\n vals = traj[:, i]\n av = np.average(a=vals, weights=w_times)\n var = np.average(a=(vals-av)**2, weights=w_times)\n species_var.append(var/av**2)\n species_avg.append(av)\n\n return species_var, species_avg\n\ndef get_var_dists(mod, n_iter, n_steps):\n # Get the distribution of the variances of the species\n # for n_iter realisations of n_steps each\n n_species = np.shape(mod.S)[0]\n var_dists = np.zeros((n_iter, n_species))\n\n for i in xrange(n_iter):\n sim_res = simulate_model(mod, n_steps)\n vars, avgs = get_res_dist(sim_res)\n var_dists[i, :] = vars\n\n return var_dists\n\ndef get_devs(dists):\n # Returns deviations of statistics between 2 species\n # across differente simulation trajectories\n devs = list(np.abs(dists[i, 0]-dists[i, 1]) for i in range(np.shape(dists)[0]))\n\n return np.array(devs)\n\ndef calc_approx_var(avgs, params):\n beta = params[1]\n C = params[2]\n x1 = avgs[0]\n x2 = avgs[1]\n \n E = (C*x1*x2) / (C*x1*x2 + beta*x1)\n approx_var = (1/x1) * ((1-(E**2/2)) / (1-E**2))\n \n return approx_var\n \ndef get_approx_vars(avgs, params):\n # Takes a list of pairs of averages and \n # returns the approx variance\n n_sims = np.shape(avgs)[0]\n avars = []\n for i in xrange(n_sims):\n avar = calc_approx_var(avgs[i, :], params[i])\n avars.append(avar)\n \n return avars\n \ndef plot_results(sim_res):\n # plot one example trace of the gillespie algorithm\n # for a particular set of params\n t = np.cumsum(sim_res)\n plt.plot(t, sim_res.trajectories)\n plt.show()\n\ndef sim_anab_model():\n mod = init_model(la=0.5, beta=0.3, C=1.0)\n sim_results = simulate_model(model=mod, n_steps=100)\n\n return sim_results\n", "sub_path": "sim.py", "file_name": "sim.py", "file_ext": "py", "file_size_in_byte": 3780, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.namedtuple", "line_number": 6, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random.exponential", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random.multinomial", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 24, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 34, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 35, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 36, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}]} +{"seq_id": "322304719", "text": "from PIL import Image\r\nimport numpy as np\r\n\r\na = np.array(Image.open(\"C:/Users/Erdi Chen/Desktop/Python/Datenanalyse/SH.jpg\").convert('L'))\r\n\r\n##d = 255* (a/255) **2 # 黑白图像\r\n\r\ndepth = 10. # 预设为10，取值0-100\r\ngrad = np.gradient(a)\r\ngrad_x,grad_y = grad\r\ngrad_x = grad_x * depth / 100.\r\ngrad_y = grad_y * depth / 100.\r\n\r\nvec_el = np.pi / 2.2 # 俯仰角\r\nvec_az = np.pi / 4. # 方位角\r\ndx = np.cos(vec_el) * np.cos(vec_az) # 单位光线在地面的投影长度\r\ndy = np.cos(vec_el) * np.sin(vec_az)\r\ndz = np.sin(vec_el) # dx,dy,dz 是光源对x,y,z三个方向的物体明暗的影响程度\r\n\r\nA = np.sqrt(grad_x **2 + grad_y **2 + 1.) # 归一化\r\nuni_x = grad_x / A\r\nuni_y = grad_y / A\r\nuni_z = 1. / A\r\n\r\nb = 255* (dx*uni_x + dy* uni_y + dz * uni_z) # 梯度与光源相互作用，将梯度转化为灰度\r\n\r\nb = b.clip(0,255)\r\n\r\nim = Image.fromarray(b.astype('uint8'))\r\nim.save(\"C:/Users/Erdi Chen/Desktop/Python/Datenanalyse/Gray_SH.jpg\")", "sub_path": "Hand-drawn_Image.py", "file_name": "Hand-drawn_Image.py", "file_ext": "py", "file_size_in_byte": 997, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.array", "line_number": 4, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 4, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 4, "usage_type": "name"}, {"api_name": "numpy.gradient", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 29, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 29, "usage_type": "name"}]} +{"seq_id": "246823459", "text": "#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n\r\nfrom random import getrandbits\r\nfrom base64 import urlsafe_b64encode\r\nfrom datetime import date, datetime, timedelta\r\n\r\nimport psycopg2\r\nfrom psycopg2.extras import DictCursor\r\n\r\nimport pygments\r\nfrom pygments import highlight\r\nfrom pygments.lexers import get_lexer_by_name, guess_lexer, get_all_lexers\r\nfrom pygments.formatters import HtmlFormatter\r\n\r\nfrom flask import Flask, \\\r\n\t\trender_template, url_for, flash, \\\r\n\t\trequest, redirect, Response, abort\r\n\r\nfrom stats import pasteview, pastecount, getstats\r\nimport config\r\n\r\napp = Flask(__name__)\r\napp.secret_key = config.secret_key\r\napp.config['MAX_CONTENT_LENGTH'] = config.max_content_length\r\n\r\n\r\nlexers_all = get_all_lexers()\r\nyear = date.today().year\r\n\r\n\r\n\r\ndef base_encode(num):\r\n\tif not num:\r\n\t\treturn config.url_alph[0]\r\n\tresult = ''\r\n\twhile num:\r\n\t\tnum, rem = divmod(num, config.base)\r\n\t\tresult = result.join(config.url_alph[rem])\r\n\treturn result\r\n\r\ndef plain(text):\r\n\tresp = Response(text)\r\n\tresp.headers['Content-Type'] = 'text/plain; charset=utf-8'\r\n\treturn resp\r\n\r\ndef paste_stats(text):\r\n\tstats = {}\r\n\tstats['lines'] = len(text.split('\\n'))\r\n\tstats['sloc'] = stats['lines'] - len(text.split('\\n\\n'))\r\n\tstats['size'] = len(text.encode('utf-8'))\r\n\treturn stats\r\n\r\ndef url_collision(db, route):\r\n\tfor rule in app.url_map.iter_rules():\r\n\t\tif rule.rule == '/' + route:\r\n\t\t\treturn True\r\n\twith db.cursor() as cur: \r\n\t\tcur.execute(\"SELECT pasteid FROM pastes WHERE pasteid = %s;\", (route,))\r\n\t\tif cur.fetchone():\r\n\t\t\treturn True\r\n\treturn False\r\n\r\ndef db_newpaste(db, opt, stats):\r\n\tdate = datetime.utcnow()\r\n\tdate += timedelta(hours=float(opt['ttl']))\r\n\twith db.cursor() as cur:\r\n\t\tcur.execute(\"\"\"INSERT INTO \r\n\t\t\tpastes (pasteid, token, lexer, expiration, burn, \r\n\t\t\tpaste, size, lines, sloc)\r\n\t\t\tVALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s);\"\"\", \\\r\n\t\t\t(opt['pasteid'], opt['token'], opt['lexer'], \\\r\n\t\t\tdate, opt['burn'], opt['paste'], \\\r\n\t\t\tstats['size'], stats['lines'], stats['sloc']))\r\n\r\ndef db_getpaste(db, pasteid):\r\n\twith db.cursor(cursor_factory = DictCursor) as cur:\r\n\t\tcur.execute((\"\"\"SELECT * FROM pastes WHERE pasteid=%s;\"\"\"), (pasteid,))\r\n\t\tr = cur.fetchone()\r\n\treturn r\r\n\r\n\r\ndef db_deletepaste(db, pasteid):\r\n\twith db.cursor() as cur:\r\n\t\tcur.execute((\"\"\"DELETE FROM pastes WHERE pasteid=%s;\"\"\"), (pasteid,))\r\n\r\ndef db_burn(db, pasteid):\r\n\twith db.cursor() as cur:\r\n\t\tcur.execute((\"\"\"UPDATE pastes SET burn = burn - 1 WHERE pasteid=%s;\"\"\"), (pasteid,))\r\n\r\n@app.route('/', methods=['GET', 'POST'])\r\n@app.route('/newpaste', methods=['POST']) #only used via html form\r\ndef newpaste():\r\n\tif request.method == 'POST':\r\n\t\tpaste_opt = {}\r\n\t\tfor param in config.defaults: #init form parameters with defaults\r\n\t\t\t\tpaste_opt[param] = config.defaults[param]\r\n\t\tfor param in request.form:\r\n\t\t\tif param in paste_opt:\r\n\t\t\t\tpaste_opt[param] = request.form[param]\r\n\t\tif paste_opt['paste'] == '':\r\n\t\t\treturn config.empty_paste\r\n\t\ttry:\r\n\t\t\tif not config.paste_limits['ttl_min'] < \\\r\n\t\t\t\t\t\tfloat(paste_opt['ttl']) < \\\r\n\t\t\t\t\t\tconfig.paste_limits['ttl_max']:\r\n\t\t\t\treturn config.invalid_ttl\r\n\t\texcept ValueError:\r\n\t\t\treturn config.invalid_ttl\r\n\t\ttry:\r\n\t\t\tif paste_opt['lexer'] == 'auto':\r\n\t\t\t\tpaste_opt['lexer'] = guess_lexer(paste_opt['paste']).aliases[0]\r\n\t\texcept pygments.util.ClassNotFound:\r\n\t\t\tpaste_opt['lexer'] = 'text'\r\n\t\ttry:\r\n\t\t\tif paste_opt['burn'] == '' or paste_opt['burn'] == 0 or paste_opt['burn'] == config.defaults['burn']:\r\n\t\t\t\tpaste_opt['burn'] = config.defaults['burn']\r\n\t\t\telif not config.paste_limits['burn_min'] <= int(paste_opt['burn']) <= config.paste_limits['burn_max']:\r\n\t\t\t\treturn config.invalid_burn\r\n\t\texcept ValueError:\r\n\t\t\treturn config.invalid_burn\r\n\r\n\t\twith psycopg2.connect(config.dsn) as db:\r\n\t\t\turl_len = config.url_len\r\n\t\t\tpaste_opt['pasteid'] = ''\r\n\t\t\twhile url_collision(db, paste_opt['pasteid']):\r\n\t\t\t\tfor i in range(url_len):\r\n\t\t\t\t\tpaste_opt['pasteid'] += base_encode(getrandbits(6))\t\r\n\t\t\t\turl_len += 1\r\n\t\t\t\r\n\t\t\tpaste_opt['token'] = \\\r\n\t\t\t\turlsafe_b64encode(getrandbits(48).to_bytes(config.token_len, 'little')).decode('utf-8')\r\n\t\t\t\r\n\t\t\tstats = paste_stats(paste_opt['paste']) #generate text stats\r\n\t\t\t\r\n\t\t\tdb_newpaste(db, paste_opt, stats)\r\n\t\t\t\r\n\t\t\tpastecount(db) #increment total pastes\r\n\r\n\t\t\tif request.path != '/newpaste': #plaintext reply \r\n\t\t\t\treturn \"token: \" + paste_opt['token'] + \" | \" + config.domain + url_for('viewraw', pasteid = paste_opt['pasteid']) + \"\\n\"\r\n\t\t\t\r\n\t\t\tflash(paste_opt['token'])\r\n\t\treturn redirect(paste_opt['pasteid'])\r\n\telif request.method == 'GET':\r\n\t\treturn render_template('newpaste.html', \\\r\n\t\t\t\tlexers_all = lexers_all, lexers_common = config.lexers_common, \\\r\n\t\t\t\tttl = config.ttl_options, paste_limits = config.paste_limits, year = year)\r\n\telse:\r\n\t\tabort(405)\r\n\r\n\r\n@app.route('/', methods=['GET', 'DELETE'])\r\ndef viewpaste(pasteid):\r\n\tif request.method == 'GET':\r\n\t\tdirection = 'ltr'\r\n\t\twith psycopg2.connect(config.dsn) as db:\r\n\t\t\tresult = db_getpaste(db, pasteid)\r\n\t\t\tif not result:\r\n\t\t\t\tabort(404)\r\n\t\t\tif result['burn'] == 0 or result['expiration'] < datetime.utcnow():\r\n\t\t\t\tdb_deletepaste(db, pasteid)\r\n\t\t\t\tabort(404)\r\n\t\t\telif result['burn'] > 0:\r\n\t\t\t\tdb_burn(db, pasteid)\r\n\t\t\t\r\n\t\t\tpasteview(db) #count towards total paste views\r\n\r\n\t\t\tif request.args.get('raw') is not None:\r\n\t\t\t\treturn plain(result['paste'])\r\n\t\t\t\r\n\t\t\tif request.args.get('d') is not None:\r\n\t\t\t\tdirection = 'rtl'\r\n\t\t\t\r\n\t\t\tlexer = get_lexer_by_name(result['lexer'])\r\n\t\t\tformatter = HtmlFormatter(nowrap=True, cssclass='paste')\r\n\t\t\tpaste = highlight(result['paste'], lexer, formatter)\r\n\r\n\t\t\tstats = {'lines': result['lines'],\r\n\t\t\t\t\t'sloc': result['sloc'],\r\n\t\t\t\t\t'size': result['size'],\r\n\t\t\t\t\t'lexer': lexer.name\r\n\t\t\t}\r\n\t\t\tdel_url = url_for('deletepaste', pasteid=pasteid, token=result['token'])\r\n\t\t\treturn render_template('viewpaste.html', \\\r\n\t\t\t\tstats=stats, paste=paste.split(\"\\n\"), direction=direction, delete=del_url, year=year)\r\n\t\tabort(500)\r\n\telif request.method == 'DELETE':\r\n\t\twith psycopg2.connect(config.dsn) as db:\r\n\t\t\tresult = db_getpaste(db, pasteid)\r\n\t\t\tif not result:\r\n\t\t\t\treturn config.msg_err_404, 404\r\n\t\t\telif 'token' in request.form and result['token'] == request.form['token']:\r\n\t\t\t\tdb_deletepaste(db, pasteid)\r\n\t\t\t\treturn config.msg_paste_deleted, 200\r\n\t\t\telif 'token' in request.headers and result['token'] == request.headers.get('token'):\r\n\t\t\t\tdb_deletepaste(db, pasteid)\r\n\t\t\t\treturn config.msg_paste_deleted, 200\r\n\t\t\telse:\r\n\t\t\t\treturn config.msg_err_401, 401\t\r\n\telse:\r\n\t\tabort(405)\r\n\r\n@app.route('/plain/', methods=['GET', 'DELETE'])\r\n@app.route('/raw/', methods=['GET', 'DELETE'])\r\ndef viewraw(pasteid):\r\n\tif request.method == 'GET':\r\n\t\twith psycopg2.connect(config.dsn) as db:\r\n\t\t\tresult = db_getpaste(db, pasteid)\r\n\t\t\tif not result:\r\n\t\t\t\treturn config.msg_err_404, 404\r\n\t\t\tif result['burn'] == 0 or result['expiration'] < datetime.utcnow():\r\n\t\t\t\tdb_deletepaste(db, pasteid)\r\n\t\t\t\treturn config.msg_err_404, 404\r\n\t\t\telif result['burn'] > 0:\r\n\t\t\t\tdb_burn(db, pasteid)\r\n\t\r\n\t\t\tpasteview(db) #count towards total paste views\r\n\t\t\t\r\n\t\t\treturn result['paste']\r\n\r\n\telif request.method == 'DELETE':\r\n\t\twith psycopg2.connect(config.dsn) as db:\r\n\t\t\tresult = db_getpaste(db, pasteid)\r\n\t\t\tif not result:\r\n\t\t\t\treturn config.msg_err_404, 404\r\n\t\t\telif 'token' in request.form and result['token'] == request.form['token']:\r\n\t\t\t\tdb_deletepaste(db, pasteid)\r\n\t\t\t\treturn config.msg_paste_deleted, 200\r\n\t\t\telif 'token' in request.headers and result['token'] == request.headers.get('token'):\r\n\t\t\t\tdb_deletepaste(db, pasteid)\r\n\t\t\t\treturn config.msg_paste_deleted, 200\r\n\t\t\telse:\r\n\t\t\t\treturn config.msg_err_401, 401\r\n\telse:\r\n\t\treturn \"invalid http method\\n\"\r\n\r\n@app.route('//', methods=['GET'])\r\ndef\tdeletepaste(pasteid, token):\r\n\twith psycopg2.connect(config.dsn) as db:\r\n\t\tresult = db_getpaste(db, pasteid)\r\n\t\tif not result:\r\n\t\t\tabort(404)\r\n\t\telif result['token'] == token:\r\n\t\t db_deletepaste(db, pasteid)\r\n\t\t return render_template('deleted.html')\r\n\t\telse:\r\n\t\t\tabort(401)\r\n\t\t\t\r\n@app.route('/about/api')\r\ndef aboutapi():\r\n\treturn render_template('api.html', year=year)\r\n\r\n@app.route('/about')\r\ndef aboutpage():\r\n\treturn render_template('about.html', year=year)\r\n\r\n@app.route('/stats')\r\ndef statspage():\r\n\twith psycopg2.connect(config.dsn) as db:\r\n\t\tstats = getstats(db)\r\n\t\treturn render_template('stats.html', year=year, stats = stats)\r\n\r\n\r\n@app.errorhandler(404)\r\ndef page_not_found(e):\r\n\treturn render_template('404.html'), 404\r\n\r\n@app.errorhandler(500)\r\ndef internal_server_error():\r\n\treturn render_template('500.html'), 500\r\n\r\nif __name__ == '__main__':\r\n\tapp.debug = False\r\n\tapp.run()\r\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 8592, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 23, "usage_type": "call"}, {"api_name": "config.secret_key", "line_number": 24, "usage_type": "attribute"}, {"api_name": "config.max_content_length", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygments.lexers.get_all_lexers", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 29, "usage_type": "name"}, {"api_name": "config.url_alph", "line_number": 35, "usage_type": "attribute"}, {"api_name": "config.base", "line_number": 38, "usage_type": "attribute"}, {"api_name": "config.url_alph", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.Response", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 65, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 66, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 73, "usage_type": "name"}, {"api_name": "psycopg2.extras.DictCursor", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 94, "usage_type": "name"}, {"api_name": "config.defaults", "line_number": 96, "usage_type": "attribute"}, {"api_name": "config.defaults", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 100, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 100, "usage_type": "name"}, {"api_name": "config.empty_paste", "line_number": 102, "usage_type": "attribute"}, {"api_name": "config.paste_limits", "line_number": 104, "usage_type": "attribute"}, {"api_name": "config.paste_limits", "line_number": 106, "usage_type": "attribute"}, {"api_name": "config.invalid_ttl", "line_number": 107, "usage_type": "attribute"}, {"api_name": "config.invalid_ttl", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pygments.lexers.guess_lexer", "line_number": 112, "usage_type": "call"}, {"api_name": "pygments.util", "line_number": 113, "usage_type": "attribute"}, {"api_name": "config.defaults", "line_number": 116, "usage_type": "attribute"}, {"api_name": "config.defaults", "line_number": 117, "usage_type": "attribute"}, {"api_name": "config.paste_limits", "line_number": 118, "usage_type": "attribute"}, {"api_name": "config.invalid_burn", "line_number": 119, "usage_type": "attribute"}, {"api_name": "config.invalid_burn", "line_number": 121, "usage_type": "attribute"}, {"api_name": "psycopg2.connect", "line_number": 123, "usage_type": "call"}, {"api_name": "config.dsn", "line_number": 123, "usage_type": "attribute"}, {"api_name": "config.url_len", "line_number": 124, "usage_type": "attribute"}, {"api_name": "random.getrandbits", "line_number": 128, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64encode", "line_number": 132, "usage_type": "call"}, {"api_name": "random.getrandbits", "line_number": 132, "usage_type": "call"}, {"api_name": "config.token_len", "line_number": 132, "usage_type": "attribute"}, {"api_name": "stats.pastecount", "line_number": 138, "usage_type": "call"}, {"api_name": "flask.request.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 140, "usage_type": "name"}, {"api_name": "config.domain", "line_number": 141, "usage_type": "attribute"}, {"api_name": "flask.url_for", "line_number": 141, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 143, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 145, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 145, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 146, "usage_type": "call"}, {"api_name": "config.lexers_common", "line_number": 147, "usage_type": "attribute"}, {"api_name": "config.ttl_options", "line_number": 148, "usage_type": "attribute"}, {"api_name": "config.paste_limits", "line_number": 148, "usage_type": "attribute"}, {"api_name": "flask.abort", "line_number": 150, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 155, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 155, "usage_type": "name"}, {"api_name": "psycopg2.connect", "line_number": 157, "usage_type": "call"}, {"api_name": "config.dsn", "line_number": 157, "usage_type": "attribute"}, {"api_name": "flask.abort", "line_number": 160, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 161, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 161, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 163, "usage_type": "call"}, {"api_name": "stats.pasteview", "line_number": 167, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 169, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 169, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 169, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 172, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 172, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 172, "usage_type": "name"}, {"api_name": "pygments.lexers.get_lexer_by_name", "line_number": 175, "usage_type": "call"}, {"api_name": "pygments.formatters.HtmlFormatter", "line_number": 176, "usage_type": "call"}, {"api_name": "pygments.highlight", "line_number": 177, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 184, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 185, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 187, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 188, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 188, "usage_type": "name"}, {"api_name": "psycopg2.connect", "line_number": 189, "usage_type": "call"}, {"api_name": "config.dsn", "line_number": 189, "usage_type": "attribute"}, {"api_name": "config.msg_err_404", "line_number": 192, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 193, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 193, "usage_type": "name"}, {"api_name": "config.msg_paste_deleted", "line_number": 195, "usage_type": "attribute"}, {"api_name": "flask.request.headers", "line_number": 196, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 196, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 196, "usage_type": "call"}, {"api_name": "config.msg_paste_deleted", "line_number": 198, "usage_type": "attribute"}, {"api_name": "config.msg_err_401", "line_number": 200, "usage_type": "attribute"}, {"api_name": "flask.abort", "line_number": 202, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 207, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 207, "usage_type": "name"}, {"api_name": "psycopg2.connect", "line_number": 208, "usage_type": "call"}, {"api_name": "config.dsn", "line_number": 208, "usage_type": "attribute"}, {"api_name": "config.msg_err_404", "line_number": 211, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 212, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 212, "usage_type": "name"}, {"api_name": "config.msg_err_404", "line_number": 214, "usage_type": "attribute"}, {"api_name": "stats.pasteview", "line_number": 218, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 222, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 222, "usage_type": "name"}, {"api_name": "psycopg2.connect", "line_number": 223, "usage_type": "call"}, {"api_name": "config.dsn", "line_number": 223, "usage_type": "attribute"}, {"api_name": "config.msg_err_404", "line_number": 226, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 227, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 227, "usage_type": "name"}, {"api_name": "config.msg_paste_deleted", "line_number": 229, "usage_type": "attribute"}, {"api_name": "flask.request.headers", "line_number": 230, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 230, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 230, "usage_type": "call"}, {"api_name": "config.msg_paste_deleted", "line_number": 232, "usage_type": "attribute"}, {"api_name": "config.msg_err_401", "line_number": 234, "usage_type": "attribute"}, {"api_name": "psycopg2.connect", "line_number": 240, "usage_type": "call"}, {"api_name": "config.dsn", "line_number": 240, "usage_type": "attribute"}, {"api_name": "flask.abort", "line_number": 243, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 246, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 248, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 252, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 256, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 260, "usage_type": "call"}, {"api_name": "config.dsn", "line_number": 260, "usage_type": "attribute"}, {"api_name": "stats.getstats", "line_number": 261, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 262, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 267, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 271, "usage_type": "call"}]} +{"seq_id": "330183199", "text": "## Package import\r\n\r\nfrom __future__ import absolute_import\r\nfrom __future__ import print_function\r\nfrom __future__ import division\r\n\r\nimport sys\r\nimport matplotlib\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport imageio\r\nfrom tqdm import tqdm_notebook as tqdm\r\nimport pandas as pd\r\nnp.set_printoptions(threshold=sys.maxsize)\r\n\r\n## Helper functions\r\n\r\ndef pairwise_dist(x, y):\r\n \"\"\"\r\n Args:\r\n x: N x D numpy array\r\n y: M x D numpy array\r\n Return:\r\n dist: N x M array, where dist2[i, j] is the euclidean distance between \r\n x[i, :] and y[j, :]\r\n \"\"\"\r\n # ||(Xi - Yj)||_2 = Xi^2 + Yj^2 - 2XiYj\r\n # Get sum-squares of x along columns\r\n xssq = np.sum(np.square(x),axis=1)\r\n # Get sum-squares of y along columns\r\n yssq = np.sum(np.square(y),axis=1)\r\n # Matrix multiplication of x and y\r\n # transpose lines up D dimensions to get N x M output\r\n xdoty = np.dot(x,np.transpose(y))\r\n \r\n # Apply formula\r\n # use newaxis so that array dimensions match\r\n # otherwise addition operation won't work\r\n dist = np.sqrt(xssq[:,np.newaxis] + yssq - 2*xdoty)\r\n return dist\r\n \r\ndef softmax(logits):\r\n \r\n # get the max of each row\r\n rowmax = np.max(logits, axis=1)\r\n # subtract the row max from each element in that row\r\n # then exponentiate that result\r\n num = np.exp(logits - rowmax[:,np.newaxis])\r\n # get the sum of each row\r\n # turn keepdims on so axes are the same as num\r\n denom = np.sum(num,axis=1,keepdims=True)\r\n # num/denom is then the correct shape\r\n return num/denom\r\n\r\ndef logsumexp(logits):\r\n \"\"\"\r\n Args:\r\n logits: N x D numpy array\r\n Return:\r\n s: N x 1 array where s[i,0] = logsumexp(logits[i,:])\r\n \"\"\"\r\n # take out the max before doing the sum\r\n # so we don't have overflow. add back in\r\n # at the end\r\n # Get max of each row same as before\r\n rowmax = np.max(logits, axis=1)\r\n # Exponentiate each element minus its row max\r\n exp = np.exp(logits - rowmax[:,np.newaxis])\r\n # take the sum along each row\r\n # with keepdims on so it's a column vector\r\n sumexp = np.sum(exp,axis=1,keepdims=True)\r\n # return the log of sumexp\r\n # and add the max of each row back in\r\n # with newaxis so it can add along that dim\r\n return np.log(sumexp) + rowmax[:,np.newaxis]\r\n\r\n# GMM Functions\r\nclass GMM(object):\r\n def __init__(self): # No need to implement\r\n pass\r\n \r\n def _init_components(self, points, K, ids, centers, **kwargs):\r\n \"\"\"\r\n Args:\r\n points: NxD numpy array, the observations\r\n K: number of components\r\n kwargs: any other args you want\r\n Return:\r\n pi: numpy array of length K, prior\r\n mu: KxD numpy array, the center for each gaussian. \r\n sigma: KxD numpy array, the diagonal standard deviation of each gaussian.\r\n \r\n Hint: You could use the K-means results to initial GMM. It will help to converge. \r\n For instance, you could use ids, mu = KMeans()(points, K) to initialize.\r\n \"\"\"\r\n # initialize with k means centers\r\n # ids, mu = KMeans()(points, K, trimEmpty=True)\r\n # allocate arrays\r\n ids = np.array(ids, dtype=np.float32)\r\n points = np.array(points, dtype=np.float32)\r\n mu = np.array(centers, dtype=np.float32)\r\n pi = np.zeros(mu.shape[0])\r\n sigma = np.zeros((mu.shape[0],points.shape[1]))\r\n # iterate on number of components (clusters)\r\n for i in range(0, mu.shape[0]):\r\n # pi = 1/i\r\n pi[i] = 1/(i+1)\r\n # get standard deviation of the points in each cluster\r\n # assemble into diagonal matrix\r\n sigma[i,:] = np.diagonal(np.sqrt(np.abs(np.cov(points[ids == i], rowvar=False))))\r\n\r\n return pi, mu, sigma\r\n \r\n raise NotImplementedError\r\n\r\n def _ll_joint(self, points, pi, mu, sigma):\r\n \"\"\"\r\n Args:\r\n points: NxD numpy array, the observations\r\n pi: np array of length K, the prior of each component\r\n mu: KxD numpy array, the center for each gaussian. \r\n sigma: KxD numpy array, the diagonal standard deviation of each gaussian.\r\n Return:\r\n ll(log-likelihood): NxK array, where ll(i, j) = log pi(j) + log NormalPDF(points_i | mu[j], sigma[j])\r\n \r\n Hint: Assume that the three dimensions of our multivariate gaussian are independent. \r\n This allows you to write treat it as a product of univariate gaussians.\r\n \"\"\"\r\n \r\n # allocate ll array\r\n ll = np.zeros((points.shape[0], len(pi)))\r\n # each iteration should create 1 column of ll\r\n # broadcast over points, loop over clusters\r\n for j in range(0,pi.shape[0]):\r\n col = np.log(pi[j]) + np.log((1/np.sqrt(2*np.pi))**points.shape[1] * 1/np.prod(sigma[j,:]) \r\n * np.exp(-0.5*( np.sum(((points[:,:]-mu[j,:])/sigma[j,:])**2, axis=1) )))\r\n ll[:,j] = col[:]\r\n \r\n return ll\r\n \r\n raise NotImplementedError\r\n\r\n def _E_step(self, points, pi, mu, sigma):\r\n \"\"\"\r\n Args:\r\n points: NxD numpy array, the observations\r\n pi: np array of length K, the prior of each component\r\n mu: KxD numpy array, the center for each gaussian. \r\n sigma: KxD numpy array, the diagonal standard deviation of each gaussian.\r\n Return:\r\n gamma: NxK array, the posterior distribution (a.k.a, the soft cluster assignment) for each observation.\r\n \r\n Hint: You should be able to do this with just a few lines of code by using _ll_joint() and softmax() defined above. \r\n \"\"\"\r\n ll = self._ll_joint(points, pi, mu, sigma)\r\n gamma = softmax(ll)\r\n return gamma\r\n \r\n raise NotImplementedError\r\n\r\n def _M_step(self, points, gamma):\r\n \"\"\"\r\n Args:\r\n points: NxD numpy array, the observations\r\n gamma: NxK array, the posterior distribution (a.k.a, the soft cluster assignment) for each observation.\r\n Return:\r\n pi: np array of length K, the prior of each component\r\n mu: KxD numpy array, the center for each gaussian. \r\n sigma: KxD numpy array, the diagonal standard deviation of each gaussian. \r\n \r\n Hint: There are formulas in the slide.\r\n \"\"\"\r\n mu = np.zeros((gamma.shape[1], points.shape[1]))\r\n pi = np.zeros(gamma.shape[1])\r\n sigma = np.zeros((gamma.shape[1],points.shape[1]))\r\n \r\n # iterate over clusters to fill in sigma, mu, pi arrays\r\n for j in range(0, gamma.shape[1]):\r\n Nk = np.sum(gamma[:,j], axis=0)\r\n mu[j,:] = np.dot(gamma[:,j],points[:,:])/Nk\r\n pi[j] = Nk / gamma.shape[0]\r\n xmm = np.dot(gamma[:,j],(points[:,:]-mu[j,:])*(points[:,:]-mu[j,:]))\r\n sigma[j,:] = np.sqrt(np.abs(xmm/Nk))\r\n \r\n return pi, mu, sigma\r\n raise NotImplementedError\r\n\r\n def __call__(self, points, K, ids, centers, max_iters=100, abs_tol=1e-16, rel_tol=1e-16, **kwargs):\r\n \"\"\"\r\n Args:\r\n points: NxD numpy array, where N is # points and D is the dimensionality\r\n K: number of clusters\r\n max_iters: maximum number of iterations\r\n abs_tol: convergence criteria w.r.t absolute change of loss\r\n rel_tol: convergence criteria w.r.t relative change of loss\r\n kwargs: any additional arguments you want\r\n Return:\r\n gamma: NxK array, the posterior distribution (a.k.a, the soft cluster assignment) for each observation.\r\n (pi, mu, sigma): (1xK np array, KxD numpy array, KxD numpy array), mu and sigma.\r\n \r\n Hint: You do not need to change it. For each iteration, we process E and M steps, then \r\n \"\"\" \r\n pi, mu, sigma = self._init_components(points, K, ids, centers, **kwargs)\r\n \r\n pbar = tqdm(range(max_iters))\r\n for it in pbar:\r\n # E-step\r\n gamma = self._E_step(points, pi, mu, sigma)\r\n \r\n # M-step\r\n pi, mu, sigma = self._M_step(points, gamma)\r\n \r\n # calculate the negative log-likelihood of observation\r\n joint_ll = self._ll_joint(points, pi, mu, sigma)\r\n loss = -np.sum(logsumexp(joint_ll))\r\n if it:\r\n diff = np.abs(prev_loss - loss)\r\n if diff < abs_tol and diff / prev_loss < rel_tol:\r\n break\r\n prev_loss = loss\r\n pbar.set_description('iter %d, loss: %.4f' % (it, loss))\r\n return gamma, (pi, mu, sigma)\r\n\r\n\r\n## Read in data\r\n\r\nadv_stats = pd.read_csv('advancedstats.csv')\r\npg = pd.read_csv('pergamestats.csv')\r\npoints = np.array(adv_stats.as_matrix())\r\npgpts = np.array(pg.as_matrix())\r\nht = pd.read_csv('player_data.csv')\r\nheight = np.array(ht.as_matrix())\r\n\r\n# which dimensions do we actually care about for clustering\r\n# adv stats:\r\n# 3PAr, FTr, ORB%, DRB%, TRB%, AST%, STL%, BLK%, TOV%, USG%\r\n# col ids: 9, 10, 11, 12, 13, 14, 15, 16, 17, 18\r\n# pg:\r\n# fg, fga, 3p, 3pa, 2p, 2pa\r\n# col ids: 8, 9, 11, 12, 14, 15\r\n\r\nleft = pgpts[:, 8:16]\r\nright = points[:, 9:19]\r\n\r\n# excluding 3par and usg\r\nright = np.delete(right, [0,9], 1)\r\n# converting to 3par, 2par\r\nleft = np.delete(left, [0, 2, 3, 5, 6], 1)\r\n# now array is fga, 3pa, 2pa\r\n# final array should be 3par, 2par\r\nnew_left = np.zeros((left.shape[0],2))\r\nfor i in range(left.shape[0]):\r\n if left[i,0] != 0:\r\n new_left[i, :] = np.array([left[i, 1]/left[i,0], left[i, 2]/left[i,0]])\r\n\r\n# final stat set is:\r\n# 3par, 2par, ftr, orb%, drb%, trb%, ast%, stl%, blk%, tov%\r\npoints_san = np.hstack((new_left,right))\r\npoints_san = np.array(points_san, dtype=np.float32)\r\n# delete columns for ftr, trb, and tov\r\npoints_san = np.delete(points_san, [2, 5, 9], axis=1)\r\npoints_san[np.isnan(points_san)] = 0.0\r\npoints_san[:, 0:2] = points_san[:, 0:2] * 100. # scale up attempt rates to match magnitude\r\n\r\n## Set initial center locations\r\ncenters = np.zeros((5, points_san.shape[1]))\r\n\r\n## Set up holder arrays to find median for each standard position\r\npg_all = np.zeros((1, points_san.shape[1]))\r\nsg_all = np.zeros((1, points_san.shape[1]))\r\nsf_all = np.zeros((1, points_san.shape[1]))\r\npf_all = np.zeros((1, points_san.shape[1]))\r\nc_all = np.zeros((1, points_san.shape[1]))\r\n\r\n## Set initial assignments for GMM\r\n# Initial clusters are just the players listed positions\r\nK = 5 # the five standard positions\r\nids = np.zeros(points.shape[0])\r\n\r\nfor i in range(points.shape[0]):\r\n if points[i, 2] in ('PG','PG-SG','SG-PG'):\r\n ids[i] = 0\r\n pg_all = np.vstack((pg_all, points_san[i,:]))\r\n if points[i, 2] in ('SG','SG-SF','SF-SG'):\r\n ids[i] = 1\r\n sg_all = np.vstack((sg_all, points_san[i,:]))\r\n if points[i, 2] in ('SF','SF-PF','PF-SF'):\r\n ids[i] = 2\r\n sf_all = np.vstack((sf_all, points_san[i,:]))\r\n if points[i, 2] in ('PF','PF-C'):\r\n ids[i] = 3\r\n pf_all = np.vstack((pf_all, points_san[i,:]))\r\n if points[i, 2] in ('C', 'C-PF'):\r\n ids[i] = 4\r\n c_all = np.vstack((c_all, points_san[i,:]))\r\n\r\norig_ids = ids\r\ncenters[0, :] = np.median(pg_all[1:pg_all.shape[0]+1,:], axis=0)\r\ncenters[1, :] = np.median(sg_all[1:sg_all.shape[0]+1,:], axis=0)\r\ncenters[2, :] = np.median(sf_all[1:sf_all.shape[0]+1,:], axis=0)\r\ncenters[3, :] = np.median(pf_all[1:pf_all.shape[0]+1,:], axis=0)\r\ncenters[4, :] = np.median(c_all[1:c_all.shape[0]+1,:], axis=0)\r\n\r\n# Get rid of players from previous years\r\nfor i in range(height.shape[0]):\r\n if np.float32(height[i, 2]) != 2018.:\r\n height[i, :] = 0.0\r\n \r\nprint(height[0,2])\r\n\r\n# Get player heights\r\nhtcol = np.zeros((points.shape[0], 1))\r\nfor i in range(points.shape[0]):\r\n for j in range(height.shape[0]):\r\n if points[i, 1].split('\\\\')[0] == height[j, 0]:\r\n if len(height[j,4].split('-')) == 2:\r\n htcol[i] = np.float32(height[j,4].split('-')[0]) + np.float32(height[j,4].split('-')[1])/12. + (np.random.randint(200)-100)/1200.\r\n # added random noise so the heights aren't all piled on 1\" intervals\r\n else:\r\n htcol[i] = np.float32(height[j,4].split('-')[0]) + (np.random.randint(200)-100)/2400.\r\n\r\n\r\n\r\n# run GMM\r\ngamma, (pi, mu, sigma) = GMM()(points_san, 5, ids, centers, max_iters=300)\r\n\r\npoints_p = points\r\ngamma_p = gamma\r\norig_ids_p = orig_ids\r\nfor i in range(gamma_p.shape[0]):\r\n if htcol[i] == 0:\r\n points_p[i,:] = -1.\r\n gamma_p[i,:] = -1.\r\n orig_ids_p[i] = -1.\r\n\r\npoints_p = points_p[~np.all(points_p == -1., axis=1)]\r\ngamma_p = gamma_p[~np.all(gamma_p == -1., axis=1)]\r\norig_ids_p = orig_ids_p[orig_ids_p != -1.]\r\nhtcol_p = htcol[htcol != 0.]\r\nhtp = np.hstack((htcol_p.reshape((-1,1)),points_p))\r\n\r\nboxdata = [htcol_p[orig_ids_p == 0], htcol_p[orig_ids_p == 1], htcol_p[orig_ids_p == 2], htcol_p[orig_ids_p == 3], htcol_p[orig_ids_p == 4]]\r\n\r\n#for i in range(gamma_p.shape[0]):\r\n# if htp[i,0] >= 6.79:\r\n# \tprint(points_p[i,1])\r\n# \tprint(gamma_p[i,:])\r\n\r\nplt.figure()\r\n#plt.boxplot(boxdata)\r\n#plt.title('Listed Position vs. Height')\r\n#plt.yticks(ticks=[6.0, 6.25, 6.5, 6.75, 7.0, 7.25], labels=['6-0', '6-3', '6-6', '6-9', '7-0', '7-3'])\r\n#plt.xticks(ticks=[1, 2, 3, 4, 5], labels=['PG', 'SG', 'SF', 'PF', 'C']))\r\n\r\n#plt.figure()\r\nplt.scatter(gamma_p[htp[:,0]>=6.79, 3]+gamma_p[htp[:,0]>=6.79, 4], gamma_p[htp[:,0]>=6.79, 0]+gamma_p[htp[:,0]>=6.79, 1])\r\nplt.title('Guard/Wing/Big Components for Players > 6-10')\r\nplt.xlabel('Big Component Sum')\r\nplt.ylabel('Guard Component Sum')\r\n#plt.figure()\r\n#plt.scatter(htp[:, 0], gamma_p[:, 1] + gamma_p[:, 2])\r\n#plt.title('Wing Similarity vs. Height')\r\n#plt.figure()\r\n#plt.scatter(htp[:, 0], gamma_p[:, 3] + gamma_p[:, 4])\r\n#plt.title('Big Similarity vs. Height')\r\nplt.show()\r\n", "sub_path": "gmm.py", "file_name": "gmm.py", "file_ext": "py", "file_size_in_byte": 14045, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.set_printoptions", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.maxsize", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 68, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 75, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 99, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.diagonal", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 135, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 183, "usage_type": "call"}, {"api_name": "tqdm.tqdm_notebook", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 217, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 227, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 230, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 259, "usage_type": "attribute"}, {"api_name": "numpy.delete", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 299, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 317, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 320, "usage_type": "attribute"}, {"api_name": "numpy.all", "line_number": 336, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 340, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 349, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 349, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 356, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 356, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 357, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 357, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 358, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 358, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 359, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 359, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 366, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 366, "usage_type": "name"}]} +{"seq_id": "40471084", "text": "import os\nimport lmdb # install lmdb by \"pip install lmdb\"\nimport cv2\nimport re\nfrom PIL import Image\nimport numpy as np\nimport imghdr\nimport glob\nimport tqdm\nimport pdb\n\n\ndef checkImageIsValid(imageBin):\n if imageBin is None:\n return False\n try:\n imageBuf = np.fromstring(imageBin, dtype=np.uint8)\n img = cv2.imdecode(imageBuf, cv2.IMREAD_GRAYSCALE)\n imgH, imgW = img.shape[0], img.shape[1]\n except:\n return False\n else:\n if imgH * imgW == 0:\n return False\t\t\n return True\n\n\ndef writeCache(env, cache):\n with env.begin(write=True) as txn:\n for k, v in cache.items():\n try:\n txn.put(k, v)\n except:\n pdb.set_trace()\n\t\t\t\ndef createDataset(outputPath, imagePathList, labelList, lexiconList=None, checkValid=True):\n \"\"\"\n Create LMDB dataset for CRNN training.\n ARGS:\n outputPath : LMDB output path\n imagePathList : list of image path\n labelList : list of corresponding groundtruth texts\n lexiconList : (optional) list of lexicon lists\n checkValid : if true, check the validity of every image\n \"\"\"\n assert(len(imagePathList) == len(labelList))\n nSamples = len(imagePathList)\n env = lmdb.open(outputPath, map_size=1099511627776)\n cache = {}\n cnt = 1\n for i in range(nSamples):\n imagePath = imagePathList[i]\n # imagePath = ''.join(imagePathList[i]).split()[0].replace('\\n','').replace('\\r\\n','')\n #print(imagePath)\n label = ''.join(labelList[i])\n print(label)\n # if not os.path.exists(imagePath):\n # print('%s does not exist' % imagePath)\n # continue\t\n\t\t\n with open(imagePath, 'r') as f:\n imageBin = f.read()\n\n\n if checkValid:\n if not checkImageIsValid(imageBin):\n print('%s is not a valid image' % imagePath)\n continue\n imageKey = 'image-%09d' % cnt\n labelKey = 'label-%09d' % cnt\n cache[imageKey] = imageBin\n cache[labelKey] = label\n if lexiconList:\n lexiconKey = 'lexicon-%09d' % cnt\n cache[lexiconKey] = ' '.join(lexiconList[i])\n if cnt % 1000 == 0:\n writeCache(env, cache)\n cache = {}\n print('Written %d / %d' % (cnt, nSamples))\n cnt += 1\n print(cnt)\n nSamples = cnt-1\n cache['num-samples'] = str(nSamples)\n writeCache(env, cache)\n print('Created dataset with %d samples' % nSamples)\n\t\n\nif __name__ == '__main__':\n home_path = os.path.expanduser('~')\n data_root = os.path.join(home_path, 'data/Datafountain')\n text_path = os.path.join(data_root, 'text')\n text_image_dir = os.path.join(text_path, 'verify_image')\n text_label_dir = os.path.join(text_path, 'verify_label')\n outputPath = \"./verify_lmdb\"\n\n imagePathList = glob.glob(text_image_dir + '/*.jpg')\n labelList = []\n print('reading label txt...')\n for img_path in tqdm.tqdm(imagePathList):\n name = os.path.split(img_path)[-1][:-4] + '.txt'\n label_path = os.path.join(text_label_dir, name)\n with open(label_path, 'r') as f:\n words = f.readline().strip()\n labelList.append(words)\n print('done.')\n\n # sort\n label_length = [(len(x),i) for i,x in enumerate(labelList)]\n label_length = sorted(label_length, key=lambda x:x[0])\n index = [x[1] for x in label_length]\n labelList = [labelList[i] for i in index]\n imagePathList = [imagePathList[i] for i in index]\n\n createDataset(outputPath, imagePathList, labelList)\n\n", "sub_path": "to_lmdb/tolmdb.py", "file_name": "tolmdb.py", "file_ext": "py", "file_size_in_byte": 3616, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.fromstring", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cv2.imdecode", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pdb.set_trace", "line_number": 34, "usage_type": "call"}, {"api_name": "lmdb.open", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 96, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}]} +{"seq_id": "651443932", "text": "import folium \nimport gpxpy\nimport os\nimport base64\nfrom PIL import Image\nimport PIL\nimport folium.plugins as plugins\t\t# tylko tak działają pluginsy\nfrom branca.element import Template, MacroElement\nfrom image_edition import image_edition\nfrom image_edition import image_edition_1\nfrom image_edition import image_edition_2\n# https://towardsdatascience.com/how-to-deploy-your-data-science-as-web-apps-easily-with-python-955dd462a9b5\n\n\ndef overlayGPX(gpxDataList, Colours, Labels, zoom):\n\tmyMap = folium.Map(location=[50.443627, 16.869285],zoom_start=zoom)\n\t# add the layer control\n\tLC = folium.map.LayerControl(position='topleft', collapsed=True, autoZIndex=True)\n\n\t# deklaracja warstw\n\tL1 = folium.FeatureGroup()\n\tL2 = folium.FeatureGroup()\n\tL3 = folium.FeatureGroup()\n\tL4 = folium.FeatureGroup()\n\tL5 = folium.FeatureGroup()\n\tL6 = folium.FeatureGroup()\n\tL7 = folium.FeatureGroup()\n\t\n\t# tyt warstwy\n\tL1.layer_name = 'Szlaki komunikacyjne'\n\tL2.layer_name = 'Niekóre atrakcje obecne w roku 2020'\n\tL3.layer_name = 'Ważna działka w centrum miasta'\n\tL4.layer_name = 'Proponowany podział ważnej działki w centrum miasta'\n\tL5.layer_name = 'Propozycje uczestników SLL'\n\tL6.layer_name = 'Nowe atrakcje'\n\tL7.layer_name = 'Przegląd ankietyzacji kolarzy górskich'\n\n\t# dodawanie tytułu\n\ttitle_html = '''\n\t\t\t

Gmina Złoty Stok - przestrzenne rozmieszczenie elementów raportu

\n\t\t\t '''\n\tmyMap.get_root().html.add_child(folium.Element(title_html))\n\n\t# dodawanie szlakow z plików GPX\n\tfor gpxData, color, label in zip(gpxDataList, Colours, Labels):\n\t\tgpx_file = open(gpxData, 'r')\n\t\tLon = []\n\t\tLat = []\n\t\tfor line in gpx_file:\n\t\t\tX = line.split('\"')\n\t\t\t\n\t\t\tfor i in X:\n\t\t\t\ttry:\n\t\t\t\t\tif float(i) < 20:\n\t\t\t\t\t\tLon.append(float(i))\n\t\t\t\t\telif float(i) > 20:\n\t\t\t\t\t\tLat.append(float(i))\n\t\t\t\texcept:\n\t\t\t\t\tpass\n\t\tpoints = [];\n\t\tfor i, j in zip(Lat[2:], Lon[2:]):\n\t\t\tpoints.append([i, j])\n\t\tif gpxData == 'zloty-stok-czerwona.gpx' or gpxData == 'zloty-stok-niebieska.gpx':\n\t\t\t(folium.vector_layers.PolyLine(points, width = '200%', popup=label, tooltip=None, smooth_factor = 2, color=color, dash_array='5')).add_to(L1)\n\t\telse:\n\t\t\t(folium.vector_layers.PolyLine(points, width = '200%', popup=label, tooltip=None, smooth_factor = 2, color=color)).add_to(L1)\n\n\t################################################################\n\t################################################################\n\t# OBRAZY ISTNIEJACYCH ATRAKCJI\n\t################################################################\n\t# singletracki\n\tpng = \"single.png\"\n\tencoded = base64.b64encode(open(png, 'rb').read())\n\thtml = '''

Sieć szlaków rowerowych Singletrack Glacensis

\n\t
\n\t

'''.format\n\tiframe = folium.IFrame(html(encoded.decode('UTF-8')), width=420, height=420)\n\tpopup = folium.Popup(iframe, max_width=500)\n\ticon = folium.Icon(color=\"red\", icon=\"ok\")\n\tmarker = folium.Marker(location=[50.441834, 16.865620], popup=popup, icon=icon)\n\tmarker.add_to(L2)\n\n\t# wypożyczalnia rowerów elektrycznych\n\tpng = 'ebike.png'\n\tencoded = base64.b64encode(open(png, 'rb').read())\n\thtml = '''

Wypożyczalnia rowerów elektrycznych Segbi

\n\t
\n\t

'''.format\n\tiframe = folium.IFrame(html(encoded.decode('UTF-8')), width=430, height=340)\n\tpopup = folium.Popup(iframe, max_width=500)\n\ticon = folium.Icon(color=\"red\", icon=\"ok\")\n\tmarker = folium.Marker(location=[50.442673, 16.875021], popup=popup, icon=icon)\n\tmarker.add_to(L2)\n\n\t# kaplica cmentarna\n\tpng = 'cmentarz_2.png'\n\tencoded = base64.b64encode(open(png, 'rb').read())\n\thtml = '''

Późnogotycka Kaplica Cmentarna

\n\t
\n\t

'''.format\n\tiframe = folium.IFrame(html(encoded.decode('UTF-8')), width=310, height=520)\n\tpopup = folium.Popup(iframe, max_width=310)\n\ticon = folium.Icon(color=\"red\", icon=\"ok\")\n\tmarker = folium.Marker(location=[50.445561, 16.875297], popup=popup, icon=icon)\n\tmarker.add_to(L2)\n\n\t# kamienica na rynku z portretami\n\tpng = 'zloty1_kamienica_z_portretami.png'\n\tencoded = base64.b64encode(open(png, 'rb').read())\n\thtml = '''

Rynek i jedna z wielu
zabytkowych kamienic

\n\t
\n\t

'''.format\n\tiframe = folium.IFrame(html(encoded.decode('UTF-8')), width=420, height=420)\n\tpopup = folium.Popup(iframe, max_width=420)\n\ticon = folium.Icon(color=\"red\", icon=\"fa-university\", prefix = 'fa')\n\tmarker = folium.Marker(location=[50.442969, 16.874835], popup=popup, icon=icon)\n\tmarker.add_to(L2)\n\n\t# 'wyrobisko'/skala za kopalnią\n\tpng = 'skala.png'\n\tencoded = base64.b64encode(open(png, 'rb').read())\n\thtml = '''

Widok na potężne wyrobisko i tyrolkę

\n\t
\n\t

'''.format\n\tiframe = folium.IFrame(html(encoded.decode('UTF-8')), width=420, height=400)\n\tpopup = folium.Popup(iframe, max_width=420)\n\ticon = folium.Icon(color=\"red\", icon=\"ok\")\n\tmarker = folium.Marker(location=[50.436968, 16.872321], popup=popup, icon=icon)\n\tmarker.add_to(L2)\n\n\t# meleks zdjecie z gazety\n\tpng = 'melex.png'\n\tencoded = base64.b64encode(open(png, 'rb').read())\n\thtml = '''

Punkt Informacji Turystycznej i możliwość zwiedzania miasta melexem

\n\t
\n\t

'''.format\n\tiframe = folium.IFrame(html(encoded.decode('UTF-8')), width=420, height=400)\n\tpopup = folium.Popup(iframe, max_width=420)\n\ticon = folium.Icon(color=\"red\", icon=\"ok\")\n\tmarker = folium.Marker(location=[50.442837, 16.874020], popup=popup, icon=icon)\n\tmarker.add_to(L2)\n\n\t# park techniki\n\tpng = 'park_techniki_2.png'\n\tencoded = base64.b64encode(open(png, 'rb').read())\n\thtml = '''

Średniowieczny Park Techniki

\n\t
\n\t

'''.format\n\tiframe = folium.IFrame(html(encoded.decode('UTF-8')), width=420, height=460)\n\tpopup = folium.Popup(iframe, max_width=420)\n\ticon = folium.Icon(color=\"red\", icon=\"ok\")\n\tmarker = folium.Marker(location=[50.444541, 16.879349], popup=popup, icon=icon)\n\tmarker.add_to(L2)\n\n\t################################################################\n\t################################################################\n\t# NASZE POMYSŁY\n\t################################################################\n\n\t# wiata gastronomiczna na rynku lub obok singla\n\tpng = 'wiata.png'\n\tencoded = base64.b64encode(open(png, 'rb').read())\n\thtml = '''

Przykładowe wykonanie i poglądowe umiejscowienie wiaty gastronomicznej

\n\t
\n\t

'''.format\n\tiframe = folium.IFrame(html(encoded.decode('UTF-8')), width=420, height=400)\n\tpopup = folium.Popup(iframe, max_width=420)\n\ticon = folium.Icon(color=\"green\", icon=\"home\")\n\tmarker = folium.Marker(location=[50.440978, 16.874471], popup=popup, icon=icon)\n\tmarker.add_to(L5)\n\n\t# napisy promocyjne z hashtagiem przy wjazdach do miasta\n\tpng = 'zdjecie_napisu.png'\n\tencoded = base64.b64encode(open(png, 'rb').read())\n\thtml = '''

Napisy powiązane z akcją promocyjną

\n\t
\n\t

'''.format\n\tiframe = folium.IFrame(html(encoded.decode('UTF-8')), width=410, height=400)\n\tpopup = folium.Popup(iframe, max_width=420)\n\ticon = folium.Icon(color=\"green\", icon=\"fa-hashtag\", prefix = 'fa')\n\tmarker = folium.Marker(location=[50.447121, 16.864869], popup=popup, icon=icon)\n\tmarker.add_to(L5)\n\n\t# atrakcyjne dzialki\n\tpng = 'pole.png'\n\tencoded = base64.b64encode(open(png, 'rb').read())\n\thtml = '''

Korzystny dla miasta podział działki

\n\t
\n\t

'''.format\n\tiframe = folium.IFrame(html(encoded.decode('UTF-8')), width=420, height=485)\n\tpopup = folium.Popup(iframe, max_width = 420)\n\ticon = folium.Icon(color = \"green\", icon = \"fa-pie-chart\", prefix = 'fa')\n\tmarker = folium.Marker(location=[50.445954, 16.873068], popup=popup, icon=icon)\n\tmarker.add_to(L5)\n\n\n\t###############################################################\n\t# PODZIAŁ DZIAŁEK\n\t###############################################################\n\n\t# Wyznaczanie (pogladowe) dzialki\n\toryginal_field = folium.vector_layers.Polygon([[50.446811, 16.873717], [50.445823, 16.873545], [50.445416, 16.873196], [50.445481, 16.873051]], popup=\"Działka w aktualnej postaci\", fill_color='blue')\n\tbiedronka_field = folium.vector_layers.Polygon([[50.445922, 16.871966], [50.446862, 16.873631], [50.446811, 16.873717], [50.446227, 16.873619], [50.445643, 16.872632]], popup=\"Działka supermarketu po zamianie\", fill_color='green')\n\tgolden_field = folium.vector_layers.Polygon([[50.445823, 16.873545], [50.445416, 16.873196], [50.445643, 16.872637], [50.446215, 16.873622]], popup = \"Działka miasta po zamianie\", fill_color = 'red')\n\toryginal_field.add_to(L3)\n\tbiedronka_field.add_to(L4)\n\tgolden_field.add_to(L4)\n\t\n\t################################################################\n\t################################################################\n\n\n\t################################################################\n\t################################################################\n\t# POWSTAJACE ATRAKCJE\n\t################################################################\n\n\t# sciezka laczaca kopalnie z rynkiem\n\tpng = 'trasa.png'\n\tencoded = base64.b64encode(open(png, 'rb').read())\n\thtml = '''

Atrakcyjna ścieżka kopalnia-rynek

\n\t
\n\t

'''.format\n\tiframe = folium.IFrame(html(encoded.decode('UTF-8')), width=420, height=400)\n\tpopup = folium.Popup(iframe, max_width = 420)\n\ticon = folium.Icon(color = \"darkgreen\", icon = \"fa-child\", prefix = 'fa')\n\tmarker = folium.Marker(location=[50.440397, 16.875159], popup=popup, icon=icon)\n\tmarker.add_to(L6)\n\n\t# wieża widokowa na kościele ewangelickim\n\tpng = \"wieza.png\"\n\tencoded = base64.b64encode(open(png, 'rb').read())\n\thtml = '''

Platforma widokowa na szczycie wieży kościelnej

\n\t
\n\t

'''.format\n\tiframe = folium.IFrame(html(encoded.decode('UTF-8')), width = 320, height=520)\n\tpopup = folium.Popup(iframe, max_width = 320)\n\ticon = folium.Icon(color = \"darkgreen\", icon = \"fa-child\", prefix = 'fa')\n\tmarker = folium.Marker(location=[50.442686, 16.873486], popup=popup, icon=icon)\n\tmarker.add_to(L6)\n\n\t################################################################\n\t################################################################\n\t# ANALIZA ANKIETY\n\t################################################################\n\n\t# wieża widokowa na kościele ewangelickim\n\tpng = image_edition_1(\"pie_chart\")\n\tencoded = base64.b64encode(open(png, 'rb').read())\n\thtml = '''
\n\t

'''.format\n\tiframe = folium.IFrame(html(encoded.decode('UTF-8')), width = 820, height=620)\n\tpopup = folium.Popup(iframe, max_width = 820)\n\ticon = folium.Icon(color = \"cadetblue\", icon = \"fa-line-chart\", prefix = 'fa')\n\tmarker = folium.Marker(location=[50.435845, 16.861167], popup=popup, icon=icon)\n\tmarker.add_to(L7)\n\n\t# dodawanie warstw do mapy\n\tL1.add_to(myMap)\n\tL2.add_to(myMap)\n\tL3.add_to(myMap)\n\tL4.add_to(myMap)\n\tL5.add_to(myMap)\n\tL6.add_to(myMap)\n\tL7.add_to(myMap)\n\tLC.add_to(myMap)\n\n\t\n\ttemplate = \"\"\"\n\t{% macro html(this, kwargs) %}\n\n\t\n\t\n\t\n\t\n\t\n\tjQuery UI Draggable - Default functionality\n\t\n\n\t\n\t\n\n\t\n\t\n\t\n\n\n\t
\n\t \n\t
Legenda znaczników
\n\t
\n\t
    \n\t
  • Atrakcje obecne w roku 2020
    • \n\t
  • Propozycje uczestników SLL
    • \n\t
  • Powstające atrakcje
    • \n\t
  • Ścieżka prowadząca od kopalni do rynku
    • \n\t
  • (i inne odcienie niebieskiego) Ważne szlaki komunikacyjne
    • \n\t
  • Wyniki ankietyzacji kolarzy
    • \n\n\t
\n\t
\n\t
\n\n\t\n\t\n\n\t\n\t{% endmacro %}\"\"\"\n\n\tmacro = MacroElement()\n\tmacro._template = Template(template)\n\n\tmyMap.get_root().add_child(macro)\n\n\tfolium.plugins.Fullscreen(\n\tposition='topright',\n\ttitle='wypełnij ekran',\n\ttitle_cancel='wyłącz tryb pełnego ekranu',\n\tforce_separate_button=True).add_to(myMap)\n\treturn(myMap)\n\noverlayGPX(['klodzko_valid.gpx', 'kopalnia.gpx', \n\t'bila_woda.gpx', 'zloty-stok-czerwona.gpx', \n\t'zloty-stok-niebieska.gpx'], ['#349ceb', '#FF5050', \n\t'#3459eb', '#038cfc', '#038cfc'], \n\t['Trasa łącząca Złoty Stok z Kłodzkiem', \n\t'Ścieżka między rynkiem a kopalnią', \n\t'Trasa łącząca Złoty Stok z Białą Wodą', \n\t'Trasa Single Track Glacensis Złoty Stok Czerwona', \n\t'Trasa Single Track Glacensis Złoty Stok Niebieska'], 15).save(\"SLL_map.html\")\n\n# dodaj zielony znacznik do pól\n# WAZNE w przyszlosci aby miec pole do popisu z kolorami markerow\n# mozna uzyc class folium.plugins.BeautifyIcon\n#
  • Atrakcje obecne w roku 2020
    • \n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 14688, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "folium.Map", "line_number": 16, "usage_type": "call"}, {"api_name": "folium.map.LayerControl", "line_number": 18, "usage_type": "call"}, {"api_name": "folium.map", "line_number": 18, "usage_type": "attribute"}, {"api_name": "folium.FeatureGroup", "line_number": 21, "usage_type": "call"}, {"api_name": "folium.FeatureGroup", "line_number": 22, "usage_type": "call"}, {"api_name": "folium.FeatureGroup", "line_number": 23, "usage_type": "call"}, {"api_name": "folium.FeatureGroup", "line_number": 24, "usage_type": "call"}, {"api_name": "folium.FeatureGroup", "line_number": 25, "usage_type": "call"}, {"api_name": "folium.FeatureGroup", "line_number": 26, "usage_type": "call"}, {"api_name": "folium.FeatureGroup", "line_number": 27, "usage_type": "call"}, {"api_name": "folium.Element", "line_number": 42, "usage_type": "call"}, {"api_name": "folium.vector_layers.PolyLine", "line_number": 64, "usage_type": "call"}, {"api_name": "folium.vector_layers", "line_number": 64, "usage_type": "attribute"}, {"api_name": "folium.vector_layers.PolyLine", "line_number": 66, "usage_type": "call"}, {"api_name": "folium.vector_layers", "line_number": 66, "usage_type": "attribute"}, {"api_name": "base64.b64encode", "line_number": 74, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 78, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 79, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 80, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 81, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 86, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 90, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 91, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 92, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 93, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 98, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 102, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 103, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 104, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 105, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 110, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 114, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 115, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 116, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 117, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 122, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 126, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 127, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 128, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 129, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 134, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 138, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 139, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 140, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 141, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 146, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 150, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 151, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 152, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 153, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 163, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 167, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 168, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 169, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 170, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 175, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 179, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 180, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 181, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 182, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 187, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 191, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 192, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 193, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 194, "usage_type": "call"}, {"api_name": "folium.vector_layers.Polygon", "line_number": 203, "usage_type": "call"}, {"api_name": "folium.vector_layers", "line_number": 203, "usage_type": "attribute"}, {"api_name": "folium.vector_layers.Polygon", "line_number": 204, "usage_type": "call"}, {"api_name": "folium.vector_layers", "line_number": 204, "usage_type": "attribute"}, {"api_name": "folium.vector_layers.Polygon", "line_number": 205, "usage_type": "call"}, {"api_name": "folium.vector_layers", "line_number": 205, "usage_type": "attribute"}, {"api_name": "base64.b64encode", "line_number": 221, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 225, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 226, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 227, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 228, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 233, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 237, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 238, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 239, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 240, "usage_type": "call"}, {"api_name": "image_edition.image_edition_1", "line_number": 249, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 250, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 253, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 254, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 255, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 256, "usage_type": "call"}, {"api_name": "branca.element.MacroElement", "line_number": 364, "usage_type": "call"}, {"api_name": "branca.element.Template", "line_number": 365, "usage_type": "call"}, {"api_name": "folium.plugins.Fullscreen", "line_number": 369, "usage_type": "call"}, {"api_name": "folium.plugins", "line_number": 369, "usage_type": "attribute"}]} +{"seq_id": "20844087", "text": "#trainyouraim by Jonas Knobloch\n#version 0.1 30.12.2014\n\nimport pygame, sys, random\nfrom pygame.tests.base_test import pygame_quit\nfrom pygame.locals import *\n\npygame.init()\n\nwindowwidth = 500\nwindowheight = 500\nscreen = pygame.display.set_mode([windowwidth, windowheight])\npygame.display.set_caption(\"Train Your Aim by Knobi\")\npygame.mouse.set_visible(True)\n\nblack = [0,0,0]\nred = [255,0,0]\ngreen = [0,255,0]\nblue = [0,0,255]\nwhite = [255,255,255]\nyellow = [255,165, 0]\n\nmyfont = pygame.font.SysFont('Arial', 20)\nx = 0\ny = 0\n\naim_pos = (0, 0)\ncounter = 0\nmissed = 0\ndim = (20, 20) #vergroessert zielbereich\nnsposxy = (windowwidth - 22, windowheight -25)\nsposxy = (windowwidth - 45, windowheight - 25)\nborder = 40\n#xy_tmp in eventloops\n\nstarttext = myfont.render(('press SPACE to continue'), 1, black)\n\nscreen.fill(white)\nscreen.blit(starttext, (140, 240))\n\ndef screenreset():\n\tpygame.draw.rect(screen, white, (0, 0, windowwidth, windowheight))\n\ndef negscorereset():\n\tpygame.draw.rect(screen, white, (nsposxy[0], nsposxy[1],40,40))\n\ndef renderscore():\n\tscreen.blit(score, sposxy)\n\ndef rendernegscore():\n\tscreen.blit(negscore, (nsposxy[0], nsposxy[1]))\n\t\nwhile True:\n\t\n\tfor event in pygame.event.get():\n\t\t\n\t\tprint(event)\n\t\t\t\t\t\t\t\n\t\tif event.type == QUIT:\n\t\t\tpygame.quit()\n\t\t\tsys.exit()\n\t\t\t\n\t\tif event.type == pygame.KEYDOWN:\n\t\t\t\n\t\t\tif event.key == K_ESCAPE:\n\t\t\t\tpygame_quit()\n\t\t\t\tquit()\n\t\t\t\t\n\t\t\tif event.key == K_SPACE:\n\t\t\t\tscreenreset()\n\t\t\t\tcounter = 0\n\t\t\t\tmissed = 0\n\t\t\t\tx = 240\n\t\t\t\ty = 240\n\t\t\t\txy_tmp = (x+10,y+10) #verschiebt grafik\n\t\t\t\tpygame.draw.circle(screen, black, (xy_tmp), 10, 0)\n\t\t\t\tscore = myfont.render(str(counter), 1, black)\n\t\t\t\tnegscore = myfont.render(str(missed), 1, red)\n\t\t\t\tscreen.blit(score, sposxy)\n\t\t\t\tscreen.blit(negscore, nsposxy)\n\t\t\t\trenderscore()\n\t\t\t\trendernegscore()\n\t\t\t\t\n\t\tif event.type == pygame.MOUSEBUTTONDOWN:\n\t\t\tscore = myfont.render(str(counter), 1, black)\n\t\t\tnegscore = myfont.render(str(missed), 1, red)\n\t\t\taim_pos = pygame.mouse.get_pos()\n\t\t\t\n\t\t\tif (aim_pos[0] >= x and aim_pos[0] <= x + dim[0]) and (aim_pos[1] >= y and aim_pos[1] <= y + dim[1]):\n\t\t\t\tscreenreset()\n\t\t\t\tcounter = counter +1\n\t\t\t\tscreen.blit(score, sposxy)\n\t\t\t\tscreen.blit(negscore, nsposxy)\t\t\t\n\t\t\t\tx = random.randint(border, windowwidth - border)\n\t\t\t\ty = random.randint(border, windowheight - border)\n\t\t\t\txy_tmp = (x+10,y+10)\n\t\t\t\tpygame.draw.circle(screen, red, (xy_tmp), 10, 0)\n\t\t\telse:\n\t\t\t\tmissed = missed +1\n\t\t\t\tnegscorereset()\n\t\t\t\tnegscore = myfont.render(str(missed), 1, red) #haelt punktestand aktuell\n\t\t\t\tscreen.blit(negscore, nsposxy)\n\t\t\t\t\n\t\tpygame.display.update()", "sub_path": "trainyouraim.py", "file_name": "trainyouraim.py", "file_ext": "py", "file_size_in_byte": 2577, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.init", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.mouse.set_visible", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 60, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pygame.tests.base_test.pygame_quit", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 87, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 87, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 94, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 95, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 97, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 104, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 104, "usage_type": "attribute"}]} +{"seq_id": "185733461", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 6 15:38:00 2018\n@author: KRUEGKJ\nta_momentum_studies.py\n\"\"\"\nimport talib as ta\nfrom config import *\n\nclass TALibMomentumStudies:\n \"\"\"Group of Momentum studies utilized fromTALib \"\"\"\n def RSI(self, df, period):\n \"\"\"Relative Strenth Index, suppose Welles Wilder verison\n Args:\n close: Closing price of instrument\n period: number of time periods in the calculation\n feature_dict: Dictionary of added features\n Return:\n RSI signal\n feature_dict\n \"\"\"\n col_name = 'RSI_' + str(period)\n current_feature['Latest'] = col_name\n feature_dict[col_name] = 'Keep'\n \n df[col_name] = ta.RSI(df.Close, period)\n return df\n\n def PPO(self, df, fast, slow):\n \"\"\" Percentage Price Oscillator\n Difference between two moving averages of a security's price\n Args:\n close: Closing price of instrument\n fast: fast MA\n slow: slowMA\n feature_dict: Dictionary of added features\n Return:\n PPO signal\n feature_dict\n \"\"\"\n col_name = 'PPO_f' + str(fast) + '_s' + str(slow)\n current_feature['Latest'] = col_name\n feature_dict[col_name] = 'Keep'\n \n df[col_name] = ta.PPO(df.Close,\n # defaults are 0\n # The FastLimit and SlowLimit parameters\n # should be between 0.01 and 0.99\n fast,\n slow\n )\n return df\n\n def CMO(self, df, period):\n \"\"\" Chande Momentum Oscillator\n Modified RSI, measures momentum on both up and down days\n Args:\n close: Closing price of instrument\n period: number of time periods in the calculation\n feature_dict: Dictionary of added features\n Return:\n CMO signal\n feature_dict\n \"\"\"\n col_name = 'CMO_' + str(period)\n current_feature['Latest'] = col_name\n feature_dict[col_name] = 'Keep'\n \n df[col_name] = ta.CMO(df.Close, period)\n return df\n\n def CCI(self, df, period):\n \"\"\" Commodity Channel Index\n CCI measures the current price level relative to an average price\n level over a given period of time. CCI is relatively high\n when prices are far above their average. CCI is relatively\n low when prices are far below their average. In this manner,\n CCI can be used to identify overbought and oversold levels.\n\n Args:\n high, low, close: HLC of instrument\n period: number of time periods in the calculation\n feature_dict: Dictionary of added features\n Return:\n CCI signal\n feature_dict\n \"\"\"\n col_name = 'CCI_' + str(period)\n current_feature['Latest'] = col_name\n feature_dict[col_name] = 'Keep'\n \n df[col_name] = ta.CCI(df.High,\n df.Low,\n df.Close,\n period\n )\n return df\n\n def UltOsc(self, df, t1=7, t2=14, t3=28):\n \"\"\" Ultimate Oscillator\n Uses weighted sums of three oscillators, designed to capture\n momentum across three different timeframes, each of which uses\n a different time period\n\n Args:\n high, low, close: HLC of instrument\n t1, t2, t3: various time periods in the calculation,\n default: 7,14,28\n feature_dict: Dictionary of added features\n Return:\n UO signal\n feature_dict\n \"\"\"\n t1t = 'UltOsc_t1' + str(t1)\n t2t = '_t2' + str(t2)\n t3t = '_t3' + str(t3)\n col_name = t1t + t2t + t3t\n current_feature['Latest'] = col_name\n feature_dict[col_name] = 'Keep'\n df[col_name] = ta.ULTOSC(df.High,\n df.Low,\n df.Close,\n t1, t2, t3\n )\n return df\n\n def rate_OfChg(self, df, period):\n \"\"\"The Rate of Change (ROC) is a technical indicator that\n measures the percentage change between the most recent price\n and the price “n” day’s ago. The indicator fluctuates around\n the zero line.\n Args:\n close: close of instrument\n feature_dict: Dictionary of added features\n Return:\n UO signal\n feature_dict\n \"\"\"\n col_name = 'ROC_' + str(period)\n current_feature['Latest'] = col_name\n feature_dict[col_name] = 'Keep'\n \n df[col_name] = ta.ROC(df.Close, period)\n return df\n\nif __name__ == \"__main__\":\n from plot_utils import *\n from retrieve_data import *\n from config import *\n \n plotIt = PlotUtility()\n taLibMomSt = TALibMomentumStudies()\n dSet = DataRetrieve()\n \n dataLoadStartDate = \"2014-04-01\"\n dataLoadEndDate = \"2018-04-01\"\n issue = \"TLT\"\n\n dataSet = dSet.read_issue_data(issue)\n\n dataSet = dSet.set_date_range(dataSet, dataLoadStartDate, dataLoadEndDate)\n\n dataSet = taLibMomSt.RSI(dataSet, 20)\n dataSet = taLibMomSt.PPO(dataSet, 12, 26)\n dataSet = taLibMomSt.CMO(dataSet, 20)\n dataSet = taLibMomSt.CCI(dataSet, 20)\n dataSet = taLibMomSt.UltOsc(dataSet, 7, 24, 28)\n dataSet = taLibMomSt.rate_OfChg(dataSet, 10)\n\n startDate = \"2015-02-01\"\n endDate = \"2015-06-30\"\n plotDF = dataSet[startDate:endDate]\n \n # Set up dictionary and plot HigherClose\n plot_dict = {}\n plot_dict['Issue'] = issue\n plot_dict['Plot_Vars'] = list(feature_dict.keys())\n plot_dict['Volume'] = 'Yes'\n plotIt.price_Ind_Vol_Plot(plot_dict, plotDF)\n", "sub_path": "Code/lib/ta_momentum_studies.py", "file_name": "ta_momentum_studies.py", "file_ext": "py", "file_size_in_byte": 6157, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "talib.RSI", "line_number": 26, "usage_type": "call"}, {"api_name": "talib.PPO", "line_number": 45, "usage_type": "call"}, {"api_name": "talib.CMO", "line_number": 69, "usage_type": "call"}, {"api_name": "talib.CCI", "line_number": 92, "usage_type": "call"}, {"api_name": "talib.ULTOSC", "line_number": 120, "usage_type": "call"}, {"api_name": "talib.ROC", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "247183584", "text": "import logging\nimport sys\nimport time\nimport os\nimport numpy as np\n\nCODE_ROOT = '/home/lab-xiong.jiangfeng/Projects/SiameseRPN'\nsys.path.insert(0, CODE_ROOT)\n\nimport tensorflow as tf\nfrom utils.misc_utils import load_cfgs\nfrom inference.OnlineTracker import OnlineTracker,OnlineNet\nfrom utils.infer_utils import Rectangle\n\nfrom configs import get_model\n\nfrom utils.misc_utils import auto_select_gpu\nonline_config={'select_channel_num':96,\n 'template_size': 64,\n 'conv_dims': [96],\n 'conv1_ksizes': [3,7,11],\n 'conv_ksizes': [],\n 'use_part_filter':1,\n 'finetune_part_filter': 1,\n 'online_lr': 0.01,\n 'epsilon': 0.1,\n 'OnlineRankWeight':0.3,\n 'dropout_keep_rate': 0.9,\n 'bn_decay':0.8,\n 'weight_decay': 1e-4,\n 'conf_th':0.3,\n 'debug': 0}\nonline_config['output_size'] = online_config['template_size'] - max(online_config['conv1_ksizes']) - sum(online_config['conv_ksizes']) + 1 + len(online_config['conv_ksizes'])\n\ndef run_SiamRPN_OPF(seq, rp, bSaveImage):\n os.environ['CUDA_VISIBLE_DEVICES']=auto_select_gpu()\n config_name = \"SiamRPN_ftall\"\n CHECKPOINT = '/home/lab-xiong.jiangfeng/Projects/SiameseRPN/Logs/%s/track_model_checkpoints/%s'%(config_name, config_name)\n logging.info('Evaluating {}...'.format(CHECKPOINT))\n \n # Read configurations from json\n model_config, _, track_config = load_cfgs(CHECKPOINT)\n track_config['log_level'] = 0 # Skip verbose logging for speed\n\n \n np.random.seed(1234)\n tf.set_random_seed(1234)\n g = tf.Graph()\n \n with g.as_default():\n model = get_model(model_config['Model'])(model_config=model_config,mode='inference')\n model.build(reuse=tf.AUTO_REUSE)\n model.online_net = OnlineNet(online_config,is_training=True, reuse=False)\n model.online_valnet = OnlineNet(online_config,is_training=False, reuse=True)\n global_variables_init_op = tf.global_variables_initializer()\n \n gpu_options = tf.GPUOptions(allow_growth=True)\n sess_config = tf.ConfigProto(gpu_options=gpu_options)\n sess_config.gpu_options.per_process_gpu_memory_fraction = 0.2\n\n with tf.Session(graph=g, config=sess_config) as sess:\n sess.run(global_variables_init_op)\n model.restore_weights_from_checkpoint(sess, 605000)\n tracker = OnlineTracker(sess, model, track_config, online_config, show_video=0)\n\n tic = time.clock()\n frames = seq.s_frames\n init_rect = seq.init_rect\n x, y, width, height = init_rect # OTB format\n init_bb = Rectangle(x - 1, y - 1, width, height)\n trajectory_py = tracker.track(init_bb, frames, bSaveImage, rp)\n #print(trajectory_py)\n trajectory = [Rectangle(val.x + 1, val.y + 1, val.width, val.height) for val in\n trajectory_py] # x, y add one to match OTB format\n duration = time.clock() - tic\n\n result = dict()\n result['res'] = trajectory\n result['type'] = 'rect'\n result['fps'] = round(seq.len / duration, 3)\n return result\n", "sub_path": "benchmark/OTB100_bechmark/scripts/bscripts/run_SiamRPN_OPF.py", "file_name": "run_SiamRPN_OPF.py", "file_ext": "py", "file_size_in_byte": 3053, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.insert", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 36, "usage_type": "attribute"}, {"api_name": "utils.misc_utils.auto_select_gpu", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.misc_utils.load_cfgs", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 46, "usage_type": "attribute"}, {"api_name": "tensorflow.set_random_seed", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.Graph", "line_number": 48, "usage_type": "call"}, {"api_name": "configs.get_model", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.AUTO_REUSE", "line_number": 52, "usage_type": "attribute"}, {"api_name": "inference.OnlineTracker.OnlineNet", "line_number": 53, "usage_type": "call"}, {"api_name": "inference.OnlineTracker.OnlineNet", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.GPUOptions", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 61, "usage_type": "call"}, {"api_name": "inference.OnlineTracker.OnlineTracker", "line_number": 64, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 66, "usage_type": "call"}, {"api_name": "utils.infer_utils.Rectangle", "line_number": 70, "usage_type": "call"}, {"api_name": "utils.infer_utils.Rectangle", "line_number": 73, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "40917683", "text": "import numpy as np\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nimport matplotlib.pyplot as plt\r\n\r\n# определяет, нарисован + или -\r\ndata = np.array([[0, 1, 0, 1, 1, 1, 0, 1, 0], # +\r\n [0, 1, 0, 1, 1, 1, 0, 1, 0], # +\r\n [0, 0, 0, 1, 1, 1, 0, 0, 0], # -\r\n [0, 0, 0, 1, 1, 1, 0, 0, 0], # -\r\n [0, 1, 0, 1, 1, 1, 0, 1, 0], # +\r\n [0, 0, 0, 1, 1, 1, 0, 0, 0], # -\r\n [0, 0, 0, 1, 1, 1, 0, 0, 0], # -\r\n [0, 1, 0, 1, 1, 1, 0, 1, 0], # +\r\n ])\r\nlabel = np.array([1, 1, 0, 0, 1, 0, 0, 1])\r\ntrain_data = data[:4]\r\ntrain_label = label[:4]\r\n\r\ntest_data = data[4:]\r\ntest_label = label[4:]\r\n\r\nmodel = keras.Sequential()\r\nmodel.add(keras.layers.Dense(16, input_shape=[9]))\r\nmodel.add(keras.layers.Dense(64, activation=tf.nn.sigmoid))\r\nmodel.add(keras.layers.Dense(128, activation=tf.nn.relu))\r\nmodel.add(keras.layers.Dense(1, activation='sigmoid'))\r\n\r\nmodel.compile(optimizer=tf.train.AdamOptimizer(),\r\n loss='mse',\r\n metrics=['accuracy'])\r\n\r\nmodel.summary()\r\nhistory = model.fit(train_data,\r\n train_label,\r\n batch_size=2,\r\n epochs=100,\r\n validation_data=(test_data, test_label),\r\n verbose=1)\r\n\r\ntest_loss, test_acc = model.evaluate(test_data, test_label)\r\nprint('\\nТочность на проверочных данных', test_acc)\r\npredictions = model.predict(test_data)\r\nprint(predictions)\r\nhistory_dict = history.history\r\nhistory_dict.keys()\r\n\r\nacc = history.history['acc']\r\nval_acc = history.history['val_acc']\r\nloss = history.history['loss']\r\nval_loss = history.history['val_loss']\r\n\r\nepochs = range(1, len(acc) + 1)\r\n\r\n# \"bo\" означает \"blue dot\", синяя точка\r\nplt.plot(epochs, loss, 'bo', label='Потери обучения')\r\n# \"b\" означает \"solid blue line\", непрерывная синяя линия\r\nplt.plot(epochs, val_loss, 'b', label='Потери проверки')\r\nplt.title('Потери во время обучения и проверки')\r\nplt.xlabel('Эпохи')\r\nplt.ylabel('Потери')\r\nplt.legend()\r\n\r\nplt.show()\r\n\r\nplt.clf() # Очистим график\r\nacc_values = history_dict['acc']\r\nval_acc_values = history_dict['val_acc']\r\n\r\nplt.plot(epochs, acc, 'bo', label='Точность обучения')\r\nplt.plot(epochs, val_acc, 'b', label='Точность проверки')\r\nplt.title('Точность во время обучения и проверки')\r\nplt.xlabel('Эпохи')\r\nplt.ylabel('Точность')\r\nplt.legend()\r\nplt.show()\r\n", "sub_path": "myNet.py", "file_name": "myNet.py", "file_ext": "py", "file_size_in_byte": 2687, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.array", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 23, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 24, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 25, "usage_type": "name"}, {"api_name": "tensorflow.nn", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 26, "usage_type": "name"}, {"api_name": "tensorflow.nn", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 27, "usage_type": "name"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 29, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}]} +{"seq_id": "83533574", "text": "import asyncio\nimport discord\nimport re\nimport datetime\nimport youtube_dl\nimport os\nimport traceback\n\nif not discord.opus.is_loaded():\n discord.opus.load_opus('libopus-0.dll')\n\ntry:\n with open('blacklist.txt') as f:\n blacklist = f.readlines()\n for i, item in enumerate(blacklist):\n whitelist[i] = item.rstrip()\n with open('whitelist.txt') as f:\n whitelist = f.readlines()\n for i, item in enumerate(whitelist):\n whitelist[i] = item.rstrip()\n with open('options.txt') as f:\n options = f.readlines()\n for i, item in enumerate(options):\n options[i] = item.rstrip()\nexcept:\n print('one of the text files was deleted, reinstall')\n\nsavedir = \"playlist\"\nif not os.path.exists(savedir):\n os.makedirs(savedir)\n \nMAX_VIDEO_LENGTH = 15*60\ndirective = 'none'\nisPlaying = False\nfirstTime = True\n\nownerID = options[4]\nskipsRequired = int(options[5])\nskipCount = 0\nskipperlist = []\ntimeSinceLast = 0\n\nplaylist = []\ncurrentlyPlaying = ''\n\nhelpmessage = '`!play [youtube link]` will allow me to play a new song or add it to the queue.\\n`!play playlist` will print out all links to youtube videos currently in the queue!\\n`!play skip` will make it skip to the next song after 4 people vote to skip the current one!'\n\nchannel = 0\n\nclient = discord.Client()\n\n@client.async_event\ndef on_ready():\n print('Connected!')\n print('Username: ' + client.user.name)\n print('ID: ' + client.user.id)\n print('--Server List--')\n for server in client.servers:\n print(server.name)\n \n@client.async_event\ndef on_message(message):\n global directive\n global ownerID\n global firstTime\n global skipCount\n global channel\n global skipperlist\n deleteMyMsg = False\n\n if message.author == client.user:\n return\n\n if message.channel.is_private:\n yield from client.send_message(message.channel, 'You cannot use this bot in private messages.')\n\n if '!whatismyuserid' in message.content.lower():\n print(message.author.id)\n\n if '!whitelist' in message.content.lower() and message.author.id == ownerID:\n msg = message.content\n substrStart = msg.find('!whitelist') + 11\n msg = msg[substrStart: ]\n msg.strip()\n msg = re.sub('<|@|>', '', msg)\n f = open('whitelist.txt', 'a')\n f.write(msg + \"\\r\")\n f.close()\n whitelist.append(msg)\n\n elif '!blacklist' in message.content.lower() and message.author.id == ownerID:\n msg = message.content\n substrStart = msg.find('!blacklist') + 11\n msg = msg[substrStart: ]\n msg.strip()\n msg = re.sub('<|@|>', '', msg)\n f = open('blacklist.txt', 'a')\n f.write(msg + \"\\r\")\n f.close()\n blacklist.append(msg)\n\n elif '!playlist' in message.content.lower():\n print('they want playlist')\n if playlist :\n endmsg = getPlaylist()\n yield from client.send_message(message.channel,endmsg)\n else:\n yield from client.send_message(message.channel,'tits no playlist bitch')\n\n elif '!skip' in message.content.lower():\n if message.author.id == ownerID:\n yield from client.send_message(message.channel,'\\u26A0 `'+message.author.name+'` used his malicious fascist powers to skip song.')\n skipperlist = []\n skipCount = 0\n directive = 'skip'\n elif message.author.id not in skipperlist:\n skipperlist.append(message.author.id)\n skipCount+=1\n yield from client.send_message(message.channel,'\\u23E9 `'+message.author.name+'` wants to skip song `'+str(skipCount)+'/'+str(skipsRequired)+'`')\n else:\n print('already voted to skip')\n if skipCount >= skipsRequired:\n skipperlist = []\n skipCount = 0\n directive = 'skip'\n\n elif '!play' in message.content.lower():\n\n # Clean up the message\n msg = message.content\n msg2 = msg\n substrStart = msg.find('!play') + 6\n msg = msg[substrStart: ]\n msg.strip()\n addSong = False\n\n # No reason to do anything if they didn't type anything after \"play\"\n if len(msg.lower()) < 2:\n yield from client.send_message(message.channel, '\\u26A0 ' + message.author.name + ' is a fucking moron')\n return\n\n # Don't allow blacklisted users to use this command.\n if message.author.id in blacklist :\n print('no, blacklisted')\n\n # If the whitelist if active, #checkyourprivilege\n elif (options[2]=='1' and not is_long_member(message.author.joined_at)) and message.author.id not in whitelist:\n print('no, not whitelisted and new')\n\n # This is still here for some reason. \n elif msg.lower() == 'help':\n hotsmessage = yield from client.send_message(message.channel,helpmessage)\n deleteMyMsg = True\n\n # Allow owner to move the bot to his channel\n elif msg.lower() == 'move' and message.author.id == ownerID:\n yield from client.voice.disconnect()\n vce = yield from client.join_voice_channel(message.author.voice_channel)\n\n # Allow owner to tell the bot to leave voice\n elif msg.lower() == 'leave' and message.author.id == ownerID:\n yield from client.voice.disconnect()\n firstTime = True\n directive = 'none'\n\n # Default behaviour once play command is sent.\n else:\n channel = message.channel\n if firstTime is True:\n vce = yield from client.join_voice_channel(message.author.voice_channel)\n yield from client.send_message(message.channel, '\\uD83C\\uDFB6 `' + message.author.name + '` queued `' + msg + '`')\n addSong = True\n\n try:\n yield from client.delete_message(message)\n except:\n print('Couldn\\'t delete message for some reason')\n\n # Adding song is handled separately after command parsing because of how long it takes the program to search youtube\n if addSong:\n error = addSongToPlaylist(msg, message.author.name)\n print('Error: ' + error)\n if error == 'length':\n yield from client.send_message(message.channel, '\\u26A0 `' + message.author.name + '` tried to queue a song longer than 15 minutes like the idiot he is.')\n elif error == 'dunno':\n yield from client.send_message(message.channel, '\\u26A0 `' + message.author.name + '` tried to queue a song with a broken link. Nice.')\n firstTime = False\n\n # Wait for a bit before deleting my message\n if deleteMyMsg is True:\n deleteMyMsg = False\n yield from asyncio.sleep(4)\n yield from client.delete_message(hotsmessage)\n\ndef is_long_member(dateJoined):\n convDT = dateJoined.date()\n today = datetime.date.today()\n optDays = options[1]\n margin = datetime.timedelta(days = int(options[1]))\n return today - margin > convDT\n\ndef addSongToPlaylist(unfixedsongURL, user):\n global firstTime\n\n # Clean up youtube links from junk\n if 'youtube' in unfixedsongURL:\n if '&' in unfixedsongURL:\n substrStart = unfixedsongURL.find('&')\n songURL = unfixedsongURL[ :substrStart]\n songURL.strip()\n else:\n songURL = unfixedsongURL\n else:\n songURL = unfixedsongURL\n\n options = {\n 'format': 'bestaudio/best',\n 'noplaylist' : True,\n 'nocheckcertificate' : True,\n 'default_search': 'auto',\n 'simulate': True}\n ydl = youtube_dl.YoutubeDL(options)\n\n try:\n info = ydl.extract_info(songURL, download=False)\n\n try:\n title = info['title']\n if info['duration'] > MAX_VIDEO_LENGTH: \n return 'length'\n playlist.append([songURL, title, user])\n\n except KeyError:\n print('youtube_dl gave me a list. probably a search then.')\n return addSongToPlaylist(info['entries'][0]['webpage_url'], user)\n except youtube_dl.utils.DownloadError:\n return 'dunno'\n except Exception as e:\n print(\"Can't access song! %s\\n\" % traceback.format_exc())\n return 'none'\n \n return 'none'\n\ndef getPlaylist():\n endmsg = ''\n count = 0\n for songs in playlist:\n count+=1\n endmsg = endmsg + str(count) + '. `' + songs[1] + '` requested by `' + songs[2] + '` \\n'\n return endmsg\n\ndef make_savepath(title, savedir=savedir):\n return os.path.join(savedir, \"%s.mp3\" % (title))\n\ndef download_song(song):\n options = {\n 'format': 'bestaudio/best',\n 'extractaudio' : True,\n 'audioformat' : \"mp3\",\n 'outtmpl': '%(id)s',\n 'noplaylist' : True,\n 'nocheckcertificate' : True}\n ydl = youtube_dl.YoutubeDL(options)\n\n try:\n title = re.sub(r'\\W+', '', song[1])\n savepath = make_savepath(title)\n except Exception as e:\n print(\"Can't access song! %s\\n\" % traceback.format_exc())\n return 'invalid'\n\n try:\n os.stat(savepath)\n return savepath\n except OSError:\n try:\n result = ydl.extract_info(song[0], download=True)\n os.rename(result['id'], savepath)\n return savepath\n except Exception as e:\n print (\"Can't download audio! %s\\n\" % traceback.format_exc())\n return 'invalid'\n\n@asyncio.coroutine\ndef playlist_update():\n global isPlaying\n global timeSinceLast\n global directive\n global firstTime\n global channel\n yield from client.wait_until_ready()\n count = 0\n time = 0\n while count!= -1:\n print('Ding! Directive: ' + directive)\n if isPlaying is False and firstTime is False:\n if playlist:\n vce = client.voice\n nextSong = playlist[0]\n try:\n path = download_song(nextSong)\n print('Downloaded, path is ' + path)\n if path != 'invalid':\n player = vce.create_ffmpeg_player(path, options='''-filter:a \"volume={}\"'''.format(0.2))\n player.start()\n isPlaying = True\n timeSinceLast = 0\n\n yield from client.send_message(channel, '\\u25B6 Now: `' + nextSong[1] + '` requested by `' + nextSong[2] + '`')\n if len(playlist) > 1: \n yield from client.send_message(channel, '\\u23E9 Next: `' + playlist[1][1] + '` requested by `' + playlist[1][2] + '`')\n\n while nextSong in playlist: playlist.remove(nextSong)\n directive = 'sleep'\n else:\n while nextSong in playlist: playlist.remove(nextSong)\n except:\n while nextSong in playlist: playlist.remove(nextSong)\n else:\n print(\"Playlist empty, waiting... \" + str(timeSinceLast))\n if timeSinceLast > 180:\n yield from client.voice.disconnect()\n firstTime = True\n directive = 'none'\n timeSinceLast = 0\n else:\n timeSinceLast += 1\n yield from asyncio.sleep(1)\n directive = 'sleep'\n if directive == 'sleep' or directive == 'skip':\n try:\n print('sleep/skip')\n cnt = 0\n while directive!='skip' and player.is_playing():\n cnt+=1\n yield from asyncio.sleep(1)\n player.stop()\n isPlaying = False\n directive = \"none\"\n except UnboundLocalError:\n directive = \"none\"\n else:\n yield from asyncio.sleep(1)\n\nloop = asyncio.get_event_loop()\ntry:\n loop.create_task(playlist_update())\n loop.run_until_complete(client.login(options[0], options[1]))\n loop.run_until_complete(client.connect())\nexcept Exception:\n loop.run_until_complete(client.close())\nfinally:\n loop.close()", "sub_path": "MusicBot.py", "file_name": "MusicBot.py", "file_ext": "py", "file_size_in_byte": 12173, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "discord.opus.is_loaded", "line_number": 9, "usage_type": "call"}, {"api_name": "discord.opus", "line_number": 9, "usage_type": "attribute"}, {"api_name": "discord.opus.load_opus", "line_number": 10, "usage_type": "call"}, {"api_name": "discord.opus", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 30, "usage_type": "call"}, {"api_name": "discord.Client", "line_number": 50, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 85, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 96, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 192, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 197, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 197, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 199, "usage_type": "call"}, {"api_name": "youtube_dl.YoutubeDL", "line_number": 222, "usage_type": "call"}, {"api_name": "youtube_dl.utils", "line_number": 236, "usage_type": "attribute"}, {"api_name": "traceback.format_exc", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 253, "usage_type": "call"}, {"api_name": "os.path", "line_number": 253, "usage_type": "attribute"}, {"api_name": "youtube_dl.YoutubeDL", "line_number": 263, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 266, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 269, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 273, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 278, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 281, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 328, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 336, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 343, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 284, "usage_type": "attribute"}, {"api_name": "asyncio.get_event_loop", "line_number": 345, "usage_type": "call"}]} +{"seq_id": "469937447", "text": "from collections import defaultdict\n\n\nclass Dep:\n\n def __init__(self):\n self.dep_map = defaultdict(set)\n\n def add(self, key, deps):\n self.dep_map[key] |= set(deps)\n\n def deps(self, key, out=None):\n if out is None:\n out = set()\n final = True\n else:\n final = False\n\n for k in self.dep_map.get(key, {}):\n if k not in out:\n out.add(k)\n self.deps(k, out)\n\n # don't list self-references\n if final:\n out -= {key}\n\n return out\n", "sub_path": "kata20190424/dependencies.py", "file_name": "dependencies.py", "file_ext": "py", "file_size_in_byte": 569, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.defaultdict", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "207165891", "text": "import pytest\nimport six\n\nfrom cmdtree import parser\nfrom cmdtree.exceptions import ArgumentParseError\n\n\ndef mk_obj(property_dict):\n class TestObject(object):\n pass\n obj = TestObject()\n for key, value in six.iteritems(property_dict):\n setattr(obj, key, value)\n return obj\n\n\n@pytest.fixture()\ndef aparser():\n from cmdtree.parser import AParser\n return AParser()\n\n\n@pytest.fixture()\ndef test_func():\n def func():\n return \"result\"\n return func\n\n\n@pytest.mark.parametrize(\n \"p_dict, expected\",\n (\n ({\"_k\": \"v\", \"k\": \"v\"}, {\"k\": \"v\"}),\n ({\"__k\": \"v\", \"k\": \"v\"}, {\"k\": \"v\"}),\n ({\"k1\": \"v\", \"k\": \"v\"}, {\"k\": \"v\", \"k1\": \"v\"}),\n )\n)\ndef test_vars_should_return_right_dict(p_dict, expected):\n obj = mk_obj(p_dict)\n assert parser.vars_(\n obj\n ) == expected\n\n\nclass TestAParser:\n def test_should_execute_func(self, aparser, test_func):\n aparser.add_cmd(\"test\", func=test_func)\n assert aparser.run([\"test\"]) == \"result\"\n\n def test_should_execute_child_cmd(self, aparser, test_func):\n parent = aparser.add_cmd(\"parent\")\n parent.add_cmd(\"child\", func=test_func)\n assert aparser.run(['parent', 'child']) == \"result\"\n\n @pytest.mark.parametrize(\n \"silent_exit, exception\",\n (\n (False, ArgumentParseError),\n (True, SystemExit)\n )\n )\n def test_should_parent_cmd_exit_or_raise_error(self, silent_exit, exception, test_func, aparser):\n from cmdtree.registry import env\n env.silent_exit = silent_exit\n parent = aparser.add_cmd(\"parent\")\n parent.add_cmd(\"child\", func=test_func)\n with pytest.raises(exception):\n aparser.run(['parent'])", "sub_path": "src/cmdtree/tests/test_parser.py", "file_name": "test_parser.py", "file_ext": "py", "file_size_in_byte": 1761, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "six.iteritems", "line_number": 12, "usage_type": "call"}, {"api_name": "cmdtree.parser.AParser", "line_number": 20, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 17, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 23, "usage_type": "call"}, {"api_name": "cmdtree.parser.vars_", "line_number": 40, "usage_type": "call"}, {"api_name": "cmdtree.parser", "line_number": 40, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 30, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 30, "usage_type": "attribute"}, {"api_name": "cmdtree.registry.env.silent_exit", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cmdtree.registry.env", "line_number": 64, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 67, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 55, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cmdtree.exceptions.ArgumentParseError", "line_number": 58, "usage_type": "name"}]} +{"seq_id": "141356650", "text": "# -*- coding: utf-8 -*-\nfrom sklearn.externals import joblib\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport numpy as np\n\n\ndef main():\n pca = joblib.load('pca.pkl')\n vocab = joblib.load('vocab.pkl')\n words = list(vocab.keys())\n vec = pca[vocab['Spain']] - pca[vocab['Madrid']] + pca[vocab['Athens']]\n top = [[0, 0] for i in range(10)]\n for index, t in enumerate(pca):\n if index % 10000 == 0:\n print(index)\n cs = cosine_similarity(np.reshape(vec, (1, -1)), np.reshape(t, (1, -1)))\n if cs > top[9][0]:\n top[9][0] = cs\n top[9][1] = words[index]\n top.sort()\n top.reverse()\n print(top)\n\ndef sim():\n pca = joblib.load('pca.pkl')\n vocab = joblib.load('vocab.pkl')\n norm = np.linalg.norm(pca, ord=2, axis=1)\n pca = pca / norm[:, np.newaxis]\n words = list(vocab.keys())\n vec = pca[vocab['Spain']] - pca[vocab['Madrid']] + pca[vocab['Athens']]\n top = [[0, 0] for i in range(10)]\n for index, t in enumerate(pca):\n if index % 10000 == 0:\n print(index)\n result = np.dot(pca[index], vec)\n if result > top[9][0]:\n top[9][0] = result\n top[9][1] = words[index]\n top.sort()\n top.reverse()\n print(top)\n\n\nif __name__ == '__main__':\n main()\n sim()\n\n# [[array([[0.86879466]]), 'Spain'], [array([[0.77158251]]), 'Italy'], [array([[0.76376002]]), 'Austria'],\n# [array([[0.76037371]]), 'Sweden'], [array([[0.74444281]]), 'France'], [array([[0.73941769]]), 'Netherlands'],\n# [array([[0.73808361]]), 'Germany'], [array([[0.7004954]]), 'Belgium'], [array([[0.69737437]]), 'Denmark'],\n# [array([[0.69105679]]), 'Télévisions']]\n", "sub_path": "hotate/chapter09/knock89.py", "file_name": "knock89.py", "file_ext": "py", "file_size_in_byte": 1721, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.externals.joblib.load", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 8, "usage_type": "name"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 9, "usage_type": "name"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 25, "usage_type": "name"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "451922088", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jan 31 14:48:30 2016\n\n@author: Kevin\n\"\"\"\nimport numpy\nimport time\nimport math\nimport random\nimport matplotlib.pyplot as plt\nfrom lmfit import Model\n\ndef createF(N):\n F = numpy.asarray([[0. for x in range(N)] for x in range(N)])\n for i in range(N):\n for j in range(N):\n F[i][j] = random.random()\n return F\ndef createT(N):\n T = numpy.asarray([[0. for x in range(N)] for x in range(N)])\n for i in range(N):\n for j in range(N):\n T[i][j] = random.random()\n return T\n\ndef simultaneous_mult(A,B):\n N = len(A)\n C = numpy.asarray([[0. for x in range(N)] for x in range(N)])\n for i in range(N):\n for j in range(N):\n part = 0.\n for p in range(N):\n for q in range(N):\n part = part + A[p][q]*B[p][i]*B[q][j]\n C[i][j]= part\n return C\n\ndef sequential_mult(A,B):\n N = len(A)\n C = numpy.asarray([[0. for x in range(N)] for x in range(N)])\n D = numpy.asarray([[0. for x in range(N)] for x in range(N)])\n for j in range(N):\n for p in range(N):\n part = 0.\n for q in range(N):\n part = part + A[p][q]*B[q][j]\n D[p][j]= part\n for j in range(N):\n for i in range(N):\n part = 0.\n for p in range(N):\n part = part + B[p][i]*D[p][j]\n C[i][j]=part\n return C\n \ndef exp_func(x, a, b):\n return a+b*x\n\ndef line_ratios(c,d):\n s = numpy.asarray([0. for x in range(len(c))])\n for i in range(len(c)):\n s[i]=c[i]/d[i]\n return s\n \ndef conv_ln(vec):\n N = len(vec)\n ln_vec = numpy.asarray([0. for x in range(N)])\n for i in range(N):\n ln_vec[i] = math.log(vec[i])\n return ln_vec\n \nrandom.seed()\nsizes = [x for x in range(64,129, 4)]\nN = len(sizes)\nbTimes = [0. for x in range(64,129, 4)]\nsTimes = [0. for x in range(64,129, 4)]\nbTimes = numpy.asarray(bTimes)\nsTimes = numpy.asarray(sTimes)\nsizes=numpy.asarray(sizes)\n\ncount = 0\nfor i in sizes:\n F = createF(i)\n T = createT(i)\n sizes[count] = i\n \n #Product with simultaneous summation\n bstart = time.time()\n E = simultaneous_mult(F,T)\n bTime = time.time() - bstart\n if (bTime > 0.001):\n bTimes[count] = bTime\n else:\n bTimes[count] = .001\n #End simultaneous summation\n \n #Product with sequential summation\n sstart = time.time()\n G = sequential_mult(F,T)\n sTime = time.time() - sstart\n if (sTime > 0.001):\n sTimes[count] = sTime\n else:\n sTimes[count] = .001\n #End sequential summation\n count = count + 1\n \nratio_times = line_ratios(bTimes,sTimes)\nratio_times = conv_ln(ratio_times)\nbTimes = conv_ln(bTimes)\nsTimes = conv_ln(sTimes)\nsizes = conv_ln(sizes)\n\nmodel = Model(exp_func)\n\nsim_pars = model.make_params(a = 0, b = 4)\nsim_model_fit = model.fit(bTimes, sim_pars, x=sizes)\nprint(sim_model_fit.fit_report())\nplt.figure()\nsim_model_fit.plot_fit()\nplt.xlabel('ln(N)')\nplt.ylabel('ln(Time)')\nplt.title('Matrix Transformation with simultaneous summation')\n\nseq_pars = model.make_params(a = 0, b = 3)\nseq_model_fit = model.fit(sTimes, seq_pars, x=sizes)\nprint(seq_model_fit.fit_report())\nplt.figure()\nseq_model_fit.plot_fit()\nplt.xlabel('ln(N)')\nplt.ylabel('ln(Time)')\nplt.title('Matrix Transformation with sequential summation')\n\nratio_pars = model.make_params(a = 0, b = 1)\nratio_model_fit = model.fit(ratio_times, ratio_pars, x=sizes)\nprint(ratio_model_fit.fit_report())\nplt.figure()\nseq_model_fit.plot_fit()\nplt.xlabel('ln(N)')\nplt.ylabel('ln(Time Simultaneous / Time Sequential)')\nplt.title('Scaling Comparison')", "sub_path": "HPC_2/HW1/VanSlyke-HW1-Source.py", "file_name": "VanSlyke-HW1-Source.py", "file_ext": "py", "file_size_in_byte": 3687, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.asarray", "line_number": 15, "usage_type": "call"}, {"api_name": "random.random", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 21, "usage_type": "call"}, {"api_name": "random.random", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 68, "usage_type": "call"}, {"api_name": "math.log", "line_number": 70, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 80, "usage_type": "call"}, {"api_name": "time.time", "line_number": 89, "usage_type": "call"}, {"api_name": "time.time", "line_number": 91, "usage_type": "call"}, {"api_name": "time.time", "line_number": 99, "usage_type": "call"}, {"api_name": "time.time", "line_number": 101, "usage_type": "call"}, {"api_name": "lmfit.Model", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}]} +{"seq_id": "347181779", "text": "import json\nimport numpy as np\n\nwith open('clusters.json') as f:\n data = json.load(f)\nn = data['n']\nprint(data['list'])\nprint(data['json_files'])\nS = np.zeros((len(data['clusters']),len(data['clusters'])))\nidxi=0\nidxj=0\nfor i in data['clusters']:\n for j in data['clusters']:\n A = n*(n-1)/2\n for ii in i:\n for jj in j:\n iii = set(ii)\n jjj = set(jj)\n n_ij = len(iii.intersection(jjj))\n if n_ij >0:\n A += n_ij*(n_ij-1)\n for ii in i:\n if len(ii) > 1:\n A -= len(ii)*(len(ii)-1)/2\n for jj in j:\n if len(jj) > 1:\n A -= len(jj)*(len(jj)-1)/2\n S[idxi,idxj] = 2*A/(n*(n-1))\n idxj += 1;\n idxj = 0\n idxi += 1;\nprint(np.array_str(S,precision=2))\n", "sub_path": "compare_clusters.py", "file_name": "compare_clusters.py", "file_ext": "py", "file_size_in_byte": 829, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.load", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array_str", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "238759622", "text": "import error\r\nfrom tkinter import Tk, Canvas, PhotoImage\r\nfrom tkinter.constants import *\r\nfrom sys import exit\r\n#from sys import exit\r\n#from numpy import square\r\nfrom math import floor, pow\r\nfrom time import perf_counter\r\nimport PIL.ImageTk\r\nimport PIL.Image\r\n\r\nclass main:\r\n\r\n def __init__(self, all_key, akey, keynum=2, \r\n show_plate=True, plate_change_color=True, plate_base_color='#00ffff',\r\n show_akey_kps=True, akey_change_color=False, akey_base_color=('black','black'), akey_text_size=(120,25),\r\n show_key_kps=True, key_change_color=True, \r\n show_key_button=True, key_button_effect=True,\r\n color_stepmul=(0.6, 1.5, 1.8), arc_center_mul=4, effct_mul=0.6, \r\n kn_base_color=('black','black'), kn_text_size=(40,40), kn_position=((200, 340), (300, 340)), kn_size=((75, 75),(75,75)),\r\n ####################################################################################\r\n show_lag=False, window_name='Kps_Sensor'):\r\n\r\n self.all_key, self.akey = all_key, akey\r\n self.keynum = keynum\r\n self.sp, self.pcc, self.pbc = show_plate, plate_change_color, plate_base_color\r\n self.sak, self.acc, self.abc, self.ats = show_akey_kps, akey_change_color, akey_base_color, akey_text_size\r\n self.skk, self.kcc, = show_key_kps, key_change_color\r\n self.skbt, self.kbte = show_key_button, key_button_effect\r\n self.knbc, self.knts, self.knp, self.kns = kn_base_color, kn_text_size, kn_position, kn_size\r\n ########################################################################################\r\n self.csm = color_stepmul\r\n self.acm = arc_center_mul\r\n self.em = effct_mul\r\n self.show_lag = show_lag\r\n\r\n self.frames = 0\r\n self.time = perf_counter()\r\n self.ptime = self.time\r\n self.hun_time = 0.0\r\n\r\n self.kbwin = Tk()\r\n try: self.kbwin.iconbitmap('pht/kps.ico')\r\n except: error.img_error('kps.ico')\r\n self.kbwin.title(window_name)\r\n self.kbwin.geometry('500x500')\r\n self.kbwin.resizable(width=False, height=False)\r\n self.kbwin.protocol('WM_DELETE_WINDOW', self.close_window)\r\n self.cv = Canvas(self.kbwin, width=500, height=500)\r\n\r\n self.arc = self.cv.create_arc(0, 0, 500, 500, width=0, fill=self.pbc, outline=self.pbc, start=240, extent=-0,)\r\n # open all the image\r\n self.key_image = []\r\n self.keyup_image = []\r\n self.keydown_image = []\r\n self.keyeffect_image = []\r\n self.dkey_image_file = ['']*10\r\n try: self.plate_image = PIL.Image.open('pht/bg.png').resize((500,500), PIL.Image.ANTIALIAS)\r\n except: error.img_error('bg.png')\r\n self.main_image_file = PIL.ImageTk.PhotoImage(self.plate_image)\r\n self.main_image = self.cv.create_image(250, 250, anchor='center', image=self.main_image_file)\r\n if self.skbt:\r\n for i in range(self.keynum):\r\n self.key_image += [self.cv.create_image(*self.knp[i], anchor='center',image='')]\r\n try: self.keyup_image += [PIL.Image.open('pht/key{}_up.png'.format(i+1)).resize(self.kns[i], PIL.Image.ANTIALIAS)]\r\n except: error.img_error('key{}_up.png'.format(i+1))\r\n try: self.keydown_image += [PIL.Image.open('pht/key{}_down.png'.format(i+1)).resize(self.kns[i], PIL.Image.ANTIALIAS)]\r\n except: error.img_error('key{}_down.png'.format(i+1))\r\n try: self.keyeffect_image += [PIL.Image.open('pht/key{}_effect.png'.format(i+1)).resize(self.kns[i], PIL.Image.ANTIALIAS)]\r\n except: error.img_error('key{}_effect.png'.format(i+1))\r\n\r\n if self.sak:\r\n self.KPS_text = self.cv.create_text(350, 250, anchor='e', text='', fill=self.abc[0], justify = CENTER, font=('Visitor TT2 (BRK)', self.ats[0], 'bold'))\r\n self.KPS_text1 = self.cv.create_text(370, 275, text='Loading',fill=self.abc[1],justify = CENTER, font=('Visitor TT2 (BRK)', self.ats[1]))\r\n if self.show_lag:\r\n self.cal_lag = self.cv.create_text(495, 495, anchor='se', text='', justify=RIGHT, font=('Calibri', 10))\r\n self.dis_lag = self.cv.create_text(495, 480, anchor='se', text='', justify=RIGHT, font=('Calibri', 10))\r\n if self.skk:\r\n self.key_text = []\r\n for i in range(self.keynum):\r\n self.key_text += [self.cv.create_text(*self.knp[i], text='', anchor='center',fill=self.knbc[i],justify=CENTER, font=('Visitor TT2 (BRK)', self.knts[i], 'bold'))]\r\n self.cv.pack()\r\n\r\n self.first = True\r\n\r\n def _cal_time(self):\r\n if self.frames == 0:\r\n self.time = perf_counter()\r\n self.frames += 1\r\n else:\r\n self.ptime = self.time\r\n self.time = perf_counter()\r\n dt = self.time - self.ptime\r\n self.hun_time += dt\r\n\r\n if self.frames <= 100:\r\n self.frames += 1\r\n else:\r\n self.hun_time -= (self.hun_time / 100)\r\n return self.hun_time\r\n \r\n def start(self):\r\n def pframe():\r\n self._cal_time()\r\n if self.all_key[1].frames < 100:\r\n pass\r\n else:\r\n if self.first:\r\n self.cv.itemconfigure(self.KPS_text1, text='Kps')\r\n self.first = False\r\n KPS, kkps, kp, kdp=self.akey.dKPS, [], [], []\r\n for i in range(self.keynum):\r\n kkps += [self.all_key[i].dKPS]\r\n kp += [self.all_key[i].position]\r\n kdp += [self.all_key[i].dposition]\r\n \r\n self.change_text(KPS, kkps)\r\n if self.sp:\r\n self.change_arc(KPS)\r\n if self.skbt:\r\n self.change_key(kp, kdp)\r\n\r\n if self.show_lag:\r\n self.Show_lag()\r\n self.kbwin.after(20, pframe)\r\n\r\n self.kbwin.after(0, pframe)\r\n self.kbwin.mainloop()\r\n\r\n def change_text(self, KPS, kkps):\r\n if self.sak:\r\n kps_str = '{:.1f}'.format(KPS)\r\n self.cv.itemconfigure(self.KPS_text, text=kps_str)\r\n if self.acc:\r\n self.cv.itemconfigure(self.KPS_text, fill=self.cal_color(KPS, self.keynum))\r\n if self.skk:\r\n for i in range(self.keynum):\r\n kps_str = '{:.1f}'.format(kkps[i])\r\n self.cv.itemconfigure(self.key_text[i], text=kps_str)\r\n if self.kcc:\r\n for i in range(self.keynum):\r\n self.cv.itemconfigure(self.key_text[i], fill=self.cal_color(kkps[i]))\r\n\r\n def change_arc(self, KPS):\r\n self.cv.itemconfigure(self.arc, extent=-self.cal_angle(KPS))\r\n if self.pcc:\r\n color = self.cal_color(KPS,self.keynum)\r\n self.cv.itemconfigure(self.arc, fill=color, outline=color)\r\n\r\n def cal_angle(self, KPS):\r\n center = self.acm * self.keynum\r\n if KPS <= center:\r\n angle = (KPS/center)*150\r\n else:\r\n angle = 300 - (150*pow(center/KPS, 2))\r\n return angle\r\n\r\n def cal_color(self, KPS, keynum=1):\r\n R, G, B = 0, 255, 255\r\n step1 = self.acm * keynum * self.csm[0]\r\n step2 = self.acm * keynum * self.csm[1]\r\n step3 = self.acm * keynum * self.csm[2]\r\n if KPS <= step1:\r\n B = floor(255 - pow(KPS/step1, 2)*255)\r\n elif KPS <= step2:\r\n B = 0\r\n R = floor(pow((KPS-step1)/(step2-step1), 2)*255)\r\n G = floor(255 - pow((KPS-step1)/(step2-step1), 2)*55)\r\n elif KPS <= step3:\r\n B = 0\r\n R = 255\r\n G = floor(200 - pow((KPS-step2)/(step3-step2), 2)*200)\r\n else:\r\n B = floor(255 - pow(step3/KPS, 2)*255)\r\n R = floor(155 + pow(step3/KPS, 2)*100)\r\n G = 0\r\n return self.RGB2hex(R, G, B)\r\n\r\n def change_key(self, keyp_args, keydp_args):\r\n for i in range(self.keynum):\r\n if self.kbte:\r\n if keyp_args[i]:\r\n dkey_image = PIL.Image.blend(self.keydown_image[i], self.keyeffect_image[i], keydp_args[i]*self.em)\r\n else:\r\n dkey_image = PIL.Image.blend(self.keyup_image[i], self.keyeffect_image[i], keydp_args[i]*self.em)\r\n else:\r\n dkey_image = self.keydown_image[i] if keyp_args[i] else self.keyup_image[i]\r\n\r\n self.dkey_image_file[i] = PIL.ImageTk.PhotoImage(dkey_image)\r\n self.cv.itemconfigure(self.key_image[i],image=self.dkey_image_file[i])\r\n\r\n def Show_lag(self):\r\n cal_lag = 0.0\r\n dis_lag = 0.0\r\n\r\n if self.all_key[0].frames <= 100:\r\n pass\r\n else:\r\n allkey_lag = 0.0\r\n for i in range(self.keynum):\r\n perkey_lag = (self.all_key[i].hun_time/100)-0.01\r\n allkey_lag += perkey_lag\r\n cal_lag = (allkey_lag/self.keynum)*1000\r\n\r\n if self.frames <= 100:\r\n pass\r\n else:\r\n dis_lag = ((self.hun_time/100)-0.02)*1000\r\n\r\n self.cv.itemconfigure(self.cal_lag, text='cal: {:.2f} ms'.format(cal_lag))\r\n self.cv.itemconfigure(self.dis_lag, text='dis: {:.2f} ms'.format(dis_lag))\r\n\r\n################################################\r\n def close_window(self):\r\n self.akey.is_openning = False\r\n exit()\r\n\r\n @staticmethod\r\n def RGB2hex(R=int, G=int, B=int):\r\n def check(colornum):\r\n if colornum > 255:\r\n return 255\r\n elif colornum < 0:\r\n return 0\r\n else:\r\n return colornum\r\n\r\n RGBhex = '#'\r\n Rstr = ('%x'%check(R)).zfill(2)\r\n Gstr = ('%x'%check(G)).zfill(2)\r\n Bstr = ('%x'%check(B)).zfill(2)\r\n RGBhex += (Rstr+Gstr+Bstr)\r\n return RGBhex\r\n\r\n", "sub_path": "display.py", "file_name": "display.py", "file_ext": "py", "file_size_in_byte": 10174, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.perf_counter", "line_number": 38, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 42, "usage_type": "call"}, {"api_name": "error.img_error", "line_number": 44, "usage_type": "call"}, {"api_name": "tkinter.Canvas", "line_number": 49, "usage_type": "call"}, {"api_name": "PIL.ImageTk.Image.open", "line_number": 58, "usage_type": "call"}, {"api_name": "PIL.ImageTk.Image", "line_number": 58, "usage_type": "attribute"}, {"api_name": "PIL.ImageTk", "line_number": 58, "usage_type": "name"}, {"api_name": "error.img_error", "line_number": 59, "usage_type": "call"}, {"api_name": "PIL.ImageTk.ImageTk.PhotoImage", "line_number": 60, "usage_type": "call"}, {"api_name": "PIL.ImageTk.ImageTk", "line_number": 60, "usage_type": "attribute"}, {"api_name": "PIL.ImageTk", "line_number": 60, "usage_type": "name"}, {"api_name": "PIL.ImageTk.Image.open", "line_number": 65, "usage_type": "call"}, {"api_name": "PIL.ImageTk.Image", "line_number": 65, "usage_type": "attribute"}, {"api_name": "PIL.ImageTk", "line_number": 65, "usage_type": "name"}, {"api_name": "error.img_error", "line_number": 66, "usage_type": "call"}, {"api_name": "PIL.ImageTk.Image.open", "line_number": 67, "usage_type": "call"}, {"api_name": "PIL.ImageTk.Image", "line_number": 67, "usage_type": "attribute"}, {"api_name": "PIL.ImageTk", "line_number": 67, "usage_type": "name"}, {"api_name": "error.img_error", "line_number": 68, "usage_type": "call"}, {"api_name": "PIL.ImageTk.Image.open", "line_number": 69, "usage_type": "call"}, {"api_name": "PIL.ImageTk.Image", "line_number": 69, "usage_type": "attribute"}, {"api_name": "PIL.ImageTk", "line_number": 69, "usage_type": "name"}, {"api_name": "error.img_error", "line_number": 70, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 88, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 92, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 155, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 164, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 164, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 167, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 167, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 168, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 168, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 172, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 172, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 174, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 174, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 175, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 175, "usage_type": "call"}, {"api_name": "PIL.ImageTk.Image.blend", "line_number": 183, "usage_type": "call"}, {"api_name": "PIL.ImageTk.Image", "line_number": 183, "usage_type": "attribute"}, {"api_name": "PIL.ImageTk", "line_number": 183, "usage_type": "name"}, {"api_name": "PIL.ImageTk.Image.blend", "line_number": 185, "usage_type": "call"}, {"api_name": "PIL.ImageTk.Image", "line_number": 185, "usage_type": "attribute"}, {"api_name": "PIL.ImageTk", "line_number": 185, "usage_type": "name"}, {"api_name": "PIL.ImageTk.ImageTk.PhotoImage", "line_number": 189, "usage_type": "call"}, {"api_name": "PIL.ImageTk.ImageTk", "line_number": 189, "usage_type": "attribute"}, {"api_name": "PIL.ImageTk", "line_number": 189, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 216, "usage_type": "call"}]} +{"seq_id": "571156500", "text": "from hashlib import sha256\nimport hmac\nimport binascii\n\nfrom six import text_type, binary_type\n\n\ndef convert_to_bytes(string):\n if string is None:\n return None\n if type(string) is text_type:\n return string.encode('ascii')\n elif type(string) is binary_type:\n return string\n else:\n raise TypeError(\"Must be a string or bytes object.\")\n\n\ndef convert_to_string(bytes):\n if bytes is None:\n return None\n if type(bytes) is text_type:\n return bytes\n elif type(bytes) is binary_type:\n return bytes.decode('ascii')\n else:\n raise TypeError(\"Must be a string or bytes object.\")\n\n\ndef truncate_or_pad(byte_string, size=None):\n if size is None:\n size = 32\n byte_array = bytearray(byte_string)\n length = len(byte_array)\n if length > size:\n return bytes(byte_array[:size])\n elif length < size:\n return bytes(byte_array + b\"\\0\"*(size-length))\n else:\n return byte_string\n\n\ndef generate_derived_key(key):\n return hmac_digest(b'macaroons-key-generator', key)\n\n\ndef hmac_digest(key, data):\n return hmac.new(\n key,\n msg=data,\n digestmod=sha256\n ).digest()\n\n\ndef sign_first_party_caveat(signature, predicate):\n return binascii.hexlify(hmac_digest(signature, predicate))\n\n\ndef sign_third_party_caveat(signature, verification_id, caveat_id):\n verification_id_hash = hmac_digest(signature, verification_id)\n caveat_id_hash = hmac_digest(signature, caveat_id)\n combined = verification_id_hash + caveat_id_hash\n return binascii.hexlify(hmac_digest(signature, combined))\n\n\ndef equals(val1, val2):\n \"\"\"\n Returns True if the two strings are equal, False otherwise.\n\n The time taken is independent of the number of characters that match.\n\n For the sake of simplicity, this function executes in constant time only\n when the two strings have the same length. It short-circuits when they\n have different lengths.\n \"\"\"\n if len(val1) != len(val2):\n return False\n result = 0\n for x, y in zip(val1, val2):\n result |= ord(x) ^ ord(y)\n return result == 0\n", "sub_path": "macaroons/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2132, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "six.text_type", "line_number": 11, "usage_type": "name"}, {"api_name": "six.binary_type", "line_number": 13, "usage_type": "name"}, {"api_name": "six.text_type", "line_number": 22, "usage_type": "name"}, {"api_name": "six.binary_type", "line_number": 24, "usage_type": "name"}, {"api_name": "hmac.new", "line_number": 48, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 51, "usage_type": "name"}, {"api_name": "binascii.hexlify", "line_number": 56, "usage_type": "call"}, {"api_name": "binascii.hexlify", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "206880160", "text": "import discord\nfrom discord.ext import commands\nimport urllib.parse\nimport os\ntheOwner = 194852876902727680\nfrom cogs.databases import check_blacklist, create_connection\ndatabase = os.getcwd()+r\"/db/database.db\"\n\nclass Support(commands.Cog):\n \"\"\"Commands used to give help\"\"\"\n\n def __init__(self,client):\n self.client = client\n self.conn = create_connection(database)\n \n @commands.group()\n async def support(self, ctx):\n \"\"\"Commands used to give help\"\"\"\n if check_blacklist(self.conn, ctx.author.id) != None:\n return\n if ctx.invoked_subcommand is None:\n await ctx.send(\"That's not a valid `support` command\")\n\n @support.command()\n async def root(self,ctx):\n if check_blacklist(self.conn, ctx.author.id) != None:\n return\n embed=discord.Embed(color=0xeea4f2)\n embed.set_image(url='https://images-ext-1.discordapp.net/external/KUr_9m9wLNNNNIo-9vxcyL36hB9bAhJwGNk8yp0Uhak/https/i.imgur.com/7PIvVjJ.png')\n await ctx.send(\"\",embed=embed)\n\n @support.command()\n async def writelock(self,ctx):\n if check_blacklist(self.conn, ctx.author.id) != None:\n return\n embed=discord.Embed(color=0xeea4f2)\n embed.add_field(name=\"Disable SD Card Write Protection\", value=\"This switch on the SD card should be pushed up, as the image below shows.\\n If it is write locked, applications may not work properly\")\n embed.set_image(url=\"https://cdn.discordapp.com/attachments/738767885572505641/743602826919542866/move-lock-switch-to-remove-write-protection.png\")\n await ctx.send(\"\",embed=embed)\n\n\n\ndef setup(client):\n client.add_cog(Support(client))\n", "sub_path": "cogs/support.py", "file_name": "support.py", "file_ext": "py", "file_size_in_byte": 1694, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.getcwd", "line_number": 7, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 9, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 9, "usage_type": "name"}, {"api_name": "cogs.databases.create_connection", "line_number": 14, "usage_type": "call"}, {"api_name": "cogs.databases.check_blacklist", "line_number": 19, "usage_type": "call"}, {"api_name": "discord.ext.commands.group", "line_number": 16, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 16, "usage_type": "name"}, {"api_name": "cogs.databases.check_blacklist", "line_number": 26, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 28, "usage_type": "call"}, {"api_name": "cogs.databases.check_blacklist", "line_number": 34, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "138573815", "text": "import json\nimport urlparse\n\nimport requests\nfrom structlog import get_logger\n\nfrom . import common\n\n\nclass CloudFlareDns(common.BaseDns):\n \"\"\"\n \"\"\"\n\n def __init__(\n self,\n CLOUDFLARE_DNS_ZONE_ID,\n CLOUDFLARE_EMAIL,\n CLOUDFLARE_API_KEY,\n CLOUDFLARE_API_BASE_URL='https://api.cloudflare.com/client/v4/'):\n\n self.CLOUDFLARE_DNS_ZONE_ID = CLOUDFLARE_DNS_ZONE_ID\n self.CLOUDFLARE_EMAIL = CLOUDFLARE_EMAIL\n self.CLOUDFLARE_API_KEY = CLOUDFLARE_API_KEY\n self.CLOUDFLARE_API_BASE_URL = CLOUDFLARE_API_BASE_URL\n self.dns_provider_name = 'cloudflare'\n self.HTTP_TIMEOUT = 65 # seconds\n\n if CLOUDFLARE_API_BASE_URL[-1] != '/':\n self.CLOUDFLARE_API_BASE_URL = CLOUDFLARE_API_BASE_URL + '/'\n else:\n self.CLOUDFLARE_API_BASE_URL = CLOUDFLARE_API_BASE_URL\n\n self.logger = get_logger(__name__).bind(\n dns_provider_name=self.dns_provider_name)\n\n def create_dns_record(self, domain_name, base64_of_acme_keyauthorization):\n self.logger.info('create_dns_record')\n\n # delete any prior existing DNS authorizations that may exist already\n self.delete_dns_record(\n domain_name=domain_name,\n base64_of_acme_keyauthorization=base64_of_acme_keyauthorization)\n url = urlparse.urljoin(\n self.CLOUDFLARE_API_BASE_URL,\n 'zones/{0}/dns_records'.format(self.CLOUDFLARE_DNS_ZONE_ID))\n headers = {\n 'X-Auth-Email': self.CLOUDFLARE_EMAIL,\n 'X-Auth-Key': self.CLOUDFLARE_API_KEY,\n 'Content-Type': 'application/json'\n }\n body = {\n \"type\": \"TXT\",\n \"name\": '_acme-challenge' + '.' + domain_name + '.',\n \"content\": \"{0}\".format(base64_of_acme_keyauthorization)\n }\n create_cloudflare_dns_record_response = requests.post(\n url,\n headers=headers,\n data=json.dumps(body),\n timeout=self.HTTP_TIMEOUT)\n self.logger.info(\n 'create_cloudflare_dns_record_response',\n status_code=create_cloudflare_dns_record_response.status_code,\n response=self.log_response(create_cloudflare_dns_record_response))\n\n def delete_dns_record(self, domain_name, base64_of_acme_keyauthorization):\n self.logger.info('delete_dns_record')\n\n class MockResponse(object):\n\n def __init__(self, status_code=200, content='mock-response'):\n self.status_code = status_code\n self.content = content\n super(MockResponse, self).__init__()\n\n def json(self):\n return {}\n\n delete_dns_record_response = MockResponse()\n headers = {\n 'X-Auth-Email': self.CLOUDFLARE_EMAIL,\n 'X-Auth-Key': self.CLOUDFLARE_API_KEY,\n 'Content-Type': 'application/json'\n }\n\n dns_name = '_acme-challenge' + '.' + domain_name\n list_dns_payload = {'type': 'TXT', 'name': dns_name}\n list_dns_url = urlparse.urljoin(\n self.CLOUDFLARE_API_BASE_URL,\n 'zones/{0}/dns_records'.format(self.CLOUDFLARE_DNS_ZONE_ID))\n\n list_dns_response = requests.get(\n list_dns_url,\n params=list_dns_payload,\n headers=headers,\n timeout=self.HTTP_TIMEOUT)\n\n for i in range(0, len(list_dns_response.json()['result'])):\n dns_record_id = list_dns_response.json()['result'][i]['id']\n url = urlparse.urljoin(self.CLOUDFLARE_API_BASE_URL,\n 'zones/{0}/dns_records/{1}'.format(\n self.CLOUDFLARE_DNS_ZONE_ID,\n dns_record_id))\n headers = {\n 'X-Auth-Email': self.CLOUDFLARE_EMAIL,\n 'X-Auth-Key': self.CLOUDFLARE_API_KEY,\n 'Content-Type': 'application/json'\n }\n delete_dns_record_response = requests.delete(\n url, headers=headers, timeout=self.HTTP_TIMEOUT)\n self.logger.info(\n 'delete_dns_record_response',\n status_code=delete_dns_record_response.status_code,\n response=self.log_response(delete_dns_record_response))\n", "sub_path": "sewer/dns_providers/cloudflare.py", "file_name": "cloudflare.py", "file_ext": "py", "file_size_in_byte": 4321, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "structlog.get_logger", "line_number": 33, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 43, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 56, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 59, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 88, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 92, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 100, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 109, "usage_type": "call"}]} +{"seq_id": "300212191", "text": "from sqlalchemy import Boolean, Integer\nfrom sqlalchemy import Table, Column, String, MetaData\n\n\nclass EventTriggerOutputDeviceMapping:\n def __init__(self, metadata: MetaData):\n self.eventTriggerOutputDeviceMapping = Table('EventTriggerOutputDeviceMapping', metadata,\n Column('EventTriggerId', Integer, primary_key=True,\n nullable=False),\n Column('DeviceAddress', String, primary_key=True, nullable=False),\n Column('IsEnable', Boolean)\n )\n", "sub_path": "Table/EventTriggerOutputDeviceMapping.py", "file_name": "EventTriggerOutputDeviceMapping.py", "file_ext": "py", "file_size_in_byte": 716, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlalchemy.MetaData", "line_number": 6, "usage_type": "name"}, {"api_name": "sqlalchemy.Table", "line_number": 7, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 8, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 10, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 10, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 11, "usage_type": "argument"}]} +{"seq_id": "533315406", "text": "import FCHD\nimport csv\nfrom multiprocessing import Pool\nimport numpy as np\n\n\ndef simul_simulate(n = 100, m = 200, T = 1):\n tps = np.linspace(0,T,m) # Discrétisation du temps\n B = np.zeros((n,m))\n a1 = np.zeros((n))\n a2 = np.zeros((n))\n for i in range(0,n):\n a1[i] = 0.05 * np.random.random()\n a2[i] = 0.05 * np.random.random()\n for j in range(0,m):\n B[i,j] = a1[i] * np.cos(tps[j] *\n 2 * np.pi) + a2[i] * np.sin(tps[j] * 2 * np.pi)\n return B;\n\nnp.random.seed(42)\nm = 40\nn = 100\nY = simul_simulate(n=n, m=m, T=1)\ntimes = np.linspace(0,1,m)\n\nW = np.zeros((4,m))\nfor j in range(0,m):\n W[0,j] = 0.025 * np.cos(times[j] *\n 2 * np.pi) + 0.025 * np.sin(times[j] * 2 * np.pi)\n W[1,j] = 0.025 * np.cos(times[j] *\n 2 * np.pi) + 0.025 * np.sin(times[j] * 2 * np.pi)+np.random.normal(0,0.005)\n W[2,j] = 0.055 * np.cos(times[j] *\n 2 * np.pi) + 0.055 * np.sin(times[j] * 2 * np.pi)\n W[3,j] = 0.055 * np.cos((times[j]+0.5) *\n 2 * np.pi+0.5) + 0.055 * np.sin((times[j]+0.5) * 2 * np.pi)\n\n\n\nl_sample = [20, 50, 100, 200, 500, 1000, 2000]\n\n\ndef boucle(l):\n score11 = np.zeros((200))\n score22 = np.zeros((200))\n score33 = np.zeros((200))\n score44 = np.zeros((200))\n np.random.seed(42)\n for k in range(100):\n Y = simul_simulate(n=l, m=m, T=1)\n FCHD1 = FCHD.FuncCHD(Y,times,)\n S1 = FCHD1.compute_depth(W)\n\n FCHD2 = FCHD.FuncCHD(Y,times, Subsampling=True, J=2)\n S2 = FCHD2.compute_depth(W)\n\n\n score11[k] = S1[0]\n score11[100+k] = S2[0]\n\n\n score22[k] = S1[1]\n score22[100+k] = S2[1]\n\n\n score33[k] = S1[2]\n score33[100+k] = S2[2]\n\n\n score44[k] = S1[3]\n score44[100+k] = S2[3]\n\n\n return score11, score22, score33, score44\n\nif __name__ == '__main__': # excute on main process only\n #with Pool(4) as p:\n p = Pool(7)\n result0 = p.map(boucle, l_sample) \n\n\nAA = np.zeros((200,len(l_sample)))\nBB = np.zeros((200,len(l_sample)))\nCC = np.zeros((200,len(l_sample)))\nDD = np.zeros((200,len(l_sample)))\n\nfor i in range(len(l_sample)):\n\tAA[:,i] = result0[i][0]\n\nfor i in range(len(l_sample)):\n\tBB[:,i] = result0[i][1]\n\nfor i in range(len(l_sample)):\n\tCC[:,i] = result0[i][2] \n\nfor i in range(len(l_sample)):\n\tDD[:,i] = result0[i][3] \t\n\nwith open(\"variance_deux_0_Jegal2.csv\", \"w\") as f_write:\n writer = csv.writer(f_write)\n writer.writerows(AA)\nwith open(\"variance_deux_1_Jegal2.csv\", \"w\") as f_write:\n writer = csv.writer(f_write)\n writer.writerows(BB)\nwith open(\"variance_deux_2_Jegal2.csv\", \"w\") as f_write:\n writer = csv.writer(f_write)\n writer.writerows(CC)\nwith open(\"variance_deux_3_Jegal2.csv\", \"w\") as f_write:\n writer = csv.writer(f_write)\n writer.writerows(DD)", "sub_path": "Supplementary/variance_deux_Jegal2.py", "file_name": "variance_deux_Jegal2.py", "file_ext": "py", "file_size_in_byte": 2784, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.linspace", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 47, "usage_type": "attribute"}, {"api_name": "FCHD.FuncCHD", "line_number": 50, "usage_type": "call"}, {"api_name": "FCHD.FuncCHD", "line_number": 53, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 99, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 102, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 105, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "156412342", "text": "import pymysql\r\ncxn=pymysql.connect(host='localhost',user='root',passwd='geforce460',charset='utf8')\r\ncur =cxn.cursor()\r\ncur.execute('use school_list')\r\nart_and_sci=['文科','理科']\r\n\r\nyears=range(2000,2016)\r\ncount=0\r\nschoolid_trans={'重庆':10028,'安徽':10008,'北京':10003,'福建':10024,'广东':10011,'广西':10012,'甘肃':10023,'贵州':10026,'河北':10016,'河南':10017,'湖南':10022,'湖北':10021,'海南':10019,'黑龙江':10031,'吉林':10004,'江苏':10014,'江西':10015,'辽宁':10027,'内蒙古':10002,'宁夏':10007,'青海':10030,'上海':10000,'四川':10005,'山西':10010,'山东':10009,'陕西':10029,'天津':10006,'新疆':10013,'西藏':10025,'云南':10001,'浙江':10018}\r\nfor province in schoolid_trans:\r\n for year in years:\r\n for AS in art_and_sci:\r\n cur.execute(\r\n \"select avg(low_grade),avg(avg_grade) from major_line3_test WHERE art_sci=%s AND province_id=%s\",\r\n (AS, schoolid_trans[province]))\r\n overall=cur.fetchone()\r\n alter=overall[0]-overall[1]\r\n try:\r\n cur.execute(\r\n \"select avg(low_grade),avg(avg_grade) from major_line3_test WHERE year=%s AND art_sci=%s AND province_id=%s\",\r\n (year, AS, schoolid_trans[province]))\r\n print(AS,year,province)\r\n simulate = cur.fetchone()\r\n change=simulate[0]-simulate[1]\r\n cur.execute(\"update major_line3_test set low_grade=avg_grade+%s WHERE low_grade is null and avg_grade is not null AND year=%s AND art_sci=%s AND province_id=%s\",(change,year, AS, schoolid_trans[province]))\r\n except:\r\n print(\"bug\")\r\n cur.execute(\r\n \"update major_line3_test set low_grade=avg_grade+%s WHERE low_grade is null and avg_grade is not null AND year=%s AND art_sci=%s AND province_id=%s\",\r\n (alter, year, AS, schoolid_trans[province]))\r\ncur.close()\r\ncxn.commit()\r\ncxn.close()", "sub_path": "source_code/data_collection/消除null2.py", "file_name": "消除null2.py", "file_ext": "py", "file_size_in_byte": 2018, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pymysql.connect", "line_number": 2, "usage_type": "call"}]} +{"seq_id": "56146110", "text": "# uncompyle6 version 3.6.7\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)]\n# Embedded file name: build/bdist.macosx-10.7-x86_64/egg/airflow/contrib/operators/gcs_to_s3.py\n# Compiled at: 2019-09-11 03:47:34\n# Size of source mod 2**32: 6121 bytes\nfrom airflow.contrib.hooks.gcs_hook import GoogleCloudStorageHook\nfrom airflow.contrib.operators.gcs_list_operator import GoogleCloudStorageListOperator\nfrom airflow.utils.decorators import apply_defaults\nfrom airflow.hooks.S3_hook import S3Hook\n\nclass GoogleCloudStorageToS3Operator(GoogleCloudStorageListOperator):\n \"\"\"GoogleCloudStorageToS3Operator\"\"\"\n template_fields = ('bucket', 'prefix', 'delimiter', 'dest_s3_key')\n ui_color = '#f0eee4'\n\n @apply_defaults\n def __init__(self, bucket, prefix=None, delimiter=None, google_cloud_storage_conn_id='google_cloud_storage_default', delegate_to=None, dest_aws_conn_id=None, dest_s3_key=None, dest_verify=None, replace=False, *args, **kwargs):\n (super(GoogleCloudStorageToS3Operator, self).__init__)(args, bucket=bucket, prefix=prefix, delimiter=delimiter, google_cloud_storage_conn_id=google_cloud_storage_conn_id, delegate_to=delegate_to, **kwargs)\n self.dest_aws_conn_id = dest_aws_conn_id\n self.dest_s3_key = dest_s3_key\n self.dest_verify = dest_verify\n self.replace = replace\n\n def execute(self, context):\n files = super(GoogleCloudStorageToS3Operator, self).execute(context)\n s3_hook = S3Hook(aws_conn_id=(self.dest_aws_conn_id), verify=(self.dest_verify))\n if not self.replace:\n bucket_name, prefix = S3Hook.parse_s3_url(self.dest_s3_key)\n existing_files = s3_hook.list_keys(bucket_name, prefix=prefix)\n existing_files = existing_files if existing_files is not None else []\n existing_files = [file.replace(prefix, '', 1) for file in existing_files]\n files = list(set(files) - set(existing_files))\n else:\n if files:\n hook = GoogleCloudStorageHook(google_cloud_storage_conn_id=(self.google_cloud_storage_conn_id),\n delegate_to=(self.delegate_to))\n for file in files:\n file_bytes = hook.download(self.bucket, file)\n dest_key = self.dest_s3_key + file\n self.log.info('Saving file to %s', dest_key)\n s3_hook.load_bytes(file_bytes, key=dest_key,\n replace=(self.replace))\n\n self.log.info('All done, uploaded %d files to S3', len(files))\n else:\n self.log.info('In sync, no files needed to be uploaded to S3')\n return files", "sub_path": "pycfiles/apache_beam-2.20.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64/gcs_to_s3.cpython-36.py", "file_name": "gcs_to_s3.cpython-36.py", "file_ext": "py", "file_size_in_byte": 2738, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "airflow.contrib.operators.gcs_list_operator.GoogleCloudStorageListOperator", "line_number": 12, "usage_type": "name"}, {"api_name": "airflow.utils.decorators.apply_defaults", "line_number": 17, "usage_type": "name"}, {"api_name": "airflow.hooks.S3_hook.S3Hook", "line_number": 27, "usage_type": "call"}, {"api_name": "airflow.hooks.S3_hook.S3Hook.parse_s3_url", "line_number": 29, "usage_type": "call"}, {"api_name": "airflow.hooks.S3_hook.S3Hook", "line_number": 29, "usage_type": "name"}, {"api_name": "airflow.contrib.hooks.gcs_hook.GoogleCloudStorageHook", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "482642481", "text": "import pickle as pkl\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nos.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n\n\npath = './log_ptb/best/AMSGrad_bs{20}_hs{300}_lr{5.0e-04}/history.pkl'\ndf_amsgrad = pkl.load(open(path, 'rb'))\n\npath = './log_ptb/best/ADAM_bs{20}_hs{300}_lr{1.0e-03}/history.pkl'\ndf_adam = pkl.load(open(path, 'rb'))\n\npath = './log_ptb/best/RMSProp_bs{20}_hs{300}_lr{1.0e-03}/history.pkl'\ndf_rms = pkl.load(open(path, 'rb'))\n\npath = './log_ptb/best/THEOPOULA_bs{20}_hs{300}_lr{5.0e-01}_beta{1.0e+10}_eps{1.0e-01}/history.pkl'\ndf_theopoula = pkl.load(open(path, 'rb'))\n\n\nplt.figure(1)\n\n\n\nfor i, key in zip(range(1, 5), df_adam.keys()):\n plt.figure(i)\n plt.plot(df_amsgrad[key], label='AMSGRAD')\n plt.plot(df_adam[key], label='ADAM')\n plt.plot(df_rms[key], label='RMSProp')\n plt.plot(df_theopoula[key], label='TheoPoula')\n plt.legend()\n plt.title(key)\n plt.xlim([0, 100])\n\n\nprint('AMSGrad: test_loss -',np.array(df_amsgrad['test_loss']).min().item())\nprint('ADAM: test_loss -',np.array(df_adam['test_loss']).min().item())\nprint('RMSprop: test_loss -',np.array(df_rms['test_loss']).min().item())\nprint('THEOPOULA: test_loss -',np.array(df_theopoula['test_loss']).min().item())\nplt.show()\n\n", "sub_path": "plots_lstm.py", "file_name": "plots_lstm.py", "file_ext": "py", "file_size_in_byte": 1260, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 13, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 16, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "346329146", "text": "# standard packages\nimport pandas as pd\nimport numpy as np\nfrom math import sqrt\nfrom sklearn.metrics import mean_squared_error\nfrom statistics import mean\nfrom tqdm import tqdm\nimport os\nimport matplotlib.pyplot as plt \nplt.set_loglevel('error')\n\n# preprocessing\nfrom tools.airPLS import airPLS\nfrom tools.spectres import spectres\nfrom sklearn.preprocessing import normalize\n\n# modelling\nfrom sklearn.linear_model import Lasso, Ridge, ElasticNet, LinearRegression, OrthogonalMatchingPursuit\nfrom sklearn.cross_decomposition import PLSRegression\nfrom sklearn.decomposition import PCA, KernelPCA\nfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.svm import SVR\nfrom sklearn.pipeline import Pipeline\n\n'''\nby Cai Ytsma (cai@caiconsulting.co.uk)\nLast updated 16 August 2023\n\nHelper functions and classes used by other programs in auto-modelling.\n'''\n\n########################\n# STANDALONE FUNCTIONS #\n########################\n# check format of input .asc filename\ndef check_asc(filename):\n if filename[-4:] != '.asc':\n filename = filename + '.asc'\n\n return filename\n\n# check format of input .csv filename\ndef check_csv(filename):\n if filename[-4:] != '.csv':\n filename = filename + '.csv'\n\n return filename \n\n# convert y/n response to boolean\ndef make_bool(val):\n if val not in ['y','n']:\n return 'error'\n if val == 'y':\n return True\n elif val == 'n':\n return False\n\n# check if num is float (for coef plot)\ndef isfloat(num):\n try:\n float(num)\n return True\n except ValueError:\n return False\n \n# convert spectra df to np array for modelling\ndef convert_spectra(spectra):\n first_col = spectra.columns[0]\n if first_col != 'wave':\n cont = make_bool(input(f'Warning: convert_spectra assumes the first column is the wavelength axis and ignores it. The first column of your data is {first_col}. Continue? (y/n):'))\n if not cont:\n raise ValueError('Aborting')\n\n conv_spectra = np.array(spectra[spectra.columns[1:]].T)\n return conv_spectra\n\n# select spectra from df and convert to array for modelling\ndef select_spectra(spectra, sample_names):\n conv_spectra = np.array(spectra[sample_names].T)\n return conv_spectra\n\n# get min axis value for plotting\ndef get_min(value, buffer=0.1):\n if value > 0:\n value = 0\n else:\n value = value + (buffer * value)\n return value\n\n# get max axis value for plotting\ndef get_max(value, buffer=0.1):\n if value > 0:\n value = value + (buffer * value)\n else:\n value = 0\n return value\n\n# for choosing best parameter during CV\ndef get_first_local_minimum(li):\n min = li[0]\n for i in li[1:]:\n if i < min:\n min = i \n elif i > min:\n return min\n elif i == min:\n continue\n # in case the last point is the lowest\n return min\n\n# get user-input data folder\ndef get_data_folder():\n in_prompt = 'Folder path containing data: '\n data_folder = input(in_prompt)\n while not os.path.exists(data_folder):\n print(f'Error: path {data_folder} does not exist\\n')\n data_folder = input(in_prompt)\n all_files = os.listdir(data_folder)\n return data_folder, all_files\n\n# get user-input spectra path\ndef get_spectra_path(data_folder, all_files):\n spectra_prompt = 'Spectra filename: '\n spectra_file = check_csv(input(spectra_prompt))\n while spectra_file not in all_files:\n print(f'Error: file {spectra_file} not in data folder\\n')\n spectra_file = check_csv(input(spectra_prompt))\n spectra_path = os.path.join(data_folder, spectra_file)\n return spectra_path\n\n# get user-input metadata path\ndef get_meta_path(data_folder, all_files):\n meta_prompt = 'Metadata filename: '\n meta_file = check_csv(input(meta_prompt))\n while meta_file not in all_files:\n print(f'Error: file {meta_file} not in data folder\\n')\n meta_file = check_csv(input(meta_prompt))\n meta_path = os.path.join(data_folder, meta_file)\n return meta_path\n\n# get user-input output folder\ndef get_out_folder():\n out_prompt = 'Folder path to export results: '\n outpath = input(out_prompt)\n while not os.path.exists(outpath):\n print(f'Error: path {outpath} does not exist\\n')\n outpath = input(out_prompt)\n return outpath\n \n##########################################\n\nclass Preprocess():\n \n '''\n Functions used to preprocess spectra\n\n \n RESAMPLE SPECTRA\n '''\n\n # resample spectra to given axis\n def resample_to_match(spectra, spectra_to_match = None):\n\n spectra_to_resample = spectra.copy()\n \n # using Spectres package\n ## https://spectres.readthedocs.io/en/latest/\n ## https://arxiv.org/pdf/1705.05165v1.pdf\n\n if spectra_to_match is None:\n print('Resampling to SuperCam -2px shifted wavelength axis by default')\n spectra_to_match = pd.read_csv('data\\\\SuperCam_cal_shift-2pix_axis.csv')\n\n # if input is a dataframe\n if isinstance(spectra_to_match,pd.DataFrame):\n if 'wave' not in spectra_to_match.columns:\n print('Axis to match needs \"wave\" header')\n return\n new_axis = spectra_to_match['wave'].to_numpy()\n\n # if input is an array\n elif isinstance(spectra_to_match,np.ndarray):\n new_axis = spectra_to_match\n\n else:\n print('Spectra to match must either be a dataframe with \"wave\" column, or a numpy array of values')\n return\n\n old_axis = spectra_to_resample['wave'].to_numpy()\n\n if list(new_axis) == list(old_axis):\n print('Spectral axes already matched')\n return\n\n spectra_to_resample.drop('wave', axis=1, inplace=True)\n old_spectra = spectra_to_resample.T.to_numpy()\n\n new_spectra = spectres(new_axis, old_axis, old_spectra, fill=0, verbose=False)\n new_spectra = pd.DataFrame(new_spectra).T\n new_spectra.columns = spectra_to_resample.columns\n new_spectra.insert(0,'wave',new_axis)\n\n return new_spectra\n\n # resample uniformly to minimum step size\n def resample_to_min_step(spectra):\n \n spectra_to_resample = spectra.copy()\n\n if 'wave' not in spectra_to_resample.columns:\n print('Input spectra must have \"wave\" axis column')\n return\n\n axis = spectra_to_resample['wave'].to_numpy()\n\n # get step sizes\n step_set = set()\n for i in np.arange(len(wave))[:-1]:\n current = wave[i]\n next = wave[i+1]\n step = next-current\n step_set.add(step)\n\n # get minimum\n min_step = min(step_set)\n\n # populate new axis with this step size\n min_step_axis = np.arange(start = axis[0], stop = axis[-1]+min_step, step=min_step)\n\n # resample spectra to match this\n resampled_spectra = Resample.resample_to_match(spectra_to_resample, min_step_axis)\n return resampled_spectra\n\n '''\n BASELINE REMOVAL\n '''\n\n # airPLS baseline removal\n ## recommend that resample to min step size first ##\n def AirPLS(spectra,\n l = 100):\n\n spectra_to_blr = spectra.copy()\n \n if spectra_to_blr.isnull().values.any():\n raise ValueError('The spectra contains NA values - remove and rerun')\n \n if spectra_to_blr.columns[0] != 'wave':\n raise ValueError('This function needs the first column to be the axis, \"wave\"')\n\n spec_list = []\n\n for column in spectra_to_blr.columns[1:]:\n spectrum = spectra_to_blr[column]\n bl = airPLS(spectrum, lambda_ = 1)\n blr_spectrum = spectrum - bl\n blr_spectrum = blr_spectrum.tolist()\n spec_list.append(blr_spectrum)\n\n blr_spectra = pd.DataFrame(spec_list).T\n blr_spectra.columns = spectra_to_blr.columns[1:]\n blr_spectra.insert(0, 'wave', spectra_to_blr['wave'])\n\n return blr_spectra\n \n '''\n NORMALIZATION\n '''\n\n # normalize each df subset of data, then concatenate\n def normalize_regions(df_list: list,\n method = 'l1'):\n\n count = 0\n\n # default, but leaving option open for other methods\n if method == 'l1':\n def normalization(array):\n return (array/sum(array))\n else:\n print('Method not defined')\n return\n\n for df in df_list:\n spectra_list = []\n\n # first, add wavelength\n wave = list(df['wave'])\n spectra_list.append(wave)\n\n # get names\n names = df.columns\n\n for sample in df.columns[1:]:\n # convert spectrum to array\n spectrum = df[sample].T.to_numpy()\n # normalize spectrum\n norm_spectrum = normalization(spectrum)\n # add to list\n spectra_list.append(norm_spectrum)\n\n # then, make df or add to df\n if count == 0:\n normed_dataset = pd.DataFrame(spectra_list).T\n normed_dataset.columns = names\n\n else:\n df_to_add = pd.DataFrame(spectra_list).T\n df_to_add.columns = names\n\n normed_dataset = pd.concat([normed_dataset, df_to_add], ignore_index=True)\n\n count+=1\n\n return normed_dataset\n\n # normalize by SuperCam method\n def norm5_SC(spectra):\n \n spectra_tonorm = spectra.copy()\n\n # limits from Anderson et al. 2021, Table 1.\n # https://doi.org/10.1016/j.sab.2021.106347\n\n uv = spectra_tonorm[(spectra_tonorm['wave'] >= 243.79) & (spectra_tonorm['wave'] <= 341.36)].copy()\n vis = spectra_tonorm[(spectra_tonorm['wave'] >= 379.26) & (spectra_tonorm['wave'] <= 464.54)].copy()\n vnir_1 = spectra_tonorm[(spectra_tonorm['wave'] >= 537.57) & (spectra_tonorm['wave'] <= 619.82)].copy()\n vnir_2 = spectra_tonorm[(spectra_tonorm['wave'] >= 620.08) & (spectra_tonorm['wave'] <= 712.14)].copy()\n vnir_3 = spectra_tonorm[(spectra_tonorm['wave'] >= 712.17) & (spectra_tonorm['wave'] <= 852.77)].copy()\n\n df_list = [uv, vis, vnir_1, vnir_2, vnir_3]\n\n normed_spectra = Preprocess.normalize_regions(df_list)\n\n return normed_spectra\n\n # normalize by ChemCam method\n def norm3_CL(spectra):\n \n spectra_tonorm = spectra.copy()\n\n uv = spectra_tonorm[(spectra_tonorm['wave'] >= 246.68) & (spectra_tonorm['wave'] <= 338.42)].copy()\n vis = spectra_tonorm[(spectra_tonorm['wave'] >= 387.9) & (spectra_tonorm['wave'] <= 469.1)].copy()\n vnir = spectra_tonorm[(spectra_tonorm['wave'] >= 492.65) & (spectra_tonorm['wave'] <= 849.1)].copy()\n\n df_list = [uv, vis, vnir]\n\n normed_spectra = Preprocess.normalize_regions(df_list)\n\n return normed_spectra\n\n # normalize by SuperLIBS 10K method\n def norm3_SL_10K(spectra):\n \n spectra_tonorm = spectra.copy()\n\n uv = spectra_tonorm[(spectra_tonorm['wave'] >= 233.12) & (spectra_tonorm['wave'] <= 351.35)].copy()\n vis = spectra_tonorm[(spectra_tonorm['wave'] >= 370.16) & (spectra_tonorm['wave'] <= 479.07)].copy()\n vnir = spectra_tonorm[(spectra_tonorm['wave'] >= 498.14) & (spectra_tonorm['wave'] <= 859.44)].copy()\n\n df_list = [uv, vis, vnir]\n\n normed_spectra = Preprocess.normalize_regions(df_list)\n\n return normed_spectra\n\n # normalize by SuperLIBS 18K method\n def norm3_SL_18K(spectra):\n \n spectra_tonorm = spectra.copy()\n\n uv = spectra_tonorm[(spectra_tonorm['wave'] >= 233.12) & (spectra_tonorm['wave'] <= 351.35)].copy()\n vis = spectra_tonorm[(spectra_tonorm['wave'] >= 370.16) & (spectra_tonorm['wave'] <= 479.07)].copy()\n vnir = spectra_tonorm[(spectra_tonorm['wave'] >= 508.3) & (spectra_tonorm['wave'] <= 869.2)].copy()\n\n df_list = [uv, vis, vnir]\n\n normed_spectra = Preprocess.normalize_regions(df_list)\n\n return normed_spectra\n\n##########################################\n\nclass Format():\n \n '''\n Functions used to prepare input data for modelling\n '''\n\n def __init__(self, spectra, meta):\n \n self.spectra = spectra\n self.meta = meta\n\n # identify relevant fold column\n def get_fold_col(self, var):\n\n if f'{var}_Folds' in self.meta.columns:\n fold_col = f'{var}_Folds'\n elif 'Folds' in self.meta.columns:\n fold_col = 'Folds'\n else:\n raise ValueError(f\"Must either have an assigned '{var}_Folds' or general 'Folds' column\")\n\n return fold_col\n\n # convert data to dict of train/test dfs per fold\n def make_data_dict(self, var, fold_col, test_fold=None):\n \n # find minimum number of samples in the folds, \n # to specify model params later\n n_samples_list = []\n\n # remove test data if using it\n if test_fold is not None:\n temp_meta = self.meta[self.meta[fold_col] != test_fold].copy()\n else:\n temp_meta = self.meta.copy()\n\n all_folds = list(temp_meta[fold_col].unique())\n if -1 in all_folds:\n all_folds.remove(-1)\n\n data_dict = {}\n for fold in all_folds:\n\n # training data\n train_meta = temp_meta[(temp_meta[fold_col] != fold) &\n (temp_meta[fold_col] != -1)].reset_index(drop=True)\n X_train = select_spectra(self.spectra, train_meta.pkey)\n y_train = train_meta[var].values\n n_samples_list.append(len(y_train))\n\n # held-out data\n test_meta = temp_meta[temp_meta[fold_col] == fold].reset_index(drop=True)\n X_test = select_spectra(self.spectra, test_meta.pkey)\n y_test = test_meta[var].values\n n_samples_list.append(len(y_test))\n\n # add datasets to dictionary\n data_dict[fold] = {'train_spectra':X_train,\n 'train_metadata':y_train,\n 'test_spectra':X_test,\n 'test_metadata':y_test}\n\n min_samples = min(n_samples_list)\n return data_dict, min_samples\n\n # convert data to correct format for modelling\n def format_spectra_meta(self, var, fold_col, test_fold=None):\n if test_fold is not None:\n # training\n train_meta = self.meta[(~self.meta[fold_col].isin([-1, test_fold])) &\n (~self.meta[fold_col].isnull())]\n y_train = train_meta[var].values\n train_names = train_meta['pkey'].values\n X_train = select_spectra(self.spectra, train_names)\n\n # testing\n test_meta = self.meta[(self.meta[fold_col] == test_fold) &\n (~self.meta[fold_col].isnull())]\n y_test = test_meta[var].values\n test_names = test_meta['pkey'].values\n X_test = select_spectra(self.spectra, test_names)\n\n return train_names, X_train, y_train, test_names, X_test, y_test\n else:\n train_meta = self.meta[(self.meta[fold_col] != -1) &\n (~self.meta[fold_col].isnull())]\n y_train = train_meta[var].values\n train_names = train_meta['pkey'].values\n X_train = select_spectra(self.spectra, train_names)\n\n return train_names, X_train, y_train\n\n########################################## \n\nclass Model():\n \n '''\n Functions that optimize regression models\n '''\n \n def __init__(self, data_dict, hide_progress=False):\n self.data_dict = data_dict\n self.hide_progress = hide_progress\n \n # perform manual CV using data_dict and return RMSECV\n def run_CV(self, model):\n\n rmsep_list = []\n for fold in list(self.data_dict.keys()):\n\n # get data\n X_train = self.data_dict[fold]['train_spectra']\n X_test = self.data_dict[fold]['test_spectra']\n y_train = self.data_dict[fold]['train_metadata']\n y_test = self.data_dict[fold]['test_metadata']\n\n # run model\n model.fit(X_train, y_train)\n preds = model.predict(X_test)\n test_df = pd.DataFrame({\n 'actual': y_test.flatten().tolist(),\n 'pred' : preds.flatten().tolist()\n })\n rmsep = sqrt(mean_squared_error(test_df.actual, test_df.pred))\n rmsep_list.append(rmsep)\n\n rmsecv = mean(rmsep_list)\n return rmsecv\n \n # CV-optimize, return best model info\n def run_PLS(self, max_components):\n \n component_range = np.arange(start=2, stop=max_components+1, step=1)\n\n cv_dict = {}\n for n_components in tqdm(component_range, desc='component value', disable=self.hide_progress):\n # define model\n model = PLSRegression(n_components = n_components, scale=False)\n # run CV and get RMSE\n temp_rmsecv = Model.run_CV(self, model)\n # add results to dictionary\n cv_dict[temp_rmsecv] = n_components\n \n rmsecv = get_first_local_minimum(list(cv_dict.keys()))\n component = cv_dict[rmsecv]\n model = PLSRegression(n_components = component, scale=False)\n\n if self.hide_progress is False:\n print(f'\\tLowest RMSE-CV of {round(rmsecv,2)} obtained from {component}-component model')\n \n return component, rmsecv, model\n\n def run_LASSO(self, num_alphas):\n \n alpha_range = np.logspace(-10, 1, num_alphas)\n\n cv_dict = dict()\n for alpha in tqdm(alpha_range, desc='alpha value', disable=self.hide_progress):\n model = Lasso(alpha=alpha)\n temp_rmsecv = Model.run_CV(self, model)\n cv_dict[temp_rmsecv] = alpha\n\n rmsecv = get_first_local_minimum(list(cv_dict.keys()))\n alpha = cv_dict[rmsecv]\n model = Lasso(alpha=alpha)\n \n if self.hide_progress is False:\n print(f'\\tLowest RMSE-CV of {round(rmsecv,2)} obtained from model with an alpha of {round(alpha,5)}')\n \n return alpha, rmsecv, model\n \n def run_Ridge(self, num_alphas):\n \n alpha_range = np.logspace(-10, 1, num_alphas)\n \n cv_dict = dict()\n for alpha in tqdm(alpha_range, desc='alpha value', disable=self.hide_progress):\n model = Ridge(alpha=alpha)\n temp_rmsecv = Model.run_CV(self, model)\n cv_dict[temp_rmsecv] = alpha\n \n rmsecv = get_first_local_minimum(list(cv_dict.keys()))\n alpha = cv_dict[rmsecv]\n model = Ridge(alpha=alpha)\n \n if self.hide_progress is False:\n print(f'\\tLowest RMSE-CV of {round(rmsecv,2)} obtained from model with an alpha of {round(alpha,5)}')\n \n return alpha, rmsecv, model\n \n def run_ElasticNet(self, num_alphas):\n \n # suggested by documentation to skew to lasso\n ratio_range = [.1, .5, .7, .9, .95, .99, 1]\n # slightly raise min because takes longer\n alpha_range = np.logspace(-7, 1, num_alphas)\n\n cv_dict = dict()\n for ratio in tqdm(ratio_range, desc='L1 ratio', leave=False, disable=self.hide_progress):\n for alpha in tqdm(alpha_range, desc='alpha value', leave=False, disable=self.hide_progress):\n model = ElasticNet(alpha=alpha, l1_ratio=ratio)\n temp_rmsecv = Model.run_CV(self, model)\n cv_dict[temp_rmsecv] = [alpha, ratio]\n \n rmsecv = min(list(cv_dict.keys()))\n params = cv_dict[rmsecv]\n model = ElasticNet(alpha=params[0], l1_ratio=params[1])\n \n if self.hide_progress is False:\n print(f'\\tLowest RMSE-CV of {round(rmsecv,2)} obtained from model with an alpha of {round(params[0],5)} and an l1_ratio of {params[1]}')\n param = f'alpha={params[0]} l1_ratio={params[1]}'\n \n return param, rmsecv, model \n \n \n def run_SVR_linear(self, num_epsilons):\n \n # smaller range here\n epsilon_range = np.logspace(-4, 1, num_epsilons)\n\n cv_dict = dict()\n for epsilon in tqdm(epsilon_range, desc='epsilon value', disable=self.hide_progress):\n model = SVR(kernel='linear', epsilon=epsilon)\n temp_rmsecv = Model.run_CV(self, model)\n cv_dict[temp_rmsecv] = epsilon\n \n rmsecv = min(list(cv_dict.keys()))\n epsilon = cv_dict[rmsecv]\n model = SVR(kernel='linear', epsilon=epsilon)\n \n if self.hide_progress is False:\n print(f'\\tLowest RMSE-CV of {round(rmsecv,2)} obtained from model with an epsilon of {round(epsilon,5)}')\n \n return epsilon, rmsecv, model\n \n def run_SVR_poly(self, num_epsilons, poly_deg):\n \n print(f'Currently using a polynomial degree of {poly_deg}')\n \n epsilon_range = np.logspace(-4, 1, num_epsilons)\n\n cv_dict = dict()\n for epsilon in tqdm(epsilon_range, desc='epsilon value', disable=self.hide_progress):\n model = SVR(kernel='poly', degree=poly_deg, epsilon=epsilon)\n temp_rmsecv = Model.run_CV(self, model)\n cv_dict[temp_rmsecv] = epsilon\n \n rmsecv = min(list(cv_dict.keys()))\n epsilon = cv_dict[rmsecv]\n model = SVR(kernel='poly', degree=poly_deg, epsilon=epsilon)\n \n if self.hide_progress is False:\n print(f'\\tLowest RMSE-CV of {round(rmsecv,2)} obtained from model with an epsilon of {round(epsilon,5)}')\n \n return epsilon, rmsecv, model\n \n def run_PCR_linear(self):\n \n print('PCR-lin does not optimize')\n # removed component range because different thing\n model = Pipeline([('PCA', PCA()), ('linear', LinearRegression())])\n rmsecv = Model.run_CV(self, model)\n \n if self.hide_progress is False:\n print(f'\\tRMSE-CV of {round(rmsecv,2)} obtained from model')\n \n return 'NA', rmsecv, model\n \n def run_PCR_poly(self, poly_deg):\n \n print('PCR-py does not optimize')\n #print(f'Currently using a polynomial degree of {poly_deg}')\n\n pca = KernelPCA(kernel='poly', degree=poly_deg)\n model = Pipeline([('PCA',pca), ('linear', LinearRegression())])\n rmsecv = Model.run_CV(self, model)\n \n if self.hide_progress is False:\n print(f'\\tRMSE-CV of {round(rmsecv,2)} obtained from model')\n \n return 'NA', rmsecv, model\n \n def run_OMP(self):\n \n print('OMP does not optimize')\n model = OrthogonalMatchingPursuit()\n rmsecv = Model.run_CV(self, model)\n \n if self.hide_progress is False:\n print(f'\\tRMSE-CV of {round(rmsecv,2)} obtained from model')\n \n return 'NA', rmsecv, model\n \n def run_RF(self):\n \n feat_range = ['sqrt', 'log2'] # `None` took long\n\n cv_dict = dict()\n for feat in tqdm(feat_range, desc='max features', disable=self.hide_progress):\n model = RandomForestRegressor(max_features=feat)\n temp_rmsecv = Model.run_CV(self, model)\n cv_dict[temp_rmsecv] = feat\n \n rmsecv = min(list(cv_dict.keys()))\n feat = cv_dict[rmsecv]\n model = RandomForestRegressor(max_features=feat)\n \n if self.hide_progress is False:\n print(f'\\tLowest RMSE-CV of {round(rmsecv,2)} obtained from model with {feat} max features')\n \n return feat, rmsecv, model\n \n def run_GBR(self):\n \n feat_range = ['sqrt', 'log2'] # `None` took long\n\n cv_dict = dict()\n for feat in tqdm(feat_range, desc='max features', disable=self.hide_progress):\n model = GradientBoostingRegressor(random_state=0, max_features=feat)\n temp_rmsecv = Model.run_CV(self, model)\n cv_dict[temp_rmsecv] = feat\n \n rmsecv = min(list(cv_dict.keys()))\n feat = cv_dict[rmsecv]\n model = GradientBoostingRegressor(random_state=0, max_features=feat)\n \n if self.hide_progress is False:\n print(f'\\tRMSE-CV of {round(rmsecv,2)} obtained from model with {feat} max features')\n \n return feat, rmsecv, model\n \n def run_OLS(self):\n \n print('OLS does not optimize')\n model = LinearRegression()\n rmsecv = Model.run_CV(self, model)\n \n if self.hide_progress is False:\n print(f'\\tRMSE-CV of {round(rmsecv,2)} obtained from model')\n \n return 'NA', rmsecv, model\n \n def run_kNN(self, max_neighbors):\n\n if max_neighbors > 1:\n neighbor_range = np.arange(1,max_neighbors)\n else:\n neighbor_range = [1]\n \n weight_range = ['uniform','distance']\n\n cv_dict = dict()\n for neighbor in tqdm(neighbor_range, desc='# neighbors', disable=self.hide_progress):\n for weight in weight_range:\n model = KNeighborsRegressor(n_neighbors=neighbor, weights=weight)\n temp_rmsecv = Model.run_CV(self, model)\n cv_dict[temp_rmsecv] = [neighbor, weight]\n \n rmsecv = min(list(cv_dict.keys()))\n params = cv_dict[rmsecv]\n model = KNeighborsRegressor(n_neighbors=params[0], weights=params[1])\n \n if self.hide_progress is False:\n print(f'\\tLowest RMSE-CV of {round(rmsecv,2)} obtained from model with {round(params[0],5)} neighbors and {params[1]} weights')\n param = f'n_neighbors={params[0]} weights={params[1]}'\n \n return param, rmsecv, model\n \n########################################## \n \nclass Plot():\n \n '''\n Functions that plot model information\n '''\n \n # model coefficients overlaid over example spectrum\n def coeffs(df, spectrum, var, method, path):\n \n # add example spectrum to df\n df['spectrum'] = spectrum \n\n # check for and remove non-numeric channels\n all_n = len(df)\n df = df[df['wave'].map(isfloat)].reset_index(drop=True)\n df['wave'] = df['wave'].astype(float)\n new_n = len(df)\n n_removed = all_n - new_n\n if n_removed > 0:\n print(f'{n_removed} non-numeric channels removed from coefficient plot')\n\n # PARAMETERS\n color1 = 'black' # spectrum\n color2 = '#e1dd01' # coeffs fill\n color3 = '#e07b00' # coeffs outline + text\n\n fsize = 14 # font size\n dsize = 30 # dot size\n\n opacity1 = 0.6 # spectrum\n opacity2 = 0.8 # coeffs\n\n # drop 0s\n coef_df = df[df['coef']!=0].reset_index(drop=True)\n\n # make plot\n fig, ax = plt.subplots(figsize=(10,5))\n\n # spectrum\n ax.plot(df['wave'], \n df['spectrum'],\n color=color1, \n lw=2, \n alpha=opacity1)\n # coefficients\n ax2 = ax.twinx()\n ax2.scatter(coef_df['wave'], \n coef_df['coef'], \n alpha = opacity2, \n c=color2, \n marker='o',\n edgecolors=color3,\n s=dsize)\n\n ax.set_xlabel('Channel', fontsize=fsize)\n ax.set_ylabel('Intensity', fontsize=fsize)\n ax2.set_ylabel('Coefficient Weight', fontsize=fsize, color = color3)\n plt.title(var, size=fsize+2)\n \n # save plot\n plt.tight_layout()\n plt.savefig(f'{path}\\\\{var}_{method}_coefs_plot.jpg', dpi=600)\n plt.savefig(f'{path}\\\\{var}_{method}_coefs_plot.eps', dpi=600)\n plt.close()\n \n # predicted vs true scatter plot with 1:1 line\n def pred_true(df, var, method, type, rmse, adj_r2, path):\n \n actual_col = f'{var}_actual'\n pred_col = f'{var}_pred'\n\n # PARAMETERS\n size=14 # font size\n color = 'black' #'#3f997c' # dot color\n opacity = 0.6 # dot opacity\n\n # get plot limits for 1:1\n plt_max = get_max(max(max(df[actual_col].values), max(df[pred_col].values))) \n plt_min = get_min(min(min(df[actual_col].values), min(df[pred_col].values)))\n\n \n # get X limits\n x_min = get_min(min(df[actual_col].values))\n x_max = get_max(max(df[actual_col].values))\n \n # get \\y limits\n y_min = get_min(min(df[pred_col].values))\n y_max = get_max(max(df[pred_col].values))\n\n # make plot\n fig, ax = plt.subplots(figsize=(6,6))\n ax.scatter(df[actual_col], df[pred_col], c=color, alpha=opacity)\n ax.plot([plt_min,plt_max], [plt_min,plt_max], 'k--')\n ax.plot([plt_min,plt_max], [0,0], 'k')\n plt.xlim(x_min,x_max)\n plt.ylim(y_min, y_max)\n\n plt.title(f'{type} RMSE: {round(rmse, 2)} Adj. R2: {round(adj_r2, 2)}', fontsize=size)\n ax.set_ylabel(f'Predicted {var}', fontsize=size)\n ax.set_xlabel(f'Actual {var}', fontsize=size)\n\n # save plot\n plt.tight_layout()\n plt.savefig(f'{path}\\\\{var}_{method}_{type}_pred_true_plot.jpg', dpi=600)\n plt.savefig(f'{path}\\\\{var}_{method}_{type}_pred_true_plot.eps', dpi=600)\n plt.close()\n", "sub_path": ".ipynb_checkpoints/model_tools-checkpoint.py", "file_name": "model_tools-checkpoint.py", "file_ext": "py", "file_size_in_byte": 29491, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.set_loglevel", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 173, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 176, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 183, "usage_type": "attribute"}, {"api_name": "tools.spectres.spectres", "line_number": 199, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 229, "usage_type": "call"}, {"api_name": "tools.airPLS.airPLS", "line_number": 256, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 261, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 305, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 309, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 312, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 504, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 508, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 508, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 511, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 517, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 520, "usage_type": "call"}, {"api_name": "sklearn.cross_decomposition.PLSRegression", "line_number": 522, "usage_type": "call"}, {"api_name": "sklearn.cross_decomposition.PLSRegression", "line_number": 530, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 539, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 542, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Lasso", "line_number": 543, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Lasso", "line_number": 549, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 558, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 561, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Ridge", "line_number": 562, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Ridge", "line_number": 568, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 580, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 583, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 584, "usage_type": "call"}, {"api_name": "sklearn.linear_model.ElasticNet", "line_number": 585, "usage_type": "call"}, {"api_name": "sklearn.linear_model.ElasticNet", "line_number": 591, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 603, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 606, "usage_type": "call"}, {"api_name": "sklearn.svm.SVR", "line_number": 607, "usage_type": "call"}, {"api_name": "sklearn.svm.SVR", "line_number": 613, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 624, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 627, "usage_type": "call"}, {"api_name": "sklearn.svm.SVR", "line_number": 628, "usage_type": "call"}, {"api_name": "sklearn.svm.SVR", "line_number": 634, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 645, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 645, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 645, "usage_type": "call"}, {"api_name": "sklearn.decomposition.KernelPCA", "line_number": 658, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 659, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 659, "usage_type": "call"}, {"api_name": "sklearn.linear_model.OrthogonalMatchingPursuit", "line_number": 670, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 683, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 684, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 690, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 702, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingRegressor", "line_number": 703, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingRegressor", "line_number": 709, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 719, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 730, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 737, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsRegressor", "line_number": 739, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsRegressor", "line_number": 745, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 791, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 791, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 812, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 812, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 815, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 815, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 816, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 816, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 817, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 817, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 818, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 818, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 845, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 845, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 849, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 849, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 850, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 850, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 852, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 852, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 857, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 857, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 858, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 858, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 859, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 859, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 860, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 860, "usage_type": "name"}]} +{"seq_id": "561112569", "text": "import numpy as np \nimport cv2\n\ncap = cv2.VideoCapture('http://70.12.241.130:4747/video')\nfps = cap.get(cv2.CAP_PROP_FPS)\ndelay = int(1000/fps)\n\nwhile(cap.isOpened()):\n ret, frame = cap.read()\n ret, buffer = cv2.imencode('.jpg',frame)\n\n with open('c:/temp/output.jpg','wb') as f:\n f.write(buffer)\n \n cv2.imshow('frame',buffer)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\ncap.release()\ncv2.destroyAllWindows()", "sub_path": "machine-workspace/Keras/IP.py", "file_name": "IP.py", "file_ext": "py", "file_size_in_byte": 443, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.VideoCapture", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 5, "usage_type": "attribute"}, {"api_name": "cv2.imencode", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "331534256", "text": "from project.server import db\nfrom flask import Blueprint, jsonify, request, current_app\nfrom project.common.response import Response\nfrom project.common.request_validator import RequestValidator\nfrom werkzeug.utils import secure_filename\nimport os\n\nfrom docx import Document\n\ndocumentdb_blueprint = Blueprint('__document__', __name__)\n\nclass DocumentDBModule(db.Model):\n\n __tablename__ = 'document_master'\n document_id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n document_title = db.Column(db.String(255), nullable=False)\n document_description = db.Column(db.String(255), nullable=False)\n document_author = db.Column(db.Integer, nullable=False)\n\n\n def __repr__(self):\n return {\n 'id': self.document_id,\n 'title': self.document_title,\n 'description': self.document_description,\n 'author': self.document_author\n }\n\n@documentdb_blueprint.route('/api/1.0/get/docx-contents', methods=['POST'])\ndef upload_file():\n\n aResponse = Response()\n\n try:\n file = request.files['file']\n filename = secure_filename(file.filename)\n file.save(os.path.join(current_app.config['UPLOAD_DIR'], filename))\n\n file = open(os.path.join(current_app.config['UPLOAD_DIR'] + '/' + filename), 'rb')\n document = Document(file)\n sections = []\n new_section = {}\n new_section['content'] = []\n for para in document.paragraphs:\n if para.style.name.startswith('Heading'):\n if 'title' in new_section and len(new_section['content']) > 0:\n sections.append(new_section)\n new_section = {}\n new_section['content'] = []\n new_section['title'] = para.text\n else:\n new_section['content'].append(para.text)\n\n sections.append(new_section)\n\n aResponse.data = sections\n\n\n except Exception as e:\n aResponse.error = 'FILE UPLOAD FAIL'\n print(e.__cause__)\n\n return jsonify(aResponse.__repr__())\n\n@documentdb_blueprint.route('/api/1.0/save/document', methods=['POST'])\ndef save_document_details():\n\n\n from .document_sections_db import DocumentSectionsDB\n from .questions_db import QuestionsDB\n\n aResponse = Response()\n in_data = request.get_json()\n valid_keys = ['documentTitle', 'documentDescription', 'userId', 'sectionData']\n a_validator = RequestValidator(in_data, valid_keys)\n if a_validator.has_valid_keys():\n aDocument = DocumentDBModule()\n aDocument.document_author = in_data['userId']\n aDocument.document_title = in_data['documentTitle']\n aDocument.document_description = in_data['documentDescription']\n db.session.add(aDocument)\n db.session.commit()\n\n for section in in_data['sectionData']:\n aSection = DocumentSectionsDB()\n aSection.section_title = section['sectionTitle']\n aSection.section_contents = section['sectionContents']\n aSection.document_id = aDocument.document_id\n db.session.add(aSection)\n db.session.commit()\n\n for question in section['questions']:\n aQuestion = QuestionsDB()\n aQuestion.question_text = question['text']\n aQuestion.questions_opt1 = question['opt1']\n aQuestion.questions_opt2 = question['opt2']\n aQuestion.questions_opt3 = question['opt3']\n aQuestion.questions_opt4 = question['opt4']\n aQuestion.questions_ans = question['ans']\n aQuestion.section_id = aSection.map_id\n\n db.session.add(aQuestion)\n db.session.commit()\n else:\n aResponse.error = 'API KEYS NOT FOUND'\n\n return jsonify(aResponse.__repr__())\n\n\n@documentdb_blueprint.route('/api/1.0/view/document', methods=['POST'])\ndef get_document():\n from .document_sections_db import DocumentSectionsDB\n from .questions_db import QuestionsDB\n\n aResponse = Response()\n in_data = request.get_json()\n valid_keys = ['documentId']\n a_validator = RequestValidator(in_data, valid_keys)\n\n response_data = {'document': None}\n if a_validator.has_valid_keys():\n fetched_document = DocumentDBModule.query.filter(DocumentDBModule.document_id == int(in_data['documentId'])).first()\n if fetched_document is not None:\n response_data['document'] = fetched_document.__repr__()\n fetched_sections = DocumentSectionsDB.query.filter(DocumentSectionsDB.document_id == fetched_document.document_id).all()\n\n sections = []\n\n for section in fetched_sections:\n fetched_questions = QuestionsDB.query.filter(QuestionsDB.section_id == section.map_id).all()\n questions = []\n for q in fetched_questions:\n questions.append(q.__repr__())\n section_repr = section.__repr__()\n section_repr['questions'] = questions\n sections.append(section_repr)\n\n\n response_data['document']['sections'] = sections\n\n aResponse.data = response_data\n\n\n else:\n aResponse.data = []\n\n else:\n aResponse.error = 'API KEYS NOT FOUND'\n\n return jsonify(aResponse.__repr__())\n\n\n@documentdb_blueprint.route('/api/1.0/get/all-documents', methods=['POST'])\ndef get_all_documents():\n\n from .userdb import UserDBModel\n\n in_data = request.get_json()\n valid_keys = ['teamId']\n a_validator = RequestValidator(in_data, valid_keys)\n\n a_response = Response()\n if a_validator.has_valid_keys():\n team_id = int(in_data['teamId'])\n author_users = UserDBModel.query.filter(UserDBModel.user_team_id == team_id).all()\n all_documents = []\n if author_users is not None:\n for user in author_users:\n fetched_documents = DocumentDBModule.query.filter(DocumentDBModule.document_author == user.user_id).all()\n for document in fetched_documents:\n all_documents.append(document.__repr__())\n\n a_response.data = all_documents\n\n else:\n a_response.data = []\n else:\n a_response.error = 'API KEYS MISSING'\n\n return jsonify(a_response.__repr__())", "sub_path": "Middleware/src/project/db_models/documentdb.py", "file_name": "documentdb.py", "file_ext": "py", "file_size_in_byte": 6301, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Blueprint", "line_number": 10, "usage_type": "call"}, {"api_name": "project.server.db.Model", "line_number": 12, "usage_type": "attribute"}, {"api_name": "project.server.db", "line_number": 12, "usage_type": "name"}, {"api_name": "project.server.db.Column", "line_number": 15, "usage_type": "call"}, {"api_name": "project.server.db", "line_number": 15, "usage_type": "name"}, {"api_name": "project.server.db.Integer", "line_number": 15, "usage_type": "attribute"}, {"api_name": "project.server.db.Column", "line_number": 16, "usage_type": "call"}, {"api_name": "project.server.db", "line_number": 16, "usage_type": "name"}, {"api_name": "project.server.db.String", "line_number": 16, "usage_type": "call"}, {"api_name": "project.server.db.Column", "line_number": 17, "usage_type": "call"}, {"api_name": "project.server.db", "line_number": 17, "usage_type": "name"}, {"api_name": "project.server.db.String", "line_number": 17, "usage_type": "call"}, {"api_name": "project.server.db.Column", "line_number": 18, "usage_type": "call"}, {"api_name": "project.server.db", "line_number": 18, "usage_type": "name"}, {"api_name": "project.server.db.Integer", "line_number": 18, "usage_type": "attribute"}, {"api_name": "project.common.response.Response", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.current_app.config", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 37, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.current_app.config", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 39, "usage_type": "name"}, {"api_name": "docx.Document", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 63, "usage_type": "call"}, {"api_name": "project.common.response.Response", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "project.common.request_validator.RequestValidator", "line_number": 75, "usage_type": "call"}, {"api_name": "project.server.db.session.add", "line_number": 81, "usage_type": "call"}, {"api_name": "project.server.db.session", "line_number": 81, "usage_type": "attribute"}, {"api_name": "project.server.db", "line_number": 81, "usage_type": "name"}, {"api_name": "project.server.db.session.commit", "line_number": 82, "usage_type": "call"}, {"api_name": "project.server.db.session", "line_number": 82, "usage_type": "attribute"}, {"api_name": "project.server.db", "line_number": 82, "usage_type": "name"}, {"api_name": "document_sections_db.DocumentSectionsDB", "line_number": 85, "usage_type": "call"}, {"api_name": "project.server.db.session.add", "line_number": 89, "usage_type": "call"}, {"api_name": "project.server.db.session", "line_number": 89, "usage_type": "attribute"}, {"api_name": "project.server.db", "line_number": 89, "usage_type": "name"}, {"api_name": "project.server.db.session.commit", "line_number": 90, "usage_type": "call"}, {"api_name": "project.server.db.session", "line_number": 90, "usage_type": "attribute"}, {"api_name": "project.server.db", "line_number": 90, "usage_type": "name"}, {"api_name": "questions_db.QuestionsDB", "line_number": 93, "usage_type": "call"}, {"api_name": "project.server.db.session.add", "line_number": 102, "usage_type": "call"}, {"api_name": "project.server.db.session", "line_number": 102, "usage_type": "attribute"}, {"api_name": "project.server.db", "line_number": 102, "usage_type": "name"}, {"api_name": "project.server.db.session.commit", "line_number": 103, "usage_type": "call"}, {"api_name": "project.server.db.session", "line_number": 103, "usage_type": "attribute"}, {"api_name": "project.server.db", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 107, "usage_type": "call"}, {"api_name": "project.common.response.Response", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 116, "usage_type": "name"}, {"api_name": "project.common.request_validator.RequestValidator", "line_number": 118, "usage_type": "call"}, {"api_name": "document_sections_db.DocumentSectionsDB.query.filter", "line_number": 125, "usage_type": "call"}, {"api_name": "document_sections_db.DocumentSectionsDB.query", "line_number": 125, "usage_type": "attribute"}, {"api_name": "document_sections_db.DocumentSectionsDB", "line_number": 125, "usage_type": "name"}, {"api_name": "document_sections_db.DocumentSectionsDB.document_id", "line_number": 125, "usage_type": "attribute"}, {"api_name": "questions_db.QuestionsDB.query.filter", "line_number": 130, "usage_type": "call"}, {"api_name": "questions_db.QuestionsDB.query", "line_number": 130, "usage_type": "attribute"}, {"api_name": "questions_db.QuestionsDB", "line_number": 130, "usage_type": "name"}, {"api_name": "questions_db.QuestionsDB.section_id", "line_number": 130, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 150, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 158, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 158, "usage_type": "name"}, {"api_name": "project.common.request_validator.RequestValidator", "line_number": 160, "usage_type": "call"}, {"api_name": "project.common.response.Response", "line_number": 162, "usage_type": "call"}, {"api_name": "userdb.UserDBModel.query.filter", "line_number": 165, "usage_type": "call"}, {"api_name": "userdb.UserDBModel.query", "line_number": 165, "usage_type": "attribute"}, {"api_name": "userdb.UserDBModel", "line_number": 165, "usage_type": "name"}, {"api_name": "userdb.UserDBModel.user_team_id", "line_number": 165, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 180, "usage_type": "call"}]} +{"seq_id": "239938255", "text": "from BaseApp import BasePage\nfrom selenium.webdriver.common.by import By\nfrom selenium.common.exceptions import NoSuchElementException\nfrom config import *\nimport time\n\nclass epic_games_locators:\n LOCATOR_SEARCH_FIELD = (By.ID, 'searchInput')\n LOCATOR_CLEAR_FIELD_SEARCH = (By.CLASS_NAME, 'css-682sma')\n LOCATOR_SEARCH_RESULT = (By.ID, 'search-bar-autocomplete')\n LOCATOR_ADULTS_PAGE_BUTTON = (By.CSS_SELECTOR, '.css-vjttra-CallToActionText__callToActionText')\n LOCATOR_CHOOSE_VERSION_GAME = (By.CSS_SELECTOR, '#Red\\ Dead\\ Redemption\\ 2_button > span:nth-child(1)')\n LOCATOR_LIST_VERSION_GAME = (By.CSS_SELECTOR, '#Red\\ Dead\\ Redemption\\ 2_menu')\n LOCATOR_ADD_TO_FAVORITES = (By.CSS_SELECTOR, '.css-2o2uxw-CallToActionText__callToActionText')\n LOCATOR_APPLE_ID = (By.ID, 'login-with-apple')\n LOCATOR_CHECK_APPLE = (By.CSS_SELECTOR, '.si-container-title')\n\nclass search_game(BasePage):\n\n def click_field_search(self, word):\n field_search = self.find_element(epic_games_locators.LOCATOR_SEARCH_FIELD)\n field_search.click()\n field_search.clear()\n field_search.send_keys(word)\n return field_search\n\n def clear_field_search(self):\n clear_button = self.find_element(epic_games_locators.LOCATOR_CLEAR_FIELD_SEARCH)\n clear_button.click()\n\n def check_empty_field(self):\n text_of_field = self.find_element(epic_games_locators.LOCATOR_SEARCH_FIELD)\n text_of_field=text_of_field.get_attribute('value')\n print('\\n' + 'field_search: ' + ' ' + text_of_field)\n assert text_of_field == ''\n\n def search_result(self, text):\n list_results = self.find_element(epic_games_locators.LOCATOR_SEARCH_RESULT)\n items = list_results.find_elements_by_tag_name(\"li\")\n if items[0].text == 'Искать все игры':\n time.sleep(2)\n #list_results = self.find_element_by_id('search-bar-autocomplete')\n list_results = self.find_element(epic_games_locators.LOCATOR_SEARCH_RESULT)\n items = list_results.find_elements_by_tag_name(\"li\")\n print('all games')\n else:\n True\n for results in items:\n if str(results.text) == text_point5:\n print('\\n' + 'Result: ' + str(results.text))\n results.click() # open Red Dead Redemption 2\n break\n\n def adults_page(self):\n continue_button = self.find_element(epic_games_locators.LOCATOR_ADULTS_PAGE_BUTTON)\n continue_button.click()\n\n def choose_version(self, text):\n choose = self.find_element(epic_games_locators.LOCATOR_CHOOSE_VERSION_GAME)\n choose.click()\n list_results = self.find_element(epic_games_locators.LOCATOR_LIST_VERSION_GAME)\n item = list_results.find_elements_by_tag_name(\"li\")\n for result in item:\n if str(result.text) == text_point7:\n print('\\n' + 'Result: ' + str(result.text))\n result.click()\n break\n\n def add_to_favorite(self):\n time.sleep(1)\n add_to_favorite = self.find_element(epic_games_locators.LOCATOR_ADD_TO_FAVORITES)\n add_to_favorite.click()\n\n def apple_id(self):\n apple_id = self.find_element((epic_games_locators.LOCATOR_APPLE_ID))\n apple_id.click()\n\n def check_apple(self):\n try:\n apple_text = self.find_element(epic_games_locators.LOCATOR_CHECK_APPLE)\n except NoSuchElementException:\n return False\n return True", "sub_path": "epicgamesstore.py", "file_name": "epicgamesstore.py", "file_ext": "py", "file_size_in_byte": 3514, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 8, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 8, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 9, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 9, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 10, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 10, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 11, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 11, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 12, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 12, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 13, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 13, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 14, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 14, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 15, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 15, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 16, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 16, "usage_type": "name"}, {"api_name": "BaseApp.BasePage", "line_number": 18, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 41, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 70, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 81, "usage_type": "name"}]} +{"seq_id": "540596886", "text": "# -*- coding: utf-8 -*-\n\nimport unittest\n\nfrom flask_sqlalchemy import SQLAlchemy\n\nfrom flask import Flask\n\nfrom coaster.manage import init_manager, set_alembic_revision\nimport coaster\n\n\nclass TestManagePy(unittest.TestCase):\n def setUp(self):\n self.app = Flask(__name__)\n self.app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n coaster.app.init_app(self.app)\n self.db = SQLAlchemy(self.app)\n\n self.manage = init_manager(self.app, self.db)\n\n def test_sqlalchemy_database_uri(self):\n \"\"\"Check settings file loaded properly\"\"\"\n assert 'postgresql:///coaster_test' == self.app.config.get(\n 'SQLALCHEMY_DATABASE_URI'\n )\n\n def test_set_alembic_revision(self):\n set_alembic_revision(path='tests/alembic')\n", "sub_path": "tests/test_manage.py", "file_name": "test_manage.py", "file_ext": "py", "file_size_in_byte": 785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "unittest.TestCase", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 15, "usage_type": "call"}, {"api_name": "coaster.app.init_app", "line_number": 17, "usage_type": "call"}, {"api_name": "coaster.app", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 18, "usage_type": "call"}, {"api_name": "coaster.manage.init_manager", "line_number": 20, "usage_type": "call"}, {"api_name": "coaster.manage.set_alembic_revision", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "232578423", "text": "from flask import Flask\nfrom flask import jsonify\n\nimport requests\n\napp = Flask(__name__)\n\ntarget = 0\ntarget_till = 0\n\n\n@app.route('/')\ndef home():\n return 'Here is the server for the heating controller. See the site at https://hello1024.github.io/104-Home/fordhook.html ...'\n\n\ndef getAllData():\n data_heatingcontroller = requests.get('http://heating.lan/cm?cmnd=status%2010').json()\n data_stairs = requests.get('http://sonoff-2286.lan/cm?cmnd=status%2010').json()\n data_setpoints = requests.get('http://heating.lan/cm?cmnd=mem1').json()\n\n global target\n return {\n 'oceanRoom': data_heatingcontroller['StatusSNS']['DS18B20-3']['Temperature'],\n 'hotWater': data_heatingcontroller['StatusSNS']['DS18B20-1']['Temperature'],\n 'hallTemperature': data_stairs['StatusSNS']['DS18B20']['Temperature'],\n 'setOceanRoomTemperature': data_setpoints['Mem1'],\n 'targetHallTemperature': target,\n }\n\ndef setSetpoint(temp):\n requests.get('http://heating.lan/cm?cmnd=mem1%20'+str(temp))\n\n print(time.strftime(\"%A, %d. %B %Y %I:%M:%S %p\") + \"temp target is now \" + str(temp) )\n\n\n@app.route('/temp')\ndef temp():\n return jsonify(getAllData())\n\ndef setTarget(offset):\n x = getAllData();\n\n # Set setpoint to 2 deg hotter than now for 3 hours.\n global target\n global target_till\n\n target = x['hallTemperature'] + offset;\n target_till = time.time()+(3*3600);\n\n\n update_setpoints();\n\n\n@app.route('/hotter')\ndef hotter():\n setTarget(2);\n return \"ok\"\n\n\n@app.route('/colder')\ndef colder():\n setTarget(-2);\n return \"ok\"\n\n\nimport time\nimport atexit\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\n\ndef update_setpoints():\n global target\n global target_till\n if time.time()>target_till:\n # No specific instruction. Use default temp chart.\n hour = int(time.strftime('%H'))\n\n temps_each_hour = [0,0,0,0,0,0,16,16,16, 14, 14, 14, 14, 14, 14, 14, 14, 14, 17, 17, 17, 17, 0, 0, 0 ];\n\n target = temps_each_hour[hour]\n\n x = getAllData();\n\n if x['hallTemperature'] d1:\n total_day = tday - d1\n f1 = int(str(total_day)[:-14]) * 5\n d2 = int(str(total_day)[:-14])\n else:\n f1=0\n d2=0\n error=True\n d={'fine':f1,'late':d2,'error':error}\n return render(request,'fineview.html',d)\n\ndef Fine2(request,pid):\n if not request.user.is_authenticated:\n return redirect('loginstudent')\n error=False\n order1=Studentinfo.objects.filter(user=request.user.id).first()\n data = Student.objects.filter(studentinfo=order1,id=pid).first()\n tday = datetime.date.today()\n mon1 = data.expiry_date.month\n d1 = data.expiry_date.day\n f1=0\n d2=0\n if mon1 == tday.month:\n if d1 < tday.day:\n d2=tday.day-d1\n f1=d2*5\n error=True\n else:\n pass\n elif mon1 < tday.month:\n m2=tday.month-mon1\n d3=(30*m2)+tday.day\n d2=d3-d1\n f1=d2*5\n error=True\n\n else:\n f1=0\n d2=0\n error=True\n d={'fine':f1,'late':d2,'error':error}\n return render(request,'fine2.html',d)\n\ndef Logout(request):\n logout(request)\n return redirect('home')\ndef viewprofile(request,pid):\n if not request.user.is_authenticated:\n return redirect('studentlogin')\n user=User.objects.get(id=pid)\n data=Studentinfo.objects.filter(user=user).first()\n d={'data':data}\n return render(request,'viewprofile.html',d)\ndef edit(request,pid):\n if not request.user.is_authenticated:\n return redirect('studentlogin')\n error = \"\"\n user=User.objects.get(id=pid)\n data1=Studentinfo.objects.filter(user=user).get()\n if request.method == \"POST\":\n b=request.POST['branch']\n e=request.POST['enroll']\n f=request.POST['fname']\n l=request.POST['lname']\n data1.user.first_name=f\n data1.user.last_name=l\n data1.branch=b\n data1.rollNo=e\n try:\n data1.user.save()\n data1.save()\n error = \"no\"\n except:\n error = \"yes\"\n d = {'data':data1,'error':error}\n return render(request,'editprofile.html',d)\n\ndef edit_book(request,pid):\n if not request.user.is_authenticated:\n return redirect('loginadmin')\n error=False\n data1=Book.objects.get(id=pid)\n if request.method == \"POST\":\n i=request.POST['isbn']\n b=request.POST['book']\n a=request.POST['author']\n q=request.POST['quantity']\n c=request.POST['cat']\n data1.isbn=i\n data1.book_name=b\n data1.category=c\n data1.quantity=q\n data1.author=a\n data1.save()\n error=True\n\n d = {'error':error,'data':data1}\n return render(request,'editbook.html',d)\n\ndef bookdelete(request,pid):\n if not request.user.is_authenticated:\n return redirect('loginadmin')\n data2=Book.objects.all()\n\n error1=False\n data = Book.objects.get(id=pid)\n data.delete()\n error1=True\n d = {'error1':error1,'data':data2}\n return render(request,'bookview.html',d)\ndef viewstudent(request):\n if not request.user.is_authenticated:\n return redirect('loginadmin')\n order1 = Studentinfo.objects.all()\n d = {'data1':order1}\n return render(request,'viewstudent.html',d)\ndef editstudent(request,pid):\n if not request.user.is_authenticated:\n return redirect('loginadmin')\n error = \"\"\n data1=Studentinfo.objects.get(id=pid)\n if request.method == \"POST\":\n b=request.POST['branch']\n e=request.POST['enroll']\n f=request.POST['fname']\n l=request.POST['lname']\n data1.user.first_name=f\n data1.user.last_name=l\n data1.branch=b\n data1.rollNo=e\n try:\n data1.user.save()\n data1.save()\n error = \"no\"\n except:\n error=\"yes\"\n d = {'data':data1,'error':error}\n return render(request,'editstudent.html',d)\n\ndef deletestudent(request,pid):\n if not request.user.is_authenticated:\n return redirect('loginadmin')\n error1=False\n data2=Studentinfo.objects.get(id=pid)\n data2.delete()\n return redirect('viewstudent')\n error1=True\n d = {'error1':error1,'data':data2}\n return render(request,'viewstudent.html',d)\ndef Registerstudent(request):\n error=False\n if request.method == \"POST\":\n f=request.POST['firstname']\n l=request.POST['lastname']\n b=request.POST['branch']\n g=request.POST['email']\n u=request.POST['username']\n p=request.POST['password']\n e=request.POST['enroll']\n user = User.objects.filter(username = u)\n if user:\n error= True\n else:\n us = User.objects.create_user(username = u,password=p, first_name=f,last_name=l )\n Studentinfo.objects.create(user=us,rollNo=e,branch=b)\n return redirect('viewstudent')\n d = {\"error\":error}\n return render(request,'registerstudent.html',d)\ndef searchbook(request):\n if not request.user.is_authenticated:\n return redirect('loginadmin')\n data=Book.objects.all()\n d={'data':data}\n return render(request,'studentbooksearch.html',d)\ndef placeorder(request):\n if not request.user.is_authenticated:\n return redirect('loginadmin')\n data = Book.objects.all()\n d = {'data': data}\n return render(request, 'placeorder.html', d)\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "library/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 10163, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.shortcuts.render", "line_number": 9, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 12, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 30, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 39, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 49, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 57, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 72, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 73, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 80, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 83, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 105, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 112, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 114, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 119, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 123, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 127, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 131, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 134, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 138, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 145, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 150, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 153, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 153, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 166, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 170, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 174, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 174, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 198, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 201, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 202, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 205, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 209, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 212, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 232, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 236, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 254, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 258, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 266, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 269, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 272, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 275, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 294, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 298, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 302, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 305, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 322, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 324, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 327, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 330, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 333, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 336, "usage_type": "call"}]} +{"seq_id": "313608833", "text": "from warnings import warn\nfrom django import forms\nfrom django.core.exceptions import ValidationError\nfrom django.utils.translation import ugettext_lazy as _\nfrom vies import VATIN, VIES_COUNTRY_CHOICES\nfrom vies.widgets import VATINWidget, VATINHiddenWidget\n\n\nclass VATINField(forms.MultiValueField):\n \"\"\"VIES VAT field. That verifies on the fly.\"\"\"\n hidden_widget = VATINHiddenWidget\n\n def __init__(self, choices=VIES_COUNTRY_CHOICES, *args, **kwargs):\n max_length = kwargs.pop('max_length', 14)\n fields = (\n forms.ChoiceField(required=False, choices=choices),\n forms.CharField(required=False, max_length=max_length)\n )\n kwargs['widget'] = VATINWidget(choices=choices)\n super(VATINField, self).__init__(fields=fields, *args, **kwargs)\n\n def compress(self, data_list):\n if data_list:\n return \"\".join(data_list)\n return ''\n\n def clean(self, value):\n if not value or not isinstance(value, (list, tuple)):\n if not value or not [v for v in value if v not in self.empty_values]:\n if self.required:\n raise ValidationError(self.error_messages['required'], code='required')\n else:\n return self.compress([])\n else:\n try:\n vatin = VATIN(*value)\n if vatin.is_valid():\n self._vies_result = vatin.result\n return super(VATINField, self).clean(value)\n except ValueError:\n pass\n\n raise ValidationError(_('%(value)s is not a valid European VAT.'), code='invalid',\n params={'value': self.compress(value)})\n\n def vatinData(self):\n return self._vies_result if hasattr(self, '_vies_result') else None\n\n\nclass VIESField(VATINField):\n \"\"\"Deprecated in favor of VATINField\"\"\"\n def __init__(self, *args, **kwargs):\n warn(DeprecationWarning, '%(class)s has been deprecated in favor of VATINField')\n super(VIESField, self).__init__(*args, **kwargs)", "sub_path": "vies/fields.py", "file_name": "fields.py", "file_ext": "py", "file_size_in_byte": 2091, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.forms.MultiValueField", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "vies.widgets.VATINHiddenWidget", "line_number": 11, "usage_type": "name"}, {"api_name": "vies.VIES_COUNTRY_CHOICES", "line_number": 13, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 16, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 17, "usage_type": "name"}, {"api_name": "vies.widgets.VATINWidget", "line_number": 19, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 31, "usage_type": "call"}, {"api_name": "vies.VATIN", "line_number": 36, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 43, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 43, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "264606382", "text": "from flask import Flask, request, jsonify, make_response, render_template, flash, redirect\nimport tweepy\nfrom tweepy import API \nfrom tweepy import Cursor\nfrom tweepy.streaming import StreamListener\nfrom tweepy import OAuthHandler\nfrom tweepy import Stream\nimport twitter_credentials\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom flask_mysqldb import MySQL\nimport hashlib\n\napp = Flask(__name__, template_folder='template')\n\napp.config['MYSQL_HOST'] = 'remotemysql.com' \napp.config['MYSQL_USER'] = 'KfZiCHimwl'\napp.config['MYSQL_PASSWORD'] = '3UfECxWJRp'\napp.config['MYSQL_DB'] = 'KfZiCHimwl'\napp.config['Templates_AUTO_RELOAD']=True\napp.config['MYSQL_CURSORCLASS'] = 'DictCursor'\n\nmysql = MySQL(app)\n\nclass TwitterAuthenticator(): \n def authenticate_twitter_app(self):\n auth = OAuthHandler(twitter_credentials.CONSUMER_KEY, twitter_credentials.CONSUMER_SECRET)\n auth.set_access_token(twitter_credentials.ACCESS_TOKEN, twitter_credentials.ACCESS_TOKEN_SECRET)\n return auth\n\nclass TweetAnalyzer():\n \"\"\"\n Functionality for analyzing and categorizing content from tweets.\n \"\"\"\n def tweets_to_data_frame(self, tweets):\n df = pd.DataFrame(data=[tweet.text for tweet in tweets], columns=['tweets'])\n\n #df['id'] = np.array([tweet.id for tweet in tweets])\n #df['len'] = np.array([len(tweet.text) for tweet in tweets])\n df['date'] = np.array([tweet.created_at for tweet in tweets])\n #df['source'] = np.array([tweet.source for tweet in tweets])\n df['likes'] = np.array([tweet.favorite_count for tweet in tweets])\n #df['retweets'] = np.array([tweet.retweet_count for tweet in tweets])\n\n return df\n\n \nclass TwitterClient():\n def __init__(self, twitter_user=None):\n self.auth = TwitterAuthenticator().authenticate_twitter_app()\n self.twitter_client = API(self.auth)\n\n self.twitter_user = twitter_user\n\n def get_twitter_client_api(self):\n return self.twitter_client\n\n def get_user_timeline_tweets(self, num_tweets):\n tweets = []\n for tweet in Cursor(self.twitter_client.user_timeline, id=self.twitter_user).items(num_tweets):\n tweets.append(tweet)\n return tweets\n\n def get_friend_list(self, num_friends):\n friend_list = []\n for friend in Cursor(self.twitter_client.friends, id=self.twitter_user).items(num_friends):\n friend_list.append(friend)\n return friend_list\n\n def get_home_timeline_tweets(self, num_tweets):\n home_timeline_tweets = []\n for tweet in Cursor(self.twitter_client.home_timeline, id=self.twitter_user).items(num_tweets):\n home_timeline_tweets.append(tweet)\n return home_timeline_tweets\n\nclass MyStreamListener(tweepy.StreamListener):\n \"\"\"\n This is a basic listener that just prints received tweets to stdout.\n \"\"\"\n def __init__(self, fetched_tweets_filename):\n self.fetched_tweets_filename = fetched_tweets_filename\n\n def on_data(self, data):\n try:\n print(data)\n with open(self.fetched_tweets_filename, 'a') as tf:\n tf.write(data)\n return True\n except BaseException as e:\n print(\"Error on_data %s\" % str(e))\n return True\n \n def on_error(self, status):\n if status == 420:\n # Returning False on_data method in case rate limit occurs.\n return False\n print(status)\n\n\n\n\n\n\n@app.route('/', methods = ['GET','POST'])\ndef twitter_stream():\n request_data = request.form\n insert = request_data.get('list')\n authenticator = TwitterAuthenticator()\n auth = authenticator.authenticate_twitter_app()\n user = TwitterClient()\n user.__init__(insert)\n analyzer = TweetAnalyzer()\n tweets = user.get_user_timeline_tweets(30)\n df = analyzer.tweets_to_data_frame(tweets)\n tweets=[]\n tweets = df.values\n \n time_favs = pd.Series(data=df['likes'].values, index=df['date'])\n time_favs.plot(figsize=(16, 4), color='r')\n plt.savefig(\"static/img/my_plot.png\")\n \n return render_template('home.html', x=tweets)\n\n\n@app.route(\"/listings\")\ndef show():\n cur=mysql.connection.cursor()\n cur.execute('''SELECT * from Channels''')\n check=cur.fetchall()\n return str(check)\n\n@app.route('/form2')\ndef form2():\n return render_template('listing.html')\n\n\n@app.route('/listingsadd', methods=['POST'])\ndef add():\n request_data = request.form\n cur = mysql.connection.cursor()\n insert = request_data.get('list')\n myString = \"INSERT INTO Channels (Channel) VALUES ('\" + insert + \"')\"\n cur.execute(myString)\n mysql.connection.commit()\n return \"Done\"\n\n\n@app.route('/form')\ndef form():\n return render_template('register.html')\n\n\n@app.route('/api/users', methods=['POST'])\ndef registerUser():\n if request.form: # Check if the user info is submitted by an HTML form\n request_data = request.form\n else: # Information is submitted via an API request using JSON\n request_data = request.get_json()\n # retrieve the data from the request from the client\n username = request_data.get('username')\n password = request_data.get('password')\n email= request_data.get('email')\n cur = mysql.connection.cursor()\n hashed_password = hashlib.sha1(username.encode('utf-8')).hexdigest()\n cur.execute(\"INSERT INTO User (Username, Password, Email) VALUES ('\" + username + \"','\" + hashed_password + \"','\" + email + \"')\")\n mysql.connection.commit()\n return \"DONE\"\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 5629, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 15, "usage_type": "call"}, {"api_name": "flask_mysqldb.MySQL", "line_number": 24, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 28, "usage_type": "call"}, {"api_name": "twitter_credentials.CONSUMER_KEY", "line_number": 28, "usage_type": "attribute"}, {"api_name": "twitter_credentials.CONSUMER_SECRET", "line_number": 28, "usage_type": "attribute"}, {"api_name": "twitter_credentials.ACCESS_TOKEN", "line_number": 29, "usage_type": "attribute"}, {"api_name": "twitter_credentials.ACCESS_TOKEN_SECRET", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 52, "usage_type": "call"}, {"api_name": "tweepy.Cursor", "line_number": 61, "usage_type": "call"}, {"api_name": "tweepy.Cursor", "line_number": 67, "usage_type": "call"}, {"api_name": "tweepy.Cursor", "line_number": 73, "usage_type": "call"}, {"api_name": "tweepy.StreamListener", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 107, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 107, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 123, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 140, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 140, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 156, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 156, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 157, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 157, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 159, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 159, "usage_type": "name"}, {"api_name": "hashlib.sha1", "line_number": 165, "usage_type": "call"}]} +{"seq_id": "521967151", "text": "\"\"\"\n- Every process has its own address space(virtual memory).\n- Thus program variables are not shared between two processes.\n- We need to use interprocess communication (IPC) techniques to share data between process\n\"\"\"\n\nimport time\nimport multiprocessing\n\nsquare_result = []\n\ndef calc_square(nums):\n global square_result\n print(\"Calculatin Square\")\n for n in nums:\n print(\"Square : %s \" % (n*n))\n square_result.append(n*n)\n \n print(\"With in process Result \"+str(square_result))\n \n \ndef calc_cube(nums):\n print(\"Calculatin Cube\")\n for n in nums:\n print(\"Cube : %s \" % (n*n*n))\n \ndef main():\n arr = [1,2,3,4,5]\n p1 = multiprocessing.Process(target=calc_square, args=(arr,))\n #p2 = multiprocessing.Process(target=calc_cube, args=(arr,))\n \n p1.start()\n #p2.start()\n \n p1.join()\n #p2.join()\n \n print(\"Result \"+str(square_result))\n print(\"Done!\")\n \nif __name__ == \"__main__\":\n main()\n \n ", "sub_path": "Parallalism/4-multiProcessExample-3-GlobalVariable-Copy1.py", "file_name": "4-multiProcessExample-3-GlobalVariable-Copy1.py", "file_ext": "py", "file_size_in_byte": 1002, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "multiprocessing.Process", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "98797259", "text": "from inqbus.rpi.widgets.base.render import Renderer\nfrom inqbus.rpi.widgets.gauge import Gauge\nfrom inqbus.rpi.widgets.interfaces.interfaces import IRenderer\n\nfrom inqbus.rpi.widgets.interfaces.widgets import (\n IGaugeTargetWidget, )\nfrom zope.component import getGlobalSiteManager\nfrom zope.interface import Interface, implementer\n\n\n@implementer(IGaugeTargetWidget)\nclass GaugeTarget(Gauge):\n \"\"\"\n Gauge Widget. Representing a single line Gauge.\n \"\"\"\n _can_focus = True\n\n def __init__(\n self,\n label,\n initial_value=0,\n initial_reading_value=0,\n increment=1,\n format='.2f',\n unit=None,\n read_only=False,\n value_callback=None,\n up_handler=None,\n down_handler=None,\n **kwargs,\n ):\n super(Gauge, self).__init__(label=label, **kwargs)\n self._desired_height = 1\n self._content = initial_value\n self._reading_value = initial_reading_value\n self._increment = increment\n self._format = format\n self._unit = unit\n self._value_callback = value_callback\n self._up_handler = up_handler\n self._down_handler = down_handler\n self.is_activated = False\n self.is_read_only = read_only\n\n def release_focus(self):\n self.is_activated = False\n super(Gauge, self).release_focus()\n\n\n@implementer(IRenderer)\nclass GaugeTargetRenderer(Renderer):\n \"\"\"\n Renderer for a LineWidget\n \"\"\"\n __used_for__ = (IGaugeTargetWidget, Interface)\n\n def render_content(self):\n \"\"\"\n Render the Gauge at the given position\n\n Returns: the new x, y position\n \"\"\"\n\n fc = {}\n # Label handling\n if self.widget._label is not None:\n fc['label'] = self.widget._label\n else:\n fc['label'] = ''\n\n # Do we have a unit?\n if self.widget._unit is not None:\n fc['unit'] = self.widget._unit\n else:\n fc['unit'] = ''\n\n # Handling of the content\n fc['reading'] = self.widget._reading_value\n fc['level'] = self.widget._content\n fc['format'] = self.widget._format\n\n # If the Gauge is activated\n if self.widget.is_activated:\n fc['operator'] = '?'\n else:\n if self.widget._reading_value < self.widget._content:\n fc['operator'] = '<'\n elif self.widget._reading_value == self.widget._content:\n fc['operator'] = '='\n else:\n fc['operator'] = '>'\n\n out_str = \"\"\"{label}:{reading:{format}}{operator}{level:{format}}{unit}\"\"\".format(**fc) # noqa: E501\n\n out_str_focus = self.render_focus(out_str)\n return out_str_focus\n\n\n# Register the adapters\ngsm = getGlobalSiteManager()\ngsm.registerAdapter(\n GaugeTargetRenderer,\n (IGaugeTargetWidget, Interface),\n IRenderer)\n", "sub_path": "inqbus/rpi/widgets/gauge_target.py", "file_name": "gauge_target.py", "file_ext": "py", "file_size_in_byte": 2958, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "inqbus.rpi.widgets.gauge.Gauge", "line_number": 12, "usage_type": "name"}, {"api_name": "inqbus.rpi.widgets.gauge.Gauge", "line_number": 32, "usage_type": "argument"}, {"api_name": "inqbus.rpi.widgets.gauge.Gauge", "line_number": 47, "usage_type": "argument"}, {"api_name": "zope.interface.implementer", "line_number": 11, "usage_type": "call"}, {"api_name": "inqbus.rpi.widgets.interfaces.widgets.IGaugeTargetWidget", "line_number": 11, "usage_type": "argument"}, {"api_name": "inqbus.rpi.widgets.base.render.Renderer", "line_number": 51, "usage_type": "name"}, {"api_name": "inqbus.rpi.widgets.interfaces.widgets.IGaugeTargetWidget", "line_number": 55, "usage_type": "name"}, {"api_name": "zope.interface.Interface", "line_number": 55, "usage_type": "name"}, {"api_name": "zope.interface.implementer", "line_number": 50, "usage_type": "call"}, {"api_name": "inqbus.rpi.widgets.interfaces.interfaces.IRenderer", "line_number": 50, "usage_type": "argument"}, {"api_name": "zope.component.getGlobalSiteManager", "line_number": 100, "usage_type": "call"}, {"api_name": "inqbus.rpi.widgets.interfaces.interfaces.IRenderer", "line_number": 104, "usage_type": "argument"}, {"api_name": "inqbus.rpi.widgets.interfaces.widgets.IGaugeTargetWidget", "line_number": 103, "usage_type": "name"}, {"api_name": "zope.interface.Interface", "line_number": 103, "usage_type": "name"}]} +{"seq_id": "65385942", "text": "from telegram.ext import Updater\nimport logging\nfrom telegram.ext import CommandHandler, MessageHandler, Filters, InlineQueryHandler\nfrom telegram import InlineQueryResultGif, InputTextMessageContent\nimport requests\n# import giphypop\nimport json\nfrom random import randint\n\n\ndef start(bot, update):\n bot.send_message(chat_id=update.message.chat_id, text=\"Just some text to test\")\n\n\ndef search(search_text):\n prev_search = search_text\n offset = 0\n\n def get_gif_search(search_text):\n nonlocal prev_search\n nonlocal offset\n if prev_search == search_text:\n offset += 25\n else:\n offset = 0\n prev_search = search_text\n\n params = {\n 'api_key': GIPHY_APIKEY,\n 'q': search_text,\n 'offset': offset\n }\n response = requests.get('http://api.giphy.com/v1/gifs/search', params=params)\n result = response.json()['data']\n return result\n return get_gif_search\n\n\ndef gifs_choice(bot, update):\n query = update.inline_query.query\n if not query:\n return\n # result1 = get_gif_random(query)\n result1 = search_func(query)\n results = list()\n for i, result in enumerate(result1):\n url = result['images']['original']['url']\n results.append(\n InlineQueryResultGif(\n type=\"gif\",\n id=i,\n gif_url=url,\n thumb_url=url,\n title=query,\n caption=query,\n )\n )\n bot.answer_inline_query(update.inline_query.id, results, cache_time=0)\n\nwith open(\"keys.json\", \"r\") as fp:\n keys = json.load(fp)\n\nTELEGRAM_TOKEN = keys[\"bots\"][\"search\"]\nGIPHY_APIKEY = keys[\"giphy\"]\n\nupdater = Updater(token=TELEGRAM_TOKEN)\ndispatcher = updater.dispatcher\nlogging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n level=logging.INFO)\n\nstart_handler = CommandHandler('start', start)\ndispatcher.add_handler(start_handler)\n\nsearch_func = search(\"\")\ninline_handler = InlineQueryHandler(gifs_choice)\ndispatcher.add_handler(inline_handler)\n\nupdater.start_polling()", "sub_path": "gifysbot.py", "file_name": "gifysbot.py", "file_ext": "py", "file_size_in_byte": 2047, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "telegram.InlineQueryResultGif", "line_number": 49, "usage_type": "call"}, {"api_name": "json.load", "line_number": 61, "usage_type": "call"}, {"api_name": "telegram.ext.Updater", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 68, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 69, "usage_type": "attribute"}, {"api_name": "telegram.ext.CommandHandler", "line_number": 71, "usage_type": "call"}, {"api_name": "telegram.ext.InlineQueryHandler", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "164723071", "text": "\"\"\" make_sqlite_fts.py\n make an inverted index (fts = full text search) from a csv file.\n Assume no header in csv file and ':' as separator\n\"\"\"\nfrom __future__ import print_function\nimport sys,re,codecs;\nimport sqlite3\nimport time # for performance checks\ndef remove(fileout):\n import os\n if os.path.exists(fileout):\n os.remove(fileout)\n print(\"removed previous\",fileout)\n\ndef get_dict_code(fileout):\n # assume fileout is xxx.sqlite\n m = re.search(r'^(.*?)[.]sqlite$',fileout)\n if not m:\n print('sqlite.py ERROR: cannot get dictionary code')\n print('fileout=',fileout)\n exit(1)\n code = m.group(1).lower() # should be lower case?\n print('sqlite.py: dictionary code=',code)\n return code\n\ndef create_virtual_table(c,conn,tabname,colnames):\n #tabcols = ['%s TEXT' %colname for colname in colnames]\n #tabcols_string = ','.join(colnames)\n # default tokenizer is\n # unicode61 (normalise all tokens into unicode characters\n # ascii: converts all non-ascii characters into ascii version\n # e.g., remove diacritics\n # porter: porter stemming algorithm for english 'stems'\n # for this purpose, we DON'T wwant porter stemming\n tokenizers = '' #'tokenize = \"porter\"'\n ftsargs = colnames + [tokenizers]\n ftsargs_string = ','.join(ftsargs)\n # can also use fts4 or fts3\n template = 'CREATE VIRTUAL TABLE %s USING fts4(%s);' % (tabname,ftsargs_string)\n if False: #dbg\n print('DBG: table template=')\n print(template)\n c.execute(template)\n conn.commit()\n\ndef insert_batch(c,conn,tabname,rows,colnames):\n # if rows is empty, nothing to do\n if len(rows) == 0:\n return\n # one placehold per colname\n placeholders = ['?' for x in colnames]\n placeholders_string = ','.join(placeholders)\n sql = 'INSERT INTO %s VALUES (%s)' % (tabname,placeholders_string)\n if False: #dbg.\n print('sql = ',sql)\n c.executemany(sql,rows)\n conn.commit()\n \nif __name__ == \"__main__\":\n time0 = time.time() # a real number\n\n filein = sys.argv[1] # xxx.txt\n fileout = sys.argv[2] # xxx.sqlite\n mbatch = 10000\n separator = ':' # column separator in filein as csv file\n remove(fileout) \n # infer dictionary name from fileout. And use this as table name\n dictlo = get_dict_code(fileout)\n tabname = dictlo\n print('table name=',tabname)\n # establish connection to xxx.sqlite, \n # also creates xxx.sqlite if it doesn't exist\n conn = sqlite3.connect(fileout)\n c = conn.cursor() # prepare cursor for further use\n import csv,codecs\n rows = [] # data rows (exclude first row which has column names\n with codecs.open(filein,\"r\",\"utf-8\") as f:\n reader = csv.reader(f,delimiter = separator)\n nrow = 0\n for irow,row in enumerate(reader):\n if irow == 0:\n colnames = row\n create_virtual_table(c,conn,tabname,colnames)\n elif nrow < mbatch:\n rows.append(row)\n nrow = nrow + 1\n else:\n # insert records of a batch, and commit\n insert_batch(c,conn,tabname,rows,colnames)\n # reinit batch\n rows = []\n nrow = 0\n # add this row to\n rows.append(row)\n nrow = nrow + 1\n #insert last batch\n insert_batch(c,conn,tabname,rows,colnames)\n\n \n # create index\n #create_index(c,conn,dictlo)\n conn.close() # close the connection to xxx.sqlite\n time1 = time.time() # ending time\n print(irow,'lines read from',filein)\n #print(nrow,'rows written to',fileout)\n timediff = time1 - time0 # seconds\n print('%0.2f seconds for batch size %s' %(timediff,mbatch))\n", "sub_path": "simple-search/simpleslp/make_sqlite_fts.py", "file_name": "make_sqlite_fts.py", "file_ext": "py", "file_size_in_byte": 3335, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.exists", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 12, "usage_type": "call"}, {"api_name": "re.search", "line_number": 17, "usage_type": "call"}, {"api_name": "time.time", "line_number": 60, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 62, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 63, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 73, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 77, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 78, "usage_type": "call"}, {"api_name": "time.time", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "43422224", "text": "\"\"\"liftit_demo URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url\nfrom django.contrib import admin\nfrom django.urls import path\nfrom django.urls import re_path\nfrom rest_framework_swagger.views import get_swagger_view\nfrom invoices.views import InvoiceList\nfrom invoices.views import InvoiceDetail\nfrom invoices.views import FileList\nfrom invoices.views import FileDetail\nfrom invoices.views import index\nfrom invoices.views import UploadFileView\n\nschema_view = get_swagger_view(title='Liftit DEMO API')\n\n\nurlpatterns = [\n path('', index, name='index'),\n url(r'^admin/', admin.site.urls),\n url(r'^swagger/', schema_view),\n\n path(\n 'upload-file/',\n UploadFileView.as_view(),\n name='upload file'\n ),\n\n\n # Invoice Endpoint's\n path(\n 'invoice/',\n InvoiceList.as_view(),\n name='invoice list'\n ),\n re_path(\n '^invoice/(?P[0-9a-f-]+)/$',\n InvoiceDetail.as_view(),\n name='invoice detail'\n ),\n\n\n # File Endpoint's\n path(\n 'file/',\n FileList.as_view(),\n name='file list'\n ),\n re_path(\n '^file/(?P[0-9a-f-]+)/$',\n FileDetail.as_view(),\n name='file detail'\n ),\n]", "sub_path": "liftit_demo/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1820, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rest_framework_swagger.views.get_swagger_view", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "invoices.views.index", "line_number": 32, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 33, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 33, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 34, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "invoices.views.UploadFileView.as_view", "line_number": 38, "usage_type": "call"}, {"api_name": "invoices.views.UploadFileView", "line_number": 38, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 44, "usage_type": "call"}, {"api_name": "invoices.views.InvoiceList.as_view", "line_number": 46, "usage_type": "call"}, {"api_name": "invoices.views.InvoiceList", "line_number": 46, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 49, "usage_type": "call"}, {"api_name": "invoices.views.InvoiceDetail.as_view", "line_number": 51, "usage_type": "call"}, {"api_name": "invoices.views.InvoiceDetail", "line_number": 51, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 57, "usage_type": "call"}, {"api_name": "invoices.views.FileList.as_view", "line_number": 59, "usage_type": "call"}, {"api_name": "invoices.views.FileList", "line_number": 59, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 62, "usage_type": "call"}, {"api_name": "invoices.views.FileDetail.as_view", "line_number": 64, "usage_type": "call"}, {"api_name": "invoices.views.FileDetail", "line_number": 64, "usage_type": "name"}]} +{"seq_id": "507692188", "text": "import pandas as pd\nfrom sklearn.metrics.pairwise import euclidean_distances\n\ndf = pd.read_csv(\"winequality-red.csv\")\nx = df[[\"fixed acidity\", \"alcohol\"]]\n\n\ndef kmeans(df, k=2):\n \"\"\"add documentation here, WHY instead of WHAT\"\"\"\n\n # Step 0: check whether data is a dataframe\n\n # step 1: check whether the data is numeric and continuous\n try:\n for variable in x.columns:\n df[variable].dtype in ('float64', 'int')\n except ValueError:\n print(\"All variables need to be numeric (float64 or int)\")\n\n # step 2: select k random points\n k_init = df.sample(n=k)\n\n # step 3: calculate the euclidean distance from each point to each k\n k_distances = pd.DataFrame(euclidean_distances(df, k_init))\n\n # step 3.1: assign each observation to the nearest cluster\n k_distances['cluster'] = k_distances.apply(lambda x: k_distances.columns[x.idxmin()], axis=1)\n\n # step 3.2: calculate the position of the mean of each newly formed cluster\n new_means = k_distances.groupby(['cluster']).mean()\n\n # step 3.3: calculate the euclidean distance from each point to each newly formed mean\n k_distances = pd.DataFrame(euclidean_distances(df, new_means))\n k_distances['cluster'] = k_distances.apply(lambda x: k_distances.columns[x.idxmin()], axis=1)\n\n # step 4: calculate the total within sum of squares\n ss_per_k = k_distances.apply(lambda x: sum(x**2))\n ss_total = sum(ss_per_k)\n\n # step 5: label and return each observation\n\n return k_distances, ss_per_k, ss_total\n\ntry_out = kmeans(x, k=2)\n\ntype(try_out.groupby(['cluster']).mean())\nprint(try_out.groupby(['cluster']).count())\n\nk_try = x.sample(n=2)\nk_try_dist = pd.DataFrame(euclidean_distances(x, k_try))\n\nfor variable in k_try_dist:\n print(k_try_dist[variable]**2)\n\nk_try_dist.head()\nk_try_dist.apply(lambda x: sum(x**2))\n", "sub_path": "first_function.py", "file_name": "first_function.py", "file_ext": "py", "file_size_in_byte": 1839, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 4, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.euclidean_distances", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.euclidean_distances", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.euclidean_distances", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "115313066", "text": "import enum\n\nfrom dataclasses import is_dataclass\n\nfrom .str_converters import to_camel_case\n\n\ndef dict_to_type(dict, cls):\n fields = cls.__dataclass_fields__\n\n kwargs = {}\n\n for name, field in fields.items():\n dict_name = name\n\n if hasattr(field, \"field_name\") and field.field_name:\n dict_name = field.field_name\n else:\n dict_name = to_camel_case(name)\n\n if is_dataclass(field.type):\n kwargs[name] = dict_to_type(dict.get(dict_name, {}), field.type)\n else:\n kwargs[name] = dict.get(dict_name)\n\n # Convert Enum fields to instances using the value. This is safe\n # because graphql-core has already validated the input.\n if isinstance(field.type, enum.EnumMeta) and kwargs[name]:\n kwargs[name] = field.type(kwargs[name])\n\n return cls(**kwargs)\n", "sub_path": "strawberry/utils/dict_to_type.py", "file_name": "dict_to_type.py", "file_ext": "py", "file_size_in_byte": 881, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "str_converters.to_camel_case", "line_number": 19, "usage_type": "call"}, {"api_name": "dataclasses.is_dataclass", "line_number": 21, "usage_type": "call"}, {"api_name": "enum.EnumMeta", "line_number": 28, "usage_type": "attribute"}]} +{"seq_id": "645191725", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n@Author : Leo\n@Connect : lipf0627@163.com\n@File : m_city.py\n@site : \n@Time : 2018/9/6 14:00\n@Software: PyCharm Community Edition\n\"\"\"\n\nfrom enum import Enum, unique\n\n\n@unique\nclass CityLevel(Enum):\n 直辖市 = 1\n 省会 = 2\n 地级市 = 3\n 县级市 = 4\n 乡镇 = 5\n 村 = 6\n\n\nclass ImCity:\n \"\"\"城市类\"\"\"\n\n def __init__(self):\n self._name = '' # 城市名\n self._province = '' # 所在省\n self._level = 0 # 行政级别\n\n @property\n def name(self):\n return self._name\n\n @name.setter\n def name(self, name):\n if not isinstance(name, str):\n raise TypeError('name')\n self._name = name\n\n @property\n def province(self):\n return self._province\n\n @province.setter\n def province(self, province):\n if not isinstance(province, str):\n raise TypeError('province')\n self._province = province\n\n @property\n def level(self):\n return self._level\n\n @level.setter\n def level(self, level):\n if level not in CityLevel.__members__.items():\n raise ValueError('level')\n self._level = level.name\n", "sub_path": "lib/college/m_city.py", "file_name": "m_city.py", "file_ext": "py", "file_size_in_byte": 1231, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "enum.Enum", "line_number": 16, "usage_type": "name"}, {"api_name": "enum.unique", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "381240237", "text": "\"\"\"Compatibility stuff (especially for 2.x - 3.x bridging)\"\"\"\n\nimport sys\nimport functools\nimport itertools\ntry:\n from io import StringIO\nexcept ImportError:\n from cStringIO import StringIO\n\nif sys.version_info < (3,):\n int_types = (int, long)\n bytes = str\n unicode = unicode\n xrange = xrange\n long = long\n maxint = sys.maxint\n reduce = reduce\n imap = itertools.imap\n izip = itertools.izip\n def u_lit(s):\n r\"\"\"Make an unicode string from a regular string literal,\n intepreting \\uXXXX escapes\"\"\"\n return eval('u\"\"\"' + s + '\"\"\"')\n def print_(*args, **kargs):\n sep = kargs.pop('sep', ' ')\n end = kargs.pop('end', '\\n')\n file = kargs.pop('file', sys.stdout)\n if kargs:\n raise TypeError(\"unexpected keyword arguments %r\" % (list(kargs),))\n file.write(sep.join(map(str, args)) + end)\n def next(x):\n return x.next()\nelse:\n import builtins\n int_types = (int,)\n bytes = bytes\n unicode = str\n xrange = range\n long = int\n maxint = sys.maxsize # good enough\n reduce = functools.reduce\n imap = map\n izip = zip\n def u_lit(s):\n return s\n # Avoid syntax errors under 2.5\n _builtin_print = getattr(builtins, 'print')\n def print_(*args, **kargs):\n _builtin_print(*args, **kargs)\n next = next\n\n\n", "sub_path": "pre-benchmarks/mike/performance-typed/compat.py", "file_name": "compat.py", "file_ext": "py", "file_size_in_byte": 1352, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.version_info", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.maxint", "line_number": 17, "usage_type": "attribute"}, {"api_name": "itertools.imap", "line_number": 19, "usage_type": "attribute"}, {"api_name": "itertools.izip", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.maxsize", "line_number": 41, "usage_type": "attribute"}, {"api_name": "functools.reduce", "line_number": 42, "usage_type": "attribute"}]} +{"seq_id": "24834497", "text": "# coding=utf-8\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.metrics import classification_report\nfrom DLforCV.Starter.chap12_Shallow_Net.shallownet import ShallowNet\nfrom keras.optimizers import SGD\nfrom keras.datasets import cifar10\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\n# 加载图像并归一化\nprint('[INFO] 正在加载CIFAR10...')\n((trainX, trainY), (testX, testY)) = cifar10.load_data()\ntrainX = trainX.astype(\"float\") / 255.0\ntestX = testX.astype(\"float\") / 255.0\n\n# 类别转成向量形式\nlb = LabelBinarizer()\ntrainY = lb.fit_transform(trainY)\ntestY = lb.transform(testY)\n\n# initialize the label names for the CIFAR-10 dataset\nlabelNames = [\"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\",\n \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"]\n\n\n# 初始化优化器和模型\nprint(\"[INFO] 正在编译模型...\")\nopt = SGD(lr=0.001)\nmodel = ShallowNet.build(width=32, height=32, depth=3, classes=10)\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])\n\n# 训练网络\nprint(\"[INFO] 正在训练网络...\")\nH = model.fit(trainX, trainY, validation_data=(testX, testY),\n batch_size=32, epochs=40, verbose=1)\n\nmodel.save_weights('./shallownet_weights.hdf5')\nmodel.save_model('./shallownet_model.hdf5')\n\n# 评估网络\nprint(\"[INFO] 正在评估网络...\")\npredictions = model.predict(testX, batch_size=32)\nprint(classification_report(testY.argmax(axis=1),\n predictions.argmax(axis=1), target_names=labelNames))\n\n# 绘制图像\nplt.style.use(\"ggplot\")\nplt.figure()\nplt.plot(np.arange(0, 40), H.history[\"loss\"], label=\"train_loss\")\nplt.plot(np.arange(0, 40), H.history[\"val_loss\"], label=\"val_loss\")\nplt.plot(np.arange(0, 40), H.history[\"acc\"], label=\"train_acc\")\nplt.plot(np.arange(0, 40), H.history[\"val_acc\"], label=\"val_acc\")\nplt.title(\"Training Loss and Accuracy\")\nplt.xlabel(\"Epoch #\")\nplt.legend()\nplt.show()\n", "sub_path": "Starter/chap12_Shallow_Net/shallownet_cifar10.py", "file_name": "shallownet_cifar10.py", "file_ext": "py", "file_size_in_byte": 1931, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "keras.datasets.cifar10.load_data", "line_number": 13, "usage_type": "call"}, {"api_name": "keras.datasets.cifar10", "line_number": 13, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.LabelBinarizer", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 29, "usage_type": "call"}, {"api_name": "DLforCV.Starter.chap12_Shallow_Net.shallownet.ShallowNet.build", "line_number": 30, "usage_type": "call"}, {"api_name": "DLforCV.Starter.chap12_Shallow_Net.shallownet.ShallowNet", "line_number": 30, "usage_type": "name"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 48, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "15410249", "text": "import pox.openflow.libopenflow_01 as of\nimport pox.lib.packet as pkt\nimport pox\nimport struct\nimport time\nfrom pox.core import core\nfrom pox.lib.packet.ethernet import ethernet\nfrom pox.lib.packet.ipv4 import ipv4\nfrom pox.lib.packet.arp import arp\nfrom pox.lib.packet.icmp import icmp, echo\nfrom pox.lib.addresses import IPAddr, EthAddr\nfrom pox.lib.recoco import Timer\nfrom pox.lib.util import str_to_bool, dpid_to_str\nfrom pox.lib.revent import *\n\nlog = core.getLogger()\n\n# Router configurations\ninfo_table = {}\ninfo_table[1] = {'local_IP' : '10.0.1.1', 'local_MAC': 'AA:BB:CC:DD:EE:01', 'local_net': '10.0.1.0/24'}\ninfo_table[2] = {'local_IP' : '10.0.2.1', 'local_MAC': 'AA:BB:CC:DD:EE:02', 'local_net': '10.0.2.0/24'}\ninfo_table[3] = {'local_IP' : '10.0.3.1', 'local_MAC': 'AA:BB:CC:DD:EE:03', 'local_net': '10.0.3.0/24'}\n\nclass Router(object):\n\n def __init__(self, connection):\n log.debug('router is up')\n\n # Keep track of connection to the switch so that we can send it messages!\n self.connection = connection\n\n # This binds our PacketIn event listener\n connection.addListeners(self)\n\n # Switch dipd\n self.dpid = connection.dpid\n\n # Use this table to keep track of which ethernet address is on\n # which switch port (keys are MACs, values are ports).\n self.mac_to_port = {}\n\n # ARP cash\n self.arp_cash = {}\n self.arp_cash['10.0.1.1'] = 'AA:BB:CC:DD:EE:01'\n self.arp_cash['10.0.2.1'] = 'AA:BB:CC:DD:EE:02'\n self.arp_cash['10.0.3.1'] = 'AA:BB:CC:DD:EE:03'\n\n # Buffer the packets if the router does not have the destination MAC address\n self.buffer = {}\n\n # Router Interface\n self.interface = {}\n self.interface[info_table[self.dpid]['local_IP']] = {'MAC': info_table[self.dpid]['local_MAC'], 'net': info_table[self.dpid]['local_net']}\n\n # Routing table to the router ip address(It is static)\n self.routing_table_ip = {}\n self.routing_table_ip['10.0.1.2'] = '10.0.1.1'\n self.routing_table_ip['10.0.1.3'] = '10.0.1.1'\n self.routing_table_ip['10.0.1.4'] = '10.0.1.1'\n self.routing_table_ip['10.0.2.2'] = '10.0.2.1'\n self.routing_table_ip['10.0.2.3'] = '10.0.2.1'\n self.routing_table_ip['10.0.2.4'] = '10.0.2.1'\n self.routing_table_ip['10.0.3.2'] = '10.0.3.1'\n self.routing_table_ip['10.0.3.3'] = '10.0.3.1'\n self.routing_table_ip['10.0.3.4'] = '10.0.3.1'\n self.routing_table_ip['10.0.1.1'] = '10.0.1.1'\n self.routing_table_ip['10.0.2.1'] = '10.0.2.1'\n self.routing_table_ip['10.0.3.1'] = '10.0.3.1'\n\n # Routing table to the port(It is static)\n self.routing_table_port = {}\n self.routing_table_port['10.0.1.2'] = 1\n self.routing_table_port['10.0.1.3'] = 2\n self.routing_table_port['10.0.1.4'] = 3\n self.routing_table_port['10.0.2.2'] = 1\n self.routing_table_port['10.0.2.3'] = 2\n self.routing_table_port['10.0.2.4'] = 3\n self.routing_table_port['10.0.2.1'] = 4\n self.routing_table_port['10.0.3.2'] = 1\n self.routing_table_port['10.0.3.3'] = 2\n self.routing_table_port['10.0.3.4'] = 3\n self.routing_table_port['10.0.3.1'] = 4\n\n\n # Routing table to the router port(It is static)\n self.routing_table_router_port = {}\n self.routing_table_router_port['10.0.1.1'] = {'10.0.2.1': 4, '10.0.3.1': 5}\n self.routing_table_router_port['10.0.2.1'] = {'10.0.1.1': 4, '10.0.3.1': 5}\n self.routing_table_router_port['10.0.3.1'] = {'10.0.1.1': 4, '10.0.2.1': 5}\n\n # Install the route default flow\n for dest in self.arp_cash.keys():\n msg = of.ofp_flow_mod()\n msg.priority = 100\n msg.match.dl_type = ethernet.IP_TYPE\n msg.match.nw_dst = IPAddr(dest)\n msg.actions.append(of.ofp_action_output(port = of.OFPP_CONTROLLER))\n self.connection.send(msg)\n\n def resend_packet (self, packet_in, out_port):\n \"\"\"\n Instructs the switch to resend a packet that it had sent to us.\n \"packet_in\" is the ofp_packet_in object the switch had sent to the\n controller due to a table-miss.\n \"\"\"\n msg = of.ofp_packet_out()\n msg.data = packet_in\n\n # Add an action to send to the specified port\n action = of.ofp_action_output(port = out_port)\n msg.actions.append(action)\n\n # Send message to switch\n self.connection.send(msg)\n\n def ARP_Process(self, packet, packet_in):\n log.debug('Network %s ARP frame is going to process' % self.dpid)\n\n # Check the frame is or not the ARP request\n if packet.payload.opcode == arp.REQUEST:\n arp_dst_ip = str(packet.payload.protodst)\n log.debug('This is netwrok %s ARP request' % self.dpid)\n\n # Check the frame is for the hosts or default router\n if arp_dst_ip in self.interface:\n log.debug('This is netwrok %s ARP request for the router gateway' % self.dpid)\n self.ARP_Request_Gateway(packet, packet_in)\n else:\n log.debug('This is network %s ARP request for the router interface' % self.dpid)\n self.ARP_Request_Interface(packet, packet_in)\n \t\n # Check the frame is or not the ARP reply\n if packet.payload.opcode == arp.REPLY:\n log.debug('This is netwrok %s ARP reply' % self.dpid)\n self.ARP_Reply(packet, packet_in)\n\n def ARP_Request_Interface(self, packet, packet_in):\n self.resend_packet(packet_in, of.OFPP_ALL)\n log.debug('Netwrok %s ARP request has flooded to other sports' % self.dpid)\n\n def ARP_Request_Gateway(self, packet, packet_in):\n arp_reply = arp()\n arp_reply.opcode = arp.REPLY\n arp_reply.hwsrc = EthAddr(self.interface[str(packet.payload.protodst)]['MAC'])\n arp_reply.hwdst = packet.payload.hwsrc\n arp_reply.protosrc = packet.payload.protodst\n arp_reply.protodst = packet.payload.protosrc\n ether_packet = ethernet()\n ether_packet.type = ether_packet.ARP_TYPE\n ether_packet.src = EthAddr(self.interface[str(packet.payload.protodst)]['MAC'])\n ether_packet.dst = packet.payload.hwsrc\n ether_packet.payload = arp_reply\n\n self.resend_packet(ether_packet, packet_in.in_port)\n log.debug('Network %s ARP reply has sent' % self.dpid)\n\n def ARP_Reply(self, packet, packet_in):\n src_ip = str(packet.payload.protosrc)\n if src_ip not in self.arp_cash:\n self.arp_cash[src_ip] = str(packet.payload.hwsrc)\n if str(packet.payload.hwsrc) not in self.mac_to_port:\n log.debug('Add %s -> %d into mac_to_port' % (packet.payload.hwsrc, packet_in.in_port))\n self.mac_to_port[str(packet.payload.hwsrc)] = packet_in.in_port\n if src_ip in self.buffer.keys():\n icmp_packet = self.buffer[src_ip]\n outPort = self.routing_table_port[src_ip]\n ether_packet = ethernet()\n ether_packet.type = ether_packet.IP_TYPE\n ether_packet.src = EthAddr(self.arp_cash[self.routing_table_ip[src_ip]])\n ether_packet.dst = EthAddr(self.arp_cash[src_ip])\n ether_packet.payload = icmp_packet\n\n self.resend_packet(ether_packet, outPort)\n log.debug('ICMP packet has sent')\n\n msg = of.ofp_flow_mod()\n msg.priority = 10\n msg.match.dl_type = ethernet.IP_TYPE\n msg.match.nw_dst = IPAddr(icmp_packet.dstip)\n msg.actions.append(of.ofp_action_dl_addr.set_src(EthAddr(self.arp_cash[self.routing_table_ip[src_ip]])))\n msg.actions.append(of.ofp_action_dl_addr.set_dst(EthAddr(self.arp_cash[src_ip])))\n msg.actions.append(of.ofp_action_output(port = outPort))\n log.debug('Installing flow:')\n log.debug('From %s to %s' % (icmp_packet.srcip, icmp_packet.dstip))\n self.connection.send(msg) \n self.buffer.pop(src_ip) \n\n def ICMP_Process(self, packet, packet_in):\n\n # Check the ICMP packet is or not request\n if packet.payload.payload.type == 8:\n log.debug('This is an ICMP request')\n\n # Check the ICMP request is for the local or remote\n if self.routing_table_ip[str(packet.payload.dstip)] == info_table[self.dpid]['local_IP']:\n\n # Check the ICMP request is for the local hosts or for the local router\n if str(packet.payload.dstip) in self.interface:\n log.debug('This is an %s ICMP request to the local router' % self.dpid)\n self.ICMP_Request_Router_local(packet, packet_in)\n else:\n log.debug('This is an %s ICMP request to the local host' % self.dpid)\n self.ICMP_Request_Host(packet, packet_in)\n else:\n\n # Check the ICMP request is for the remote hosts or for the remote router\n if str(packet.payload.dstip) == self.routing_table_ip[str(packet.payload.dstip)]:\n log.debug('This is an %s ICMP request to the remote router' % self.dpid)\n self.ICMP_Request_Router(packet, packet_in)\n else:\n log.debug('This is an %s ICMP request to the remote hosts' % self.dpid)\n self.ICMP_Request_Router(packet, packet_in) \n\n # Check the ICMP packet is or not reply\n if packet.payload.payload.type == 0:\n\n # Check the ICMP reply is for the router or for the host\n if self.routing_table_ip[str(packet.payload.dstip)] == info_table[self.dpid]['local_IP']:\n self.ICMP_Request_Host(packet, packet_in)\n else:\n self.ICMP_Request_Router(packet, packet_in)\n\n def ICMP_Request_Router_local(self, packet, packet_in):\n icmp_reply = icmp()\n icmp_reply.code = 0\n icmp_reply.type = 0\n icmp_reply.payload = packet.payload.payload.payload\n ip_reply = ipv4()\n ip_reply.srcip = packet.payload.dstip\n ip_reply.dstip = packet.payload.srcip\n ip_reply.protocol = ipv4.ICMP_PROTOCOL\n ip_reply.payload = icmp_reply\n ether_packet = ethernet()\n ether_packet.type = ethernet.IP_TYPE\n ether_packet.src = packet.dst\n ether_packet.dst = packet.src\n ether_packet.payload = ip_reply\n \n self.resend_packet(ether_packet, packet_in.in_port)\n log.debug('ICMP/TCP/UDP reply has sent')\n\n def ICMP_Request_Router(self, packet, pakcet_in):\n src_ip = str(packet.payload.srcip)\n dst_ip = str(packet.payload.dstip)\n\n packet.src = EthAddr(self.arp_cash[self.routing_table_ip[src_ip]])\n packet.dst = EthAddr(self.arp_cash[self.routing_table_ip[dst_ip]])\n\n self.resend_packet(packet, self.routing_table_router_port[self.routing_table_ip[src_ip]][self.routing_table_ip[dst_ip]])\n log.debug('ICMP/TCP/UDP packet has sent')\n\n def ICMP_Request_Host(self, packet, packet_in):\n dst_ip = str(packet.payload.dstip)\n outPort = self.routing_table_port[dst_ip]\n\n # Check the arp_cash has or not has destination address\n if dst_ip in self.arp_cash:\n packet.src = EthAddr(self.arp_cash[self.routing_table_ip[dst_ip]])\n packet.dst = EthAddr(self.arp_cash[dst_ip])\n\n self.resend_packet(packet, outPort)\n log.debug('ICMP/TCP/UDP packet has sent')\n\n # Check the arp_cash has or not has destination address\n if dst_ip not in self.arp_cash:\n\n # Buffer the packet(keys are the IP address, values are the ICMP pakcet)\n self.buffer[dst_ip] = packet.payload\n\n # Construct an ARP packet to get the MAC address of destination MAC address\n arp_request_packet = arp()\n arp_request_packet.opcode = arp.REQUEST\n arp_request_packet.protosrc = IPAddr(self.routing_table_ip[dst_ip])\n arp_request_packet.protodst = IPAddr(dst_ip)\n arp_request_packet.hwsrc = EthAddr(self.arp_cash[self.routing_table_ip[dst_ip]])\n arp_request_packet.hwdst = EthAddr('00:00:00:00:00:00')\n ether_packet = ethernet()\n ether_packet.type = ethernet.ARP_TYPE\n ether_packet.src = EthAddr(self.arp_cash[self.routing_table_ip[dst_ip]])\n ether_packet.dst = EthAddr('FF:FF:FF:FF:FF:FF')\n ether_packet.payload = arp_request_packet\n\n self.resend_packet(ether_packet, outPort)\n log.debug('ARP request has sent')\n\n def ICMP_Unreachable(self, packet, packet_in):\n icmp_reply_unreachable = icmp()\n icmp_reply_unreachable.code = 0\n icmp_reply_unreachable.type = 3\n icmp_reply_unreachable.payload = packet.payload.payload.payload\n ip_reply_unreachable = ipv4()\n ip_reply_unreachable.srcip = packet.payload.dstip\n ip_reply_unreachable.dstip = packet.payload.srcip\n ip_reply_unreachable.protocol = ipv4.ICMP_PROTOCOL\n ip_reply_unreachable.payload = icmp_reply_unreachable\n ether_packet = ethernet()\n ether_packet.type = ethernet.IP_TYPE\n ether_packet.src = packet.dst\n ether_packet.dst = packet.src\n ether_packet.payload = ip_reply_unreachable\n\n self.resend_packet(ether_packet, packet_in.in_port)\n log.debug('ICMP reply for unreachable packet has sent')\n\n def TCP_Process(self, packet, packet_in):\n if self.routing_table_ip[str(packet.payload.dstip)] == info_table[self.dpid]['local_IP']:\n self.ICMP_Request_Host(packet, packet_in)\n else:\n self.ICMP_Request_Router(packet, packet_in)\n\n def _handle_PacketIn (self, event):\n \"\"\"\n Handles packet in messages from the switch.\n \"\"\"\n packet = event.parsed # This is the parsed packet data.\n\n if not packet.parsed:\n log.warning(\"Ignoring incomplete packet\")\n return\n\n packet_in = event.ofp # The actual ofp_packet_in message.\n\n # If the frame is LLTP, retrun\n if packet.type == ethernet.LLDP_TYPE:\n log.warning(\"Ignoring LLDP\")\n return \n\n # Check this frame is or not for the network\n if packet.type == ethernet.ARP_TYPE:\n if self.routing_table_ip[str(packet.payload.protodst)] != info_table[self.dpid]['local_IP']:\n return\n\n # Comment out the following line and uncomment the one after\n # when starting the exercise.\n # self.act_like_hub(packet, packet_in)\n log.debug('Deals with packets from %s to %s' % (packet.src, packet.dst))\n\n # Add the MAC address to the mac_to_port dictionary\n if str(packet.src) not in self.mac_to_port:\n log.debug('Add %s -> %d into mac_to_port' % (packet.src, packet_in.in_port))\n self.mac_to_port[str(packet.src)] = packet_in.in_port\n\n # Check the frame destination is or not in the router\n if str(packet.dst) in self.mac_to_port:\n self.resend_packet(packet, self.mac_to_port[(str(packet.dst))])\n log.debug('The network %s frame is to the local hosts' % self.dpid)\n else:\n\n # Check the ethernet frame is or not an ARP frame\n if packet.type == ethernet.ARP_TYPE:\n log.debug('This network %s frame is an ARP frame' % self.dpid)\n self.ARP_Process(packet, packet_in)\n\n # Check the ethernet frame is or not an IP frame \n if packet.type == ethernet.IP_TYPE:\n \n # Check the ethernet frame is an ICMP frame or an TCP/UDP frame\n if packet.payload.protocol == ipv4.ICMP_PROTOCOL:\n log.debug('This network %s frame is an ICMP frame' % self.dpid)\n if str(packet.payload.dstip) not in self.routing_table_ip:\n log.debug('This network %s packet is not routable' % self.dpid)\n self.ICMP_Unreachable(packet, packet_in)\n else:\n log.debug('This network %s packet is routable' % self.dpid)\n self.ICMP_Process(packet, packet_in)\n else:\n log.debug('This frame is not an ICMP frame')\n self.TCP_Process(packet, packet_in)\n\ndef launch():\n \"\"\"\n Starts the component\n \"\"\"\n def start_switch (event):\n log.debug(\"Controlling %s\" % (event.connection,))\n Router(event.connection)\n core.openflow.addListenerByName(\"ConnectionUp\", start_switch)", "sub_path": "Scenario4/controller4.py", "file_name": "controller4.py", "file_ext": "py", "file_size_in_byte": 16892, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pox.core.core.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "pox.core.core", "line_number": 16, "usage_type": "name"}, {"api_name": "pox.openflow.libopenflow_01.ofp_flow_mod", "line_number": 93, "usage_type": "call"}, {"api_name": "pox.openflow.libopenflow_01", "line_number": 93, "usage_type": "name"}, {"api_name": "pox.lib.packet.ethernet.ethernet.IP_TYPE", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 95, "usage_type": "name"}, {"api_name": "pox.lib.addresses.IPAddr", "line_number": 96, "usage_type": "call"}, {"api_name": "pox.openflow.libopenflow_01.ofp_action_output", "line_number": 97, "usage_type": "call"}, {"api_name": "pox.openflow.libopenflow_01", "line_number": 97, "usage_type": "name"}, {"api_name": "pox.openflow.libopenflow_01.OFPP_CONTROLLER", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pox.openflow.libopenflow_01.ofp_packet_out", "line_number": 106, "usage_type": "call"}, {"api_name": "pox.openflow.libopenflow_01", "line_number": 106, "usage_type": "name"}, {"api_name": "pox.openflow.libopenflow_01.ofp_action_output", "line_number": 110, "usage_type": "call"}, {"api_name": "pox.openflow.libopenflow_01", "line_number": 110, "usage_type": "name"}, {"api_name": "pox.lib.packet.arp.arp.REQUEST", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.arp.arp", "line_number": 120, "usage_type": "name"}, {"api_name": "pox.lib.packet.arp.arp.REPLY", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.arp.arp", "line_number": 133, "usage_type": "name"}, {"api_name": "pox.openflow.libopenflow_01.OFPP_ALL", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pox.openflow.libopenflow_01", "line_number": 138, "usage_type": "name"}, {"api_name": "pox.lib.packet.arp.arp", "line_number": 142, "usage_type": "call"}, {"api_name": "pox.lib.packet.arp.arp.REPLY", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.arp.arp", "line_number": 143, "usage_type": "name"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 144, "usage_type": "call"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 148, "usage_type": "call"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 150, "usage_type": "call"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 167, "usage_type": "call"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 169, "usage_type": "call"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 170, "usage_type": "call"}, {"api_name": "pox.openflow.libopenflow_01.ofp_flow_mod", "line_number": 176, "usage_type": "call"}, {"api_name": "pox.openflow.libopenflow_01", "line_number": 176, "usage_type": "name"}, {"api_name": "pox.lib.packet.ethernet.ethernet.IP_TYPE", "line_number": 178, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 178, "usage_type": "name"}, {"api_name": "pox.lib.addresses.IPAddr", "line_number": 179, "usage_type": "call"}, {"api_name": "pox.openflow.libopenflow_01.ofp_action_dl_addr.set_src", "line_number": 180, "usage_type": "call"}, {"api_name": "pox.openflow.libopenflow_01.ofp_action_dl_addr", "line_number": 180, "usage_type": "attribute"}, {"api_name": "pox.openflow.libopenflow_01", "line_number": 180, "usage_type": "name"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 180, "usage_type": "call"}, {"api_name": "pox.openflow.libopenflow_01.ofp_action_dl_addr.set_dst", "line_number": 181, "usage_type": "call"}, {"api_name": "pox.openflow.libopenflow_01.ofp_action_dl_addr", "line_number": 181, "usage_type": "attribute"}, {"api_name": "pox.openflow.libopenflow_01", "line_number": 181, "usage_type": "name"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 181, "usage_type": "call"}, {"api_name": "pox.openflow.libopenflow_01.ofp_action_output", "line_number": 182, "usage_type": "call"}, {"api_name": "pox.openflow.libopenflow_01", "line_number": 182, "usage_type": "name"}, {"api_name": "pox.lib.packet.icmp.icmp", "line_number": 224, "usage_type": "call"}, {"api_name": "pox.lib.packet.ipv4.ipv4", "line_number": 228, "usage_type": "call"}, {"api_name": "pox.lib.packet.ipv4.ipv4.ICMP_PROTOCOL", "line_number": 231, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.ipv4.ipv4", "line_number": 231, "usage_type": "name"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 233, "usage_type": "call"}, {"api_name": "pox.lib.packet.ethernet.ethernet.IP_TYPE", "line_number": 234, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 234, "usage_type": "name"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 246, "usage_type": "call"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 247, "usage_type": "call"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 258, "usage_type": "call"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 259, "usage_type": "call"}, {"api_name": "pox.lib.packet.arp.arp", "line_number": 271, "usage_type": "call"}, {"api_name": "pox.lib.packet.arp.arp.REQUEST", "line_number": 272, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.arp.arp", "line_number": 272, "usage_type": "name"}, {"api_name": "pox.lib.addresses.IPAddr", "line_number": 273, "usage_type": "call"}, {"api_name": "pox.lib.addresses.IPAddr", "line_number": 274, "usage_type": "call"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 275, "usage_type": "call"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 276, "usage_type": "call"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 277, "usage_type": "call"}, {"api_name": "pox.lib.packet.ethernet.ethernet.ARP_TYPE", "line_number": 278, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 278, "usage_type": "name"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 279, "usage_type": "call"}, {"api_name": "pox.lib.addresses.EthAddr", "line_number": 280, "usage_type": "call"}, {"api_name": "pox.lib.packet.icmp.icmp", "line_number": 287, "usage_type": "call"}, {"api_name": "pox.lib.packet.ipv4.ipv4", "line_number": 291, "usage_type": "call"}, {"api_name": "pox.lib.packet.ipv4.ipv4.ICMP_PROTOCOL", "line_number": 294, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.ipv4.ipv4", "line_number": 294, "usage_type": "name"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 296, "usage_type": "call"}, {"api_name": "pox.lib.packet.ethernet.ethernet.IP_TYPE", "line_number": 297, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 297, "usage_type": "name"}, {"api_name": "pox.lib.packet.ethernet.ethernet.LLDP_TYPE", "line_number": 324, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 324, "usage_type": "name"}, {"api_name": "pox.lib.packet.ethernet.ethernet.ARP_TYPE", "line_number": 329, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 329, "usage_type": "name"}, {"api_name": "pox.lib.packet.ethernet.ethernet.ARP_TYPE", "line_number": 350, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 350, "usage_type": "name"}, {"api_name": "pox.lib.packet.ethernet.ethernet.IP_TYPE", "line_number": 355, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.ethernet.ethernet", "line_number": 355, "usage_type": "name"}, {"api_name": "pox.lib.packet.ipv4.ipv4.ICMP_PROTOCOL", "line_number": 358, "usage_type": "attribute"}, {"api_name": "pox.lib.packet.ipv4.ipv4", "line_number": 358, "usage_type": "name"}, {"api_name": "pox.core.core.openflow.addListenerByName", "line_number": 377, "usage_type": "call"}, {"api_name": "pox.core.core.openflow", "line_number": 377, "usage_type": "attribute"}, {"api_name": "pox.core.core", "line_number": 377, "usage_type": "name"}]} +{"seq_id": "618169147", "text": "# -*- coding: utf-8 -*-\n\n\"\"\"\n Time : 2019-11-25 14:45\n Author : Thinkgamer\n File : DataTranslate.py\n Software: PyCharm\n Desc : 数据翻译\n\"\"\"\nimport http.client\nimport hashlib\nimport urllib\nimport random\nimport json\nimport pandas as pd\n\nclass TransWithBaidu:\n def __init__(self):\n self.appid = '20191125000360239' # 填写你的appid\n self.secretKey = 'Kcf53Ws63weAD0jgDkjj' # 填写你的密钥\n self.httpClient = None\n self.fromLang = 'auto' # 原文语种\n self.toLang = 'zh' # 译文语种\n self.salt = random.randint(32768, 65536)\n\n\n def line(self, text):\n sign = self.appid + text + str(self.salt) + self.secretKey\n newSign = hashlib.md5(sign.encode()).hexdigest()\n myurl = '/api/trans/vip/translate' + \\\n '?appid=' + self.appid + \\\n '&q=' + urllib.parse.quote(text) + \\\n '&from=' + self.fromLang + \\\n '&to=' + self.toLang + \\\n '&salt=' + str(self.salt) + \\\n '&sign=' + newSign\n return myurl\n\n def translate(self, text):\n myurl = self.line(text)\n try:\n httpClient = http.client.HTTPConnection('api.fanyi.baidu.com')\n httpClient.request('GET', myurl)\n\n # response是HTTPResponse对象\n response = httpClient.getresponse()\n result_all = response.read().decode(\"utf-8\")\n result = json.loads(result_all)\n print(result[\"trans_result\"][0][\"dst\"])\n return result[\"trans_result\"][0][\"dst\"]\n except Exception as e:\n print(e)\n return text\n finally:\n if httpClient:\n httpClient.close()\n\n\n def categories(self):\n needTransKey = [\"category_name\",\"shortname\",\"sort_name\"]\n file =\"../data/meetup/categories.csv\"\n df = pd.read_csv(file)\n for key in df.keys():\n df[key] = df[key].map(lambda x: self.translate(x) if key in needTransKey else x)\n\n df.to_csv(\"../data/meetup_zh/categories.csv\", index=False)\n print(df.head(35))\n\nif __name__ == \"__main__\":\n trans = TransWithBaidu()\n trans.translate(\"Fitness\")\n # trans.categories()", "sub_path": "Experiment/DataTranslate.py", "file_name": "DataTranslate.py", "file_ext": "py", "file_size_in_byte": 2245, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.parse.quote", "line_number": 32, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 32, "usage_type": "attribute"}, {"api_name": "http.client.client.HTTPConnection", "line_number": 42, "usage_type": "call"}, {"api_name": "http.client.client", "line_number": 42, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 42, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "370265169", "text": "import unittest\n\nfrom django.core.urlresolvers import reverse\nfrom django.test import TestCase\nfrom django.test import override_settings\nfrom django_factory_boy import auth as auth_factories\n\nfrom conference.tests.factories.attendee_profile import AttendeeProfileFactory\nfrom conference.tests.factories.conference import ConferenceFactory\nfrom p3.tests.factories.schedule import ScheduleFactory\n\n\nclass TestWhosComing(TestCase):\n def setUp(self):\n self.user = auth_factories.UserFactory(password='password1234', is_superuser=True)\n is_logged = self.client.login(username=self.user.username,\n password='password1234')\n AttendeeProfileFactory(user=self.user)\n self.assertTrue(is_logged)\n\n @override_settings(CONFERENCE_CONFERENCE='epbeta', DEBUG=False)\n def test_p3_whos_coming_no_conference(self):\n url = reverse('p3-whos-coming')\n conference = ConferenceFactory(code='epbeta')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 302)\n self.assertRedirects(response, reverse('p3-whos-coming-conference', kwargs={\n 'conference': conference.pk,\n }))\n\n def test_p3_whos_coming_with_conference(self):\n # p3-whos-coming-conference -> p3.views.whos_coming\n # FIXME: The conference parameter has a default value to None, but the url does not accept a empty value\n conference = ConferenceFactory(code='epbeta')\n url = reverse('p3-whos-coming-conference', kwargs={\n 'conference': conference.pk\n })\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n # FIXME: Test the query string speaker, tags, country\n\n\nclass TestView(TestCase):\n def setUp(self):\n self.user = auth_factories.UserFactory(password='password1234', is_superuser=True)\n is_logged = self.client.login(username=self.user.username,\n password='password1234')\n AttendeeProfileFactory(user=self.user)\n self.assertTrue(is_logged)\n\n def test_p3_billing_with_no_user_cart(self):\n # p3-billing -> p3.views.cart.billing\n url = reverse('p3-billing')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 302)\n self.assertRedirects(response, reverse('p3-cart'), fetch_redirect_response=False)\n\n def test_p3_billing_no_ticket(self):\n # p3-billing -> p3.views.cart.billing\n url = reverse('p3-billing')\n response = self.client.get(url)\n self.assertRedirects(response, reverse('p3-cart'), fetch_redirect_response=False)\n\n @override_settings(DEBUG=False)\n def test_p3_calculator_get_default_values(self):\n # p3-calculator -> p3.views.cart.calculator\n url = reverse('p3-calculator')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response.get('content-type'), 'application/json')\n self.assertJSONEqual(response.content, {'tickets': [], 'coupon': 0, 'total': 0})\n\n @override_settings(CONFERENCE_CONFERENCE='epbeta')\n def test_p3_my_schedule(self):\n # p3-my-schedule -> p3.views.schedule.jump_to_my_schedule\n conference = ConferenceFactory(code='epbeta')\n\n url = reverse('p3-my-schedule')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 302)\n\n redirect_url = reverse('p3-schedule-my-schedule', kwargs={\n 'conference': conference.code\n })\n\n self.assertRedirects(response, redirect_url, fetch_redirect_response=False)\n\n @override_settings(CONFERENCE_CONFERENCE='epbeta')\n def test_p3_schedule_ics(self):\n # p3-schedule-ics -> p3.views.schedule.schedule_ics\n conference = ConferenceFactory(code='epbeta')\n\n url = reverse('p3-schedule-ics', kwargs={\n 'conference': conference.code,\n })\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n\n def test_p3_schedule(self):\n # p3-schedule -> p3.views.schedule.schedule\n conference = ConferenceFactory()\n url = reverse('p3-schedule', kwargs={\n 'conference': conference.code\n })\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n \n # @unittest.skip(\"FIXME\")\n def test_p3_schedule_list(self):\n # p3-schedule-list -> p3.views.schedule.schedule_list\n conference = ConferenceFactory()\n schedule = ScheduleFactory(conference=conference.code)\n\n url = reverse('p3-schedule-list', kwargs={\n 'conference': conference.code,\n })\n response = self.client.get(url)\n\n values = response.context['sids'].values()\n\n dict_of_schedule = {\n 'conference': schedule.conference,\n 'date': schedule.date.date(),\n 'description': schedule.description,\n 'id': schedule.id,\n 'slug': schedule.slug,\n }\n\n self.assertDictEqual(values[0], dict_of_schedule)\n self.assertEqual(response.status_code, 200)\n \n @unittest.skip(\"FIXME\")\n def test_p3_schedule_my_schedule(self):\n # p3-schedule-my-schedule -> p3.views.schedule.my_schedule\n url = reverse('p3-schedule-my-schedule')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n \n @unittest.skip(\"FIXME\")\n def test_p3_sprint_submission(self):\n # p3-sprint-submission -> p3.views.sprint_submission\n url = reverse('p3-sprint-submission')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n \n @unittest.skip(\"FIXME\")\n def test_p3_sprints(self):\n # p3-sprints -> p3.views.sprints\n url = reverse('p3-sprints')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n \n @unittest.skip(\"FIXME\")\n def test_p3_sprint(self):\n # p3-sprint -> p3.views.sprint\n url = reverse('p3-sprint')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n \n @unittest.skip(\"FIXME\")\n def test_p3_tickets(self):\n # p3-tickets -> p3.views.tickets\n url = reverse('p3-tickets')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n \n @unittest.skip(\"FIXME\")\n def test_p3_ticket(self):\n # p3-ticket -> p3.views.ticket\n url = reverse('p3-ticket')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n \n @unittest.skip(\"FIXME\")\n def test_p3_user(self):\n # p3-user -> p3.views.user\n url = reverse('p3-user')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n\n @override_settings(CONFERENCE_CONFERENCE='epbeta')\n def test_p3_schedule_my_schedule_ics(self):\n # p3-schedule-my-schedule-ics -> p3.views.schedule.schedule_ics\n\n conference = ConferenceFactory(code='epbeta')\n\n url = reverse('p3-schedule-my-schedule-ics', kwargs={\n 'conference': conference.code,\n })\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response.get('content-type'), 'text/calendar')\n\n @override_settings(CONFERENCE_CONFERENCE='epbeta')\n def test_p3_schedule_my_schedule_ics_error_404(self):\n # p3-schedule-my-schedule-ics -> p3.views.schedule.schedule_ics\n self.client.logout()\n conference = ConferenceFactory(code='epbeta')\n\n url = reverse('p3-schedule-my-schedule-ics', kwargs={\n 'conference': conference.code,\n })\n response = self.client.get(url, follow=True)\n self.assertEqual(response.status_code, 404)\n", "sub_path": "p3/tests/test_views.py", "file_name": "test_views.py", "file_ext": "py", "file_size_in_byte": 7863, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.test.TestCase", "line_number": 13, "usage_type": "name"}, {"api_name": "django_factory_boy.auth.UserFactory", "line_number": 15, "usage_type": "call"}, {"api_name": "django_factory_boy.auth", "line_number": 15, "usage_type": "name"}, {"api_name": "conference.tests.factories.attendee_profile.AttendeeProfileFactory", "line_number": 18, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 23, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 24, "usage_type": "name"}, {"api_name": "conference.tests.factories.conference.ConferenceFactory", "line_number": 24, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 27, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile.pk", "line_number": 28, "usage_type": "attribute"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 28, "usage_type": "name"}, {"api_name": "django.test.override_settings", "line_number": 21, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 34, "usage_type": "name"}, {"api_name": "conference.tests.factories.conference.ConferenceFactory", "line_number": 34, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 35, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile.pk", "line_number": 36, "usage_type": "attribute"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 36, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 43, "usage_type": "name"}, {"api_name": "django_factory_boy.auth.UserFactory", "line_number": 45, "usage_type": "call"}, {"api_name": "django_factory_boy.auth", "line_number": 45, "usage_type": "name"}, {"api_name": "conference.tests.factories.attendee_profile.AttendeeProfileFactory", "line_number": 48, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 53, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 56, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 60, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 62, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 67, "usage_type": "call"}, {"api_name": "django.test.override_settings", "line_number": 64, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 76, "usage_type": "name"}, {"api_name": "conference.tests.factories.conference.ConferenceFactory", "line_number": 76, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 78, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 82, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile.code", "line_number": 83, "usage_type": "attribute"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 83, "usage_type": "name"}, {"api_name": "django.test.override_settings", "line_number": 73, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 91, "usage_type": "name"}, {"api_name": "conference.tests.factories.conference.ConferenceFactory", "line_number": 91, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 93, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile.code", "line_number": 94, "usage_type": "attribute"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 94, "usage_type": "name"}, {"api_name": "django.test.override_settings", "line_number": 88, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 101, "usage_type": "name"}, {"api_name": "conference.tests.factories.conference.ConferenceFactory", "line_number": 101, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 102, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile.code", "line_number": 103, "usage_type": "attribute"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 103, "usage_type": "name"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 111, "usage_type": "name"}, {"api_name": "conference.tests.factories.conference.ConferenceFactory", "line_number": 111, "usage_type": "call"}, {"api_name": "p3.tests.factories.schedule.ScheduleFactory", "line_number": 112, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile.code", "line_number": 112, "usage_type": "attribute"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 112, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 114, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile.code", "line_number": 115, "usage_type": "attribute"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 115, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 135, "usage_type": "call"}, {"api_name": "unittest.skip", "line_number": 132, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 142, "usage_type": "call"}, {"api_name": "unittest.skip", "line_number": 139, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 149, "usage_type": "call"}, {"api_name": "unittest.skip", "line_number": 146, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 156, "usage_type": "call"}, {"api_name": "unittest.skip", "line_number": 153, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 163, "usage_type": "call"}, {"api_name": "unittest.skip", "line_number": 160, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 170, "usage_type": "call"}, {"api_name": "unittest.skip", "line_number": 167, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 177, "usage_type": "call"}, {"api_name": "unittest.skip", "line_number": 174, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 185, "usage_type": "name"}, {"api_name": "conference.tests.factories.conference.ConferenceFactory", "line_number": 185, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 187, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile.code", "line_number": 188, "usage_type": "attribute"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 188, "usage_type": "name"}, {"api_name": "django.test.override_settings", "line_number": 181, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 198, "usage_type": "name"}, {"api_name": "conference.tests.factories.conference.ConferenceFactory", "line_number": 198, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 200, "usage_type": "call"}, {"api_name": "conference.tests.factories.attendee_profile.code", "line_number": 201, "usage_type": "attribute"}, {"api_name": "conference.tests.factories.attendee_profile", "line_number": 201, "usage_type": "name"}, {"api_name": "django.test.override_settings", "line_number": 194, "usage_type": "call"}]} +{"seq_id": "611477024", "text": "import sys\nfrom PyQt5.QtWidgets import *\nfrom PyQt5 import uic\n\nimport ComicDL\n# from ComicDL import download_marumaru\n# from ComicDL import download_manazero\n\n\n# ui 파일 로드 후 class 생성\nmain_window_class = uic.loadUiType(\"main_window.ui\")[0]\n\n\nclass MainWindow(QMainWindow, main_window_class):\n def __init__(self):\n super().__init__()\n self.setupUi(self)\n\n #직접검색-검색 버튼\n self.pushButton.clicked.connect(self.btn_directly_download)\n\n\n #직접검색-검색 버튼\n def btn_directly_download(self):\n # QMessageBox.about(self, \"message\", \"클릭됨\")\n gallery_url = self.lineEdit.text()\n ComicDL.download_marumaru_request(gallery_url, \"C:/Downloaded Files/죠죠1부/\")\n\ndef downloader_marumaru_console():\n print(\"=============================마루마루 다운로더 Console=============================\")\n request_url = input(\"다운로드 요청할 주소를 입력하세요: \")\n destination = input(\"저장 위치를 입력하세요: \")\n ComicDL.download_marumaru_request(request_url, destination)\n\napp = None\nmainWindow = None\n\nif __name__ == \"__main__\":\n # # GUI APP\n # app = QApplication(sys.argv)\n # mainWindow = MainWindow()\n # mainWindow.show()\n # app.exec_()\n\n # # CONSOLE APP\n downloader_marumaru_console()\n # ComicDL.download_marumaru_request(\"http://marumaru.in/b/manga/132529\", \"D:/Downloaded Comic Files/\")\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1440, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PyQt5.uic.loadUiType", "line_number": 11, "usage_type": "call"}, {"api_name": "PyQt5.uic", "line_number": 11, "usage_type": "name"}, {"api_name": "ComicDL.download_marumaru_request", "line_number": 27, "usage_type": "call"}, {"api_name": "ComicDL.download_marumaru_request", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "255664273", "text": "from django.utils.safestring import mark_safe\nfrom django.core.urlresolvers import reverse\nfrom django.utils.html import format_html, conditional_escape\nfrom django import forms\nfrom functools import partial\nfrom django.forms.models import modelform_factory\n\n\nclass ForbidDeleteAdd(object):\n\n def has_delete_permission(self, request, obj=None):\n return False\n\n def has_add_permission(self, request, obj=None):\n return False\n\n\nclass FkAdminLink(object):\n \"\"\"\n Link to object in admin\n Example usage:\n class Book(models.Model):\n author = models.ForeignKey('user')\n content = models.TextField()\n\n\n class BookAdmin(models.ModelAdmin):\n readonly_fields = ('_author', )\n\n def _author(self, obj):\n return self._admin_url(obj.author)\n # or\n # return self._admin_url(obj.author,\n obj.author.last_name + obj.author.first_name[0])\n \"\"\"\n\n def _admin_url(self, obj, title=None):\n if not title:\n title = conditional_escape(obj.__str__())\n\n admin_url_str = 'admin:{}_{}_change'.format(\n obj._meta.app_label,\n obj._meta.object_name.lower()\n )\n admin_url = reverse(admin_url_str, args=[obj.pk])\n\n return format_html(mark_safe(\n \"{}\".format(admin_url, title)))\n\n\nclass AllFieldsReadOnly(object):\n '''\n Simple mixin if you want to make all fields readonly\n without specifying fields attribute\n '''\n\n def get_readonly_fields(self, request, obj=None):\n if self.fields:\n return self.fields\n\n # took this django sources\n if self.exclude is None:\n exclude = []\n else:\n exclude = list(self.exclude)\n if self.exclude is None and hasattr(self.form, '_meta') and self.form._meta.exclude:\n # Take the custom ModelForm's Meta.exclude into account only if the\n # ModelAdmin doesn't define its own.\n exclude.extend(self.form._meta.exclude)\n # if exclude is an empty list we pass None to be consistent with the\n # default on modelform_factory\n exclude = exclude or None\n\n defaults = {\n \"form\": self.form,\n \"fields\": forms.ALL_FIELDS,\n \"exclude\": exclude,\n \"formfield_callback\": partial(self.formfield_for_dbfield, request=request),\n }\n form = modelform_factory(self.model, **defaults)\n return list(form.base_fields)\n", "sub_path": "output_test)/manoj/WEBPR_Project/WebPR/libs/admin/mixins/model_admin.py", "file_name": "model_admin.py", "file_ext": "py", "file_size_in_byte": 2534, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.utils.html.conditional_escape", "line_number": 39, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 45, "usage_type": "call"}, {"api_name": "django.utils.html.format_html", "line_number": 47, "usage_type": "call"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 47, "usage_type": "call"}, {"api_name": "django.forms.ALL_FIELDS", "line_number": 76, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 76, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 78, "usage_type": "call"}, {"api_name": "django.forms.models.modelform_factory", "line_number": 80, "usage_type": "call"}]} +{"seq_id": "58379708", "text": "import io,os\nimport requests\n#import pynmea2\nfrom threading import Thread\nfrom requests.auth import HTTPBasicAuth\n\nimport time\n\nCHECK_SERVER_URL = \"https://dev.r2b.stax.tlabs.cloud/vehicle/qwe/check-booking-status\"\nTIME_INTERVAL = 1\n\nBASE_URL = \"https://dev.r2b.stax.tlabs.cloud/\"\n\n\n\nSTAT_DATA = \"https://dev.r2b.stax.tlabs.cloud/vehicle/{address}/dynamic-info\"\nSTAT_DATA = \"https://dev.r2b.stax.tlabs.cloud/vehicle/0xbE73928c57B6e7191EfCdDA9426204202Cd9dE6c/dynamic-info\"\n\n\nTOKEN = \"did:jolo:1031d19d6fe6520fcbb6c4542b4e37b512493a5bf0b81b5ccc78afcc27bdce11\"\n\nlatitude = -1\nlongitude = -1\npower = 0\n\ndef check_status_loop():\n\tr = requests.post(CHECK_SERVER_URL, json={\"did\": TOKEN})\n\twhile(True):\n\t\ttry:\n\t\t\twhile(True):\n\t\t\t\tprint(latitude)\n\t\t\t\tif RUN_STATUS == r.json()['isBookedByUser']:\n\t\t\t\t\tchange_power()\n\t\t\t\t\tprint(\"Skyter is booked\")\n\t\t\t\ttime.sleep(TIME_INTERVAL)\n\t\texcept Exception as exp:\n\t\t\tprint(exp)\n\t\t\n\n#thread = Thread(target=check_status_loop)\n#thread.start()\n\ndef check_key_file_loop():\n\twhile(True):\n\t\tif os.path.exists(\"book.key\"):\n\t\t\tif not RUN_STATUS:\n\t\t\t\tchange_power()\n\t\t\tos.remove(\"book.key\")\n\t\ttime.sleep(TIME_INTERVAL)\n\ndef send_gps_loop():\n\tglobal latitude\n\tglobal longitude\n\tglobal power\n\twhile(True):\n\t\tprint(latitude)\n\t\tprint(longitude)\n\t\tprint(power)\n\t\tif latitude != -1 and longitude != -1:\n\t\t\tr = requests.post(STAT_DATA, json={\"latitude\": latitude,\"longitude\": longitude,\"batteryLevel\": power})\n\t\ttime.sleep(TIME_INTERVAL)\n\nthread = Thread(target=send_gps_loop)\nthread.start()\n\n\n# gep position\ndef update_position(new_latitude, new_longitude):\n\t# TODO add blocking function\n\tglobal latitude \n\tlatitude = float('{:.4f}'.format(new_latitude))\n\tglobal longitude \n\tlongitude = float('{:.4f}'.format(new_longitude))\n\n\ndef generate_random_gps_and_power():\n\timport random\n\tglobal power\n\twhile True:\n\t\tupdate_position(random.random()*180,random.random()*180)\n\t\tpower = random.randint(0, 90)\n\n\ngps_read_thread = Thread(target=generate_random_gps_and_power)\ngps_read_thread.start()\n\n\n\ndef gps():\n\ttty = io.TextIOWrapper(io.FileIO(os.open(\"/dev/ttyS0\",os.O_NOCTTY | os.O_RDWR),\"r+\" ))\n\ttty = open(\"walking3_data.txt.compl\")\n\tfor line in iter(tty.readline,None):\n\t\tline = line.replace(\"\\n\",\"\")\n\t\tif line == \"\" or line == \"\\n\":\n\t\t\tcontinue\n\t\tif line == \"$GPTXT,01,01,01,NMEA unknown msg*58\":\n\t\t\tcontinue\n\t\ttry:\n\t\t\tmsg = pynmea2.parse(line)\n\t\t\tif msg.sentence_type == \"GLL\":\n\t\t\t\tprint(msg)\n\t\t\t\tif msg.is_valid :\n\t\t\t\t\tupdate_position(msg.latitude,msg.longitude)\n\t\t\t\tprint(\"--\")\n\t\texcept Exception as exp:\n\t\t\tpass\n\n\n\t\t\n\n\n\t\nthread.join()\n", "sub_path": "r2b-rasp-vehicle-controller/NFC/gps.py", "file_name": "gps.py", "file_ext": "py", "file_size_in_byte": 2555, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.post", "line_number": 27, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 48, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 60, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 63, "usage_type": "call"}, {"api_name": "random.random", "line_number": 80, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 81, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 84, "usage_type": "call"}, {"api_name": "io.TextIOWrapper", "line_number": 90, "usage_type": "call"}, {"api_name": "io.FileIO", "line_number": 90, "usage_type": "call"}, {"api_name": "os.open", "line_number": 90, "usage_type": "call"}, {"api_name": "os.O_NOCTTY", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.O_RDWR", "line_number": 90, "usage_type": "attribute"}]} +{"seq_id": "256665343", "text": "# Module to run tests on spectra.lsf\nfrom __future__ import print_function, absolute_import, \\\n division, unicode_literals\n\n\nimport os\nimport pytest\nimport astropy.units as u\nimport numpy as np\nfrom astropy.table import Table\n\nfrom linetools.spectra.lsf import LSF\n\ndef test_interpolate_to_wv0(plot=False):\n err_msg = 'Something is wrong with LSF.interpolate_to_wv0()'\n wv0 = 1160*u.AA\n cos_dict = dict(name='COS',grating='G130M',life_position='2',cen_wave='1309')\n lsf_cos = LSF(cos_dict)\n lsf_tab = lsf_cos.interpolate_to_wv0(wv0)\n assert lsf_tab[len(lsf_tab)//2]['wv'] == wv0.value, err_msg\n assert lsf_tab[len(lsf_tab)//2]['kernel'] == np.max(lsf_tab['kernel']), err_msg\n if plot:\n import matplotlib.pyplot as plt\n wv_array = np.arange(1200,1400,10)*u.AA\n for wv in wv_array:\n lsf_tab = lsf_cos.interpolate_to_wv0(wv)\n plt.plot(lsf_tab['wv']-wv.value,lsf_tab['kernel'],'-')\n plt.show()\n\ndef test_interpolate_to_wv_array(plot=False):\n err_msg = 'Something is wrong with LSF.interpolate_to_wv_array()'\n wv_array = np.arange(1600,1601,0.001)*u.AA\n wv_array = np.arange(1600,1650,0.001)*u.AA\n cen_waves = ['1577','1589A','1600','1611','1623']\n colors = ['k','b','g','r','orange']\n lsf_dict = dict()\n for i,cen_wave in enumerate(cen_waves):\n cos_dict_aux = dict(name='COS',grating='G160M',life_position='2',cen_wave=cen_wave)\n lsf_dict[cen_wave] = LSF(cos_dict_aux)\n lsf_tab = lsf_dict[cen_wave].interpolate_to_wv_array(wv_array)\n assert isinstance(lsf_tab,Table), err_msg\n if plot:\n import matplotlib.pyplot as plt\n plt.plot(wv_array,lsf_tab['kernel'],'-',color=colors[i])\n if plot:\n plt.show()\n\ndef test_get_lsf(plot=False):\n err_msg = 'Something is wrong with LSF.get_lsf()'\n wv_array = np.arange(1250,1251,0.0001)*u.AA\n cen_waves = ['1291','1300','1309','1318A','1327A']\n colors = ['k','b','g','r','orange']\n lsf_dict = dict()\n for i,cen_wave in enumerate(cen_waves):\n cos_dict_aux = dict(name='COS',grating='G130M',life_position='2',cen_wave=cen_wave)\n lsf_dict[cen_wave] = LSF(cos_dict_aux)\n lsf_kernel = lsf_dict[cen_wave].get_lsf(wv_array)\n assert isinstance(lsf_kernel,np.ndarray), err_msg\n if plot:\n import matplotlib.pyplot as plt\n plt.plot(wv_array,lsf_kernel,'-',color=colors[i])\n if plot:\n plt.show()\n\n\n", "sub_path": "linetools/spectra/tests/test_lsf_use.py", "file_name": "test_lsf_use.py", "file_ext": "py", "file_size_in_byte": 2467, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "astropy.units.AA", "line_number": 16, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 16, "usage_type": "name"}, {"api_name": "linetools.spectra.lsf.LSF", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 24, "usage_type": "call"}, {"api_name": "astropy.units.AA", "line_number": 24, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 32, "usage_type": "call"}, {"api_name": "astropy.units.AA", "line_number": 32, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 33, "usage_type": "call"}, {"api_name": "astropy.units.AA", "line_number": 33, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 33, "usage_type": "name"}, {"api_name": "linetools.spectra.lsf.LSF", "line_number": 39, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 41, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 50, "usage_type": "call"}, {"api_name": "astropy.units.AA", "line_number": 50, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 50, "usage_type": "name"}, {"api_name": "linetools.spectra.lsf.LSF", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 58, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}]} +{"seq_id": "10318982", "text": "import copy\n\nimport torch\nfrom torch.nn.utils import clip_grad_norm_\nfrom common.loss_fn import ActorLoss, Retrace\nfrom common.replay_buffer import SharedReplayBuffer\n\n\nclass Learner:\n def __init__(self,\n actor: torch.nn.Module,\n critic: torch.nn.Module,\n replay_buffer: SharedReplayBuffer,\n device: str,\n num_actions: int,\n num_obs: int,\n argp,\n logger=None):\n\n self.actor = actor\n self.critic = critic\n\n self.target_actor = copy.deepcopy(self.actor).to(device)\n self.target_actor.freeze_net()\n self.target_critic = copy.deepcopy(self.critic).to(device)\n self.target_critic.freeze_net()\n\n self.replay_buffer = replay_buffer\n\n self.num_actions = num_actions\n self.num_obs = num_obs\n self.device = device\n\n self.logger = logger\n self.logging = argp.logging\n self.log_every = argp.log_interval\n\n self.actor_loss = ActorLoss(alpha=argp.entropy_reg)\n self.critic_loss = Retrace(num_actions=self.num_actions, reward_scale=argp.reward_scale)\n\n self.actor_opt = torch.optim.Adam(self.actor.parameters(), argp.actor_lr)\n self.critic_opt = torch.optim.Adam(self.critic.parameters(), argp.critic_lr)\n\n self.update_targnets_every = argp.update_targnets_every\n self.learning_steps = argp.learning_steps\n self.smoothing_coefficient = argp.smoothing_coefficient\n self.global_gradient_norm = argp.global_gradient_norm\n\n def learn(self) -> None:\n\n \"\"\"\n Calculates gradients w.r.t. the actor and the critic and sends them to a shared parameter server. Whenever\n the server has accumulated G gradients, the parameter of the shared critic and actor are updated and sent\n to the worker. However, the parameters of the shared actor and critic are copied to the worker after each\n iteration since it is unknown to the worker when the gradient updates were happening.\n\n Returns:\n No return value\n \"\"\"\n self.actor.train()\n self.critic.train()\n\n for i in range(self.learning_steps):\n\n # Update the target networks\n if i % self.update_targnets_every == 0:\n self.update_targnets(smoothing_coefficient=self.smoothing_coefficient)\n\n self.actor.train()\n self.critic.train()\n\n states, actions, rewards, behaviour_log_pr = self.replay_buffer.sample()\n states = states.to(self.device)\n actions = actions.to(self.device)\n rewards = rewards.to(self.device)\n behaviour_log_pr = behaviour_log_pr.to(self.device)\n\n # Q(a_t, s_t)\n batch_Q = self.critic(torch.cat([actions / 2, states], dim=-1))\n\n # Q_target(a_t, s_t)\n target_Q = self.target_critic(torch.cat([actions / 2, states], dim=-1))\n\n # Compute 𝔼_π_target [Q(s_t,•)] with a ~ π_target(•|s_t), log(π_target(a|s)) with 1 sample\n mean, log_std = self.target_actor(states)\n mean, log_std = mean.to(self.device), log_std.to(self.device)\n\n action_sample, _ = self.target_actor.action_sample(mean, log_std)\n # action_sample = torch.tanh(mean)\n expected_target_Q = self.target_critic(torch.cat([action_sample / 2, states], dim=-1))\n\n # log(π_target(a_t | s_t))\n target_action_log_prob = self.target_actor.get_log_prob(actions=actions, mean=mean, log_std=log_std)\n\n # a ~ π(•|s_t), log(π(a|s_t))\n current_mean, current_log_std = self.actor(states)\n current_actions, current_action_log_prob = self.actor.action_sample(current_mean, current_log_std)\n current_actions.to(self.device)\n\n # Reset the gradients\n self.critic.zero_grad()\n self.actor.zero_grad()\n\n # Critic update\n critic_loss = self.critic_loss(Q=batch_Q,\n expected_target_Q=expected_target_Q,\n target_Q=target_Q,\n rewards=rewards,\n target_policy_probs=target_action_log_prob,\n behaviour_policy_probs=behaviour_log_pr,\n logger=self.logger)\n\n self.critic.zero_grad()\n # critic_loss.backward(retain_graph=True)\n critic_loss.backward()\n if self.global_gradient_norm != -1:\n clip_grad_norm_(self.critic.parameters(), self.global_gradient_norm)\n self.critic_opt.step()\n\n # Q(a, s_t)\n current_Q = self.critic(torch.cat([current_actions / 2, states], dim=-1))\n\n # print(\"1\", self.critic.grad_norm)\n\n # Actor update\n actor_loss = self.actor_loss(Q=current_Q, action_log_prob=current_action_log_prob.unsqueeze(-1))\n self.actor.zero_grad()\n actor_loss.backward()\n if self.global_gradient_norm != -1:\n clip_grad_norm_(self.actor.parameters(), self.global_gradient_norm)\n self.actor_opt.step()\n # print(\"2\", self.critic.grad_norm)\n\n # Keep track of different values\n if self.logging and i % self.log_every == 0:\n self.logger.add_scalar(scalar_value=actor_loss.item(), tag=\"Loss/Actor_loss\")\n self.logger.add_scalar(scalar_value=critic_loss.item(), tag=\"Loss/Critic_loss\")\n self.logger.add_scalar(scalar_value=current_log_std.exp().mean(), tag=\"Statistics/Action_std_mean\")\n self.logger.add_scalar(scalar_value=current_log_std.exp().std(), tag=\"Statistics/Action_std_std\")\n self.logger.add_scalar(scalar_value=batch_Q.mean(), tag=\"Statistics/Q\")\n\n # self.logger.add_scalar(scalar_value=self.critic.param_norm, tag=\"Critic/param norm\")\n # self.logger.add_scalar(scalar_value=self.critic.grad_norm, tag=\"Critic/grad norm\")\n # self.logger.add_scalar(scalar_value=self.actor.param_norm, tag=\"Actor/param norm\")\n # self.logger.add_scalar(scalar_value=self.actor.grad_norm, tag=\"Actor/grad norm\")\n\n self.logger.add_histogram(values=current_mean, tag=\"Statistics/Action_mean\")\n self.logger.add_histogram(values=rewards.sum(dim=-1), tag=\"Cumm Reward/Action_mean\")\n # print(current_mean[:10])\n self.logger.add_histogram(values=current_actions, tag=\"Statistics/Action\")\n\n def update_targnets(self, smoothing_coefficient=1.) -> None:\n \"\"\"\n Update the target actor and the target critic by copying the parameter from the updated networks. If the\n smoothing coefficient is 1 then updates are hard otherwise the parameter update is smoothed according to.\n\n param' = (1 - smoothing_coefficient) * target param + smoothing_coefficient * param\n\n Returns:\n No return value\n \"\"\"\n if smoothing_coefficient == 1:\n self.target_actor.load_state_dict(self.actor.state_dict())\n self.target_critic.load_state_dict(self.critic.state_dict())\n else:\n assert 0 < smoothing_coefficient < 1\n with torch.no_grad():\n for a_param, a_target_param in zip(self.actor.parameters(), self.target_actor.parameters()):\n a_target_param.data.mul_(1 - smoothing_coefficient)\n torch.add(a_target_param.data, a_param.data, alpha=smoothing_coefficient,\n out=a_target_param.data)\n\n for c_param, c_target_param in zip(self.critic.parameters(), self.target_critic.parameters()):\n c_target_param.data.mul_(1 - smoothing_coefficient)\n torch.add(c_target_param.data, c_param.data, alpha=smoothing_coefficient,\n out=c_target_param.data)\n", "sub_path": "sp_carl/learner.py", "file_name": "learner.py", "file_ext": "py", "file_size_in_byte": 8101, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.nn", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "attribute"}, {"api_name": "common.replay_buffer.SharedReplayBuffer", "line_number": 13, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 23, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 25, "usage_type": "call"}, {"api_name": "common.loss_fn.ActorLoss", "line_number": 38, "usage_type": "call"}, {"api_name": "common.loss_fn.Retrace", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 42, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.add", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.add", "line_number": 175, "usage_type": "call"}]} +{"seq_id": "310070448", "text": "\"\"\" \nMakes a figure providing an overview of our dataset with a focus on lineages\nlaid out as follows:\n\na - Patient metadata\nb - Donut plot of our lineage distributions vs the world\nc - Timeline of patient sampling vs lineages identified\nd - Choropleth of lineages by region\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom typing import Dict\n\nimport logging\nimport matplotlib\nfrom matplotlib.lines import Line2D\nfrom mpl_toolkits.axes_grid.inset_locator import (inset_axes, InsetPosition,\n mark_inset)\n\nfrom covid_bronx import lineage_colors_dict, lineage_colors_dict_rgb\nfrom covid_bronx.quality import fasta_files, sam_files, variant_files\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\nimport matplotlib\nmatplotlib.rcParams['pdf.fonttype'] = 42\nmatplotlib.rcParams['ps.fonttype'] = 42\n\nsavefile_a = \"figures_final/figure1a\"\nsavefile_b = \"figures_final/figure1b\"\n\nmonths = {\n 1: 'Jan',\n 2: 'Feb',\n 3: 'Mar',\n 4: 'Apr',\n 5: 'May',\n 6: 'Jun',\n 7: 'Jul',\n 8: 'Aug',\n 9: 'Sep',\n 10: 'Oct',\n 11: 'Nov',\n 12: 'Dec',\n}\n\nfrom covid_bronx.metadata import preprocess_metadata\nfrom matplotlib.colors import colorConverter\n\n# a) Timeline of lineages\nlogger.info(\"Plotting 1a\")\ntimeline = pd.read_csv(\"data/external/global_lineages.csv\")\nfrom covid_bronx.metadata import get_metadata\nmetadata = get_metadata()\nindex = pd.date_range(metadata['collection_date'].min(), metadata['collection_date'].max())\nmetadata.index = metadata['name']\ndf = pd.read_csv(\"data/external/pangolin2.csv\")\ndf.index = df['Sequence name'].apply(lambda x: x.split(\" \")[0])\ndf.index = df.index.map(lambda x: \"AECOM-\" + str(int(x.split(\"-\")[1])))\n\nmetadata[df.columns] = df\nlineages_df = pd.read_csv(\"data/external/Lineages_updated.csv\", index_col=0)\nlineages = lineages_df['lineage'].dropna()\nlineages.index = lineages.index.map(lambda x: x.replace(\"_\", \"-\"))\nmetadata['Lineage'] = lineages\nmetadata = pd.concat([metadata,metadata.loc[['AECOM-126','AECOM-127','AECOM-128','AECOM-129','AECOM-130']]]).drop_duplicates(keep=False)\n\nddf = pd.DataFrame([ # Incremental Values\n {\n l: (metadata[metadata['collection_date'] == d]['Lineage']==l).sum()\n for l in lineages\n }\n for d in index\n ],\n index=index\n)\n\nddf.index = ddf.index.map(lambda x: months[x.month])\nddmf = pd.DataFrame({k: v.sum(0) for k,v in ddf.groupby(ddf.index)})\n\ncdf = pd.DataFrame([ # Cumulative Values\n {\n l: (metadata[metadata['collection_date'] <= d]['Lineage']==l).sum()\n for l in lineages\n }\n for d in index\n ],\n index=index\n)\n\ndd = pd.read_csv(\"data/external/data-by-day.csv\", index_col=0)\ndd.index = pd.to_datetime(dd.index)\ndd['month'] = dd.index.map(lambda x: months[x.month])\n\nbronx_sampling = ddmf.sum(0)\nsampling = pd.read_csv(\"data/external/sampling.csv\", index_col=0) # TODO: Verify this\nsampling['date'] = pd.to_datetime(sampling['date'])\nsampling['month'] = sampling['date'].apply(lambda x: months[x.month])\n# deathsdmf = pd.Series({k:v['Deaths'].sum() for k,v in sampling.groupby('month')})\n# casesdmf = pd.Series({k:v['Cases'].sum() for k,v in sampling.groupby('month')})\n# hospitalizationdmf = pd.Series({k:v['Hospitalizations'].sum() for k,v in sampling.groupby('month')})\n\ndeathsdmf = pd.Series({k:v['DEATH_COUNT'].sum() for k,v in dd.groupby('month')})\ncasesdmf = pd.Series({k:v['CASE_COUNT'].sum() for k,v in dd.groupby('month')})\nhospitalizationdmf = pd.Series({k:v['HOSPITALIZED_COUNT'].sum() for k,v in dd.groupby('month')})\n\nsampling_df = pd.DataFrame({\"Sampling\": bronx_sampling, \"Cases\": casesdmf, \"Deaths\": deathsdmf, \"Hospitalizations\": hospitalizationdmf}).fillna(0.)\n\n##########################################################\n\n# Start Plotting \nmatplotlib.rcParams.update({'font.size': 16})\nplt.clf()\nplt.close()\nfig1a = plt.figure(figsize=(24,24))\n\nfrom covid_bronx.geography import gen_points_in_gdf_polys, blank_background_choropleth, get_zip_codes_metadata_geo\nimport geopandas as gpd\n\nmetadata = preprocess_metadata()\ncoverage_levels = pd.read_csv(\"data/processed/sequencing/coverage.csv\", index_col=0)['0']\ncoverage_levels = pd.read_csv(\"data/processed/sequencing/coverage.csv\", index_col=0)['0']\npassed = coverage_levels[coverage_levels>=.95].index.intersection(sam_files.keys()).map(lambda x: x.replace(\"_\",\"-\").replace(\"-0\", \"-\").replace(\"-0\", \"-\"))\nnum_samples = len(passed)\n\nfrom covid_bronx.metadata import preprocess_metadata\nfrom matplotlib import colors\n\ndef colorizer(df: pd.DataFrame, color_dict: Dict) -> pd.Series:\n \"\"\"\n Given a dataframe where the rows are zip codes and columns are lineages,\n along with a dict explaining what the RGB color values are, returns a series\n linking zip codes to a color output.\n \"\"\"\n scale_factors = df.sum(1) / max(df.sum(1))\n weights = (df.T / df.sum(1))\n color_series = pd.DataFrame( [np.sum(weights[z][c]*v for c,v in color_dict.items()) for z in weights.columns], index=weights.columns, columns=['r','g','b'])\n\n return color_series.T\n\ndf = pd.read_csv(\"data/external/lineages_final.csv\", index_col=0)\ndf.index = df['taxon'].apply(lambda x: x.split(\" \")[0])\nmetadata[df.columns] = df\n\nzips = metadata.loc[metadata.index.intersection(passed)]['zip_code'].to_numpy()\nzips = np.array(sorted(zips)[2:])\n\n# Get a listing of coordinates by zip code\nbronx_zip_codes = [10453, 10457, 10460,\t10458, 10467, 10468,10451, 10452, 10456,10454, 10455, 10459, 10474,\t10463, 10471,10466, 10469, 10470, 10475,10461, 10462,10464, 10465, 10472, 10473]\ngdf = gpd.read_file(\"data/external/ZIP_CODE_040114/ZIP_CODE_040114.geojson\")\ngdf.index = gdf['ZIPCODE']\ngdf = gdf.loc[list(map(str, bronx_zip_codes))]\n# Remove extraneous zip codes\n\nlatlons = gpd.GeoDataFrame({\"ZIPCODE\": gdf['ZIPCODE'], 'geometry': gdf['geometry'].centroid}).set_index(\"ZIPCODE\")\n\nzdf, bzip = get_zip_codes_metadata_geo()\nzdf.index = zdf['zip_code'].map(lambda x: str(int(float(x))))\ngdf[zdf.columns] = zdf\ngdf = gdf.fillna(0.)\n\ngeocolor_dict = {k: lineage_colors_dict_rgb[k] for k in ['B.1', 'B.1.3', 'B.1.1']} # {'B.1': np.array([1,0,0]), 'B.1.3': np.array([0,1,0]), 'B.1.1': np.array([0,0,1])}\nlineage_colors = colorizer(zdf[['B.1', 'B.1.3', 'B.1.1']], geocolor_dict).to_numpy()\nlineage_colors = np.nan_to_num(lineage_colors, 0.)\ngdf['lineage_colors'] = pd.Series([colors.to_rgba(lineage_colors[:,i]/256) for i in range(len(lineage_colors.T))], index=zdf.index)\ngdf['lineage_colors'] = gdf['lineage_colors'].fillna('#000000')\n\nfig, ax = plt.subplots()\ngdf.fillna(0.).plot(column='count', cmap='Purples',ax=ax, legend=True, legend_kwds={'shrink': 0.3})\ngdf.boundary.plot(color='black', ax=ax)\nax.set_axis_off()\n\n# Plot hospital locations\nfrom shapely.geometry import Point\n\nhospitals = [Point(-73.846184,40.849010)]\nhospitals_df = gpd.GeoDataFrame(geometry=hospitals)\n# hospitals_df.plot(ax=ax, markersize=500, color='black', marker='.', label=\"Collection Site\") # We decided not to do this\n\nplt.tight_layout(pad=.3)\n\nplt.savefig(savefile_a + '.pdf')\nplt.savefig(savefile_a + '.svg')\nplt.clf()\n\n# Plot lineage colored distribution\n\ngeocolor_dict = {k: lineage_colors_dict_rgb[k] for k in ['B.1', 'B.1.3', 'B.1.1']} # {'B.1': np.array([1,0,0]), 'B.1.3': np.array([0,1,0]), 'B.1.1': np.array([0,0,1])}\nlineage_colors = colorizer(zdf[['B.1', 'B.1.3', 'B.1.1']], geocolor_dict).to_numpy()\nlineage_colors = np.nan_to_num(lineage_colors, 0.)\ngdf['lineage_colors'] = pd.Series([colors.to_rgba(lineage_colors[:,i]/256) for i in range(len(lineage_colors.T))], index=zdf.index)\ngdf['lineage_colors'] = gdf['lineage_colors'].fillna('#000000')\nfig, ax = plt.subplots()\ngdf.plot(ax=ax, color=gdf['lineage_colors'])\ngdf.boundary.plot(color='black', ax=ax)\nax.set_axis_off()\nplt.savefig(\"figures_final/figure1a_lineage.pdf\")\nplt.savefig(\"figures_final/figure1a_lineage.svg\")\nplt.show()\nplt.clf()\n\n# Figure 1b. Sampling Density\n\nfig, ax = plt.subplots(figsize=(15,10))\nax_2 = ax.twinx()\nsampling_df[['Cases', 'Hospitalizations', 'Deaths']].loc[['Feb','Mar','Apr','May','Jun','Jul','Aug','Sep', 'Oct']].plot(ax=ax, label=True, color=['yellowgreen','orange','red'], linewidth=6)\nax.grid(linestyle='--', linewidth=1)\nax.set_ylim([0,115000])\nax_2.set_ylim([0,80])\nax.set_ylabel(\"Count of Cases / Hospitalizations / Deaths\")\nax.legend()\nax_2.set_ylabel(\"Count of Genomes Sequenced\")\nax.set_xlabel(\"Month\")\nax.set_xticklabels(['Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct'])\nsampling_df['Sampling'][['Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct']].plot.bar(ax=ax_2, alpha=.5)\nax_2.grid(linestyle='--', color='blue', alpha=.5, linewidth=1)\nax_2.spines['right'].set_color('blue')\nax_2.yaxis.label.set_color('blue')\nax_2.tick_params(axis='y', colors='blue')\n\nplt.savefig(savefile_b + '.pdf')\nplt.savefig(savefile_b + '.svg')\nplt.show()\nplt.clf()\n\n# Figure 2b. Lineages Over Time\nfig, ax = plt.subplots(figsize=(30,15))\ncdf_colors = [lineage_colors_dict[k] for k in ['B.1.26', 'B.1', 'B.2', 'B.2.1', 'A.1', 'B.1.3', 'B.1.1.1', 'B.1.1']]\ncdf[['A.1', 'B.1', 'B.1.1', 'B.1.1.1', 'B.1.26', 'B.1.3', 'B.2', 'B.2.1',]].plot.line(legend=True, color=cdf_colors, ax=ax, linewidth=6)\nax.set_ylabel(\"Cumulative Sample Counts by Lineage\")\n\nplt.savefig('figures_final/figure2a' + '.pdf')\nplt.savefig('figures_final/figure2a' + '.svg')\nplt.clf()\n\n# b) Donut Plot showing lineage distributions in world, US, NYS, and Bronx\n# ax_q = fig1.add_subplot(gs[0:7, 13:])\nimport matplotlib\nmatplotlib.rcParams.update({'font.size':24})\nfig, ax_q = plt.subplots(figsize=(30,30))\nfacecolor = colorConverter.to_rgba('white', alpha=0)\ncirculo = lambda r: plt.Circle((0,0), r, ec='white', fc=facecolor, lw=2)\nlogger.info(\"Plotting 1b\")\ndonut = pd.read_csv(\"data/external/Donut_churro_plot.csv\", index_col=0)\ndonut_colors = [lineage_colors_dict[k] for k in donut.index]\nartist = donut['world'].plot.pie(radius=1, ax=ax_q, colors=donut_colors)\ncircle_1 = circulo(.8)\nax_q.add_artist(circle_1)\ndonut['USA'].plot.pie(radius=.8, ax=ax_q, labels=None, colors=donut_colors)\ncircle_1a = circulo(.6)\nax_q.add_artist(circle_1a)\ndonut['NYS'].plot.pie(radius=.6, ax=ax_q, labels=None, colors=donut_colors)\ncircle_2 = circulo(.4)\nax_q.add_artist(circle_2)\ndonut['Bronx'].plot.pie(radius=.4, ax=ax_q, labels=None, colors=donut_colors)\ncircle_3 = circulo(.2)\ncircle_4 = plt.Circle((0,0), .2, color='white')\nax_q.add_artist(circle_3)\nax_q.add_artist(circle_4)\nax_q.set_ylabel('')\nplt.savefig(\"figures_final/figure2b.pdf\")\nplt.savefig(\"figures_final/figure2b.svg\")\nplt.show()\n\n# Plot a triangular legend\nfig, ax = plt.subplots()\nx = np.array([-1,0])\ny = np.array([1,0])\nz = np.array([0,1])\nx_c = geocolor_dict['B.1']/256\ny_c = geocolor_dict['B.1.3']/256\nz_c = geocolor_dict['B.1.1']/256\n\n# Do convex combinations of everything\ncoordinates = []\nk = 100\nfor lambd in np.linspace(0,1,k):\n for mu in np.linspace(0, 1-lambd, int(k*(1-lambd))):\n for w in np.linspace(0, 1-lambd-mu, int(k*(1-mu))):\n combo = lambd*x + mu*y + w*z\n color = colors.to_hex(max(lambd,0)*x_c + max(mu,0)*y_c + max(w,0)*z_c)\n coordinates.append([combo[0], combo[1], color])\n\ncoordinates = np.array(coordinates)\nxy = coordinates[:, 0:2].astype(float)\nax.scatter(xy[:,0],xy[:,1], c=coordinates[:,2])\nax.text(-1.4,-.1, 'B.1')\nax.text(1.05,-.1, 'B.1.3')\nax.text(-.25,1.1, 'B.1.1')\nax.set_axis_off()\nplt.savefig(\"figures_final/figure1a_lineage_legend.pdf\")\nplt.savefig(\"figures_final/figure1a_lineage_legend.svg\")\nplt.show()\nplt.clf()\n", "sub_path": "scripts/demographics/figure1_2_final.py", "file_name": "figure1_2_final.py", "file_ext": "py", "file_size_in_byte": 11515, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 26, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 29, "usage_type": "attribute"}, {"api_name": "matplotlib.rcParams", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 55, "usage_type": "call"}, {"api_name": "covid_bronx.metadata.get_metadata", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 60, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 65, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 69, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 82, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 84, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 94, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 95, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 99, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 100, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 106, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 107, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 108, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.rcParams.update", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 115, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "covid_bronx.metadata.preprocess_metadata", "line_number": 123, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 124, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 125, "usage_type": "call"}, {"api_name": "covid_bronx.quality.sam_files.keys", "line_number": 126, "usage_type": "call"}, {"api_name": "covid_bronx.quality.sam_files", "line_number": 126, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 132, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 132, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 140, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 149, "usage_type": "call"}, {"api_name": "geopandas.read_file", "line_number": 153, "usage_type": "call"}, {"api_name": "geopandas.GeoDataFrame", "line_number": 158, "usage_type": "call"}, {"api_name": "covid_bronx.geography.get_zip_codes_metadata_geo", "line_number": 160, "usage_type": "call"}, {"api_name": "covid_bronx.lineage_colors_dict_rgb", "line_number": 165, "usage_type": "name"}, {"api_name": "numpy.nan_to_num", "line_number": 167, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.colors.to_rgba", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "shapely.geometry.Point", "line_number": 179, "usage_type": "call"}, {"api_name": "geopandas.GeoDataFrame", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "covid_bronx.lineage_colors_dict_rgb", "line_number": 191, "usage_type": "name"}, {"api_name": "numpy.nan_to_num", "line_number": 193, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.colors.to_rgba", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 230, "usage_type": "name"}, {"api_name": "covid_bronx.lineage_colors_dict", "line_number": 231, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}, {"api_name": "matplotlib.rcParams.update", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 242, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 243, "usage_type": "name"}, {"api_name": "matplotlib.colors.colorConverter.to_rgba", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.colors.colorConverter", "line_number": 244, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Circle", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 245, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 247, "usage_type": "call"}, {"api_name": "covid_bronx.lineage_colors_dict", "line_number": 248, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Circle", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 264, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 264, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 265, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 265, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 266, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 266, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.colors.to_hex", "line_number": 284, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 284, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 294, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 294, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 295, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 295, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 296, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 296, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 297, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 297, "usage_type": "name"}]} +{"seq_id": "22941232", "text": "from tkinter import *\nimport Recognition.Recognition as R\nimport Speech.Speech as S\nimport speech_recognition as sr\nimport Logic.TrialTalkLogic as T\nimport Wikipedia.TrialWikipedia as W\nimport Main.animatedGif as ag\nimport threading\n\n\ndef decide(input):\n try:\n if \"search for\" in input:\n newinput = input.replace(\"search for\", \"\")\n W.search(newinput)\n\n elif \"tell me about\" in input or \"tell me about the\" in input:\n if \"tell me about\" in input:\n newinput = input.replace(\"tell me about\", \"\")\n W.summary(newinput)\n else:\n newinput = input.replace(\"tell me about the\", \"\")\n W.summary(newinput)\n\n else:\n T.deduction(input)\n\n except sr.UnknownValueError:\n S.horizonSpeakPy(\"Sorry I am Unable to find the information you request\")\n\n\ndef response():\n input = R.recognition_trial()\n print(input)\n outputLog.insert(0.0, \"[You Said]:\" + input + \"\\n\")\n newinput = input.lower()\n threading.Thread(target=decide(newinput)).start()\n\n\ndef main():\n response()\n\n\nroot = Tk()\nroot.title(\"Indigo\")\nroot.config(bg='white')\nroot.resizable(0,0)\nroot.iconbitmap('favi_256px.ico')\nindigoLeftFrame = Frame(root, width=200, height = 200,bg='white',borderwidth=0)\nindigoLeftFrame.grid(row=0, column=0, padx=0, pady=0)\n\ngif = ag.AnimatedGIF(indigoLeftFrame, \"indigo.gif\")\ngif.pack()\n\nindigoRightFrame = Frame(root, width=200, height = 200, bg='white',borderwidth=0)\nindigoRightFrame.grid(row=0, column=1, padx=10, pady=0)\n\nspeakerImage = PhotoImage(file=\"speakerimg.png\")\n\nspeakerButton = Button(indigoRightFrame,text=\"Hello\", image=speakerImage,width=30, height=30, bg='white',borderwidth=0.4,command=main)\nspeakerButton.grid(row=0, column=0, padx=10, pady=2)\n\noutputLog = Text(indigoRightFrame, width = 30, height = 10, takefocus=0,borderwidth=0)\noutputLog.grid(row=2, column=0, padx=10, pady=2)\n\noutputLog.insert(0.0, \"Click the microphone above\\nSay 'Search for...' \\nor 'Tell me about...'\\n\")\nroot.mainloop()\n", "sub_path": "Main/Horizon.py", "file_name": "Horizon.py", "file_ext": "py", "file_size_in_byte": 2055, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "Wikipedia.TrialWikipedia.search", "line_number": 15, "usage_type": "call"}, {"api_name": "Wikipedia.TrialWikipedia", "line_number": 15, "usage_type": "name"}, {"api_name": "Wikipedia.TrialWikipedia.summary", "line_number": 20, "usage_type": "call"}, {"api_name": "Wikipedia.TrialWikipedia", "line_number": 20, "usage_type": "name"}, {"api_name": "Wikipedia.TrialWikipedia.summary", "line_number": 23, "usage_type": "call"}, {"api_name": "Wikipedia.TrialWikipedia", "line_number": 23, "usage_type": "name"}, {"api_name": "Logic.TrialTalkLogic.deduction", "line_number": 26, "usage_type": "call"}, {"api_name": "Logic.TrialTalkLogic", "line_number": 26, "usage_type": "name"}, {"api_name": "speech_recognition.UnknownValueError", "line_number": 28, "usage_type": "attribute"}, {"api_name": "Speech.Speech.horizonSpeakPy", "line_number": 29, "usage_type": "call"}, {"api_name": "Speech.Speech", "line_number": 29, "usage_type": "name"}, {"api_name": "Recognition.Recognition.recognition_trial", "line_number": 33, "usage_type": "call"}, {"api_name": "Recognition.Recognition", "line_number": 33, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 37, "usage_type": "call"}, {"api_name": "Main.animatedGif.AnimatedGIF", "line_number": 52, "usage_type": "call"}, {"api_name": "Main.animatedGif", "line_number": 52, "usage_type": "name"}]} +{"seq_id": "430796119", "text": "#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 19 17:27:06 2017\n\n@author: arvardaz\n\"\"\"\n\nimport bpy\nimport numpy as np\nfrom mathutils import Matrix\nmeshObj = bpy.data.objects['blue_tube_2k.002']\ncamObj = bpy.data.objects['Camera']\npath = '/home/arvardaz/SFT_with_CNN/3D_models/tube/extra/3d/2k/'\nfilename = 'tube_2k_coords.csv'\n\nn_vert = len(meshObj.data.vertices)\npoints = np.empty((n_vert, 3)) \n\nworldCamMat = camObj.matrix_world.inverted()\n\ni = Matrix(np.eye(3)*[1,-1,-1])\n\npoints = np.empty((n_vert, 3))\nfor vertex in meshObj.data.vertices:\n cam_co = meshObj.matrix_world * vertex.co#World to Camera frame\n points[vertex.index,:] = cam_co.to_tuple() \n\npoints = points.flatten()\n\ndata = points\nnp.savetxt(path+filename, data.reshape(1, data.shape[0]), delimiter=\",\", fmt=\"%s\")", "sub_path": "datagen/save_template_coords.py", "file_name": "save_template_coords.py", "file_ext": "py", "file_size_in_byte": 810, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "bpy.data", "line_number": 12, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 18, "usage_type": "call"}, {"api_name": "mathutils.Matrix", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "437409008", "text": "import sys\nimport os\nsys.path.append(os.path.join(os.getcwd(),'src'))\n\n\nimport torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.optim as optim\nimport progressbar\nfrom torch.nn.utils.rnn import pad_packed_sequence,pack_padded_sequence\nfrom config import LSTMConfig,TrainingConfig\nfrom itertools import zip_longest\nfrom copy import deepcopy\nfrom utils import tensorized,sort_by_lengths,cal_loss,cal_lstm_crf_loss\n\n\nclass HMM(object):\n def __init__(self,N,M):\n '''\n N 隐藏状态数\n M 观测状态数\n '''\n self.N=N\n self.M=M\n\n #torch tensor 默认类型 float32\n #状态转移矩阵\n self.A=torch.zeros(N,N)\n #观测产生矩阵\n self.B=torch.zeros(N,M)\n #初始状态概率分布\n self.Pi=torch.zeros(N)\n\n #在进行解码的时候涉及很多小数概率相乘，我们对其取对数\n self.logA=torch.zeros(N,N)\n self.logB=torch.zeros(N,M)\n self.logPi=torch.zeros(N)\n\n def train(self,word_lists,tag_lists,word2id,tag2id):\n '''\n word_lists = 句子列表 [ [ '北','京','欢','迎','你'], [ ],[ ]]\n tag_lists [ ['B-LOC','I-LOC','O','O','O'],[ ],[ ],[ ] ]\n word2id 映射到index\n tag2id 映射到index\n '''\n assert len(word_lists)==len(tag_lists)\n #统计转移矩阵 和 初始概率分布矩阵\n for tag_list in tag_lists:\n l=len(tag_list)-1\n for j in range(l):\n next_tag_id=tag2id[tag_list[j+1]]\n tag_id=tag2id[tag_list[j]]\n self.A[tag_id][next_tag_id]+=1\n self.Pi[tag_id]+=1\n if j==l-1: self.Pi[next_tag_id]+=1 \n Asum=torch.sum(self.A,1).unsqueeze(1)\n self.A=self.A/Asum\n pisum=torch.sum(self.Pi)\n self.Pi=self.Pi/pisum\n \n #统计生成矩阵\n for i in range(len(tag_lists)):\n tag_list=tag_lists[i]\n word_list=word_lists[i]\n for j in range(len(tag_list)):\n tag_id=tag2id[tag_list[j]]\n word_id=word2id[word_list[j]]\n self.B[tag_id][word_id]+=1\n Bsum=torch.sum(self.B,1).unsqueeze(1)\n self.B=self.B/Bsum\n\n self.logA=torch.log(self.A)\n self.logB=torch.log(self.B)\n self.logPi=torch.log(self.Pi)\n\n def test(self,test_word_lists,_,word2id,tag2id):\n pred_tag_lists=[]\n # for test_word_list in test_word_lists:\n # pred_tag_list=self.decoding(test_word_list,word2id,tag2id)\n # pred_tag_lists.append(pred_tag_list)\n for i in progressbar.progressbar(range(len(test_word_lists))):\n test_word_list=test_word_lists[i]\n pred_tag_list=self.decoding(test_word_list,word2id,tag2id)\n pred_tag_lists.append(pred_tag_list)\n\n return pred_tag_lists\n\n\n def decoding(self,word_list,word2id,tag2id):\n '''\n 使用维特比算法进行状态序列求解\n '''\n length=len(word_list)\n \n #定义delta[t][n]记录 t 时刻 隐藏状态为n的 概率最大值\n delta=torch.zeros(length,self.N)\n #定义Psi[t][n] 当t时刻，隐藏状态为n，概率最大路径上 t-1 时的 隐藏状态\n psi=torch.zeros(length,self.N).long()\n\n #进行转置，便于并行计算\n Bt=self.logB.t()\n At=self.logA.t()\n\n #初始化 递推状态\n first_word_id=word2id.get(word_list[0],None)\n if first_word_id==None:\n #word UNK 字典里不存在,认为隐藏状态的分布是平均分布\n bt=torch.log(torch.ones(self.N)/self.N)\n else:\n bt=Bt[first_word_id]\n\n delta[0]=self.logPi+bt\n psi[0]=torch.zeros(self.N).long()\n\n #开始递推\n #递推公式 \n for t in range(1,length):\n word_id=word2id.get(word_list[t],None)\n if word_id==None:\n bt=torch.log(torch.ones(self.N)/self.N)\n else:\n bt=Bt[word_id]\n \n for i in range(self.N):\n at=At[i] # 1,2,...,N 转到 状态i 的转移概率 向量\n dd=delta[t-1] # 1,2,...,N 最大概率\n tmp=at+dd\n\n dd=delta[t-1] \n tmp=At+dd # max[ delta[t-1] * a]\n delta[t],psi[t]=torch.max(tmp,dim=1) #计算最大概率\n delta[t]+=bt\n \n best_path=[]\n #使用回溯法，找到最佳隐藏序列\n\n #最后一个单词对应的隐藏状态\n i_=torch.argmax(delta[length-1]).item()\n best_path.append(i_)\n for t in range(length-1,0,-1):\n i_=psi[t][i_].item()\n best_path.append(i_)\n \n id2tag=dict((id_,tag) for tag,id_ in tag2id.items())\n best_path=[ id2tag[id_] for id_ in reversed(best_path)]\n\n return best_path\n ", "sub_path": "src/models/hmm.py", "file_name": "hmm.py", "file_ext": "py", "file_size_in_byte": 4916, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 3, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 75, "usage_type": "call"}, {"api_name": "progressbar.progressbar", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "600176314", "text": "# -*- coding: utf-8 -*-\nimport hashlib, time, random, decimal, json\nfrom application import app, db\nfrom common.models.food.Food import Food\nfrom common.models.food.FoodSaleChangeLog import FoodSaleChangeLog\nfrom common.models.pay.PayOrder import PayOrder\nfrom common.models.pay.PayOrderItem import PayOrderItem\nfrom common.models.pay.PayOrderCallbackData import PayOrderCallbackDatum\nfrom common.libs.Helper import getCurrentData\n# from common.libs.queue.QueueService import QueueService\nfrom common.libs.food.FoodService import FoodService\n\n\nclass PayService():\n\n def __init__(self):\n pass\n\n def createOrder(self, member_id, items=None, params=None): # 创建订单（哪个用户，商品列表，params额外字段[留言] ）\n \"\"\"\n 实现下单并发，库存减少\n :param member_id:\n :param items:\n :param params:\n :return:\n \"\"\"\n resp = {'code': 200, 'msg': '操作成功~', 'data': {}}\n pay_price = decimal.Decimal(0.00) # 商品总价格\n continue_cnt = 0\n food_ids = []\n for item in items: # 遍历所有下单的商品\n if decimal.Decimal(item['price']) < 0: # 如果有的商品价格<0。那么统计次数，并且跳过\n continue_cnt += 1\n continue\n\n pay_price = pay_price + decimal.Decimal(item['price']) * int(item['number']) # 此时的，商品总价格。就是，初始价格0.00 + 上面跳过的商品价格 * 下单数量\n food_ids.append(item['id']) # 在这里面添加，通过的商品的 id\n\n if continue_cnt >= len(items): # 如果跳过的次数 >= 下单商品的数量。说明没有选择商品\n resp['code'] = -1\n resp['msg'] = '商品items为空~~'\n return resp\n\n yun_price = params['yun_price'] if params and 'yun_price' in params else 0\n note = params['note'] if params and 'note' in params else ''\n express_address_id = params['express_address_id'] if params and 'express_address_id' in params else 0\n express_info = params['express_info'] if params and 'express_info' in params else {}\n yun_price = decimal.Decimal(yun_price)\n total_price = pay_price + yun_price\n\n # 并发处理 乐观锁和悲观锁。这里采用的是观锁。（悲观锁：锁数据表行记录。乐观锁：数据表增加一个字段，每次更新时对它进行判断 ）\n try:\n # 为了防止并发库存出问题了，我们坐下selectfor update, 这里可以给大家演示下\n tmp_food_list = db.session.query(Food).filter(Food.id.in_(food_ids)) \\\n .with_for_update().all() # 锁定所有本次下单的商品id，行记录\n\n tmp_food_stock_mapping = {} # 临时的商品库存 map，方便对比\n for tmp_item in tmp_food_list:\n tmp_food_stock_mapping[tmp_item.id] = tmp_item.stock # 被锁定的商品 库存\n\n model_pay_order = PayOrder()\n model_pay_order.order_sn = self.geneOrderSn() # 随机订单号，通过随机算法算出\n model_pay_order.member_id = member_id\n model_pay_order.total_price = total_price\n model_pay_order.yun_price = yun_price\n model_pay_order.pay_price = pay_price\n model_pay_order.note = note # 备注信息\n model_pay_order.status = -8 # 默认状态：-8待付款\n model_pay_order.express_status = -8 # 待支付\n model_pay_order.express_address_id = express_address_id\n model_pay_order.express_info = json.dumps(express_info)\n model_pay_order.updated_time = model_pay_order.created_time = getCurrentData()\n db.session.add(model_pay_order)\n db.session.flush()\n\n for item in items: # 第一次判断，剩下的商品（跳出的商品）\n tmp_left_stock = tmp_food_stock_mapping[item['id']]\n\n if decimal.Decimal(item['price']) < 0: # 如果是价格<=0，就停止本次操作，继续\n continue\n\n if int(item['number']) > int(tmp_left_stock): # 如果下单的商品数量 > 库存\n raise Exception(\"您购买的这美食太火爆了，剩余：%s,您购买%s~~\" % (tmp_left_stock, item['number']))\n\n tmp_ret = Food.query.filter_by(id=item['id']).update({\n \"stock\": int(tmp_left_stock) - int(item['number'])\n }) # 更新库存\n if not tmp_ret:\n raise Exception(\"下单失败请重新下单\")\n\n tmp_pay_item = PayOrderItem() # 生成订单\n tmp_pay_item.pay_order_id = model_pay_order.id\n tmp_pay_item.member_id = member_id\n tmp_pay_item.quantity = item['number'] # 下单数量\n tmp_pay_item.price = item['price'] # 商品单价\n tmp_pay_item.food_id = item['id'] # 商品id\n tmp_pay_item.note = note # 备注信息\n tmp_pay_item.updated_time = tmp_pay_item.created_time = getCurrentData()\n db.session.add(tmp_pay_item)\n db.session.flush()\n\n FoodService.setStockChangeLog(item['id'], -item['number'], \"在线购买\") # 商品变更记录。商品id，-数量，备注\n db.session.commit() # 直到完成本次提交，行锁才解开\n resp['data'] = { # 下单成功，返回数据\n 'id': model_pay_order.id,\n 'order_sn': model_pay_order.order_sn,\n 'total_price': str(total_price)\n }\n except Exception as e:\n pass\n db.session.rollback() # 如果出现异常，数据回滚，回到操作前的状态\n print(\"*\"*50,e)\n resp['code'] = -1\n resp['msg'] = \"下单失败请重新下单\"\n resp['msg'] = str(e)\n return resp\n return resp\n\n # def closeOrder(self, pay_order_id=0):\n # if pay_order_id < 1:\n # return False\n # pay_order_info = PayOrder.query.filter_by(id=pay_order_id, status=-8).first()\n # if not pay_order_info:\n # return False\n #\n # pay_order_items = PayOrderItem.query.filter_by(pay_order_id=pay_order_id).all()\n # if pay_order_items:\n # # 需要归还库存\n # for item in pay_order_items:\n # tmp_food_info = Food.query.filter_by(id=item.food_id).first()\n # if tmp_food_info:\n # tmp_food_info.stock = tmp_food_info.stock + item.quantity\n # tmp_food_info.updated_time = getCurrentData()\n # db.session.add(tmp_food_info)\n # db.session.commit()\n # FoodService.setStockChangeLog(item.food_id, item.quantity, \"订单取消\")\n #\n # pay_order_info.status = 0\n # pay_order_info.updated_time = getCurrentData()\n # db.session.add(pay_order_info)\n # db.session.commit()\n # return True\n #\n # def orderSuccess(self, pay_order_id=0, params=None):\n # try:\n # pay_order_info = PayOrder.query.filter_by(id=pay_order_id).first()\n # if not pay_order_info or pay_order_info.status not in [-8, -7]:\n # return True\n #\n # pay_order_info.pay_sn = params['pay_sn'] if params and 'pay_sn' in params else ''\n # pay_order_info.status = 1\n # pay_order_info.express_status = -7\n # pay_order_info.updated_time = getCurrentData()\n # db.session.add(pay_order_info)\n #\n # pay_order_items = PayOrderItem.query.filter_by(pay_order_id=pay_order_id).all()\n # for order_item in pay_order_items:\n # tmp_model_sale_log = FoodSaleChangeLog()\n # tmp_model_sale_log.food_id = order_item.food_id\n # tmp_model_sale_log.quantity = order_item.quantity\n # tmp_model_sale_log.price = order_item.price\n # tmp_model_sale_log.member_id = order_item.member_id\n # tmp_model_sale_log.created_time = getCurrentData()\n # db.session.add(tmp_model_sale_log)\n #\n # db.session.commit()\n # except Exception as e:\n # db.session.rollback()\n # print(e)\n # return False\n #\n # # 加入通知队列，做消息提醒和\n # QueueService.addQueue(\"pay\", {\n # \"member_id\": pay_order_info.member_id,\n # \"pay_order_id\": pay_order_info.id\n # })\n # return True\n #\n # def addPayCallbackData(self, pay_order_id=0, type='pay', data=''):\n # model_callback = PayOrderCallbackData()\n # model_callback.pay_order_id = pay_order_id\n # if type == \"pay\":\n # model_callback.pay_data = data\n # model_callback.refund_data = ''\n # else:\n # model_callback.refund_data = data\n # model_callback.pay_data = ''\n #\n # model_callback.created_time = model_callback.updated_time = getCurrentData()\n # db.session.add(model_callback)\n # db.session.commit()\n # return True\n #\n def geneOrderSn(self): # 随机订单号（随机产生 md5）\n m = hashlib.md5()\n sn = None\n while True: # 为什么while true？因为生成的sn是唯一的，所以先到数据库查查存不存在\n str = \"%s-%s\" % (int(round(time.time() * 1000)), random.randint(0, 9999999)) # 当前时间戳为标准\n m.update(str.encode(\"utf-8\")) # 生成 md5\n sn = m.hexdigest() #订单号\n if not PayOrder.query.filter_by(order_sn=sn).first(): # 如果 这个订单号不存在，就跳过这个死循环。否则继续生成新的md5订单号\n break\n return sn # 返回订单号\n", "sub_path": "common/libs/pay/PayService.py", "file_name": "PayService.py", "file_ext": "py", "file_size_in_byte": 9938, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "decimal.Decimal", "line_number": 28, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 32, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 36, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 48, "usage_type": "call"}, {"api_name": "application.db.session.query", "line_number": 54, "usage_type": "call"}, {"api_name": "common.models.food.Food.Food", "line_number": 54, "usage_type": "argument"}, {"api_name": "application.db.session", "line_number": 54, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 54, "usage_type": "name"}, {"api_name": "common.models.food.Food.Food.id.in_", "line_number": 54, "usage_type": "call"}, {"api_name": "common.models.food.Food.Food.id", "line_number": 54, "usage_type": "attribute"}, {"api_name": "common.models.pay.PayOrder.PayOrder", "line_number": 61, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 71, "usage_type": "call"}, {"api_name": "common.libs.Helper.getCurrentData", "line_number": 72, "usage_type": "call"}, {"api_name": "application.db.session.add", "line_number": 73, "usage_type": "call"}, {"api_name": "application.db.session", "line_number": 73, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 73, "usage_type": "name"}, {"api_name": "application.db.session.flush", "line_number": 74, "usage_type": "call"}, {"api_name": "application.db.session", "line_number": 74, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 74, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 79, "usage_type": "call"}, {"api_name": "common.models.food.Food.Food.query.filter_by", "line_number": 85, "usage_type": "call"}, {"api_name": "common.models.food.Food.Food.query", "line_number": 85, "usage_type": "attribute"}, {"api_name": "common.models.food.Food.Food", "line_number": 85, "usage_type": "name"}, {"api_name": "common.models.pay.PayOrderItem.PayOrderItem", "line_number": 91, "usage_type": "call"}, {"api_name": "common.libs.Helper.getCurrentData", "line_number": 98, "usage_type": "call"}, {"api_name": "application.db.session.add", "line_number": 99, "usage_type": "call"}, {"api_name": "application.db.session", "line_number": 99, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 99, "usage_type": "name"}, {"api_name": "application.db.session.flush", "line_number": 100, "usage_type": "call"}, {"api_name": "application.db.session", "line_number": 100, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 100, "usage_type": "name"}, {"api_name": "common.libs.food.FoodService.FoodService.setStockChangeLog", "line_number": 102, "usage_type": "call"}, {"api_name": "common.libs.food.FoodService.FoodService", "line_number": 102, "usage_type": "name"}, {"api_name": "application.db.session.commit", "line_number": 103, "usage_type": "call"}, {"api_name": "application.db.session", "line_number": 103, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 103, "usage_type": "name"}, {"api_name": "application.db.session.rollback", "line_number": 111, "usage_type": "call"}, {"api_name": "application.db.session", "line_number": 111, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 111, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 195, "usage_type": "call"}, {"api_name": "time.time", "line_number": 198, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 198, "usage_type": "call"}, {"api_name": "common.models.pay.PayOrder.PayOrder.query.filter_by", "line_number": 201, "usage_type": "call"}, {"api_name": "common.models.pay.PayOrder.PayOrder.query", "line_number": 201, "usage_type": "attribute"}, {"api_name": "common.models.pay.PayOrder.PayOrder", "line_number": 201, "usage_type": "name"}]} +{"seq_id": "257770461", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jun 12 23:05:33 2020\r\n\r\n@author: ritesh\r\n\"\"\"\r\n\r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\nimport qmpy_rester as qr\r\nfrom qmpy import io\r\nimport pandas as pd\r\nfrom ase.spacegroup import crystal as crys\r\nfrom ase.io import read, write\r\nfrom ase import Atoms\r\n\r\n## OQMD-API format\r\ncomp = []; space_group = []; oqmd_id = []; formation_energy = []; stability = []\r\nn_atoms = []\r\nwith qr.QMPYRester() as q:\r\n kwargs = {\r\n # 'element_set': '(Fe-Mn),O', # composition include (Fe OR Mn) AND O\r\n 'composition': 'Ni-C-N-S',\r\n 'stability': '<0', # hull distance smaller than -0.1 eV\r\n # 'natom': '<10', # number of atoms less than 10\r\n # 'ntypes': '<4',\r\n # 'filter': 'element_set=Ni OR C OR N',\r\n\r\n }\r\n list_of_data = q.get_oqmd_phases(**kwargs)\r\n\r\n## OPTIMADE-API format\r\n# with qr.QMPYRester() as q:\r\n # kwargs = {\r\n # 'elements': 'Ni-C-N', # composition include (Fe OR Mn) AND O\r\n # '_oqmd_stability': '<0', # hull distance smaller than -0.1 eV\r\n # 'nelements': '<4', # number of atoms less than 10\r\n # }\r\n # list_of_data = q.get_optimade_structures(**kwargs)\r\n\r\nprint(len(list_of_data['data']))\r\n# print(list_of_data['data'][0])\r\n\r\ndef make_sym_coor(lis):\r\n\tsit = lis['sites']\r\n\tbas = [sit[i].split('@') for i in range(len(sit))]\r\n\tsymbols = []; coordinate = []\r\n\tfor i in range(len(bas)):\r\n\t\tsym = bas[i][0].split()[0]\r\n\t\tsymbols.append(sym)\r\n\t\tcoor = bas[i][1].split()\r\n\t\tcoor_ = [float(coor[i]) for i in range(len(coor))]\r\n\t\tcoordinate.append(coor_)\r\n\treturn symbols, coordinate\r\n\r\ndef make_crys(lis):\r\n\tspg = lis['spacegroup']\r\n\tsymbols, coordinate = make_sym_coor(lis)\r\n\tlatt = lis['unit_cell']\r\n\treturn Atoms(symbols=symbols, scaled_positions=coordinate, cell=latt)\r\n\r\nfor i in range(len(list_of_data['data'])):\r\n\tname = list_of_data['data'][i]['name']\r\n\tcomp.append(name)\r\n\t# sg = list_of_data['data'][i]['spacegroup']\r\n\tspace_group.append(list_of_data['data'][i]['spacegroup'])\r\n\tstability.append(list_of_data['data'][i]['stability'])\r\n\tformation_energy.append(list_of_data['data'][i]['delta_e'])\r\n\toqmd_id.append(list_of_data['data'][i]['entry_id'])\r\n\tn_atoms.append(list_of_data['data'][i]['natoms'])\r\n\tstruc = make_crys(list_of_data['data'][i])\r\n\tf = str(name) + '.vasp'\r\n\twrite(f, struc)\r\n\r\ndict = {'composition': comp, 'space group':space_group, 'hull distance':stability, \r\n 'formation energy':formation_energy, 'oqmd id': oqmd_id, '# atoms': n_atoms}\r\n\r\ndf = pd.DataFrame(dict)\r\ndf.to_csv('list.csv', index=False)", "sub_path": "Miscellaneous/using-APIs/oqmd-api-working.py", "file_name": "oqmd-api-working.py", "file_ext": "py", "file_size_in_byte": 2632, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "warnings.filterwarnings", "line_number": 9, "usage_type": "call"}, {"api_name": "qmpy_rester.QMPYRester", "line_number": 20, "usage_type": "call"}, {"api_name": "ase.Atoms", "line_number": 60, "usage_type": "call"}, {"api_name": "ase.io.write", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "251028277", "text": "from django.core.management.base import BaseCommand, CommandError\nfrom energy.models import *\nfrom django.db.models import Sum\n\nclass Command(BaseCommand):\n help = 'Closes the specified poll for voting'\n\n def add_arguments(self, parser):\n parser.add_argument('--area', type=str)\n parser.add_argument('--timeres', type=str)\n parser.add_argument('--date', type=str)\n parser.add_argument('--month', type=str)\n parser.add_argument('--year', type=str)\n\n def handle(self, *args, **options):\n area = options['area']\n timeres = options['timeres']\n r = Resolutioncode.objects.get(resolutioncodetext=timeres)\n if options['date']:\n date = options['date']\n s = date.split('-')\n l = Actualtotalload.objects.filter(areaname=area, year=int(s[0]), month=int(s[1]), day=int(s[2]), resolutioncodeid=r.id).order_by('datetime').values('areaname', 'areatypecodeid', 'resolutioncodeid', 'year', 'month', 'day', 'datetime', 'totalloadvalue', 'updatetime', 'mapcodeid')\n elif options['month']:\n month=options['month']\n s = month.split('-')\n l = Actualtotalload.objects.filter(areaname=area, year=int(s[0]), month=int(s[1]), resolutioncodeid=r.id).order_by('datetime').\\\n values('areaname', 'areatypecodeid', 'mapcodeid', 'resolutioncodeid', 'year', 'month', 'day').\\\n annotate(totalload=Sum('totalloadvalue'))\n elif options['year']:\n l = Actualtotalload.objects.filter(areaname=area, year=int(options['year']), resolutioncodeid=r.id).\\\n values('areaname', 'areatypecodeid', 'mapcodeid', 'resolutioncodeid', 'year').\\\n order_by('datetime').annotate(totalload=Sum('totalloadvalue')) \n for d in l:\n self.stdout.write(str(d))\n", "sub_path": "backend/energy/management/commands/ActualTotalLoad.py", "file_name": "ActualTotalLoad.py", "file_ext": "py", "file_size_in_byte": 1785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "119280390", "text": "# !/bin/python2\nimport pynmea2 as nmea\nfrom serial import Serial as ser\nfrom time import sleep\n# import binascii as bina\nfrom gpio import gps_off, gps_on\nfrom radium import read, send\n\n\ndef writeFile(file_name, strings, form):\n \"\"\"\n Create or write to an existing file\n File_name: string representations of the file name\n String: the string to be writing to the file\n form: a,w, ... the format of the file to be open.\n Return: true when done\n \"\"\"\n with open(file_name, form) as fil:\n fil.write(strings)\n # print('Writing data')\n return True\n\n\nclass binex():\n \"\"\"\n Get binex data from GPS module and save it as a binary file.\n is_saved: if the read line has been saved to the log file\n interval: interval or frequency to acquire data\n timeOut: How long to acquire data during each interval\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n self.sequence = 1\n self.is_saved = False\n self.interval = 15\n self.timeout = 15\n try:\n self.port = ser('/dev/ttyS0') # set the port\n self.port.baudrate = 115200 # set baudrate\n self.port.timeOut = None # set port time out\n except:\n self.port = None\n print('Unable to setup port')\n\n def __binex_proce(self, byte, keys, is_byte):\n \"\"\"\n Process the binex file and saved it to the log file\n byte: the read binex string (in binary)\n keys: the nmea keys\n is_byte: is set when all binex is written to file\n \"\"\"\n key = 0\n for key, objects in enumerate(keys): # loop through the keys\n # check if object is present in the byte data read\n if byte.find(objects) != 3:\n # in case no objects is found till the end of the loop\n if byte.find(objects) == -1 and key == 14 and byte.find(\"\\r\\n\") == -1:\n # check the byte for empty string or end of line\n if byte != '\\n' or byte != '':\n self.is_saved = writeFile('gps_binex_data.log', byte, 'ab')\n sleep(2)\n break\n # object found a position 3 do nothing (no a binex)\n elif byte.find(objects) == 3:\n break\n # object not found, keep looping till all the end of the keys array\n elif byte.find(objects) == -1:\n pass\n # The binex and the nmea are attached, here we separate them\n else:\n bytes = byte[0:int(byte.find(objects))-3] # separate the binex\n # it could happen that the last string is just ATT or $PTP\n if bytes == '$PTP' or byte.find('ATT') != -1:\n break\n # write the cut byte to the file\n elif bytes or bytes != '\\n':\n # print(3, bytes)\n self.is_saved = writeFile('gps_binex_data.log', bytes, 'ab')\n sleep(2)\n is_byte = True\n break\n # Check if that is saved and the byte has GGA in it\n if self.is_saved == True and byte.find('GGA') != -1:\n is_byte = True # All the binex has been written to file\n self.sequence = self.sequence+1 # increment sequence\n self.is_saved = False # reset the variable is_saved\n sleep(self.interval) # wait for interval\n break\n return is_byte # return that All the binex has been written to file\n\n def get_binex(self):\n \"\"\"\n Initiate the reading of the binex language from GPS module to Titron\n Take no argument\n Return None\n \"\"\"\n try:\n # try opening the port\n self.port.open()\n except:\n self.port = None\n print('Unable to open port')\n else:\n # turn the GPS module on\n gps_on(bit=1)\n # Wait for 40 s for GPS module to set up\n # It was notice that if the wait is not long enough, the data received from the module are incomplete\n sleep(40)\n # set the sequence: how many junk of reading has to be taken\n while self.sequence <= self.timeout*60/self.interval:\n # flush the port to delete very data before reading, so we get fresh reading\n self.port.flushInput()\n # This are the kys persent in all nmea reading\n keys = ('GGA', 'GLL', 'GMP', 'GNS', 'GRS', 'GSA', 'GST', 'GSV',\n 'HDT', 'RMC', 'ROT', 'VTG', 'ZDA', 'UID', 'ATT')\n # No binex has been saved to set the is_byte to false\n is_byte = False\n while not is_byte:\n # Read a line of string from the port\n byte = self.port.readline()\n # Process the reading and save it to the log file\n is_byte = self.__binex_proce(byte, keys, is_byte)\n\n finally:\n # At every exit close the port, and turn off the GPS\n if self.port:\n self.port.close()\n gps_off(bit=1)\n\n\nclass nmea():\n \"\"\"\n Get NMEA data from GPS module and save it as a binary file.\n is_done: Check wether all lines has been saved for the sequence\n interval: interval or frequency to acquire data\n timeOut: How long to acquire data during each interval\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n\n self.sequence = 1\n self.done = False\n self.interval = 15\n self.timeout = 15\n try:\n self.port = ser('/dev/ttyS0') # set the port\n self.port.baudrate = 115200 # set baudrate\n self.port.timeOut = None # set port time out\n except:\n self.port = None\n print('Unable to setup port')\n\n def __get_GPGGA_GPVTG(self):\n \"\"\"\n Read raw data in nmea format from the GPS module. The GPGGA, GPVTG only\n Take not argument\n Return the GPGGA, GPVTG variable\n \"\"\"\n try:\n # try opening the port\n self.port.open()\n except:\n self.port = None\n print('Unable to open port')\n else:\n # set GPGGA, GPVTG to None\n gps_on(1)\n print('Waiting for 1 minute. GPS module not fully started!')\n sleep(60)\n GPVTG = None\n GPGGA = None\n # loop and find the GPGGA, GPVTG variable\n while GPGGA is None or GPVTG is None:\n data = self.port.readline()\n try:\n if data.find('GPVTG') != -1:\n GPVTG = data\n elif data.find('GPGGA') != -1:\n GPGGA = data\n except:\n print(\"Unable to read. Check GPS module configuration or try again\")\n return 0, 0\n finally:\n # Close port and turn off GPS module\n if self.port:\n self.port.close()\n gps_off(1)\n\n sleep(1)\n return GPGGA, GPVTG\n\n def __get_all(self):\n \"\"\"\n Get all the NMEA data from the GPS module\n Take no argument\n Return None\n \"\"\"\n # NMEA language keys\n keys = ('GGA', 'GLL', 'GMP', 'GNS', 'GRS', 'GSA', 'GST', 'GSV',\n 'HDT', 'RMC', 'ROT', 'VTG', 'ZDA', 'UID', 'PSR')\n self.done = False\n while not self.done: # loop till all keys are saved\n byte = self.port.readline() # read a line from the port\n key = 0\n for key, objects in enumerate(keys):\n if byte.find('PSR') == 3: # Check for tge last key in at the port\n self.done = True\n if byte.find(objects) != -1: # if the key is found is the string\n # the key is not at the begining of the string then NMEA and BINEX are attached\n if byte.find(objects) != 3:\n # separate NMEA from BINEX and save the string\n bytes = byte[int(byte.find(objects))-3:]\n writeFile('gps_nmea_data.log', bytes, 'a')\n sleep(.3)\n break\n\n elif byte.find(objects) == 3: # Key at the bigging then save the string\n writeFile('gps_nmea_data.log', byte, 'a')\n sleep(.3)\n break\n else:\n pass\n self.sequence = self.sequence+1 # increment sequence\n writeFile('gps_nmea_data.log', '-'*50 + '\\n', 'a') # write a delimiter\n\n def __Nmea_parse(self):\n \"\"\"\n Parser the nmea language code into human readable location code GPGGA, GPVTG\n Take no argument\n Return GPGGA_parser, GPVTG_parser\n \"\"\"\n GPGGA, GPVTG = self.__get_GPGGA_GPVTG() # get the rwa data\n GPGGA_parser = None\n GPVTG_parser = None\n if GPGGA and GPVTG:\n GPGGA_parser = nmea.parse(GPGGA) # parser the data\n GPVTG_parser = nmea.parse(GPVTG)\n # print(GPGGA_parser, GPVTG_parser)\n return GPGGA_parser, GPVTG_parser\n\n def quick_gps_data(self):\n \"\"\"\n Output the location of the amigos module with less precision\n take no argument\n Return nothing\n \"\"\"\n gps_data = self.__Nmea_parse() # call the parser function to get location\n Altitude = gps_data[0].altitude # retrieve altitude\n Longitude = gps_data[0].lon # retrive longitude\n Longitude_Dir = gps_data[0].lon_dir # retrive longitude direction\n Latitude = gps_data[0].lat # retrive latitude\n Latitude_Dir = gps_data[0].lat_dir # retrive latitude direction\n print(\" Altitude: {0}\\n Longitude: {1}\\n Longitude Dir: {2}\\n Latitude: {3}\\n Latitude Dir: {4}\\n\".format(\n Altitude, Longitude, Longitude_Dir, Latitude, Latitude_Dir))\n\n def get_nmea(self):\n \"\"\"\n Get the nmea language from gps module and save it to file.\n Take no argument\n Return None\n \"\"\"\n try:\n # Try to open the port\n self.port.open()\n except:\n self.port = None\n print('Unable to open port')\n else:\n gps_on(bit=1) # turn GPS on\n sleep(40) # wait for 40 s for module to fully start\n # start reading\n while self.sequence <= self.timeout*60/self.interval:\n self.port.flushInput()\n self.__get_all()\n sleep(self.interval) # wait for the interval\n\n finally:\n # close port and turn gps module off\n if self.port:\n self.port.close()\n gps_off(bit=1)\n\n # def cover_to_ascii():\n # n = ''\n # with open('gps_binex_data.log', 'rb') as fil:\n # m = fil.readline()\n # # print(m)\n # for b in m:\n # h = bina.b2a_uu(b)[0:-2]\n # # if '\\n' in h:\n # # print('hey')\n # if h != '\\n':\n # n = n+h\n # with open('gps_binex_ascii_data.log.log', 'w') as logg:\n # logg.write(n)\n # print(n)\n\n\nif __name__ == \"__main__\":\n with open('gps_nmea_data.log', 'w') as fil:\n fil.write('')\n bn = nmea()\n bn.timeout = 2\n bn.interval = 2\n bn.get_nmea()\n", "sub_path": "amigos/amigos/gps_data.py", "file_name": "gps_data.py", "file_ext": "py", "file_size_in_byte": 11618, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "serial.Serial", "line_number": 38, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 79, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 87, "usage_type": "call"}, {"api_name": "gpio.gps_on", "line_number": 105, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 108, "usage_type": "call"}, {"api_name": "gpio.gps_off", "line_number": 128, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 146, "usage_type": "call"}, {"api_name": "gpio.gps_on", "line_number": 167, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 169, "usage_type": "call"}, {"api_name": "gpio.gps_off", "line_number": 187, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 189, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 214, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 219, "usage_type": "call"}, {"api_name": "gpio.gps_on", "line_number": 269, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 270, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 275, "usage_type": "call"}, {"api_name": "gpio.gps_off", "line_number": 281, "usage_type": "call"}]} +{"seq_id": "15494334", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport climlab\nfrom climlab.radiation import RRTMG\nimport xarray as xr\nimport constants\nimport os\nfrom netCDF4 import Dataset\n\nlocation = constants.home\nmodel = 'CMIP6-AMIP-GFDL-CM4'\nos.chdir( location + 'Data/' + model )\n\n\n# Take global, annual average\n\nwith xr.open_dataset(constants.variable_to_filename( 'cl' ), decode_times=False) as cl:\n weight = np.cos(np.deg2rad(cl.lat)) / np.cos(np.deg2rad(cl.lat)).mean(dim='lat')\n CLglobal = (cl.cl * weight).mean(dim=('lat','lon','time')) / 100\n a = cl.ap\n b = cl.b\n\nwith xr.open_dataset(constants.variable_to_filename( 'ps' ), decode_times=False) as ps:\n ps = ps.ps\n\np = a + b*ps\nplev = (p * weight).mean(dim=('lat','lon','time'))\n\nwith xr.open_dataset(constants.variable_to_filename( 'clw' ), decode_times=False) as clw:\n CLWglobal = (clw.clw * weight).mean(dim=('lat','lon','time'))\n\nwith xr.open_dataset(constants.variable_to_filename( 'cli' ), decode_times=False) as cli:\n CLIglobal = (cli.cli * weight).mean(dim=('lat','lon','time'))\n\nwith xr.open_dataset(constants.variable_to_filename( 'ta' ), decode_times=False) as ta:\n Tglobal = (ta.ta * weight).mean(dim=('lat','lon','time'))\n\nwith xr.open_dataset(constants.variable_to_filename( 'hus' ), decode_times=False) as hus:\n SHglobal = (hus.hus * weight).mean(dim=('lat','lon','time')) # kg/kg\n\nwith xr.open_dataset(constants.variable_to_filename( 'o3' ), decode_times=False) as o3:\n O3global = (o3.o3 * weight).mean(dim=('lat','lon','time')) # kg/kg\n\nstate = climlab.column_state(num_lev=50)\nlev = state.Tatm.domain.axes['lev'].points * 100\n\n# interpolate to model pressure levels\nTinterp = np.interp(lev, np.flipud(Tglobal.plev[:18]), np.flipud(Tglobal[:18]))\nSHinterp = np.interp(lev, np.flipud(SHglobal.plev[:18]), np.flipud(SHglobal[:18]))\nO3interp = np.interp(lev, np.flipud(O3global.plev[:18]), np.flipud(O3global[:18]))\nCLinterp = np.interp(lev, np.flipud(plev), np.flipud(CLglobal))\nCLWinterp = np.interp(lev, np.flipud(plev), np.flipud(CLWglobal))\nCLIinterp = np.interp(lev, np.flipud(plev), np.flipud(CLIglobal))\n# Need to 'flipud' because the interpolation routine \n# needs the pressure data to be in increasing order\n# Create a state dictionary with corresponsing number of cl data levels\n\n# Set the temperature to the observed values\nstate.Tatm[:] = Tinterp\n\nr_liq = 14. # Cloud water drop effective radius (microns)\nr_ice = 60. # Cloud water drop effective radius (microns)\n\n# Convert mixing ratio (kg/kg) into cloud water content in (g/m3)\n\nalt = constants.p_to_alt( lev ) * 1000 # in km\nair_density = ((lev) / (286.9 * Tinterp))\nclwc = (CLWinterp * air_density) * 1000\nciwc = (CLIinterp * air_density) * 1000\n\nprev_alt = alt[0] + (alt[0] - alt[1])\nCLWPglobal = np.zeros_like(lev)\nfor i, ( row, altitude ) in enumerate( zip( clwc, alt ) ):\n CLWPglobal[i] = row * (prev_alt - altitude)\n prev_alt = altitude\n\nprev_alt = alt[-1] + (alt[-1] - alt[-2])\nCIWPglobal = np.zeros_like(lev)\nfor i, ( row, altitude ) in enumerate( zip( ciwc, alt ) ):\n CIWPglobal[i] = row * (prev_alt - altitude)\n prev_alt = altitude\n# Convert cloud water content to water path (g/m2)\n# WP = WC * deltaz\n# CLWPglobal = constants.wc_to_wp( clwc, alt )\n# CIWPglobal = constants.wc_to_wp( ciwc, alt )\n\n\n# Loop through all pressure levels\n# Set up a radiation model with the cloud layer at the current pressure level\n# Compute CRE and store the results\nCRE_LW = {}\nCRE_SW = {}\nOLR = np.zeros_like(lev)\nASR = np.zeros_like(lev)\nOLRclr = np.zeros_like(lev)\nASRclr = np.zeros_like(lev)\nfor i in range(lev.size):\n # Whole-column cloud characteristics\n # The cloud fraction is a Gaussian bump centered at the current level \n rad = RRTMG(state=state, \n albedo=0.2,\n O3 = O3interp,\n specific_humidity=SHinterp,\n cldfrac=CLinterp*np.exp(-(lev-lev[i])**2/(2*25.)**2),\n verbose=False,\n clwp = CLWPglobal *1000,\n ciwp = CIWPglobal *1000,\n r_liq = np.zeros_like(state.Tatm) + r_liq,\n r_ice = np.zeros_like(state.Tatm) + r_ice, \n )\n rad.compute_diagnostics()\n OLR[i] = rad.OLR\n OLRclr[i] = rad.OLRclr\n ASR[i] = rad.ASR\n ASRclr[i] = rad.ASRclr\nCRE_LW = -(OLR - OLRclr)\nCRE_SW = (ASR - ASRclr)\n\n# Make some plots of the CRE dependence on cloud height\nfig, axes = plt.subplots(1,3, figsize=(16,6))\nax = axes[0]\nax.plot(CRE_LW, lev)\nax.set_ylabel('Pressure (hPa)')\nax.set_xlabel('LW cloud radiative effect (W/m2)')\n\nax = axes[1]\nax.plot(CRE_SW, lev)\nax.set_xlabel('SW cloud radiative effect (W/m2)')\n\nax = axes[2]\nax.plot(CRE_SW + CRE_LW, lev)\nax.set_xlabel('Net cloud radiative effect (W/m2)')\n\nfor ax in axes:\n ax.invert_yaxis()\n ax.legend()\n ax.grid()\nfig.suptitle('Cloud Radiative Effect as a function of the vertical height of the cloud layer', fontsize=16)\nplt.show()", "sub_path": "backup/test_radiation_column_original.py", "file_name": "test_radiation_column_original.py", "file_ext": "py", "file_size_in_byte": 4954, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "constants.home", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 12, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 17, "usage_type": "call"}, {"api_name": "constants.variable_to_filename", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 18, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 23, "usage_type": "call"}, {"api_name": "constants.variable_to_filename", "line_number": 23, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 29, "usage_type": "call"}, {"api_name": "constants.variable_to_filename", "line_number": 29, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 32, "usage_type": "call"}, {"api_name": "constants.variable_to_filename", "line_number": 32, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 35, "usage_type": "call"}, {"api_name": "constants.variable_to_filename", "line_number": 35, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 38, "usage_type": "call"}, {"api_name": "constants.variable_to_filename", "line_number": 38, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 41, "usage_type": "call"}, {"api_name": "constants.variable_to_filename", "line_number": 41, "usage_type": "call"}, {"api_name": "climlab.column_state", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 53, "usage_type": "call"}, {"api_name": "constants.p_to_alt", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 96, "usage_type": "call"}, {"api_name": "climlab.radiation.RRTMG", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}]} +{"seq_id": "309374258", "text": "import cv2\nimport os\nimport numpy as np\nimport scipy.fftpack as fft\nfrom proto_mpeg import quantization_matrix,zz_indices,reversed_zz_indices,Frame\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport pickle\nfrom PIL import Image\n\ndef decode_block(F,QF):\n def zigzag_to_flattened_block(encoded_string):\n \"\"\"\n @param encoded_string: A list in the form \n @return rst: A zigzag series of coeffs\n \"\"\"\n rst = [encoded_string[0]]\n for i in encoded_string[1:-1]:\n #print(i)\n for _ in range(i[0]):\n rst.append(0)\n rst.append(i[1])\n #print(16-len(rst))\n for i in range(64-len(rst)):\n rst.append(0)\n return rst\n\n def de_quantize(F,QF):\n quant_matrix=np.ceil(quantization_matrix*QF)\n quant_matrix[quant_matrix>255]=255\n tmp = []\n for i in range(8):\n for j in range(8):\n #tmp1 = F[i][j]*quant_matrix.A[i][j]\n #print(tmp1)\n tmp.append(int(F[i][j]*quant_matrix.A[i][j]))\n return np.array(tmp).reshape(8,8)\n\n def idct(y):\n return fft.idct(fft.idct(y, axis=0, norm='ortho', type=2), axis=1, norm='ortho', type=2)\n\n def regenerate(src8_8):\n \"\"\"\n @param src8_8: A 8*8 downsampled block \n @return regenerated_16_16: A 16*16 block, expanding each pixel to 2*2.\n \"\"\"\n #print(src8_8)\n rst16_16_flattened=[]\n for i in range(8):\n for j in range(8):\n #print(src8_8[i][j])\n rst16_16_flattened.append(src8_8[i][j])\n rst16_16_flattened.append(src8_8[i][j])\n for j in range(8):\n #print(src8_8[i][j])\n rst16_16_flattened.append(src8_8[i][j])\n rst16_16_flattened.append(src8_8[i][j])\n regenerated_16_16 = np.array(rst16_16_flattened).reshape(16,16)\n return regenerated_16_16\n\n rst = []\n for i in F:\n decoded_F = zigzag_to_flattened_block(i)\n de_flattened_F = np.array([decoded_F[i] for i in reversed_zz_indices]).reshape(8,8)\n de_quantized_F = de_quantize(de_flattened_F,QF)\n regenerated_F = idct(de_quantized_F).astype(int)\n rst.append(regenerated_F)\n Y1,Y2,Y3,Y4,subsample_Cb,subsample_Cr = rst\n rst=[]\n for i in range(8):\n tmp = list(Y1[i])+list(Y2[i])\n rst+=tmp\n #print(rst)\n for i in range(8):\n tmp = list(Y3[i])+list(Y4[i])\n rst+=tmp\n regenerated_Y = np.array(rst).reshape(16,16)\n regenerate_Cb = regenerate(subsample_Cb)\n regenerate_Cr = regenerate(subsample_Cr)\n regenerated_block = cv2.merge((regenerated_Y, regenerate_Cb, regenerate_Cr)).astype(np.uint8)\n regenerated_block = cv2.cvtColor(regenerated_block,cv2.COLOR_YCR_CB2RGB)\n return regenerated_block\n \n# dre refers to DC_term,Run_level,EOB\ndef decode_dre_to_pic(v,h,blocks,QF):\n decoded_blocks = []\n for block in blocks:\n decoded_block = decode_block(block,QF)\n decoded_blocks.append(decoded_block)\n f=[]\n for m in range(v):\n rst = []\n for i in range(16):\n for n in range(h):\n block = decoded_blocks[m*h+n]\n rst+=list(block[i])\n #print(len(rst))\n f+=rst\n #print(len(f))\n f = np.array(f).reshape(v*16,h*16,3)\n return f\n\ndef decode_bit_to_dre_1(input): \n with open(input, \"rb\") as fp: # Unpickling\n decoded_dre = pickle.load(fp)\n return decoded_dre\n\ndef decode_pic(v,h,input,QF,output=\"output.jpg\"):\n decoded_dre = decode_bit_to_dre_1(input)\n #print(decoded_dre) \n decoded = decode_dre_to_pic(v,h,decoded_dre,QF)\n #plt.imshow(decoded)\n #plt.show()\n matplotlib.image.imsave(output, decoded)\n\nfrom PIL import Image\ndef pics_to_video(fname,fps,output):\n os.system(\"ffmpeg -r \"+str(fps)+\" -i \"+fname+\" -vcodec mpeg4 -y \"+output)\n\ndef decode_video(input,fps=10,output='decoded_movie.mp4'):\n \n imgs = decode_bit_to_dre_1(input)\n headtag = imgs.pop(0)\n if not headtag == 'EC504':\n print(\"invalid binary file - must use a file encoded using this project... aborting\")\n return None\n numimg = imgs.pop(0)\n QF = imgs.pop(0)\n v = imgs.pop(0)\n h = imgs.pop(0)\n print(\"Number of Frames: \"+str(numimg))\n print(\"QF: \"+str(QF))\n print(\"decoding start!\")\n i=1\n if not os.path.exists(\"./decoded_pics\"):\n os.makedirs(\"./decoded_pics\")\n for img in imgs:\n print(i)\n tmp = decode_dre_to_pic(v,h,img,QF=QF)\n output1 = \"./decoded_pics/output%04d.png\" % i\n plt.imsave(output1, tmp, format='png')\n i+=1\n print(\"decoding done!\")\n pics_to_video(\"./decoded_pics/output%04d.png\",fps,output)\n #pics_to_video(\"./pics/sample_images/scene00%03d.jpg\", 24,'original_movie.mp4')", "sub_path": "decoder.py", "file_name": "decoder.py", "file_ext": "py", "file_size_in_byte": 4868, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.ceil", "line_number": 29, "usage_type": "call"}, {"api_name": "proto_mpeg.quantization_matrix", "line_number": 29, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "scipy.fftpack.idct", "line_number": 40, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "proto_mpeg.reversed_zz_indices", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 80, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.COLOR_YCR_CB2RGB", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.image.imsave", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imsave", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}]} +{"seq_id": "599260646", "text": "#!/usr/bin/python3\n\nfrom Crypto import Random\nfrom Crypto.Cipher import AES\nimport binascii\n\ndef decrypt(textEncrypted, key,iv): \n \n cipher = AES.new(key, AES.MODE_CBC, iv)\n plaintext = cipher.decrypt(textEncrypted[AES.block_size:])\n return plaintext.rstrip(b\"\\0\")\n\ndef encrypt_file(file_name, key):\n with open(file_name, 'rb') as fo:\n plaintext = fo.read()\n enc = encrypt(plaintext, key)\n with open(file_name + \".aes256\", 'wb') as fo:\n fo.write(enc)\n\ndef decrypt_file(file_name, key, iv):\n with open(file_name, 'rb') as fo:\n textEncrypted = fo.read()\n dec = decrypt(textEncrypted, key,iv)\n with open(file_name + \".decrypt\", 'wb') as fo:\n fo.write(dec)\n\n\nprint(\"### This script only work for AES-CBC-256. ###\\n## The decrypted will have the .decrypt extension added ##\")\nkey = binascii.unhexlify(input(\"please enter the key :\"))\niv = binascii.unhexlify(input(\"Please enter the IV : \" ))\nfilename = input(\"please enter the filename to decrypt : \")\ndecrypt_file(filename,key,iv)", "sub_path": "AES-256-FILE.py", "file_name": "AES-256-FILE.py", "file_ext": "py", "file_size_in_byte": 1035, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "Crypto.Cipher.AES.new", "line_number": 9, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 9, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_CBC", "line_number": 9, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.AES.block_size", "line_number": 10, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.AES", "line_number": 10, "usage_type": "name"}, {"api_name": "binascii.unhexlify", "line_number": 29, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "221642816", "text": "from .helpers import data\nfrom .helpers import values\n\nimport discord\n\nfrom discord.ext import commands, tasks\n\nclass Tools(commands.Cog):\n def __init__(self, client):\n self.client = client\n\n @commands.command(help='🔧An online counter! Add \"server\" to the command to only count your server.')\n async def online(self, ctx, mode='servers'):\n mode = mode.lower()\n\n if mode == 'server':\n minimum = len([m for m in ctx.guild.members if (str(m.status) != 'offline' and (not m.bot))])\n maximum = len([m for m in ctx.guild.members if (not m.bot)])\n\n elif mode == 'bots':\n minimum = len([m for m in ctx.guild.members if (str(m.status) != 'offline' and (m.bot))])\n maximum = len([m for m in ctx.guild.members if (m.bot)])\n\n else:\n minimum = 0\n maximum = 0\n already_counted = []\n\n for g in self.client.guilds:\n for m in g.members:\n if not m.id in already_counted and (not m.bot):\n if str(m.status) != 'offline':\n minimum += 1\n maximum += 1\n \n already_counted.append(m.id)\n \n embed = discord.Embed(\n title=f'Online Percentage for {\"Bots in \" if mode == \"bots\" else \"\"}{\"this Server\" if mode != \"servers\" else \"all Servers I have access to\"}',\n description=f'> {minimum}/{maximum} (**{round(minimum/maximum*100)}%**)',\n color=values.color()\n ).set_footer(text=f'Bots are {\"not\" if mode != \"bots\" else \"exclusively\"} counted')\n\n await ctx.send(embed=embed)\n\ndef setup(client):\n client.add_cog(Tools(client))\n", "sub_path": "src/cogs/tools.py", "file_name": "tools.py", "file_ext": "py", "file_size_in_byte": 1754, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 8, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 8, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 38, "usage_type": "call"}, {"api_name": "helpers.values.color", "line_number": 41, "usage_type": "call"}, {"api_name": "helpers.values", "line_number": 41, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 12, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "468479043", "text": "import re\nfrom os.path import splitext, basename\n\nimport pandas as pd\nfrom pandas import DataFrame, Series\n\nfrom cs_dictionary import CSDictionary\nfrom utils.file_io import save_dataframe\nfrom utils.logger import Logger\n\n\nclass Ensemble:\n num_preds = 0\n net_names = []\n\n def __init__(self, params: CSDictionary):\n self.params = params\n self.log: Logger = params.log\n\n self.log.info(\n 'Generating ensemble with: {}'.format(self.params.input_file))\n self.ensemble_predictions(single_ensemble=self.params.ensemble)\n\n def ensemble_predictions(self, single_ensemble: bool):\n df = self.read_predictions_file()\n weights = self.compute_weights(df)\n if single_ensemble:\n json_predictions = pd.DataFrame(\n pd.read_json(self.params.json_prediction,\n typ='series', orient='records')).T\n ensemble_data = self.compute_weighted_prediction(\n json_predictions,\n weights)\n else:\n ensemble_data = self.compute_weighted_prediction(df, weights)\n if self.params.save_predictions:\n self.save_ensemble(ensemble_data)\n else:\n if single_ensemble:\n to_show = ensemble_data[['w_avg']]\n else:\n to_show = ensemble_data[['actual', 'w_avg']]\n if self.params.predict:\n print(pd.DataFrame(\n to_show.iloc[-1]).T.to_string())\n else:\n pd.set_option('display.max_rows', -1)\n to_show.T[0].to_json(self.params.json_forecast)\n self.log.info('Saved forecast: {}'.format(\n self.params.json_forecast))\n print(to_show.to_string())\n\n def read_predictions_file(self) -> DataFrame:\n self.log.debug(\n 'Reading predictions file: {}'.format(self.params.input_file))\n df = pd.read_csv(self.params.input_file,\n delimiter=self.params.delimiter)\n if 'avg' in df.columns:\n self.num_preds = df.columns.get_loc('avg') - 1\n else:\n self.log.error('Column called not present in pred_ file')\n return df\n\n def compute_weights(self, preds: DataFrame) -> DataFrame:\n self.log.debug('Computing weights from different networks')\n # Take only the names of the networks\n from_position = list(preds.columns).index('actual') + 1\n self.net_names = preds.columns[from_position:self.num_preds + 1]\n self.log.debug('Network names: {}'.format(self.net_names))\n # Compute proportions\n proportions = preds.winner.value_counts()\n weights = pd.DataFrame({'proportion': proportions,\n 'weight': pd.Series(index=proportions.index)})\n weights.weight = weights.proportion / weights.proportion.sum()\n return weights\n\n def compute_weighted_prediction(self, df, weights):\n self.log.debug('Computing final weighted prediction')\n preds = df.copy(deep=True)\n\n def weighted_prediction(x: Series): # , weights: DataFrame):\n w = weights.reindex(list(x.index)).weight\n x_w = x * w\n self.log.debug(\n 'Weighting X·W = {}·{} = {}'.format(\n x.values, w.values, x_w.values))\n self.log.debug('sum(X·W) = {}'.format(x_w.sum()))\n return x_w.sum()\n\n preds['w_avg'] = preds[self.net_names].apply(\n lambda x: weighted_prediction(x), axis=1)\n\n return preds\n\n def save_ensemble(self, preds: DataFrame):\n # Set the filename of the output file.\n if self.params.output is not None:\n new_filename = self.params.output\n else:\n current_filename = splitext(basename(self.params.input_file))[0]\n if re.search('^pred_', current_filename) is not None:\n new_filename = current_filename.replace('pred_', 'forecast_')\n else:\n new_filename = 'forecast_' + current_filename\n\n # Prepare the data frame to be saved.\n date_column = self.params.csv_dict['d']\n preds.reset_index(drop=True, inplace=True)\n preds.rename(columns={'w_avg': 'forecast'}, inplace=True)\n saved_file, _ = save_dataframe(\n new_filename,\n preds[[date_column, 'actual', 'forecast']].round(2),\n self.params.predictions_path,\n index=False)\n self.log.info('Saved forecast file: {}'.format(saved_file))\n", "sub_path": "src/predictor/ensemble.py", "file_name": "ensemble.py", "file_ext": "py", "file_size_in_byte": 4571, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cs_dictionary.CSDictionary", "line_number": 16, "usage_type": "name"}, {"api_name": "utils.logger.Logger", "line_number": 18, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 53, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 64, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 81, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 95, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 100, "usage_type": "call"}, {"api_name": "re.search", "line_number": 101, "usage_type": "call"}, {"api_name": "utils.file_io.save_dataframe", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "397205729", "text": "import numpy as np \nimport torch\nimport matplotlib.pyplot as plt\nimport pickle\n\n# num_epoch = 2000\n# results_dict_file = \"BioTac_info_400/results_dict.pkl\"\n# new_name = \"BioTac_info_400/results_dict_smooth_\" + str(num_epoch) + \".pkl\"\n# results_dict = pickle.load(open(results_dict_file,'rb'))\n# pickle.dump(results_dict,open(new_name,'wb'))\n# results = results_dict[\"results\"]\n# loss = results_dict[\"loss\"]\n# conf_mat = results_dict[\"conf_mat\"]\n\n# print(conf_mat)\n\n# fig, ax = plt.subplots(figsize=(15, 7))\n# epoch_num = np.arange(len(loss), dtype=np.int)\n# ax.plot(epoch_num, loss, label=\"train:\" + str(results[0])+ \" test:\" + str(results[1]))\n# ax.set_xlabel('epoch')\n# ax.set_ylabel('loss')\n# ax.grid(True)\n# plt.legend(loc='upper right')\n# figname = \"figures/smooth_\" + str(num_epoch) + \"_loss.png\"\n# plt.savefig(figname)\n# plt.show()\n\n\nnum_epoch = 5000\nresults_dict_file = \"BioTac_info_400/results_dict_\" + str(num_epoch) + \".pkl\"\nnew_name = \"BioTac_info_400/results_dict_smooth_\" + str(num_epoch) + \".pkl\"\nresults_dict = pickle.load(open(results_dict_file,'rb'))\n# pickle.dump(results_dict,open(new_name,'wb'))\nprint(\"keys\", [key for key in results_dict])\nresults = results_dict[\"results\"]\ntrain_loss = results_dict[\"train_loss\"]\ntest_loss = results_dict[\"test_loss\"]\ntrain_acc = results_dict[\"train_acc\"]\ntest_acc = results_dict[\"test_acc\"]\nconf_mat = results_dict[\"conf_mat\"]\n\n# print(conf_mat)\nassert len(train_loss) == len(test_loss), \"different loss len, train: {}, test: {}\".format(len(train_loss), len(test_loss))\nepoch_num = np.arange(len(train_loss), dtype=np.int)\n\n# # if single plot\n# fig, ax = plt.subplots(figsize=(15, 7))\n# ax.plot(epoch_num, train_loss, label=\"train:\" + str(results[0]))\n# ax.plot(epoch_num, test_loss, label=\"test: \" + str(results[1]))\n# ax.set_xlabel('epoch')\n# ax.set_ylabel('loss')\n# ax.grid(True)\n# plt.legend(loc='upper right')\n\n# if double plots\nfig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 7), sharex=True)\n# # make a little extra space between the subplots\n# fig.subplots_adjust(hspace=0.5)\nax1.plot(epoch_num, train_loss, label=\"train loss\")\nax1.plot(epoch_num, test_loss, label=\"test loss\")\nax1.set_ylabel('loss')\nax1.grid(True)\nax2.plot(epoch_num, train_acc, label=\"train acc \"+ str(results[0]))\nax2.plot(epoch_num, test_acc, label=\"test acc \"+ str(results[1]))\nax2.set_ylabel(\"acc\")\nax1.grid(True)\n\nax2.set_xlabel('epoch')\nplt.legend(loc='upper right')\n\n\n# save the figure\nfigname = \"figures/smooth_\" + str(num_epoch) + \"_loss_acc.png\" \nplt.savefig(figname)\nplt.show()", "sub_path": "plot_loss.py", "file_name": "plot_loss.py", "file_ext": "py", "file_size_in_byte": 2524, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pickle.load", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 44, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}]} +{"seq_id": "619381522", "text": "# coding: utf-8\n\"\"\"\nEste processamento realiza a exportação de registros SciELO para o formato RSPS\n\"\"\"\n\nimport os\nimport argparse\nimport logging\nimport codecs\nimport json\nfrom io import StringIO\n\nimport packtools\nfrom packtools.catalogs import XML_CATALOG\n\nimport utils\n\nos.environ['XML_CATALOG_FILES'] = XML_CATALOG\nlogger = logging.getLogger(__name__)\n\ndef _config_logging(logging_level='INFO', logging_file=None):\n\n allowed_levels = {\n 'DEBUG': logging.DEBUG,\n 'INFO': logging.INFO,\n 'WARNING': logging.WARNING,\n 'ERROR': logging.ERROR,\n 'CRITICAL': logging.CRITICAL\n }\n\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\n logger.setLevel(allowed_levels.get(logging_level, 'INFO'))\n\n if logging_file:\n hl = logging.FileHandler(logging_file, mode='a')\n else:\n hl = logging.StreamHandler()\n\n hl.setFormatter(formatter)\n hl.setLevel(allowed_levels.get(logging_level, 'INFO'))\n\n logger.addHandler(hl)\n\n return logger\n\ndef summarize(validator):\n\n def _make_err_message(err):\n \"\"\" An error message is comprised of the message itself and the\n element sourceline.\n \"\"\"\n err_msg = {'message': err.message}\n\n try:\n err_element = err.get_apparent_element(validator.lxml)\n except ValueError:\n logger.info('Could not locate the element name in: %s' % err.message)\n err_element = None\n\n if err_element is not None:\n err_msg['apparent_line'] = err_element.sourceline\n else:\n err_msg['apparent_line'] = None\n\n return err_msg\n\n\n dtd_is_valid, dtd_errors = validator.validate()\n sps_is_valid, sps_errors = validator.validate_style()\n\n summary = {\n 'dtd_errors': [_make_err_message(err) for err in dtd_errors],\n 'sps_errors': [_make_err_message(err) for err in sps_errors],\n }\n\n summary['dtd_is_valid'] = validator.validate()[0]\n summary['sps_is_valid'] = validator.validate_style()[0]\n summary['is_valid'] = bool(validator.validate()[0] and validator.validate_style()[0])\n\n return summary\n\ndef analyze_xml(xml, document):\n \"\"\"Analyzes `file` against packtools' XMLValidator.\n \"\"\"\n\n f = StringIO(xml)\n\n try:\n xml = packtools.XMLValidator(f)\n except:\n logger.error('Could not read file %s' % document.publisher_id)\n summary = {}\n summary['dtd_is_valid'] = False\n summary['sps_is_valid'] = False\n summary['is_valid'] = False\n summary['parsing_error'] = True\n return summary\n else:\n summary = summarize(xml)\n return summary\n\n\nclass Dumper(object):\n\n def __init__(self, collection, issns=None):\n\n self._articlemeta = utils.articlemeta_server()\n self.collection = collection\n self.issns = issns or [None]\n\n def fmt_json(self, data, xml_result):\n\n fmt = {}\n\n fmt['code'] = data.publisher_id\n fmt['collection'] = data.collection_acronym\n fmt['id'] = '_'.join([data.collection_acronym, data.publisher_id])\n fmt['document_type'] = data.document_type\n fmt['publication_year'] = data.publication_date[0:4]\n fmt['document_type'] = data.document_type\n fmt['data_version'] = 'legacy' if data.data_model_version == 'html' else 'xml'\n fmt.update(xml_result)\n return json.dumps(fmt)\n\n def run(self):\n for issn in self.issns:\n for document in self._articlemeta.documents(collection=self.collection, issn=issn):\n try:\n xml = self._articlemeta.document(document.publisher_id, document.collection_acronym, fmt='xmlrsps')\n except Exception as e:\n logger.exception(e)\n logger.error('Fail to read document: %s_%s' % (document.publisher_id, document.collection_acronym))\n xml = u''\n logger.debug('Reading document: %s' % document.publisher_id)\n validation_result = analyze_xml(xml, document)\n print(self.fmt_json(document, validation_result))\n\n\ndef main():\n\n parser = argparse.ArgumentParser(\n description='Dump languages distribution by article'\n )\n\n parser.add_argument(\n 'issns',\n nargs='*',\n help='ISSN\\'s separated by spaces'\n )\n\n parser.add_argument(\n '--collection',\n '-c',\n help='Collection Acronym'\n )\n\n parser.add_argument(\n '--logging_file',\n '-o',\n help='Full path to the log file'\n )\n\n parser.add_argument(\n '--logging_level',\n '-l',\n default='DEBUG',\n choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],\n help='Logggin level'\n )\n\n args = parser.parse_args()\n _config_logging(args.logging_level, args.logging_file)\n logger.info('Dumping data for: %s' % args.collection)\n\n issns = None\n if len(args.issns) > 0:\n issns = utils.ckeck_given_issns(args.issns)\n\n dumper = Dumper(args.collection, issns)\n\n dumper.run()", "sub_path": "export/xml_rsps.py", "file_name": "xml_rsps.py", "file_ext": "py", "file_size_in_byte": 5089, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "packtools.catalogs.XML_CATALOG", "line_number": 18, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 24, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 25, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 26, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 27, "usage_type": "attribute"}, {"api_name": "logging.CRITICAL", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 38, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 87, "usage_type": "call"}, {"api_name": "packtools.XMLValidator", "line_number": 90, "usage_type": "call"}, {"api_name": "utils.articlemeta_server", "line_number": 108, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 124, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 142, "usage_type": "call"}, {"api_name": "utils.ckeck_given_issns", "line_number": 178, "usage_type": "call"}]} +{"seq_id": "313253161", "text": "import lib.marking_evaluation as meval\nimport copy\n\nfrom time import time, asctime\nfrom tqdm import tqdm\nimport pandas as pd\n\n# Lists all the sucessors of a team proposal\ndef list_successors(current_proposal, step):\n successors = []\n\n # This chained fors generate every possible movement for all the players\n # according to the step globally defined\n for i in range(10):\n for j in range(2):\n for k in [-step, step]:\n changed_proposal = copy.deepcopy(current_proposal)\n changed_proposal[i][j] += k\n successors.append(changed_proposal)\n \n # Returns a list with all the successors of the team proposal\n return successors\n\n# Calculate sucessor fitness\ndef calculate_sucessor_fitnesses(adversary, successors):\n # Calculating the fitness for all successors\n successor_fitnesses = []\n for successor in successors:\n successor_fitnesses.append(adversary.calculate_heuristic(meval.create_team(successor)))\n\n # Returns list with the fitness of all successors\n return successor_fitnesses\n\n# Finds the best proposal from a list\ndef find_best(successors, successor_fitnesses):\n # Assuming the first \"best\" is the first element of the list\n best = successors[0]\n best_fitness = successor_fitnesses[0]\n\n # Calculating the fitness for all successors\n for successor, fitness in zip(successors[1:], successor_fitnesses[1:]):\n # If sucessor is the best, save the result \n if fitness > best_fitness:\n best = successor\n best_fitness = fitness\n \n # Returns the best proposal and the heuristic\n return best, best_fitness\n\n# Hill climb algorithm\ndef hillclimb():\n # Create register to save information about the run\n register = {'iteration': [], 'proposal': [], 'fitness': [], 'cycle_time': []}\n\n # Starting from a random position\n proposed_team = meval.generate_random_start(adversary.x_min, adversary.x_max)\n best_fitness = adversary.calculate_heuristic(meval.create_team(proposed_team))\n\n # Defining step related variables\n step = 10\n step_decrease_cycle = 125\n max_steps_at_current_step = 250\n counter = 0\n internal_counter = 0\n\n # Search for a better solution until:\n # No better solution can be found and step cannot be lowered\n while 1:\n counter += 1\n \n # Reset timer\n start_time = time()\n \n # Halve step every *step_decrease_cycle* iterations\n internal_counter += 1\n if internal_counter % step_decrease_cycle == 0:\n internal_counter = 0\n step = int(step/2)\n\n # Find the best sucessor\n successors = list_successors(proposed_team, step)\n successor_fitnesses = calculate_sucessor_fitnesses(adversary, successors)\n proposed_team, fitness = find_best(successors, successor_fitnesses)\n\n # If fitness did not improve\n if fitness <= best_fitness:\n # If step is already one, end optimization\n if step == 1:\n break\n \n # If we can decrease the step, do it before ending\n else:\n internal_counter = 0\n step = int(step/2)\n \n # If fitness improved\n else:\n # If the hill climb is stuck at the minimum temperature, break\n if internal_counter > max_steps_at_current_step:\n break\n\n # Else, save the result and continue\n best_fitness = fitness\n\n # Save results for post-hoc analysis\n for successor, successor_fitness in zip(successors, successor_fitnesses):\n register['iteration'].append(counter)\n register['proposal'].append(successor)\n register['fitness'].append(successor_fitness)\n register['cycle_time'].append(time() - start_time)\n\n # Export registers to CSV\n current_time = asctime().replace(':','_').split(' ')\n export_time = f'{current_time[1]}_{current_time[2]}_{current_time[3]}'\n pd.DataFrame(register).to_csv(f'results/012_hillclimb_{export_time}.csv', index=False)\n\n# Define adversary\nadversary = meval.default_adversary_1\n\n# Run algorithm multiple times\nfor _ in tqdm(range(20)):\n hillclimb()", "sub_path": "012_HillClimb.py", "file_name": "012_HillClimb.py", "file_ext": "py", "file_size_in_byte": 4250, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "copy.deepcopy", "line_number": 17, "usage_type": "call"}, {"api_name": "lib.marking_evaluation.create_team", "line_number": 29, "usage_type": "call"}, {"api_name": "lib.marking_evaluation", "line_number": 29, "usage_type": "name"}, {"api_name": "lib.marking_evaluation.generate_random_start", "line_number": 56, "usage_type": "call"}, {"api_name": "lib.marking_evaluation", "line_number": 56, "usage_type": "name"}, {"api_name": "lib.marking_evaluation.create_team", "line_number": 57, "usage_type": "call"}, {"api_name": "lib.marking_evaluation", "line_number": 57, "usage_type": "name"}, {"api_name": "time.time", "line_number": 72, "usage_type": "call"}, {"api_name": "time.time", "line_number": 110, "usage_type": "call"}, {"api_name": "time.asctime", "line_number": 113, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 115, "usage_type": "call"}, {"api_name": "lib.marking_evaluation.default_adversary_1", "line_number": 118, "usage_type": "attribute"}, {"api_name": "lib.marking_evaluation", "line_number": 118, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 121, "usage_type": "call"}]} +{"seq_id": "17323976", "text": "from model import lrcnModel\nimport numpy as np\nimport os\nfrom skimage.io import imread\nfrom keras.utils import to_categorical\nfrom KerasBatchGenerator import KerasBatchGenerator\n\n\ndef load_data(folder_path, dictionary):\n if 'train' in folder_path:\n names = ['Dana', 'liz', 'Jamiee', 'Naomi', 'Tyler','Lana']\n elif 'test' in folder_path:\n names = ['Dana', 'liz', 'Jamiee', 'Naomi', 'Tyler','Lana']\n else:\n raise ValueError('folder_path must be for train or test only')\n folders = os.listdir(folder_path)\n X, y = [], []\n for folder in folders:\n if folder[0] == '.':\n continue\n files = os.listdir(os.path.join(folder_path, folder))\n for file in files:\n if file.endswith('.jpg'):\n basename, ext = file.split('.')\n for name in names:\n if name in basename:\n img_path = os.path.join(folder_path, folder, file)\n img = imread(img_path)\n X.append(img)\n y.append(dictionary[folder])\n return np.asarray(X), np.asarray(y)\n\n\ndef make_dictionary():\n dictionary = {}\n with open('sign_list') as f:\n for line in f.readlines():\n if '.' in line:\n idx, word = line.split('.')\n word = word.strip()\n dictionary[word] = int(idx) - 1\n return dictionary\n\n\n\ndef train(seq_length, model, saved_model=None, class_limit=None, image_shape=None,\n batch_size=32, nb_epochs=100):\n\n # dictionary = make_dictionary()\n # X_train, y_train = load_data('train', dictionary)\n # X_test, y_test = load_data('test', dictionary)\n # np.save('X_train.npy', X_train)\n # np.save('y_train.npy', y_train)\n # np.save('X_test.npy', X_test)\n # np.save('y_test.npy', y_test)\n\n X_train = np.load('X_train.npy')\n y_train = np.load('y_train.npy')\n X_test = np.load('X_test.npy')\n y_test = np.load('y_test.npy')\n\n y_train.shape #19385\n y_test.shape#4620\n\n y_train_onehot = to_categorical(y_train)\n y_test_onehot = to_categorical(y_test)\n\n train_data_generator = KerasBatchGenerator(X_train, y_train_onehot, batch_size)\n test_data_generator = KerasBatchGenerator(X_test, y_test_onehot, batch_size)\n\n lrcnm = lrcnModel(59, model, seq_length, saved_model)\n\n lrcnm.model.fit_generator(train_data_generator.frame_generator(),\n len(X_train)//(batch_size *seq_length),\n validation_data=test_data_generator.frame_generator(),\n epochs = nb_epochs,\n validation_steps= len(X_test) // (batch_size *seq_length))\n\n lrcnm.model.save('my_model3.h5')\n\ndef main():\n model = 'lrcn'\n saved_model = None\n class_limit = None\n seq_length = 40\n batch_size = 1\n nb_epochs = 20\n\n if model is 'lrcn':\n image_shape = (360, 360, 3)\n\n train(seq_length, model, saved_model=saved_model, class_limit=class_limit,\n image_shape=image_shape, batch_size=batch_size, nb_epochs=nb_epochs)\n\nif __name__ == '__main__':\n main()\n", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 3072, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.listdir", "line_number": 16, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "skimage.io.imread", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 66, "usage_type": "call"}, {"api_name": "KerasBatchGenerator.KerasBatchGenerator", "line_number": 68, "usage_type": "call"}, {"api_name": "KerasBatchGenerator.KerasBatchGenerator", "line_number": 69, "usage_type": "call"}, {"api_name": "model.lrcnModel", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "271467944", "text": "#!/usr/bin/env python\nimport sys\nimport os\nimport argparse\nimport numpy as np\n\n\ndef str2int(string):\n if string.isdigit():\n return int(string)\n else:\n return None\n\n\ndef get_stress_from_line(line):\n\n stress = np.zeros((3, 3)) * np.nan\n\n # stress[0, 0] = float(line.split()[2])\n # stress[1, 1] = float(line.split()[3])\n # stress[2, 2] = float(line.split()[4])\n # stress[0, 1] = float(line.split()[5])\n # stress[1, 2] = float(line.split()[6])\n # stress[2, 0] = float(line.split()[7])\n stress[0, 0] = float(line[ 9:21])\n stress[1, 1] = float(line[21:33])\n stress[2, 2] = float(line[33:45])\n stress[0, 1] = float(line[45:57])\n stress[1, 2] = float(line[57:69])\n stress[2, 0] = float(line[69:81])\n stress[1, 0] = stress[0, 1]\n stress[2, 1] = stress[1, 2]\n stress[0, 2] = stress[2, 0]\n\n stress *= -1.0\n stress *= 0.1 # (GPa)\n\n return stress\n\n\ndef generate_properties_per_atom(dictionary, nions):\n properties = [\n \"NELECT\",\n \"volume\",\n \"free_energy\",\n \"energy_T\",\n \"energy\",\n ]\n for p in properties:\n p_per_atom = p + \"_per_atom\"\n if nions == 0:\n dictionary[p_per_atom] = None\n elif p not in dictionary:\n dictionary[p_per_atom] = None\n else:\n dictionary[p_per_atom] = dictionary[p] / nions\n\n\nclass Outcar:\n def __init__(self, filename):\n self._filename = filename\n self._initialize_dictionary()\n self.generate_dictionary()\n\n def __getitem__(self, key):\n return self._dictionary[key]\n\n def as_dict(self):\n return self._dictionary\n\n get_dictionary = as_dict\n\n def _initialize_dictionary(self):\n self._dictionary = {\n \"filename\": self._filename,\n \"NIONS\": 0,\n \"NITYP\": [],\n \"magnetization\": None,\n \"energy\": np.inf,\n \"MAGMOM\": None,\n \"ionic_steps\": [],\n \"warning\": [],\n \"status\": \"running\",\n \"time\": np.inf,\n }\n\n @staticmethod\n def _open(filename):\n if os.path.splitext(filename)[1] == '.gz':\n import gzip\n return gzip.open(\n filename, 'rt', encoding='utf-8', errors='ignore')\n else:\n return open(filename, \"r\")\n\n def _read_pomass(self, lines):\n d = {}\n\n tmp = []\n for line in lines:\n if \" TITEL =\" in line:\n tmp.append(line.split()[3])\n d['TITEL'] = tmp\n\n # POMASS after \"Ionic relaxation\" has smaller decimals\n tmp = []\n for line in lines:\n if \" POMASS =\" in line and \"mass and valenz\" in line:\n tmp.append(float(line[11:20]))\n elif \" Following reciprocal coordinates:\" in line:\n break\n d['POMASS'] = np.array(tmp)\n\n self._dictionary.update(d)\n\n def _read_kpoint_weights(self, lines):\n for i, l in enumerate(lines):\n if \" Following reciprocal coordinates:\" in l:\n wtkpt = []\n j = 0\n while True:\n j += 1\n line2 = lines[i + j + 1]\n if len(line2.split()) != 4:\n break\n wtkpt.append(float(line2.split()[-1]))\n wtkpt = np.array(wtkpt)\n wtkpt /= sum(wtkpt)\n self._dictionary[\"WTKPT\"] = wtkpt\n break\n\n def _read_dimension_of_arrays(self, lines):\n d = {}\n for i, l in enumerate(lines):\n if ' Dimension of arrays:' in l:\n line = lines[i + 1] # \" k-points NKPTS =\"\n d[\"NKPTS\"] = int(line.split()[3])\n d[\"NBANDS\"] = int(line.split()[-1])\n\n line = lines[i + 2]\n tmp = line.split()\n d[\"NEDOS\"] = int(tmp[5])\n d[\"NIONS\"] = int(tmp[-1])\n\n line = lines[i + 4]\n d[\"NPLWV\"] = str2int(line.split()[-1])\n line = lines[i + 6]\n # Initialization is done using -1.\n ng = np.zeros(3, dtype=int) * -1\n ng[0] = int(line.split()[4])\n ng[1] = int(line.split()[7])\n ng[2] = int(line.split()[10])\n d[\"NG\"] = ng\n line = lines[i + 9] # \" ions per type =\"\n d[\"NITYP\"] = np.array([int(x) for x in line.split()[4:]])\n break\n self._dictionary.update(d)\n\n def _read_warning(self, lines):\n outcar_dictionary = self._dictionary\n for i, l in enumerate(lines):\n if (\"W W AA RRRRR N N II N N GGGG\" in l or\n \"ADVICE TO THIS USER RUNNING 'VASP/VAMP'\" in l):\n j = i\n while True:\n j += 1\n line = lines[j]\n if \"The number of bands has been changed\" in line:\n outcar_dictionary[\"warning\"].append(\"Changed NBANDS\")\n break\n elif \"Your highest band is occupied at some k-points!\" in line:\n outcar_dictionary[\"warning\"].append(\"Occupied highest band\")\n break\n elif \"The distance between some ions is very small\" in line:\n outcar_dictionary[\"warning\"].append(\"small_distance\")\n break\n elif \"One of the lattice vectors is very long\" in line:\n outcar_dictionary[\"warning\"].append(\"AMIN\")\n break\n elif \"-\" * 77 in line:\n outcar_dictionary[\"warning\"].append(\"others\")\n break\n self._dictionary.update(outcar_dictionary)\n\n def _read_startparameter(self, lines):\n d = {}\n for i, line in enumerate(lines):\n if \" ISPIN =\" in line:\n d[\"ISPIN\"] = int(line.split()[2])\n break\n self._dictionary.update(d)\n\n def _read_electronic_relaxation_1(self, lines):\n d = {}\n for i, l in enumerate(lines):\n if ' Electronic Relaxation 1' in l:\n line = lines[i + 1] # \" ENCUT =\"\n d[\"ENCUT\"] = float(line.split()[2])\n line = lines[i + 4] # \" NELM =\"\n d[\"NELM\"] = int(line.split()[2].strip(\";\"))\n line = lines[i + 5]\n d[\"EDIFF\"] = float(line.split()[2])\n break\n self._dictionary.update(d)\n\n def _read_ionic_relaxation(self, lines):\n d = {}\n for i, l in enumerate(lines):\n if \" Ionic relaxation\" in l:\n line2 = lines[i + 1]\n d[\"EDIFFG\"] = float(line2.split()[2])\n line2 = lines[i + 2]\n d[\"NSW\"] = int(line2.split()[2])\n line2 = lines[i + 4]\n d[\"IBRION\"] = int(line2.split()[2])\n line2 = lines[i + 6]\n d[\"ISIF\"] = int(line2.split()[2])\n line2 = lines[i + 8]\n d[\"ISYM\"] = int(line2.split()[2])\n line = lines[i + 18] # \" PSTRESS=\"\n d[\"PSTRESS\"] = float(line.split()[1])\n break\n self._dictionary.update(d)\n\n def _read_dos(self, lines):\n d = {}\n for i, l in enumerate(lines):\n if ' DOS related values' in l:\n line = lines[i + 3] # \" ISMEAR =\"\n d[\"ISMEAR\"] = int(line.split()[2].strip(\";\"))\n d[\"SIGMA\"] = float(line.split()[5])\n break\n self._dictionary.update(d)\n\n def _read_ionic_relaxation_ex(self, lines):\n d = {}\n for i, l in enumerate(lines):\n if \" ZVAL =\" in l and \"mass and valenz\" not in l:\n line = lines[i]\n d[\"ZVAL\"] = np.array([float(x) for x in line.split()[2:]])\n line = lines[i + 2]\n d[\"RWIGS\"] = np.array([float(x) for x in line.split()[2:]])\n line = lines[i + 4] # \" VCA =\"\n d[\"VCA\"] = [float(x) for x in line.split()[2:]]\n line = lines[i + 5]\n d[\"NELECT\"] = float(line.split()[2])\n line = lines[i + 6]\n d[\"NUPDOWN\"] = float(line.split()[1])\n break\n self._dictionary.update(d)\n\n def _read_electronic_relaxation_2(self, lines):\n d = {}\n for i, l in enumerate(lines):\n if ' Electronic relaxation 2 (details)' in l:\n line = lines[i + 9] # \" IMIX =\"\n d[\"IMIX\"] = int(line.split()[2])\n line = lines[i + 10]\n d[\"AMIX\"] = float(line.split()[2].strip(\";\"))\n d[\"BMIX\"] = float(line.split()[5].strip(\";\"))\n line = lines[i + 11]\n d[\"AMIX_MAG\"] = float(line.split()[2].strip(\";\"))\n d[\"BMIX_MAG\"] = float(line.split()[5].strip(\";\"))\n break\n self._dictionary.update(d)\n\n def _read_exchange_correlation_treatment(self, lines):\n d = {}\n for i, l in enumerate(lines):\n if ' Exchange correlation treatment:' in l:\n line = lines[i + 1] # \" GGA =\"\n d[\"GGA\"] = str(line.split()[2])\n break\n self._dictionary.update(d)\n\n def _read_elastic_constants(self, lines):\n # elastic constants\n ec_order = [0, 1, 2, 4, 5, 3] # Used to reorder EC correctly\n d = {}\n for j, l in enumerate(lines):\n if \"SYMMETRIZED ELASTIC MODULI (kBar)\" in l:\n ec_electron = np.zeros((6, 6)) * np.nan\n for i in range(6):\n line = lines[i + j + 3]\n ec_electron[i, :] = [float(x) for x in line.split()[1:]]\n ec_electron = ec_electron[ec_order, :][:, ec_order]\n # kBar -> GPa\n ec_electron *= 0.1\n d[\"elastic_constants_electron\"] = ec_electron\n\n elif \"ELASTIC MODULI CONTR FROM IONIC RELAXATION (kBar)\" in l:\n ec_ion = np.zeros((6, 6)) * np.nan\n for i in range(6):\n line = lines[i + j + 3]\n ec_ion[i, :] = [float(x) for x in line.split()[1:]]\n ec_ion = ec_ion[ec_order, :][:, ec_order]\n # kBar -> GPa\n ec_ion *= 0.1\n d[\"elastic_constants_ion\"] = ec_ion\n\n elif \"TOTAL ELASTIC MODULI (kBar)\" in l:\n ec_total = np.zeros((6, 6)) * np.nan\n for i in range(6):\n line = lines[i + j + 3]\n ec_total[i, :] = [float(x) for x in line.split()[1:]]\n ec_total = ec_total[ec_order, :][:, ec_order]\n # kBar -> GPa\n ec_total *= 0.1\n d[\"elastic_constants_total\"] = ec_total\n\n self._dictionary.update(d)\n\n def _read_kpoint_vectors(self, lines):\n nkpts = self._dictionary['NKPTS']\n for j, line in enumerate(lines):\n if \" k-points in reciprocal lattice and weights:\" in line:\n vkpt = np.zeros((nkpts, 3), dtype=float) * np.nan\n for i in range(nkpts):\n line2 = lines[i + j + 1]\n vkpt[i] = [float(x) for x in line2.split()[0:3]]\n self._dictionary[\"VKPT\"] = vkpt\n break\n\n def _read_plane_waves(self, lines):\n nkpts = self._dictionary['NKPTS']\n for j, line in enumerate(lines):\n if \"plane waves:\" in line:\n # Initialization is done using -1.\n nplwkp_tot = np.zeros(nkpts, dtype=int) * -1\n nplwkp_tot[0] = int(line.split()[-1])\n for i in range(1, nkpts):\n line2 = lines[j + i]\n nplwkp_tot[i] = int(line2.split()[-1])\n self._dictionary[\"NPLWKP_TOT\"] = nplwkp_tot\n break\n\n def _read_nstep(self, lines):\n self._dictionary['NSTEP'] = len(\n [line for line in lines if 'energy w' in line])\n\n def _read_ionic_steps(self, lines):\n outcar_dictionary = self._dictionary\n\n nstep = self._dictionary[\"NSTEP\"]\n nions = self._dictionary[\"NIONS\"]\n\n energies_zero = np.full(nstep, np.nan)\n atoms_spins = np.full((nstep, nions), np.nan)\n\n ionic_step_dict = {}\n for i, line in enumerate(lines):\n if \"-\" * 39 in line and \" Iteration\" in line: # For VASP 5.3 and 5.4\n start = line.find(\"(\") + 1\n end = line.find(\")\")\n ionic_step_dict['N'] = int(line[start:end])\n\n istep = int(line[51:start-1]) - 1\n\n elif 'total charge ' in line:\n ionic_step_dict['electrons'] = self._read_electrons(\n lines, i)\n\n elif 'magnetization (x)' in line:\n magmom = []\n j = 3\n while True:\n j += 1\n line2 = lines[i + j]\n magmom.append(float(line2.split()[-1]))\n if int(line2.split()[0]) == outcar_dictionary[\"NIONS\"]:\n break\n magmom = np.array(magmom)\n atoms_spins[istep] = magmom\n\n elif \"free energy TOTEN =\" in line:\n ionic_step_dict[\"free_energy\"] = float(line.split()[-2])\n elif \" energy without entropy=\" in line:\n ionic_step_dict[\"energy_T\"] = float(line.split()[3])\n ionic_step_dict[\"energy\"] = float(line.split()[-1])\n\n elif 'TOTAL-FORCE (eV/Angst)' in line:\n positions = [] # Cartesian coordinates\n forces = []\n j = 0\n while True:\n j += 1\n line2 = lines[i + j + 1]\n if \"-\" * 83 in line2:\n break\n list_float = [float(x) for x in line2.split()]\n positions.append(list_float[0:3])\n forces.append(list_float[3:6])\n\n positions = np.array(positions)\n forces = np.array(forces)\n\n ionic_step_dict[\"positions\"] = positions\n ionic_step_dict[\"forces\"] = forces\n\n elif \" FORCE on cell =-STRESS in cart. coord.\" in line:\n j = 0\n while True:\n j += 1\n line2 = lines[i + j]\n if \" in kB\" in line2: # (kBar)\n stress = get_stress_from_line(line2)\n pressure = stress[0, 0] + stress[1, 1] + stress[2, 2]\n pressure /= -3.0\n break\n\n ionic_step_dict[\"stress\"] = stress\n ionic_step_dict[\"pressure\"] = pressure\n\n elif \" VOLUME and BASIS-vectors are now :\" in line:\n avec = np.full((3, 3), np.nan, dtype=float)\n bvec = np.full((3, 3), np.nan, dtype=float)\n for i2 in range(3):\n line2 = lines[i + i2 + 5]\n # avec[i] = [float(x) for x in line2.split()[0:3]]\n # bvec[i] = [float(x) for x in line2.split()[3:6]]\n k = 3\n avec[i2] = [\n float(line2[k+13*j:k+13*(j+1)]) for j in range(3)\n ]\n k = 45\n bvec[i2] = [\n float(line2[k+13*j:k+13*(j+1)]) for j in range(3)\n ]\n\n ionic_step_dict[\"avec\"] = avec\n ionic_step_dict[\"bvec\"] = bvec\n\n elif \"LOOP+:\" in line:\n outcar_dictionary[\"ionic_steps\"].append(ionic_step_dict)\n energies_zero[istep] = ionic_step_dict['energy']\n ionic_step_dict = {}\n\n outcar_dictionary['energy_zero'] = energies_zero\n outcar_dictionary['atoms_spins'] = atoms_spins\n outcar_dictionary['MAGMOM'] = atoms_spins[-1]\n\n self._dictionary.update(outcar_dictionary)\n\n def _read_electrons_last(self, lines):\n i = -1\n for j, line in enumerate(lines):\n if 'total charge ' in line:\n i = j\n self._dictionary['electrons'] = self._read_electrons(lines, i)\n\n def _read_electrons(self, lines, i):\n outcar_dictionary = self._dictionary\n electrons = []\n j = 3\n while True:\n j += 1\n line2 = lines[i + j]\n electrons.append(float(line2.split()[-1]))\n if int(line2.split()[0]) == outcar_dictionary[\"NIONS\"]:\n break\n return np.array(electrons)\n\n def _read_magnetization(self, lines):\n for line in reversed(lines): # Only the last one is read\n if \"number of electron\" in line and \"magnetization\" in line:\n magnetization = line.split()[-1]\n if magnetization == \"magnetization\": # NM\n self._dictionary[\"magnetization\"] = 0.0\n else:\n self._dictionary[\"magnetization\"] = float(magnetization)\n break\n\n def _read_energies(self, lines):\n d = {}\n for i, l in enumerate(lines):\n if \" entropy T*S EENTRO =\" in l:\n line = lines[i]\n d[\"EENTRO\"] = float(line.split()[-1])\n # weighted sum of eigenvalues\n # elif \" eigenvalues EBANDS =\" in line:\n line = lines[i + 1]\n value = line.split()[-1]\n try:\n value = float(value)\n except:\n pass\n d[\"EBANDS\"] = value\n\n line = lines[i + 2]\n if \"EATOM\" in line: # False for the perturbation theory\n d[\"EATOM\"] = float(line.split()[-1])\n\n self._dictionary.update(d)\n\n def _read_status(self, lines):\n text = \" General timing and accounting informations for this job:\"\n for line in reversed(lines):\n if text in line:\n self._dictionary[\"status\"] = \"finished\"\n break\n\n def _read_cpu_time(self, lines):\n for line in reversed(lines):\n if \" Total CPU time used (sec):\" in line:\n self._dictionary[\"time\"] = float(line.split()[-1])\n break\n\n def generate_dictionary(self):\n\n filename = self._filename\n\n with self._open(filename) as f:\n lines = f.readlines()\n self._lines = lines\n\n try:\n self._read_pomass(lines)\n self._read_kpoint_weights(lines)\n self._read_dimension_of_arrays(lines)\n self._read_startparameter(lines)\n self._read_electronic_relaxation_1(lines)\n self._read_ionic_relaxation(lines)\n self._read_ionic_relaxation_ex(lines)\n self._read_electronic_relaxation_2(lines)\n self._read_exchange_correlation_treatment(lines)\n self._read_dos(lines)\n self._read_kpoint_vectors(lines)\n self._read_plane_waves(lines)\n self._read_nstep(lines)\n self._read_ionic_steps(lines)\n self._read_electrons_last(lines)\n self._read_magnetization(lines)\n self._read_energies(lines)\n if 5 <= self._dictionary['IBRION'] <= 8:\n self._read_elastic_constants(lines)\n self._read_warning(lines)\n self._read_status(lines)\n self._read_cpu_time(lines)\n except:\n pass\n\n outcar_dictionary = self._dictionary\n\n if outcar_dictionary[\"status\"] == \"finished\":\n if outcar_dictionary['ionic_steps'][-1]['N'] == outcar_dictionary[\"NELM\"]:\n outcar_dictionary[\"status\"] = \"finished (not converged)\"\n else:\n outcar_dictionary[\"status\"] = \"finished (converged)\"\n\n self._dictionary.update(outcar_dictionary)\n self.analyze_outcar_dictionary()\n\n def analyze_outcar_dictionary(self):\n\n self._create_symbols()\n outcar_dictionary = self._dictionary\n nions = outcar_dictionary[\"NIONS\"]\n\n if \"elastic_constants_total\" in outcar_dictionary:\n w, v = np.linalg.eigh(outcar_dictionary[\"elastic_constants_total\"])\n outcar_dictionary[\"elastic_constants_eigvals\"] = w\n outcar_dictionary[\"elastic_constants_eigvecs\"] = v\n\n if len(outcar_dictionary[\"ionic_steps\"]) == 0:\n return self\n\n if (outcar_dictionary[\"ionic_steps\"][-1].keys() == [\"MAGMOM\"] or\n outcar_dictionary[\"ionic_steps\"][-1].keys() == []):\n outcar_dictionary[\"ionic_steps\"].pop()\n\n for dictionary_is in outcar_dictionary[\"ionic_steps\"]:\n if \"avec\" in dictionary_is:\n volume = np.linalg.det(dictionary_is[\"avec\"])\n dictionary_is[\"volume\"] = volume\n\n if \"forces\" in dictionary_is:\n forces_norm = np.linalg.norm(dictionary_is[\"forces\"], axis=1)\n index_force_max = np.argmax(forces_norm)\n index_force_min = np.argmin(forces_norm)\n force_max = forces_norm[index_force_max]\n force_min = forces_norm[index_force_min]\n force_average = np.average(forces_norm)\n force_sd = np.std(forces_norm)\n\n dictionary_is[\"index_force_max\"] = index_force_max\n dictionary_is[\"index_force_min\"] = index_force_min\n dictionary_is[\"force_max\"] = force_max\n dictionary_is[\"force_min\"] = force_min\n dictionary_is[\"force_average\"] = force_average\n dictionary_is[\"force_s.d.\"] = force_sd\n\n if \"stress\" in dictionary_is:\n stress_norm = np.linalg.norm(dictionary_is[\"stress\"])\n dictionary_is[\"stress_norm\"] = stress_norm\n\n generate_properties_per_atom(dictionary_is, nions)\n\n keys = outcar_dictionary[\"ionic_steps\"][0].keys()\n for k in keys:\n if k in outcar_dictionary[\"ionic_steps\"][-1]:\n outcar_dictionary[k] = outcar_dictionary[\"ionic_steps\"][-1][k]\n # loop counter for ionic movement\n outcar_dictionary['NSTEP'] = len(outcar_dictionary[\"ionic_steps\"])\n\n self.create_masses_mean()\n\n return self\n\n def _create_symbols(self):\n d = self._dictionary\n if 'TITEL' in d and len(d['TITEL']) != 0:\n x = [d['TITEL'][i] for i, n in enumerate(d['NITYP']) for j in range(n)]\n d['symbols'] = np.array(x)\n self._dictionary = d\n\n def parse_eigenvalues(self):\n \"\"\"Parse eigenvalues written in OUTCAR file.\n\n Variables \"eigenvalues\" and \"weights\" are stored as numpy arrays.\n\n EFERMI: energies_fermi[-1]\n \"\"\"\n nstep = self._dictionary[\"NSTEP\"]\n ispin = self._dictionary[\"ISPIN\"]\n nkpts = self._dictionary[\"NKPTS\"]\n nbands = self._dictionary[\"NBANDS\"]\n ibrion = self._dictionary[\"IBRION\"]\n\n eigenvalues = np.full((nstep, ispin, nkpts, nbands), np.nan)\n occupancies = np.full((nstep, ispin, nkpts, nbands), np.nan)\n energies_fermi = np.full(nstep, np.nan)\n\n lines = self._lines\n istep = 0\n for i, line in enumerate(lines):\n if \" E-fermi :\" in line:\n energies_fermi[istep] = float(line.split()[2])\n j = 2\n for isp in range(ispin):\n if ispin > 1:\n j += 2\n for ik in range(nkpts):\n j += 2\n for ib in range(nbands):\n j += 1\n line2 = lines[i + j]\n e, w = line2.split()[1:]\n eigenvalues[istep, isp, ik, ib] = e\n occupancies[istep, isp, ik, ib] = w\n j += 1\n\n # Elastic constants calculations\n # Only the eigenvalues of the first step is collected.\n # After the second step, the number of k points could\n # change.\n if 5 <= ibrion <= 8:\n break\n\n istep += 1\n\n self._dictionary['energies_fermi'] = energies_fermi\n self._dictionary[\"eigenvalues\"] = eigenvalues\n self._dictionary[\"occupancies\"] = occupancies\n\n def create_masses_mean(self):\n data = self._dictionary\n concentrations = data['NITYP'] / float(sum(data['NITYP']))\n atomic_weights = data['POMASS']\n if len(data['POMASS']) == 0:\n return\n mass_amean = np.average(atomic_weights, weights=concentrations)\n mass_gmean = np.exp(\n np.average(np.log(atomic_weights), weights=concentrations))\n data['mass_amean'] = float(mass_amean)\n data['mass_gmean'] = float(mass_gmean)\n self._dictionary = data\n\n def write_properties(self):\n print(\"-\" * 80)\n print(self._filename)\n print(\"-\" * 80)\n for k, v in self._dictionary.items():\n print(k, v)\n\n def write_specified_properties(self,\n keys,\n precision=16,\n is_recursive=False,\n file_written=None,\n is_appended=False):\n if file_written is None:\n f = sys.stdout\n else:\n if is_appended:\n f = open(file_written, \"a\")\n else:\n f = open(file_written, \"w\")\n\n width = precision + 6\n width_int = 5\n for k in keys:\n if k in self._dictionary:\n value = self._dictionary[k]\n else:\n value = None\n f.write(\" \")\n f.write(\"{:s}\".format(k))\n f.write(\" \")\n\n def write_value(value):\n if is_recursive and hasattr(value, \"__iter__\"):\n for v in value:\n write_value(v)\n else:\n if isinstance(value, float):\n f.write(\n \"{:{width}.{precision}f}\".format(\n value,\n width=width,\n precision=precision,))\n elif isinstance(value, int):\n f.write(\n \"{:{width}d}\".format(\n value,\n width=width_int,))\n f.write(\" \" * (precision + 1))\n else:\n f.write(\"{:s}\".format(str(value)))\n write_value(value)\n f.write(\"\\n\")\n if f is not sys.stdout:\n f.close()\n\n\ndef get_MAGMOM(filename):\n f = open(filename, 'r')\n magmom = None\n for line in f:\n if 'magnetization (x)' in line:\n magmom = []\n for i in range(3):\n next(f)\n while True:\n line2 = next(f)\n if(line2.find(\"-\" * 48) != -1):\n break\n else:\n # sys.stdout.write(\"%s\" % line2)\n magmom.append(float(line2.split()[-1]))\n return magmom\n\n\ndef main():\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-f\", \"--filename\",\n default=[\"OUTCAR\"],\n nargs=\"*\",\n type=str,\n help=\"OUTCAR file name\")\n parser.add_argument(\"-k\", \"--key\",\n nargs=\"+\",\n type=str,\n help=\"keys to be printed\")\n parser.add_argument(\"-p\", \"--precision\",\n default=16,\n type=int,\n help=\"Precision for the floating point values.\")\n parser.add_argument(\"-r\", \"--recursive\",\n action=\"store_true\",\n help=\"Properties are recursively printed.\")\n parser.add_argument(\"--eigenvalues\",\n action=\"store_true\",\n help=\"Read eigenvalues\")\n args = parser.parse_args()\n\n for filename in args.filename:\n if not os.path.isfile(filename):\n print(\"{} does not exist.\".format(filename))\n continue\n outcar = Outcar(filename=filename)\n if args.eigenvalues:\n outcar.parse_eigenvalues()\n if args.key is None:\n outcar.write_properties()\n else:\n outcar.write_specified_properties(\n args.key,\n precision=args.precision,\n is_recursive=args.recursive)\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "cmtools/io/vasp/outcar.py", "file_name": "outcar.py", "file_ext": "py", "file_size_in_byte": 29093, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.zeros", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "gzip.open", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 282, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 292, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 302, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 317, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 329, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 347, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 348, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 372, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 395, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 415, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 416, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 416, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 461, "usage_type": "call"}, {"api_name": "numpy.linalg.eigh", "line_number": 560, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 560, "usage_type": "attribute"}, {"api_name": "numpy.linalg.det", "line_number": 573, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 573, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 577, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 577, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 578, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 579, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 582, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 583, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 593, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 593, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 613, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 629, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 629, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 630, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 630, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 631, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 631, "usage_type": "attribute"}, {"api_name": "numpy.average", "line_number": 671, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 672, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 673, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 673, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 692, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 731, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 755, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 778, "usage_type": "call"}, {"api_name": "os.path", "line_number": 778, "usage_type": "attribute"}]} +{"seq_id": "617090194", "text": "import numpy as np\r\nimport cv2\r\nm=18\r\nn=15\r\nc=(134,132,133)\r\nmat=np.zeros((80*m,80*n,3),dtype=\"uint8\")\r\nfor i in range(m+1):\r\n for j in range(n+1):\r\n cv2.rectangle(mat,(80*j,80*i),(80,80),c)\r\ncv2.imwrite(\"blocks.jpg\",mat)\r\n", "sub_path": "tools/blockdrawer.py", "file_name": "blockdrawer.py", "file_ext": "py", "file_size_in_byte": 233, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.zeros", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "165527060", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Dec 22 22:09:19 2018\n\n@author: omi\n\"\"\"\n\nfrom sklearn.ensemble import BaggingClassifier\nimport pandas as pd\nfrom sklearn.metrics import classification_report,confusion_matrix\nfrom sklearn.metrics import roc_curve\nfrom sklearn.metrics import roc_auc_score\nimport matplotlib.pyplot as plt\n\neeg_data=pd.read_csv(\"E:\\\\Research\\\\Ensemble DT in BMI\\\\2_3_class_data\\\\2omitrain.csv\")\neeg_test_data=pd.read_csv(\"E:\\\\Research\\\\Ensemble DT in BMI\\\\2_3_class_data\\\\2omitest.csv\")\n\n\neeg_data.head()\nfeatute_col=['Theta','Alpha','Low_beta','High_beta','Gamma']\n\nX_train=eeg_data[featute_col]\ny_train=eeg_data['Class']\n\nX_test=eeg_test_data[featute_col]\ny_test=eeg_test_data['Class']\n\nprint('ok')\n\nclf = BaggingClassifier()\nclf = clf.fit(X_train, y_train)\n\nprint('Accuracy on the test subset: {:.3f}'.format(clf.score(X_test, y_test)))\n\npredicted=clf.predict(X_test)\nprint('completed')\n#confusion=confusion_matrix(y_test, predicted, labels=[\"lefthand\", \"steady\", \"righthand\"])\nconfusion=confusion_matrix(y_test, predicted, labels=[\"steady\", \"righthand\"])\nprint(confusion)\n\nprint(classification_report(y_test, predicted))\n\nprint('ok')\n\n\nprint('start roc')\nprobs = clf.predict_proba(X_test)\nprobs = probs[:, 1]\n# calculate AUC\n# Binarize the output\ny=y_test\ny=y.replace(['righthand'],0)\ny=y.replace(['steady'],1)\nauc_random = roc_auc_score(y, probs)\nprint('AUC: %.3f' % auc_random)\n# calculate roc curve\nfpr_random, tpr_random, thresholds_random = roc_curve(y, probs)\n\nplt.figure()\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.plot([0, 1], [0, 1], color='navy', linestyle='--')\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.title('SVM Classifier ROC')\nplt.plot(fpr_random, tpr_random, color='blue', lw=2, label='AUC = %0.2f)' % auc_random)\nplt.legend(loc=\"lower right\")\nplt.show()\n\n#probs = clf.predict_proba(X_test)\n#probs = probs[:, 1]\n## calculate AUC\n## Binarize the output\n#y = y_test\n#y=y.replace(['righthand'],1)\n#y=y.replace(['steady'],0)\n#y=y.replace(['lefthand'],0)\n#auc = roc_auc_score(y, probs)\n#print('AUC: %.3f' % auc)\n## calculate roc curve\n#fpr, tpr, thresholds = roc_curve(y, probs)\n#\n#plt.figure()\n#plt.xlabel('False Positive Rate')\n#plt.ylabel('True Positive Rate')\n#plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n#plt.xlim([0.0, 1.0])\n#plt.ylim([0.0, 1.05])\n#plt.title('SVM Classifier ROC')\n#plt.plot(fpr, tpr, color='blue', lw=2, label='AUC = %0.2f)' % auc)\n#plt.legend(loc=\"lower right\")\n#plt.show()\n\nprint('end roc')", "sub_path": "Machine-Learning/bagging.py", "file_name": "bagging.py", "file_ext": "py", "file_size_in_byte": 2499, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.ensemble.BaggingClassifier", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 41, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}]} +{"seq_id": "454304482", "text": "from django.urls import path\nfrom django.views.decorators.csrf import csrf_exempt\nfrom .views import *\n\nurlpatterns = [\n path('overview/', overView, name=\"overView\"),\n path('product//', productView, name=\"productView\"),\n path('cart/', cartView, name='cart' ),\n path('', LoginView.as_view(), name=\"loginView\"),\n path('deletecart//', deleteCartItem, name='deleteCart'),\n # path('updatecart/', updateCartItem, name=\"updateCart\"),\n path('checkout/', checkOut, name=\"checkout\"),\n\n path('register/', RegistrationView.as_view(), name=\"register\"),\n path('validate-username', csrf_exempt(UsernameValidationView.as_view()), name=\"validate-username\"),\n path('validate-first_name', csrf_exempt(First_NameValidationView.as_view()), name=\"validate-first_name\"),\n path('validate-last_name', csrf_exempt(Last_NameValidationView.as_view()), name=\"validate-last_name\"),\n path('validate-email', csrf_exempt(EmailValidationView.as_view()), name=\"validate-email\"),\n path('validate-mobile_no', csrf_exempt(Mobile_NumberValidationView.as_view()), name=\"validate-mobile_no\"),\n path('activate///', VerificationView.as_view(), name=\"activate\"),\n path('logout/', LogoutView.as_view(), name=\"logout\"),\n]", "sub_path": "mysite/myapp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1251, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "34406199", "text": "from utils.fetcher_abstract import AbstractFetcher\nfrom datetime import datetime\nimport logging\nimport pandas as pd\nimport numpy as np\n\n__all__ = ('BRA_MSHMFetcher',)\n\n\"\"\" \n site-location: https://github.com/elhenrico/covid19-Brazil-timeseries\n \n COVID19-Brazil Data for Brazil created, maintained and hosted by elhenrico.\n \n The data sources include: official communications of the Brazilian Ministry of Health.\n \n\"\"\"\nlogger = logging.getLogger(__name__)\n\n\nclass BRA_MSHMFetcher(AbstractFetcher):\n LOAD_PLUGIN = True\n\n def province_confirmed_fetch(self):\n\n \"\"\"\n This url mainly provide cumulative data of confirmed cases on the province-level.\n \"\"\"\n\n url = 'https://raw.githubusercontent.com/elhenrico/covid19-Brazil-timeseries/master/confirmed-cases.csv'\n logger.debug('Fetching Brazil province-level confirmed cases from BRA_MSHM')\n return pd.read_csv(url)\n\n def province_dead_fetch(self):\n\n \"\"\"\n This url mainly provide cumulative data of death data on the province-level.\n \"\"\"\n\n url = 'https://raw.githubusercontent.com/elhenrico/covid19-Brazil-timeseries/master/deaths.csv'\n logger.debug('Fetching Brazil province-level death cases from BRA_MSHM')\n return pd.read_csv(url)\n\n def run(self):\n\n \"\"\"\n This run functions mainly created province-level cumulative confirmed&dead collection from\n \n province_confirmed_fetch and province_dead_fetch;\n \n \"\"\"\n\n province_confirmed_data = self.province_confirmed_fetch()\n province_dead_data = self.province_dead_fetch()\n\n ### Get province names list\n province_list = list(province_confirmed_data[\"Unnamed: 1\"])[1:]\n\n ### Get dates list\n time_list = list(province_confirmed_data.columns)[2:]\n\n for k in range(len(time_list)):\n\n ### Translating data format from DD/MM to YYYY-MM-DD\n date_ddmm = time_list[k]\n date = datetime.strptime(date_ddmm + \"/2020\", '%d/%m/%Y').strftime('%Y-%m-%d')\n\n ### Get confirmed and dead list for current date\n current_confirm_list = np.array(province_confirmed_data[date_ddmm])\n current_dead_list = np.array(province_dead_data[date_ddmm])\n\n ### Fetch confirmed number and dead number for each province one by one\n for i in range(len(province_list)):\n province = province_list[i]\n confirmed = current_confirm_list[1 + i]\n dead = current_dead_list[1 + i]\n\n upsert_obj = {\n # source is mandatory and is a code that identifies the source\n 'source': 'BRA_MSHM',\n # date is also mandatory, the format must be YYYY-MM-DD\n 'date': date,\n # country is mandatory and should be in English\n # the exception is \"Ships\"\n 'country': \"Brazil\",\n # countrycode is mandatory and it's the ISO Alpha-3 code of the country\n # an exception is ships, which has \"---\" as country code\n 'countrycode': 'BRA',\n # adm_area_1, when available, is a wide-area administrative region, like a\n # Canadian province in this case. There are also subareas adm_area_2 and\n # adm_area_3\n 'adm_area_1': province,\n 'adm_area_2': None,\n 'adm_area_3': None,\n 'confirmed': int(confirmed),\n # dead is the number of people who have died because of covid19, this is cumulative\n 'dead': int(dead)\n\n }\n\n self.db.upsert_epidemiology_data(**upsert_obj)\n", "sub_path": "src/plugins/BRA_MSHM/fetcher.py", "file_name": "fetcher.py", "file_ext": "py", "file_size_in_byte": 3896, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "utils.fetcher_abstract.AbstractFetcher", "line_number": 20, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "364764994", "text": "\n\"\"\"\nauthor : sera\ndate: 2019\n\"\"\"\n\n# coding: utf-8\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nplt.rc('font',family='Times New Roman')\nget_ipython().run_line_magic('matplotlib', 'inline')\nfrom IPython.core.pylabtools import figsize # import figsize\n\ndef result_pic(result):\n \"\"\"\n 雷达图的绘制\n :param result: 分类数据\n :return: 雷达图\n \"\"\"\n labels = ['SN', 'SP', 'Precision','NPV','F1', 'ACC', 'MCC']\n kinds = list(result.iloc[:, 0])\n \n result = pd.concat([result, result[['SN']]], axis=1)\n centers = np.array(result.iloc[:, 1:])\n \n # circle\n n = len(labels)\n angle = np.linspace(0, 2 * np.pi, n, endpoint=False)\n angle = np.concatenate((angle, [angle[0]]))\n\n # plot\n fig = plt.figure()\n ax = fig.add_subplot(111, polar=True) \n ax.spines['polar'].set_visible(False) \n ax.set_rlim(0,1) \n\n # plot line\n for i in range(len(kinds)):\n ax.plot(angle, centers[i], linewidth=2, label=kinds[i])\n\n ax.set_thetagrids(angle * 180 / np.pi, labels)\n \n\n plt.rcParams['savefig.dpi'] = 300 #\n plt.rcParams['figure.dpi'] = 300 \n \n plt.title('Evaluation Values of Different Classifiers')\n ax.set_xlabel('D1209')\n plt.legend(loc=(1.05, 0.05))\n plt.savefig('./data/D1209-CV.jpg', dpi=300) # save figure \n plt.show()\n \n\nif __name__ == '__main__':\n\n result = pd.read_csv('./data/D1209-CV.csv', sep=',')\n result_pic(result)\n", "sub_path": "Brain Cancer/plot Radar figures.py", "file_name": "plot Radar figures.py", "file_ext": "py", "file_size_in_byte": 1455, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.rc", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "pandas.concat", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 43, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 46, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 47, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 58, "usage_type": "call"}]} +{"seq_id": "28774697", "text": "import numpy as np\nimport pandas as pd\nimport time\nimport pickle\nfrom config import Config\n\ndef addLabel(fileInput, dictInput, fileOutput):\n\tstart_time = time.time()\n\n\tprint(\"Reading clean file\")\n\tdf = pd.read_csv(fileInput,delimiter=',',names=['id', 'article','numP','numA'])\n\n\t# Nueva columna que guarda el label de hiperpartidista\n\tdf['hyperpartisan'] = pd.Series(np.zeros(df.shape[0]), index=df.index,dtype = np.int8)\n\n\tprint(\"Adding label\")\n\n\tpickle_in = open(dictInput,\"rb\")\n\tidToLabel = pickle.load(pickle_in)\n\tfor index, row in df.iterrows():\n\t\tmyId = row['id']\n\t\tdf.set_value(index,'hyperpartisan',idToLabel[myId])\n\n\tdf.to_csv(fileOutput,header=False,index=False)\n\tprint(\"Saved\")\n\tprint('Total time: %.3f s' % (time.time() - start_time))\n\ndef main(args):\n err_msg = 'Unknown function, options: train, validate'\n if len(args) > 1:\n func_name = args[1]\n if func_name == 'train':\n addLabel(Config.FILE_FREQ_ID,\"dict.pickle\",Config.FILE_TRAIN)\n elif func_name == 'validate':\n addLabel(Config.FILE_FREQ_ID_VAL,\"dictVal.pickle\",Config.FILE_VAL)\n elif func_name == 'test':\n addLabel(Config.FILE_FREQ_ID_TEST,\"dictTest.pickle\",Config.FILE_TEST)\n else:\n print(err_msg)\n else:\n print(err_msg)\n return 0\n\nif __name__ == '__main__':\n import sys\n sys.exit(main(sys.argv))", "sub_path": "LogRegression/add_result.py", "file_name": "add_result.py", "file_ext": "py", "file_size_in_byte": 1311, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.time", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 19, "usage_type": "call"}, {"api_name": "time.time", "line_number": 26, "usage_type": "call"}, {"api_name": "config.Config.FILE_FREQ_ID", "line_number": 33, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 33, "usage_type": "name"}, {"api_name": "config.Config.FILE_TRAIN", "line_number": 33, "usage_type": "attribute"}, {"api_name": "config.Config.FILE_FREQ_ID_VAL", "line_number": 35, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 35, "usage_type": "name"}, {"api_name": "config.Config.FILE_VAL", "line_number": 35, "usage_type": "attribute"}, {"api_name": "config.Config.FILE_FREQ_ID_TEST", "line_number": 37, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 37, "usage_type": "name"}, {"api_name": "config.Config.FILE_TEST", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 46, "usage_type": "attribute"}]} +{"seq_id": "335070914", "text": "import os.path\nfrom PyQt4.QtGui import (QDockWidget, QWidget, QFileDialog, QPushButton,\n QGridLayout, QMessageBox, QFont, QTabWidget)\nfrom PyQt4.QtCore import Qt, pyqtSignal\n\nfrom spyderlib.widgets.sourcecode.codeeditor import CodeEditor\n\nclass PluginEditorDock(QDockWidget):\n \"\"\" A dock for editing plugins.\n \"\"\"\n\n template_code = \\\n\"\"\"from spykeviewer.plugin_framework.analysis_plugin import AnalysisPlugin\n\nclass SamplePlugin(AnalysisPlugin):\n def get_name(self):\n return 'New plugin'\n\n def start(self, current, selections):\n print 'Plugin started.'\n\"\"\"\n\n plugin_saved = pyqtSignal()\n\n def __init__(self, title='Analysis Editor', parent=None):\n QDockWidget.__init__(self, title, parent)\n self.setupUi()\n\n\n def populate_groups(self):\n self.filterGroupComboBox.clear()\n self.filterGroupComboBox.addItem('')\n for g in sorted(self.groups[self.filterTypeComboBox.currentText()]):\n self.filterGroupComboBox.addItem(g)\n\n\n def setupUi(self):\n self.saveButton = QPushButton('Save', self)\n self.saveButton.clicked.connect(self.button_pressed)\n\n self.tabs = QTabWidget()\n self.tabs.setTabsClosable(True)\n self.tabs.tabCloseRequested.connect(self.close_file)\n\n self.content_widget = QWidget()\n layout = QGridLayout(self.content_widget)\n layout.addWidget(self.tabs)\n layout.addWidget(self.saveButton)\n\n self.setWidget(self.content_widget)\n\n\n def add_file(self, file_name):\n font = QFont('Some font that does not exist')\n font.setStyleHint(font.TypeWriter, font.PreferDefault)\n editor = CodeEditor()\n editor.setup_editor(linenumbers=True, language='py',\n scrollflagarea=False, codecompletion_enter=True,\n tab_mode=False, edge_line=False, font=font,\n codecompletion_auto=True, go_to_definition=True,\n codecompletion_single=True)\n editor.setCursor(Qt.IBeamCursor)\n editor.horizontalScrollBar().setCursor(Qt.ArrowCursor)\n editor.verticalScrollBar().setCursor(Qt.ArrowCursor)\n editor.file_name = file_name\n\n if file_name.endswith('py'):\n editor.set_text_from_file(file_name)\n tab_name = os.path.split(file_name)[1]\n else:\n editor.set_text(self.template_code)\n tab_name = 'New Analysis'\n\n editor.file_was_changed = False\n editor.textChanged.connect(lambda: self.file_changed(editor))\n\n self.tabs.addTab(editor, tab_name)\n self.tabs.setCurrentWidget(editor)\n\n self.setVisible(True)\n self.raise_()\n\n\n #noinspection PyCallByClass,PyTypeChecker,PyArgumentList\n def close_file(self, tab_index):\n if self.tabs.widget(tab_index).file_was_changed:\n fname = os.path.split(self.tabs.widget(tab_index).file_name)[1]\n if not fname:\n fname = 'New Analysis'\n ans = QMessageBox.question(self, 'File was changed',\n 'Do you want to save \"%s\" before closing?' % fname,\n QMessageBox.Yes | QMessageBox.No | QMessageBox.Cancel)\n if ans == QMessageBox.Yes:\n return self.save_file(self.tabs.widget(tab_index))\n elif ans == QMessageBox.Cancel:\n return False\n self.tabs.removeTab(tab_index)\n return True\n\n\n def closeEvent(self, event):\n if not self.close_all():\n event.ignore()\n else:\n event.accept()\n\n\n def close_all(self):\n while self.tabs.count():\n if not self.close_file(0):\n return False\n return True\n\n\n def file_changed(self, editor):\n editor.file_was_changed = True\n\n fname = os.path.split(editor.file_name)[1]\n if not fname:\n fname = 'New Analysis'\n text = '*' + fname\n\n self.tabs.setTabText(self.tabs.indexOf(editor), text)\n\n\n def code(self):\n return [self.tabs.currentWidget().get_text_line(l)\n for l in xrange(self.tabs.currentWidget().get_line_count())]\n\n\n def code_has_errors(self):\n code = '\\n'.join(self.code())\n try:\n compile(code, '', 'exec')\n except SyntaxError as e:\n return e.msg + ' (Line %d)' % (e.lineno)\n return None\n\n\n #noinspection PyCallByClass,PyTypeChecker,PyArgumentList\n def save_file(self, editor):\n if not editor.file_name.endswith('py'):\n d = QFileDialog(self, 'Choose where to save selection',\n self.tabs.currentWidget().file_name)\n d.setAcceptMode(QFileDialog.AcceptSave)\n d.setNameFilter(\"Python files (*.py)\")\n d.setDefaultSuffix('py')\n if d.exec_():\n editor.file_name = str(d.selectedFiles()[0])\n else:\n return False\n #else:\n # if QMessageBox.question(self, 'Warning',\n # 'Do you really want to overwrite the existing file?',\n # QMessageBox.Yes | QMessageBox.No) != QMessageBox.Yes:\n # return\n\n err = self.code_has_errors()\n if err:\n QMessageBox.critical(self, 'Error saving analysis',\n 'Compile error:\\n' + err)\n return False\n\n f = open(editor.file_name, 'w')\n f.write('\\n'.join(self.code()))\n f.close()\n\n editor.file_was_changed = False\n fname = os.path.split(editor.file_name)[1]\n self.tabs.setTabText(self.tabs.indexOf(editor), fname)\n self.plugin_saved.emit()\n return True\n\n\n def button_pressed(self):\n editor = self.tabs.currentWidget()\n self.save_file(editor)\n", "sub_path": "spykeviewer/ui/plugin_editor_dock.py", "file_name": "plugin_editor_dock.py", "file_ext": "py", "file_size_in_byte": 5734, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PyQt4.QtGui.QDockWidget", "line_number": 8, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.pyqtSignal", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QDockWidget.__init__", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QDockWidget", "line_number": 26, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QPushButton", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QTabWidget", "line_number": 41, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QWidget", "line_number": 45, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QGridLayout", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QFont", "line_number": 54, "usage_type": "call"}, {"api_name": "spyderlib.widgets.sourcecode.codeeditor.CodeEditor", "line_number": 56, "usage_type": "call"}, {"api_name": "PyQt4.QtCore.Qt.IBeamCursor", "line_number": 62, "usage_type": "attribute"}, {"api_name": "PyQt4.QtCore.Qt", "line_number": 62, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.Qt.ArrowCursor", "line_number": 63, "usage_type": "attribute"}, {"api_name": "PyQt4.QtCore.Qt", "line_number": 63, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.Qt.ArrowCursor", "line_number": 64, "usage_type": "attribute"}, {"api_name": "PyQt4.QtCore.Qt", "line_number": 64, "usage_type": "name"}, {"api_name": "os.path.path.split", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 69, "usage_type": "name"}, {"api_name": "os.path.path.split", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 87, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QMessageBox.question", "line_number": 90, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 90, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QMessageBox.Yes", "line_number": 92, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 92, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QMessageBox.No", "line_number": 92, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui.QMessageBox.Cancel", "line_number": 92, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui.QMessageBox.Yes", "line_number": 93, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 93, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QMessageBox.Cancel", "line_number": 95, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 95, "usage_type": "name"}, {"api_name": "os.path.path.split", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 118, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QFileDialog", "line_number": 143, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QFileDialog.AcceptSave", "line_number": 145, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui.QFileDialog", "line_number": 145, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QMessageBox.critical", "line_number": 160, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 160, "usage_type": "name"}, {"api_name": "os.path.path.split", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 169, "usage_type": "name"}]} +{"seq_id": "600904436", "text": "import csv\nimport re\nimport pymongo\nimport datetime\n\n\ndef read_data(csv_file, db):\n with open(csv_file, encoding='utf8') as f:\n reader = csv.DictReader(f)\n for line in reader:\n day, month = map(int, line['Дата'].split('.'))\n event = {'artist': line['Исполнитель'],\n 'price': int(line['Цена']),\n 'place': line['Место'],\n 'date': datetime.datetime(year=2020, month=month, day=day),\n }\n db.event.insert_one(event)\n\n\ndef find_cheapest(db):\n sorted_by_price = db.event.find().sort('price')\n return [(event['artist'], f\"{event['price']}\", event['place'], str(event['date']))\n for event in sorted_by_price]\n\n\ndef find_by_name(name, db):\n regex = re.compile(f'.*{name}.*', re.IGNORECASE)\n search_by_name = db.event.find({'artist': regex}).sort('price')\n return [(event['artist'], f\"{event['price']}\", event['place'], str(event['date']))\n for event in search_by_name]\n\n\ndef find_earlist(db):\n sorted_by_date = db['event'].find().sort('date')\n return [(event['artist'], f\"{event['price']}\", event['place'], str(event['date']))\n for event in sorted_by_date]\n\n\nif __name__ == '__main__':\n with pymongo.MongoClient() as client:\n db = client['netology']\n\n read_data('artists.csv', db)\n\n print('---sort by price---')\n print(*find_cheapest(db), sep='\\n')\n part_of_name = 'on'\n print(f'---search by \"{part_of_name}\"---')\n print(*find_by_name(part_of_name, db), sep='\\n')\n print('---sort by date---')\n print(*find_earlist(db), sep='\\n')\n \n ", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1702, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "csv.DictReader", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 27, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pymongo.MongoClient", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "463918636", "text": "import torch\nimport numpy as np\nfrom typing import Sequence, Union\nfrom torch_geometric.data import Batch\n\n\nEdge_Index = Union[np.ndarray, None]\nEdge_Weight = Union[np.ndarray, None]\nNode_Features = Sequence[Union[np.ndarray, None]]\nTargets = Sequence[Union[np.ndarray, None]]\nBatches = Union[np.ndarray, None]\nAdditional_Features = Sequence[np.ndarray]\n\n\nclass StaticGraphTemporalSignalBatch(object):\n r\"\"\"A data iterator object to contain a static graph with a dynamically\n changing constant time difference temporal feature set (multiple signals).\n The node labels (target) are also temporal. The iterator returns a single\n constant time difference temporal snapshot for a time period (e.g. day or week).\n This single temporal snapshot is a Pytorch Geometric Batch object. Between two\n temporal snapshots the feature matrix, target matrices and optionally passed\n attributes might change. However, the underlying graph is the same.\n\n Args:\n edge_index (Numpy array): Index tensor of edges.\n edge_weight (Numpy array): Edge weight tensor.\n features (Sequence of Numpy arrays): Sequence of node feature tensors.\n targets (Sequence of Numpy arrays): Sequence of node label (target) tensors.\n batches (Numpy array): Batch index tensor.\n **kwargs (optional Sequence of Numpy arrays): Sequence of additional attributes.\n \"\"\"\n\n def __init__(\n self,\n edge_index: Edge_Index,\n edge_weight: Edge_Weight,\n features: Node_Features,\n targets: Targets,\n batches: Batches,\n **kwargs: Additional_Features\n ):\n self.edge_index = edge_index\n self.edge_weight = edge_weight\n self.features = features\n self.targets = targets\n self.batches = batches\n self.additional_feature_keys = []\n for key, value in kwargs.items():\n setattr(self, key, value)\n self.additional_feature_keys.append(key)\n self._check_temporal_consistency()\n self._set_snapshot_count()\n\n def _check_temporal_consistency(self):\n assert len(self.features) == len(\n self.targets\n ), \"Temporal dimension inconsistency.\"\n for key in self.additional_feature_keys:\n assert len(self.targets) == len(\n getattr(self, key)\n ), \"Temporal dimension inconsistency.\"\n\n def _set_snapshot_count(self):\n self.snapshot_count = len(self.features)\n\n def _get_edge_index(self):\n if self.edge_index is None:\n return self.edge_index\n else:\n return torch.LongTensor(self.edge_index)\n\n def _get_batch_index(self):\n if self.batches is None:\n return self.batches\n else:\n return torch.LongTensor(self.batches)\n\n def _get_edge_weight(self):\n if self.edge_weight is None:\n return self.edge_weight\n else:\n return torch.FloatTensor(self.edge_weight)\n\n def _get_feature(self, time_index: int):\n if self.features[time_index] is None:\n return self.features[time_index]\n else:\n return torch.FloatTensor(self.features[time_index])\n\n def _get_target(self, time_index: int):\n if self.targets[time_index] is None:\n return self.targets[time_index]\n else:\n if self.targets[time_index].dtype.kind == \"i\":\n return torch.LongTensor(self.targets[time_index])\n elif self.targets[time_index].dtype.kind == \"f\":\n return torch.FloatTensor(self.targets[time_index])\n\n def _get_additional_feature(self, time_index: int, feature_key: str):\n feature = getattr(self, feature_key)[time_index]\n if feature.dtype.kind == \"i\":\n return torch.LongTensor(feature)\n elif feature.dtype.kind == \"f\":\n return torch.FloatTensor(feature)\n\n def _get_additional_features(self, time_index: int):\n additional_features = {\n key: self._get_additional_feature(time_index, key)\n for key in self.additional_feature_keys\n }\n return additional_features\n\n def __getitem__(self, time_index: Union[int, slice]):\n if isinstance(time_index, slice):\n snapshot = StaticGraphTemporalSignalBatch(\n self.edge_index,\n self.edge_weight,\n self.features[time_index],\n self.targets[time_index],\n self.batches,\n **{key: getattr(self, key)[time_index] for key in self.additional_feature_keys}\n )\n else:\n x = self._get_feature(time_index)\n edge_index = self._get_edge_index()\n edge_weight = self._get_edge_weight()\n batch = self._get_batch_index()\n y = self._get_target(time_index)\n additional_features = self._get_additional_features(time_index)\n\n snapshot = Batch(x=x, edge_index=edge_index, edge_attr=edge_weight,\n y=y, batch=batch, **additional_features)\n return snapshot\n\n def __next__(self):\n if self.t < len(self.features):\n snapshot = self[self.t]\n self.t = self.t + 1\n return snapshot\n else:\n self.t = 0\n raise StopIteration\n\n def __iter__(self):\n self.t = 0\n return self\n", "sub_path": "torch_geometric_temporal/signal/static_graph_temporal_signal_batch.py", "file_name": "static_graph_temporal_signal_batch.py", "file_ext": "py", "file_size_in_byte": 5380, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.Union", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 7, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 8, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 8, "usage_type": "attribute"}, {"api_name": "typing.Sequence", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 9, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 9, "usage_type": "attribute"}, {"api_name": "typing.Sequence", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 10, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 11, "usage_type": "attribute"}, {"api_name": "typing.Sequence", "line_number": 12, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 104, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 113, "usage_type": "name"}, {"api_name": "torch_geometric.data.Batch", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "121553730", "text": "from bs4 import BeautifulSoup\nimport sys\nimport io\nimport os\nimport urllib.parse as rep\nimport urllib.request as req\nimport requests\nimport pytube\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nimport csv\nimport errno\nimport time\nfrom tqdm import tqdm\nimport random\n\nsys.stdout = io.TextIOWrapper(sys.stdout.detach(), encoding='utf-8')\nsys.stderr = io.TextIOWrapper(sys.stderr.detach(), encoding = 'utf-8')\n\ndef get_keyword(keyword):\n base = \"https://www.youtube.com/results?search_query=\"\n quote = rep.quote_plus(keyword)\n return base+quote\n\ndef get_page(url):\n driver = webdriver.Chrome('/Users/dai/Desktop/ml_py/dlyt/chromedriver')\n driver.get(url)\n time.sleep(random.randint(3,8))\n html = driver.page_source\n href = get_link(html)\n driver.close()\n return href\n\n\ndef get_link(source):\n time.sleep(2)\n soup = BeautifulSoup(source, 'lxml')\n titles = soup.find_all('h3')\n title = []\n base = \"https://www.youtube.com\"\n for title in titles:\n if hasattr(title.a, 'href'):\n url = base+title.a['href']\n href.append(url)\n return href\n\n \n \ndef make_csv(csv_path, href):\n try:\n if not (os.path.isfile(csv_path)):\n with open(csv_path, mode='w',encoding='utf-8', newline='') as file:\n writer = csv.writer(file)\n for row in href:\n writer.writerow([row])\n else:\n with open(csv_path, mode='r') as file:\n reader = csv.reader(file)\n temp_href = list(reader)\n for row in href:\n if row in temp_href:\n href.remove([row])\n tot = temp_href+href\n with open(csv_path, mode='w',encoding='utf-8', newline='') as file:\n writer = csv.writer(file)\n for row in tot:\n writer.writerow([row])\n print(\"csv file 만들기 완료!\")\n except OSError as e:\n if e.errno != errno.EEXIST:\n print('csv file 만들기 실패')\n raise\n return href\n\n#폴더 명 -> 파일 명 -> 파일명 유튜브에 검색 -> 영상 링크 받아서 -> csv file에 쓰기\n# /Users/dai/Desktop/sample/get_csv.py\n\nvideo_dir = '/Users/dai/desktop/190117-190228/captions/'\ncsv_dir = '/Users/dai/desktop/190117-190228/'\nfolders =['면접합격법', '입사후포부'] \n# folders2 = ['대답해드립니다', '질문해주세요', '하울', '면접 탈락 사례', '지원동기', '자기소개법']\nfor f in folders:\n href = []\n dir = video_dir+f\n csv_path = csv_dir + f + '.csv'\n video = [f for f in os.listdir(dir) if os.path.isfile(os.path.join(dir, f))]\n\n with open(csv_path, mode='w',encoding='utf-8', newline='') as file:\n writer = csv.writer(file)\n for v in tqdm(video):\n v= v.replace('.srt','')\n link = get_keyword(v)\n page = get_page(link)\n if len(page) != 0:\n hyperlink = page[0]\n writer.writerow([hyperlink])\n # print(page[0], flush= True)\n page.clear()\n # print(link, flush = True)\n # print(page[0], flush=True)\n # print(len(href), flush=True)\n # test = set(href)\n # print(len(test))\n # print(len(href))\n # make_csv(csv_path, href)\n\n", "sub_path": "get_csv.py", "file_name": "get_csv.py", "file_ext": "py", "file_size_in_byte": 3351, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.stdout", "line_number": 17, "usage_type": "attribute"}, {"api_name": "io.TextIOWrapper", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.stdout.detach", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 18, "usage_type": "attribute"}, {"api_name": "io.TextIOWrapper", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.stderr.detach", "line_number": 18, "usage_type": "call"}, {"api_name": "urllib.parse.quote_plus", "line_number": 22, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 22, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 26, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 26, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 53, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 58, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 65, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 89, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 90, "usage_type": "call"}]} +{"seq_id": "133899988", "text": "import tempfile\nfrom datetime import datetime\nfrom pathlib import Path\n\nfrom serenity.marketdata.fh.feedhandler import feedhandler_capnp\nfrom serenity.marketdata.fh.txlog import TransactionLog\n\n\ndef test_txlog_read_write():\n tmp_txfile_dir = tempfile.TemporaryDirectory()\n tmp_txfile_path = Path(tmp_txfile_dir.name)\n txlog = TransactionLog(tmp_txfile_path)\n\n txlog_writer = txlog.create_writer()\n\n book_msg = feedhandler_capnp.Level1BookUpdateMessage.new_message()\n book_msg.time = datetime.utcnow().timestamp()\n book_msg.bestBidQty = 5.0\n book_msg.bestBidPx = 38000.0\n book_msg.bestAskQty = 9.0\n book_msg.bestAskPx = 38001.0\n\n txlog_writer.append_msg(book_msg)\n txlog_writer.close()\n\n txlog_reader = txlog.create_reader()\n book_msgs = txlog_reader.read_messages(feedhandler_capnp.Level1BookUpdateMessage)\n loaded_book_msg = next(book_msgs)\n assert book_msg.bestBidQty == loaded_book_msg.bestBidQty\n assert book_msg.bestBidPx == loaded_book_msg.bestBidPx\n assert book_msg.bestAskQty == loaded_book_msg.bestAskQty\n assert book_msg.bestAskPx == loaded_book_msg.bestAskPx\n", "sub_path": "tests/serenity/marketdata/fh/test_txlog.py", "file_name": "test_txlog.py", "file_ext": "py", "file_size_in_byte": 1127, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tempfile.TemporaryDirectory", "line_number": 10, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 11, "usage_type": "call"}, {"api_name": "serenity.marketdata.fh.txlog.TransactionLog", "line_number": 12, "usage_type": "call"}, {"api_name": "serenity.marketdata.fh.feedhandler.feedhandler_capnp.Level1BookUpdateMessage.new_message", "line_number": 16, "usage_type": "call"}, {"api_name": "serenity.marketdata.fh.feedhandler.feedhandler_capnp.Level1BookUpdateMessage", "line_number": 16, "usage_type": "attribute"}, {"api_name": "serenity.marketdata.fh.feedhandler.feedhandler_capnp", "line_number": 16, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "name"}, {"api_name": "serenity.marketdata.fh.feedhandler.feedhandler_capnp.Level1BookUpdateMessage", "line_number": 27, "usage_type": "attribute"}, {"api_name": "serenity.marketdata.fh.feedhandler.feedhandler_capnp", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "226991086", "text": "#!/usr/bin/env python\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import *\nimport sys\nimport numpy as np\nimport PIL.Image\nimport tr\n\nclass Main(QDialog):\n @staticmethod\n def QPixmapToArray(pixmap):\n size = pixmap.size()\n w = size.width()\n h = size.height()\n\n qimg = pixmap.toImage()\n b = qimg.bits()\n b.setsize(w*h*4)\n img = np.frombuffer(b, np.uint8).reshape((h, w, 4))\n\n img = PIL.Image.fromarray(img).convert(\"L\")\n img = np.array(img)\n return img\n\n def __init__(self):\n super().__init__()\n\n self.textEdit = QPlainTextEdit(self)\n\n font = QFont()\n font.setPointSize(18)\n self.textEdit.setFont(font)\n self.resize(640, 240)\n self.setWindowTitle(\"请使用飞书、微信等软件进行截图，只支持单行文本识别 v2.3\")\n self.setWindowFlags(Qt.WindowStaysOnTopHint)\n\n layout = QGridLayout(self)\n layout.addWidget(self.textEdit, 1, 0)\n self.setLayout(layout)\n\n self.timer = QTimer(self)\n self.timer.timeout.connect(self.task)\n self.timer.start(200)\n \n def task(self):\n clipboard = QApplication.clipboard()\n pixmap = clipboard.pixmap()\n if pixmap.width() * pixmap.height() <= 0: return\n\n img = self.QPixmapToArray(pixmap)\n txt, _ = tr.recognize(img)\n \n clipboard.setText(txt)\n self.textEdit.appendPlainText(txt)\n\n\nif __name__ == \"__main__\":\n app = QApplication(sys.argv)\n main = Main()\n main.show()\n sys.exit(app.exec_())\n\n\n\n", "sub_path": "test_crnn_pyqt5.py", "file_name": "test_crnn_pyqt5.py", "file_ext": "py", "file_size_in_byte": 1609, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.frombuffer", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 20, "usage_type": "attribute"}, {"api_name": "PIL.Image.Image.fromarray", "line_number": 22, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 22, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "tr.recognize", "line_number": 52, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 59, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "574405478", "text": "from mido import MidiFile, MetaMessage\nimport sys\n\nDEFAULT_TEMPO = 0.5\n\nARDUINO_CODE_MODE = False\nNUM_NOTES_TO_OUTPUT = None\nINSERT_DELAY = False\n\n\ndef ticks2s(ticks, tempo, ticks_per_beat):\n \"\"\"\n Converts ticks to seconds\n \"\"\"\n return ticks/ticks_per_beat * tempo\n\n\ndef note2freq(x):\n \"\"\"\n Convert a MIDI note into a frequency (given in Hz)\n \"\"\"\n a = 440\n return (a/32) * (2 ** ((x-9)/12))\n\ndef commandLineHelp():\n print(\"Usage:\\npython main.py {-a} {-n NUM_NOTES} inputfile.mid\\n\")\n print(\"-a, --arduino\\t\\t\\tOutput textfile as list of tone(outPin, freq, duration); calls that can be pasted into arduino sketch.\\n\")\n print(\"-d, --delay\\t\\t\\tInsert delays in arduino C code so that song will play as pasted into editor.\\n\")\n print(\"-n, --num-notes\\t[NUM_NOTES]\\tOnly output the first NUM_NOTES that have duration greater than zero into output file\\n\")\n print(\"-h, --help\\t\\t\\tDisplay this help\")\n\n\nif __name__ == '__main__':\n\n \n # Parse command line options\n if(len(sys.argv) < 2):\n commandLineHelp()\n sys.exit(1)\n for i in range(1,len(sys.argv)):\n if( sys.argv[i] in ['-a', '--arduino']):\n print(\"Arduino code output mode enabled\")\n ARDUINO_CODE_MODE = True\n \n elif(sys.argv[i] in ['-n', '--num-notes']):\n try:\n NUM_NOTES_TO_OUTPUT = int(sys.argv[i+1])\n except ValueError:\n print(\"-n option takes an INTEGER as an argument. Ex.\\npython [filename].py -n 100\")\n print(\"Only outputting first %d notes\" % NUM_NOTES_TO_OUTPUT)\n \n elif(sys.argv[i] in ['-d', '--delay']):\n INSERT_DELAY = True\n print(\"Inserting delays in output arduino C code.\")\n \n elif(sys.argv[i] in ['-h', '--help']):\n commandLineHelp()\n sys.exit(0)\n \n # Import the MIDI file... \n mid = MidiFile(sys.argv[-1])\n print(\"TYPE: \" + str(mid.type))\n print(\"LENGTH: \" + str(mid.length))\n print(\"TICKS PER BEAT: \" + str(mid.ticks_per_beat))\n\n if mid.type == 3:\n print(\"Unsupported type.\")\n exit()\n\n \"\"\"\n First read all the notes in the MIDI file\n \"\"\"\n\n tracksMerged = []\n notes = {}\n\n for i, track in enumerate(mid.tracks):\n tempo = DEFAULT_TEMPO\n totaltime = 0\n print(\"Track: \" + str(i))\n\n for message in track:\n t = ticks2s(message.time, tempo, mid.ticks_per_beat)\n totaltime += t\n\n if isinstance(message, MetaMessage): # Tempo change\n if message.type == \"set_tempo\":\n tempo = message.tempo / 10**6\n elif message.type == \"end_of_track\":\n pass\n else:\n print(\"Unsupported metamessage: \" + str(message))\n\n else: # Note\n if message.type == \"control_change\" or \\\n message.type == \"program_change\":\n pass\n\n elif message.type == \"note_on\" or message.type == \"note_off\":\n if message.note not in notes:\n notes[message.note] = 0\n if message.type == \"note_on\" and message.velocity != 0:\n notes[message.note] += 1\n if(notes[message.note] == 1):\n tracksMerged += \\\n [(totaltime, message.note, message.velocity)]\n\n else:\n notes[message.note] -= 1\n if(notes[message.note] == 0):\n tracksMerged += \\\n [(totaltime, message.note, message.velocity)]\n\n else:\n print(message)\n\n print(\"totaltime: \" + str(totaltime)+\"s\")\n\n \"\"\"\n Now merge all the tracks alltogether\n \"\"\"\n\n tracksMerged = sorted(tracksMerged, key=lambda x: x[0])\n music = []\n\n for i in range(len(tracksMerged)-1):\n a = tracksMerged[i][0]\n b = tracksMerged[i+1][0]\n t = round(b-a, 3)\n m = tracksMerged[i]\n music += [(m[0], t, round(note2freq(m[1])), m[2])]\n \"\"\"\n Finally write it in CSV format in a file\n \"\"\"\n\n he = \"\"\n \n if(not ARDUINO_CODE_MODE):\n for msg in music:\n he += str(msg[0])+\",\" + str(msg[1]) + \",\" +str(msg[2])+\",\"+str(msg[3])+\"\\n\"\n else:\n nloop = len(music)\n if(NUM_NOTES_TO_OUTPUT is not None):\n nloop = NUM_NOTES_TO_OUTPUT\n count = 0\n i = 0\n while count < nloop and i < len(music):\n msg = music[i]\n if(msg[1] > 0):\n he += \"tone(outPin, \" + str(msg[2]) + \", \" +str(int(msg[1]*1000)) + \");\\n\"\n if(INSERT_DELAY):\n he += \"delay(\" + str(int(msg[1]*1000)) + \");\\n\"\n count += 1\n i += 1\n if(ARDUINO_CODE_MODE):\n f = open(\"./music.c\", \"w\")\n else:\n f = open(\"./music.csv\",\"w\")\n f.write(\"#Total Time,Note Len,note2freq,velocity\\n\")\n f.write(he)\n f.close()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5136, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.argv", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 39, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 52, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 56, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 58, "usage_type": "call"}, {"api_name": "mido.MidiFile", "line_number": 61, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 61, "usage_type": "attribute"}, {"api_name": "mido.MetaMessage", "line_number": 86, "usage_type": "argument"}]} +{"seq_id": "111650631", "text": "'''\n@Description\n@Autor 朱俊\n@Date 2020-07-19 15:30:18\n@LastEditors 朱俊\n@LastEditTime 2020-07-19 21:15:02\n'''\n'''\n请将以下的 SQL 语句翻译成 pandas 语句：\n1. SELECT * FROM data;\n\n2. SELECT * FROM data LIMIT 10;\n\n3. SELECT id FROM data; //id 是 data 表的特定一列\n\n4. SELECT COUNT(id) FROM data;\n\n5. SELECT * FROM data WHERE id<1000 AND age>30;\n\n6. SELECT id,COUNT(DISTINCT order_id) FROM table1 GROUP BY id;\n\n7. SELECT * FROM table1 t1 INNER JOIN table2 t2 ON t1.id = t2.id;\n\n8. SELECT * FROM table1 UNION SELECT * FROM table2;\n\n9. DELETE FROM table1 WHERE id=10;\n\n10. ALTER TABLE table1 DROP COLUMN column_name;\n'''\nimport pandas as pd\nimport pymysql\nimport os\npath_file = os.path.dirname(__file__)+'/movies.csv'\ncon = pymysql.connect(\n host=\"localhost\", user=\"root\", password=\"root\", db=\"pytest\")\n\n\n# 1. SELECT * FROM data;\ndf = pd.read_sql(\"SELECT * FROM movies\", con)\n\n# 2 SELECT * FROM data LIMIT 10;\n# df.loc[:,'id':'type'] 这里如果我选取指定两列 id type df.loc[:,['id','type']]\ndf.iloc[:10]\n\n# 3. SELECT id FROM data; //id 是 data 表的特定一列\nid = df['id']\nname = df.loc[:, 'name']\n\n\n# 4. SELECT COUNT(id) FROM data;\nrow_num = df.shape[0]\ncol_num = df.shape[1]\n\n# 5. SELECT * FROM data WHERE id<1000 AND age>30;\n# df[(df['id']<1000) & (df['age']>30)]\n# 需要注意的是这里多条件进行筛选的时候需要进行加上（）\ndf[(df['id'] < 13) & (df['id'] > 10)]\n\n\n# 6. SELECT id,COUNT(DISTINCT order_id) FROM table1 GROUP BY id;\ndf = pd.DataFrame({'A': ['foo1', 'bar2', 'foo1', 'bar4',\n 'foo5', 'bar6', 'foo1', 'foo9'],\n 'B': [2, 3, 3, 3,\n 2, 2, 1, 4]})\ndf.groupby([\"A\"])['A'].value_counts()\n\ndf = pd.DataFrame({'A': ['foo1', 'bar2', 'foo3', 'bar4',\n 'foo5', 'bar6', 'foo7', 'foo9'],\n 'B': ['one', 'one', 'two', 'three',\n 'two', 'two', 'one', 'three']})\ndf1 = pd.DataFrame({'A': ['foo1', 'bar2', 'foo3', 'bar4',\n 'foo5', 'bar6', 'foo7', 'foo8'],\n 'B1': ['one1', 'one1', 'two1', 'three1',\n 'two1', 'two1', 'one1', 'three1']})\n\n\n# 7. SELECT * FROM table1 t1 INNER JOIN table2 t2 ON t1.id = t2.id;\n# 可以看到merge的使用方式 与 concat的使用方式还是不同的 在传参上 concat传入一个df列表的\npd.merge(df, df1, how=\"inner\", on='A')\n\n\n# 8. SELECT * FROM table1 UNION SELECT * FROM table2;\npd.concat([df, df1])\n# 9. DELETE FROM table1 WHERE id=10;\n# df[df['id']!=10]\ndf[df['A'] != 'foo1']\ndf.drop(df[df['A'] == 'foo1'].index) # 这个也没有改变原来的df的\n\n# 10. ALTER TABLE table1 DROP COLUMN column_name;\ndf2 = df.drop(['A'], axis=1)\n", "sub_path": "week04/zy/index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 2736, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pymysql.connect", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 67, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "533702526", "text": "from collections import deque, namedtuple\nfrom heapq import heappop, heappush\n\nfrom ..geometry.utilities import triangle_area_squared\nfrom ..coordinates import Vector\n\nfrom network.iterators import BidirectionalIterator, look_ahead\n\n__all__ = \"Funnel\", \"PathNotFoundException\", \"AlgorithmNotImplementedException\", \"AStarAlgorithm\", \"FunnelAlgorithm\", \\\n \"PathfinderAlgorithm\"\n\n\nforward_vector = Vector((0, 1, 0))\nEndPortal = namedtuple(\"EndPortal\", [\"left\", \"right\"])\nBoundVector = type(\"BoundVector\", (Vector,), {\"__slots__\": \"data\"})\n\n\ndef manhattan_distance_heuristic(a, b):\n return (b.position - a.position).length_squared\n\n\nclass Funnel:\n __slots__ = \"left\", \"right\", \"_apex\", \"_apex_callback\"\n\n def __init__(self, apex, left, right, on_apex_changed):\n self.left = left\n self.right = right\n self._apex = apex\n self._apex_callback = on_apex_changed\n\n @property\n def apex(self):\n return self._apex\n\n @apex.setter\n def apex(self, value):\n self._apex = value\n self._apex_callback(value)\n\n def update(self, portals):\n portals = BidirectionalIterator(portals)\n left_index = right_index = portals.index\n\n # Increment index and then return entry at index\n for portal in portals:\n # Check if left is inside of left margin\n if triangle_area_squared(self.apex, self.left, portal.left) >= 0.0:\n # Check if left is inside of right margin or\n # we haven't got a proper funnel\n if self.apex == self.left or (triangle_area_squared(self.apex, self.right, portal.left) < 0.0):\n # Narrow funnel\n self.left = portal.left\n left_index = portals.index\n\n else:\n # Otherwise add apex to path\n self.left = self.apex = self.right\n # Set portal to consider from the corner we pivoted around\n # This index is incremented by the for loop\n portals.index = right_index\n continue\n\n # Check if right is inside of right margin\n if triangle_area_squared(self.apex, self.right, portal.right) <= 0.0:\n # Check if right is inside of left margin or\n # we haven't got a proper funnel\n if self.apex == self.right or (triangle_area_squared(self.apex, self.left, portal.right) > 0.0):\n # Narrow funnel\n self.right = portal.right\n right_index = portals.index\n\n else:\n # Otherwise add apex to path\n self.right = self.apex = self.left\n # Set portal to consider from the corner we pivoted around\n # This index is incremented by the for loop\n portals.index = left_index\n continue\n\n\nclass PathNotFoundException(Exception):\n pass\n\n\nclass AlgorithmNotImplementedException(Exception):\n pass\n\n\nclass AStarAlgorithm:\n\n def __init__(self, get_neighbours, get_h_score, get_g_score, check_completed):\n self.get_h_score = get_h_score\n self.get_g_score = get_g_score\n self.get_neighbours = get_neighbours\n self.is_finished = check_completed\n\n @staticmethod\n def reconstruct_path(node, path):\n result = deque()\n while node:\n result.appendleft(node)\n node = path.get(node)\n\n return result\n\n def find_path(self, start):\n open_set = {start}\n closed_set = set()\n\n f_scored = [(0, start)]\n g_scored = {start: 0}\n\n get_h_score = self.get_h_score\n get_g_score = self.get_g_score\n get_neighbours = self.get_neighbours\n is_complete = self.is_finished\n\n path = {}\n\n while open_set:\n current = heappop(f_scored)[1]\n if is_complete(current, path):\n return self.reconstruct_path(current, path)\n\n open_set.remove(current)\n closed_set.add(current)\n\n for neighbour in get_neighbours(current):\n if neighbour in closed_set:\n continue\n\n tentative_g_score = g_scored[current] + get_g_score(current, neighbour)\n\n if not neighbour in open_set or tentative_g_score < g_scored[neighbour]:\n path[neighbour] = current\n g_scored[neighbour] = tentative_g_score\n heappush(f_scored, (tentative_g_score + get_h_score(neighbour), neighbour))\n\n if not neighbour in open_set:\n open_set.add(neighbour)\n\n raise PathNotFoundException(\"Couldn't find path for given nodes\")\n\n\nclass FunnelAlgorithm:\n\n def find_path(self, source, destination, nodes):\n path = [source]\n\n # Account for main path\n portals = [source.get_portal_to(destination) for source, destination in look_ahead(nodes)]\n portals.append(EndPortal(destination, destination))\n\n funnel = Funnel(source, source, source, path.append)\n funnel.update(portals)\n\n # Account for last destination point\n if funnel is None:\n return []\n\n path.append(destination)\n return path\n\n\nclass PathfinderAlgorithm:\n\n def __init__(self, low_fidelity, high_fidelity, spatial_lookup):\n self.low_resolution = low_fidelity\n self.high_resolution = high_fidelity\n self.spatial_lookup = spatial_lookup\n\n def find_path(self, source, destination, nodes, low_resolution=False):\n source_node = self.spatial_lookup(source)\n destination_node = self.spatial_lookup(destination)\n\n try:\n path_finder = self.low_resolution.find_path\n\n except AttributeError:\n raise AlgorithmNotImplementedException(\"Couldn't find low resolution finder algorithm\")\n\n low_resolution_path = path_finder(source_node, destination_node, nodes)\n if low_resolution:\n return low_resolution_path\n\n try:\n path_finder = self.high_resolution.find_path\n\n except AttributeError:\n raise AlgorithmNotImplementedException(\"Couldn't find high resolution finder algorithm\")\n\n high_resolution_path = path_finder(source, destination, low_resolution_path)\n return high_resolution_path", "sub_path": "game_system/pathfinding/algorithm.py", "file_name": "algorithm.py", "file_ext": "py", "file_size_in_byte": 6423, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "coordinates.Vector", "line_number": 13, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 14, "usage_type": "call"}, {"api_name": "coordinates.Vector", "line_number": 15, "usage_type": "name"}, {"api_name": "network.iterators.BidirectionalIterator", "line_number": 41, "usage_type": "call"}, {"api_name": "geometry.utilities.triangle_area_squared", "line_number": 47, "usage_type": "call"}, {"api_name": "geometry.utilities.triangle_area_squared", "line_number": 50, "usage_type": "call"}, {"api_name": "geometry.utilities.triangle_area_squared", "line_number": 64, "usage_type": "call"}, {"api_name": "geometry.utilities.triangle_area_squared", "line_number": 67, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 99, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 121, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 137, "usage_type": "call"}, {"api_name": "network.iterators.look_ahead", "line_number": 151, "usage_type": "call"}]} +{"seq_id": "259539452", "text": "from map import Lane,Map\nfrom pid import PID\nimport rospy\nfrom autominy_msgs.msg import SpeedCommand, NormalizedSteeringCommand, SteeringCommand\nfrom nav_msgs.msg import Odometry\nfrom std_msgs.msg import Int32\nimport math\nimport numpy as np\nimport tf.transformations\nfrom visualization_msgs.msg import Marker\n\n\n\n\n\n\nclass Navigation():\n\n def __init__(self):\n rospy.init_node(\"Navigation\")\n\n self.map = Map()\n self.pid = PID()\n self.lane = 0\n self.lane_pub = rospy.Publisher('lane_num', Int32, queue_size=10)\n self.lane_pub.publish(self.lane)\n self.lane_sub = rospy.Subscriber(\"/lane_num\", Int32, self.set_lane, queue_size=10)\n self.localization_sub = rospy.Subscriber(\"/sensors/localization/filtered_map\", Odometry, self.on_localization,\n queue_size=10)\n self.pid_desired_angle = rospy.Publisher(\"/desired_angle/angle\", NormalizedSteeringCommand, queue_size=10)\n self.lookahead_pub = rospy.Publisher(\"lane\", Marker, queue_size=10)\n\n self.rate = rospy.Rate(100)\n rospy.on_shutdown(self.on_shutdown)\n while not rospy.is_shutdown():\n self.rate.sleep()\n\n def set_lane(self, lane_num):\n self.lane = lane_num.data\n\n def on_localization(self,data):\n current_pos = np.array([data.pose.pose.position.x,data.pose.pose.position.y])\n look_ahead_point = self.map.lanes[self.lane].lookahead_point(current_pos,0.5)[0]# [0] for lookahead point inner lane\n self.publish_looakhead(look_ahead_point)\n car_yaw = self.get_car_yaw(data)\n yaw = self.calculateYaw(current_pos,look_ahead_point,car_yaw) # yaw difference from two points\n print(\"I want this yaw:\"+str(yaw))\n steering_msg = NormalizedSteeringCommand()\n steering_msg.value = yaw\n self.pid_desired_angle.publish(steering_msg)\n\n def calculateYaw(self,current_pos,look_ahead_point,car_yaw):\n\n v = [look_ahead_point[0] - current_pos[0], look_ahead_point[1] - current_pos[1]]\n theta = car_yaw\n c, s = np.cos(theta), np.sin(theta)\n R = np.array(((c, -s), (s, c)))\n v_r = np.matmul(v, R)\n myradians = math.atan2(v_r[1], v_r[0])\n return myradians\n\n\n def get_car_yaw(self,data):\n quat = [data.pose.pose.orientation.x, data.pose.pose.orientation.y, data.pose.pose.orientation.z,\n data.pose.pose.orientation.w]\n roll, pitch, yaw = tf.transformations.euler_from_quaternion(quat)\n return yaw\n\n def on_shutdown(self):\n speed_msg = SpeedCommand()\n speed_msg.value = 0.0\n self.pid.speed_pub.publish(speed_msg)\n\n def publish_looakhead(self,look_ahead):\n i= 0\n msg = Marker(type=Marker.SPHERE, action=Marker.ADD)\n msg.header.frame_id = \"map\"\n msg.scale.x = 0.2\n msg.scale.y = 0.2\n msg.scale.z = 0.2\n msg.color.b = 1.0\n msg.color.a = 1.0\n msg.id = i\n\n #p, param = lane.closest_point(point)\n msg.pose.position.x = look_ahead[0]\n msg.pose.position.y = look_ahead[1]\n\n # i += 1\n #\n # self.lane_pub.publish(msg)\n # # green lookahead point\n # msg.color.b = 0.0\n # msg.color.g = 1.0\n # p, param = lane.lookahead_point(point, 0.5)\n # msg.pose.position.x = p[0]\n # msg.pose.position.y = p[1]\n # msg.id = i\n #\n # i += 1\n\n self.lookahead_pub.publish(msg)\n\nif __name__ == \"__main__\":\n Navigation()", "sub_path": "src/assignment9_navigation/navigation.py", "file_name": "navigation.py", "file_ext": "py", "file_size_in_byte": 3530, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rospy.init_node", "line_number": 20, "usage_type": "call"}, {"api_name": "map.Map", "line_number": 22, "usage_type": "call"}, {"api_name": "pid.PID", "line_number": 23, "usage_type": "call"}, {"api_name": "rospy.Publisher", "line_number": 25, "usage_type": "call"}, {"api_name": "std_msgs.msg.Int32", "line_number": 25, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 27, "usage_type": "call"}, {"api_name": "std_msgs.msg.Int32", "line_number": 27, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 28, "usage_type": "call"}, {"api_name": "nav_msgs.msg.Odometry", "line_number": 28, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 30, "usage_type": "call"}, {"api_name": "autominy_msgs.msg.NormalizedSteeringCommand", "line_number": 30, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 31, "usage_type": "call"}, {"api_name": "visualization_msgs.msg.Marker", "line_number": 31, "usage_type": "argument"}, {"api_name": "rospy.Rate", "line_number": 33, "usage_type": "call"}, {"api_name": "rospy.on_shutdown", "line_number": 34, "usage_type": "call"}, {"api_name": "rospy.is_shutdown", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "autominy_msgs.msg.NormalizedSteeringCommand", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 58, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 59, "usage_type": "call"}, {"api_name": "tf.transformations.transformations.euler_from_quaternion", "line_number": 66, "usage_type": "call"}, {"api_name": "tf.transformations.transformations", "line_number": 66, "usage_type": "attribute"}, {"api_name": "tf.transformations", "line_number": 66, "usage_type": "name"}, {"api_name": "autominy_msgs.msg.SpeedCommand", "line_number": 70, "usage_type": "call"}, {"api_name": "visualization_msgs.msg.Marker", "line_number": 76, "usage_type": "call"}, {"api_name": "visualization_msgs.msg.Marker.SPHERE", "line_number": 76, "usage_type": "attribute"}, {"api_name": "visualization_msgs.msg.Marker.ADD", "line_number": 76, "usage_type": "attribute"}]} +{"seq_id": "499090753", "text": "\r\n\"\"\" zth title screen v1 \"\"\"\r\n\r\nimport pygame\r\nimport cfg, common_functions\r\n\r\n\"\"\" ------------------------------------------------------------------------ \"\"\"\r\n\r\ndef main():\r\n\r\n titleScreen()\r\n\r\n\r\ndef titleScreen():\r\n\r\n backgroundImageOne = pygame.image.load('images/title/title1.png')\r\n backgroundImageTwo = pygame.image.load('images/title/title2.png')\r\n\r\n titleBackSurf = pygame.Surface((780, 100))\r\n titleBackSurf.set_alpha(45)\r\n titleBackRect = titleBackSurf.get_rect()\r\n titleBackRect.topleft = (10, 10)\r\n\r\n titleSurf = cfg.AR74.render('Zero to Hero', True, cfg.WHITE)\r\n titleRect = titleSurf.get_rect()\r\n titleRect.center = titleBackRect.center\r\n\r\n pygame.mixer.music.load('bgm/title.ogg')\r\n pygame.mixer.music.play(-1, 0.0)\r\n\r\n timer = 0\r\n while True:\r\n\r\n common_functions.standardEventHandling()\r\n\r\n # if timer > 40 and timer < 60:\r\n # cfg.DISPLAYSURF.blit(backgroundImageTwo, (0,0))\r\n # else:\r\n cfg.DISPLAYSURF.blit(backgroundImageOne, (0,0))\r\n\r\n cfg.DISPLAYSURF.blit(titleBackSurf, titleBackRect)\r\n cfg.DISPLAYSURF.blit(titleSurf, titleRect)\r\n\r\n timer += 1\r\n if timer > 200:\r\n timer = 0\r\n\r\n if cfg.mouseClicked:\r\n pygame.mixer.music.fadeout(500)\r\n return\r\n\r\n pygame.display.update()\r\n cfg.FPSCLOCK.tick(cfg.FPS)\r\n\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "sub_path": "title_screen.py", "file_name": "title_screen.py", "file_ext": "py", "file_size_in_byte": 1438, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.image.load", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 19, "usage_type": "call"}, {"api_name": "cfg.AR74.render", "line_number": 24, "usage_type": "call"}, {"api_name": "cfg.AR74", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cfg.WHITE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 29, "usage_type": "attribute"}, {"api_name": "common_functions.standardEventHandling", "line_number": 34, "usage_type": "call"}, {"api_name": "cfg.DISPLAYSURF.blit", "line_number": 39, "usage_type": "call"}, {"api_name": "cfg.DISPLAYSURF", "line_number": 39, "usage_type": "attribute"}, {"api_name": "cfg.DISPLAYSURF.blit", "line_number": 41, "usage_type": "call"}, {"api_name": "cfg.DISPLAYSURF", "line_number": 41, "usage_type": "attribute"}, {"api_name": "cfg.DISPLAYSURF.blit", "line_number": 42, "usage_type": "call"}, {"api_name": "cfg.DISPLAYSURF", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cfg.mouseClicked", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.fadeout", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 52, "usage_type": "attribute"}, {"api_name": "cfg.FPSCLOCK.tick", "line_number": 53, "usage_type": "call"}, {"api_name": "cfg.FPSCLOCK", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cfg.FPS", "line_number": 53, "usage_type": "attribute"}]} +{"seq_id": "433874699", "text": "import requests\n\n#CI_Slack_webhook = 'https://hooks.slack.com/services/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'\n\ndef publish_results_to_slack(channel, global_result='1', message='default', jobID='481857404', browser='chrome', scope='ci'):\n # get only last line of gloabal suite message\n # messageLine = message.split('\\n')\n # message = messageLine[len(messageLine) - 1]\n print(message)\n print(global_result)\n\n\n test_nature = 'Tests on *' + browser + '*'\n\n url = channel\n #artifacts_path = 'https://gitlab.insideboard.com/qa/qa-' + project + '/-/jobs/' + jobID + '/artifacts/download'\n\n if str(global_result) == '0':\n feedback = ':heavy_check_mark: ' + test_nature + ' are passed !! :tada: :tada:'\n msg_color = 'good'\n else:\n feedback = ':red_circle: ' + test_nature + ' are failing !!'\n msg_color = '#ed5c5c'\n\n headers = { 'Content-Type': 'application/json' }\n formattedPayload = {\n \"attachments\":\n [\n {\n \"mrkdwn_in\": [\"text\"],\n \"color\": msg_color,\n \"pretext\": feedback,\n # \"type\": \"section\",\n # \"color\": msg_color,\n \"fields\":\n [\n {\n \"value\": \"*Test results*: \" + message,\n \"short\": False\n }\n ],\n \"actions\": [\n {\n \"name\": \"Artifacts\",\n \"text\": \"Github build\",\n \"type\": \"button\",\n \"url\": 'https://github.com/Qualitybox/syaanh-client/actions/runs/' + jobID\n }\n ]\n }\n ]\n }\n\n response = requests.post(url, headers=headers, json=formattedPayload)\n print(response.status_code)\n print(response.content)\n return response.status_code\n\n#publish_results_to_slack()\n\n", "sub_path": "Utils/Slack_notification.py", "file_name": "Slack_notification.py", "file_ext": "py", "file_size_in_byte": 2262, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.post", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "418888451", "text": "\"\"\"All Abstract Repositories\"\"\"\n\n# Libraries\nfrom typing import Union\nfrom abc import ABC, abstractmethod\nfrom pydantic import BaseModel\n\n# Modules\nfrom . import models\n\n\nclass ProductAbstractRepository(ABC):\n def __init__(self, session):\n self.session = session\n self.seen = set()\n\n def add(self, new_element: models.Item, list_id: int) -> models.ItemRepository:\n item_to_save = models.ItemRepository(**new_element.__dict__, list_id=list_id)\n n_element = _add(item_to_save)\n self.seen.add(n_element)\n return n_element\n\n @abstractmethod\n def _add(self, new_element: models.ItemRepository) -> models.ItemRepository:\n raise NotImplementedError\n\n\nclass ListAbstractRepository(ABC):\n def __init__(self, session):\n self.session = session\n self.seen = set()\n\n\nall_repositories = Union[ProductAbstractRepository, ListAbstractRepository]\n", "sub_path": "src/domain/repositories.py", "file_name": "repositories.py", "file_ext": "py", "file_size_in_byte": 908, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "abc.ABC", "line_number": 12, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 23, "usage_type": "name"}, {"api_name": "abc.ABC", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "183238103", "text": "import pyfiglet \r\nfrom pyfiglet import Figlet\r\nfrom termcolor import colored, cprint\r\nimport music\r\nimport movies \r\nimport time\r\n\r\ndef ascii_title():\r\n ascii_format = Figlet(font='trek',justify=\"center\")\r\n ascii_title = ascii_format.renderText('Savender')\r\n print(\"\\n\")\r\n print(colored(ascii_title, 'red'))\r\n\r\ndef menu():\r\n menu = \"1. Get Music Recs!\"\r\n x = menu.center(65)\r\n print(colored(x, 'green'))\r\n menu = \"2. Get Movie Recs!\"\r\n x = menu.center(65)\r\n print(colored(x, 'green'))\r\n menu = \"3. Terminate Program\"\r\n x = menu.center(65)\r\n print(colored(x, 'green'))\r\n\r\ndef menu_loop():\r\n ascii_title()\r\n print(\"\\n\")\r\n menu()\r\n\r\ndef movie_recs():\r\n movies.main()\r\n\r\ndef music_recs():\r\n music.main()\r\ndef main():\r\n ascii_title()\r\n print(\"Welcome!\\n\".center(65))\r\n time.sleep(0.8)\r\n menu()\r\n userInput = 0\r\n while(userInput != 3):\r\n userInput = int(input(colored(\"\\nEnter a number to choose: \", 'green')))\r\n if (userInput == 1):\r\n music_recs()\r\n menu_loop()\r\n elif (userInput == 2):\r\n movie_recs()\r\n menu_loop()\r\n elif (userInput == 3):\r\n print(colored(\"\\nGoodbye!\".center(65), 'green'))\r\n break\r\n else:\r\n print(\"Enter a valid number or choice!\")\r\n menu_loop()\r\nif __name__ == '__main__':\r\n main() \r\n\r\n", "sub_path": "src/savender.py", "file_name": "savender.py", "file_ext": "py", "file_size_in_byte": 1402, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pyfiglet.Figlet", "line_number": 9, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 12, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 17, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 20, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 23, "usage_type": "call"}, {"api_name": "movies.main", "line_number": 31, "usage_type": "call"}, {"api_name": "music.main", "line_number": 34, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 42, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "635890842", "text": "import numpy as np\nfrom keras.models import Model\nfrom keras.layers import Input, LSTM, Dense\n\n# import os\n# os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\n\nfrom keras import backend as K\n#with K.tf.device('/gpu:0'):\ngpu_options = K.tf.GPUOptions(per_process_gpu_memory_fraction = 0.3, visible_device_list = \"0,1\");\nsess = K.tf.Session(config=K.tf.ConfigProto(gpu_options=gpu_options))\nK.set_session(sess)\n\n\n\nbatch_size = 64\nepochs = 5\nlatent_dim = 256\nnum_samples = 10000\n\ninput_texts = []\ntarget_texts = []\ninput_chars = {}\nrev_input_chars = {}\nnum_encoder_tokens = 0\nnum_decoder_tokens = 0\ntarget_chars = {}\nrev_target_chars = {}\nwith open(\"open_corpus\", \"r\", encoding='utf-8') as f:\n lines = f.readlines()\nfor line in lines:\n input_text, target_text = line.split('\\t')\n target_text = '\\t ' + target_text\n input_texts.append(input_text)\n target_texts.append(target_text)\n words_input=input_text.split(\" \")\n words_target = target_text.split(\" \")\n for word in words_input:\n if word not in input_chars:\n input_chars[word] = num_encoder_tokens\n rev_input_chars[num_encoder_tokens] = word\n num_encoder_tokens += 1\n for word in words_target:\n if word not in target_chars:\n target_chars[word] = num_decoder_tokens\n rev_target_chars[num_decoder_tokens] = word\n num_decoder_tokens += 1\n\n# Define max input length and output length\n\nmax_encoder_seq_length = max([len(txt) for txt in input_texts])\nmax_decoder_seq_length = max([len(txt) for txt in target_texts])\n\n#print(num_encoder_tokens, num_decoder_tokens, max_encoder_seq_length, max_decoder_seq_length)\n\n# Define a 3D array with one hot representation of every sentence\nenc_input_data = np.zeros((num_samples, max_encoder_seq_length, num_encoder_tokens), dtype='float32')\n\ndec_input_data = np.zeros((num_samples, max_decoder_seq_length, num_decoder_tokens), dtype='float32')\n\ndec_target_data = np.zeros((num_samples, max_decoder_seq_length, num_decoder_tokens), dtype='float32')\n\n# Filling my enc_input_data and dec_data one hot representation\n\n\n\n# Defining the encoder portion\n\nencoder_inputs = Input(shape=(None, num_encoder_tokens))\nencoder_lstm = LSTM(latent_dim, return_state=True)\nencoder_out, state_h, state_c = encoder_lstm(encoder_inputs)\nencoder_states = [state_h, state_c]\n\n# define the decoder part\ndecoder_inputs = Input(shape=(None, num_decoder_tokens))\ndecoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)\ndecoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)\ndecoder_dense = Dense(num_decoder_tokens, activation='softmax')\ndecoder_outputs = decoder_dense(decoder_outputs)\n\n# Define the model\nmodel = Model([encoder_inputs, decoder_inputs], decoder_outputs)\n\n# Training\nmodel.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n\nnum_batches = len(lines)/num_samples\n\nfor s in range(0,9):#int(num_batches)-1):\n enc_input_data = np.zeros((num_samples, max_encoder_seq_length, num_encoder_tokens), dtype='float32')\n\n dec_input_data = np.zeros((num_samples, max_decoder_seq_length, num_decoder_tokens), dtype='float32')\n\n dec_target_data = np.zeros((num_samples, max_decoder_seq_length, num_decoder_tokens), dtype='float32')\n\n for i, (input_text, target_text) in enumerate(zip(input_texts[s*num_samples:s*num_samples+num_samples], target_texts[s*num_samples:s*num_samples+num_samples])):\n for t, char in enumerate(input_text):\n enc_input_data[i, t, input_chars[char]] = 1.0\n for t, char in enumerate(target_text):\n dec_input_data[i, t, target_chars[char]] = 1.0\n if t > 0:\n dec_target_data[i, t - 1, target_chars[char]] = 1.\n model.fit([enc_input_data, dec_input_data], dec_target_data, batch_size=batch_size, epochs=epochs, validation_split=0.2)\n\n#model.save('fr_eng_model.model')\n\n# Inference\n\nencoder_model = Model(encoder_inputs, encoder_states)\n\ndecoder_state_h = Input(shape=(latent_dim,))\ndecoder_state_c = Input(shape=(latent_dim,))\ndecoder_states_input = [decoder_state_h, decoder_state_c]\n\ndecoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_states_input)\ndecoder_states = [state_h, state_c]\n\ndecoder_outputs = decoder_dense(decoder_outputs)\n\ndecoder_model = Model([decoder_inputs] + decoder_states_input, [decoder_outputs] + decoder_states)\n\n\ndef decode_seq(input_seq):\n enc_states_values = encoder_model.predict(input_seq)\n\n target_seq = np.zeros((1, 1, num_decoder_tokens))\n\n target_seq[0, 0, target_chars['\\t']] = 1.\n\n stop_cond = False\n\n decoded_sentence = ''\n\n while not stop_cond:\n output_tokens, h, c = decoder_model.predict([target_seq] + enc_states_values)\n\n # Sample a token ??\n sampled_token_ind = np.argmax(output_tokens[0, -1, :])\n sampled_char = rev_target_chars[sampled_token_ind]\n decoded_sentence += sampled_char\n\n if sampled_char == '\\n' or len(decoded_sentence) > max_decoder_seq_length:\n stop_cond = True\n\n target_seq = np.zeros((1, 1, num_decoder_tokens))\n target_seq[0, 0, sampled_token_ind] = 1.\n\n enc_states_values = [h, c]\n\n return decoded_sentence\n\n\nfor seq_index in range(5000,6000):\n input_seq = enc_input_data[seq_index:seq_index + 1]\n decoded_sentence = decode_seq(input_seq)\n print('-')\n print('Input Sentence: ', input_texts[seq_index])\n print('Decoded Sentence: ', decoded_sentence)\n", "sub_path": "word_model.py", "file_name": "word_model.py", "file_ext": "py", "file_size_in_byte": 5519, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "keras.backend.tf.GPUOptions", "line_number": 11, "usage_type": "call"}, {"api_name": "keras.backend.tf", "line_number": 11, "usage_type": "attribute"}, {"api_name": "keras.backend", "line_number": 11, "usage_type": "name"}, {"api_name": "keras.backend.tf.Session", "line_number": 12, "usage_type": "call"}, {"api_name": "keras.backend.tf", "line_number": 12, "usage_type": "attribute"}, {"api_name": "keras.backend", "line_number": 12, "usage_type": "name"}, {"api_name": "keras.backend.tf.ConfigProto", "line_number": 12, "usage_type": "call"}, {"api_name": "keras.backend.set_session", "line_number": 13, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 95, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 110, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 112, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 146, "usage_type": "call"}]} +{"seq_id": "413076604", "text": "import os\nimport datetime\nimport json\nfrom functools import wraps # 登录装饰器\n\nfrom werkzeug.utils import secure_filename\nfrom . import home\nfrom flask import render_template, redirect, url_for, flash, session, request, jsonify\nfrom werkzeug.security import generate_password_hash\nfrom decimal import Decimal, ROUND_CEILING\n\nfrom app import db, config\n\nfrom home.forms import RegisterForm, LoginForm, UserForm, PasswordForm\n\nfrom app.models import User, AdminUser, Userlog, Product, Tag, Order, OrderInfo, Cart, CartInfo, Comment\n\n\nALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'} # 上传通过检查\n\n\"\"\"\n 工具类型函数\n\"\"\"\n\n\ndef allowed_file(filename):\n \"\"\"\n 上传通过检查\n \"\"\"\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n\ndef change_name(filename):\n \"\"\"\n 修改文件名称\n \"\"\"\n file_info = os.path.splitext(filename)\n filename = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\") + file_info[-1]\n return filename\n\n\n\"\"\"\n 用户逻辑函数\n\"\"\"\n\n\ndef user_login_dec(f):\n \"\"\"\n 登录装饰器\n 已登录就可以访问，否则重定向至登录，next页面为当前请求的url\n \"\"\"\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if \"user\" not in session:\n return redirect(url_for('home.login', next=request.url))\n return f(*args, **kwargs)\n return decorated_function\n\n\n@home.route(\"/login/\", methods=[\"GET\", \"POST\"])\ndef login():\n \"\"\"\n 登录视图\n :return: login.html\n \"\"\"\n if request.method == 'GET': # get请求\n form = LoginForm()\n return render_template('home/login.html', form=form)\n else: # post请求\n form = LoginForm(request.form)\n if form.validate_on_submit():\n data = form.data\n user = User.query.filter_by(name=data['name']).first()\n if user:\n if not user.check_password(data['password']):\n flash(\"密码错误\", 'err')\n return redirect(url_for('home.login'))\n else:\n flash(\"输入的账户不存在\", 'err')\n return redirect(url_for('home.login'))\n\n session['user'] = user.name\n session['user_id'] = user.id\n userlog = Userlog(\n user_id=user.id\n )\n db.session.add(userlog)\n db.session.commit()\n return redirect(url_for('home.user'))\n return render_template('home/login.html', form=form)\n\n\n@home.route('/pay/', methods=['GET', 'POST'])\ndef pay():\n order_id = request.form['order_id']\n order_sub_total = request.form['order_sub_total']\n order = Order.query.filter(Order.id == order_id).first()\n order.status = 1\n db.session.commit()\n orderinfos = OrderInfo.query.filter(OrderInfo.order_id == order_id).all()\n for orderinfo in orderinfos:\n product = Product.query.filter(Product.id == orderinfo.product_id).first()\n product.stock = product.stock - orderinfo.quantity\n product.sell = product.sell + orderinfo.quantity\n db.session.commit()\n return render_template('home/pay.html', order_id=order_id, order_subTotal=order_sub_total)\n\n\n@home.route('/pay_to/', methods=['GET', 'POST'])\ndef pay_to():\n order_id = request.form['order_id']\n order_subTotal = request.form['order_subTotal']\n order = Order.query.filter(Order.id == order_id).first()\n order.status = 1\n order.add_time = datetime.datetime.now()\n db.session.commit()\n orderinfos = OrderInfo.query.filter(OrderInfo.order_id == order_id).all()\n for orderinfo in orderinfos:\n product = Product.query.filter(Product.id == orderinfo.product_id).first()\n product.stock = product.stock - orderinfo.quantity\n product.sell = product.sell + orderinfo.quantity\n db.session.commit()\n return render_template('home/pay.html', order_id=order_id, order_subTotal=order_subTotal)\n\n\n@home.route(\"/register/\", methods=[\"GET\", \"POST\"])\ndef register():\n \"\"\"\n 注册视图\n :return: register.html\n \"\"\"\n form = RegisterForm()\n if form.validate_on_submit():\n data = form.data\n user = User(\n name=data[\"name\"],\n email=data[\"email\"],\n phone=data[\"phone\"],\n password=generate_password_hash(data[\"password\"])\n )\n db.session.add(user)\n db.session.commit()\n # 数据库事务回滚\n # except\n # db.session.rollback()\n\n flash(\"你已经成功注册\", \"ok\")\n\n return render_template(\"home/register.html\", form=form)\n\n\n@home.route(\"/user/\", methods=[\"GET\", \"POST\"])\n@user_login_dec\ndef user():\n \"\"\"\n 用户信息中心视图\n :return: user.html\n \"\"\"\n form = UserForm()\n user = User.query.get(int(session['user_id']))\n form.face.validators = []\n if request.method == \"GET\":\n form.name.data = user.name\n form.email.data = user.email\n form.phone.data = user.phone\n form.card.data = user.card\n form.info.data = user.info\n form.address.data = user.address\n form.location.data = user.location\n if form.validate_on_submit():\n data = form.data\n if form.face.data != \"\":\n file_face = secure_filename(form.face.data.filename)\n if not os.path.exists(config.FACE_FOLDER):\n os.makedirs(config.FACE_FOLDER)\n os.chmod(config.FACE_FOLDER)\n user.face = change_name(file_face)\n form.face.data.save(config.FACE_FOLDER + user.face)\n\n name_count = User.query.filter_by(name=data['name']).count()\n if data['name'] != user.name and name_count == 1:\n flash(\"用户名已经存在\", 'err')\n return redirect(url_for('home.user'))\n\n email_count = User.query.filter_by(email=data['email']).count()\n if data['email'] != user.email and email_count == 1:\n flash(\"邮箱已经存在\", 'err')\n return redirect(url_for('home.user'))\n\n phone_count = User.query.filter_by(phone=data['phone']).count()\n if data['phone'] != user.phone and phone_count == 1:\n flash(\"手机号码已经存在\", 'err')\n return redirect(url_for('home.user'))\n\n card_count = User.query.filter_by(card=data['card']).count()\n if data['card'] != user.card and card_count == 1:\n flash(\"银行卡号码已经存在\", 'err')\n return redirect(url_for('home.user'))\n\n user.name = data['name']\n user.email = data['email']\n user.phone = data['phone']\n user.card = data['card']\n user.info = data['info']\n user.address = data['address']\n user.location = data['location']\n\n db.session.add(user)\n try:\n db.session.commit()\n except:\n db.session.rollback()\n\n flash(\"你的信息已经修改成功\", 'ok')\n return redirect(url_for('home.user'))\n\n return render_template(\"home/user.html\", form=form, user=user)\n\n\n@home.route(\"/pwd/\", methods=[\"GET\", \"POST\"])\n@user_login_dec\ndef pwd():\n \"\"\"\n 修改密码视图\n :return: pwd.html\n \"\"\"\n form = PasswordForm()\n if form.validate_on_submit():\n data = form.data\n user = User.query.filter_by(name=session[\"user\"]).first()\n if not user.check_password(data[\"old_password\"]):\n flash(\"旧密码错误\", \"error\")\n return redirect(url_for('home.pwd'))\n user.pwd = generate_password_hash(data[\"new_password\"])\n db.session.add(user)\n db.session.commit()\n return redirect(url_for('home.login'))\n return render_template(\"home/pwd.html\", form=form)\n\n\n@home.route(\"/logout/\")\ndef logout():\n \"\"\"\n 退出登录视图\n :return: redirect to login\n \"\"\"\n session.pop(\"user\", None)\n session.pop(\"user_id\", None)\n\n return redirect(url_for('home.login'))\n\n\n\"\"\"\n 商品逻辑函数\n\"\"\"\n\n\n@home.route(\"/\", methods=['GET'])\ndef index():\n \"\"\"\n 商品首页视图\n \"\"\"\n tags = Tag.query.all()\n total = Product.query.all()\n total = len(total)\n discounts = Product.query.filter(Product.discount < 10).all()\n for discount in discounts:\n # discount.sale_price = discount.price * discount.discount * 0.1\n discount.true_price = Decimal(discount.price * discount.discount * 0.1).quantize(Decimal('0.00'), ROUND_CEILING)\n # db.session.commit()\n one_dollers = Product.query.filter(Product.isKilled is True).all()\n return render_template(\"home/index.html\", tags=tags, total=total, discounts=discounts, one_dollers=one_dollers)\n\n\n@home.route(\"/news/\", methods=['GET'])\ndef new_product():\n \"\"\"\n 新品视图\n \"\"\"\n page = request.args.get(\"page\", 1, type=int)\n page_data = Product.query.order_by(Product.add_time.desc())\n page_data = page_data.paginate(page=page, per_page=8, error_out=False)\n news = page_data.items\n return render_template(\"home/news.html\", news=news, page_data=page_data, page=page)\n\n\n@home.route(\"/all_product/\", methods=['GET'])\ndef all_product():\n page = request.args.get(\"page\", 1, type=int)\n page_data = Product.query\n page_data = page_data.paginate(page=page, per_page=8, error_out=False)\n products = page_data.items\n return render_template(\"home/all_product.html\", products=products, page_data=page_data, page=page)\n\n\n@home.route('/guess_like/', methods=['GET'])\ndef guess_like():\n \"\"\"\n 商品推荐视图\n \"\"\"\n page = request.args.get('page', 1, type=int)\n if \"user\" not in session:\n page_data = Product.query.order_by(Product.sell.desc())\n page_data = page_data.paginate(page=page, per_page=8, error_out=False)\n likes = page_data.items\n else:\n user = User.query.filter(User.id == session.get('user_id')).first()\n orders = Order.query.filter(Order.user_id == user.id).all()\n for order in orders:\n orderinfos = OrderInfo.query.filter(OrderInfo.order_id == order.id).all()\n for orderinfo in orderinfos:\n product_count = Product.query.filter(Product.id == orderinfo.product_id).count()\n\n\n@home.route(\"/hot_sale/\", methods=['GET'])\ndef hot_sale():\n \"\"\"\n 热销商品视图\n \"\"\"\n # hots = Product.query.order_by('sell desc').all()\n page = request.args.get(\"page\", 1, type=int)\n page_data = Product.query.order_by(Product.sell.desc())\n page_data = page_data.paginate(page=page, per_page=8, error_out=False)\n hots = page_data.items\n # print(hots)\n return render_template(\"home/hot_sale.html\", hots=hots, page_data=page_data, page=page)\n\n\n@home.route('/detail//')\ndef detail(product_id):\n \"\"\"\n 正常状态下商品详情视图\n \"\"\"\n comments_num = Comment.query.join(Product).filter(Product.id == product_id).count()\n product_model = Product.query.filter(Product.id == product_id).first()\n return render_template('home/detail.html', product=product_model, comments_num=comments_num)\n\n\n@home.route('/detail_onsale//')\ndef detail_onsale(product_id):\n \"\"\"\n 活动状态下商品详情视图\n \"\"\"\n comments = Comment.query.join(Product).filter(Product.id == product_id).all()\n users = User.query.all()\n comments_num = Comment.query.join(Product).filter(Product.id == product_id).count()\n product_model = Product.query.filter(Product.id == product_id).first()\n return render_template('home/detail_onsale.html', product=product_model, comments_num=comments_num,\n comments=comments, users=users)\n\n\n@home.route('/category//', methods=['GET'])\ndef category(tag_id):\n \"\"\"\n 商品分类视图\n \"\"\"\n tag = Tag.query.filter(Tag.id == tag_id).first()\n product_cate = Product.query.join(Tag).filter(Tag.id == tag_id).all()\n return render_template('home/category.html', product_cate=product_cate, tag=tag)\n\n\n@home.route('/search//')\ndef search(page=None):\n \"\"\"\n 搜索界面\n \"\"\"\n if page is None:\n page = 1\n key = request.args.get('key', '')\n product_count = Product.query.filter(Product.name.ilike('%' + key + '%')).count()\n page_data = Product.query.filter(Product.name.ilike('%' + key + '%')). \\\n order_by(Product.add_time.desc()).paginate(page=page, per_page=10)\n page_data.key = key\n return render_template('home/search.html', product_count=product_count, key=key, page_data=page_data)\n\n\n@home.route('/checkout/')\ndef checkout():\n \"\"\"\n 购物车\n \"\"\"\n return render_template('home/checkout.html')\n\n\n@home.route('/cart/', methods=['POST'])\ndef cart():\n if \"user\" not in session:\n flash(\"请登录后再试\")\n else:\n user = User.query.filter(User.id == session.get('user_id')).first()\n cart = Cart.query.filter(Cart.user_id == user.id).first()\n if cart is None:\n cart = Cart(user_id=user.id)\n db.session.add(cart)\n db.session.commit()\n # 得到传来的json数据\n data = json.loads(request.form.get('data'))\n # 得到商品的名称\n name = data[\"itemName\"]\n # 得到商品实际付的价格\n price = float(data[\"itemPrice\"])\n # 得到商品的数量\n quantity = int(data[\"itemQty\"])\n # 得到商品的总价\n total = data['itemTotal'].strip('￥')\n cart_infos = dict()\n cart_infos['name'] = name\n cart_infos['price'] = price\n cart_infos['quality'] = quantity\n cart_infos['total'] = total\n product = Product.query.filter(Product.name == name).first()\n cart_info = CartInfo(quantity=quantity, product_name=name, cart_id=cart.id,\n product_id=product.id, product_price=price, total=total)\n db.session.add(cart_info)\n\n try:\n db.session.commit()\n except:\n db.session.rollback()\n\n else:\n # 得到传来的json数据\n data = json.loads(request.form.get('data'))\n # 得到商品的名称\n name = data[\"itemName\"]\n # 得到商品实际付的价格\n price = float(data[\"itemPrice\"])\n # 得到商品的数量\n quantity = int(data[\"itemQty\"])\n # 得到商品的总价\n total = data['itemTotal'].strip('￥')\n cart_infos = dict()\n cart_infos['name'] = name\n cart_infos['price'] = price\n cart_infos['quality'] = quantity\n cart_infos['total'] = total\n # 得到当前session中登录的用户\n product = Product.query.filter(Product.name == name).first()\n cart_info = CartInfo(quantity=quantity, product_name=name, cart_id=cart.id,\n product_id=product.id, product_price=price, total=total)\n db.session.add(cart_info)\n try:\n db.session.commit()\n except:\n db.session.rollback()\n\n return jsonify(cart_infos)\n\n\n@home.route('/order/', methods=['GET', 'POST'])\ndef order():\n # 得到当前访问的订单所属用户\n user = User.query.filter(User.id == session.get('user_id')).first()\n order = Order.query.filter(Order.user_id == user.id).first()\n\n order = Order(user_id=user.id, subTotal=0)\n db.session.add(order)\n db.session.commit()\n # cartinfos = CartInfo.query.join(Cart).filter(Cart.user_id == user.id).all()\n cartinfos = CartInfo.query.join(Cart).filter(Cart.user_id == session.get('user_id')).all()\n orderinfos = list()\n\n for cartinfo in cartinfos:\n\n orderinfo = OrderInfo(quantity=cartinfo.quantity, product_name=cartinfo.product_name, order_id=order.id,\n product_id=cartinfo.product_id, product_price=cartinfo.product_price, total=\n cartinfo.total)\n db.session.add(orderinfo)\n db.session.delete(cartinfo)\n # db.session.commit()\n # try:\n # db.session.commit()\n # except:\n # db.session.rollback()\n order.subTotal = order.subTotal + orderinfo.total\n\n\n cart = Cart.query.filter(Cart.user_id == session.get('user_id')).first()\n\n orderinfos = OrderInfo.query.join(Order).filter(Order.id == order.id).all()\n\n # db.session.delete(cart)\n db.session.commit()\n # orderinfos = OrderInfo.query.join(Order).filter(Order.user_id == user.id).all()\n # orderinfos = OrderInfo.query.join(Order).filter(Order.id == order.id).all()\n\n # else:\n #\n # cartinfos = CartInfo.query.join(Cart).filter(Cart.user_id == user.id).all()\n # for cartinfo in cartinfos:\n # orderinfo = OrderInfo(quantity=cartinfo.quantity, product_name=cartinfo.product_name, order_id=order.id,\n # product_id=cartinfo.product_id, product_price=cartinfo.product_price,\n # total=cartinfo.total)\n # db.session.add(orderinfo)\n # db.session.delete(cartinfo)\n # try:\n # db.session.commit()\n # except:\n # db.session.rollback()\n # order.subTotal = order.subTotal + orderinfo.total\n #\n # cart = Cart.query.filter(Cart.user_id == user.id).first()\n #\n # db.session.delete(cart)\n # db.session.commit()\n #\n # orderinfos = OrderInfo.query.join(Order).filter(Order.user_id == user.id).all()\n return render_template('home/order.html', user=user, order=order, orderinfos=orderinfos)\n\n\n@home.route('/order_manage/', methods=['GET', 'POST'])\ndef order_manage():\n # 得到当前用户的信息\n user = User.query.filter(User.id == session.get('user_id')).first()\n # 得到所有订单的list\n orders = Order.query.filter(Order.user_id == user.id).order_by(Order.add_time.desc()).all()\n order_infos = OrderInfo.query.all()\n products = Product.query.all()\n # 得到未付款订单的list\n order_nopays = Order.query.filter(Order.user_id == user.id, Order.status == 0).order_by(Order.add_time.desc()).all()\n # 得到已付款订单的list\n order_haspays = Order.query.filter(Order.user_id == user.id, Order.status == 1).order_by(Order.add_time.desc()).all()\n return render_template('home/ordermanage.html', orders=orders, order_infos=order_infos, products=products\n , order_nopays=order_nopays, order_haspays=order_haspays)\n", "sub_path": "app/home/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 18544, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.splitext", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.request.url", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 66, "usage_type": "name"}, {"api_name": "home.forms.LoginForm", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 68, "usage_type": "call"}, {"api_name": "home.forms.LoginForm", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 70, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 70, "usage_type": "name"}, {"api_name": "app.models.User.query.filter_by", "line_number": 73, "usage_type": "call"}, {"api_name": "app.models.User.query", "line_number": 73, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 83, "usage_type": "name"}, {"api_name": "app.models.Userlog", "line_number": 84, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 87, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 87, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 87, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 88, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 88, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 88, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 90, "usage_type": "call"}, {"api_name": "home.forms.route", "line_number": 60, "usage_type": "call"}, {"api_name": "home.forms", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 95, "usage_type": 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{"api_name": "app.models.Product", "line_number": 335, "usage_type": "name"}, {"api_name": "app.models.Product.id", "line_number": 335, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 336, "usage_type": "call"}, {"api_name": "home.forms.route", "line_number": 329, "usage_type": "call"}, {"api_name": "home.forms", "line_number": 329, "usage_type": "name"}, {"api_name": "app.models.Comment.query.join", "line_number": 344, "usage_type": "call"}, {"api_name": "app.models.Product", "line_number": 344, "usage_type": "argument"}, {"api_name": "app.models.Comment.query", "line_number": 344, "usage_type": "attribute"}, {"api_name": "app.models.Comment", "line_number": 344, "usage_type": "name"}, {"api_name": "app.models.Product.id", "line_number": 344, "usage_type": "attribute"}, {"api_name": "app.models.User.query.all", "line_number": 345, "usage_type": "call"}, {"api_name": "app.models.User.query", "line_number": 345, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 345, "usage_type": "name"}, {"api_name": "app.models.Comment.query.join", "line_number": 346, "usage_type": "call"}, {"api_name": "app.models.Product", "line_number": 346, "usage_type": "argument"}, {"api_name": "app.models.Comment.query", "line_number": 346, "usage_type": "attribute"}, {"api_name": "app.models.Comment", "line_number": 346, "usage_type": "name"}, {"api_name": "app.models.Product.id", "line_number": 346, "usage_type": "attribute"}, {"api_name": "app.models.Product.query.filter", "line_number": 347, "usage_type": "call"}, {"api_name": "app.models.Product.query", "line_number": 347, "usage_type": "attribute"}, {"api_name": "app.models.Product", "line_number": 347, "usage_type": "name"}, {"api_name": "app.models.Product.id", "line_number": 347, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 348, "usage_type": "call"}, {"api_name": "home.forms.route", "line_number": 339, "usage_type": "call"}, {"api_name": "home.forms", "line_number": 339, "usage_type": "name"}, {"api_name": "app.models.Tag.query.filter", "line_number": 357, "usage_type": "call"}, {"api_name": "app.models.Tag.query", "line_number": 357, "usage_type": "attribute"}, {"api_name": "app.models.Tag", "line_number": 357, "usage_type": "name"}, {"api_name": "app.models.Tag.id", "line_number": 357, "usage_type": "attribute"}, {"api_name": "app.models.Product.query.join", "line_number": 358, "usage_type": "call"}, {"api_name": "app.models.Tag", "line_number": 358, "usage_type": "argument"}, {"api_name": "app.models.Product.query", "line_number": 358, "usage_type": "attribute"}, {"api_name": "app.models.Product", "line_number": 358, "usage_type": "name"}, {"api_name": "app.models.Tag.id", "line_number": 358, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 359, "usage_type": "call"}, {"api_name": "home.forms.route", "line_number": 352, "usage_type": "call"}, {"api_name": "home.forms", "line_number": 352, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 369, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 369, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 369, "usage_type": "name"}, {"api_name": "app.models.Product.query.filter", "line_number": 370, "usage_type": "call"}, {"api_name": "app.models.Product.query", "line_number": 370, "usage_type": "attribute"}, {"api_name": "app.models.Product", "line_number": 370, "usage_type": "name"}, {"api_name": "app.models.Product.name.ilike", "line_number": 370, "usage_type": "call"}, {"api_name": "app.models.Product.name", "line_number": 370, "usage_type": "attribute"}, {"api_name": "app.models.Product.query.filter", "line_number": 371, "usage_type": "call"}, {"api_name": "app.models.Product.query", "line_number": 371, "usage_type": "attribute"}, {"api_name": "app.models.Product", "line_number": 371, "usage_type": "name"}, {"api_name": "app.models.Product.name.ilike", "line_number": 371, "usage_type": "call"}, {"api_name": "app.models.Product.name", "line_number": 371, "usage_type": "attribute"}, {"api_name": "app.models.Product.add_time.desc", "line_number": 372, "usage_type": "call"}, {"api_name": "app.models.Product.add_time", "line_number": 372, "usage_type": "attribute"}, {"api_name": "app.models.Product", "line_number": 372, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 374, "usage_type": "call"}, {"api_name": "home.forms.route", "line_number": 362, "usage_type": "call"}, {"api_name": "home.forms", "line_number": 362, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 382, "usage_type": "call"}, {"api_name": "home.forms.route", "line_number": 377, "usage_type": "call"}, {"api_name": "home.forms", "line_number": 377, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 387, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 388, "usage_type": "call"}, {"api_name": "app.models.User.query.filter", "line_number": 390, "usage_type": "call"}, {"api_name": "app.models.User.query", "line_number": 390, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 390, "usage_type": "name"}, {"api_name": "app.models.User.id", "line_number": 390, "usage_type": "attribute"}, {"api_name": "flask.session.get", "line_number": 390, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 390, "usage_type": "name"}, {"api_name": "app.models.Cart.query.filter", "line_number": 391, "usage_type": "call"}, {"api_name": "app.models.Cart.query", "line_number": 391, "usage_type": "attribute"}, {"api_name": "app.models.Cart", "line_number": 391, "usage_type": "name"}, {"api_name": "app.models.Cart.user_id", "line_number": 391, "usage_type": "attribute"}, {"api_name": "app.models.Cart", "line_number": 393, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 394, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 394, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 394, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 395, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 395, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 395, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 397, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 397, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 397, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 397, "usage_type": "name"}, {"api_name": "app.models.Product.query.filter", "line_number": 411, "usage_type": "call"}, {"api_name": "app.models.Product.query", "line_number": 411, "usage_type": "attribute"}, {"api_name": "app.models.Product", "line_number": 411, "usage_type": "name"}, {"api_name": "app.models.Product.name", "line_number": 411, "usage_type": "attribute"}, {"api_name": "app.models.CartInfo", "line_number": 412, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 414, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 414, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 414, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 417, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 417, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 417, "usage_type": "name"}, {"api_name": "app.db.session.rollback", "line_number": 419, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 419, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 419, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 423, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 423, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 423, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 423, "usage_type": "name"}, {"api_name": "app.models.Product.query.filter", "line_number": 438, "usage_type": "call"}, {"api_name": "app.models.Product.query", "line_number": 438, "usage_type": "attribute"}, {"api_name": "app.models.Product", "line_number": 438, "usage_type": "name"}, {"api_name": "app.models.Product.name", "line_number": 438, "usage_type": "attribute"}, {"api_name": "app.models.CartInfo", "line_number": 439, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 441, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 441, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 441, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 443, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 443, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 443, "usage_type": "name"}, {"api_name": "app.db.session.rollback", "line_number": 445, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 445, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 445, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 447, "usage_type": "call"}, {"api_name": "home.forms.route", "line_number": 385, "usage_type": "call"}, {"api_name": "home.forms", "line_number": 385, "usage_type": "name"}, {"api_name": "app.models.User.query.filter", "line_number": 453, "usage_type": "call"}, {"api_name": "app.models.User.query", "line_number": 453, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 453, "usage_type": "name"}, {"api_name": "app.models.User.id", "line_number": 453, "usage_type": "attribute"}, {"api_name": "flask.session.get", "line_number": 453, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 453, "usage_type": "name"}, {"api_name": "app.models.Order.query.filter", "line_number": 454, "usage_type": "call"}, {"api_name": "app.models.Order.query", "line_number": 454, "usage_type": "attribute"}, {"api_name": "app.models.Order", "line_number": 454, "usage_type": "name"}, {"api_name": "app.models.Order.user_id", "line_number": 454, "usage_type": "attribute"}, {"api_name": "app.models.Order", "line_number": 456, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 457, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 457, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 457, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 458, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 458, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 458, "usage_type": "name"}, {"api_name": "app.models.CartInfo.query.join", "line_number": 460, "usage_type": "call"}, {"api_name": "app.models.Cart", "line_number": 460, "usage_type": "argument"}, {"api_name": "app.models.CartInfo.query", "line_number": 460, "usage_type": "attribute"}, {"api_name": "app.models.CartInfo", "line_number": 460, "usage_type": "name"}, {"api_name": "app.models.Cart.user_id", "line_number": 460, "usage_type": "attribute"}, {"api_name": "flask.session.get", "line_number": 460, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 460, "usage_type": "name"}, {"api_name": "app.models.OrderInfo", "line_number": 465, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 468, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 468, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 468, "usage_type": "name"}, {"api_name": "app.db.session.delete", "line_number": 469, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 469, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 469, "usage_type": "name"}, {"api_name": "app.models.Cart.query.filter", "line_number": 478, "usage_type": "call"}, {"api_name": "app.models.Cart.query", "line_number": 478, "usage_type": "attribute"}, {"api_name": "app.models.Cart", "line_number": 478, "usage_type": "name"}, {"api_name": "app.models.Cart.user_id", "line_number": 478, "usage_type": "attribute"}, {"api_name": "flask.session.get", "line_number": 478, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 478, "usage_type": "name"}, {"api_name": "app.models.OrderInfo.query.join", "line_number": 480, "usage_type": "call"}, {"api_name": "app.models.Order", "line_number": 480, "usage_type": "argument"}, {"api_name": "app.models.OrderInfo.query", "line_number": 480, "usage_type": "attribute"}, {"api_name": "app.models.OrderInfo", "line_number": 480, "usage_type": "name"}, {"api_name": "app.models.Order.id", "line_number": 480, "usage_type": "attribute"}, {"api_name": "app.db.session.commit", "line_number": 483, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 483, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 483, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 508, "usage_type": "call"}, {"api_name": "home.forms.route", "line_number": 450, "usage_type": "call"}, {"api_name": "home.forms", "line_number": 450, "usage_type": "name"}, {"api_name": "app.models.User.query.filter", "line_number": 514, "usage_type": "call"}, {"api_name": "app.models.User.query", "line_number": 514, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 514, "usage_type": "name"}, {"api_name": "app.models.User.id", "line_number": 514, "usage_type": "attribute"}, {"api_name": "flask.session.get", "line_number": 514, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 514, "usage_type": "name"}, {"api_name": "app.models.Order.query.filter", "line_number": 516, "usage_type": "call"}, {"api_name": "app.models.Order.query", "line_number": 516, "usage_type": "attribute"}, {"api_name": "app.models.Order", "line_number": 516, "usage_type": "name"}, {"api_name": "app.models.Order.user_id", "line_number": 516, "usage_type": "attribute"}, {"api_name": "app.models.Order.add_time.desc", "line_number": 516, "usage_type": "call"}, {"api_name": "app.models.Order.add_time", "line_number": 516, "usage_type": "attribute"}, {"api_name": "app.models.OrderInfo.query.all", "line_number": 517, "usage_type": "call"}, {"api_name": "app.models.OrderInfo.query", "line_number": 517, "usage_type": "attribute"}, {"api_name": "app.models.OrderInfo", "line_number": 517, "usage_type": "name"}, {"api_name": "app.models.Product.query.all", "line_number": 518, "usage_type": "call"}, {"api_name": "app.models.Product.query", "line_number": 518, "usage_type": "attribute"}, {"api_name": "app.models.Product", "line_number": 518, "usage_type": "name"}, {"api_name": "app.models.Order.query.filter", "line_number": 520, "usage_type": "call"}, {"api_name": "app.models.Order.query", "line_number": 520, "usage_type": "attribute"}, {"api_name": "app.models.Order", "line_number": 520, "usage_type": "name"}, {"api_name": "app.models.Order.user_id", "line_number": 520, "usage_type": "attribute"}, {"api_name": "app.models.Order.status", "line_number": 520, "usage_type": "attribute"}, {"api_name": "app.models.Order.add_time.desc", "line_number": 520, "usage_type": "call"}, {"api_name": "app.models.Order.add_time", "line_number": 520, "usage_type": "attribute"}, {"api_name": "app.models.Order.query.filter", "line_number": 522, "usage_type": "call"}, {"api_name": "app.models.Order.query", "line_number": 522, "usage_type": "attribute"}, {"api_name": "app.models.Order", "line_number": 522, "usage_type": "name"}, {"api_name": "app.models.Order.user_id", "line_number": 522, "usage_type": "attribute"}, {"api_name": "app.models.Order.status", "line_number": 522, "usage_type": "attribute"}, {"api_name": "app.models.Order.add_time.desc", "line_number": 522, "usage_type": "call"}, {"api_name": "app.models.Order.add_time", "line_number": 522, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 523, "usage_type": "call"}, {"api_name": "home.forms.route", "line_number": 511, "usage_type": "call"}, {"api_name": "home.forms", "line_number": 511, "usage_type": "name"}]} +{"seq_id": "103320067", "text": "import json\nimport random\n\nimport numpy as np\n\nimport question_engine as qeng\n\n\n### Utilities.py ###\ndef node_shallow_copy(node):\n new_node = {\n 'type': node['type'],\n 'inputs': node['inputs'],\n }\n if 'side_inputs' in node:\n new_node['side_inputs'] = node['side_inputs']\n return new_node\n\n\ndef precompute_filter_options(scene_struct, metadata):\n # Keys are tuples (size, color, shape, material) (where some may be None)\n # and values are lists of object idxs that match the filter criterion\n attribute_map = {}\n\n if metadata['dataset'] == 'CLEVR-v1.0':\n attr_keys = ['size', 'color', 'material', 'shape']\n else:\n assert False, 'Unrecognized dataset'\n\n # Precompute masks\n masks = []\n for i in range(2 ** len(attr_keys)):\n mask = []\n for j in range(len(attr_keys)):\n mask.append((i // (2 ** j)) % 2)\n masks.append(mask)\n\n for object_idx, obj in enumerate(scene_struct['objects']):\n if metadata['dataset'] == 'CLEVR-v1.0':\n keys = [tuple(obj[k] for k in attr_keys)]\n\n for mask in masks:\n for key in keys:\n masked_key = []\n for a, b in zip(key, mask):\n if b == 1:\n masked_key.append(a)\n else:\n masked_key.append(None)\n masked_key = tuple(masked_key)\n if masked_key not in attribute_map:\n attribute_map[masked_key] = set()\n attribute_map[masked_key].add(object_idx)\n\n scene_struct['_filter_options'] = attribute_map\n\n\ndef find_filter_options(object_idxs, scene_struct, metadata):\n # Keys are tuples (size, color, shape, material) (where some may be None)\n # and values are lists of object idxs that match the filter criterion\n\n if '_filter_options' not in scene_struct:\n precompute_filter_options(scene_struct, metadata)\n\n attribute_map = {}\n object_idxs = set(object_idxs)\n for k, vs in scene_struct['_filter_options'].items():\n attribute_map[k] = sorted(list(object_idxs & vs))\n return attribute_map\n\n\ndef find_relate_filter_options(object_idx, scene_struct, metadata,\n unique=False, include_zero=False, trivial_frac=0.1):\n options = {}\n if '_filter_options' not in scene_struct:\n precompute_filter_options(scene_struct, metadata)\n\n # TODO: Right now this is only looking for nontrivial combinations; in some\n # cases I may want to add trivial combinations, either where the intersection\n # is empty or where the intersection is equal to the filtering output.\n trivial_options = {}\n for relationship in scene_struct['relationships']:\n related = set(scene_struct['relationships'][relationship][object_idx])\n for filters, filtered in scene_struct['_filter_options'].items():\n intersection = related & filtered\n trivial = (intersection == filtered)\n if unique and len(intersection) != 1: continue\n if not include_zero and len(intersection) == 0: continue\n if trivial:\n trivial_options[(relationship, filters)] = sorted(list(intersection))\n else:\n options[(relationship, filters)] = sorted(list(intersection))\n\n N, f = len(options), trivial_frac\n num_trivial = int(round(N * f / (1 - f)))\n trivial_options = list(trivial_options.items())\n random.shuffle(trivial_options)\n for k, v in trivial_options[:num_trivial]:\n options[k] = v\n\n return options\n\n\ndef add_empty_filter_options(attribute_map, metadata, num_to_add):\n # Add some filtering criterion that do NOT correspond to objects\n\n if metadata['dataset'] == 'CLEVR-v1.0':\n attr_keys = ['Size', 'Color', 'Material', 'Shape']\n else:\n assert False, 'Unrecognized dataset'\n\n attr_vals = [metadata['types'][t] + [None] for t in attr_keys]\n if '_filter_options' in metadata:\n attr_vals = metadata['_filter_options']\n\n target_size = len(attribute_map) + num_to_add\n while len(attribute_map) < target_size:\n k = (random.choice(v) for v in attr_vals)\n if k not in attribute_map:\n attribute_map[k] = []\n\n\n### Utilities.py ###\n\n\n### Blender Utilities.py ###\ndef compute_all_relationships(scene_struct, eps=0.2):\n \"\"\"\n Computes relationships between all pairs of objects in the scene.\n\n Returns a dictionary mapping string relationship names to lists of lists of\n integers, where output[rel][i] gives a list of object indices that have the\n relationship rel with object i. For example if j is in output['left'][i] then\n object j is left of object i.\n \"\"\"\n all_relationships = {}\n for name, direction_vec in scene_struct['directions'].items():\n if name == 'above' or name == 'below': continue\n all_relationships[name] = []\n for i, obj1 in enumerate(scene_struct['objects']):\n coords1 = obj1['3d_coords']\n related = set()\n for j, obj2 in enumerate(scene_struct['objects']):\n if obj1 == obj2: continue\n coords2 = obj2['3d_coords']\n diff = [coords2[k] - coords1[k] for k in [0, 1, 2]]\n dot = sum(diff[k] * direction_vec[k] for k in [0, 1, 2])\n if dot > eps:\n related.add(j)\n all_relationships[name].append(sorted(list(related)))\n return all_relationships\n\n\n# def add_random_objects(current_item, scene_struct, args, camera, old_behaviour=False):\n# positions = []\n# objects = []\n# blender_objects = []\n# for i in range(args.num_objects[current_item]):\n# x = args.object_properties[str(current_item)][i]['x']\n# y = args.object_properties[str(current_item)][i]['y']\n# # Check to make sure the new object is further than min_dist from all\n# # other objects, and further than margin along the four cardinal directions\n# dists_good = True\n# margins_good = True\n# for (xx, yy, rr) in positions:\n# dx, dy = x - xx, y - yy\n# dist = math.sqrt(dx * dx + dy * dy)\n# if dist - r - rr < args.min_dist:\n# dists_good = False\n# break\n# for direction_name in ['left', 'right', 'front', 'behind']:\n# direction_vec = scene_struct['directions'][direction_name]\n# assert direction_vec[2] == 0\n# margin = dx * direction_vec[0] + dy * direction_vec[1]\n# if 0 < margin < args.margin:\n# margins_good = False\n# break\n# if not margins_good:\n# break\n#\n# if not dists_good or not margins_good:\n# print(\"[DEBUG] Failed for Object, \", x, \" \", y, \"\\n\")\n# return None, None\n# return\n### Blender Utilities.py ###\n\ndef instantiate_templates_dfs(scene_struct, program, metadata):\n q = {'nodes': program}\n outputs = qeng.answer_question(q, metadata, scene_struct, all_outputs=True)\n answer = outputs[-1]\n if answer == '__INVALID__':\n return answer\n return answer\n\n\nwith open('./metadata.json', 'r') as f:\n metadata = json.load(f)\n dataset = metadata['dataset']\n functions_by_name = {}\n for f in metadata['functions']:\n functions_by_name[f['name']] = f\n metadata['_functions_by_name'] = functions_by_name\n\n\ndef get_scene_struct_(scale):\n return dict({'split': 'Rendered', 'directions': {'below': [-0.0, -0.0, -1.0],\n 'front': [0.754490315914154, -0.6563112735748291, -0.0],\n 'above': [0.0, 0.0, 1.0],\n 'right': [0.6563112735748291, 0.7544902563095093, -0.0],\n 'behind': [-0.754490315914154, 0.6563112735748291, 0.0],\n 'left': [-0.6563112735748291, -0.7544902563095093, 0.0]},\n \"objects\": [\n {\n \"3d_coords\": [\n -1.4927695989608765 + np.random.normal(scale=scale),\n -2.0407912731170654 + np.random.normal(scale=scale),\n 0.699999988079071\n ],\n \"shape\": \"cylinder\",\n \"rotation\": 0.7320286457359669,\n \"size\": \"large\",\n \"color\": \"brown\",\n \"pixel_coords\": [\n 117,\n 135,\n 10.785210609436035\n ],\n \"material\": \"rubber\"\n },\n {\n \"3d_coords\": [\n 1.5566600561141968 + np.random.normal(scale=scale),\n -2.1519246101379395 + np.random.normal(scale=scale),\n 0.699999988079071\n ],\n \"shape\": \"cube\",\n \"rotation\": 0.7320286457359669,\n \"size\": \"large\",\n \"color\": \"gray\",\n \"pixel_coords\": [\n 203,\n 197,\n 8.6880521774292\n ],\n \"material\": \"rubber\"\n },\n {\n \"3d_coords\": [\n -2.341233015060425 + np.random.normal(scale=scale),\n -0.5676895380020142 + np.random.normal(scale=scale),\n 0.3499999940395355\n ],\n \"shape\": \"cylinder\",\n \"rotation\": 0.38202865169643135,\n \"size\": \"small\",\n \"color\": \"green\",\n \"pixel_coords\": [\n 157,\n 118,\n 12.36081600189209\n ],\n \"material\": \"rubber\"\n },\n {\n \"3d_coords\": [\n -0.8063592314720154 + np.random.normal(scale=scale),\n 1.8669357299804688 + np.random.normal(scale=scale),\n 0.699999988079071\n ],\n \"shape\": \"sphere\",\n \"rotation\": 0.7320286457359669,\n \"size\": \"large\",\n \"color\": \"purple\",\n \"pixel_coords\": [\n 277,\n 98,\n 12.562734603881836\n ],\n \"material\": \"metal\"\n },\n {\n \"3d_coords\": [\n 2.677332878112793 + np.random.normal(scale=scale),\n -0.01264934055507183 + np.random.normal(scale=scale),\n 0.3499999940395355\n ],\n \"shape\": \"cube\",\n \"rotation\": 0.38202865169643135,\n \"size\": \"small\",\n \"color\": \"gray\",\n \"pixel_coords\": [\n 338,\n 198,\n 9.331548690795898\n ],\n \"material\": \"metal\"\n }\n ],\n 'image_filename': 'CLEVR_Rendered_000000.png', 'image_index': 0})\n\n\nMAGNITUDES_UNDER_TEST = [0.0001, 0.01, 0.1, 0.5, 0.8, 1]\nmagnitude_fails = {k: 0 for k in MAGNITUDES_UNDER_TEST}\n\nwith open('../questions/CLEVR_Rendered_questions.json', 'r') as fin:\n questions = json.load(fin)\n questions = questions['questions']\n\nfor trial in questions[:80]:\n program = []\n dirty_program = trial['program']\n for dirty_entry in dirty_program:\n if dirty_entry['type'] == 'scene':\n pass\n else:\n dirty_entry.pop('_output')\n program.append(dirty_entry)\n answer = str(trial['answer']).lower()\n qq = trial['question']\n\n for magnitude in MAGNITUDES_UNDER_TEST:\n for i in range(0, 1000):\n scene_struct_ = get_scene_struct_(magnitude)\n scene_struct_['relationships'] = compute_all_relationships(scene_struct_)\n answer_ = instantiate_templates_dfs(scene_struct=scene_struct_, program=program, metadata=metadata)\n program = []\n dirty_program = trial['program']\n for dirty_entry in dirty_program:\n if dirty_entry['type'] == 'scene':\n pass\n else:\n try:\n dirty_entry.pop('_output')\n except KeyError:\n pass\n program.append(dirty_entry)\n\n if answer != str(answer_).lower():\n magnitude_fails[magnitude] += (1 / (len(questions[:80]) * 1000.0))\nprint(magnitude_fails)\n\n", "sub_path": "generation/Oracle_CLEVR.py", "file_name": "Oracle_CLEVR.py", "file_ext": "py", "file_size_in_byte": 13433, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "random.shuffle", "line_number": 97, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 118, "usage_type": "call"}, {"api_name": "question_engine.answer_question", "line_number": 189, "usage_type": "call"}, {"api_name": "json.load", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 215, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 216, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 232, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 233, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 249, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 250, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 266, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 267, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 283, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 284, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 306, "usage_type": "call"}]} +{"seq_id": "506173674", "text": "# -*- coding: utf-8 -*-\n#\n# (c) 2017 Kilian Kluge\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__all__ = [\"EightLevelSystem\"]\n\nimport itertools\n\nimport numpy as np\nimport qutip\n\nfrom ..polarization import normalize\n\n# Clebsch-Gordan coefficients (as calculated with ytterbium.clebsch_gordan)\ncg = np.zeros((8, 8))\n\n# pi transitions\ncg[0][6] = cg[6][0] = 1 / np.sqrt(3)\ncg[1][5] = cg[5][1] = -1 / np.sqrt(3)\ncg[2][4] = cg[4][2] = 1 / np.sqrt(3)\ncg[3][7] = cg[7][3] = 1 / np.sqrt(3)\n\n# sigma+ transitions\ncg[0][7] = cg[7][0] = -1 / np.sqrt(3)\ncg[1][4] = cg[4][1] = 1 / np.sqrt(3)\ncg[1][6] = cg[6][1] = 1 / np.sqrt(3)\ncg[2][7] = cg[7][2] = 1 / np.sqrt(3)\n\n# sigma- transitions\ncg[0][5] = cg[5][0] = -1 / np.sqrt(3)\ncg[2][5] = cg[5][2] = -1 / np.sqrt(3)\ncg[3][4] = cg[4][3] = 1 / np.sqrt(3)\ncg[3][6] = cg[6][3] = -1 / np.sqrt(3)\n\n# mF factors\nmF = np.zeros(8)\nmF[1] = mF[5] = -1\nmF[3] = mF[7] = 1\n\n\n# noinspection PyPep8Naming\nclass EightLevelSystem:\n linewidth = 19.7 * 10 ** 6 # Hz\n\n # hyperfine splitting\n s_splitting = 12642.812118466 # MHz\n p_splitting = 2105 # MHz\n\n quadratic_shift = 310.8 # B**2 Hz (B in Gauss)\n\n def __init__(self, sat=0.0, delta=0.0, polarization=(1, 0, 0), B=0.0):\n \"\"\"Model of the 2S1/2-2P1/2 transition in 171Yb+ as an eight-level system.\n\n :param delta: Laser detuning from resonance in MHz.\n :param sat: Laser intensity expressed as a multiple of the saturation\n intensity.\n :param polarization: Laser polarization as a 3-tuple\n (pi, sigma+, sigma-).\n :param B: Magnetic field in Gauss.\n \"\"\"\n self._polarization = (1, 0, 0)\n\n self.sat = sat\n self.delta = delta\n self.polarization = polarization\n self.B = B\n\n self.basis = [qutip.states.basis(8, i) for i in range(8)]\n\n @property\n def polarization(self):\n return self._polarization\n\n @polarization.setter\n def polarization(self, vector):\n self._polarization = normalize(vector)\n\n @property\n def H(self):\n \"\"\"Full Hamiltonian of the system.\"\"\"\n laser_field = [2 * np.pi * -self.delta * 10 ** 6\n * self.basis[4] * self.basis[4].dag()]\n laser_field += [2 * np.pi * (-self.delta + self.p_splitting) * 10 ** 6\n * self.basis[i] * self.basis[i].dag()\n for i in (5, 6, 7)]\n laser_field += [2 * np.pi * -self.s_splitting * 10 ** 6\n * self.basis[0] * self.basis[0].dag()]\n\n magnetic_field = [2 * np.pi * mF[i] * 1.4 * 10 ** 6 * self.B\n * self.basis[i] * self.basis[i].dag()\n for i in (1, 3)]\n magnetic_field += [2 * np.pi * mF[i] * 0.47 * 10 ** 6 * self.B\n * self.basis[i] * self.basis[i].dag()\n for i in (5, 7)]\n magnetic_field += [2 * np.pi * self.quadratic_shift * self.B ** 2\n * self.basis[0] * self.basis[0].dag()]\n\n off_diagonal_elements = [self.omega[i][j] / 2 * cg[i][j]\n * self.basis[i] * self.basis[j].dag()\n for i, j in itertools.product(range(8),\n range(8))]\n\n H = sum(laser_field) + sum(magnetic_field) + sum(off_diagonal_elements)\n\n return H\n\n @property\n def decay(self):\n \"\"\"Decay terms prepared for use in `qutip.mesolve`.\"\"\"\n return list(itertools.chain(*self.raw_decay))\n\n @property\n def raw_decay(self):\n \"\"\"All decay terms.\"\"\"\n decay = [[] for _ in range(8)]\n decay[4] = [np.sqrt(2 * np.pi * self.linewidth) * cg[i][4]\n * self.basis[i] * self.basis[4].dag()\n for i in range(4)]\n decay[5] = [np.sqrt(2 * np.pi * self.linewidth) * cg[i][5]\n * self.basis[i] * self.basis[5].dag()\n for i in range(4)]\n decay[6] = [np.sqrt(2 * np.pi * self.linewidth) * cg[i][6]\n * self.basis[i] * self.basis[6].dag()\n for i in range(4)]\n decay[7] = [np.sqrt(2 * np.pi * self.linewidth) * cg[i][7]\n * self.basis[i] * self.basis[7].dag()\n for i in range(4)]\n\n return decay\n\n @property\n def omega(self):\n \"\"\"Rabi frequencies.\"\"\"\n pi, sigma_plus, sigma_minus = self.polarization\n _omega = 2 * np.pi * self.linewidth * np.sqrt(self.sat / 2)\n\n omega = np.zeros((8, 8))\n omega[0][7] = omega[7][0] = \\\n omega[1][4] = omega[4][1] = \\\n omega[1][6] = omega[6][1] = \\\n omega[2][7] = omega[7][2] = _omega * sigma_plus\n\n omega[0][5] = omega[5][0] = \\\n omega[2][5] = omega[5][2] = \\\n omega[3][4] = omega[4][3] = \\\n omega[3][6] = omega[6][3] = _omega * sigma_minus\n\n omega[0][6] = omega[6][0] = \\\n omega[1][5] = omega[5][1] = \\\n omega[2][4] = omega[4][2] = \\\n omega[3][7] = omega[7][3] = _omega * pi\n\n return omega\n", "sub_path": "ytterbium/Yb171/eightlevel.py", "file_name": "eightlevel.py", "file_ext": "py", "file_size_in_byte": 5675, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.zeros", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "qutip.states.basis", "line_number": 80, "usage_type": "call"}, {"api_name": "qutip.states", "line_number": 80, "usage_type": "attribute"}, {"api_name": "polarization.normalize", "line_number": 88, "usage_type": "call"}, {"api_name": "polarization.setter", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 104, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 107, "usage_type": "attribute"}, {"api_name": "itertools.product", "line_number": 112, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 131, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 134, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 149, "usage_type": "call"}]} +{"seq_id": "400805543", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Nov 12 21:14:06 2018\r\n\r\n@author: chandra.shekhar\r\n\"\"\"\r\n# Importing the libraries\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\n\r\n# Importing the dataset\r\ndataset = pd.read_csv('Position_Salaries.csv')\r\nX = dataset.iloc[:, 1:2].values\r\ny = dataset.iloc[:, 2].values\r\n\r\n# Splitting the dataset into the Training set and Test set\r\n'''from sklearn.cross_validation import train_test_split\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)'''\r\n\r\n# Fitting Linear Regression to the dataset\r\nfrom sklearn.linear_model import LinearRegression\r\nlinReg = LinearRegression()\r\nlinReg.fit(X,y)\r\n\r\n\r\n# Fitting Polynomial Regression to the dataset\r\nfrom sklearn.preprocessing import PolynomialFeatures\r\npolyReg = PolynomialFeatures(degree = 3)\r\nX_poly = polyReg.fit_transform(X)\r\nlinReg2 = LinearRegression()\r\nlinReg2.fit(X_poly, y)\r\n\r\n# Visualising the Linear Regression results\r\nplt.scatter(X, y, c='red')\r\nplt.plot(X, linReg.predict(X), color='blue')\r\nplt.title('Truth or Bluff (Linear Regression)')\r\nplt.xlabel('Position level')\r\nplt.ylabel('Salary')\r\nplt.show()\r\n\r\n# Visualising the Polynomial Regression results (for higher resolution and smoother curve)\r\nplt.scatter(X,y,c='red')\r\nplt.plot(X, linReg2.predict(polyReg.fit_transform(X)), c='blue')\r\nplt.title('Truth or Bluff (Polynomial Regression)')\r\nplt.xlabel('Position level')\r\nplt.ylabel('Salary')\r\nplt.show()\r\n\r\nX1 = X\r\nX1 = np.append(arr=X1, values=np.ones((1,1)).astype(float), axis=0)\r\nX1[10,:] = [6.5]\r\ny1 = linReg2.predict(polyReg.fit_transform(X1[10:11,0:1] ))\r\n\r\nlinReg.predict(6.5)\r\nlinReg2.predict(polyReg.fit_transform(6.5))", "sub_path": "Part 2 - Regression/Section 6 - Polynomial Regression/polynomial_regression_new.py", "file_name": "polynomial_regression_new.py", "file_ext": "py", "file_size_in_byte": 1698, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.append", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "233926129", "text": "from django.db.models import Q\nfrom django.utils.decorators import method_decorator\nfrom drf_yasg2 import openapi\nfrom drf_yasg2.utils import swagger_auto_schema\nfrom rest_framework import mixins, viewsets\n\nfrom app.pagination import CustomPagination\nfrom audit.models import AuditLog\nfrom audit.serializers import AuditLogSerializer, AuditLogsQueryParamSerializer\n\nproject_query_param = openapi.Parameter(\n \"project\",\n openapi.IN_QUERY,\n description=\"ID of the project to filter on\",\n type=openapi.TYPE_INTEGER,\n)\nenvironment_query_param = openapi.Parameter(\n \"environment\",\n openapi.IN_QUERY,\n description=\"ID of the environment to filter on \"\n \"(Note `id` required, not `api_key`)\",\n type=openapi.TYPE_INTEGER,\n)\n\n\n@method_decorator(\n name=\"list\",\n decorator=swagger_auto_schema(\n manual_parameters=[project_query_param, environment_query_param]\n ),\n)\nclass AuditLogViewSet(mixins.ListModelMixin, viewsets.GenericViewSet):\n serializer_class = AuditLogSerializer\n pagination_class = CustomPagination\n filterset_fields = [\"is_system_event\"]\n\n def get_queryset(self):\n q = Q(project__organisation__users=self.request.user)\n serializer = AuditLogsQueryParamSerializer(data=self.request.GET)\n serializer.is_valid(raise_exception=True)\n project = serializer.data.get(\"project\")\n environments = serializer.data.get(\"environments\")\n if project:\n q = q & Q(project__id=project)\n if environments:\n q = q & Q(environment__id__in=environments)\n\n search = serializer.data.get(\"search\")\n if search:\n q = q & Q(log__icontains=search)\n return AuditLog.objects.filter(q).select_related(\n \"project\", \"environment\", \"author\"\n )\n", "sub_path": "api/audit/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1788, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "drf_yasg2.openapi.Parameter", "line_number": 11, "usage_type": "call"}, {"api_name": "drf_yasg2.openapi", "line_number": 11, "usage_type": "name"}, {"api_name": "drf_yasg2.openapi.IN_QUERY", "line_number": 13, "usage_type": "attribute"}, {"api_name": "drf_yasg2.openapi", "line_number": 13, "usage_type": "name"}, {"api_name": "drf_yasg2.openapi.TYPE_INTEGER", "line_number": 15, "usage_type": "attribute"}, {"api_name": "drf_yasg2.openapi", "line_number": 15, "usage_type": "name"}, {"api_name": "drf_yasg2.openapi.Parameter", "line_number": 17, "usage_type": "call"}, {"api_name": "drf_yasg2.openapi", "line_number": 17, "usage_type": "name"}, {"api_name": "drf_yasg2.openapi.IN_QUERY", "line_number": 19, "usage_type": "attribute"}, {"api_name": "drf_yasg2.openapi", "line_number": 19, "usage_type": "name"}, {"api_name": "drf_yasg2.openapi.TYPE_INTEGER", "line_number": 22, "usage_type": "attribute"}, {"api_name": "drf_yasg2.openapi", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 32, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 32, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 32, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 32, "usage_type": "name"}, {"api_name": "audit.serializers.AuditLogSerializer", "line_number": 33, "usage_type": "name"}, {"api_name": "app.pagination.CustomPagination", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 38, "usage_type": "call"}, {"api_name": "audit.serializers.AuditLogsQueryParamSerializer", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 46, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 50, "usage_type": "call"}, {"api_name": "audit.models.AuditLog.objects.filter", "line_number": 51, "usage_type": "call"}, {"api_name": "audit.models.AuditLog.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "audit.models.AuditLog", "line_number": 51, "usage_type": "name"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 26, "usage_type": "call"}, {"api_name": "drf_yasg2.utils.swagger_auto_schema", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "36562395", "text": "from numpy import *\nimport matplotlib.pyplot as plt\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.wrappers.scikit_learn import KerasClassifier\nfrom sklearn.model_selection import cross_val_score\n\ndef plot(h, metric):\n fig, ax = plt.subplots()\n epochs = range(len(h[metric]))\n ax.plot(epochs, h[metric], c='blue', label='loss')\n ax.plot(epochs, h['val_'+metric], c='green', label='acc')\n ax.legend()\n plt.title('train X test')\n ax.set_xlabel('epochs')\n plt.show()\n\ndef create_baseline_model():\n model = Sequential()\n\n model.add(Dense(units=10, input_dim=10, activation='relu'))\n model.add(Dense(units=1, activation='sigmoid'))\n\n model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n return model\n\nif __name__ == '__main__':\n\n train_file = '../mlz/test/kaggle/datasets/train.csv'\n test_file = '../mlz/test/kaggle/datasets/test.csv'\n\n X_test = loadtxt(test_file, usecols=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), unpack=True, delimiter=',').T\n Y_test = loadtxt(test_file, unpack=True, usecols=(11), delimiter=',')\n Y_test = where((Y_test > 0.15), 1, 0)\n\n X = loadtxt(train_file, usecols=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), unpack=True, delimiter=',').T\n Y = loadtxt(train_file, unpack=True, usecols=(11), delimiter=',')\n Y = where((Y > 0.15), 1, 0)\n\n # 0: low redshift, 1: high redshift\n\n print('Data loaded!')\n\n estimator = KerasClassifier(build_fn=create_baseline_model, epochs=10)\n\n results = cross_val_score(estimator, X, Y)\n print(\"Results: %.2f%% (%.2f%%)\" % (results.mean() * 100, results.std() * 100))\n\n hist = estimator.fit(X, Y)\n\n preds = estimator.predict(X_test)\n\n plot(hist.history, 'acc')\n plot(hist.history, 'loss')", "sub_path": "unit_test/cnn_fully_connected_test.py", "file_name": "cnn_fully_connected_test.py", "file_ext": "py", "file_size_in_byte": 1789, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.wrappers.scikit_learn.KerasClassifier", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "466119061", "text": "from django.contrib import admin\nfrom django.urls import path, include\n\n#swagger\nfrom .swagger import schema_view\nfrom django.conf.urls import url\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('account/', include('accounts.urls')),\n path('item/', include('items.urls')),\n path('mission/', include('missions.urls')),\n path('ai-images/', include('ai_images.urls')),\n #swagger\n url(r'^swagger(?P\\.json|\\.yaml)$', schema_view.without_ui(cache_timeout=0), name='schema-json'),\n url(r'^swagger/$', schema_view.with_ui('swagger', cache_timeout=0), name='schema-swagger-ui'),\n url(r'^redoc/$', schema_view.with_ui('redoc', cache_timeout=0), name='schema-redoc'),\n]\n", "sub_path": "backend/Django/bexperts/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 703, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "swagger.schema_view.without_ui", "line_number": 15, "usage_type": "call"}, {"api_name": "swagger.schema_view", "line_number": 15, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "swagger.schema_view.with_ui", "line_number": 16, "usage_type": "call"}, {"api_name": "swagger.schema_view", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "swagger.schema_view.with_ui", "line_number": 17, "usage_type": "call"}, {"api_name": "swagger.schema_view", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "567599282", "text": "import contextlib\nimport copy\nfrom io import StringIO\n\nimport pandas as pd\nimport pytest\nimport torch\n\nfrom ludwig.api import LudwigModel\nfrom ludwig.data.preprocessing import preprocess_for_training\nfrom ludwig.features.feature_registries import update_config_with_metadata\nfrom ludwig.data.dataset_synthesizer import build_synthetic_dataset\nfrom tests.integration_tests.utils import ENCODERS\nfrom tests.integration_tests.utils import generate_data\nfrom tests.integration_tests.utils import run_experiment\nfrom tests.integration_tests.utils import sequence_feature, numerical_feature\n\n#\n# this test is focused on testing input sequence features with all encoders\n# and output sequence feature with Generator decoder. Except for specified\n# configuration parameters all other parameters assume default values.\n#\n\nTEST_VOCAB_SIZE = 132\nTEST_HIDDEN_SIZE = 32\nTEST_STATE_SIZE = 16\nTEST_EMBEDDING_SIZE = 64\nTEST_NUM_FILTERS = 24\n\n\n# generates dataset that can be used for rest of test\n@pytest.fixture(scope='module')\ndef generate_sequence_training_data():\n input_features = [\n sequence_feature(\n vocab_size=TEST_VOCAB_SIZE,\n embedding_size=TEST_EMBEDDING_SIZE,\n state_size=TEST_STATE_SIZE,\n hidden_size=TEST_HIDDEN_SIZE,\n num_filters=TEST_NUM_FILTERS,\n min_len=5,\n max_len=10,\n encoder='rnn',\n cell_type='lstm',\n reduce_output=None\n )\n ]\n\n output_features = [\n sequence_feature(\n min_len=5,\n max_len=10,\n decoder='generator',\n cell_type='lstm',\n attention='bahdanau',\n reduce_input=None\n )\n ]\n\n # generate synthetic data set testing\n dataset = build_synthetic_dataset(\n 150,\n copy.deepcopy(input_features) + copy.deepcopy(output_features)\n )\n raw_data = '\\n'.join([r[0] + ',' + r[1] for r in dataset])\n df = pd.read_csv(StringIO(raw_data))\n\n return df, input_features, output_features\n\n\n# setups up minimal number of data structures required to support initialized\n# input and output features. The function returns initialized LudwigModel\n# and batcher for training dataset\n@contextlib.contextmanager\ndef setup_model_scaffolding(\n raw_df,\n input_features,\n output_features\n):\n # setup input feature for testing\n config = {'input_features': input_features,\n 'output_features': output_features}\n\n # setup model scaffolding to for testing\n model = LudwigModel(config)\n training_set, _, _, training_set_metadata = preprocess_for_training(\n config,\n training_set=raw_df,\n skip_save_processed_input=True\n )\n model.training_set_metadata = training_set_metadata\n update_config_with_metadata(\n model.config,\n training_set_metadata\n )\n model.model = model.create_model(model.config)\n\n # setup batcher to go through synthetic data\n with training_set.initialize_batcher() as batcher:\n yield model, batcher\n\n\n# TODO(#1333): refactor test once torch sequence generator work is complete\n#\n# tests output feature sequence with `Generator` decoder\n# pytest parameters\n# dec_cell_type: decoder cell type\n# dec_attention: decoder's attention mechanism\n# dec_beam_width: decoder's beam search width\n# combiner_output_shapes: is a 2-tuple specifies the possible types of\n# tensors that the combiner may generate for sequences.\n# combiner_output_shapes[0]: specifies shape for hidden key\n# combiner_output_shapes[1]: is either None or 1 or 2-tuple representing\n# the encoder_output_state key. None: no encoder_output_state key,\n# 1-tuple: generate tf.Tensor, 2-tuple: generate list with 2 tf.Tensors\n#\n@pytest.mark.skip(reason=\"Issue #1333: Sequence output generation.\")\n@pytest.mark.parametrize('dec_num_layers', [1, 2])\n@pytest.mark.parametrize('dec_beam_width', [1, 2])\n@pytest.mark.parametrize('dec_attention', ['bahdanau', 'luong', None])\n@pytest.mark.parametrize('dec_cell_type', ['lstm', 'rnn', 'gru'])\n@pytest.mark.parametrize(\n 'combiner_output_shapes',\n [\n ((128, 10, 8), None),\n ((128, 10, 32), None),\n ((128, 10, 8), ((128, 8), (128, 8))),\n ((128, 10, 8), ((128, 8),))\n\n ]\n)\ndef test_sequence_decoders(\n dec_cell_type,\n dec_attention,\n dec_beam_width,\n dec_num_layers,\n combiner_output_shapes,\n generate_sequence_training_data\n):\n # retrieve pre-computed dataset and features\n raw_df = generate_sequence_training_data[0]\n input_features = generate_sequence_training_data[1]\n output_features = generate_sequence_training_data[2]\n output_feature_name = output_features[0]['name']\n output_features[0]['cell_type'] = dec_cell_type\n output_features[0]['attention'] = dec_attention\n output_features[0]['beam_width'] = dec_beam_width\n output_features[0]['num_layers'] = dec_num_layers\n\n with setup_model_scaffolding(\n raw_df,\n input_features,\n output_features\n ) as (model, _):\n\n # generate synthetic encoder_output tensors and make it look like\n # it came out of the combiner\n encoder_output = torch.randn(combiner_output_shapes[0])\n combiner_outputs = {'hidden': encoder_output}\n\n if combiner_output_shapes[1] is not None:\n if len(combiner_output_shapes[1]) > 1:\n encoder_output_state = [\n torch.randn(combiner_output_shapes[1][0]),\n torch.randn(combiner_output_shapes[1][1])\n ]\n else:\n encoder_output_state = \\\n torch.randn(combiner_output_shapes[1][0])\n\n combiner_outputs['encoder_output_state'] = encoder_output_state\n\n decoder = model.model.output_features[output_feature_name].decoder_obj\n decoder_out = decoder(combiner_outputs)\n\n # gather expected components of the shape\n batch_size = combiner_outputs['hidden'].shape[0]\n seq_size = output_features[0]['max_len']\n num_classes = model.config['output_features'][0]['num_classes']\n\n # confirm output is what is expected\n assert len(decoder_out) == 5\n logits, lengths, preds, last_preds, probs = decoder_out\n\n # confirm shape and format of deocoder output\n if dec_beam_width > 1:\n assert logits is None\n else:\n assert isinstance(logits, torch.Tensor)\n assert logits.shape.as_list() == [\n batch_size, seq_size, num_classes]\n\n assert isinstance(lengths, torch.Tensor)\n assert lengths.shape.as_list() == [batch_size]\n\n assert isinstance(preds, torch.Tensor)\n assert preds.shape.as_list() == [batch_size, seq_size]\n\n assert isinstance(last_preds, torch.Tensor)\n assert last_preds.shape.as_list() == [batch_size]\n\n assert isinstance(probs, torch.Tensor)\n assert probs.shape.as_list() == [batch_size, seq_size, num_classes]\n\n\n# todo: refactor test once torch sequence generator work is complete\n#\n# final sanity test. Checks a subset of sequence parameters\n#\n@pytest.mark.skip(reason=\"Issue #1333: Sequence output generation.\")\n@pytest.mark.parametrize('dec_num_layers', [1, 2])\n@pytest.mark.parametrize('dec_beam_width', [1, 2])\n@pytest.mark.parametrize('dec_attention', ['bahdanau', 'luong', None])\n@pytest.mark.parametrize('dec_cell_type', ['lstm', 'rnn', 'gru'])\n@pytest.mark.parametrize('enc_cell_type', ['lstm', 'rnn', 'gru'])\n@pytest.mark.parametrize('enc_encoder', ['embed', 'rnn'])\ndef test_sequence_generator(\n enc_encoder,\n enc_cell_type,\n dec_cell_type,\n dec_attention,\n dec_beam_width,\n dec_num_layers,\n csv_filename\n):\n # Define input and output features\n input_features = [\n sequence_feature(\n min_len=5,\n max_len=10,\n encoder='rnn',\n cell_type='lstm',\n reduce_output=None\n )\n ]\n output_features = [\n sequence_feature(\n min_len=5,\n max_len=10,\n decoder='generator',\n cell_type='lstm',\n attention='bahdanau',\n reduce_input=None\n )\n ]\n\n # Generate test data\n rel_path = generate_data(input_features, output_features, csv_filename)\n\n # setup encoder specification\n input_features[0]['encoder'] = enc_encoder\n input_features[0]['cell_type'] = enc_cell_type\n\n # setup decoder specification\n output_features[0]['cell_type'] = dec_cell_type\n output_features[0]['attention'] = dec_attention\n output_features[0]['beam_width'] = dec_beam_width\n output_features[0]['num_layers'] = dec_num_layers\n\n # run the experiment\n run_experiment(input_features, output_features, dataset=rel_path)\n", "sub_path": "tests/integration_tests/test_sequence_features.py", "file_name": "test_sequence_features.py", "file_ext": "py", "file_size_in_byte": 8854, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tests.integration_tests.utils.sequence_feature", "line_number": 35, "usage_type": "call"}, {"api_name": "tests.integration_tests.utils.sequence_feature", "line_number": 50, "usage_type": "call"}, {"api_name": "ludwig.data.dataset_synthesizer.build_synthetic_dataset", "line_number": 61, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 66, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 66, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 32, "usage_type": "call"}, {"api_name": "ludwig.api.LudwigModel", "line_number": 85, "usage_type": "call"}, {"api_name": "ludwig.data.preprocessing.preprocess_for_training", "line_number": 86, "usage_type": "call"}, {"api_name": "ludwig.features.feature_registries.update_config_with_metadata", "line_number": 92, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 74, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 189, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 193, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 196, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 199, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 202, "usage_type": "attribute"}, {"api_name": "pytest.mark.skip", "line_number": 117, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 118, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 119, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 120, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 121, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 121, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 122, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 122, "usage_type": "attribute"}, {"api_name": "tests.integration_tests.utils.sequence_feature", "line_number": 228, "usage_type": "call"}, {"api_name": "tests.integration_tests.utils.sequence_feature", "line_number": 237, "usage_type": "call"}, {"api_name": "tests.integration_tests.utils.generate_data", "line_number": 248, "usage_type": "call"}, {"api_name": "tests.integration_tests.utils.run_experiment", "line_number": 261, "usage_type": "call"}, {"api_name": "pytest.mark.skip", "line_number": 210, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 210, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 211, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 211, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 212, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 212, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 213, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 213, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 214, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 214, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 215, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 215, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 216, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 216, "usage_type": "attribute"}]} +{"seq_id": "403287294", "text": "\"\"\"Name:- Triparna Bhattacharya, UIN:- 677270035, CS 582 Assignment 4 \"\"\"\n\nimport os\nimport re\nfrom nltk.stem import PorterStemmer\nimport heapq\nfrom collections import defaultdict\nimport math\nimport sys\n\npath_gold = sys.argv[1]\npath_abstract = sys.argv[2]\n\nstemmer = PorterStemmer()\npunctuations='!\"#$%&\\'()\\\\n*+,-./:;<=>?@[\\\\]^`{|}~'\nreg_pattern = '[' + punctuations + ']'\n\nstop_words = []\nf = open(\"stopwords.txt\") # the stopwords list provided in the assignment\nstop_words = f.read().splitlines()\n\nglobal_page_rank = {}\nvocab_di={}\ndi = {}\ndi_tf ={}\ndi_tf_idf = {}\ndi2 = {}\ndi_node_score= {}\ntop_ten_ngrams = {}\ntop_ten_words = []\nsum_rank=0\nresult = [0] * 10\ntop_ten_ngrams_tf_idf ={}\ntop_ten_words_tf_idf = []\n\npos_tag = ['nn', 'nns', 'nnp', 'nnps', 'jj']\n\n\ndef preprocess_abstract_file(file_content,file):\n\n abstract_final = []\n abstract_final1 = []\n for word in file_content.split():\n y = re.sub(reg_pattern, '', word).lower()\n abstract_final.append(str(y))\n\n for word in abstract_final:\n word_pos_pair = word.split(\"_\")\n if word_pos_pair[1] in pos_tag:\n stemmed_word = stemmer.stem(word_pos_pair[0])\n if stemmed_word not in stop_words:\n abstract_final1.append(stemmed_word)\n\n else:\n word = word.replace(word, \"\")\n abstract_final1.append(word)\n\n add_graph_node(abstract_final1, file)\n\n\ndef add_graph_node(abstract_final1, file):\n di.clear()\n termfreq = defaultdict(int)\n for word in abstract_final1:\n if (word not in di) & (word != \"\"):\n di[word] = {}\n dfreq[word] += 1\n termfreq[word] += 1\n\n termfreq_main[file] = termfreq\n\n create_vocab_graph(abstract_final1,file)\n for key in di_node_score:\n global_page_rank[key] = di_node_score[key].copy()\n\n\ndfreq = defaultdict(int)\ntermfreq_main = defaultdict(int)\n\ndef create_vocab_graph(abstract_final1,file):\n vocab_di.clear()\n for index, word in enumerate(abstract_final1):\n new_word_index = index + 1\n if new_word_index < len(abstract_final1):\n word2 = abstract_final1[new_word_index]\n if (word!= \"\") & (word2 != \"\"):\n if word in di[word2]:\n di[word2][word] += 1\n else:\n di[word2][word] = 1\n if word2 in di[word]:\n di[word][word2] += 1\n else:\n di[word][word2] = 1\n vocab_di[file] = di\n\n node_score(vocab_di,file)\n calculate_bigram_score(abstract_final1, file)\n calculate_trigram_score(abstract_final1, file)\n\ndef node_score(vocab_di,file):\n di_node_score.clear()\n di2.clear()\n probability = float(1/len(vocab_di[file]))\n alpha = 0.85\n constant = ((1 - alpha) * (probability))\n\n for key in di:\n if key not in di2:\n di2[key] = probability\n di_node_score[file] = di2\n\n for i in range(10):\n for word in vocab_di[file]:\n sum = 0\n for key in vocab_di[file][word]:\n sum += calculate_weight(key, word, file)\n sum = (alpha * sum) + constant\n di_node_score[file][word] = sum\n\ndef calculate_weight(key, word, file):\n\n numerator = vocab_di[file][key][word]\n denominator = 0\n\n for w in vocab_di[file][key]:\n denominator = denominator + vocab_di[file][key][w]\n score = float(((numerator/denominator) * di_node_score[file][key]))\n\n return score\n\ndef calculate_tf(abstract_final1, file):\n for word in abstract_final1:\n if word != \"\":\n if word not in di_tf:\n di_tf[word] = {}\n if word in di_tf:\n if file in di_tf[word]:\n di_tf[word][file] += 1\n else:\n di_tf[word][file] = 1\n\ndef calculate_bigram_score(abstract_final1, file):\n\n for index, word in enumerate(abstract_final1):\n new_word_index = index + 1\n if new_word_index < len(abstract_final1):\n word2 = abstract_final1[new_word_index]\n if (word != \"\") & (word2 != \"\"):\n bigram_score = float((di_node_score[file][word]) + (di_node_score[file][word2]))\n di_node_score[file][word, word2] = bigram_score\n\ndef calculate_trigram_score(abstract_final1, file):\n top_ten_ngrams.clear()\n top_ten_words.clear()\n for index, word in enumerate(abstract_final1):\n new_word_index = index + 1\n new_word_index2 = new_word_index + 1\n if new_word_index < len(abstract_final1):\n word2 = abstract_final1[new_word_index]\n if new_word_index2 < len(abstract_final1):\n word3 = abstract_final1[new_word_index2]\n if (word != \"\") & (word2 != \"\") & (word3 != \"\"):\n trigram_score = float((di_node_score[file][word]) + (di_node_score[file][word2]) + (di_node_score[file][word3]))\n di_node_score[file][word, word2, word3] = trigram_score\n\n top_ten_ngrams[file] = find_top_ten_ngrams(di_node_score, file)\n\n for key in top_ten_ngrams[file]:\n top_ten_words.append(key)\n\n read_gold(file, top_ten_words)\n\ndef find_top_ten_ngrams(di_node_score, file):\n\n score_sorted = dict(heapq.nlargest(10, di_node_score[file].items(), key=lambda x: x[1]))\n return score_sorted\n\ndef read_gold(file, top_ten_words):\n \n gold_file_path = \"gold/\"\n if file in os.listdir(gold_file_path):\n with open(gold_file_path + file, 'r') as f:\n gold_file_content = f.read()\n preprocess_gold_file(gold_file_content, file, top_ten_words)\n\ndef preprocess_gold_file(gold_file_content, file, top_ten_words):\n\n gold_final = []\n for line in gold_file_content.split(\"\\n\"):\n stemmed_word=[stemmer.stem(word) for word in line.split()]\n stemmed_word = \" \".join(stemmed_word)\n gold_final.append(stemmed_word)\n\n\n find_exact_match(gold_final, file, top_ten_words)\n\ndef find_exact_match(gold_final, file, top_ten_words):\n\n rank_list = [0] * 10\n for k in range(1, 11):\n flag = False\n rank_sum = 0\n for key, item in enumerate(top_ten_words[:k]):\n for word in gold_final:\n if type(item) == tuple:\n item = \" \".join(item)\n if item == word:\n rank = (1/(key+1))\n rank_sum += rank\n flag = True\n break\n if flag:\n break\n rank_list[k-1]=rank_sum\n for ij in range(len(rank_list)-1):\n if rank_list[ij+1] < rank_list[ij]:\n break\n\n calculate_final_mrr(rank_list)\n\n\ndef calculate_final_mrr(rank_list):\n global result\n mrr_value = [rank * (1/1330) for rank in rank_list]\n result = [x + y for x,y in zip(result, mrr_value)]\n\n\ntf_idf_main = {}\n\ndef calculate_tf_idf():\n for doc in global_page_rank:\n tf_idf = defaultdict(int)\n words = global_page_rank[doc].keys()\n tf_dict = termfreq_main[doc]\n for word in words:\n if type(word) == tuple:\n for tok in word:\n tf_idf[word] += tf_idf[tok]\n else:\n tf_idf[word] = tf_dict[word] * math.log2(1330 / dfreq[word])\n tf_idf_main[doc] = tf_idf\n\ndef calculate_mrr_tf_idf():\n global result\n result = [0] * 10\n\n for doc in tf_idf_main:\n top_ten_ngrams_tf_idf.clear()\n top_ten_words_tf_idf.clear()\n top_ten_ngrams_tf_idf[doc] = find_top_ten_ngrams(tf_idf_main, doc)\n for key in top_ten_ngrams_tf_idf[doc]:\n top_ten_words_tf_idf.append(key)\n read_gold(doc, top_ten_words_tf_idf)\n\n for index, value in enumerate(result):\n\n print (\"MRR of Top \" + str(index+1) + \" word using TF-IDF: \" + str(value))\n print(\"\")\n print(\"**********************\" + \"Process Completed\" + \"**********************\")\n\n\n\ndef main():\n gold_file_path = path_gold + '/'\n list = os.listdir(gold_file_path)\n\n path = path_abstract + '/'\n for file in os.listdir(path):\n if file in list[:]:\n with open(path + file, 'r') as f:\n file_content = f.read()\n\n preprocess_abstract_file(file_content, file)\n\n for index, value in enumerate(result):\n print (\"MRR of Top \" + str(index+1) + \" word using Page Rank: \" + str(value))\n print(\"\")\n print(\"***********************\" + \"Comparison \" + \"***************************\")\n print(\"\")\n\n calculate_tf_idf()\n calculate_mrr_tf_idf()\n\n\n\nmain()\n\n", "sub_path": "PageRank/PageRank.py", "file_name": "PageRank.py", "file_ext": "py", "file_size_in_byte": 8632, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "nltk.stem.PorterStemmer", "line_number": 14, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 44, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 63, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 77, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 78, "usage_type": "call"}, {"api_name": "heapq.nlargest", "line_number": 176, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 182, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 233, "usage_type": "call"}, {"api_name": "math.log2", "line_number": 241, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 266, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 269, "usage_type": "call"}]} +{"seq_id": "226189071", "text": "from django.db import models\nfrom django.utils import timezone\nfrom autoslug import AutoSlugField\nfrom taggit.managers import TaggableManager\nfrom exclusivebooleanfield.fields import ExclusiveBooleanField\n\n\n\nclass Post(models.Model):\n author = models.ForeignKey('auth.User')\n title = models.CharField(max_length=200)\n #excerpt=models.TextField()\n text = models.TextField()\n image= models.FileField()\n #tags=models.CharField(max_length=200, blank=True)\n slug = AutoSlugField(populate_from='title'\n )\n published=models.BooleanField(default=True)\n created_date = models.DateTimeField(\n default=timezone.now)\n tags = TaggableManager()\n topstory=ExclusiveBooleanField()\n\n\n\n\n\n\n def publish(self):\n self.published_date = timezone.now()\n self.save()\n\n def __unicode__(self):\n return self.slug\n\n\n\n\n", "sub_path": "reviewwriters/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 884, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.db.models.Model", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "autoslug.AutoSlugField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 20, "usage_type": "name"}, {"api_name": "taggit.managers.TaggableManager", "line_number": 21, "usage_type": "call"}, {"api_name": "exclusivebooleanfield.fields.ExclusiveBooleanField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 30, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "288429577", "text": "import endpoints.models as m\nimport boto\nfrom boto.s3.key import Key\nimport os\n\n\ndef delete_equipment():\n m.Equipment.objects.all().delete()\n\n\ndef delete_files():\n m.File.objects.all().delete()\n\n\ndef create_equipment():\n eq1 = m.Equipment(\n serial_number=\"AXNOTER3235\",\n make=\"Cent-o-whirl\",\n model=\"RX1000\",\n description=\"A Centrifuge\",\n eqType='1',\n )\n eq2 = m.Equipment(\n serial_number=\"AXNOTER3243\",\n make=\"Cent-o-whirl\",\n model=\"RX1000\",\n description=\"A Centrifuge\",\n eqType='1',\n )\n eq3 = m.Equipment(\n serial_number=\"AXNOTER3255\",\n make=\"Cent-o-whirl\",\n model=\"RX1000\",\n description=\"A Centrifuge\",\n eqType='1',\n )\n eq4 = m.Equipment(\n serial_number=\"VEC-R3235\",\n make=\"Eyeshield\",\n model=\"20X-TREME\",\n description=\"Lab goggles\",\n eqType='2',\n )\n eq5 = m.Equipment(\n serial_number=\"VEC-R3252\",\n make=\"Eyeshield\",\n model=\"20X-TREME\",\n description=\"Lab goggles\",\n eqType='2',\n )\n eq6 = m.Equipment(\n serial_number=\"EP-FU123\",\n make=\"Eye-pro\",\n model=\"\",\n description=\"Lab goggles\",\n eqType='2',\n )\n eq7 = m.Equipment(\n serial_number=\"EP-FG111\",\n make=\"Eyes-pro\",\n model=\"\",\n description=\"Lab goggles\",\n eqType='2',\n )\n eq8 = m.Equipment(\n serial_number=\"\",\n make=\"\",\n model=\"\",\n description=\"Generic Lab goggles\",\n eqType='2',\n )\n eq1.save()\n eq2.save()\n eq3.save()\n eq4.save()\n eq5.save()\n eq6.save()\n eq7.save()\n eq8.save()\n\n\ndef create_files():\n aws_id = os.environ['AWS_ID']\n aws_key = os.environ['AWS_KEY']\n bucket_name = 'vala_test_filestore'\n conn = boto.connect_s3(aws_id, aws_key)\n conn.create_bucket(bucket_name)\n bucket = conn.get_bucket(bucket_name)\n k = Key(bucket)\n for i in range(0, 10):\n k.key = 'Example File ' + str(i)\n k.set_contents_from_string('This is the contents of \"Example File ' + str(i) + '\"')\n new_file = m.File(key=k.key)\n new_file.save()\n\n\ndef create_data():\n create_equipment()\n create_files()\n\n\ndef delete_data():\n delete_equipment()\n delete_files()\n\n\ndelete_data()\ncreate_data()\n", "sub_path": "createData.py", "file_name": "createData.py", "file_ext": "py", "file_size_in_byte": 2340, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "endpoints.models.Equipment.objects.all", "line_number": 8, "usage_type": "call"}, {"api_name": "endpoints.models.Equipment", "line_number": 8, "usage_type": "attribute"}, {"api_name": "endpoints.models", "line_number": 8, "usage_type": "name"}, {"api_name": "endpoints.models.File.objects.all", "line_number": 12, "usage_type": "call"}, {"api_name": "endpoints.models.File", "line_number": 12, "usage_type": "attribute"}, {"api_name": "endpoints.models", "line_number": 12, "usage_type": "name"}, {"api_name": "endpoints.models.Equipment", "line_number": 16, "usage_type": "call"}, {"api_name": "endpoints.models", "line_number": 16, "usage_type": "name"}, {"api_name": "endpoints.models.Equipment", "line_number": 23, "usage_type": "call"}, {"api_name": "endpoints.models", "line_number": 23, "usage_type": "name"}, {"api_name": "endpoints.models.Equipment", "line_number": 30, "usage_type": "call"}, {"api_name": "endpoints.models", "line_number": 30, "usage_type": "name"}, {"api_name": "endpoints.models.Equipment", "line_number": 37, "usage_type": "call"}, {"api_name": "endpoints.models", "line_number": 37, "usage_type": "name"}, {"api_name": "endpoints.models.Equipment", "line_number": 44, "usage_type": "call"}, {"api_name": "endpoints.models", "line_number": 44, "usage_type": "name"}, {"api_name": "endpoints.models.Equipment", "line_number": 51, "usage_type": "call"}, {"api_name": "endpoints.models", "line_number": 51, "usage_type": "name"}, {"api_name": "endpoints.models.Equipment", "line_number": 58, "usage_type": "call"}, {"api_name": "endpoints.models", "line_number": 58, "usage_type": "name"}, {"api_name": "endpoints.models.Equipment", "line_number": 65, "usage_type": "call"}, {"api_name": "endpoints.models", "line_number": 65, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 84, "usage_type": "attribute"}, {"api_name": "boto.connect_s3", "line_number": 86, "usage_type": "call"}, {"api_name": "boto.s3.key.Key", "line_number": 89, "usage_type": "call"}, {"api_name": "endpoints.models.File", "line_number": 93, "usage_type": "call"}, {"api_name": "endpoints.models", "line_number": 93, "usage_type": "name"}]} +{"seq_id": "113335464", "text": "from tkinter import *\nimport register_page\nimport os\nimport sqlite3 as base\nimport datetime\n# create or connect a data base\ndata_base = base.connect(\"demo1.db\")\n# create a cursor\ncursor = data_base.cursor()\n\nlogin_page =Tk()\nlogin_page.title(\"KM bank\")\nlogin_page.configure(bg=\"#93D5FF\")\nlogin_page.iconbitmap(\"dbmsicon.ico\")\n#login_page.geometry(\"900x700\")\nLabel(login_page,text = \"KMC BANK\",font =\"Algerian 35\",bg =\"#93D5FF\").grid(sticky=E)\n\n##########################################################################\n# Only functions()\ndef login():\n u=\"admin\"\n p=\"1234\"\n user_i= username_box.get()\n passw_i=password_box.get()\n cursor.execute(\"SELECT PASSWORD from customer WHERE CUST_ID = :CUSTID;\",{ 'CUSTID':user_i})\n password_real = cursor.fetchall()\n cust_id_=user_i\n def manager_view():\n admin_page =Tk()\n admin_page.title(\"KM bank\")\n admin_page.configure(bg=\"#93D5FF\")\n admin_page.iconbitmap(\"dbmsicon.ico\")\n\n #functions\n def employee_details_f():\n all_emp_details_page=Toplevel()\n all_emp_details_page.title(\"KM bank\")\n all_emp_details_page.configure(bg=\"#93D5FF\")\n all_emp_details_page.iconbitmap(\"dbmsicon.ico\") \n\n cursor.execute(\"select * from OFFICER \")\n x =cursor.fetchall()\n i=0\n j=0 \n var=''\n result=[]\n for i in range(len(x)):\n for j in range(6):\n var=var+\"\\t\" +str(x[i][j]) \n result.append(var)\n var=' '\n Label(all_emp_details_page,text =\"EMP_ID BRANCH_ID NAME EMAIL GENDER CONTACT \",font =\"none 15\",bg =\"#93D5FF\", borderwidth=2, relief=\"ridge\",pady=10,padx=10).grid(row=0,column=0,pady=10,padx=10,sticky = E)\n for i in range(len(result)):\n Label(all_emp_details_page,text = result[i] +\"\\t\",font =\"none 15\",bg =\"#93D5FF\", borderwidth=2, relief=\"ridge\").grid(row=i+1,column=0,pady=10,padx=10,sticky = E)\n def close_button_f():\n all_emp_details_page.destroy()\n close_button=Button(all_emp_details_page,text=\"Close\",padx=20,pady=5,height = 2, width = 20,command=close_button_f)\n close_button.grid(row=30,column=0,pady=10,padx=20)\n all_emp_details_page.grab_set()\n\n\n def account_details_f():\n all_ac_details_page=Toplevel()\n all_ac_details_page.title(\"KM bank\")\n all_ac_details_page.configure(bg=\"#93D5FF\")\n all_ac_details_page.iconbitmap(\"dbmsicon.ico\") \n\n cursor.execute(\"select * from account \")\n x =cursor.fetchall()\n i=0\n j=0 \n var=''\n result=[]\n for i in range(len(x)):\n for j in range(8):\n var=var+\"\\t\" +str(x[i][j]) \n result.append(var)\n var=' '\n Label(all_ac_details_page,text =\"ACCCOUNT NO | INT_ID | CUSTOMER_ID | AC_TYPE | BALANCE | INT_AMOUNT | INT_RATE | OPEN_DATE| \",font =\"none 15\",bg =\"#93D5FF\", borderwidth=2, relief=\"ridge\",pady=10,padx=10).grid(row=0,column=0,pady=10,padx=10,sticky = E)\n for i in range(len(result)):\n Label(all_ac_details_page,text = result[i] +\"\\t\",font =\"none 15\",bg =\"#93D5FF\", borderwidth=2, relief=\"ridge\").grid(row=i+1,column=0,pady=10,padx=10,sticky = W)\n def close_button_f():\n all_ac_details_page.destroy()\n close_button=Button(all_ac_details_page,text=\"Close\",padx=20,pady=5,height = 2, width = 20,command=close_button_f)\n close_button.grid(row=30,column=0,pady=10,padx=20)\n all_ac_details_page.grab_set()\n\n \n def account_activity_details_f():\n account_activity_details_tk = Toplevel()\n account_activity_details_tk.title(\"KM bank\")\n account_activity_details_tk.configure(bg=\"#93D5FF\")\n account_activity_details_tk.iconbitmap(\"dbmsicon.ico\")\n\n\n Label(account_activity_details_tk,text = \"Enter account no.\",font =\"none 25\",bg =\"#93D5FF\").grid(row=0,column=0,pady=10,padx=20)\n acc_no_input_activity =Entry(account_activity_details_tk,font =\"none 20\",bg =\"#93D5FF\")\n acc_no_input_activity.grid(row=1,column=0,pady=10,padx=20)\n def get_ac_no():\n if acc_no_input_activity.get() == '':\n Label(account_activity_details_tk,text=\"Enter Account No. or close\",font =\"none 25\",bg =\"#93D5FF\").grid(row=3,column=0,pady=10,padx=20)\n else:\n ac_no=int(acc_no_input_activity.get())\n get_acc_button.destroy()\n acc_no_input_activity.destroy()\n cursor.execute(\"SELECT * FROM transactions where ac_no =:ac_no\",{'ac_no':ac_no})\n print(ac_no)\n x = cursor.fetchall()\n i=0\n j=0\n result =[]\n var=' '\n for i in range(len(x)):\n for j in range(4):\n var=var+\"\\t\" +str(x[i][j]) \n result.append(var)\n var=' '\n i=0\n Label(account_activity_details_tk,text =\"\\tACCOUNT NO\\tTRANS ID \\tACTIVITY \\tACTIVITY DATETIME\\t\",font =\"none 15\",bg =\"#93D5FF\", borderwidth=2, relief=\"ridge\",pady=10,padx=10).grid(row=0,column=0,pady=10,padx=10,sticky = E)\n for i in range(len(result)):\n Label(account_activity_details_tk,text = result[i] +\"\\t\",font =\"none 15\",bg =\"#93D5FF\", borderwidth=2, relief=\"ridge\").grid(row=i+1,column=0,pady=10,padx=10,sticky = E)\n def close_button_f():\n account_activity_details_tk.destroy()\n close_button=Button(account_activity_details_tk,text=\"Close\",padx=20,pady=5,height = 2, width = 20,command=close_button_f)\n close_button.grid(row=30,column=0,pady=10,padx=20)\n \n\n get_acc_button=Button(account_activity_details_tk,text=\"submit\",padx=20,pady=5,height = 2, width = 20,command=get_ac_no)\n get_acc_button.grid(row=2,column=0,pady=10,padx=20)\n account_activity_details_tk.grab_set()\n\n def add_employee_f():\n add_emp_page =Toplevel()\n add_emp_page.title(\"KM bank\")\n add_emp_page.configure(bg=\"#93D5FF\")\n add_emp_page.iconbitmap(\"dbmsicon.ico\")\n\n #functions only\n def go_back_button():\n add_emp_page.destroy()\n def register_button():\n branch_id = branch_id_e.get()\n name = name_e.get()\n street = street_box.get()\n state = state_box.get()\n city = city_box.get()\n pin = pin_box.get()\n email = email_e.get()\n gender = gender_e.get()\n contact = contact_e.get()\n nationality= nationality_e.get()\n dob= dob_e.get()\n username = username_e.get()\n password = password_e.get()\n if branch_id =='':\n Label(add_emp_page,text =\"Please enter Branch_id\",font=\"Android 15\",padx=10,pady=10) .grid(row=17,column=1)\n elif name =='':\n Label(add_emp_page,text =\"Please enter Name\",font=\"Android 15\",padx=10,pady=10) .grid(row=17,column=1)\n elif email =='':\n Label(add_emp_page,text =\"Please enter Email\",font=\"Android 15\",padx=10,pady=10) .grid(row=17,column=1)\n elif street =='':\n Label(add_emp_page,text =\"Please enter street\",font=\"Android 15\",padx=10,pady=10) .grid(row=17,column=1) \n elif state =='':\n Label(add_emp_page,text =\"Please enter state\",font=\"Android 15\",padx=10,pady=10) .grid(row=17,column=1) \n elif city =='':\n Label(add_emp_page,text =\"Please enter city\",font=\"Android 15\",padx=10,pady=10) .grid(row=17,column=1) \n elif pin =='':\n Label(add_emp_page,text =\"Please enter pin\",font=\"Android 15\",padx=10,pady=10) .grid(row=17,column=1) \n elif gender =='':\n Label(add_emp_page,text =\"Please enter Gender\",font=\"Android 15\",padx=10,pady=10) .grid(row=17,column=1)\n elif contact =='':\n Label(add_emp_page,text =\"Please enter Contact No.\",font=\"Android 15\",padx=10,pady=10) .grid(row=17,column=1)\n elif nationality =='':\n Label(add_emp_page,text =\"Please enter Nationality\",font=\"Android 15\",padx=10,pady=10) .grid(row=17,column=1)\n elif dob =='':\n Label(add_emp_page,text =\"Please enter dob\",font=\"Android 15\",padx=10,pady=10) .grid(row=17,column=1)\n elif username =='':\n Label(add_emp_page,text =\"Please enter username\",font=\"Android 15\",padx=10,pady=10) .grid(row=17,column=1)\n elif password =='':\n Label(add_emp_page,text =\"Please enter password\",font=\"Android 15\",padx=10,pady=10) .grid(row=17,column=1)\n else:\n cursor.execute(\"INSERT INTO OFFICER VALUES (:EMP_ID ,:BRANCH_ID , :NAME , :EMAIL ,:GENDER ,:CONTACT ,:NATIONALITY ,:DOB ,:USERNAME ,:PASSWORD )\",\n {\n 'EMP_ID':emp_id,\n 'BRANCH_ID': branch_id,\n 'NAME':name ,\n 'EMAIL':email ,\n 'GENDER':gender ,\n 'CONTACT': contact,\n 'NATIONALITY':nationality ,\n 'DOB': dob,\n 'USERNAME':username,\n 'PASSWORD':password \n })\n cursor.execute(\"INSERT INTO OFFICER_ADDRESS VALUES (:EMP_ID , :STREET ,:CITY,:STATE,:PIN)\",\n {\n 'EMP_ID':emp_id,\n 'STREET':street,\n 'STATE':state ,\n 'CITY':city ,\n 'PIN':pin\n })\n add_emp_page.destroy()\n cursor.execute(\"UPDATE employeeID_generator SET employee_id_g = :employee_id_g WHERE row=1;\",{\n 'employee_id_g':x[0][0]+1\n }\n ) \n\n cursor.execute(\"SELECT employee_id_g FROM employeeID_generator where row =1\")\n x = cursor.fetchall()\n emp_id =x[0][0] \n #Labels\n Label(add_emp_page,text = \"EMP_ID\",font =\"none 15\",bg =\"#93D5FF\") .grid(row=1,column=0,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text = emp_id,font =\"none 15\",bg =\"#93D5FF\") .grid(row=1,column=1,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text=\"BRANCH_ID \",font =\"none 15\",bg =\"#93D5FF\") .grid(row=2,column=0,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text = \"NAME \",font =\"none 15\",bg =\"#93D5FF\") .grid(row=3,column=0,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text = \"Address: Street\",font =\"none 15\",bg =\"#93D5FF\") .grid(row=4,column=0,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text=\"Address: State\",font =\"none 15\",bg =\"#93D5FF\") .grid(row=5,column=0,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text = \"Address: City\",font =\"none 15\",bg =\"#93D5FF\") .grid(row=6,column=0,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text = \"Address: pin\",font =\"none 15\",bg =\"#93D5FF\") .grid(row=7,column=0,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text = \"EMAIL \",font =\"none 15\",bg =\"#93D5FF\") .grid(row=8,column=0,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text=\"GENDER \",font =\"none 15\",bg =\"#93D5FF\") .grid(row=9,column=0,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text = \"CONTACT \",font =\"none 15\",bg =\"#93D5FF\") .grid(row=10,column=0,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text=\"NATIONALITY \",font =\"none 15\",bg =\"#93D5FF\") .grid(row=11,column=0,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text = \"DOB\",font =\"none 15\",bg =\"#93D5FF\") .grid(row=12,column=0,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text=\"USERNAME \",font =\"none 15\",bg =\"#93D5FF\") .grid(row=13,column=0,pady=10,padx=20,sticky = W)\n Label(add_emp_page,text=\"PASSWORD \",font =\"none 15\",bg =\"#93D5FF\") .grid(row=14,column=0,pady=10,padx=20,sticky = W)\n #entry\n branch_id_e=Entry(add_emp_page,font =\"none 15\",bg =\"#93D5FF\")\n name_e=Entry(add_emp_page,font =\"none 15\",bg =\"#93D5FF\")\n street_box=Entry(add_emp_page,font =\"none 15\",bg =\"#93D5FF\")\n state_box=Entry(add_emp_page,font =\"none 15\",bg =\"#93D5FF\")\n city_box=Entry(add_emp_page,font =\"none 15\",bg =\"#93D5FF\")\n pin_box=Entry(add_emp_page,font =\"none 15\",bg =\"#93D5FF\")\n email_e=Entry(add_emp_page,font =\"none 15\",bg =\"#93D5FF\")\n gender_e=Entry(add_emp_page,font =\"none 15\",bg =\"#93D5FF\")\n contact_e=Entry(add_emp_page,font =\"none 15\",bg =\"#93D5FF\")\n nationality_e=Entry(add_emp_page,font =\"none 15\",bg =\"#93D5FF\")\n dob_e=Entry(add_emp_page,font =\"none 15\",bg =\"#93D5FF\")\n username_e=Entry(add_emp_page,font =\"none 15\",bg =\"#93D5FF\")\n password_e=Entry(add_emp_page,font =\"none 15\",bg =\"#93D5FF\")\n #entry.grid()\n branch_id_e .grid(row=2,column=1,pady=10,padx=20,sticky = W)\n name_e .grid(row=3,column=1,pady=10,padx=20,sticky = W)\n street_box. grid(row=4,column=1,pady=10,padx=20,sticky = W)\n state_box. grid(row=5,column=1,pady=10,padx=20,sticky = W)\n city_box. grid(row=6,column=1,pady=10,padx=20,sticky = W)\n pin_box. grid(row=7,column=1,pady=10,padx=20,sticky = W)\n email_e .grid(row=8,column=1,pady=10,padx=20,sticky = W)\n gender_e .grid(row=9,column=1,pady=10,padx=20,sticky = W)\n contact_e .grid(row=10,column=1,pady=10,padx=20,sticky = W)\n nationality_e .grid(row=11,column=1,pady=10,padx=20,sticky = W)\n dob_e .grid(row=12,column=1,pady=10,padx=20,sticky = W)\n username_e .grid(row=13,column=1,pady=10,padx=20,sticky = W)\n password_e .grid(row=14,column=1,pady=10,padx=20,sticky = W)\n #Only Buttons()\n register=Button(add_emp_page,text=\"Register Employee\",padx=30,pady=5,command=register_button)\n go_back=Button(add_emp_page,text=\"Go Back\",padx=30,pady=5,command=go_back_button)\n #button.grid()\n register.grid(row=15,column=1,pady=10,padx=20,sticky = W)\n go_back.grid(row=16,column=1,pady=10,padx=20,sticky = W) \n\n\n def set_interest_rate_f():\n set_interest_rate = Toplevel()\n set_interest_rate.title(\"KM bank\")\n set_interest_rate.configure(bg=\"#93D5FF\")\n set_interest_rate.iconbitmap(\"dbmsicon.ico\")\n\n cursor.execute(\"SELECT SAVING_INT FROM INTEREST where INTEREST_ID =1\")\n x= cursor.fetchall()\n current_int_amt=x[0][0]\n Label(set_interest_rate,text =\"Current Interest Rate for savings account\" ,font =\"none 25\",bg =\"#93D5FF\").grid(row=0,column=0,pady=10,padx=20)\n Label(set_interest_rate,text =current_int_amt ,font =\"none 25\",bg =\"#93D5FF\").grid(row=1,column=0,pady=10,padx=20)\n Label(set_interest_rate,text =\"Enter new Interest Rate\" ,font =\"none 25\",bg =\"#93D5FF\").grid(row=2,column=0,pady=10,padx=20)\n new_interest_rate_e =Entry(set_interest_rate,font =\"none 20\",bg =\"#93D5FF\")\n new_interest_rate_e.grid(row=3,column=0,pady=10,padx=20)\n\n def set_new_int_rate():\n new_interest_rate = new_interest_rate_e.get()\n if new_interest_rate =='':\n Label(set_interest_rate,text =\"Enter any value or close\",font =\"none 15\",bg =\"#93D5FF\").grid(row=31,column=0,pady=10,padx=20)\n return\n cursor.execute(\"UPDATE INTEREST SET SAVING_INT = :SAVING_INT WHERE INTEREST_ID =1;\",{\n 'SAVING_INT':new_interest_rate\n }\n ) \n cursor.execute(\"UPDATE ACCOUNT SET INTEREST_RATE= :INTEREST_RATE WHERE INTEREST_ID =1;\",{\n 'INTEREST_RATE':new_interest_rate\n }\n )\n data_base.commit()\n submit_int_rate.destroy()\n Label(set_interest_rate,text =\"Interest rate set to \"+ str(new_interest_rate) ,font =\"none 25\",bg =\"#93D5FF\").grid(row=4,column=0,pady=10,padx=20)\n\n submit_int_rate=Button(set_interest_rate,text=\"submit\",padx=20,pady=5,height = 2, width = 20,command=set_new_int_rate)\n submit_int_rate.grid(row=4,column=0,pady=10,padx=20)\n def close_button_f():\n set_interest_rate.destroy()\n close_button=Button(set_interest_rate,text=\"Close\",padx=20,pady=5,height = 2, width = 20,command=close_button_f)\n close_button.grid(row=30,column=0,pady=10,padx=20)\n set_interest_rate.grab_set()\n\n def add_interest_to_accounts_f():\n add_int_acc_page = Tk()\n add_int_acc_page.title(\"KM bank\")\n add_int_acc_page.configure(bg=\"#93D5FF\")\n add_int_acc_page.iconbitmap(\"dbmsicon.ico\")\n\n def CONFIRM_f():\n cursor.execute(\"select AC_NO from ACCOUNT\")\n x = cursor.fetchall()\n i=0\n for i in range(len(x)):\n ac_no=x[i][0]\n cursor.execute(\"select BALANCE from ACCOUNT where AC_NO =:AC_NO\",{'AC_NO':ac_no})\n balance_fetch= cursor.fetchall()\n balance = float(balance_fetch[0][0])\n cursor.execute(\"select INTEREST_RATE from ACCOUNT where AC_NO =:AC_NO\",{'AC_NO':ac_no})\n interest_rate_fetch= cursor.fetchall()\n int_rate = float(interest_rate_fetch[0][0]) \n updated_balance = round((balance + balance*int_rate*0.001),3) \n updated_int_amt=round((balance*int_rate*0.001),3)\n cursor.execute(\"UPDATE account SET balance= :balance where AC_NO=:AC_NO;\",{\n 'balance':updated_balance,\n 'AC_NO':ac_no\n }\n )\n cursor.execute(\"UPDATE account SET INTEREST_AMOUNT = :INTEREST_AMOUNT WHERE AC_NO=:AC_NO ;\",{\n 'INTEREST_AMOUNT':updated_int_amt,\n 'AC_NO':ac_no\n }\n )\n cursor.execute(\"SELECT trans_id_g FROM transactionID_generator where row =1\")\n t = cursor.fetchall()\n trans_id =t[0][0]\n print(\"TransID = \"+ str(trans_id))\n cursor.execute(\"UPDATE transactionID_generator SET trans_id_g = :trans_id_g WHERE row=1;\",\n {\n 'trans_id_g':t[0][0]+1\n })\n date_of_trans = datetime.datetime.now()\n\n cursor.execute(\"INSERT INTO TRANSACTIONS VALUES (:AC_NO,:TRANS_ID ,:TRANS_TYPE ,:DATE_OF_TRANS )\",\n {\n \n 'AC_NO':ac_no,\n 'TRANS_ID':trans_id,\n 'TRANS_TYPE': \"\\tSavingsINT +\"+ str(updated_int_amt)+\"\\t\",\n 'DATE_OF_TRANS':date_of_trans \n })\n confirm_button.destroy()\n Label(add_int_acc_page,text =\"Interest amount added to all accounts\" ,font =\"none 25\",bg =\"#93D5FF\").grid(row=1,column=0,pady=10,padx=20)\n Label(add_int_acc_page,text =\"Confirm to add interest amount to all accounts\" ,font =\"none 25\",bg =\"#93D5FF\").grid(row=0,column=0,pady=10,padx=20)\n confirm_button=Button(add_int_acc_page,text=\"CONFIRM\",padx=20,pady=5,height = 2, width = 20,command=CONFIRM_f)\n confirm_button.grid(row=1,column=0,pady=10,padx=20)\n\n\n def log_out_f():\n admin_page.destroy()\n #Labels\n Label(admin_page,text = \"Welcome Manager\",font =\"Algerian 25\",bg =\"#93D5FF\").grid(row=0,column=0,pady=10,padx=20)\n #Button\n employee_details=Button(admin_page,text=\"Employee Details\",padx=30,pady=5,height = 2, width = 20,command=employee_details_f)\n account_details=Button(admin_page,text=\"All Account Details\",padx=30,pady=5,height = 2, width = 20,command=account_details_f)\n account_activity_details=Button(admin_page,text=\"Account activity Details\",padx=30,pady=5,height = 2, width = 20,command=account_activity_details_f)\n add_employee=Button(admin_page,text=\"Add Employee\",padx=30,pady=5,height = 2, width = 20,command=add_employee_f)\n set_interest_rate=Button(admin_page,text=\"Set Interest Rate\",padx=30,pady=5,height = 2, width = 20,command=set_interest_rate_f)\n add_interest_to_accounts=Button(admin_page,text=\"Add interest amount to accounts\",padx=30,pady=5,height = 2, width = 20,command=add_interest_to_accounts_f)\n log_out_button=Button(admin_page,text=\"LogOut\",padx=30,pady=5,height = 2, width = 20,command=log_out_f)\n\n\n #Buttone.grid()\n employee_details .grid(row=1,column=0,pady=10,padx=20)\n add_employee .grid(row=1,column=1,pady=10,padx=20)\n account_details .grid(row=2,column=0,pady=10,padx=20)\n account_activity_details.grid(row=2,column=1,pady=10,padx=20)\n set_interest_rate .grid(row=3,column=0,pady=10,padx=20)\n add_interest_to_accounts.grid(row=3,column=1,pady=10,padx=20)\n log_out_button .grid(row=4,column=0,pady=10,padx=20)\n admin_page.grab_set()\n def customer_signin_window(cust_id_):\n signin_page =Tk()\n signin_page.title(\"KM bank\")\n signin_page.configure(bg=\"#93D5FF\")\n signin_page.iconbitmap(\"dbmsicon.ico\")\n def check_balance_f():\n balance_page =Toplevel()\n balance_page.title(\"KM bank\")\n balance_page.configure(bg=\"#93D5FF\")\n balance_page.iconbitmap(\"dbmsicon.ico\")\n cursor.execute(\"SELECT BALANCE from account WHERE AC_NO = :ACNO;\",{ 'ACNO':ac_no})\n b = cursor.fetchall()\n balance =b[0][0]\n balance_l=Label(balance_page,text = \" Your Current Balance: \"+ str(balance)+\" \",font =\"none 25\",bg =\"#93D5FF\")\n balance_l.grid(row=0,column=0,pady=10,padx=20)\n def close_button_f():\n balance_page.destroy()\n close_button=Button(balance_page,text=\"Close\",padx=30,pady=10,height = 2, width = 20,command=close_button_f)\n close_button.grid(row=1,column=0,pady=10,padx=20)\n balance_page.grab_set()\n\n def deposit_f():\n deposit_page =Toplevel()\n deposit_page.title(\"KM bank\")\n deposit_page.configure(bg=\"#93D5FF\")\n deposit_page.iconbitmap(\"dbmsicon.ico\")\n deposit_test_l= Label(deposit_page,text = \" Enter amount to deposit: \",font =\"none 25\",bg =\"#93D5FF\")\n deposit_amount_entry= Entry(deposit_page,font =\"none 25\")\n deposit_test_l .grid(row=0,column=0,pady=10,padx=20)\n deposit_amount_entry .grid(row=1,column=0,pady=10,padx=20)\n def submit_button_f():\n cursor.execute(\"SELECT BALANCE from account WHERE AC_NO = :ACNO;\",{ 'ACNO':ac_no})\n b = cursor.fetchall()\n balance =b[0][0]\n if deposit_amount_entry.get() == '':\n Label(deposit_page,text = \" Enter amount to deposit or Close \",font =\"none 10\",bg =\"#93D5FF\").grid(row=1,column=1,pady=10,padx=20)\n elif deposit_amount_entry.get().isdigit():\n cursor.execute(\"SELECT trans_id_g FROM transactionID_generator where row =1\")\n t = cursor.fetchall()\n trans_id =t[0][0]\n print(\"TransID = \"+ str(trans_id))\n cursor.execute(\"UPDATE transactionID_generator SET trans_id_g = :trans_id_g WHERE row=1;\",\n {\n 'trans_id_g':t[0][0]+1\n })\n date_of_trans = datetime.datetime.now()\n\n cursor.execute(\"INSERT INTO TRANSACTIONS VALUES (:AC_NO,:TRANS_ID ,:TRANS_TYPE ,:DATE_OF_TRANS )\",\n {\n \n 'AC_NO':ac_no,\n 'TRANS_ID':trans_id,\n 'TRANS_TYPE': \"\\tDEPOSITED +\"+ str(deposit_amount_entry.get()),\n 'DATE_OF_TRANS':date_of_trans \n })\n\n\n updated_balance=balance+int(deposit_amount_entry.get())\n cursor.execute(\"UPDATE account SET balance = :balance WHERE AC_NO = :ACNO;\",{ 'balance':updated_balance,'ACNO':ac_no})\n submit_button.destroy()\n Label(deposit_page,text = \"Rs \"+str(deposit_amount_entry.get())+\" deposited\",font =\"none 25\",bg =\"#93D5FF\").grid(row=2,column=0,pady=10,padx=20)\n else:\n Label(deposit_page,text = \" Enter only numbers \",font =\"none 10\",bg =\"#93D5FF\").grid(row=1,column=1,pady=10,padx=20)\n\n \n submit_button=Button(deposit_page,text=\"Deposit\",padx=30,pady=10,height = 2, width = 20,command=submit_button_f)\n submit_button.grid(row=2,column=0,pady=10,padx=20)\n def close_button_f():\n deposit_page.destroy()\n close_button=Button(deposit_page,text=\"Close\",padx=30,pady=10,height = 2, width = 20,command=close_button_f)\n close_button.grid(row=3,column=0,pady=10,padx=20)\n deposit_page.grab_set()\n \n def withdraw_f():\n withdraw_page =Toplevel()\n withdraw_page.title(\"KM bank\")\n withdraw_page.configure(bg=\"#93D5FF\")\n withdraw_page.iconbitmap(\"dbmsicon.ico\")\n withdraw_test_l= Label(withdraw_page,text = \" Enter amount to transfer: \",font =\"none 25\",bg =\"#93D5FF\")\n withdraw_amount_entry= Entry(withdraw_page,font =\"none 25\")\n withdraw_test_l .grid(row=0,column=0,pady=10,padx=20)\n withdraw_amount_entry .grid(row=1,column=0,pady=10,padx=20)\n def submit_button_f_w():\n cursor.execute(\"SELECT BALANCE from account WHERE AC_NO = :ACNO;\",{ 'ACNO':ac_no})\n b = cursor.fetchall()\n balance =b[0][0]\n if withdraw_amount_entry.get() == '':\n Label(withdraw_page,text = \" Enter amount to transfer or Close \",font =\"none 10\",bg =\"#93D5FF\").grid(row=1,column=1,pady=10,padx=20)\n elif withdraw_amount_entry.get().isdigit():\n cursor.execute(\"SELECT trans_id_g FROM transactionID_generator where row =1\")\n t = cursor.fetchall()\n trans_id =t[0][0]\n print(\"TransID = \"+ str(trans_id))\n cursor.execute(\"UPDATE transactionID_generator SET trans_id_g = :trans_id_g WHERE row=1;\",\n {\n 'trans_id_g':t[0][0]+1\n })\n date_of_trans = datetime.datetime.now()\n\n cursor.execute(\"INSERT INTO TRANSACTIONS VALUES (:AC_NO,:TRANS_ID ,:TRANS_TYPE ,:DATE_OF_TRANS )\",\n {\n \n 'AC_NO':ac_no,\n 'TRANS_ID':trans_id,\n 'TRANS_TYPE': \"\\tWITHDREW -\"+ str(withdraw_amount_entry.get())+\"\\t\",\n 'DATE_OF_TRANS':date_of_trans \n })\n\n\n updated_balance=balance - int(withdraw_amount_entry.get())\n cursor.execute(\"UPDATE account SET balance = :balance WHERE AC_NO = :ACNO;\",{ 'balance':updated_balance,'ACNO':ac_no})\n submit_button.destroy()\n Label(withdraw_page,text = \"Rs \"+str(withdraw_amount_entry.get())+\" withdrawn\",font =\"none 25\",bg =\"#93D5FF\").grid(row=2,column=0,pady=10,padx=20)\n else:\n Label(withdraw_page,text = \" Enter only numbers \",font =\"none 10\",bg =\"#93D5FF\").grid(row=1,column=1,pady=10,padx=20)\n\n \n submit_button=Button(withdraw_page,text=\"Transfer\",padx=30,pady=10,height = 2, width = 20,command=submit_button_f_w)\n submit_button.grid(row=2,column=0,pady=10,padx=20)\n def close_button_f():\n withdraw_page.destroy()\n close_button=Button(withdraw_page,text=\"Close\",padx=30,pady=10,height = 2, width = 20,command=close_button_f)\n close_button.grid(row=3,column=0,pady=10,padx=20)\n withdraw_page.grab_set()\n\n def mini_statement_f():\n mini_statement =Toplevel()\n mini_statement.title(\"KM bank\")\n mini_statement.configure(bg=\"#93D5FF\")\n mini_statement.iconbitmap(\"dbmsicon.ico\")\n cursor.execute(\"SELECT * FROM transactions where ac_no =:ac_no\",{'ac_no':ac_no})\n print(ac_no)\n x = cursor.fetchall()\n i=0\n j=0\n result =[]\n var=' '\n for i in range(len(x)):\n for j in range(4):\n var=var+\"\\t\" +str(x[i][j]) \n result.append(var)\n var=''\n i=0\n Label(mini_statement,text =\"\\tACCOUNT NO\\t\\tTRANSACTION ID \\tACTIVITY \\t\\tACTIVITY DATETIME\\t\",font =\"none 15\",bg =\"#93D5FF\", borderwidth=2, relief=\"ridge\",pady=10,padx=10).grid(row=0,column=0,pady=10,padx=0,sticky = E)\n for i in range(len(result)):\n Label(mini_statement,text = result[i] +\"\\t\",font =\"none 15\",bg =\"#93D5FF\", borderwidth=2, relief=\"ridge\").grid(row=i+1,column=0,pady=10,padx=10,sticky = E)\n def close_button_f():\n mini_statement.destroy()\n close_button=Button(mini_statement,text=\"Close\",padx=20,pady=5,height = 2, width = 20,command=close_button_f)\n close_button.grid(row=30,column=0,pady=10,padx=20)\n mini_statement.grab_set()\n\n def log_out_f():\n signin_page.destroy()\n cursor.execute(\"SELECT AC_NO from account WHERE CUST_ID = :CUSTID;\",{ 'CUSTID':cust_id_})\n x = cursor.fetchall()\n ac_no=x[0][0]\n #print(ac_no)\n cursor.execute(\"SELECT NAME from customer WHERE CUST_ID = :CUSTID;\",{ 'CUSTID':cust_id_})\n n = cursor.fetchall()\n name = n[0][0]\n #print(balance)\n #Label\n welcome_message=Label(signin_page,text = \"Welcome \"+name,font =\"Algerian 25\",bg =\"#93D5FF\")\n #Button\n check_balance=Button(signin_page,text=\"Check Balance\",padx=30,pady=5,height = 2, width = 20,command=check_balance_f)\n deposit=Button(signin_page,text=\"Deposit\",padx=30,pady=5,height = 2, width = 20,command=deposit_f)\n withdraw=Button(signin_page,text=\"Transfer\",padx=30,pady=5,height = 2, width = 20,command=withdraw_f)\n mini_statement=Button(signin_page,text=\"Mini Statement\",padx=30,pady=5,height = 2, width = 20,command=mini_statement_f)\n log_out_button=Button(signin_page,text=\"LogOut\",padx=30,pady=5,height = 2, width = 20,command=log_out_f)\n \n #Label.grid()\n welcome_message .grid(row=0,column=0,pady=10,padx=20)\n #Buttone.grid()\n check_balance .grid(row=1,column=0,pady=10,padx=20)\n deposit .grid(row=2,column=0,pady=10,padx=20)\n withdraw .grid(row=1,column=1,pady=10,padx=20)\n mini_statement .grid(row=2,column=1,pady=10,padx=20)\n log_out_button .grid(row=3,column=0,pady=10,padx=20) \n signin_page.grab_set() \n if user_i =='':\n lab = Label(login_page,text =\"Please enter Cust_ID\",font=\"Android 15\",padx=10,pady=10)\n lab.grid(row=5,column=1)\n elif passw_i =='':\n lab = Label(login_page,text =\"Please enter Password\",font=\"Android 15\",padx=10,pady=10)\n lab.grid(row=5,column=1,columnspan=2) \n elif user_i == u and passw_i == p:\n manager_view()\n elif password_real[0][0] == passw_i:\n cursor.execute(\"SELECT NAME from customer WHERE CUST_ID = :CUSTID;\",{ 'CUSTID':user_i})\n n = cursor.fetchall()\n print(password_real[0][0])\n customer_signin_window(cust_id_)\n login_page.grab_set()\n else:\n lab = Label(login_page,text =\"Try Again\",font=\"Android 20\",padx=10,pady=10)\n lab.grid(row=5,column=1,columnspan=2)\n\n\nregister_page_exists=0\ndef signup():\n global register_page_exists\n global register_page\n if register_page_exists == 0: \n os.system('register_page.py')\n\n if register_page.register_page.winfo_exists():\n register_page_exists=1\n else:\n os.system('register_page.py')\n register_page_exists=1\n\n##########################################################################\n#Only Label()\n#picture = PhotoImage(file = \"\")\n#lab = Label(login_page, image=picture)\n#lab.grid(row=0,column=0,columnspan=2)\nusername=Label(login_page,text = \"CustomerID\",font =\"none 15\",bg =\"#93D5FF\")\npassword=Label(login_page,text=\"Password\",font =\"none 15\",bg =\"#93D5FF\")\n##########################################################################\n\n#Only Entry() \nusername_box=Entry(login_page,font=\"none 15\", w=20)\npassword_box=Entry(login_page,font=\"none 15\", w=20,show=\"*\")\n##########################################################################\n\n#Only Buttons()\nlogin=Button(login_page,text=\"Login\",padx=20,pady=3,command=login)\nsign_up=Button(login_page,text=\"SignUp\",padx=20,pady=3,command=signup)\n##########################################################################\n#GRID()\n##########################################################################\n#Only Label.grid()\nusername.grid(row=1,column=0,pady=10,padx=20)\npassword.grid(row=2,column=0,pady=5,padx=2)\n##########################################################################\n#Only Entry.grid()\nusername_box.grid(row=1,column=1,pady=10,padx=20)\npassword_box.grid(row=2,column=1,pady=10,padx=20)\n##########################################################################\n# Only Button.grid()\nlogin.grid(row=3,column=1,pady=8,padx=20,sticky = W)\nsign_up.grid(row=4,column=1,pady=8,padx=20,sticky = W)\n\n\n\nmainloop()\ndata_base.commit()\ndata_base.close()", "sub_path": "root_file.py", "file_name": "root_file.py", "file_ext": "py", "file_size_in_byte": 35243, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlite3.connect", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 342, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 342, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 427, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 427, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 479, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 479, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 586, "usage_type": "call"}, {"api_name": "register_page.register_page.winfo_exists", "line_number": 588, "usage_type": "call"}, {"api_name": "register_page.register_page", "line_number": 588, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 591, "usage_type": "call"}]} +{"seq_id": "401385121", "text": "#edit by knva\n#tool VSCODE\n#time 2018-8-2 10:12:27\nimport websocket\ntry:\n import thread\nexcept ImportError:\n import _thread as thread\nimport time\nimport json\nimport re\nclass wsgame:\n smflag = True\n yjdid = ''\n smid =''\n rc = False\n sfname = \"苏星河\"\n dxerid = ''\n dxename = \"店小二\"\n baoziid = ''\n serverip=''\n acctoken = ''\n palyer =''\n smcode=1\n die = False\n myname = ''\n def __init__(self, serverip, acctoken, palyer=\"\",smcode=\"\"):\n self.serverip = serverip\n self.acctoken=acctoken\n self.palyer = palyer\n self.smcode=smcode\n\n def convet_json(self,json_str):\n json_obj = eval(json_str, type('Dummy', (dict,), dict(__getitem__=lambda s,n:n))())\n return json_obj\n def logCat(self,msg):\n print(\"{0}: {1}: {2}\".format(time.time(),self.myname, msg))\n \n def sm(self,ws):\n ws.send(\"jh fam \"+str(self.smcode)+\" start\")\n if self.smcode==1:\n self.sfname = \"宋远桥\"\n ws.send(\"go north\")\n elif self.smcode == 2:\n self.sfname = \"清乐比丘\"\n elif self.smcode==3:\n self.sfname = \"高根明\"\n elif self.smcode==4:\n self.sfname = \"苏梦清\"\n ws.send(\"go west\")\n elif self.smcode==5:\n self.sfname = \"苏星河\"\n elif self.smcode==6:\n self.sfname = \"左全\"\n ws.send(\"go down\")\n\n\n time.sleep(1)\n self.logCat(self.smflag)\n while self.smflag:\n time.sleep(1)\n ws.send(\"task sm \"+self.smid)\n\n def baozi(self,ws):\n ws.send(\"jh fam 0 start\")\n time.sleep(1)\n ws.send(\"go north\")\n time.sleep(1)\n ws.send(\"go north\")\n time.sleep(1)\n ws.send(\"go east\")\n time.sleep(1)\n ws.send(\"list \"+self.dxerid)\n time.sleep(1)\n ws.send(\"sell all\")\n time.sleep(0.5)\n ws.send(\"buy 20 \"+self.baoziid+\" from \"+self.dxerid)\n time.sleep(1)\n\n def richang(self,ws):\n if self.rc:\n return\n time.sleep(1)\n ws.send(\"jh fb 0 start1\")\n time.sleep(1)\n ws.send(\"cr cd/wen/damen\")\n time.sleep(1)\n ws.send(\"cr\")\n time.sleep(1)\n ws.send(\"cr over\")\n time.sleep(1)\n ws.send(\"taskover signin\")\n\n def fuben(self,ws):\n ws.send('pack')\n time.sleep(5)\n for i in range(10):\n time.sleep(1)\n self.richang(ws)\n for i in range(5):\n time.sleep(1)\n if self.rc:\n return\n ws.send(\"use \"+self.yjdid)\n for i in range(10):\n time.sleep(1)\n self.richang(ws)\n \n \n def wakuang(self,ws):\n ws.send(\"jh fam 0 start\")\n time.sleep(1)\n ws.send(\"go west\")\n time.sleep(1)\n ws.send(\"go west\")\n time.sleep(1)\n ws.send(\"go west\")\n time.sleep(1)\n ws.send(\"go west\")\n time.sleep(1)\n ws.send(\"wa\")\n \n def lianxi(self,ws,e):\n if e['dialog']=='list':\n self.getitemsId(ws,e)\n if e['dialog']==\"skills\":\n self.logCat(\"技能 \"+e['id'] +\" 提升到 \"+ str(e['exp'])+\"%\")\n if 'level' in e:\n #self.logCat(e)\n self.logCat(\"升级了\"+\"技能 \"+e['id'] +\"到\"+str(e['level'])+\"级\")\n if self.yjdid ==\"\":\n if e['dialog']==\"pack\":\n if 'items' in e:\n for item in e['items']:\n #self.logCat(item)\n if \"养精丹\" in item['name']:\n self.yjdid = item['id']\n self.logCat(\"养精丹id:\"+self.yjdid)\n break\n def getsmid(self,ws ,e):\n if 'items' in e:\n for item in e[\"items\"]:\n #self.logCat(item)\n if item==0:\n continue\n if self.smid =='':\n if self.sfname in item[\"name\"]:\n self.smid = item['id']\n self.logCat(\"师门id:\"+self.smid)\n break\n if self.dxerid =='':\n if self.dxename in item[\"name\"]:\n self.dxerid = item['id']\n self.logCat(\"店小二id:\"+self.dxerid)\n break\n def getitemsId(self,ws,e):\n if self.dxerid == '':\n return\n if 'seller' in e:\n self.logCat(\"getbaozi\")\n if e['seller'] == self.dxerid:\n self.logCat(\"getbaozi1\")\n for sellitem in e['selllist']:\n if sellitem ==0:\n continue\n if self.baoziid ==\"\":\n if \"包子\" in sellitem['name']:\n self.baoziid =sellitem['id']\n self.logCat(\"包子id:\"+self.baoziid)\n break\n \n def smcmd(self,ws,e):\n self.logCat(e['items'][0]['cmd'])\n ws.send(e['items'][0]['cmd'])\n def relive(self,ws,e):\n ws.send('relive')\n self.die=True\n def login(self,ws):\n ws.send(self.acctoken)\n ws.send(\"login \"+self.palyer)\n time.sleep(1)\n ws.send('setting ban_pk 1')\n ws.send(\"stopstate\")\n ws.send('pack')\n ws.send(\"taskover signin\")\n time.sleep(1)\n self.logCat(\"3\")\n time.sleep(1)\n self.logCat(\"2\")\n time.sleep(1)\n self.logCat(\"1\")\n time.sleep(1)\n ws.send('tm aa')\n time.sleep(1)\n def getmyname(self,ws,e):\n if e['ch']=='tm' and e['uid']==self.palyer:\n self.myname = e['name']\n\n def on_message(self,ws, message):\n if \"{\" and \"}\" in message: \n e = self.convet_json(message)\n #self.logCat(e)\n if e['type']==\"dialog\":\n self.lianxi(ws,e)\n if e['type']==\"cmds\":\n self.smcmd(ws,e)\n if e['type']==\"items\":\n self.getsmid(ws,e)\n if e['type']==\"msg\":\n self.getmyname(ws,e)\n else:\n self.logCat(message)\n if \"你今天已经签到了\" in message:\n self.rc = True\n if \"休息一下吧\" in message:\n self.smflag = False\n if \"灵魂状态\" in message:\n self.relive(ws,message)\n \n def on_error(self,ws, error):\n self.logCat(error)\n\n def on_close(self,ws):\n self.logCat(\"### closed ###\")\n\n def on_open(self,ws):\n def run(*args):\n time.sleep(1)\n self.login(ws)\n self.logCat(self.rc)\n while True:\n if not self.rc:\n self.baozi(ws)\n self.sm(ws)\n self.fuben(ws)\n if not self.die:\n break\n self.wakuang(ws)\n ws.close()\n self.logCat(\"thread terminating...\")\n thread.start_new_thread(run, ())\n\n def start(self):\n websocket.enableTrace(True)\n ws = websocket.WebSocketApp(self.serverip,\n on_message = self.on_message,\n on_error = self.on_error,\n on_close = self.on_close)\n ws.on_open = self.on_open\n ws.run_forever()\n", "sub_path": "wsgame.py", "file_name": "wsgame.py", "file_ext": "py", "file_size_in_byte": 7510, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.time", "line_number": 37, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 66, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 68, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 70, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 72, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 74, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 76, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 78, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 83, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 85, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 87, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 89, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 91, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 96, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 98, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 101, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 106, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 112, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 114, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 116, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 118, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 120, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 181, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 186, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 188, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 190, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 192, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 194, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 228, "usage_type": "call"}, {"api_name": "_thread.start_new_thread", "line_number": 241, "usage_type": "call"}, {"api_name": "websocket.enableTrace", "line_number": 244, "usage_type": "call"}, {"api_name": "websocket.WebSocketApp", "line_number": 245, "usage_type": "call"}]} +{"seq_id": "24215717", "text": "import cv2\nimport numpy as np\nimport random\nimport os\nos.chdir('C:/Users/PC021/Downloads/05-컴퓨터비전-이미지파일/05-컴퓨터비전-이미지파일')\noldx = oldy = -1\n\ndef random_color():\n return tuple(sorted([i for i in range(256)]*3, key=lambda x:random.random())[:3])\n\ndef random_size():\n return random.randint(10,100)\n\ndef on_mouse(event, x, y, flags, param):\n global oldx, oldy\n if event == cv2.EVENT_LBUTTONDOWN:\n oldx, oldy = x, y\n\n elif event == cv2.EVENT_MOUSEMOVE:\n if flags & cv2.EVENT_FLAG_LBUTTON:\n cv2.line(img, (oldx, oldy), (x, y), (0, 0, 255), 4, cv2.LINE_AA)\n cv2.imshow('image', img)\n oldx, oldy = x, y\n\n elif event == cv2.EVENT_LBUTTONDBLCLK:\n cv2.circle(img, (oldx, oldy),random_size(),random_color(),-1,cv2.LINE_4,)\n cv2.imshow('image', img)\n oldx, oldy = x, y\n\nimg = cv2.imread('images/car.jpg',cv2.IMREAD_UNCHANGED)\n\ncv2.imshow('image', img)\ncv2.setMouseCallback('image', on_mouse, img)\ncv2.waitKey()\ncv2.destroyAllWindows()\n", "sub_path": "OpenCV/OpenCV_input_control_double_click_random_radius_and_color.py", "file_name": "OpenCV_input_control_double_click_random_radius_and_color.py", "file_ext": "py", "file_size_in_byte": 1046, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.chdir", "line_number": 5, "usage_type": "call"}, {"api_name": "random.random", "line_number": 9, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_MOUSEMOVE", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_FLAG_LBUTTON", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.EVENT_LBUTTONDBLCLK", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.LINE_4", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.IMREAD_UNCHANGED", "line_number": 30, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.setMouseCallback", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "19358761", "text": "#!/usr/bin/ev python\r\nimport csv\r\nimport base64\r\nimport os\r\n\r\nwith open('/disk/wukecai/msr_face/MsCelebV1-Faces-Aligned.tsv','r') as fr:\r\n\t\tcr = csv.reader(fr,delimiter = '\\t')\r\n\t\tfor line in cr:\r\n\t\t\t# fw.write(line)\r\n\t\t\tnew_path = '/disk/wukecai/msr_face/Faces_aligned/images/' + line[0]\r\n\t\t\tif not os.path.exists(new_path):\r\n\t\t\t\tos.makedirs(new_path)\r\n\t\t\tos.chdir(new_path)\r\n\t\t\timgdata = base64.b64decode(line[6])\r\n\t\t\timgname = line[1] + '.jpeg'\r\n\t\t\twith open(imgname,'w') as fimg:\r\n\t\t\t\tfimg.write(imgdata)\r\n\r\n", "sub_path": "create_images.py", "file_name": "create_images.py", "file_ext": "py", "file_size_in_byte": 512, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "csv.reader", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 12, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 13, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "250772295", "text": "import cx_Oracle\nimport time\n\n#Achtung geht nur mit dem 10g cx_Oracle3, der 11g gibt einen Fehler beim contestnect.\n#contest = cx_Oracle.contestnect('noalyze/noalyze@DAYSNAP.PUC.OV.OTTO.DE')\n\n#contest1 = cx_Oracle.contestnection('AUFTRAG/auftrag@OP01SPE')\n#contest2 = cx_Oracle.contestnection('AUFTRAG/AUFTRAG@OP01SPG')\n#contest3 = cx_Oracle.contestnection('halden/halden@SPRILL')\n\ncontest = cx_Oracle.Connection('cobra/dbt1cob@COBT1DEV.OV.OTTO.DE')\nconprax = cx_Oracle.Connection('cobra/dbpcob@COBRA_ITANIUM.OV.OTTO.DE')\nconpara = cx_Oracle.Connection('AUFTRAG/AUFTRAG@OP01SPG')\n\ncurtest = contest.cursor()\ncurprax = conprax.cursor()\ncurpara = conpara.cursor()\n\ncurtest.execute(\"update fct_setting set value = 'true' where name = 'isSprintExportEnabled_Buchung'\") \n\n# Lese Praxis\ncurprax.execute(\"\"\"\nselect * from booking_temporary where id in (1397305257, 1394904333, 1397268739, 1397254434, 1397305256) \n\"\"\")\nrow = curprax.fetchone()\nwhile row is not None:\n #Schreibe Test\n huge=row[9].read()\n curtest.execute(\"\"\"INSERT INTO booking_temporary(id, status_code, type_code, status_changed_date, create_date, booking_xml)\n VALUES (:1, 0, :3, :4, :5, :6)\"\"\", (row[0], row[3], row[4], row[5], huge))\n row = curprax.fetchone()\n \n# cursor.execute(\"insert.... values (:1, :2, :3), (1, \"String\", clobValue))\ncontest.commit()\ncontest.close()\nconprax.close()\n\n#warte etwas und mache refesh\ntime.sleep(60)\n#warte etwas und mache refesh\nprint(time.localtime())\ncurpara.callproc(\"SYS.DBMS_SNAPSHOT.REFRESH\", ('LA_LGR_DATEN_IMP_cobra',))\nprint(time.localtime())\nconpara.close()", "sub_path": "pyscript/SPRINT Tests/CopyBookingTemporary.py", "file_name": "CopyBookingTemporary.py", "file_ext": "py", "file_size_in_byte": 1590, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cx_Oracle.Connection", "line_number": 11, "usage_type": "call"}, {"api_name": "cx_Oracle.Connection", "line_number": 12, "usage_type": "call"}, {"api_name": "cx_Oracle.Connection", "line_number": 13, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 39, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 41, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "532462686", "text": "import paramiko\n\n\ndef execute(host, path, user):\n private = paramiko.RSAKey.from_private_key_file(path)\n transport = paramiko.Transport((host, 22))\n transport.connect(user, private)\n client = paramiko.SSHClient()\n client._transport = transport\n client.exec_command('python3 /opt/monitor/main.py')\n transport.close()\n\n\ndef transfer(host, path, user):\n private = paramiko.RSAKey.from_private_key_file(path)\n transport = paramiko.Transport((host, 22))\n transport.connect(user, private)\n sftp = paramiko.SFTPClient.from_transport(transport)\n sftp.put(localpath='./monitor', remotepath='/opt/monitor')\n transport.close()\n", "sub_path": "02.pythonbasis/16.GetServerInfo/monitorInfo.py", "file_name": "monitorInfo.py", "file_ext": "py", "file_size_in_byte": 655, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "paramiko.RSAKey.from_private_key_file", "line_number": 5, "usage_type": "call"}, {"api_name": "paramiko.RSAKey", "line_number": 5, "usage_type": "attribute"}, {"api_name": "paramiko.Transport", "line_number": 6, "usage_type": "call"}, {"api_name": "paramiko.SSHClient", "line_number": 8, "usage_type": "call"}, {"api_name": "paramiko.RSAKey.from_private_key_file", "line_number": 15, "usage_type": "call"}, {"api_name": "paramiko.RSAKey", "line_number": 15, "usage_type": "attribute"}, {"api_name": "paramiko.Transport", "line_number": 16, "usage_type": "call"}, {"api_name": "paramiko.SFTPClient.from_transport", "line_number": 18, "usage_type": "call"}, {"api_name": "paramiko.SFTPClient", "line_number": 18, "usage_type": "attribute"}]} +{"seq_id": "206516148", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('channels', '0007_channel_channel_content_amount'),\n ]\n\n operations = [\n migrations.AlterModelOptions(\n name='channel',\n options={'ordering': ['channel_content_amount', 'channel_created_date']},\n ),\n migrations.AddField(\n model_name='channel',\n name='channel_follows_amount',\n field=models.IntegerField(default=0, verbose_name=b'\\xe9\\xa2\\x91\\xe9\\x81\\x93\\xe5\\x85\\xb3\\xe6\\xb3\\xa8\\xe4\\xba\\xba\\xe6\\x95\\xb0'),\n ),\n ]\n", "sub_path": "channels/migrations/0008_auto_20150608_1448.py", "file_name": "0008_auto_20150608_1448.py", "file_ext": "py", "file_size_in_byte": 682, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterModelOptions", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "640494170", "text": "# -*- coding: utf-8 -*-\r\n# @Time : 2021/7/27 16:01\r\n# @Author : Kevin_liu\r\n# @Email : 87281094@qq.com\r\n# @File : db_to_excel.py\r\nimport xlwt\r\nfrom DBUtils import select\r\n\r\n\r\ndef mysql_to_excel():\r\n n = 1\r\n m = 0\r\n book = xlwt.Workbook()\r\n while True:\r\n if m < 12:\r\n sql = 'select * from %s月'\r\n data = [int(n)]\r\n model = 'all'\r\n record = select(sql, data, model, []) # 所有数据\r\n sheet = book.add_sheet(str(n) + '月', m) # 添加新的选项卡\r\n fields = ['日期', '服装名称', '价格/件', '库存数量', '销售量/每日'] # 获取所有字段名\r\n\r\n for col, field in enumerate(fields):\r\n sheet.write(0, col, field)\r\n\r\n row = 1\r\n for data in record:\r\n for col, field in enumerate(data):\r\n sheet.write(row, col, field)\r\n row += 1\r\n n = n + 1\r\n m = m + 1\r\n\r\n else:\r\n print('\\033[35;1m导入Excel完成！\\033[0m')\r\n book.save(\"2020年12个月销售情况.xls\")\r\n break\r\n", "sub_path": "day09/db_to_excel.py", "file_name": "db_to_excel.py", "file_ext": "py", "file_size_in_byte": 1141, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "xlwt.Workbook", "line_number": 13, "usage_type": "call"}, {"api_name": "DBUtils.select", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "102181897", "text": "# -*- coding:utf-8 -*-\n'''\n@time : 2018-08-20 10:49\n@author : Zephyr\n@file : 07按分区存储.py\n'''\nimport requests\nimport lxml\nfrom lxml import etree\nimport json\nimport time\nimport threading\n\nrlock = threading.RLock()\n\nheaders = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36\",\n}\n\n\ndef getArea(url):\n '''\n 获取城市分区\n :param url: 城市url\n :return: 分区字典\n '''\n response = requests.get(url, headers=headers)\n # nowurl = response.url\n mytree = lxml.etree.HTML(response.text)\n # 区域列表\n areaList = mytree.xpath('//div[@data-role=\"ershoufang\"]/div[1]/a')\n\n areaDict = {}\n\n for area in areaList:\n areaName = area.xpath('./text()')[0]\n areaurl = \"http://gz.lianjia.com\" + area.xpath('./@href')[0]\n print(areaName, areaurl)\n areaDict[areaName] = areaurl\n\n return areaDict\n\n\ndef getPage(url):\n '''\n 获取页数\n :param url: 分区url\n :return: 页数\n '''\n # url = \"https://gz.lianjia.com/ershoufang/pg1/\"\n\n response = requests.get(url, headers=headers)\n # print(response.text)\n\n mytree = lxml.etree.HTML(response.text)\n # 页数\n page = mytree.xpath('//div[@class=\"page-box house-lst-page-box\"]/@page-data')[0]\n totalPage = int(json.loads(page)['totalPage'])\n # print(totalPage)\n return totalPage\n\n\ndef getHouseInfo(urlList):\n '''\n 获取房子信息\n :param urlList :区+页 url列表\n :return:\n '''\n # url = \"https://gz.lianjia.com/ershoufang/pg1/\"\n\n for url in urlList:\n response = requests.get(url[1], headers=headers)\n # print(response.text)\n\n mytree = lxml.etree.HTML(response.text)\n\n # 房子列表\n houseList = mytree.xpath('//ul[@class=\"sellListContent\"]/li')\n\n for house in houseList:\n # 图片\n houseImg = house.xpath('./a/img/@data-original')[0]\n # 标题\n houseAlt = house.xpath('./a/img/@alt')[0]\n\n # 位置\n houseAddress = house.xpath('.//div[@class=\"houseInfo\"]/a/text()')[0] + \\\n house.xpath('.//div[@class=\"houseInfo\"]/text()')[0]\n # 楼层 小区\n positionInfo = house.xpath('.//div[@class=\"positionInfo\"]/text()')[0] + \\\n house.xpath('.//div[@class=\"positionInfo\"]/a/text()')[0]\n\n # 总价\n totalPrice = house.xpath('.//div[@class=\"totalPrice\"]/span/text()')[0] + \"万\"\n\n # 单价\n unitPrice = house.xpath('.//div[@class=\"unitPrice\"]/span/text()')[0]\n\n with rlock:\n with open('../data/' + url[0] + '.txt', 'a+', encoding='utf-8', errors='ignore') as f:\n # print(houseImg, houseAlt, houseAddress, positionInfo, totalPrice, unitPrice)\n f.write(str((houseImg, houseAlt, houseAddress, positionInfo, totalPrice, unitPrice)) + '\\n')\n f.flush()\n\n\nif __name__ == '__main__':\n url = \"https://gz.lianjia.com/ershoufang/pg1/\"\n areaDict = getArea(url)\n # 全部区+url\n newUrl = []\n for areaName, areaUrl in areaDict.items():\n totalPage = getPage(areaUrl)\n for i in range(1, totalPage + 1):\n url = areaUrl + \"pg%d\" % i\n # print(url)\n newUrl.append((areaName, url))\n\n print(newUrl)\n\n # 20 条线程\n # 二维列表\n cityAndAreaList = [[] for _ in range(20)]\n\n print(cityAndAreaList)\n\n for i in range(len(newUrl)):\n cityAndAreaList[i % 20].append(newUrl[i])\n\n # 开线程\n tList = []\n for urlList in cityAndAreaList:\n print(urlList)\n # print('*************************')\n\n t = threading.Thread(target=getHouseInfo, args=(urlList,))\n tList.append(t)\n t.start()\n\n for t in tList:\n t.join()\n", "sub_path": "Pspider/day06/coeds/07按分区存储.py", "file_name": "07按分区存储.py", "file_ext": "py", "file_size_in_byte": 3886, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "threading.RLock", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 29, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 29, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 52, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 55, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 55, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 58, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 72, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 75, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 75, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 135, "usage_type": "call"}]} +{"seq_id": "624908011", "text": "from django.core.management.base import BaseCommand, CommandError\nfrom optparse import make_option\nimport feedparser\n\nfrom content.models import Page, PageContent\nfrom editor.models import Comic, Article\nfrom django.contrib.auth.models import User\n\nclass Command( BaseCommand):\n # args = ''\n help = 'Pulls down a feed from arreseted motion for testing content.'\n requires_model_validation = True\n option_list = BaseCommand.option_list+(\n make_option('-u','--uri',\n action='store',\n type=\"string\",\n default=\"http://arrestedmotion.com/feed/\",\n ),\n )\n def handle( self, *a, **kw):\n feed = feedparser.parse( kw.get('uri'))\n am_bot, is_new = User.objects.get_or_create( \n username=\"amBot\",\n password=\"aoeu\",\n email=\"jbcurtin@arrestedmotion.com\",\n first_name=\"AM\",\n last_name=\"BOT\" )\n am_page, is_new = Page.objects.get_or_create(\n title=\"AM-Page\",\n parent=Page.objects.home_page,\n template=\"content/am.html\",\n )\n for entry in feed.entries:\n import ipdb\n ipdb.set_trace()\n content, is_new = Article.objects.get_or_create( \n title=entry.get('title'),\n content=entry.get('content')[0].get('value'), \n state=u\"base\",\n author=am_bot)\n if is_new:\n PageContent.objects.addContent( \n content=content,\n page=am_page\n )", "sub_path": "src/editor/management/commands/editor_digest_am.py", "file_name": "editor_digest_am.py", "file_ext": "py", "file_size_in_byte": 1568, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 9, "usage_type": "name"}, {"api_name": "django.core.management.base.BaseCommand.option_list", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 13, "usage_type": "name"}, {"api_name": "optparse.make_option", "line_number": 14, "usage_type": "call"}, {"api_name": "feedparser.parse", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get_or_create", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 22, "usage_type": "name"}, {"api_name": "content.models.Page.objects.get_or_create", "line_number": 28, "usage_type": "call"}, {"api_name": "content.models.Page.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "content.models.Page", "line_number": 28, "usage_type": "name"}, {"api_name": "content.models.Page.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "content.models.Page", "line_number": 30, "usage_type": "name"}, {"api_name": "ipdb.set_trace", "line_number": 35, "usage_type": "call"}, {"api_name": "content.models", "line_number": 36, "usage_type": "name"}, {"api_name": "editor.models.Article.objects.get_or_create", "line_number": 36, "usage_type": "call"}, {"api_name": "editor.models.Article.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "editor.models.Article", "line_number": 36, "usage_type": "name"}, {"api_name": "content.models.PageContent.objects.addContent", "line_number": 42, "usage_type": "call"}, {"api_name": "content.models.PageContent.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "content.models.PageContent", "line_number": 42, "usage_type": "name"}, {"api_name": "content.models", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "372337801", "text": "'''\nGiven a list of accounts where each element accounts[i] is a list of strings, where the first element accounts[i][0] is a name, and the rest of the elements are emails representing emails of the account.\n\nNow, we would like to merge these accounts. Two accounts definitely belong to the same person if there is some common email to both accounts. Note that even if two accounts have the same name, they may belong to different people as people could have the same name. A person can have any number of accounts initially, but all of their accounts definitely have the same name.\n\nAfter merging the accounts, return the accounts in the following format: the first element of each account is the name, and the rest of the elements are emails in sorted order. The accounts themselves can be returned in any order.\n\n \n\nExample 1:\n\nInput: accounts = [[\"John\",\"johnsmith@mail.com\",\"john_newyork@mail.com\"],[\"John\",\"johnsmith@mail.com\",\"john00@mail.com\"],[\"Mary\",\"mary@mail.com\"],[\"John\",\"johnnybravo@mail.com\"]]\nOutput: [[\"John\",\"john00@mail.com\",\"john_newyork@mail.com\",\"johnsmith@mail.com\"],[\"Mary\",\"mary@mail.com\"],[\"John\",\"johnnybravo@mail.com\"]]\nExplanation:\nThe first and second John's are the same person as they have the common email \"johnsmith@mail.com\".\nThe third John and Mary are different people as none of their email addresses are used by other accounts.\nWe could return these lists in any order, for example the answer [['Mary', 'mary@mail.com'], ['John', 'johnnybravo@mail.com'], \n['John', 'john00@mail.com', 'john_newyork@mail.com', 'johnsmith@mail.com']] would still be accepted.\nExample 2:\n\nInput: accounts = [[\"Gabe\",\"Gabe0@m.co\",\"Gabe3@m.co\",\"Gabe1@m.co\"],[\"Kevin\",\"Kevin3@m.co\",\"Kevin5@m.co\",\"Kevin0@m.co\"],[\"Ethan\",\"Ethan5@m.co\",\"Ethan4@m.co\",\"Ethan0@m.co\"],[\"Hanzo\",\"Hanzo3@m.co\",\"Hanzo1@m.co\",\"Hanzo0@m.co\"],[\"Fern\",\"Fern5@m.co\",\"Fern1@m.co\",\"Fern0@m.co\"]]\nOutput: [[\"Ethan\",\"Ethan0@m.co\",\"Ethan4@m.co\",\"Ethan5@m.co\"],[\"Gabe\",\"Gabe0@m.co\",\"Gabe1@m.co\",\"Gabe3@m.co\"],[\"Hanzo\",\"Hanzo0@m.co\",\"Hanzo1@m.co\",\"Hanzo3@m.co\"],[\"Kevin\",\"Kevin0@m.co\",\"Kevin3@m.co\",\"Kevin5@m.co\"],[\"Fern\",\"Fern0@m.co\",\"Fern1@m.co\",\"Fern5@m.co\"]]\n \n\nConstraints:\n\n1 <= accounts.length <= 1000\n2 <= accounts[i].length <= 10\n1 <= accounts[i][j] <= 30\naccounts[i][0] consists of English letters.\naccounts[i][j] (for j > 0) is a valid email.\n'''\nfrom typing import List\nfrom collections import defaultdict\nclass UnionFind:\n def __init__(self, size):\n self.root = list(range(size))\n def find(self, x):\n if x != self.root[x]:\n self.root[x] = self.find(self.root[x])\n return self.root[x]\n def union(self, x, y):\n rootX = self.find(x)\n rootY = self.find(y)\n if rootX != rootY:\n self.root[rootX] = rootY\n\nclass Solution:\n def accountsMerge(self, accounts: List[List[str]]) -> List[List[str]]:\n uf = UnionFind(len(accounts))\n ownerIds = {}\n for ownerId, (_, *emails) in enumerate(accounts):\n for email in emails:\n if email in ownerIds:\n uf.union(ownerId, ownerIds[email])\n ownerIds[email] = ownerId\n memo = defaultdict(list)\n for email, ownerId in ownerIds.items():\n rootOwnerId = uf.find(ownerId)\n memo[rootOwnerId].append(email)\n return [[accounts[i][0]] + sorted(emails) for i, emails in memo.items()]\n\nimport unittest\nfunctions = [Solution().__getattribute__(f) for f in dir(Solution()) if not f.startswith('__')]\nclass Test(unittest.TestCase): \n def test1(self):\n for f in functions:\n input = [[\"John\",\"johnsmith@mail.com\",\"john_newyork@mail.com\"],[\"John\",\"johnsmith@mail.com\",\"john00@mail.com\"],[\"Mary\",\"mary@mail.com\"],[\"John\",\"johnnybravo@mail.com\"]]\n expected = [[\"John\",\"john00@mail.com\",\"john_newyork@mail.com\",\"johnsmith@mail.com\"],[\"Mary\",\"mary@mail.com\"],[\"John\",\"johnnybravo@mail.com\"]]\n self.assertCountEqual(f(input), expected, f.__name__)\n def test2(self):\n for f in functions:\n input = [[\"David\",\"David0@m.co\",\"David1@m.co\"],[\"David\",\"David3@m.co\",\"David4@m.co\"],[\"David\",\"David4@m.co\",\"David5@m.co\"],[\"David\",\"David2@m.co\",\"David3@m.co\"],[\"David\",\"David1@m.co\",\"David2@m.co\"]]\n expected = [[\"David\",\"David0@m.co\",\"David1@m.co\",\"David2@m.co\",\"David3@m.co\",\"David4@m.co\",\"David5@m.co\"]]\n self.assertCountEqual(f(input), expected, f.__name__)\nunittest.main()\n \n ", "sub_path": "leetcode/LC721. Accounts Merge.py", "file_name": "LC721. Accounts Merge.py", "file_ext": "py", "file_size_in_byte": 4485, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.List", "line_number": 49, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 57, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 65, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "425979838", "text": "import itertools\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\nfrom scipy.spatial import ConvexHull\r\ndef func():\r\n dim = 4\r\n gervec = list(itertools.product(range(2), repeat=dim))\r\n pts = []\r\n for r in range(1, len(gervec)):\r\n comb = itertools.combinations(gervec, r)\r\n for lists in comb:\r\n # print('list: ', lists)\r\n pt = np.array(lists).sum(axis=0, dtype=int)\r\n pts.append(list(pt))\r\n # print(pt)\r\n\r\n pts = np.array(pts)\r\n\r\n hull = ConvexHull(pts)\r\n # f = open(\"d\" + str(dim) + \".txt\", \"w\")\r\n # f.write(pts[hull.vertices])\r\n # f.close()\r\n\r\n # pts[hull.vertices].tofile(\"d\" + str(dim) + \".txt\", sep=\",\", format=\"%s\")\r\n np.savetxt(\"d\" + str(dim) + \".csv\", pts[hull.vertices], fmt='%s', delimiter=',', newline='\\n')\r\n\r\n print(pts[hull.vertices])\r\n\r\n\r\n", "sub_path": "CmemNdcomp.py", "file_name": "CmemNdcomp.py", "file_ext": "py", "file_size_in_byte": 861, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "itertools.product", "line_number": 8, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "scipy.spatial.ConvexHull", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "490408345", "text": "from rest_framework.response import Response\nfrom rest_framework.exceptions import ValidationError\nfrom rest_framework.views import APIView\nfrom django.contrib.auth.models import AnonymousUser\nfrom rest_framework.authentication import BasicAuthentication\nfrom django.conf import settings\n\nfrom RecommendationApp.forms import GenreForm\nfrom FilmsApp.models import MarkType, Mark\nfrom WTW.csrf import CsrfExemptSessionAuthentication\nfrom .models import Voting, Room, FilmsForVoting, MarkTypesForRecommendation, Invitation\nfrom .forms import FilmForVotingRadioForm\nfrom .views import RoomView\n\nREQUIRED_GRADED = settings.REQUIRED_GRADED\n\n\nclass VoteApiView(APIView, RoomView):\n authentication_classes = (CsrfExemptSessionAuthentication, BasicAuthentication)\n\n def post(self, request, *args, **kwargs):\n if not self.request.user.is_authenticated():\n raise ValidationError('U are not authenticated')\n self.kwargs['room_id'] = kwargs[\"room_id\"]\n try:\n room = Room.objects.get(id=kwargs[\"room_id\"])\n if room.user != request.user:\n Invitation.objects.get(room=room, user=request.user)\n except Room.DoesNotExist:\n raise ValidationError('Room does not exist')\n except Invitation.DoesNotExist:\n raise ValidationError('U are not in this room')\n if not room.voting:\n raise ValidationError('Voting not in process in this room')\n chosen = request.data.get(\"films_for_voting\",[])\n kwargs = {\n \"data\" :{'films_for_voting': chosen}\n }\n films_for_voting = self._get_films_for_voting()\n kwargs.update(films_for_voting)\n form = FilmForVotingRadioForm(**kwargs)\n\n if form.is_valid():\n for film in films_for_voting[\"films_for_voting\"]:\n if str(film.film_id) == chosen:\n film_for_voting = FilmsForVoting.objects.get(room=room, film=film.film)\n\n try:\n Voting.objects.get(film_for_voting__room=room, user=request.user)\n raise ValidationError(\"You have already voted\")\n except Voting.DoesNotExist:\n Voting.objects.create(film_for_voting=film_for_voting, user=request.user)\n break\n room.send_message(chosen, \"new_vote\", AnonymousUser())\n return Response(\"OK\")\n else:\n raise ValidationError(\"Film id is not valid\")\n\n\nclass ChooseCriteriaView(APIView):\n authentication_classes = (CsrfExemptSessionAuthentication, BasicAuthentication)\n\n def post(self, request, *args, **kwargs):\n if not self.request.user.is_authenticated():\n raise ValidationError('U are not authenticated')\n self.kwargs['room_id'] = kwargs[\"room_id\"]\n try:\n room = Room.objects.get(id=kwargs[\"room_id\"])\n if room.user != request.user:\n Invitation.objects.get(room=room, user=request.user)\n except Room.DoesNotExist:\n raise ValidationError('Room does not exist')\n except Invitation.DoesNotExist:\n raise ValidationError('U are not in this room')\n if room.recommendations:\n raise ValidationError('Voting for criteria is not allowed in this room now')\n graded_movies = Mark.get_graded_number(user=request.user)\n if graded_movies < REQUIRED_GRADED:\n raise ValidationError('You need to grade ' + str(REQUIRED_GRADED - graded_movies) +\n ' more films to be able to choose criteria')\n chosen = request.data.get(\"genres\", [])\n if chosen:\n chosen = request.data.getlist(\"genres\")\n kwargs = {\n \"data\": {'genres': chosen}\n }\n form = GenreForm(language_code=self.request.LANGUAGE_CODE, **kwargs)\n if form.is_valid():\n chose = MarkTypesForRecommendation.objects.filter(room=room, user=request.user)\n if chose:\n raise ValidationError(\"You have already submitted your choice\")\n for id in form.cleaned_data[\"genres\"]:\n mark_type = MarkType.objects.get(id=id)\n MarkTypesForRecommendation.objects.create(room=room,user=request.user,mark_type=mark_type)\n room.send_message(\"\", \"new_criteria\", room.user)\n return Response(\"OK\")\n else:\n raise ValidationError(\"Mark type id is not valid\")\n\n\nclass GetRoomRecommendationView(APIView, RoomView):\n authentication_classes = (CsrfExemptSessionAuthentication, BasicAuthentication)\n\n def get(self, request, *args, **kwargs):\n if not self.request.user.is_authenticated():\n raise ValidationError('U are not authenticated')\n self.kwargs['room_id'] = kwargs[\"room_id\"]\n try:\n room = Room.objects.get(id=kwargs[\"room_id\"])\n self.kwargs['room_id'] = room.id\n except Room.DoesNotExist:\n raise ValidationError('Room does not exist')\n if request.user != room.user:\n raise ValidationError(\"U ARE NOT ADMIN IN THIS ROOM\")\n if not room.recommendations and not MarkTypesForRecommendation.objects.filter(room=room).count():\n raise ValidationError(\"Not enough choices of criteria\")\n if room.recommendations:\n MarkTypesForRecommendation.objects.filter(room=room).delete()\n data = \"reset\"\n else:\n data = self._get_recommended_films(room)\n room.recommendations = not room.recommendations\n room.save()\n room.send_message(data, \"recommendations\", request.user)\n return Response(\"OK\")\n\n\n\n\n\n\n", "sub_path": "RoomApp/apiviews.py", "file_name": "apiviews.py", "file_ext": "py", "file_size_in_byte": 5683, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.settings.REQUIRED_GRADED", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 15, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 18, "usage_type": "name"}, {"api_name": "views.RoomView", "line_number": 18, "usage_type": "name"}, {"api_name": "WTW.csrf.CsrfExemptSessionAuthentication", "line_number": 19, "usage_type": "name"}, {"api_name": "rest_framework.authentication.BasicAuthentication", "line_number": 19, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Room.objects.get", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Room.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Room", "line_number": 26, "usage_type": "name"}, {"api_name": "models.Invitation.objects.get", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Invitation.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.Invitation", "line_number": 28, "usage_type": "name"}, {"api_name": "models.Room.DoesNotExist", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.Room", "line_number": 29, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Invitation.DoesNotExist", "line_number": 31, "usage_type": "attribute"}, {"api_name": "models.Invitation", "line_number": 31, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 32, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 34, "usage_type": "call"}, {"api_name": "forms.FilmForVotingRadioForm", "line_number": 41, "usage_type": "call"}, {"api_name": "models.FilmsForVoting.objects.get", "line_number": 46, "usage_type": "call"}, {"api_name": "models.FilmsForVoting.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.FilmsForVoting", "line_number": 46, "usage_type": "name"}, {"api_name": "models.Voting.objects.get", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Voting.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "models.Voting", "line_number": 49, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Voting.DoesNotExist", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.Voting", "line_number": 51, "usage_type": "name"}, {"api_name": "models.Voting.objects.create", "line_number": 52, "usage_type": "call"}, {"api_name": "models.Voting.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "models.Voting", "line_number": 52, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.AnonymousUser", "line_number": 54, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 55, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 57, "usage_type": "call"}, {"api_name": "rest_framework.views.APIView", "line_number": 60, "usage_type": "name"}, {"api_name": "WTW.csrf.CsrfExemptSessionAuthentication", "line_number": 61, "usage_type": "name"}, {"api_name": "rest_framework.authentication.BasicAuthentication", "line_number": 61, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 65, "usage_type": "call"}, {"api_name": "models.Room.objects.get", "line_number": 68, "usage_type": "call"}, {"api_name": "models.Room.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "models.Room", "line_number": 68, "usage_type": "name"}, {"api_name": "models.Invitation.objects.get", "line_number": 70, "usage_type": "call"}, {"api_name": "models.Invitation.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "models.Invitation", "line_number": 70, "usage_type": "name"}, {"api_name": "models.Room.DoesNotExist", "line_number": 71, "usage_type": "attribute"}, {"api_name": "models.Room", "line_number": 71, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 72, "usage_type": "call"}, {"api_name": "models.Invitation.DoesNotExist", "line_number": 73, "usage_type": "attribute"}, {"api_name": "models.Invitation", "line_number": 73, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 74, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 76, "usage_type": "call"}, {"api_name": "FilmsApp.models.Mark.get_graded_number", "line_number": 77, "usage_type": "call"}, {"api_name": "FilmsApp.models.Mark", "line_number": 77, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 79, "usage_type": "call"}, {"api_name": "RecommendationApp.forms.GenreForm", "line_number": 87, "usage_type": "call"}, {"api_name": "models.MarkTypesForRecommendation.objects.filter", "line_number": 89, "usage_type": "call"}, {"api_name": "models.MarkTypesForRecommendation.objects", "line_number": 89, "usage_type": "attribute"}, {"api_name": "models.MarkTypesForRecommendation", "line_number": 89, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 91, "usage_type": "call"}, {"api_name": "FilmsApp.models.MarkType.objects.get", "line_number": 93, "usage_type": "call"}, {"api_name": "FilmsApp.models.MarkType.objects", "line_number": 93, "usage_type": "attribute"}, {"api_name": "FilmsApp.models.MarkType", "line_number": 93, "usage_type": "name"}, {"api_name": "models.MarkTypesForRecommendation.objects.create", "line_number": 94, "usage_type": "call"}, {"api_name": "models.MarkTypesForRecommendation.objects", "line_number": 94, "usage_type": "attribute"}, {"api_name": "models.MarkTypesForRecommendation", "line_number": 94, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 96, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 98, "usage_type": "call"}, {"api_name": "rest_framework.views.APIView", "line_number": 101, "usage_type": "name"}, {"api_name": "views.RoomView", "line_number": 101, "usage_type": "name"}, {"api_name": "WTW.csrf.CsrfExemptSessionAuthentication", "line_number": 102, "usage_type": "name"}, {"api_name": "rest_framework.authentication.BasicAuthentication", "line_number": 102, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 106, "usage_type": "call"}, {"api_name": "models.Room.objects.get", "line_number": 109, "usage_type": "call"}, {"api_name": "models.Room.objects", "line_number": 109, "usage_type": "attribute"}, {"api_name": "models.Room", "line_number": 109, "usage_type": "name"}, {"api_name": "models.Room.DoesNotExist", "line_number": 111, "usage_type": "attribute"}, {"api_name": "models.Room", "line_number": 111, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 112, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 114, "usage_type": "call"}, {"api_name": "models.MarkTypesForRecommendation.objects.filter", "line_number": 115, "usage_type": "call"}, {"api_name": "models.MarkTypesForRecommendation.objects", "line_number": 115, "usage_type": "attribute"}, {"api_name": "models.MarkTypesForRecommendation", "line_number": 115, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 116, "usage_type": "call"}, {"api_name": "models.MarkTypesForRecommendation.objects.filter", "line_number": 118, "usage_type": "call"}, {"api_name": "models.MarkTypesForRecommendation.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "models.MarkTypesForRecommendation", "line_number": 118, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 125, "usage_type": "call"}]} +{"seq_id": "301899858", "text": "#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n# author:黎涛\n# datetime:2018/11/24 10:37\n# software:PyCharm Community Edition\n\nfrom selenium import webdriver\n\nwd = webdriver.Chrome(executable_path=\"E:\\\\Download\\\\chromedriver.exe\")\nurl = \"https://www.baidu.com/\"\nwd.get(url)\n", "sub_path": "自动化webdriver/Test1.py", "file_name": "Test1.py", "file_ext": "py", "file_size_in_byte": 274, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 9, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "376954480", "text": "from flask import Flask, render_template, jsonify, redirect, request\nimport random\napp = Flask(__name__)\n\n\ningredients = [\"apples\", \"flour\", \"sugar\", \"love\"]\n\n@app.route('/')\ndef Hello():\n return 'Hello World 👋🏼🌍'\n\n\n@app.route('/greeting/')\ndef Greeting(name):\n return render_template('greeting.html', name=name)\n\n\n@app.route('/pie')\ndef Ingrediets():\n global ingredients\n return jsonify({'pie ingredient': random.choice(ingredients) })\n\n\n@app.route('/recipe', methods=['GET', 'POST'])\ndef Recipe():\n if request.method == 'POST':\n new_ingredient = request.form[\"ingredient\"]\n ingredients.append(new_ingredient)\n return redirect('/recipe')\n else:\n \n return render_template('recipe.html', ingredients )\n\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 770, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 21, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "624161549", "text": "import osmnx as ox\nimport networkx as nx\n\nfrom PathFinder import PathFinder\n\n\nclass DijkstraForCar(PathFinder):\n \"\"\"Поиск пути для водителя алгоритмом Дейкстры\"\"\"\n\n def __init__(self):\n super().__init__()\n # Граф дорог для велосипедиста\n self._road_graph = ox.graph_from_place(\"Barnaul, Russia\", network_type=\"drive\")\n\n def find_path(self, start_longitude: float, start_latitude: float,\n destination_longitude: float, destination_latitude: float):\n # Находим ближайщие вершины в графе дорог\n start_node = ox.get_nearest_node(self._road_graph, (start_latitude, start_longitude))\n end_node = ox.get_nearest_node(self._road_graph, (destination_latitude, destination_longitude))\n\n # Вычисялем путь алгоритмом Дейкстры\n path = nx.dijkstra_path(self._road_graph, start_node, end_node)\n\n # Добавляем в список координаты вершин\n path = self._path_id_to_path_coordinates(path)\n\n # Добавля��м в список точку назначения\n path.append({\n \"id\": \"\",\n \"longitude\": destination_longitude,\n \"latitude\": destination_latitude\n })\n\n return path\n", "sub_path": "src/DijkstraForCar.py", "file_name": "DijkstraForCar.py", "file_ext": "py", "file_size_in_byte": 1371, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PathFinder.PathFinder", "line_number": 7, "usage_type": "name"}, {"api_name": "osmnx.graph_from_place", "line_number": 13, "usage_type": "call"}, {"api_name": "osmnx.get_nearest_node", "line_number": 18, "usage_type": "call"}, {"api_name": "osmnx.get_nearest_node", "line_number": 19, "usage_type": "call"}, {"api_name": "networkx.dijkstra_path", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "465581695", "text": "#!/usr/bin/env python\r\n# coding: utf-8\r\n\r\n'''双十一渠道名称、url、pv数、ip数统计'''\r\n\r\nimport time\r\nimport smtplib\r\nfrom email.mime.text import MIMEText\r\nfrom email.header import Header\r\n\r\nimport sys\r\n\r\ntoday = time.time()\r\nyesterday = time.localtime(today - 60 * 60 * 24)\r\nyear, month, day = yesterday.tm_year, yesterday.tm_mon, yesterday.tm_mday\r\nyear = str(year)\r\nmonth = str(month) if month > 9 else '0' + str(month)\r\nday = str(day) if day > 9 else '0' + str(day)\r\ndate_str = str(year) + str(month) + str(day)\r\nmail_title = \"双11各来源渠道统计 \" + date_str\r\n# mail_list = [\"zhangwen@ichunqiu.com\", \"liuxiaoyue@ichunqiu.com\"]\r\nmail_list = [\"zhangwen@ichunqiu.com\"] \r\n# mail_list = [\r\n# \"zhangyanhua@ichunqiu.com\",\r\n# \"wujian@ichunqiu.com\",\r\n# \"liuxiaoyue@ichunqiu.com\",\r\n# \"zhengfeifei@ichunqiu.com\",\r\n# \"liyongfeng@ichunqiu.com\",\r\n# \"shida@ichunqiu.com\",\r\n# \"zhangwen@ichunqiu.com\",\r\n#]\r\nlogfile = sys.argv[1]\r\n\r\n# https://www.ichunqiu.com/active2017/Topic/index\r\nbaseurl = \"https://www.ichunqiu.com\"\r\nsuburl = \"/active2017/Topic/index\"\r\nchannel = {\r\n # i春秋\r\n '?from=banner': 'banner图',\r\n '?from=hot0': '首页热门推荐图',\r\n '?from=src': 'SRC-热门推荐图、SRC渠道：微信、朋友圈等-雅弛',\r\n '?from=so': '搜索框文字链',\r\n '?from=logo': 'i春秋-logo',\r\n '?from=login': '登录页图标',\r\n '?from=register': '注册页图标',\r\n '?from=course': '视频暂停播放页弹窗',\r\n '?from=register1': '注册右侧文字链',\r\n '?from=window': '首页弹窗',\r\n '?from=right': '全站锚点入口',\r\n\r\n # 社区\r\n '?from=bbs': '论坛banner图',\r\n '?from=bbs1': '论坛帖子页右侧图',\r\n '?from=bbs2': '论坛置顶帖-内广告图',\r\n\r\n # APP\r\n '?from=app': '开屏、banner、发现活动、短信、APP-push',\r\n\r\n # 短消息\r\n '?from=sns': '站内信',\r\n # '?from=app': '短信',\r\n # '?from=app': 'APP-push',\r\n\r\n # 社群推广\r\n '?from=qq': 'QQ用户群-长拟、用户活动群+职场群-艳华',\r\n\r\n # SRC\r\n # '?from=src': 'SRC渠道：微信、朋友圈等-雅弛',\r\n\r\n # 市场部\r\n '?from=wx': '微信推文、合作白帽子微信群-市场部',\r\n # 'from=wx': '合作白帽子微信群-市场部',\r\n '?from=wb': '微博',\r\n '?from=yidian': '一点资讯',\r\n '?from=toutiao': '今日头条',\r\n '?from=mofa': '春秋圈魔法日报',\r\n '?from=zhihu': '知乎',\r\n '?from=esafety': '易安全',\r\n '?from=aqk1': '安全客',\r\n\r\n # 站外渠道\r\n '?from=beef': 'freebuf',\r\n '?from=aqk': '安全客',\r\n '?from=sf': '指尖安全',\r\n '?from=jk': '即刻安全',\r\n '?from=wi': '威客安全',\r\n}\r\nshare_page_url = '/active2017/Topic/webGroup?'\r\ndata = {}\r\nsum_info = {\r\n 'pv': 0, # 总pv数\r\n 'name': '【渠道来源汇总】',\r\n 'url': '/',\r\n 'ip': {} # 总ip数\r\n}\r\n\r\nshare_page_info = {\r\n 'pv': 0, # 分享页面pv数\r\n 'name': '【团购分享页面汇总】',\r\n 'url': '/',\r\n 'ip': {} # 分享页面ip数\r\n}\r\n\r\nfor url in channel:\r\n data.setdefault(url, {})\r\n data[url].setdefault('channel_name', '')\r\n data[url].setdefault('ip', {})\r\n data[url].setdefault('pv', 0)\r\n\r\n\r\ndef send_email(content_str):\r\n msg = MIMEText(content_str, 'html', 'utf-8')\r\n msg['From'] = Header('i春秋', 'utf-8') # 在收到邮件时，设置发件人的显示形式\r\n msg['Subject'] = mail_title\r\n msg['To'] = ', '.join(mail_list) # 此行不写，收件人会这样显示，收件人: Undisclosed recipients:\r\n\r\n smtp = smtplib\r\n smtp = smtplib.SMTP()\r\n smtp.connect('mail.xxx.com', 587)\r\n smtp.login('service.xxx.com', 'passwd')\r\n smtp.sendmail('service@xxx.com', mail_list, msg.as_string())\r\n\r\n print('%s ：sending email...' % time.ctime())\r\n smtp.quit()\r\n\r\n\r\ndef parse_log():\r\n with open(logfile, 'r') as fd:\r\n for line in fd:\r\n msg = line.strip().split()\r\n\r\n if msg:\r\n req_url = msg[6]\r\n unique_ip = msg[-3]\r\n\r\n if req_url.startswith(suburl) or req_url.startswith(share_page_url):\r\n for item in channel:\r\n url = suburl + item\r\n\r\n if url in req_url:\r\n # print 'url: ', url, 'req_url: ', req_url\r\n req_url = req_url.split(suburl)[1] \r\n\r\n data[item]['channel_name'] = channel[item]\r\n data[item]['pv'] += 1\r\n\r\n if data[item]['ip'].has_key(unique_ip):\r\n continue\r\n else:\r\n data[item]['ip'][unique_ip] = \"1\"\r\n if req_url.startswith(share_page_url):\r\n share_page_info['pv'] += 1\r\n\r\n if share_page_info['ip'].has_key(unique_ip):\r\n continue\r\n else:\r\n # share_page_info['ip'].setdefault(unique_ip, \"1\")\r\n share_page_info['ip'][unique_ip] = \"1\"\r\n\r\n return data\r\n\r\n\r\ndef process_data(data):\r\n data_str = \"\"\"\r\n \r\n \r\n \"\"\"\r\n data_str += ''\r\n data_str += ''\r\n\r\n # 排序，\r\n sorted_list = sorted(data.items(), key=lambda item: item[1]['pv'], reverse=True)\r\n\r\n for item in sorted_list:\r\n url = baseurl + suburl + item[0]\r\n cname = item[1]['channel_name']\r\n pv = item[1]['pv']\r\n ip_dic = item[1]['ip']\r\n ip = len(ip_dic)\r\n\r\n # 总pv, uv统计\r\n sum_info['pv'] += pv\r\n for i in ip_dic:\r\n if sum_info['ip'].has_key(i):\r\n continue\r\n else:\r\n sum_info['ip'][i] = 'real ip addr'\r\n\r\n # 如果渠道名或者是pv数为0则表示这些渠道没有流量\r\n if cname == '' or pv == 0: continue\r\n\r\n data_str += '' \\\r\n '' \\\r\n '' \\\r\n '' \\\r\n '' \\\r\n ''.format(url=url, cname=cname, pv=pv, ip=ip)\r\n\r\n data_str += '' \\\r\n '' \\\r\n '' \\\r\n '' \\\r\n '' \\\r\n ''.format(url=sum_info['url'], cname=sum_info['name'], pv=sum_info['pv'], ip=len(sum_info['ip']))\r\n\r\n data_str += '' \\\r\n '' \\\r\n '' \\\r\n '' \\\r\n '' \\\r\n ''.format(url=share_page_info['url'], cname=share_page_info['name'], pv=share_page_info['pv'], ip=len(share_page_info['ip']))\r\n\r\n data_str += '
    url渠道名称pvip(独立ip数)
    {url}{cname}{pv}{ip}
    {url}{cname}{pv}{ip}
    {url}{cname}{pv}{ip}
    '\r\n\r\n return data_str\r\n\r\n\r\ndef main():\r\n data = parse_log()\r\n mail_content = process_data(data)\r\n send_email(mail_content)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n", "sub_path": "py_utils/pyscripts/double_eleven_info_statistics.py", "file_name": "double_eleven_info_statistics.py", "file_ext": "py", "file_size_in_byte": 10647, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.time", "line_number": 13, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 32, "usage_type": "attribute"}, {"api_name": "email.mime.text.MIMEText", "line_number": 112, "usage_type": "call"}, {"api_name": "email.header.Header", "line_number": 113, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 118, "usage_type": "call"}, {"api_name": "time.ctime", "line_number": 123, "usage_type": "call"}]} +{"seq_id": "145078620", "text": "#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Aug 24 14:35:23 2020\r\n\r\n@author: rhoover\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom scipy import fftpack\r\nfrom scipy import linalg\r\n\r\n## Tensor functions for evaluation ##\r\ndef t_svd(A):\r\n n1,n2,n3 = A.shape\r\n F = np.fft.fft(A,axis=0) # Python ordering (axis=0 implies the \"depth\" axis)\r\n tempU = np.zeros((n1,n2,n2),dtype=complex)\r\n tempS = np.zeros((n1,n2,n3),dtype=complex)\r\n tempVt = np.zeros((n1,n3,n3),dtype=complex)\r\n for i in range(0,n1):\r\n M = F[i,0:]\r\n U,S,Vt = linalg.svd(M,full_matrices=True)\r\n tempU[i,0:] = U\r\n tempS[i,0:] = linalg.diagsvd(S,n2,n3) # need to re-case as matrix #\r\n tempVt[i,0:] = Vt.T\r\n\r\n Ut = np.real(np.fft.ifft(tempU,axis=0))\r\n St = np.real(np.fft.ifft(tempS,axis=0))\r\n Vt_t = np.real(np.fft.ifft(tempVt,axis=0))\r\n return Ut,St,Vt_t\r\n\r\ndef t_eig(A):\r\n n1,n2,n3 = A.shape\r\n if(n2 != n3):\r\n raise RuntimeError(\"Faces are not square\")\r\n else:\r\n F = fftpack.fft(A,axis=0) # Python ordering (axis=0 implies the \"depth\" axis)\r\n tempD = np.zeros((n1,n2,n3),dtype=complex)\r\n tempV = np.zeros((n1,n2,n3),dtype=complex)\r\n for i in range(0,n1):\r\n M = F[i,0:]\r\n Dt,Vt = np.linalg.eig(M)\r\n ## need to sort in decending order for consistancy ##\r\n delta = np.abs(Dt)\r\n idx = delta.argsort()[::-1]\r\n Dt = np.diag(Dt[idx])\r\n Vt = Vt[:,idx]\r\n tempD[i,0:] = Dt\r\n tempV[i,0:] = Vt\r\n DD = np.real(fftpack.ifft(tempD,axis=0))\r\n VV = np.real(fftpack.ifft(tempV,axis=0))\r\n return DD,VV\r\n\r\ndef unfold(A):\r\n n1,n2,n3 = A.shape\r\n return A.reshape(n1*n2,n3)\r\n\r\ndef tcirc(A):\r\n n1,n2,n3 = A.shape\r\n Av = unfold(A)\r\n temp = np.zeros((n1*n2,n1*n3))\r\n Ar = np.roll(Av,n2,axis=0)\r\n temp[:,0:n3] = Av\r\n for i in range(1,n1):\r\n temp[:,i*n3:(i+1)*n3] = Ar\r\n Ar = np.roll(Ar,n2,axis=0)\r\n return temp\r\n\r\ndef fold(A,r,c,d):\r\n ## Expects a r*d x c block matrix ##\r\n return A.reshape(r,c,d)\r\n\r\ndef tprod(A,B):\r\n n1,n2,n3 = A.shape\r\n na,n4,nb = B.shape\r\n #print(B.shape)\r\n if(n1 != na) or (n3 != n4):\r\n raise RuntimeError(\"Incompatable Dimensions\")\r\n else:\r\n return fold(tcirc(A) @ unfold(B),n1,n2,nb)\r\n\r\ndef ttran(A):\r\n n1,n2,n3 = A.shape\r\n Ac = tcirc(A).T\r\n Ac = Ac[:,0:n3]\r\n return fold(Ac,n1,n2,n3)\r\n\r\ndef tinv(A):\r\n n1,n2,n3 = A.shape\r\n Ac = tcirc(A)\r\n Ac = np.linalg.inv(Ac)\r\n Ac = Ac[:,0:n3]\r\n return fold(Ac,n1,n2,n3)\r\n\r\ndef teye(n):\r\n I = np.zeros((n,n,n))\r\n I[0,:,:] = np.eye(n)\r\n return I\r\n\r\ndef tfronorm(A):\r\n temp = A*A\r\n return np.sqrt(np.sum(np.abs(temp)))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "riley/Misc/t_svd.py", "file_name": "t_svd.py", "file_ext": "py", "file_size_in_byte": 2819, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.fft.fft", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 19, "usage_type": "call"}, {"api_name": "scipy.linalg.svd", "line_number": 22, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 22, "usage_type": "name"}, {"api_name": "scipy.linalg.diagsvd", "line_number": 24, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.real", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.fft.ifft", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.real", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.fft.ifft", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.real", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.fft.ifft", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 29, "usage_type": "attribute"}, {"api_name": "scipy.fftpack.fft", "line_number": 37, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.linalg.eig", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 50, "usage_type": "call"}, {"api_name": "scipy.fftpack.ifft", "line_number": 50, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.real", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.fftpack.ifft", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 51, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 102, "usage_type": "call"}]} +{"seq_id": "138318398", "text": "# IN\n# Import the modules we use.\nimport datetime\nimport io\nimport numpy as np\nimport os\nimport pandas as pd\nimport re\nimport requests\nimport sys\nimport zipfile\n\n# Import the configuration.\nimport edgar_logs_config\n\n# Create logfile.\nlogfile = open(\"edgar-logs-logfile.txt\", \"w\")\ndef log_entry(s):\n #print('Date now: %s' % datetime.datetime.now())\n\n timestamp = '[%s] ' % datetime.datetime.now()\n log_line = timestamp + str(s)\n logfile.write(log_line)\n logfile.flush()\n\n # Also write to standard output as a convenience.\n print(log_line)\n\n# IN\n# User provided inputs.\n# year = \"2003\"\nyear = edgar_logs_config.year\nif year is None or year == \"\":\n log_entry(\"Required parameter 'year' is missing: check the edgar_logs_config.py file\")\n sys.exit()\n\n# Useful constants.\nsite = \"https://www.sec.gov/data/edgar-log-file-data-set.html\"\nlog_entry(\"Using base site URL: \" + site)\n\nlink = \"https://www.sec.gov/files/edgar{year}.html\".format(year=year)\nlog_entry(\"Looking for link: \" + link)\n\n# IN\nresponse = requests.get(site)\n\n# Exit when the page can't be retrieved.\nif response.status_code >= 400:\n log_entry(\"Unexpected status code: \" + response.status_code)\n sys.exit()\n\ncontent = response.content\n#print(content[0:200])\n\n# Check whether the given year appears on the EDGAR log page and exit if it doesn't.\ncheck_text = \"files/edgar{year}.html\".format(year=year)\nif str(content).find(check_text) != -1:\n log_entry(\"Year \" + str(year) + \" is valid\")\nelse:\n log_entry(\"Year \" + str(year) + \" is not valid\")\n sys.exit()\n\n# IN\n# Months in the year and the quarter to which a month belongs are constants.\nmonths_dict = [{\"month\":\"01\", \"quarter\":1}, {\"month\":\"02\", \"quarter\":1}, {\"month\":\"03\", \"quarter\":1},\n {\"month\":\"04\", \"quarter\":2}, {\"month\":\"05\", \"quarter\":2}, {\"month\":\"06\", \"quarter\":2},\n {\"month\":\"07\", \"quarter\":3}, {\"month\":\"08\", \"quarter\":3}, {\"month\":\"09\", \"quarter\":3},\n {\"month\":\"10\", \"quarter\":4}, {\"month\":\"11\", \"quarter\":4}, {\"month\":\"12\", \"quarter\":4}]\n\nfilenames = []\n\nfor m in months_dict:\n url = \"http://www.sec.gov/dera/data/Public-EDGAR-log-file-data/{year}/Qtr{quarter}/log{year}{month}01.zip\".format(\n year=year, month=m['month'], quarter = str(m['quarter']))\n filenames.append({'url':url,'filename':\"log\" + str(year) + m['month'] + \"01.zip\"})\n\nlog_entry(\"Formed filenames: \" + str(filenames))\n\n\n# IN\ncolumn_names = ['ip','date','time','zone','cik','accession','doc','code','filesize','idx',\n 'norefer','noagent','find','crawler','browser']\n\ncolumn_types = {'ip':np.object, 'date':np.object, 'time':np.object, 'zone':np.object,\n 'cik':np.object, 'accession':np.object, 'doc':np.object, 'code':np.object,\n 'filesize':np.object, 'idx':np.object, 'norefer':np.int32, 'noagent':np.int32,\n 'find':np.object, 'crawler':np.object, 'browser':np.object}\n\n# CIK, filesize, code, idx, find and crawler should be ints, but they have missing values.\n\ndef process_monthly_file(item):\n url = item['url']\n zip_filename = item['filename']\n csv_filename = zip_filename.replace('zip', 'csv')\n\n log_entry('Processing: ' + zip_filename)\n\n # Download and save the ZIP file (if we haven't already).\n if not os.path.isfile(csv_filename):\n log_entry('Downloading: ' + zip_filename)\n r = requests.get(url)\n z = zipfile.ZipFile(io.BytesIO(r.content))\n z.extractall()\n else:\n log_entry('Already downloaded: ' + csv_filename)\n\n # Read the data into a dataframe and return it.\n # Here we are using a TextFileReader, which is iterable with chunks of 1000 rows.\n tp = pd.read_csv(csv_filename, header=0, names=column_names, dtype=column_types, iterator=True, chunksize=1000)\n df = pd.concat(tp, ignore_index=True)\n\n # Missing values in these columns are set to 0.0 and then converted to an int.\n convert_columns = ['cik', 'code', 'filesize', 'idx', 'find', 'crawler']\n for c in convert_columns:\n log_entry('Replacing missing ' + c + ' values with 0 and converting to integer')\n df[c].fillna(0.0, inplace=True)\n df[c] = df[c].astype(float)\n df[c] = df[c].astype(int)\n\n # Missing values in browser are set to ''.\n log_entry('Replacing missing browsers with empty string')\n df['browser'].fillna('', inplace=True)\n\n return df\n\n\n# IN\n#dataframes = []\nfor f in filenames:\n monthly_df = process_monthly_file(f)\n print(monthly_df.head(5))\n print(monthly_df.info())\n print(monthly_df.shape)\n\n if not monthly_df.empty:\n log_entry(\"Summary stats for filesize: \" + monthly_df['date'][0])\n log_entry('\\n' + str(monthly_df['filesize'].describe(include=[np.number])))\n\n log_entry(\"Summary stats for categoricals: IP, time, accession, doc, browser: \" + monthly_df['date'][0])\n log_entry('\\n' + str(monthly_df[['ip', 'time', 'accession', 'doc', 'browser']].describe(include=[object])))\n\n log_entry(\"Value counts for browser: \" + monthly_df['date'][0])\n log_entry('\\n' + str(monthly_df['browser'].value_counts()))\n else:\n log_entry(\"dataframe from file \" + str(f) + \" was empty\")\n\n #dataframes.append(monthly_df)\n\n\n# Clean up the logfile.\nlogfile.close()\n", "sub_path": "Assignment_DataExtraction&Exploration/edgar-logs-image/edgar-logs.py", "file_name": "edgar-logs.py", "file_ext": "py", "file_size_in_byte": 5254, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.now", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "attribute"}, {"api_name": "edgar_logs_config.year", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 45, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 50, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.object", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.object", "line_number": 85, "usage_type": "attribute"}, {"api_name": "numpy.object", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.object", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 101, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 102, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 109, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.number", "line_number": 137, "usage_type": "attribute"}]} +{"seq_id": "468361229", "text": "import time\nimport gym\nimport tensorflow as tf\nimport keras\nfrom keras import backend as K\nimport numpy as np\nfrom priority_replay import Memory\nimport atari_wrapper\nfrom annealing_variable import AnnealingVariable\nfrom stats import Stats\nimport atari_wrapper\nimport pandas as pd\nfrom nes_py.wrappers import JoypadSpace\nimport gym_super_mario_bros\nfrom gym_super_mario_bros.actions import SIMPLE_MOVEMENT\n\n\n# the hubber loss is used instead of mse\ndef huber_loss(y_true, y_pred, delta=2.0):\n err = y_true - y_pred\n\n cond = K.abs(err) < delta\n\n L2 = 0.5 * K.square(err)\n L1 = delta * (K.abs(err) - 0.5 * delta)\n\n # if error < delta perform mse error else perform mae\n loss = tf.where(cond, L2, L1)\n\n return K.mean(loss)\n\n\nclass DQNAgent:\n def __init__(self):\n self.action_space = env.action_space.n # number of actions\n self.discount_rate = 0.99\n self.stack_frames = 4 # number of frames stacked\n self.lr = 2.5e-5\n self.model = self.build_deep_q_model() # build the main network\n self.target_model = self.build_deep_q_model() # build the target network\n mask_shape = (1, env.action_space.n)\n self.batch_size = 32 # number of batches to learn from\n self.priority_replay_size = 1000000 # max replay memory size\n\n self.priority_replay = Memory(self.priority_replay_size) # create the replay memory\n self.exploration_rate = AnnealingVariable(1., .01, 400000) # exploration rate (start_value, final_value, n_steps)\n self.mask = np.ones(mask_shape, dtype=np.float32) # mask used in the training to train only the q values for which the agent performed and action\n self.one_hot = np.eye(env.action_space.n, dtype=np.float32)\n\n self.update_target_frequency = 10000 # update target every\n self.replay_frequency = 4 # learn from memories frequency\n\n # creates the network and returns it\n def build_deep_q_model(self,\n image_size: tuple = (84, 84)\n ) -> keras.Model:\n # weights initializer - he init\n initializer = keras.initializers.VarianceScaling(scale=2.0)\n # input layer - takes (84,84,84,4) images\n cnn_input = keras.layers.Input((*image_size, self.stack_frames), name=\"cnninpt\")\n # mask input - one hot when want only q_value for particular action\n mask_input = keras.layers.Input((self.action_space,), name=\"mask\")\n\n # first Conv2D layer\n cnn = keras.layers.Conv2D(32, 8, strides=4, activation=\"relu\", padding='valid', kernel_initializer=initializer)(cnn_input)\n # second Conv2D layer\n cnn = keras.layers.Conv2D(64, 4, strides=2, activation=\"relu\", padding='valid', kernel_initializer=initializer)(cnn)\n # third Conv2D layer\n cnn = keras.layers.Conv2D(64, 3, strides=1, activation=\"relu\", padding='valid', kernel_initializer=initializer)(cnn)\n\n # flatten the kernels from previous layers\n cnn = keras.layers.Flatten()(cnn)\n\n # fully connected layer\n cnn = keras.layers.Dense(512, activation=\"relu\", kernel_initializer=initializer)(cnn)\n # output layer, q_values for every action in enviroment\n cnn = keras.layers.Dense(self.action_space, name=\"output\")(cnn)\n\n # multiply output by mask to give true output\n output = keras.layers.Multiply()([cnn, mask_input])\n\n # create the model\n model = keras.Model(inputs=[cnn_input, mask_input], outputs=output)\n\n # add loss function and method of optimization\n model.compile(loss=huber_loss, optimizer=keras.optimizers.Adam(lr=self.lr))\n print(model.summary())\n return model\n\n # samples a batch of #batch_size from the priority replay\n def sample(self):\n batch = self.priority_replay.sample(self.batch_size) # sample batch according to priorities\n X_state = [None] * len(batch) # create empty list #batch_size\n X_action = [None] * len(batch)\n X_reward = [None] * len(batch)\n X_done = [None] * len(batch)\n X_next_state = [None] * len(batch)\n # for each batch - retrieve the particualar entries\n for i in range(len(batch)):\n o = batch[i][1]\n X_state[i] = np.array(o[0], copy=False)\n X_action[i] = o[1]\n X_reward[i] = o[2]\n X_done[i] = o[3]\n X_next_state[i] = np.array(o[4], copy=False)\n\n return np.array(X_state), np.array(X_action), np.array(X_reward), np.array(X_done), np.array(X_next_state), batch\n\n # train on the samples in memory\n def learn_from_memories(self):\n X_state, X_action, X_reward, X_done, X_next_state, batch = self.sample()\n\n # repeat the base mask (all actions) for all the samples in the batch\n mask = np.repeat(self.mask, len(X_state), axis=0)\n\n # q for the next_state is 0 if episode has ended or else the max of q values according to the target network\n q_next = np.max(self.target_model.predict_on_batch([X_next_state, mask]), axis=1)\n q_next[X_done] = 0.0\n\n # the q predictions on batch\n pred = self.model.predict_on_batch([X_state, self.mask])\n\n # the q predictions for each action taken only\n pred_action = pred[range(pred.shape[0]), X_action]\n\n # calculate the target / true values\n target_q = X_reward + self.discount_rate * q_next\n\n # get the error - priorirty\n error = abs(target_q - pred_action)\n\n # update errors - priorities\n for i in range(len(batch)):\n idx = batch[i][0]\n self.priority_replay.update(idx, error[i])\n\n # assign target_q to the appropriate true_q columns for which the agent performed an action\n # all other true q values are 0 and the agent is not trained based on their value\n true_q = np.zeros((len(X_state), env.action_space.n), dtype=np.float32)\n # for every row, for the columns the agent picked that action assign target_q\n true_q[range(true_q.shape[0]), X_action] = target_q\n # train only on actions the agent chose - One hot mask from action batch\n return self.model.fit([X_state, self.one_hot[X_action]], true_q, verbose=0, epochs=1)\n\n # helper function to initialize the priority replay with init_size samples\n def init_priority_replay(self, init_size=50000):\n while init_size > 0:\n done = False\n state = env.reset() # reset the env\n while not done:\n # behave randomly\n action = env.action_space.sample()\n next_state, reward, done, _ = env.step(action)\n # save batch of experience to memory\n self.save_to_memory(state, action, reward, done, next_state)\n state = next_state\n init_size -= 1\n\n # takes the experience and stores it in replay memory\n def save_to_memory(self, state, action, reward, done, next_state):\n error = self.get_error(state, action, reward, done, next_state) # find error - priority\n self.priority_replay.add(error, (state, action, reward, done, next_state)) # save to memory according to priority\n\n # behaves epsilon greedily\n def epsilon_greedy_policy(self, state, epsilon=0.01):\n if np.random.random() < epsilon:\n return env.action_space.sample() # pick randomly\n\n q_values = self.predict_qvalues(state)\n return np.argmax(q_values) # pick optimal action - max q(s,a)\n\n # calculate the error of prediction\n def get_error(self, state, action, reward, done, next_state):\n # q next is 0 for terminal states else predict it from target network\n if done:\n q_next = 0.0\n else:\n next_state = np.expand_dims(next_state, axis=0)\n q_next = self.target_model.predict([next_state, self.mask])\n\n predicted_q = self.predict_qvalues(state)\n # error = true - predicted for action taken\n error = abs(reward + self.discount_rate*np.max(q_next) - predicted_q[0, action])\n return error\n\n # predicts and returns q values from state\n def predict_qvalues(self, state: np.ndarray):\n # keras needs first dimension to be batch size even if only one sample\n state = np.expand_dims(state, axis=0)\n return self.model.predict([state, self.mask])\n\n # trains the agent for max_steps\n def train_model(self, max_steps, stats):\n print(\"Start Training \")\n while max_steps > 0:\n r_per_episode = 0\n error = 0\n done = False\n\n state = env.reset()\n\n while not done: # the episode has not ended\n # find action according to epsilon greedy behaviour with epsilon = exploration rate\n action = self.epsilon_greedy_policy(state, self.exploration_rate.value)\n # decrease the exploration rate\n self.exploration_rate.step()\n\n max_steps -= 1\n # take the action and observe next observation, reward, done\n next_state, reward, done, _ = env.step(action)\n\n r_per_episode += reward\n\n # save this experience to memory\n self.save_to_memory(state, action, reward, done, next_state)\n\n state = next_state\n\n # time to train from priority replay\n if max_steps % self.replay_frequency == 0:\n hist = self.learn_from_memories()\n error += hist.history['loss'][0]\n\n # time to update the target network\n if max_steps % self.update_target_frequency == 0:\n self.target_model.set_weights(self.model.get_weights())\n # update stats\n stats(self, r_per_episode)\n # save stats at the end of training\n stats.save_stats()\n\n # test the agent for nepisodes\n def play(self, nepisodes, exploration_rate, stats):\n rewards_arr = np.zeros(nepisodes)\n for episode in range(nepisodes):\n episode_reward = 0\n done = False\n state = env.reset()\n while not done:\n env.render()\n # time.sleep(0.05)\n action = self.epsilon_greedy_policy(state, exploration_rate)\n next_state, reward, done, _ = env.step(action)\n episode_reward += reward\n\n state = next_state\n rewards_arr[episode] = episode_reward\n stats(self, episode_reward)\n print(episode_reward)\n stats.save_stats()\n return rewards_arr\n\n def save_weights(self):\n print(\"PRINT\")\n self.model.save_weights(\"MarioDQNWeights.h5\")\n\n def save_model(self):\n self.model.save(\"DqnMarioModel.h5\")\n\n def restore_weights(self):\n print(\"Restoring model weights Mario\")\n self.model.load_weights(\"MarioDQNWeights.h5\")\n\n\nstats = Stats()\n_env = gym_super_mario_bros.make('SuperMarioBros-1-1-v1')\n_env = JoypadSpace(_env, SIMPLE_MOVEMENT)\nenv = atari_wrapper.wrap_dqn(_env)\nagent = DQNAgent()\nagent.init_priority_replay(50000)\nagent.train_model(max_steps=50e6, stats=stats)\nenv.close()\n", "sub_path": "Deeep Q Learning/Super Mario/Train/deep_q_model.py", "file_name": "deep_q_model.py", "file_ext": "py", "file_size_in_byte": 11170, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "keras.backend.abs", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 22, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 24, "usage_type": "name"}, {"api_name": "keras.backend.abs", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 25, "usage_type": "name"}, {"api_name": "tensorflow.where", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.backend.mean", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 30, "usage_type": "name"}, {"api_name": "priority_replay.Memory", "line_number": 45, "usage_type": "call"}, {"api_name": "annealing_variable.AnnealingVariable", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 48, "usage_type": "attribute"}, {"api_name": "keras.initializers.VarianceScaling", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.initializers", "line_number": 58, "usage_type": "attribute"}, {"api_name": "keras.layers.Input", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 60, "usage_type": "attribute"}, {"api_name": "keras.layers.Input", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 62, "usage_type": "attribute"}, {"api_name": "keras.layers.Conv2D", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 65, "usage_type": "attribute"}, {"api_name": "keras.layers.Conv2D", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 67, "usage_type": "attribute"}, {"api_name": "keras.layers.Conv2D", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 69, "usage_type": "attribute"}, {"api_name": "keras.layers.Flatten", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 72, "usage_type": "attribute"}, {"api_name": "keras.layers.Dense", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 75, "usage_type": "attribute"}, {"api_name": "keras.layers.Dense", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 77, "usage_type": "attribute"}, {"api_name": "keras.layers.Multiply", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 80, "usage_type": "attribute"}, {"api_name": "keras.Model", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 86, "usage_type": "attribute"}, {"api_name": "keras.Model", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 166, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 187, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 189, "usage_type": "call"}, {"api_name": "stats.save_stats", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 234, "usage_type": "call"}, {"api_name": "stats.save_stats", "line_number": 250, "usage_type": "call"}, {"api_name": "stats.Stats", "line_number": 265, "usage_type": "call"}, {"api_name": "gym_super_mario_bros.make", "line_number": 266, "usage_type": "call"}, {"api_name": "nes_py.wrappers.JoypadSpace", "line_number": 267, "usage_type": "call"}, {"api_name": "gym_super_mario_bros.actions.SIMPLE_MOVEMENT", "line_number": 267, "usage_type": "argument"}, {"api_name": "atari_wrapper.wrap_dqn", "line_number": 268, "usage_type": "call"}]} +{"seq_id": "380344931", "text": "import geoip2.database\nfrom py2neo import Graph, Node, Relationship\nimport json\nimport os\n\nfrom parser import pull_ip_src\n\ndef geoip(ip):\n path = str(os.path.dirname(__file__)) + '/data/GeoLite2-City.mmdb'\n reader = geoip2.database.Reader(path)\n\n insights = {}\n try: \n response = reader.city(ip)\n reader.close()\n\n insights[\"ip_src\"] = ip\n insights[\"country\"] = response.country.name\n insights[\"state_subdivision\"] = response.subdivisions.most_specific.name\n insights[\"city\"] = response.city.name\n\n return insights\n except:\n reader.close()\n return 0\n\ndef geoip_insert(data, graph):\n\n if(data != 0):\n c = Node(\"Country\", data = data[\"country\"])\n ip_node = graph.nodes.match(\"IP\", data=data[\"ip_src\"]).first()\n c_node = graph.nodes.match(\"Country\", data = data[\"country\"]).first()\n\n if(c_node):\n rel = Relationship(ip_node, \"IS_LOCATED_IN\", c_node)\n graph.create(rel)\n print(\"Existing country node linked\")\n else:\n graph.create(c)\n rel = Relationship(ip_node, \"IS_LOCATED_IN\", c)\n graph.create(rel)\n print(\"New country node created and linked\")\n return 1\n else:\n print(\"No GeoIP Entry\")\n return 0\n \n\n \n\ndef ASN(ip):\n path = str(os.path.dirname(__file__)) + '/data/GeoLite2-ASN.mmdb'\n with geoip2.database.Reader(path) as reader:\n \n insights = {}\n try:\n response = reader.asn(ip)\n insights[\"ASN\"] = response.autonomous_system_number\n insights[\"ASO\"] = response.autonomous_system_organization\n insights[\"ip_src\"] = ip\n return insights\n except:\n return 0\n\ndef asn_insert(data, graph):\n\n if(data != 0):\n a = Node(\"ASN\", data = str(data[\"ASN\"]))\n ip_node = graph.nodes.match(\"IP\", data=data[\"ip_src\"]).first()\n a_node = graph.nodes.match(\"ASN\", data = str(data[\"ASN\"])).first()\n\n if(a_node):\n rel = Relationship(ip_node, \"HAS_ASN\", a_node)\n graph.create(rel)\n print(\"Existing asn node linked\")\n else:\n graph.create(a)\n rel = Relationship(ip_node, \"HAS_ASN\", a)\n graph.create(rel)\n print(\"New asn node created and linked\")\n return 1\n else:\n print(\"No asn Entry\")\n return 0\n", "sub_path": "app-server/tiweb/gip.py", "file_name": "gip.py", "file_ext": "py", "file_size_in_byte": 2742, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "geoip2.database.database.Reader", "line_number": 10, "usage_type": "call"}, {"api_name": "geoip2.database.database", "line_number": 10, "usage_type": "attribute"}, {"api_name": "geoip2.database", "line_number": 10, "usage_type": "name"}, {"api_name": "py2neo.Node", "line_number": 30, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 35, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "geoip2.database.database.Reader", "line_number": 53, "usage_type": "call"}, {"api_name": "geoip2.database.database", "line_number": 53, "usage_type": "attribute"}, {"api_name": "geoip2.database", "line_number": 53, "usage_type": "name"}, {"api_name": "py2neo.Node", "line_number": 68, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 73, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "226529234", "text": "import tkinter as tk\nfrom collections import deque as dq\nimport random\n\n\nclass Cell:\n def __init__(self, x, y, square):\n self.x = x\n self.y = y\n self.square = square\n\n def __iter__(self):\n yield self.x\n yield self.y\n\n\nclass Snake:\n def __init__(self, parent):\n self.parent = parent\n self.WIDTH = 600\n self.HEIGHT = 600\n self.CELL = 50\n self.max_speed_score = 500 # stop increasing speed at this score\n self.speed_factor = 40 # increase speed every x points\n self.canvas = tk.Canvas(root, width=self.WIDTH, height=self.HEIGHT)\n self.canvas.pack()\n self.parent.bind('', self.draw_board)\n self.parent.bind('', self.tick)\n self.parent.bind('', self.tick)\n self.parent.bind('', self.tick)\n self.parent.bind('', self.tick)\n self.draw_board()\n\n def draw_board(self, event=None):\n self.direction = 'Right'\n self.tickrate = 1000\n self.score = 1\n self.canvas.delete(tk.ALL)\n self.empty = [(x, y) for x in range(0, self.WIDTH, self.CELL)\n for y in range(0, self.HEIGHT, self.CELL)]\n x, y = self.empty.pop(len(self.empty) // 2 + int(0.5 * len(self.empty) ** 0.5))\n # the head is 0 and the tail is -1\n self.snake = dq([Cell(x, y, self.canvas.create_rectangle(x,\n y,\n x + self.CELL,\n y + self.CELL,\n fill='blue'))])\n self.create_food()\n self.ticking = self.parent.after(self.tickrate, self.tick)\n self.running = True\n\n def create_food(self):\n try:\n x, y = self.empty.pop(random.randrange(len(self.empty)))\n except ValueError:\n self.end(f'You win!\\nScore: {self.score}.')\n else:\n self.food = Cell(x, y, self.canvas.create_rectangle(x, y, x + self.CELL, y + self.CELL, fill='red'))\n if self.score <= self.max_speed_score:\n self.tickrate = 1000 // (self.score // self.speed_factor + 1)\n\n def move(self, direction):\n head = self.snake[0]\n tail = self.snake[-1]\n x, y = head\n if direction == 'Left':\n target = x - self.CELL, y\n elif direction == 'Right':\n target = x + self.CELL, y\n elif direction == 'Up':\n target = x, y - self.CELL\n else:\n target = x, y + self.CELL\n # collision detection etc.\n if target == tuple(self.food):\n self.snake.appendleft(self.food)\n self.score += 1\n self.recolor()\n self.create_food()\n return\n if target in self.empty or target == tuple(tail):\n self.empty.append(tuple(tail))\n self.empty.remove(target)\n tail.x, tail.y = target\n self.canvas.coords(tail.square, *target, *(a + self.CELL for a in target))\n self.snake.appendleft(self.snake.pop())\n self.recolor()\n return\n self.end(f'You lose.\\nScore: {self.score}.')\n\n def recolor(self):\n self.canvas.itemconfig(self.snake[0].square, fill='blue')\n if len(self.snake) > 1:\n self.canvas.itemconfig(self.snake[1].square, fill='black')\n\n def tick(self, event=None):\n if event:\n direction = event.keysym\n if {direction, self.direction} in ({'Left', 'Right'},\n {'Up', 'Down'}):\n return\n self.direction = direction\n self.parent.after_cancel(self.ticking)\n if self.running:\n self.move(self.direction)\n self.ticking = self.parent.after(self.tickrate, self.tick)\n\n def end(self, text):\n self.parent.after_cancel(self.ticking)\n self.running = False\n self.canvas.create_text(self.WIDTH // 2,\n self.HEIGHT // 2,\n text=text,\n font=('Times', self.HEIGHT // 10),\n fill='orange')\n\n\nroot = tk.Tk()\nsnake = Snake(root)\nroot.mainloop()\n", "sub_path": "snake.py", "file_name": "snake.py", "file_ext": "py", "file_size_in_byte": 4369, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tkinter.Canvas", "line_number": 25, "usage_type": "call"}, {"api_name": "tkinter.ALL", "line_number": 38, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 43, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 54, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 118, "usage_type": "call"}]} +{"seq_id": "18397628", "text": "#!/usr/bin/env python\nimport os\nimport django\n\ndef populate():\n from simdb.models import Semester, Course, Assignment, MossSubmission\n semester = Semester(term=4, year=2015)\n semester.save()\n course = Course(discipline='C S', number=1440, semester=semester, crn=12345)\n course.save()\n assignment = Assignment(name='Lab 01', course=course, file='lab01.zip')\n assignment.clean()\n assignment.save()\n moss_submission = MossSubmission(assignment=assignment, language='java')\n moss_submission.clean()\n moss_submission.save()\n\nif __name__ == \"__main__\":\n os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"mysite.settings\")\n django.setup()\n populate()\n", "sub_path": "populate.py", "file_name": "populate.py", "file_ext": "py", "file_size_in_byte": 685, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "simdb.models.Semester", "line_number": 7, "usage_type": "call"}, {"api_name": "simdb.models.Course", "line_number": 9, "usage_type": "call"}, {"api_name": "simdb.models.Assignment", "line_number": 11, "usage_type": "call"}, {"api_name": "simdb.models.MossSubmission", "line_number": 14, "usage_type": "call"}, {"api_name": "os.environ.setdefault", "line_number": 19, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "598623232", "text": "\n# ---\n# name: fullcontact-find-person\n# deployed: true\n# title: FullContact Find Person\n# description: Return a person's profile information based on their email address and LinkedIn profile.\n# params:\n# - name: email\n# type: string\n# description: The email address of the person you wish you find.\n# required: true\n# - name: linkedin\n# type: string\n# description: The LinkedIn username of the person you wish to find.\n# required: true\n# - name: properties\n# type: array\n# description: The properties to return (defaults to all properties). See \"Returns\" for a listing of the available properties.\n# required: false\n# returns:\n# - name: full_name\n# type: string\n# description: The full name of the person (default)\n# - name: age_range\n# type: string\n# description: The age range of the person\n# - name: gender\n# type: string\n# description: The gender of the person\n# - name: location\n# type: string\n# description: The location of the person (varies depending on data quality)\n# - name: title\n# type: string\n# description: The current or most recent job title of the person\n# - name: organization\n# type: string\n# description: The current or most recent place of work of the person\n# - name: twitter_url\n# type: string\n# description: The URL of the person's Twitter profile\n# - name: facebook_url\n# type: string\n# description: The URL of the person's Facebook profile\n# - name: linkedin_url\n# type: string\n# description: The URL of the person's LinkedIn profile\n# - name: bio\n# type: string\n# description: A biography of the person\n# - name: avatar_url\n# type: string\n# description: The URL of the person's photo\n# examples:\n# - '\"tcook@apple.com\"'\n# - '\"bill.gates@microsoft.com\"'\n# - '\"jeff@amazon.com\", \"full_name, title, bio\"'\n# ---\n\nimport json\nimport urllib\nimport requests\nfrom requests.adapters import HTTPAdapter\nfrom requests.packages.urllib3.util.retry import Retry\nimport itertools\nfrom datetime import *\nfrom cerberus import Validator\nfrom collections import OrderedDict\n\n# main function entry point\ndef flexio_handler(flex):\n\n # get the api key from the variable input\n auth_token = dict(flex.vars).get('fullcontact_api_key')\n if auth_token is None:\n raise ValueError\n\n # get the input\n input = flex.input.read()\n try:\n input = json.loads(input)\n if not isinstance(input, list): raise ValueError\n except ValueError:\n raise ValueError\n\n # define the expected parameters and map the values to the parameter names\n # based on the positions of the keys/values\n params = OrderedDict()\n params['email'] = {'required': True, 'type': 'string'}\n params['profile'] = {'required': True, 'type': 'string'}\n params['properties'] = {'required': False, 'validator': validator_list, 'coerce': to_list, 'default': '*'}\n input = dict(zip(params.keys(), input))\n\n # validate the mapped input against the validator\n # if the input is valid return an error\n v = Validator(params, allow_unknown = True)\n input = v.validated(input)\n if input is None:\n raise ValueError\n\n # map this function's property names to the API's property names\n property_map = OrderedDict()\n property_map['full_name'] = 'fullName'\n property_map['age_range'] = 'ageRange'\n property_map['gender'] = 'gender'\n property_map['location'] = 'location'\n property_map['title'] = 'title'\n property_map['organization'] = 'organization'\n property_map['twitter_url'] = 'twitter'\n property_map['facebook_url'] = 'facebook'\n property_map['linkedin_url'] = 'linkedin'\n property_map['bio'] = 'bio'\n property_map['avatar_url'] = 'avatar'\n\n # get the properties to return and the property map\n properties = [p.lower().strip() for p in input['properties']]\n\n # if we have a wildcard, get all the properties\n if len(properties) == 1 and properties[0] == '*':\n properties = list(property_map.keys())\n\n # see here for more info:\n # https://docs.fullcontact.com/#person-enrichment\n # https://docs.fullcontact.com/#multi-field-request\n # https://dashboard.fullcontact.com/api-ref#response-codes-&-errors\n\n email = input['email'].lower().strip()\n profile = input['profile'].lower().strip()\n\n data = {}\n if len(email) > 0:\n data['emails'] = [\n input['email'].lower().strip()\n ]\n if len(profile) > 0:\n data['profiles'] = [\n {\n \"service\": \"linkedin\",\n \"username\": input['profile'].lower().strip()\n }\n ]\n data = json.dumps(data)\n\n headers = {\n 'Content-Type': 'application/json',\n 'Authorization': 'Bearer ' + auth_token\n }\n url = 'https://api.fullcontact.com/v3/person.enrich'\n\n # get the response data as a JSON object\n response = requests_retry_session().post(url, data=data, headers=headers)\n content = response.json()\n\n # sometimes results are pending; for these, return text indicating\n # the result is pending so the user can refresh later to look for\n # the completed result\n status_code = response.status_code\n if status_code == 202:\n flex.output.content_type = \"application/json\"\n flex.output.write([['Result Pending...']])\n return\n\n # if a result can't be found or wasn't formatted properly,\n # return a blank (equivalent to not finding a bad email address)\n if status_code == 400 or status_code == 404 or status_code == 422:\n flex.output.content_type = \"application/json\"\n flex.output.write([['']])\n return\n\n # return an error for any other non-200 result\n response.raise_for_status()\n\n # limit the results to the requested properties\n properties = [content.get(property_map.get(p,''),'') or '' for p in properties]\n result = [properties]\n\n # return the results\n result = json.dumps(result, default=to_string)\n flex.output.content_type = \"application/json\"\n flex.output.write(result)\n\ndef requests_retry_session(\n retries=3,\n backoff_factor=0.3,\n status_forcelist=(429, 500, 502, 503, 504),\n session=None,\n):\n session = session or requests.Session()\n retry = Retry(\n total=retries,\n read=retries,\n connect=retries,\n backoff_factor=backoff_factor,\n status_forcelist=status_forcelist,\n )\n adapter = HTTPAdapter(max_retries=retry)\n session.mount('http://', adapter)\n session.mount('https://', adapter)\n return session\n\ndef validator_list(field, value, error):\n if isinstance(value, str):\n return\n if isinstance(value, list):\n for item in value:\n if not isinstance(item, str):\n error(field, 'Must be a list with only string values')\n return\n error(field, 'Must be a string or a list of strings')\n\ndef to_string(value):\n if isinstance(value, (date, datetime)):\n return value.isoformat()\n if isinstance(value, (Decimal)):\n return str(value)\n return value\n\ndef to_list(value):\n # if we have a list of strings, create a list from them; if we have\n # a list of lists, flatten it into a single list of strings\n if isinstance(value, str):\n return value.split(\",\")\n if isinstance(value, list):\n return list(itertools.chain.from_iterable(value))\n return None\n", "sub_path": "fullcontact-find-person.py", "file_name": "fullcontact-find-person.py", "file_ext": "py", "file_size_in_byte": 7424, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.loads", "line_number": 81, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 88, "usage_type": "call"}, {"api_name": "cerberus.Validator", "line_number": 96, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 102, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 142, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 178, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 188, "usage_type": "call"}, {"api_name": "requests.packages.urllib3.util.retry.Retry", "line_number": 189, "usage_type": "call"}, {"api_name": "requests.adapters.HTTPAdapter", "line_number": 196, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 224, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 224, "usage_type": "attribute"}]} +{"seq_id": "499639916", "text": "#!/usr/bin/env python\nimport os, sys\nimport argparse\nimport ROOT\nROOT.PyConfig.IgnoreCommandLineOptions = True\nfrom importlib import import_module\nfrom PhysicsTools.NanoAODTools.postprocessing.framework.postprocessor import PostProcessor\nfrom PhysicsTools.NanoSUSYTools.modules.eleMiniCutIDProducer import *\nfrom PhysicsTools.NanoSUSYTools.modules.Stop0lObjectsProducer import *\nfrom PhysicsTools.NanoSUSYTools.modules.Stop0lBaselineProducer import *\nfrom PhysicsTools.NanoSUSYTools.modules.DeepTopProducer import *\nfrom PhysicsTools.NanoSUSYTools.modules.updateGenWeight import *\nfrom PhysicsTools.NanoSUSYTools.modules.lepSFProducer import *\nfrom PhysicsTools.NanoSUSYTools.modules.updateJetIDProducer import *\nfrom PhysicsTools.NanoAODTools.postprocessing.modules.common.puWeightProducer import *\n\nDataDepInputs = {\n \"2016\" : { \"pileup\": \"Cert271036_284044_23Sep2016ReReco_Collisions16.root\"\n },\n \"2017\" : { \"pileup\": \"Cert294927_306462_EOY2017ReReco_Collisions17.root\"\n },\n \"2018\" : { \"pileup\": \"Cert314472_325175_PromptReco_Collisions18.root\"\n }\n}\n\ndef main(args):\n # isdata = False\n # isfastsim = False\n if \"False\" in args.isData:\n isdata = False\n else:\n isdata = True\n if \"False\" in args.isFastSim:\n isfastsim = False\n else:\n isfastsim = True\n\n mods = [\n eleMiniCutID(),\n Stop0lObjectsProducer(args.era),\n DeepTopProducer(args.era),\n Stop0lBaselineProducer(args.era, isData=isdata, isFastSim=isfastsim),\n UpdateGenWeight(isdata, args.crossSection, args.nEvents)\n ]\n if args.era == \"2018\":\n mods.append(UpdateJetID(args.era))\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ For MC ~~~~~\n if not isdata:\n pufile = \"%s/src/PhysicsTools/NanoSUSYTools/data/pileup/%s\" % (os.environ['CMSSW_BASE'], DataDepInputs[args.era][\"pileup\"])\n mods += [\n lepSFProducer(args.era),\n puWeightProducer(\"auto\", pufile, \"pu_mc\",\"pileup\", verbose=False)\n ]\n\n\n files = []\n lines = open(args.inputfile).readlines()\n for line in lines:\n files.append(line.strip())\n\n\n p=PostProcessor(args.outputfile,files,cut=None, branchsel=None, outputbranchsel=\"keep_and_drop.txt\", modules=mods,provenance=False)\n p.run()\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='NanoAOD postprocessing.')\n parser.add_argument('-i', '--inputfile',\n default = \"testing.txt\",\n help = 'Path to the input filelist.')\n parser.add_argument('-o', '--outputfile',\n default=\"./\",\n help = 'Path to the output file location.')\n parser.add_argument('-e', '--era',\n default = \"2017\", help = 'Year of production')\n parser.add_argument('-f', '--isFastSim', default = False)\n parser.add_argument('-d', '--isData', default = False)\n parser.add_argument('-c', '--crossSection',\n type=float,\n default = 1,\n help = 'Cross Section of MC')\n parser.add_argument('-n', '--nEvents',\n type=float,\n default = 1,\n help = 'Number of Events')\n args = parser.parse_args()\n main(args)\n", "sub_path": "python/processors/Stop0l_postproc.py", "file_name": "Stop0l_postproc.py", "file_ext": "py", "file_size_in_byte": 3271, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ROOT.PyConfig", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 50, "usage_type": "attribute"}, {"api_name": "PhysicsTools.NanoAODTools.postprocessing.framework.postprocessor.PostProcessor", "line_number": 63, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "153305601", "text": "import cv2\nimport mediapipe as mp\nimport time\n\n\nclass HandDetector:\n def __init__(self, mode=False, max_hands=2, detect_con=0.5, track_con=0.5):\n self.mpDraw = mp.solutions.drawing_utils\n self.mpHands = mp.solutions.hands\n self.hands = self.mpHands.Hands(mode, max_hands, detect_con, track_con)\n self.results = None\n\n def find_hands(self, img, draw=True):\n img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n self.results = self.hands.process(img_rgb)\n\n if draw:\n if self.results.multi_hand_landmarks:\n for handLms in self.results.multi_hand_landmarks:\n self.mpDraw.draw_landmarks(img, handLms, self.mpHands.HAND_CONNECTIONS)\n return img\n\n def find_position(self, img, hand_no=0, draw=True):\n lm_list = []\n if self.results.multi_hand_landmarks:\n my_hand = self.results.multi_hand_landmarks[hand_no]\n for idx, lm in enumerate(my_hand.landmark):\n h, w, c = img.shape\n cx, cy = int(lm.x * w), int(lm.y * h)\n lm_list.append([idx, cx, cy])\n if draw:\n cv2.circle(img, (cx, cy), 15, (255, 0, 255), cv2.FILLED)\n return lm_list\n\n def get_point_position(self, img, point):\n if self.results.multi_hand_landmarks:\n land_mark = self.results.multi_hand_landmarks[0].landmark[point]\n h, w, c = img.shape\n x, y = int(land_mark.x * w), int(land_mark.y * h)\n return x, y\n return None, None\n\n\ndef main():\n prev_time = 0\n video = cv2.VideoCapture(0)\n detector = HandDetector()\n while True:\n success, img = video.read()\n img = cv2.flip(img, 1)\n\n img = detector.find_hands(img)\n # lm_list = detector.find_position(img)\n # if len(lm_list) != 0:\n # print(lm_list[4])\n\n curr_time = time.time()\n fps = 1 / (curr_time - prev_time)\n prev_time = curr_time\n cv2.putText(img, str(int(fps)), (10, 70), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 255), 3)\n\n cv2.imshow('Camera', img)\n cv2.waitKey(1)\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "hand_tracking_module.py", "file_name": "hand_tracking_module.py", "file_ext": "py", "file_size_in_byte": 2188, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "mediapipe.solutions", "line_number": 8, "usage_type": "attribute"}, {"api_name": "mediapipe.solutions", "line_number": 9, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.FILLED", "line_number": 32, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 50, "usage_type": "call"}, {"api_name": "time.time", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_PLAIN", "line_number": 60, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "60599844", "text": "from flask import Flask, render_template, url_for, request,redirect\r\nimport csv\r\n\r\napp = Flask(__name__)\r\n\r\n\r\n@app.route('/')\r\ndef home():\r\n return render_template('index.html')\r\n\r\n@app.route('/')\r\ndef pages(page):\r\n return render_template(page)\r\n\r\n@app.route('/submit_form', methods=['POST', 'GET'])\r\ndef submit_form():\r\n if request.method == \"POST\":\r\n try: \r\n data = request.form.to_dict()\r\n # save_info_textfile(data)\r\n write_to_csv(data)\r\n return redirect('/thankyou.html')\r\n except:\r\n return 'Did not save to database'\r\n else:\r\n return \"something went wrong\"\r\n\r\n\r\ndef write_to_csv(data):\r\n with open('database.csv',newline='',mode='a')as csvfile:\r\n fullname=data[\"Fname\"]\r\n email = data[\"Email\"]\r\n phonenumber =data[\"Phonenumber\"]\r\n message = data[\"Message\"]\r\n csv_writer = csv.writer(csvfile, delimiter=',',quotechar='|', lineterminator='\\n', quoting=csv.QUOTE_MINIMAL)\r\n csv_writer.writerow([fullname,email,phonenumber,message])\r\n\r\n\r\ndef save_info_textfile(data):\r\n with open('database.txt', mode='a') as database:\r\n fullname=data[\"Fname\"]\r\n email = data[\"Email\"]\r\n phonenumber =data[\"Phonenumber\"]\r\n message = data[\"Message\"]\r\n database.write(f'\\n{fullname}, {email}, {phonenumber}, {message}')\r\n \r\n\r\n \r\n\r\n\r\n\r\n", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1411, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.form.to_dict", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 22, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 35, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 35, "usage_type": "attribute"}]} +{"seq_id": "450958560", "text": "## @package nltk\r\n# loading libraries\r\n#\r\n#loading pandas\r\nimport pandas as pd\r\n\r\n#loading numpy\r\nimport numpy as np\r\n\r\n#loading NLTK text precessing libaray\r\nimport nltk\r\n\r\n#import list for list of punctuations\r\nimport string\r\n\r\n#Import beautifulSoup\r\n\r\nfrom bs4 import BeautifulSoup\r\n\r\n#import stop word list\r\n\r\nfrom nltk.corpus import stopwords\r\n\r\n#import tokenizer \r\n\r\nfrom nltk.tokenize import RegexpTokenizer\r\n\r\n#import Lemmatizer\r\n\r\nfrom nltk.stem import WordNetLemmatizer\r\n\r\n#import stemmer\r\n\r\nfrom nltk.stem.porter import PorterStemmer\r\n\r\n\r\n\r\n#creating dataframe of commits\r\n\r\ndf = pd.read_csv('C:/Users/Priya/Downloads/miner_refactoring.csv')\r\n\r\n\r\n#Remove duplicate commit_ids as a part of EDA\r\ndf = (df.drop_duplicates(['CommitId'], keep ='last'));\r\n\r\n#display dataframe\r\ndf\r\n\r\n\r\n\r\n#drop NA values from commit text\r\ncommit_text= df['Message'].dropna()\r\n\r\n\r\n\r\n#remove duplicate commitId's\r\ndf = (df.drop_duplicates(['CommitId'], keep ='last'));\r\n\r\n\r\n\r\n# This function is for removing html tags from commits\r\n# \tinput\tcommit messages in data frame\r\n# \toutput\tcommit messages without html tags in dataframe\r\n#\r\n\r\ndef remove_html(text):\r\n\r\n soup = BeautifulSoup(text, 'lxml')\r\n\r\n hrml_free = soup.get_text()\r\n\r\n return hrml_free\r\n\r\n\r\n\r\n\r\n# function to remove punctuations\r\n# This function will removes punctuations from commit messages\r\n# \tinput\tcommit messages in data frame\r\n# \toutput\tcommit messages without punctuations in dataframe\r\n#\r\n\r\ndef remove_punctuation(text):\r\n no_punct = \"\".join([c for c in text if c not in string.punctuation])\r\n\r\n return no_punct\r\n\r\n\r\n\r\n#import string\r\n\r\ndir(string)\r\n\r\n#give call to remove_punctations()\r\ncommit_text= commit_text.apply(lambda x: remove_punctuation(x))\r\n\r\ncommit_text.head()\r\n\r\n#dispaly commit_text dataframe\r\ncommit_text\r\n\r\n\r\n#tokenize\r\n\r\n#instantiate tokenizer\r\n\r\n#split up by spaces\r\n\r\ntokenizer = RegexpTokenizer(r'\\w+')\r\n\r\n#call to tokenizer\r\ncommit_text = commit_text.apply(lambda x: tokenizer.tokenize(x.lower()))\r\n\r\n#display top 100 commit messages\r\ncommit_text.head(100)\r\n\r\n\r\n\r\n# remove stop words\r\n# this function will remove stopwords from commit messages\r\n# input:commit messages in data frame\r\n# output:text without stopwords\r\n#\r\n\r\ndef remove_stopwords(text):\r\n\r\n words = [w for w in text if w not in stopwords.words('english')]\r\n\r\n return words\r\n\r\n\r\n\r\n#remove stop words from english\r\ncommit_text = commit_text.apply(lambda x : remove_stopwords(x))\r\n\r\n\r\n\r\n#print starting intial commits\r\ncommit_text.head(100)\r\n\r\n\r\n\r\n#Lemmatization\r\n\r\nlemmatizer = WordNetLemmatizer()\r\n\r\n# this function will is for Lemmatizing,\r\n# on the other hand, maps common words into one base.\r\n# \tinput\tcommit messages in data frame\r\n# \toutput\tcommit message with shorten words back to their root form\r\n#\r\n\r\ndef word_lemmatizer(text):\r\n for_text= [lemmatizer.lemmatize(i) for i in text]\r\n\r\n return for_text\r\n\r\n\r\n#call to lammetization\r\ncommit_text.apply(lambda x :word_lemmatizer(x))\r\n\r\n\r\n\r\n#stemming\r\nstemmer= PorterStemmer()\r\n\r\n\r\n# this function will perform stemming on commit messages\r\n# \tinput\tcommit messages in data frame\r\n# output:text with stemmed words\r\n#\r\n\r\ndef word_stemmer(text):\r\n stem_commits = \"\".join([stemmer.stem(i) for i in text])\r\n\r\n return stem_commits\r\n\r\n\r\n\r\ncommit_text = commit_text.apply(lambda x : word_stemmer(x))\r\n\r\n#count frequency of words\r\n\r\ncommit_text.str.split(expand=True).stack().value_counts()\r\n\r\n\r\n\r\n\r\n#copy commit messages to text file\r\ndf.to_csv(r'C:\\Users\\Priya\\Desktop\\Capstone_Commits_sem2\\commits.txt', header=None, index=None, sep=' ', mode='a')\r\n\r\n\r\n\r\nfrom collections import Counter\r\n\r\n\r\n\r\n#opens the file. the with statement here will automatically close it afterwards.\r\n\r\nwith open('C:\\\\Users\\\\Priya\\\\Desktop\\\\Capstone_Commits_sem2\\\\commits.txt',encoding=\"utf8\") as input_file:\r\n\r\n #build a counter from each word in the file\r\n\r\n count = Counter(word for line in input_file\r\n\r\n for word in line.split())\r\n\r\n\r\n\r\nprint(count.most_common(200))\r\n\r\n\r\n\r\n\r\n\r\n#count most frequent words\r\n\r\nfrequent_words = count.most_common(200)\r\n\r\n\r\n\r\n#create a dataframe of frequent words\r\n\r\nfreq_w = pd.DataFrame(frequent_words)\r\n\r\n\r\n\r\nwords = freq_w[0]\r\n\r\n#created a dictioanry of frequent words\r\n\r\ndata_dict = words.to_dict()\r\n", "sub_path": "commit_preprocessing.py", "file_name": "commit_preprocessing.py", "file_ext": "py", "file_size_in_byte": 4348, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 40, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 68, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 84, "usage_type": "attribute"}, {"api_name": "nltk.tokenize.RegexpTokenizer", "line_number": 109, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 127, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 127, "usage_type": "name"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 145, "usage_type": "call"}, {"api_name": "nltk.stem.porter.PorterStemmer", "line_number": 165, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 204, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 224, "usage_type": "call"}]} +{"seq_id": "232299233", "text": "# coding=utf-8\n\"\"\"\nModule containing Nimsuggest process interface code.\n\"\"\"\nimport os\nimport re\nimport subprocess\nimport sys\nfrom threading import Thread, Lock\nfrom collections import namedtuple\n\nimport sublime\nfrom nimlime_core import configuration\nfrom nimlime_core.configuration import debug_print\n\nif sys.version_info < (3, 0):\n from Queue import Queue, Empty\nelse:\n from queue import Queue # python 3.x\n\n\nTAB_BYTE = '\\t'.encode()\nDOUBLE_NEWLINE_BYTE = (os.linesep*2).encode()\nNEWLINE_BYTE = os.linesep.encode()\nEXIT_REQUEST = object()\nANSWER_REGEX = r\"\"\"\n(?P[^\\t]*)\\t\n(?P[^\\t]*)\\t\n(?P[^\\t]*)\\t\n(?P[^\\t]*)\\t\n(?P[^\\t]*)\\t\n(?P[^\\t]*)\\t\n(?P[^\\t]*)\\t\n(?P[^\\t]*)\\n?\n\"\"\"\nNimsuggestEntry = namedtuple(\n \"NimsuggestEntry\",\n (\n 'answer_type', 'symbol_type', 'name',\n 'declaration', 'file_path', 'line',\n 'column', 'docstring'\n )\n)\n\n\nclass Nimsuggest(object):\n \"\"\"\n Used to retrieve suggestions, completions, and other IDE-like information\n for a Nim project.\n \"\"\"\n\n def __init__(self, project_file, max_failures):\n \"\"\"\n Create a Nimsuggest instance using the given project file path.\n :type project_file: str\n \"\"\"\n self.max_failures = max_failures\n self.current_failures = -1\n self.running = False\n\n # Nimsuggest process handlers\n self.input_queue = Queue()\n self.state_transition_lock = Lock() # Used for shutdown of server\n # thread.\n self.server_thread = None\n\n # Information needed to start a nimsuggest process\n self.environment = os.environ\n if os.path.isfile(configuration.nim_exe):\n self.environment = os.environ.copy()\n self.environment['PATH'] = '{0};{1}'.format(\n os.path.dirname(configuration.nim_exe),\n self.environment['PATH']\n )\n\n self.process_args = dict(\n args=[configuration.nimsuggest_exe,\n '--nimpath:\"C:\\\\x64\\\\Nim\\\\\"',\n 'stdin', '--interactive:false',\n project_file],\n env=self.environment,\n universal_newlines=False,\n creationflags=(configuration.on_windows and 0x08000000) or None,\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT,\n )\n\n def start(self):\n \"\"\"\n Start the internal nimsuggest process\n \"\"\"\n if self.running:\n raise Exception(\"Nimsuggest instance already running.\")\n self.server_thread = Thread(target=self.run)\n self.server_thread.daemon = True\n self.server_thread.start()\n\n def stop(self):\n \"\"\"\n Stop the internal nimsuggest process.\n \"\"\"\n # We use the input_queue to signal to the handler thread to cleanup.\n if not self.running:\n raise Exception(\"Nimsuggest instance already stopped.\")\n self.input_queue.put(EXIT_REQUEST)\n\n def check_process(self, process):\n result = process\n if process is None or process.poll() is not None:\n try:\n result = subprocess.Popen(**self.process_args)\n self.current_failures += 1\n except OSError:\n self.current_failures = self.max_failures\n result = None\n return result\n\n def run(self):\n self.running = True\n self.current_failures = -1\n process = self.check_process(None)\n\n if self.current_failures >= self.max_failures:\n self.running = False\n sublime.set_timeout(\n lambda: sublime.status_message(\n \"Error: Nimsuggest process couldn't be started.\"\n ), 0\n )\n self.input_queue = Queue()\n\n while self.running:\n # Retrieve the next request and send it to the process.\n request = self.input_queue.get()\n debug_print(\"Got request\", request)\n if request is EXIT_REQUEST:\n break\n\n # Check up on the process\n process = self.check_process(process)\n if self.current_failures >= self.max_failures:\n sublime.set_timeout(\n lambda: sublime.status_message(\n \"Nimsuggest process failure limit reached.\"\n )\n )\n break\n\n input_data, callback = request\n process.stdin.write(input_data)\n process.stdin.flush()\n\n # Get output from Nimsuggest.\n incomplete_data = False\n raw_output = bytearray()\n while True:\n # Get the next byte\n process.stdout.flush()\n output_char = process.stdout.read(1)\n\n # The process returns nothing if it has exited\n if output_char == b'':\n incomplete_data = True\n break\n\n raw_output.extend(output_char)\n newline_found = raw_output.find(\n DOUBLE_NEWLINE_BYTE,\n len(raw_output) - len(DOUBLE_NEWLINE_BYTE)\n )\n if newline_found != -1:\n break\n\n # Parse the data\n entries = None\n output = raw_output.decode('utf-8')\n if not incomplete_data:\n entries = re.findall(ANSWER_REGEX, output, re.X)\n if len(entries) == 0:\n print('No entries found. Output:')\n print(output)\n else:\n debug_print(\"Nimsuggest process pipe was closed.\")\n print(output)\n\n # Run the callback\n self.input_queue.task_done()\n debug_print(\"Finished request, \", len(entries), \" entries found.\")\n sublime.set_timeout(lambda: callback((raw_output, entries)), 0)\n\n # Cleanup\n with self.state_transition_lock:\n while True:\n try:\n callback = self.input_queue.get_nowait()\n sublime.set_timeout(lambda: callback('', None), 0)\n self.input_queue.task_done()\n except Empty:\n break\n\n if process is not None and process.poll() is None:\n process.kill()\n\n self.running = False\n\n def run_command(self, command, nim_file, dirty_file, line, column, cb):\n # First, check that the process and thread are active\n \"\"\"\n Run the given nimsuggest command.\n :type command: str\n :type nim_file: str\n :type dirty_file: str\n :type line: int\n :type column: int\n :type cb: (str, list[Any]) -> None\n \"\"\"\n with self.state_transition_lock:\n if not self.running:\n self.start()\n\n # Next, prepare the command\n if dirty_file:\n formatted_command = '{0}\\t\"{1}\";\"{2}\":{3}:{4}\\r\\n'.format(\n command, nim_file, dirty_file, line, column\n ).encode('utf-8')\n else:\n formatted_command = '{0}\\t\"{1}\":{2}:{3}\\r\\n'.format(\n command, nim_file, line, column\n ).encode('utf-8')\n self.input_queue.put((formatted_command, cb))\n\n def __del__(self):\n pass\n\n def find_definition(self, nim_file, dirty_file, line, column, cb):\n self.run_command('def', nim_file, dirty_file, line, column, cb)\n\n def find_usages(self, nim_file, dirty_file, line, column, cb):\n self.run_command('use', nim_file, dirty_file, line, column, cb)\n\n def find_dot_usages(self, nim_file, dirty_file, line, column, cb):\n self.run_command('dus', nim_file, dirty_file, line, column, cb)\n\n def get_suggestions(self, nim_file, dirty_file, line, column, cb):\n self.run_command('sug', nim_file, dirty_file, line, column, cb)\n\n def get_context(self, nim_file, dirty_file, line, column, cb):\n self.run_command('context', nim_file, dirty_file, line, column, cb)\n\n def get_highlights(self, nim_file, dirty_file, line, column, cb):\n self.run_command('highlight', nim_file, dirty_file, line, column, cb)\n\n def get_outline(self, nim_file, dirty_file, line, column, cb):\n self.run_command('outline', nim_file, dirty_file, line, column, cb)\n", "sub_path": "nimlime_core/utils/idetools.py", "file_name": "idetools.py", "file_ext": "py", "file_size_in_byte": 8484, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.version_info", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.linesep", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.linesep.encode", "line_number": 24, "usage_type": "call"}, {"api_name": "os.linesep", "line_number": 24, "usage_type": "attribute"}, {"api_name": "collections.namedtuple", "line_number": 36, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 62, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 63, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "nimlime_core.configuration.nim_exe", "line_number": 69, "usage_type": "attribute"}, {"api_name": "nimlime_core.configuration", "line_number": 69, "usage_type": "name"}, {"api_name": "os.environ.copy", "line_number": 70, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "nimlime_core.configuration.nim_exe", "line_number": 72, "usage_type": "attribute"}, {"api_name": "nimlime_core.configuration", "line_number": 72, "usage_type": "name"}, {"api_name": "nimlime_core.configuration.nimsuggest_exe", "line_number": 77, "usage_type": "attribute"}, {"api_name": "nimlime_core.configuration", "line_number": 77, "usage_type": "name"}, {"api_name": "nimlime_core.configuration.on_windows", "line_number": 83, "usage_type": "attribute"}, {"api_name": "nimlime_core.configuration", "line_number": 83, "usage_type": "name"}, {"api_name": "subprocess.PIPE", "line_number": 84, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 85, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 86, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 95, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 112, "usage_type": "call"}, {"api_name": "sublime.set_timeout", "line_number": 126, "usage_type": "call"}, {"api_name": "sublime.status_message", "line_number": 127, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 131, "usage_type": "call"}, {"api_name": "nimlime_core.configuration.debug_print", "line_number": 136, "usage_type": "call"}, {"api_name": "sublime.set_timeout", "line_number": 143, "usage_type": "call"}, {"api_name": "sublime.status_message", "line_number": 144, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 179, "usage_type": "call"}, {"api_name": "re.X", "line_number": 179, "usage_type": "attribute"}, {"api_name": "nimlime_core.configuration.debug_print", "line_number": 184, "usage_type": "call"}, {"api_name": "nimlime_core.configuration.debug_print", "line_number": 189, "usage_type": "call"}, {"api_name": "sublime.set_timeout", "line_number": 190, "usage_type": "call"}, {"api_name": "sublime.set_timeout", "line_number": 197, "usage_type": "call"}, {"api_name": "Queue.Empty", "line_number": 199, "usage_type": "name"}]} +{"seq_id": "354264580", "text": "import numpy as np\nfrom direpack import sudire\nimport random\nfrom scipy.linalg import inv,sqrtm\n\ndef ask_othersdrmodel_service(x, y, Other_model_config, DR_results):\n '''\n 输入x(n*p) y，降维方法\n 输出beta的列表\n 返回最优维度、空间距离\n 同时也返回SDR网络的距离\n '''\n # 转化数据类型，提取参数\n\n methods = Other_model_config['methods']\n B = Other_model_config['B']\n n_slices=Other_model_config['n_slices']\n\n x = np.matrix(x)\n y = np.matrix(y)\n\n varmatx = np.cov(x, rowvar=0)\n meanx = x.mean(axis=0)\n N2 = inv(sqrtm(varmatx))\n\n #手动进行标准化\n x=x-meanx\n x=np.matmul(x, N2)\n\n\n # 记录其他模型的距离\n for method in methods:\n # 调用函数计算最优子空间\n\n best_dim, _ = my_estimate_dim(method, x, y, B, n_slices, N2)\n\n #已经标准化过了，所以不使用它的标准化，它的标准化很垃圾\n sdr_obj = sudire(sudiremeth=method, n_components=best_dim, scale_data = False, center_data = False, n_slices=n_slices )\n sdr_obj.fit(x, y)\n\n #这个时候的x_loadings并没有反标准化，也是原包里出错的地方，必须要有这一步。\n loadingBasis = np.matmul(N2 , sdr_obj.x_loadings_ )\n Space=hat(loadingBasis)\n\n # 保存信息\n DR_results.append({'method':method,\n 'DimensionFound':best_dim,\n 'EstimatedBasis':loadingBasis,\n 'EstimatedSpace':Space\n })\n\n\ndef hat(P):\n #回归里面的帽子矩阵， x (x'x)^-1x'\n return np.matmul(np.matmul(P,inv(np.matmul(P.T,P))),P.T)\n\n\ndef my_estimate_dim(method, x, y, B, n_slices,N2):\n \"\"\"\n Estimates the dimension of the central subspace using\n the sudiremeth. This approach is based on the bootstrap method of Sheng and Yin (2016)\n\n Parameters\n ----------\n\n sudiremeth : str\n the SDR method to use in the estimation.\n\n X : numpy array or dataframe\n Input X data\n\n Y : vector or 1d matrix\n Input Y data as\n\n B : int\n Number of bootstrap replications\n\n n_slices: number of slices for SIR/SAVE/DR\n\n Returns\n ----------\n\n h : int\n representing the dimension of the central subspace\n ----------\n \"\"\"\n\n n, p = x.shape\n\n diff_b = []\n mean_diff = []\n for k in range(1, p + 1):\n print('possible dim', k)\n\n sdr_obj = sudire(sudiremeth=method, n_components=k, scale_data = False, center_data = False, n_slices=n_slices )\n sdr_obj.fit(x, y=y)\n\n loadingBasis = np.matmul(N2 , sdr_obj.x_loadings_ )\n Space=hat(loadingBasis)\n\n for b in range(B):\n idx = np.random.randint(0, n, n)\n x_b = x[idx, :].copy()\n sdr_b = sudire(sudiremeth=method, n_components=k, scale_data=False, center_data=False, n_slices=n_slices)\n sdr_b.fit(x_b, y=y)\n\n loadingBasis_b = np.matmul( N2 , sdr_b.x_loadings_)\n Space_b = hat(loadingBasis_b)\n\n uh, sh, vh = np.linalg.svd( Space - Space_b)\n diff_b.append(np.nanmax(sh))\n\n mean_diff.append(np.mean(diff_b))\n\n return (np.argmin(mean_diff) + 1, mean_diff)", "sub_path": "service/ask_othersdr_method.py", "file_name": "ask_othersdr_method.py", "file_ext": "py", "file_size_in_byte": 3283, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.matrix", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 22, "usage_type": "call"}, {"api_name": "scipy.linalg.inv", "line_number": 24, "usage_type": "call"}, {"api_name": "scipy.linalg.sqrtm", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 28, "usage_type": "call"}, {"api_name": "direpack.sudire", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 55, "usage_type": "call"}, {"api_name": "scipy.linalg.inv", "line_number": 55, "usage_type": "call"}, {"api_name": "direpack.sudire", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 102, "usage_type": "attribute"}, {"api_name": "direpack.sudire", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.linalg.svd", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.nanmax", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "556236239", "text": "# chapter 24 in python\n# sudo apt-get install python3-pip\n# python3 -m pip install pyserial\n# sudo apt-get install python3-matplotlib\nimport serial\nser = serial.Serial('/dev/ttyUSB0',230400,rtscts=1)\nprint('Opening port: ')\nprint(ser.name)\nser.write(b'1.0 1.0\\n') # Kp Ki\nsampnum = 0\nread_samples = 10\nADCval = []\nref = []\nwhile read_samples > 1:\n data_read = ser.read_until(b'\\n',50)\n data_text = str(data_read,'utf-8')\n data = list(map(int,data_text.split()))\n if(len(data)==3):\n read_samples = data[0]\n ADCval.append(data[1])\n ref.append(data[2])\n sampnum = sampnum + 1\n# plot it\nimport matplotlib.pyplot as plt\nt = range(len(ADCval)) # time array\nplt.plot(t,ADCval,'r*-',t,ref,'b*-')\nplt.ylabel('value')\nplt.xlabel('sample')\nplt.show()\n# be sure to close the port\nser.close()\n", "sub_path": "hw/hw7/ch24.py", "file_name": "ch24.py", "file_ext": "py", "file_size_in_byte": 820, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "serial.Serial", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}]} +{"seq_id": "553107972", "text": "from django.conf.urls import patterns, url\nfrom custom.ilsgateway.views import GlobalStats\nfrom custom.ilsgateway.views import ILSConfigView\n\nurlpatterns = patterns('custom.ilsgateway.views',\n url(r'^ils_config/$', ILSConfigView.as_view(), name=ILSConfigView.urlname),\n url(r'^sync_ilsgateway/$', 'sync_ilsgateway', name='sync_ilsgateway'),\n url(r'^global_stats/$', GlobalStats.as_view(), name=GlobalStats.urlname),\n # for testing purposes\n url(r'^sync_stock_data/$', 'sync_stock_data', name='sync_stock_data'),\n url(r'^clear_stock_data/$', 'clear_stock_data', name='clear_stock_data'),\n url(r'^run_reports/$', 'run_warehouse_runner', name='run_reports'),\n url(r'^end_report_run/$', 'end_report_run', name='end_report_run')\n)\n", "sub_path": "custom/ilsgateway/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 750, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "custom.ilsgateway.views.ILSConfigView.as_view", "line_number": 6, "usage_type": "call"}, {"api_name": "custom.ilsgateway.views.ILSConfigView", "line_number": 6, "usage_type": "name"}, {"api_name": "custom.ilsgateway.views.ILSConfigView.urlname", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "custom.ilsgateway.views.GlobalStats.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "custom.ilsgateway.views.GlobalStats", "line_number": 8, "usage_type": "name"}, {"api_name": "custom.ilsgateway.views.GlobalStats.urlname", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "9585440", "text": "import logging\nimport pychromecast\n\nlogger = logging.getLogger(__name__)\n\n\nclass Caster:\n def __init__(self, stream_url, preferred_chromecast=None):\n self.stream_url = stream_url\n\n logger.info(\"Searching for Chromecast devices...\")\n chromecast_list = pychromecast.get_chromecasts_as_dict().keys()\n logger.debug(\"Found Chromecasts: %s\", chromecast_list)\n\n if not chromecast_list:\n raise RuntimeError(\"Unable to find a Chromecast on the local network.\")\n\n chromecast_name = None\n if preferred_chromecast:\n preferred_index = chromecast_list.index(preferred_chromecast)\n if preferred_index:\n chromecast_name = preferred_chromecast\n else:\n logger.warn(\"Couldn't find preferred chromecast\")\n \n if chromecast_name is None:\n chromecast_name = chromecast_list[0]\n if len(chromecast_list) > 1:\n logger.warn(\"Multiple Chromecast devices detected\")\n logger.warn(\"Found Chromecasts: %s\", ', '.join(chromecast_list))\n logger.warn(\"Defaulting to Chromecast '%s'\", chromecast_name)\n\n logger.info(\"Connecting to Chromecast '%s'\", chromecast_name)\n self.chromecast = pychromecast.get_chromecast(\n friendly_name=chromecast_name)\n self.chromecast.wait()\n logger.info(\"Connected to Chromecast '%s'\", chromecast_name)\n\n def start_stream(self):\n logger.info(\"Starting stream of URL %s on Chromecast '%s'\",\n self.stream_url, self.device_name)\n\n self.chromecast.quit_app()\n\n mc = self.chromecast.media_controller\n mc.play_media(self.stream_url, 'audio/flac', stream_type=\"LIVE\")\n\n @property\n def device_name(self):\n return self.chromecast.device.friendly_name\n", "sub_path": "src/cast.py", "file_name": "cast.py", "file_ext": "py", "file_size_in_byte": 1852, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "pychromecast.get_chromecasts_as_dict", "line_number": 12, "usage_type": "call"}, {"api_name": "pychromecast.get_chromecast", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "57340802", "text": "#---------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n#---------------------------------------------------------------------------------------------\n#pylint: skip-file\n# coding=utf-8\n# --------------------------------------------------------------------------\n# Code generated by Microsoft (R) AutoRest Code Generator 0.16.0.0\n# Changes may cause incorrect behavior and will be lost if the code is\n# regenerated.\n# --------------------------------------------------------------------------\n\nfrom msrest.service_client import ServiceClient\nfrom msrest import Configuration, Serializer, Deserializer\nfrom .version import VERSION\nfrom .operations.operation import Operation\nfrom .operations.registries import Registries\nfrom .operations.subscriptions import Subscriptions\nfrom . import models\n\n\nclass ContainerRegistryConfiguration(Configuration):\n \"\"\"Configuration for ContainerRegistry\n Note that all parameters used to create this instance are saved as instance\n attributes.\n\n :param subscription_id: Gets subscription credentials which uniquely\n identify Microsoft Azure subscription.The subscription ID forms part of\n the URI for every service call.\n :type subscription_id: str\n :param api_version: Client Api Version.\n :type api_version: str\n :param credentials: Subscription credentials which uniquely identify\n client subscription.\n :type credentials: :mod:`A msrest Authentication\n object`\n :param str base_url: Service URL\n :param str filepath: Existing config\n \"\"\"\n\n def __init__(\n self, subscription_id, api_version, credentials, base_url=None, filepath=None):\n\n if subscription_id is None:\n raise ValueError(\"Parameter 'subscription_id' must not be None.\")\n if not isinstance(subscription_id, str):\n raise TypeError(\"Parameter 'subscription_id' must be str.\")\n if api_version is None:\n raise ValueError(\"Parameter 'api_version' must not be None.\")\n if not isinstance(api_version, str):\n raise TypeError(\"Parameter 'api_version' must be str.\")\n if credentials is None:\n raise ValueError(\"Parameter 'credentials' must not be None.\")\n if not base_url:\n base_url = 'https://management.azure.com'\n\n super(ContainerRegistryConfiguration, self).__init__(base_url, filepath)\n\n self.add_user_agent('containerregistry/{}'.format(VERSION))\n\n self.subscription_id = subscription_id\n self.api_version = api_version\n self.credentials = credentials\n\n\nclass ContainerRegistry(object):\n \"\"\"ContainerRegistry\n\n :param config: Configuration for client.\n :type config: ContainerRegistryConfiguration\n\n :ivar operation: Operation operations\n :vartype operation: .operations.Operation\n :ivar registries: Registries operations\n :vartype registries: .operations.Registries\n :ivar subscriptions: Subscriptions operations\n :vartype subscriptions: .operations.Subscriptions\n \"\"\"\n\n def __init__(self, config):\n\n self._client = ServiceClient(config.credentials, config)\n\n client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)}\n self._serialize = Serializer()\n self._deserialize = Deserializer(client_models)\n\n self.config = config\n self.operation = Operation(\n self._client, self.config, self._serialize, self._deserialize)\n self.registries = Registries(\n self._client, self.config, self._serialize, self._deserialize)\n self.subscriptions = Subscriptions(\n self._client, self.config, self._serialize, self._deserialize)\n", "sub_path": "src/command_modules/azure-cli-acr/azure/cli/command_modules/acr/mgmt_acr/container_registry.py", "file_name": "container_registry.py", "file_ext": "py", "file_size_in_byte": 3878, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "msrest.Configuration", "line_number": 22, "usage_type": "name"}, {"api_name": "version.VERSION", "line_number": 59, "usage_type": "argument"}, {"api_name": "msrest.service_client.ServiceClient", "line_number": 82, "usage_type": "call"}, {"api_name": "msrest.Serializer", "line_number": 85, "usage_type": "call"}, {"api_name": "msrest.Deserializer", "line_number": 86, "usage_type": "call"}, {"api_name": "operations.operation.Operation", "line_number": 89, "usage_type": "call"}, {"api_name": "operations.registries.Registries", "line_number": 91, "usage_type": "call"}, {"api_name": "operations.subscriptions.Subscriptions", "line_number": 93, "usage_type": "call"}]} +{"seq_id": "584461648", "text": "import matplotlib.pyplot as plt\r\nimport pandas as pd\r\nimport numpy as np\r\nimport torch\r\n\r\nplt.style.use('dark_background')\r\n\r\n\r\nclass DDPGTrainer:\r\n \"\"\"\r\n A class for the implementation and utilization of the training process\r\n steps for the Deep Deterministic Policy Gradient algorithm.\r\n\r\n Attributes:\r\n env: A UnityEnvironment used for Agent evaluation and training.\r\n agent: Agent object being trained in env.\r\n update_frequency: An integer designating the step frequency of\r\n updating target network parameters.\r\n num_updates: Integer number of updates desired for every\r\n update_frequency steps.\r\n max_epsiode_length: An integer for maximum number of timesteps per\r\n episode.\r\n save_dir: Path designating directory to save resulting files.\r\n score_window_size: Integer window size used in order to gather\r\n mean score to evaluate environment solution.\r\n \"\"\"\r\n\r\n def __init__(self, env, agent, update_frequency, num_updates,\r\n max_episode_length, save_dir, score_window_size):\r\n\r\n # Initialize relevant variables for training\r\n self.env = env\r\n self.brain_name = env.brain_names[0]\r\n self.agent = agent\r\n self.update_frequency = update_frequency\r\n self.num_updates = num_updates\r\n self.max_episode_length = max_episode_length\r\n self.save_dir = save_dir\r\n self.score_window_size = score_window_size\r\n\r\n # Initialize episode number and scoring array.\r\n self.i_episode = 0\r\n self.scores = []\r\n self.max_score = -np.inf\r\n\r\n def reset_env(self):\r\n \"\"\"Resets environement and returns original state.\"\"\"\r\n\r\n env_info = self.env.reset()[self.brain_name]\r\n return env_info.vector_observations\r\n\r\n def step_env(self, actions):\r\n \"\"\"\r\n Realizes actions in environment and returns relevant attributes.\r\n\r\n Parameters:\r\n actions: Actions array to be realized in the environment.\r\n\r\n Returns:\r\n states: Array with next state information.\r\n rewards: Array with rewards information.\r\n dones: Array with boolean values with 'true' designating the\r\n episode has finished.\r\n env_info: BrainInfo object with current environment data.\r\n \"\"\"\r\n\r\n # From environment information, extract states and rewards.\r\n env_info = self.env.step(actions)[self.brain_name]\r\n states = env_info.vector_observations\r\n rewards = env_info.rewards\r\n\r\n # Evaluate if episode has finished.\r\n dones = env_info.local_done\r\n\r\n return states, rewards, dones, env_info\r\n\r\n def run_episode(self, max_episode_length):\r\n \"\"\"\r\n Runs a single episode in the training process for max_episode_length\r\n timesteps.\r\n\r\n Parameters:\r\n max_episode_length: Integer number of timesteps in one episode.\r\n\r\n Returns:\r\n scores: Array with rewards aquired from episode.\r\n \"\"\"\r\n\r\n # Restart the environment and gather original states.\r\n states = self.reset_env()\r\n\r\n # Initialize scores array to hold reward values for each episode.\r\n scores = np.zeros(states.shape[0])\r\n\r\n # Act and evaluate results and networks for each timestep.\r\n for t in range(max_episode_length):\r\n\r\n # Act and evaluate results of action.\r\n actions = self.agent.act(states)\r\n next_states, rewards, dones, _ = self.step_env(actions)\r\n dones = np.array(dones).astype(int)\r\n\r\n # Add experiences to memory\r\n self.agent.add_experience(states, actions, rewards,\r\n next_states, dones)\r\n\r\n # Update networks num_update times each update_frequency timesteps.\r\n if (t + 1) % self.update_frequency == 0:\r\n for _ in range(self.num_updates):\r\n self.agent.step()\r\n\r\n # Save states and scores and break if training is complete.\r\n states = next_states\r\n scores += np.array(rewards)\r\n if any(dones):\r\n break\r\n\r\n return scores\r\n\r\n def train_step(self):\r\n \"\"\"Steps through each episode and stores mean scores output.\"\"\"\r\n\r\n self.i_episode += 1\r\n scores = self.run_episode(self.max_episode_length)\r\n self.scores.append(scores.mean())\r\n\r\n def get_running_mean_score(self):\r\n \"\"\"\r\n Returns the mean score for the last score_window_size episodes or\r\n for as many episodes that have been evaluated.\r\n \"\"\"\r\n\r\n # If less than score_window_size episodes evaluated, return mean\r\n # up until that point.\r\n if len(self.scores) < self.score_window_size:\r\n return np.mean(self.scores).item()\r\n\r\n # Return mean score for the last score_window_size episodes.\r\n return np.mean(self.scores[-self.score_window_size:]).item()\r\n\r\n def print_status(self, put_new_line):\r\n \"\"\"Displays current episode and average score to terminal.\"\"\"\r\n\r\n if put_new_line:\r\n print('\\rEpisode {0}\\tAverage Score: {1:.2f}'.format(\r\n self.i_episode, self.get_running_mean_score()))\r\n else:\r\n print('\\rEpisode {0}\\tAverage Score: {1:.2f}'.format(\r\n self.i_episode, self.get_running_mean_score()), end='')\r\n\r\n def save(self):\r\n \"\"\"Saves local network parameters for successful Actor and Critic.\"\"\"\r\n\r\n torch.save(\r\n self.agent.actor_local.state_dict(),\r\n f'{self.save_dir}/checkpoint_actor_{self.i_episode}.pth'\r\n )\r\n\r\n torch.save(\r\n self.agent.critic_local.state_dict(),\r\n f'{self.save_dir}/checkpoint_critic_{self.i_episode}.pth'\r\n )\r\n\r\n def plt_mavg(self):\r\n \"\"\"Plots cumulative moving average score by episode.\"\"\"\r\n\r\n # Initialize episode number values:\r\n x = np.arange(1, len(self.scores)+1)\r\n\r\n # Calculate cumulative moving average values.\r\n y = np.cumsum(self.scores) / x\r\n\r\n # Plot cumulative moving averages and save resulting plot.\r\n fig, ax = plt.subplots(figsize=(12, 9))\r\n ax.plot(x, y, color='paleturquoise', linewidth=1.5)\r\n ax.grid(color='w', linewidth=0.2)\r\n ax.set_title(\r\n f'Learning Curve: Deep Deterministic Policy Gradient',\r\n fontsize=30\r\n )\r\n ax.set_xlabel('Episode', fontsize=21)\r\n plt.xticks(np.arange(0, np.max(x), 20), fontsize=10)\r\n ax.set_ylabel('Score', fontsize=21)\r\n plt.yticks(np.arange(0, np.max(y)+5, 5), fontsize=10)\r\n plt.tight_layout()\r\n plt.savefig(rf'{self.save_dir}/scores_mavg_{self.i_episode}')\r\n plt.show()\r\n\r\n return fig\r\n", "sub_path": "ddpg/ddpg_trainer.py", "file_name": "ddpg_trainer.py", "file_ext": "py", "file_size_in_byte": 6874, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 6, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "numpy.inf", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}]} +{"seq_id": "26855044", "text": "import time\n\nimport allure\nfrom allure_commons.types import AttachmentType\nfrom behave import *\nfrom selenium import webdriver\nfrom webdriver_manager.chrome import ChromeDriverManager\n\nfrom Pages.homePage import HomePage\nfrom Pages.navbarValidation import myLogger, baseURL, navBar\n\n\n@given(u'Launch the browser')\ndef step_impl(context):\n context.driver = webdriver.chrome(ChromeDriverManager.install())\n myLogger.info(\"****Driver Initialized ****\")\n context.driver.get(baseURL)\n myLogger.info(\"****Browser Launched ****\")\n\n\n@when(u'navbar element found')\ndef step_impl(context):\n myLogger.info(\"****Navbar Element Found ****\")\n global hpage\n global nbar\n hpage = HomePage(context.driver)\n hpage.titleDisplayed()\n time.sleep(5)\n nbar = navBar(context.driver)\n nbar.clickonAllShoes()\n myLogger.info(\"****Navbar Element Found! ****\")\n\n\n\n\n@then(u'navigate to All Shoes')\ndef step_impl(context):\n myLogger.info(\"****Navbar Element Found ****\")\n global hpage\n global nbar\n hpage = HomePage(context.driver)\n hpage.titleDisplayed()\n time.sleep(5)\n nbar = navBar(context.driver)\n nbar.clickonAllShoes()\n myLogger.info(\"****All Shoes Found and clicked! ****\")\n\n\n#@then(u'I click the All Shoes link')\n#def step_impl(context):\n\n\n\n@then(u'close the App')\ndef step_impl(context):\n context.driver.close()\n myLogger.info(\"****Browser Closed!****\")\n", "sub_path": "ShoeStoreBehave/Features/Steps/navbarValidationSteps.py", "file_name": "navbarValidationSteps.py", "file_ext": "py", "file_size_in_byte": 1405, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "selenium.webdriver.chrome", "line_number": 15, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 15, "usage_type": "name"}, {"api_name": "webdriver_manager.chrome.ChromeDriverManager.install", "line_number": 15, "usage_type": "call"}, {"api_name": "webdriver_manager.chrome.ChromeDriverManager", "line_number": 15, "usage_type": "name"}, {"api_name": "Pages.navbarValidation.myLogger.info", "line_number": 16, "usage_type": "call"}, {"api_name": "Pages.navbarValidation.myLogger", "line_number": 16, "usage_type": "name"}, {"api_name": "Pages.navbarValidation.baseURL", "line_number": 17, "usage_type": "argument"}, {"api_name": "Pages.navbarValidation.myLogger.info", "line_number": 18, "usage_type": "call"}, {"api_name": "Pages.navbarValidation.myLogger", "line_number": 18, "usage_type": "name"}, {"api_name": "Pages.navbarValidation.myLogger.info", "line_number": 23, "usage_type": "call"}, {"api_name": "Pages.navbarValidation.myLogger", "line_number": 23, "usage_type": "name"}, {"api_name": "Pages.homePage.HomePage", "line_number": 26, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "Pages.navbarValidation.navBar", "line_number": 29, "usage_type": "call"}, {"api_name": "Pages.navbarValidation.myLogger.info", "line_number": 31, "usage_type": "call"}, {"api_name": "Pages.navbarValidation.myLogger", "line_number": 31, "usage_type": "name"}, {"api_name": "Pages.navbarValidation.myLogger.info", "line_number": 38, "usage_type": "call"}, {"api_name": "Pages.navbarValidation.myLogger", "line_number": 38, "usage_type": "name"}, {"api_name": "Pages.homePage.HomePage", "line_number": 41, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 43, "usage_type": "call"}, {"api_name": "Pages.navbarValidation.navBar", "line_number": 44, "usage_type": "call"}, {"api_name": "Pages.navbarValidation.myLogger.info", "line_number": 46, "usage_type": "call"}, {"api_name": "Pages.navbarValidation.myLogger", "line_number": 46, "usage_type": "name"}, {"api_name": "Pages.navbarValidation.myLogger.info", "line_number": 57, "usage_type": "call"}, {"api_name": "Pages.navbarValidation.myLogger", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "3891585", "text": "from __future__ import print_function\nfrom __future__ import division\n\nimport argparse\nimport gym\nimport tensorflow as tf\nimport numpy as np\nfrom policyGradientAgent import policyGradient\n\n\n\n# 定义传入参数的函数\ndef args_parse():\n # 参数解释器\n parser = argparse.ArgumentParser()\n\n # 模型路径参数， 默认值为None\n parser.add_argument(\"--model_path\",\n default=None,\n help=\"Whether to use a saved model. (*None|model path)\")\n\n # 模型保存路径， 默认存储路径为当前目录下的model文件夹\n parser.add_argument(\"--save_path\",\n default=\"./models/mountainCar/\",\n help=\"Path to save a model during training.\")\n\n # GPU使用设置, 默认值为-1，即不使用任何GPU，仅使用CPU进行训练\n parser.add_argument(\"--gpu\",\n default=-1,\n help=\"Runing on a specify GPU, -1 indicates using CPU.\")\n\n # 设定随机种子\n parser.add_argument(\"--seed\",\n default=100,\n help=\"random seed.\")\n\n # 环境的状态数\n parser.add_argument(\"--num_features\",\n type=int,\n default=2,\n help=\"Number of states.\")\n\n # agent的行为个数\n parser.add_argument(\"--num_actions\",\n type=int,\n default=3,\n help=\"Number of actions.\")\n\n # 模型最大训练轮数\n parser.add_argument(\"--max_episodes\",\n type=int,\n default=2000,\n help=\"Max number of training episodes.\")\n\n # 模型测试的轮数\n parser.add_argument(\"--test_episodes\",\n type=int,\n default=50,\n help=\"Number of test episodes.\")\n\n # 深度神经网络的learning rate\n parser.add_argument(\"--learning_rate\",\n type=float,\n default=0.005,\n help=\"Deep network Learning rate.\")\n\n # 深度网络的batchSize\n parser.add_argument(\"--batch_size\",\n type=int,\n default=128,\n help=\"Size of training batch.\"\n )\n\n # 强化学习当前状态和下一状态的discount Rate（折扣率）\n parser.add_argument(\"--discount_rate\",\n type=float,\n default=0.99,\n help=\"Discount rate.\"\n )\n\n # 返回结果\n return parser.parse_args()\n\n\ndef setRandomSeed(seed):\n np.random.seed(seed)\n tf.set_random_seed(seed)\n\n\n\ndef main(args):\n # 设定随机种子\n setRandomSeed(args.seed)\n\n # 设定环境\n env = gym.make('MountainCar-v0')\n env = env.unwrapped\n\n # 初始化agent\n agent = policyGradient(args)\n\n # 构建模型\n agent.model\n agent.sess.run(tf.global_variables_initializer())\n\n # 模型训练\n for episode_index in range(args.max_episodes):\n # 初始化当前回合的环境\n currentState = env.reset()\n\n # 当前回合的experience数\n while True:\n # 根据策略，为当前状态产生一个行为\n currentAction = agent.action_sample(currentState)\n print(currentState)\n print(currentAction)\n\n # 在当前状态下，采用当前的行为，与环境交互，获取下一个状态\n nextState, reward, done, debug = env.step(currentAction)\n\n # 解析当前experience\n experience = currentState, currentAction, reward\n\n # 当前experience存储\n agent.store_experience(experience)\n\n # 当当前回合结束时，开始更新模型\n if done:\n loss = agent.learn()\n print(\"第\", episode_index + 1, \"回合的损失函数为：\", loss)\n break\n\n # 将下一个state变成当前state\n currentState = nextState\n\n\n # 模型保存\n agent.model_saver()\n\nif __name__ == '__main__':\n main(args_parse())", "sub_path": "policyGradient/MountainCar_training.py", "file_name": "MountainCar_training.py", "file_ext": "py", "file_size_in_byte": 4222, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 86, "usage_type": "attribute"}, {"api_name": "tensorflow.set_random_seed", "line_number": 87, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 96, "usage_type": "call"}, {"api_name": "policyGradientAgent.policyGradient", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "582831508", "text": "from typing import Iterator\nfrom unittest import TestCase\n\nfrom xsdata.models.xsd import Extension\n\n\nclass ExtensionTests(TestCase):\n def test_property_extensions(self):\n obj = Extension()\n self.assertIsInstance(obj.extensions, Iterator)\n self.assertEqual([], list(obj.extensions))\n obj.base = \"a b c\"\n self.assertEqual([\"a b c\"], list(obj.extensions))\n", "sub_path": "tests/models/xsd/test_extension.py", "file_name": "test_extension.py", "file_ext": "py", "file_size_in_byte": 391, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "unittest.TestCase", "line_number": 7, "usage_type": "name"}, {"api_name": "xsdata.models.xsd.Extension", "line_number": 9, "usage_type": "call"}, {"api_name": "typing.Iterator", "line_number": 10, "usage_type": "argument"}]} +{"seq_id": "72960380", "text": "\"\"\"\nDjango settings for canvas_course_creation project.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/1.6/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/1.6/ref/settings/\n\"\"\"\nimport os\nfrom .secure import SECURE_SETTINGS\nfrom django.core.urlresolvers import reverse_lazy\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = SECURE_SETTINGS.get('django_secret_key', 'changeme')\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = SECURE_SETTINGS.get('enable_debug', False)\n\nTEMPLATE_DEBUG = DEBUG\n\nALLOWED_HOSTS = ['*']\n\n# THESE ADDRESSES WILL RECEIVE EMAIL ABOUT CERTAIN ERRORS!\nADMINS = SECURE_SETTINGS.get('admins')\n\n# Application definition\n\nINSTALLED_APPS = (\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'django_auth_lti',\n 'icommons_common',\n 'icommons_common.monitor',\n 'icommons_ui',\n 'canvas_course_site_wizard',\n 'djangular',\n 'crispy_forms',\n 'bulk_site_creation',\n\n)\n\nMIDDLEWARE_CLASSES = (\n 'djangular.middleware.DjangularUrlMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'cached_auth.Middleware',\n 'django_auth_lti.middleware_patched.MultiLTILaunchAuthMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n)\n\nAUTHENTICATION_BACKENDS = (\n 'icommons_common.auth.backends.PINAuthBackend',\n 'django_auth_lti.backends.LTIAuthBackend',\n\n)\n\nCRISPY_TEMPLATE_PACK = 'bootstrap3'\n\nLOGIN_URL = reverse_lazy('lti_auth_error')\n\nROOT_URLCONF = 'canvas_course_creation.urls'\n\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n 'DIRS': [os.path.normpath(os.path.join(BASE_DIR, 'templates'))],\n 'APP_DIRS': True,\n 'OPTIONS': {\n 'context_processors': [\n 'django.template.context_processors.debug',\n 'django.template.context_processors.request',\n 'django.contrib.auth.context_processors.auth',\n 'django.template.context_processors.i18n',\n 'django.template.context_processors.media',\n 'django.template.context_processors.static',\n 'django.template.context_processors.tz',\n 'django.contrib.messages.context_processors.messages',\n 'icommons_common.auth.context_processors.pin_context'\n ],\n },\n },\n]\n\nWSGI_APPLICATION = 'canvas_course_creation.wsgi.application'\n\n# This is the address that emails will be sent \"from\"\nSERVER_EMAIL = 'iCommons LTI Tools '\n\nSECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')\n\n# Database config\n\nDATABASE_APPS_MAPPING = {\n 'icommons_common': 'termtool',\n 'canvas_course_site_wizard': 'termtool',\n 'bulk_site_creation' : 'default',\n 'auth': 'default',\n 'contenttypes': 'default',\n 'sessions': 'default',\n}\n\nDATABASE_MIGRATION_WHITELIST = ['default']\n\nDATABASE_ROUTERS = ['icommons_common.routers.DatabaseAppsRouter', ]\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.postgresql_psycopg2',\n 'NAME': SECURE_SETTINGS.get('db_default_name', 'canvas_course_creation'),\n 'USER': SECURE_SETTINGS.get('db_default_user', 'postgres'),\n 'PASSWORD': SECURE_SETTINGS.get('db_default_password'),\n 'HOST': SECURE_SETTINGS.get('db_default_host', '127.0.0.1'),\n 'PORT': SECURE_SETTINGS.get('db_default_port', 5432), # Default postgres port\n },\n 'termtool': {\n 'ENGINE': 'django.db.backends.oracle',\n 'NAME': SECURE_SETTINGS.get('db_termtool_name'),\n 'USER': SECURE_SETTINGS.get('db_termtool_user'),\n 'PASSWORD': SECURE_SETTINGS.get('db_termtool_password'),\n 'HOST': SECURE_SETTINGS.get('db_termtool_host'),\n 'PORT': str(SECURE_SETTINGS.get('db_termtool_port')),\n 'OPTIONS': {\n 'threaded': True,\n },\n 'CONN_MAX_AGE': 0,\n }\n}\n\n# Cache config\n\nREDIS_HOST = SECURE_SETTINGS.get('redis_host', '127.0.0.1')\nREDIS_PORT = SECURE_SETTINGS.get('redis_port', 6379)\n\nCACHES = {\n 'default': {\n 'BACKEND': 'redis_cache.RedisCache',\n 'LOCATION': \"redis://%s:%s/0\" % (REDIS_HOST, REDIS_PORT),\n 'OPTIONS': {\n 'PARSER_CLASS': 'redis.connection.HiredisParser'\n },\n 'TIMEOUT': 60 * 20, # 20 minutes\n 'KEY_PREFIX': 'canvas_course_creation'\n },\n 'shared': {\n 'BACKEND': 'redis_cache.RedisCache',\n 'LOCATION': \"redis://%s:%s/0\" % (REDIS_HOST, REDIS_PORT),\n 'OPTIONS': {\n 'PARSER_CLASS': 'redis.connection.HiredisParser'\n },\n 'KEY_PREFIX': 'tlt_shared',\n 'TIMEOUT': SECURE_SETTINGS.get('default_cache_timeout_secs', 300),\n }\n}\n\n\n# Sessions\n\nSESSION_ENGINE = 'django.contrib.sessions.backends.cache'\n\nSESSION_EXPIRE_AT_BROWSER_CLOSE = True\n\n# Internationalization\n# https://docs.djangoproject.com/en/1.6/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'America/New_York'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/1.6/howto/static-files/\n\nSTATIC_URL = '/static/'\n\nSTATIC_ROOT = os.path.normpath(os.path.join(BASE_DIR, 'http_static'))\n\n\n\nLTI_OAUTH_CREDENTIALS = SECURE_SETTINGS.get('lti_oauth_credentials', None)\n\nCANVAS_URL = SECURE_SETTINGS.get('canvas_url', 'https://canvas.harvard.edu')\n\nISITES_LMS_URL = ''\n\nCANVAS_SITE_SETTINGS = {\n 'base_url': CANVAS_URL + '/',\n}\n\nCANVAS_SDK_SETTINGS = {\n 'auth_token': SECURE_SETTINGS.get('canvas_token', None),\n 'base_api_url': CANVAS_URL + '/api',\n 'max_retries': 3,\n 'per_page': 40,\n 'session_inactivity_expiration_time_secs': 50,\n}\n\nICOMMONS_COMMON = {\n 'ICOMMONS_API_HOST': SECURE_SETTINGS.get('icommons_api_host', None),\n 'ICOMMONS_API_USER': SECURE_SETTINGS.get('icommons_api_user', None),\n 'ICOMMONS_API_PASS': SECURE_SETTINGS.get('icommons_api_pass', None),\n 'CANVAS_API_BASE_URL': CANVAS_URL + '/api/v1',\n 'CANVAS_API_HEADERS': {\n 'Authorization': 'Bearer ' + SECURE_SETTINGS.get('canvas_token', 'canvas_token_missing_from_config')\n },\n}\n\nCANVAS_EMAIL_NOTIFICATION = {\n 'from_email_address': 'icommons-bounces@harvard.edu',\n 'support_email_address': 'tlt_support@harvard.edu',\n 'course_migration_success_subject': 'Course site is ready',\n 'course_migration_success_body': 'Success! \\nYour new Canvas course site has been created and is ready for you at:\\n'+\n ' {0} \\n\\n Here are some resources for getting started with your site:\\n http://tlt.harvard.edu/getting-started#teachingstaff',\n\n 'course_migration_failure_subject': 'Course site not created',\n 'course_migration_failure_body': 'There was a problem creating your course site in Canvas.\\n'+\n 'Your local academic support staff has been notified and will be in touch with you.\\n\\n'+\n 'If you have questions please contact them at:\\n'+\n ' FAS: atg@fas.harvard.edu\\n'+\n ' DCE/Summer: AcademicTechnology@dce.harvard.edu\\n'+\n ' (Let them know that course site creation failed for sis_course_id: {0} ',\n\n 'support_email_subject_on_failure': 'Course site not created',\n 'support_email_body_on_failure': 'There was a problem creating a course site in Canvas via the wizard.\\n\\n'+\n 'Course site creation failed for sis_course_id: {0}\\n'+\n 'User: {1}\\n'+\n '{2}\\n'+\n 'Environment: {3}\\n',\n 'environment' : 'Production',\n}\n\nBULK_COURSE_CREATION = {\n 'log_long_running_jobs': True,\n 'long_running_age_in_minutes': 30,\n 'notification_email_subject': 'Sites created for {school} {term} term',\n 'notification_email_body': 'Canvas course sites have been created for the '\n '{school} {term} term.\\n\\n - {success_count} '\n 'course sites were created successfully.\\n',\n 'notification_email_body_failed_count': ' - {} course sites were not '\n 'created.',\n}\n\n\n# Background task PID (lock) files\n# * If created in another directory, ensure the directory exists in runtime environment\nPROCESS_ASYNC_JOBS_PID_FILE = 'process_async_jobs.pid'\nFINALIZE_BULK_CREATE_JOBS_PID_FILE = 'finalize_bulk_create_jobs.pid'\n\n_LOG_ROOT = SECURE_SETTINGS.get('log_root', '') # Default to current directory\n\n\n# Logging\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': True,\n 'formatters': {\n 'verbose': {\n 'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s'\n },\n 'simple': {\n 'format': '%(levelname)s %(module)s %(message)s'\n }\n },\n 'filters': {\n 'require_debug_false': {\n '()': 'django.utils.log.RequireDebugFalse'\n }\n },\n 'handlers': {\n 'mail_admins': {\n 'level': 'ERROR',\n 'filters': ['require_debug_false'],\n 'class': 'django.utils.log.AdminEmailHandler',\n },\n # Log to a text file that can be rotated by logrotate\n 'logfile': {\n 'level': 'INFO',\n 'class': 'logging.handlers.WatchedFileHandler',\n 'filename': os.path.join(_LOG_ROOT, 'course_creation.log'),\n 'formatter': 'verbose',\n },\n 'console': {\n 'level': 'INFO',\n 'class': 'logging.StreamHandler',\n 'formatter': 'simple',\n },\n 'request': {\n 'level': 'DEBUG',\n 'class': 'logging.handlers.WatchedFileHandler',\n 'filename': 'request.log',\n 'formatter': 'verbose',\n },\n\n },\n 'loggers': {\n '': {\n 'handlers': ['console', 'logfile'],\n 'level': 'INFO',\n 'propagate': True,\n },\n 'django.request': {\n 'handlers': ['request'],\n 'level': 'DEBUG',\n 'propagate': False,\n },\n 'django': {\n 'handlers': ['console', 'logfile'],\n 'level': 'ERROR',\n 'propagate': True,\n },\n 'oauth2': {\n 'handlers': ['console', 'logfile'],\n 'level': 'DEBUG',\n 'propagate': True,\n },\n 'ims_lti_py': {\n 'handlers': ['console', 'logfile'],\n 'level': 'DEBUG',\n 'propagate': True,\n },\n 'django_auth_lti': {\n 'handlers': ['console', 'logfile'],\n 'level': 'DEBUG',\n 'propagate': True,\n },\n 'icommons_common': {\n 'handlers': ['console', 'logfile'],\n 'level': 'DEBUG',\n 'propagate': True,\n },\n 'bulk_site_creation': {\n 'handlers': ['console', 'logfile'],\n 'level': 'DEBUG',\n 'propagate': True,\n },\n 'canvas_course_site_wizard': {\n 'handlers': ['console', 'logfile'],\n 'level': 'INFO',\n 'propagate': True,\n },\n\n\n }\n}\n", "sub_path": "canvas_course_creation/settings/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 11339, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 15, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 18, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 18, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 21, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 21, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 28, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 28, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse_lazy", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 116, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 116, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 117, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 117, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 118, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 118, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 119, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 119, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 120, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 120, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 124, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 124, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 125, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 125, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 126, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 126, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 127, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 127, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 128, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 128, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 138, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 138, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 139, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 139, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 158, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 158, "usage_type": "name"}, {"api_name": "os.path.normpath", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path", "line_number": 188, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 188, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 192, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 192, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 194, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 194, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 203, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 203, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 211, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 211, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 212, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 212, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 213, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 213, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 216, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 216, "usage_type": "name"}, {"api_name": "secure.SECURE_SETTINGS.get", "line_number": 261, "usage_type": "call"}, {"api_name": "secure.SECURE_SETTINGS", "line_number": 261, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 291, "usage_type": "call"}, {"api_name": "os.path", "line_number": 291, "usage_type": "attribute"}]} +{"seq_id": "27626227", "text": "import json\nimport os\nimport sys\nimport logging\nimport configparser\nimport collections\nimport numpy as np\nimport pandas as pd\n\n# import tensorflow as tf\nimport LAC\nimport happybase\nimport joblib\nimport sklearn.utils\nfrom sklearn.feature_selection import RFE\n\nAPP_DIR = os.path.dirname(os.path.abspath(\"__file__\"))\nsys.path.append(APP_DIR)\nconfig_ini_dict = configparser.ConfigParser()\nconfig_ini_dict.read(os.path.join(APP_DIR, \"config.ini\"))\nlogging.info(config_ini_dict)\n\n\nclass Trade_data_deal(object):\n def __init__(\n self,\n data_input_path,\n title_tokenize_path,\n content_tokenize_path,\n trade_output_data_path,\n ):\n self.data_input_path = data_input_path\n self.title_tokenize_path = title_tokenize_path\n self.content_tokenize_path = content_tokenize_path\n self.trade_output_data_path = trade_output_data_path\n\n def trade_data_output(self):\n df_list = []\n with open(self.data_input_path) as f:\n for line in f:\n df_list.append(line.strip().split(\"\\001\"))\n df = pd.DataFrame(df_list)\n df[1] = df[1].apply(lambda x: x.lower())\n all_counter = collections.Counter([v for v in df[1]])\n\n def temp_function(x):\n # x = datetime.datetime.now().year - int(x.split(\"-\")[0])\n y = 0\n if x in set([\"it\", \"互联网\", \"tmt从业人员\", \"人工智能/前沿科技\", \"企业服务/云计算/大数据\"]):\n y = \"IT/移动互联网\"\n elif x in set([\"教育/专业服务/培训\", \"内容/营销/传播\", \"营销\", \"贸易/零售批发\", \"酒店/餐饮/旅游\"]):\n y = \"零售消费\"\n elif x in set([\"金融\", \"投资人\"]):\n y = \"金融\"\n elif x in set(\n [\n \"汽车/出行\",\n ]\n ):\n y = \"汽车\"\n elif x in set([\"电商/仓储物流\", \"仓储物流\", \"商业服务\", \"商业服务-o2o/服务/社区\"]):\n y = \"电商\"\n elif x in set([\"加工制造\"]):\n y = \"制造业\"\n elif x in set([\"智能硬件/硬件制造\"]):\n y = \"IOT\"\n elif x in set([\"文化/体育/娱乐业\"]):\n y = \"游戏\"\n elif x in set(\n [\n \"房地产\",\n ]\n ):\n y = \"房地产\"\n elif x in set(\n [\n \"制药医疗/生物/卫生保健\",\n ]\n ):\n y = \"医疗\"\n elif x in set(\n [\"创业者\", \"政府/非盈利机构\", \"法律\", \"其他\", \"媒体\", \"能源相关\", \"农业\", \"学生\", \"-1\"]\n ):\n y = \"其他\"\n else:\n temp = \"a\"\n return y\n\n df[1] = df[1].apply(lambda x: temp_function(x))\n df.columns = [\"aid\", \"trade\", *[\"c{}\".format(v) for v in range(10)]]\n article_dict = {}\n with open(self.title_tokenize_path, encoding=\"utf8\") as f:\n for line in f:\n line_dict = json.loads(line.strip())\n aid = line_dict[\"aid\"]\n article_dict[aid] = {}\n article_dict[aid][\"title_tokenize\"] = line_dict[\"title_tokenize\"]\n with open(self.content_tokenize_path, encoding=\"utf8\") as f:\n for line in f:\n line_dict = json.loads(line.strip())\n aid = line_dict[\"aid\"]\n if not aid in article_dict:\n print(aid)\n continue\n article_dict[aid][\"content_tokenize\"] = line_dict[\"content_tokenize\"][\n :512\n ]\n text_list = []\n y_list = []\n for index in df.index:\n y = df.loc[index, \"trade\"]\n temp_text_list = []\n for i in range(10):\n c_text_list = []\n c = \"c{}\".format(i)\n aid = str(df.loc[index, c]).strip()\n if aid:\n if aid in article_dict:\n c_text_list = [\n *article_dict[aid][\"title_tokenize\"],\n \"\",\n *article_dict[aid][\"content_tokenize\"][:512],\n ]\n else:\n print(\"aid不存在\", index, c, aid)\n else:\n print(index)\n temp_text_list.append(c_text_list)\n text_list.append(temp_text_list)\n y_list.append(y)\n with open(self.trade_output_data_path, \"w\") as f:\n for tag, content_tokenize in zip(y_list, text_list):\n f.write(\n json.dumps(\n {\"tag\": tag, \"all_content_tokenize\": content_tokenize},\n ensure_ascii=False,\n )\n + \"\\n\"\n )\n\n\ndata_input_path = config_ini_dict[\"file\"][\"trade_input_path\"]\ntitle_tokenize_path = config_ini_dict[\"file\"][\"title_tokenize_path\"]\ncontent_tokenize_path = config_ini_dict[\"file\"][\"content_tokenize_path\"]\ntrade_output_data_path = config_ini_dict[\"file\"][\"trade_output_data_path\"]\ntrade = Trade_data_deal(\n data_input_path=config_ini_dict[\"file\"][\"trade_input_path\"],\n title_tokenize_path=config_ini_dict[\"file\"][\"title_tokenize_path\"],\n content_tokenize_path=config_ini_dict[\"file\"][\"content_tokenize_path\"],\n trade_output_data_path=config_ini_dict[\"file\"][\"trade_output_data_path\"],\n)\ntrade.trade_data_output()\n", "sub_path": "predict_user_attribute/train_model/trade_data_deal.py", "file_name": "trade_data_deal.py", "file_ext": "py", "file_size_in_byte": 5636, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 42, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 44, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 94, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 100, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 134, "usage_type": "call"}]} +{"seq_id": "402091216", "text": "# coding: utf-8\nimport pygame\nimport random\nfrom config import *\nfrom pygame.locals import *\n\n\nclass Buraco(pygame.sprite.Sprite):\n def __init__(self, screen, x, y):\n pygame.sprite.Sprite.__init__(self)\n self.screen = screen\n self.x = x\n self.y = y\n self.image = pygame.image.load(IMAGEM_BURACO)\n self.rect_buraco = self.image.get_rect()\n self.pista = [145, 255, 355]\n self.speed = 5\n \n def move(self):\n self.y += self.speed\n if self.y > 600:\n self.y = -self.speed\n self.x = self.pista[random.randint(0, 2)]\n \n self.rect_buraco.y = self.y\n self.rect_buraco.x = self.x \n \n def render(self):\n self.screen.blit(self.image, (self.x, self.y))\n self.rect_buraco.normalize()", "sub_path": "buraco.py", "file_name": "buraco.py", "file_ext": "py", "file_size_in_byte": 814, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.sprite", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 14, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "450328757", "text": "import simpleaudio as sa\nimport time\nimport random\n\n\"\"\"\nAn example project in which three wav files are played after eachother with a\nminor break in between.\n\n------ HANDS-ON TIPS ------\n- Alter the code:\n Write a function that plays the samples a given number of times.\n Use this function. \n\n- Alter the code:\n Change the time in between samples into a random value.\n E.g. wait 0.25, 0.5, or 1 second.\n hint: there is a standard random package available with a random function\n https://docs.python.org/2/library/random.html\n A standard package does not need to be installed with pip, but it does\n need to be imported.\n\"\"\"\n#load 3 audioFiles into a list\nsamples = [ sa.WaveObject.from_wave_file(\"../audioFiles/Pop.wav\"),\n sa.WaveObject.from_wave_file(\"../audioFiles/Laser1.wav\"),\n sa.WaveObject.from_wave_file(\"../audioFiles/Dog2.wav\")]\n\ndef playNTimes(times):\n\tfor n in range(times):\n\t\tfor sample in samples:\n\t\t\tprint(sample)\n\t\t\tsample.play()\n\t\t\t#sample.wait_done()\n\t\t\ttime.sleep(random.choice([0.25, 0.5, 0.1, 1]))\n\t\t\t\n\t\t\t\n#play samples, wait 1 second in between\n#for sample in samples:\n #display the sample object\n #print(sample)\n #play sample\n #sample.play()\n #wait 1 second\n #time.sleep(1)\n\nplayNTimes(10)", "sub_path": "CSD2a/Voorbereiding Les 6/python Scripts/02_timedPlayback.py", "file_name": "02_timedPlayback.py", "file_ext": "py", "file_size_in_byte": 1268, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "simpleaudio.WaveObject.from_wave_file", "line_number": 23, "usage_type": "call"}, {"api_name": "simpleaudio.WaveObject", "line_number": 23, "usage_type": "attribute"}, {"api_name": "simpleaudio.WaveObject.from_wave_file", "line_number": 24, "usage_type": "call"}, {"api_name": "simpleaudio.WaveObject", "line_number": 24, "usage_type": "attribute"}, {"api_name": "simpleaudio.WaveObject.from_wave_file", "line_number": 25, "usage_type": "call"}, {"api_name": "simpleaudio.WaveObject", "line_number": 25, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "425423381", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\ndef plot(valuesY: dict, title: str = \"\", bits: int = 0, showGraph: bool = False) -> None:\n plt.figure(figsize=(20,10))\n plt.style.use(\"seaborn-darkgrid\")\n palette = plt.get_cmap(\"Set1\")\n df = pd.DataFrame( valuesY )\n size = df.index.size\n valuesY.update( {\"x\": range(8, size * 8 + 2, 8 ) })\n df = pd.DataFrame( valuesY )\n num = 0\n for column in df.drop(\"x\", axis=1):\n num += 1\n plt.plot(\n df[\"x\"],\n df[column],\n marker=\"\",\n color=palette(num),\n linewidth=1,\n alpha=0.9,\n label=column,\n )\n\n plt.legend(loc=2, ncol=2)\n plt.xticks(np.arange(8, size * 8 + 2, step = 8))\n plt.xlabel(\"n bits\")\n plt.ylabel(\"time spent (seconds)\")\n plt.title(f\"RSA - {title} {bits} bits\", loc=\"left\")\n plt.savefig(f'./images/{bits}_{title.replace(\" \", \"\")}')\n\n if (showGraph):\n plt.show()\n\n", "sub_path": "graph.py", "file_name": "graph.py", "file_ext": "py", "file_size_in_byte": 991, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "169124977", "text": "\nimport unittest\nimport asyncio\n\nimport rx\nfrom rx.internal.exceptions import SequenceContainsNoElementsError\nfrom rx.testing import ReactiveTest\n\non_next = ReactiveTest.on_next\non_completed = ReactiveTest.on_completed\non_error = ReactiveTest.on_error\nsubscribe = ReactiveTest.subscribe\nsubscribed = ReactiveTest.subscribed\ndisposed = ReactiveTest.disposed\ncreated = ReactiveTest.created\n\n\nclass TestToFuture(unittest.TestCase):\n def test_await_success(self):\n loop = asyncio.get_event_loop()\n result = None\n\n async def go():\n nonlocal result\n source = rx.return_value(42)\n result = await source\n\n loop.run_until_complete(go())\n assert result == 42\n\n def test_await_success_on_sequence(self):\n loop = asyncio.get_event_loop()\n result = None\n\n async def go():\n nonlocal result\n source = rx.from_([40, 41, 42])\n result = await source\n\n loop.run_until_complete(go())\n assert result == 42\n\n def test_await_error(self):\n loop = asyncio.get_event_loop()\n error = Exception(\"error\")\n result = None\n\n async def go():\n nonlocal result\n source = rx.throw(error)\n try:\n result = await source\n except Exception as ex:\n result = ex\n\n loop.run_until_complete(go())\n assert result == error\n\n def test_await_empty_observable(self):\n loop = asyncio.get_event_loop()\n result = None\n\n async def go():\n nonlocal result\n source = rx.empty()\n result = await source\n\n self.assertRaises(SequenceContainsNoElementsError, loop.run_until_complete, go())\n", "sub_path": "tests/test_observable/test_tofuture.py", "file_name": "test_tofuture.py", "file_ext": "py", "file_size_in_byte": 1752, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rx.testing.ReactiveTest.on_next", "line_number": 9, "usage_type": "attribute"}, {"api_name": "rx.testing.ReactiveTest", "line_number": 9, "usage_type": "name"}, {"api_name": "rx.testing.ReactiveTest.on_completed", "line_number": 10, "usage_type": "attribute"}, {"api_name": "rx.testing.ReactiveTest", "line_number": 10, "usage_type": "name"}, {"api_name": "rx.testing.ReactiveTest.on_error", "line_number": 11, "usage_type": "attribute"}, {"api_name": "rx.testing.ReactiveTest", "line_number": 11, "usage_type": "name"}, {"api_name": "rx.testing.ReactiveTest.subscribe", "line_number": 12, "usage_type": "attribute"}, {"api_name": "rx.testing.ReactiveTest", "line_number": 12, "usage_type": "name"}, {"api_name": "rx.testing.ReactiveTest.subscribed", "line_number": 13, "usage_type": "attribute"}, {"api_name": "rx.testing.ReactiveTest", "line_number": 13, "usage_type": "name"}, {"api_name": "rx.testing.ReactiveTest.disposed", "line_number": 14, "usage_type": "attribute"}, {"api_name": "rx.testing.ReactiveTest", "line_number": 14, "usage_type": "name"}, {"api_name": "rx.testing.ReactiveTest.created", "line_number": 15, "usage_type": "attribute"}, {"api_name": "rx.testing.ReactiveTest", "line_number": 15, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 18, "usage_type": "attribute"}, {"api_name": "asyncio.get_event_loop", "line_number": 20, "usage_type": "call"}, {"api_name": "rx.return_value", "line_number": 25, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 32, "usage_type": "call"}, {"api_name": "rx.from_", "line_number": 37, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 44, "usage_type": "call"}, {"api_name": "rx.throw", "line_number": 50, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 60, "usage_type": "call"}, {"api_name": "rx.empty", "line_number": 65, "usage_type": "call"}, {"api_name": "rx.internal.exceptions.SequenceContainsNoElementsError", "line_number": 68, "usage_type": "argument"}]} +{"seq_id": "326471049", "text": "\"\"\"\nAnother shot at dispersion, but using a distance metric restricted to be within the\ngrid.\n\nLooking for ways to refine grid-based approach.\n - ignore particles that leave the domain.\n\n - would it make sense to reverse the process, instead looking at the collection\n of the particles at a given location at t_n, and where they came from at t_m=0,axis=1)\n c1=e2c[internal,0]\n c2=e2c[internal,1]\n cc=g.cells_center()\n lengths=utils.dist(cc[c1],cc[c2])\n bi_lengths=np.concatenate([lengths,lengths])\n bi_lengths=bi_lengths.astype(np.float32) # more than enough precision.\n A=np.concatenate((c1,c2))\n B=np.concatenate((c2,c2))\n graph=sparse.csr_matrix( (bi_lengths,(A,B)),\n shape=(g.Ncells(),g.Ncells()) )\n # use scipy graph algorithms to find the connections\n # dists=csgraph.shortest_path(graph,directed=False,indices=cloud_nodes)\n # that ends up being (32,Nnodes), where 32 is the number of unique\n # nodes we started with\n return graph\n\ngraph=grid_to_graph(g)\n##\n\nparticles=part_locs\n\n# one-time preprocessing, find the cell corresponding to each particle\nparticle0_cells=[g.select_cells_nearest(particles[0,i,:])\n for i in utils.progress(range(len(particles[0,:,:])))]\nparticle0_cells=np.array(particle0_cells)\n\n##\n\ndef particle_cloud_for_point(x0,count=10):\n c0=g.select_cells_nearest(x0)\n\n # get ordering of all other cells relative to c0\n c0_dists=csgraph.shortest_path(graph,directed=False,indices=[c0])[0,:]\n\n # in-grid distance for all particles at time 0\n part_dists=c0_dists[particle0_cells]\n cloud_particles=np.argsort(part_dists)[:count]\n return cloud_particles\n\n##\n\n# a list of hashes for pair-wise distance was pretty slow\n# full calculation also too slow/large\npair_map=[dict() for i in range(g.Ncells())]\n\ndef pairwise_grid_distance(cells):\n n_extra=10*len(cells)\n missing=[]\n for ai,a in enumerate(cells):\n a_dists=pair_map[a]\n for bi,b in enumerate(cells[ai+1:],start=ai+1):\n if b not in a_dists:\n missing.append(b)\n\n if missing:\n dists=csgraph.shortest_path(graph,directed=False,indices=missing)\n for mi,m in enumerate(missing):\n for n in cells:\n pair_map[n][m]=pair_map[m][n]=dists[mi,n]\n # opportunistically grab more?\n extras=np.argsort(dists[mi,:])[:n_extra]\n for n in extras:\n pair_map[n][m]=pair_map[m][n]=dists[mi,n]\n else:\n print(\"No need to call shortest path\")\n\n result=np.zeros( (len(cells),len(cells)), np.float64)\n for ai,a in enumerate(cells):\n a_dists=pair_map[a]\n for bi,b in enumerate(cells[ai+1:],start=ai+1):\n assert b in a_dists\n result[ai,bi]=result[bi,ai]=a_dists[b]\n return result\n\n\n##\nfrom stompy import memoize\n@memoize.memoize(lru=25000)\ndef one_cell_dists(c):\n return csgraph.shortest_path(graph,directed=False,indices=[c])[0,:]\n\ndef pairwise_grid_distance(cells):\n result=np.zeros( (len(cells),len(cells)), np.float64)\n for ai,a in enumerate(cells):\n a_dists=one_cell_dists(a)\n for bi,b in enumerate(cells[ai+1:],start=ai+1):\n result[ai,bi]=result[bi,ai]=a_dists[b]\n return result\n\n\n## get polygons for particle exit:\n\nif 0:\n t,parts=ptm_group.read_timestep(1000)\n\n fig=plt.figure(1)\n fig.clf()\n ax=fig.add_subplot(1,1,1)\n g.plot_edges(ax=ax,lw=0.5,color='k')\n ax.plot(parts['x'][:,0], parts['x'][:,1], 'g.')\n ax.axis('equal')\n\n from stompy.plot import plot_utils\n res=plot_utils.draw_polyline()\n\n\npoly_geo=np.array([[ 629664., 4233211.],\n [ 629823., 4233215.],\n [ 629821., 4233119.],\n [ 629661., 4233114.]])\npoly_rio=np.array([[ 615061., 4224762.],\n [ 616046., 4224084.],\n [ 615810., 4223645.],\n [ 614768., 4224230.]])\npoly_bsp=np.array([[ 605189., 4237133.],\n [ 605242., 4237153.],\n [ 605257., 4237118.],\n [ 605204., 4237098.]])\n\n##\n\n# fast lookups via matplotlib:\nfrom matplotlib import path\nctrs=g.cells_centroid()\ndead_cells=np.zeros(g.Ncells(),np.bool8)\nfor poly in [poly_geo,poly_rio]:\n dead_cells |= path.Path(poly).contains_points(ctrs)\ng.plot_cells(mask=dead_cells,color='m',ax=ax)\n\n##\n#time_steps=range(len(part_ts))\ntime_steps=range(0,150,2)\n# timeline same for all\ntrack_times=part_ts[time_steps]\ntrack_time_s=(track_times-track_times[0])/np.timedelta64(1,'s')\n\n# truncate time series when particles leave domain\n\ndef track_cloud(cloud_particles):\n # track cloud. this ti is an index into the time steps already extracted\n track_vars=[]\n\n for ti in utils.progress(time_steps,1.0,\"processing %s timesteps\"):\n cloud_xy=particles[ti,cloud_particles,:]\n cloud_cells=np.array( [g.select_cells_nearest(xy) for xy in cloud_xy] )\n if np.any( dead_cells[cloud_cells] ):\n # print(\"Particles hit boundary - truncate time\")\n break\n pair_dists=pairwise_grid_distance(cloud_cells)\n variance_per_cell=(pair_dists**2).mean(axis=1)\n best_variance=variance_per_cell.min()\n track_vars.append(best_variance)\n\n track_vars=np.array(track_vars)\n t_s=track_time_s[:len(track_vars)]\n\n # give up if there is less than 12h or 5 data points.\n if len(t_s>5) and (t_s[-1]-t_s[0])>43200:\n mb=np.polyfit(t_s,track_vars,1)\n return mb[0]\n else:\n return np.nan\n\ndef dispersion_from_x(x0):\n cloud_particles=particle_cloud_for_point(x0)\n return track_cloud(cloud_particles)\n\n\ndef dispersion_for_cell(c):\n # wrapper which checks to make sure a particle actually started in the requested\n # cell\n cloud_particles=particle_cloud_for_point(cc[c])\n ti=0\n cloud_xy=particles[ti,cloud_particles,:]\n for xy in cloud_xy:\n if c==g.select_cells_nearest(xy):\n break\n else:\n # none of the cloud actually started in c, so this dispersion\n # \"belong\" to a different cell\n return np.nan\n return track_cloud(cloud_particles)\n\n##\n\nper_cell_K=-np.ones(g.Ncells())\n\n##\n\nretry=np.isnan(per_cell_K)\nper_cell_K[retry]=-1\n\n##\n\ncc=g.cells_center()\n\nmissing=np.nonzero(per_cell_K<0)[0]\n\n# random, to evenly fill out the map...\n# missing=missing[ np.argsort( np.random.random(len(missing)) ) ]\n# ordered, to capitalize on locality\nmissing=missing[ np.argsort( cc[missing,1] ) ]\n\nfor idx in utils.progress(missing):\n #per_cell_K[idx]=dispersion_from_x(cc[idx])\n per_cell_K[idx]=dispersion_for_cell(idx)\n\n##\n\ncell_nbrs=[ [c]+g.cell_to_cells(c) for c in range(g.Ncells())]\n\n# drop negative boundary values\ncell_nbrs=[ np.array(nbrs) for nbrs in cell_nbrs]\ncell_nbrs=[ nbrs[nbrs>=0] for nbrs in cell_nbrs]\n\n##\n# v02b: deal with BSPP exits, too.\n#ds=xr.open_dataset('dispersion_K_v02b.nc')\n#per_cell_K=ds.K.values.copy()\n\nsmooth_K=per_cell_K.clip(0,np.inf)\n\nfor it in range(3):\n print(it)\n new_K=smooth_K.copy()\n for c,nbrs in enumerate(cell_nbrs):\n new_K[c]=np.nanmean( smooth_K[nbrs] )\n smooth_K=new_K\n\n##\n\nif 1: # save to disk along with geometry\n ds=g.write_to_xarray()\n ds['K']=('face',),per_cell_K\n ds['Ksmooth']=('face',),smooth_K\n ds.to_netcdf('dispersion_K_v02b_wsmooth.nc')\n ds.close()\n\n##\n#zoom=(601804.8951374379, 633292.0594476878, 4222037.326571205, 4252542.492583467)\nzoom=(603276.0170315901, 623336.1938701726, 4230666.761713925, 4248493.5760155255)\nplt.figure(3).clf()\nfig,(ax1,ax2)=plt.subplots(1,2,sharex=True,sharey=True,num=3)\n\nvalid1=np.isfinite(per_cell_K) & (per_cell_K>=0)\n\ncoll1=g.plot_cells(values=per_cell_K.clip(1,np.inf),\n norm=colors.LogNorm(),\n mask=valid1,ax=ax1)\n\nvalid2=np.isfinite(smooth_K)&(smooth_K>=0)\ncoll2=g.plot_cells(values=smooth_K.clip(1,np.inf),\n norm=colors.LogNorm(),\n mask=valid2,ax=ax2)\nfor coll in [coll1,coll2]:\n coll.set_clim([3,300])\n coll.set_cmap('inferno_r')\n\nfor coll,ax in zip([coll1,coll2],[ax1,ax2]):\n plot_wkb.plot_polygon(grid_poly,ax=ax,fc='none',ec='k',lw=0.5)\n plt.colorbar(coll, label=\"log10 K\",ax=ax)\n ax.axis('equal')\n ax.axis(zoom)\n ax.xaxis.set_visible(0)\n ax.yaxis.set_visible(0)\n\nax1.set_title('Output')\nax2.set_title('Smoothed')\nfig.tight_layout()\n\nfig.savefig('third-cutb-dispersion.png',dpi=200)\n\n##\n\nax.plot(particles[0,:,0],\n particles[0,:,1],\n 'm.',ms=4)\n\n##\n#zoom=(601804.8951374379, 633292.0594476878, 4222037.326571205, 4252542.492583467)\nzoom=(603276.0170315901, 623336.1938701726, 4230666.761713925, 4248493.5760155255)\nplt.ioff()\n\nfig=plt.figure(3)\nfig.clf()\nfig.set_size_inches((7,10),forward=True)\nax=fig.add_axes([0,0,1,1])\n\nvalid=np.isfinite(smooth_K)&(smooth_K>=0)\ncoll=g.plot_cells(values=smooth_K.clip(1,np.inf),\n norm=colors.LogNorm(),\n mask=valid,ax=ax)\ncoll.set_clim([3,300])\ncoll.set_cmap('inferno_r')\n\nplot_wkb.plot_polygon(grid_poly,ax=ax,fc='none',ec='k',lw=0.5)\ncax=fig.add_axes([0.05,0.65,0.02,0.3])\nplt.colorbar(coll, label=\"log10 K (m$^2$ s$^{-1}$)\",cax=cax)\nax.axis('equal')\nax.axis(zoom)\nax.xaxis.set_visible(0)\nax.yaxis.set_visible(0)\n\nfig.savefig('dispersion-map-2014-04.png',dpi=250)\nplt.ion()\n\n\n# depth is an issue, as particles which move into deeper water\n# converge in the horizontal, which is advection in reality, but\n# anti-dispersion in the math.\n\n\n", "sub_path": "ptm/dispersion_map/dispersion_v02.py", "file_name": "dispersion_v02.py", "file_ext": "py", "file_size_in_byte": 11101, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "stompy.utils.log.setLevel", "line_number": 26, "usage_type": "call"}, {"api_name": "stompy.utils.log", "line_number": 26, "usage_type": "attribute"}, {"api_name": "stompy.utils", "line_number": 26, "usage_type": "name"}, {"api_name": "logging.INFO", "line_number": 26, "usage_type": "attribute"}, {"api_name": "stompy.grid.unstructured_grid.UnstructuredGrid.from_ugrid", "line_number": 30, "usage_type": "call"}, {"api_name": "stompy.grid.unstructured_grid.UnstructuredGrid", "line_number": 30, "usage_type": "attribute"}, {"api_name": "stompy.grid.unstructured_grid", "line_number": 30, "usage_type": "name"}, {"api_name": "stompy.spatial.proj_utils.mapper", "line_number": 32, "usage_type": "call"}, {"api_name": "stompy.spatial.proj_utils", "line_number": 32, "usage_type": "name"}, {"api_name": "stompy.model.fish_ptm.ptm_tools.PtmBin", "line_number": 39, "usage_type": "call"}, {"api_name": "stompy.model.fish_ptm.ptm_tools", "line_number": 39, "usage_type": "name"}, {"api_name": "stompy.utils.progress", "line_number": 56, "usage_type": "call"}, {"api_name": "stompy.utils", "line_number": 56, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "stompy.utils.to_dt64", "line_number": 63, "usage_type": "call"}, {"api_name": "stompy.utils", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.all", "line_number": 74, "usage_type": "call"}, {"api_name": "stompy.utils.dist", "line_number": 78, "usage_type": "call"}, {"api_name": "stompy.utils", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 82, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 83, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 83, "usage_type": "name"}, {"api_name": "stompy.utils.progress", "line_number": 98, "usage_type": "call"}, {"api_name": "stompy.utils", "line_number": 98, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "scipy.sparse.csgraph.shortest_path", "line_number": 107, "usage_type": "call"}, {"api_name": "scipy.sparse.csgraph", "line_number": 107, "usage_type": "name"}, {"api_name": "numpy.argsort", "line_number": 111, "usage_type": "call"}, {"api_name": "scipy.sparse.csgraph.shortest_path", "line_number": 130, "usage_type": "call"}, {"api_name": "scipy.sparse.csgraph", "line_number": 130, "usage_type": "name"}, {"api_name": "numpy.argsort", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 141, "usage_type": "attribute"}, {"api_name": "scipy.sparse.csgraph.shortest_path", "line_number": 154, "usage_type": "call"}, {"api_name": "scipy.sparse.csgraph", "line_number": 154, "usage_type": "name"}, {"api_name": "stompy.memoize.memoize", "line_number": 152, "usage_type": "call"}, {"api_name": "stompy.memoize", "line_number": 152, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 157, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "stompy.plot.plot_utils.draw_polyline", "line_number": 178, "usage_type": "call"}, {"api_name": "stompy.plot.plot_utils", "line_number": 178, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.bool8", "line_number": 199, "usage_type": "attribute"}, {"api_name": "matplotlib.path.Path", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.path", "line_number": 201, "usage_type": "name"}, {"api_name": "numpy.timedelta64", "line_number": 209, "usage_type": "call"}, {"api_name": "stompy.utils.progress", "line_number": 217, "usage_type": "call"}, {"api_name": "stompy.utils", "line_number": 217, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 236, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 255, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 276, "usage_type": "call"}, {"api_name": "stompy.utils.progress", "line_number": 278, "usage_type": "call"}, {"api_name": "stompy.utils", "line_number": 278, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 295, "usage_type": "attribute"}, {"api_name": "numpy.nanmean", "line_number": 301, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 316, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 316, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 317, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 317, "usage_type": "name"}, {"api_name": "numpy.isfinite", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 321, "usage_type": "attribute"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 322, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 322, "usage_type": "name"}, {"api_name": "numpy.isfinite", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 326, "usage_type": "attribute"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 327, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 327, "usage_type": "name"}, {"api_name": "stompy.plot.plot_wkb.plot_polygon", "line_number": 334, "usage_type": "call"}, {"api_name": "stompy.plot.plot_wkb", "line_number": 334, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 335, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 335, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ioff", "line_number": 356, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 356, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 358, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 358, "usage_type": "name"}, {"api_name": "numpy.isfinite", "line_number": 363, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 364, "usage_type": "attribute"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 365, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 365, "usage_type": "name"}, {"api_name": "stompy.plot.plot_wkb.plot_polygon", "line_number": 370, "usage_type": "call"}, {"api_name": "stompy.plot.plot_wkb", "line_number": 370, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 372, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 372, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 379, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 379, "usage_type": "name"}]} +{"seq_id": "129276623", "text": "from random import randint\nimport judy\nimport numpy as np\nimport logging\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s')\nimport pickledb\nimport pickle\nfrom preshed.maps import PreshMap\nfrom sklearn.utils.fast_dict import IntFloatDict\n\ndef test():\n logging.info(\"Starting\")\n a = {}\n a = PreshMap()\n #a = judy.JudyIntObjectMap()\n #db = pickledb.load(\"pickledb.db\", False)\n\n dict_size = 100000000\n\n keys = np.random.randint(0, 1000000000000, dict_size)\n values = np.random.randint(0, 1000000000, dict_size).astype(np.float64)\n logging.info(\"Creating\")\n a = IntFloatDict(keys, values)\n logging.info(\"Done\")\n return\n\n #a = dict(zip(np.random.randint(0, 100000000000, dict_size), np.random.randint(0, 100000000000, dict_size)))\n for i in range(0, 10000):\n if i % 1000000 == 0:\n print(i)\n\n number = randint(0, 10000000000000)\n\n a[number] = randint(0, 3000000000)\n #db.set(str(number), randint(0, 3000000000))\n\n #db.dump()\n logging.info(\"writing to file\")\n with open(\"testdict.pckl\", \"wb\") as f:\n pickle.dump(a, f, protocol=4)\n logging.info(\"Wrote to file\")\n\n\ntest()\nb = input(\"Stopp\")\n\n \n", "sub_path": "memory_test.py", "file_name": "memory_test.py", "file_ext": "py", "file_size_in_byte": 1225, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 5, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 5, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 12, "usage_type": "call"}, {"api_name": "preshed.maps.PreshMap", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 21, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.utils.fast_dict.IntFloatDict", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 24, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 32, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 38, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "324486895", "text": "import sys\r\nimport os\r\nimport pygame\r\nimport neat\r\nfrom math import hypot as dist\r\nfrom pygame import *\r\nimport game_elements\r\nfrom game_elements import *\r\n\r\n# start a pygame module and\r\npygame.init()\r\nscreen = pygame.display.set_mode(SCREEN.size)\r\npygame.display.set_caption(\"MAN VS. MACHINE\")\r\n\r\n# set fonts for the different uses in game, then the winning message when you beat the computer\r\npygame.font.init()\r\nwin_font = pygame.font.SysFont(None, 65)\r\nmenu_font = pygame.font.SysFont(None, 100)\r\nbutton_font = pygame.font.SysFont(None, 50)\r\nwin_msg = win_font.render('Win!', True, (0, 128, 0))\r\ninfo_font = pygame.font.SysFont(None, 30)\r\nscore_font = pygame.font.SysFont(None, 30)\r\nsmall_font = pygame.font.SysFont(None, 20)\r\nsmaller_font = pygame.font.SysFont(None, 10)\r\n\r\ngame_elements.ai_score = 0\r\ngame_elements.player_score = 0\r\n\r\n# set path and load images for menu icons\r\nscript_dir = os.path.dirname(__file__)\r\nhuman_img_path = os.path.join(script_dir, 'user.png')\r\nai_img_path = os.path.join(script_dir, 'bot.png')\r\nhuman_img = pygame.image.load(human_img_path)\r\nai_img = pygame.image.load(ai_img_path)\r\n\r\n# set some simple colors for use later on with buttons and whatnot\r\nred = (200, 0, 25)\r\nlightred = (255, 0, 50)\r\ngreen = (0, 128, 0)\r\nlightgreen = (0, 175, 0)\r\nblue = (0, 0, 125)\r\nlightblue = (0,0,200)\r\nmid_blue= (90, 140, 240)\r\nlighter_blue = (100, 240, 250)\r\nwhite = (255, 255, 255)\r\n\r\n# function to create the counters for score while the game plays\r\ndef scoreboard(player_score, ai_score):\r\n\r\n # creates the exit button in the top right corner\r\n make_button(\"Menu\", button_font, white, mid_blue, lighter_blue, SCREEN.width - 100, 0, 100, 50, \"menu\")\r\n\r\n # place border and background\r\n pygame.draw.rect(screen, white, (5, 5, 150, 100))\r\n pygame.draw.rect(screen, (0,0,0), (10, 10, 140, 90))\r\n\r\n # Add title of scoreboard\r\n text_surf, text_rect = text_objects(\"Score\", info_font, white)\r\n text_rect.center = (77, 22)\r\n screen.blit(text_surf, text_rect)\r\n\r\n # add rectangles to house the points\r\n pygame.draw.rect(screen, (47, 79, 79), (20, 34, 50, 62))\r\n pygame.draw.rect(screen, (47, 79, 79), (90, 34, 50, 62))\r\n\r\n # add score titles\r\n # human title first\r\n text_surf, text_rect = text_objects(\"Human\", small_font, white)\r\n text_rect.center = (44, 42)\r\n screen.blit(text_surf, text_rect)\r\n # ai title second\r\n text_surf, text_rect = text_objects(\"AI\", small_font, white)\r\n text_rect.center = (115, 42)\r\n screen.blit(text_surf, text_rect)\r\n\r\n # add scores in the boxes\r\n # player score first\r\n text_surf, text_rect = text_objects(str(player_score), button_font, white)\r\n text_rect.center = (44, 75)\r\n screen.blit(text_surf, text_rect)\r\n # ai score second\r\n text_surf, text_rect = text_objects(str(ai_score), button_font, white)\r\n text_rect.center = (115, 75)\r\n screen.blit(text_surf, text_rect)\r\n\r\n# output the information screen\r\ndef game_info():\r\n\r\n info = True\r\n\r\n # loop ensuring the objects remain created\r\n while info:\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n quit()\r\n\r\n # paint a black background that matches the game\r\n screen.fill((0,0,0))\r\n\r\n # Title block at the top of the screen\r\n text_surf, text_rect = text_objects('Man vs. Machine', menu_font, lightblue)\r\n text_rect.center = (int(SCREEN.width / 2), 50)\r\n screen.blit(text_surf, text_rect)\r\n\r\n # lines 383 - 432 simply output text explanations to the screen\r\n text_surf, text_rect = text_objects('Man vs. Machine is a small-scale platforming game', info_font, white)\r\n text_rect.center = (int(SCREEN.width / 3), 125)\r\n screen.blit(text_surf, text_rect)\r\n\r\n text_surf, text_rect = text_objects('It has been developed to implement and test basic AI', info_font, white)\r\n text_rect.center = (int(SCREEN.width / 3) + 10, 150)\r\n screen.blit(text_surf, text_rect)\r\n\r\n text_surf, text_rect = text_objects('How it works:', win_font, lightred)\r\n text_rect.center = (int(SCREEN.width / 4.7) + 10, 210)\r\n screen.blit(text_surf, text_rect)\r\n\r\n text_surf, text_rect = text_objects('The AI get a headstart of sorts and run through the program a few preliminary times', info_font, white)\r\n text_rect.center = (int(SCREEN.width / 2.05) + 10, 250)\r\n screen.blit(text_surf, text_rect)\r\n\r\n text_surf, text_rect = text_objects('Users then choose a level of difficulty: easy, medium, or hard', info_font, white)\r\n text_rect.center = (int(SCREEN.width / 2.45) + 10, 275)\r\n screen.blit(text_surf, text_rect)\r\n\r\n text_surf, text_rect = text_objects('The difficulty level corresponds to a set number of AIs the player will be up against', info_font, white)\r\n text_rect.center = (int(SCREEN.width / 1.925) + 10, 300)\r\n screen.blit(text_surf, text_rect)\r\n\r\n text_surf, text_rect = text_objects('Essentially, the more AI trying to win, the tougher it will be for you', info_font, white)\r\n text_rect.center = (int(SCREEN.width / 2.49) + 10, 325)\r\n screen.blit(text_surf, text_rect)\r\n\r\n text_surf, text_rect = text_objects('If you reach the finish before the AI, you win!', info_font, white)\r\n text_rect.center = (int(SCREEN.width / 3.43) + 10, 350)\r\n screen.blit(text_surf, text_rect)\r\n\r\n text_surf, text_rect = text_objects('If any of the AI beat you there, they win!', info_font, white)\r\n text_rect.center = (int(SCREEN.width / 3.77) + 10, 375)\r\n screen.blit(text_surf, text_rect)\r\n\r\n text_surf, text_rect = text_objects('As soon as you finish a level, you\\'ll continue on to a new one', info_font, white)\r\n text_rect.center = (int(SCREEN.width / 2.71) + 10, 400)\r\n screen.blit(text_surf, text_rect)\r\n\r\n text_surf, text_rect = text_objects('There are more AI than there are of you, so multiple AI could beat you in one round!', info_font, white)\r\n text_rect.center = (int(SCREEN.width / 2.06) + 10, 425)\r\n screen.blit(text_surf, text_rect)\r\n\r\n text_surf, text_rect = text_objects('Good luck!', win_font, lightgreen)\r\n text_rect.center = (int(SCREEN.width / 2) + 10, 525)\r\n screen.blit(text_surf, text_rect)\r\n\r\n # make a lil button to get back to the menu\r\n make_button('Back', score_font, white, lightblue, mid_blue, SCREEN.width - 200, SCREEN.height - 100, 100, 50, \"menu\")\r\n\r\n pygame.display.update()\r\n\r\n\r\n# output the starting menu\r\ndef game_menu():\r\n\r\n intro = True\r\n\r\n screen.fill((0, 0, 0))\r\n\r\n # add some dope icons in the menu to make it pretty\r\n screen.blit(human_img, (int(SCREEN.width/3), int(SCREEN.height/5)))\r\n text_surf, text_rect = text_objects('VS', button_font, white)\r\n text_rect.center = (int(SCREEN.width / 2), int(SCREEN.height / 3.5))\r\n screen.blit(text_surf, text_rect)\r\n screen.blit(ai_img, (int(SCREEN.width*2/3 - 80), int(SCREEN.height/5)))\r\n\r\n while intro:\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n quit()\r\n\r\n # title words\r\n text_surf, text_rect = text_objects('Welcome to Man vs. Machine', menu_font, lighter_blue)\r\n text_rect.center = (int(SCREEN.width / 2), int(SCREEN.height / 2))\r\n screen.blit(text_surf, text_rect)\r\n\r\n # call functions to create buttons to move to other screens or quit\r\n make_button('Start', button_font, white, lightblue, mid_blue, 150, SCREEN.height - 200, 200, 100, \"sett\")\r\n make_button('Quit', button_font, white, lightblue, mid_blue, SCREEN.width - 100, SCREEN.height-50, 100, 50, \"quit\")\r\n make_button('Info', button_font, white, lightblue, mid_blue, SCREEN.width - 200 - 150, SCREEN.height - 200, 200, 100, \"info\")\r\n\r\n pygame.display.update()\r\n\r\n\r\n# once user starts the game, let them select the difficulty with this menu\r\ndef game_settings():\r\n\r\n sett = True\r\n\r\n while sett:\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n quit()\r\n\r\n screen.fill((0, 0, 0))\r\n\r\n text_surf, text_rect = text_objects('Man vs. Machine', menu_font, lightblue)\r\n text_rect.center = (int(SCREEN.width / 2), 50)\r\n screen.blit(text_surf, text_rect)\r\n\r\n text_surf, text_rect = text_objects('Difficulty', win_font, lightred)\r\n text_rect.center = (int(SCREEN.width / 2) + 10, 125)\r\n screen.blit(text_surf, text_rect)\r\n\r\n make_button('Easy', button_font, white, green, lightgreen, SCREEN.width/4 - 100, SCREEN.height/2, 200, 100, \"easy\")\r\n make_button('Medium', button_font, white, blue, lightblue, SCREEN.width/2 - 100, SCREEN.height/2, 200, 100, \"med\")\r\n make_button('Hard', button_font, white, red, lightred, SCREEN.width*3/4 - 100, SCREEN.height/2, 200, 100, \"hard\")\r\n\r\n pygame.display.update()\r\n\r\n\r\n# button creator function\r\ndef make_button (text, font, textcolor, color_off, color_on, x_pos, y_pos, width, height, action = None):\r\n mouse = pygame.mouse.get_pos()\r\n\r\n click = pygame.mouse.get_pressed()\r\n\r\n if x_pos + width > mouse[0] > x_pos and y_pos + height > mouse[1] > y_pos:\r\n pygame.draw.rect(screen, color_on, (int(x_pos), int(y_pos), int(width), int(height)))\r\n if click[0] == 1 and action is not None:\r\n if action == 'sett':\r\n # start game action\r\n game_settings()\r\n elif action == 'quit':\r\n pygame.quit()\r\n quit()\r\n elif action == 'menu':\r\n game_menu()\r\n elif action == 'info':\r\n game_info()\r\n elif action == 'easy':\r\n run(config_easy)\r\n elif action == 'med':\r\n run(config_med)\r\n elif action == 'hard':\r\n run(config_hard)\r\n else:\r\n pygame.draw.rect(screen, color_off, (int(x_pos), int(y_pos), int(width), int(height)))\r\n\r\n text_surf, text_rect = text_objects(text, font, textcolor)\r\n text_rect.center = (int(x_pos) + int(width / 2), int(y_pos) + int(height / 2))\r\n\r\n screen.blit(text_surf, text_rect)\r\n\r\n\r\n# helper function to create text blocks\r\ndef text_objects(text, font, color):\r\n\r\n text_surface = font.render(text, True, color)\r\n return text_surface, text_surface.get_rect()\r\n\r\n# Run A.I. simulation of the game - calculates the fitness function\r\ndef eval_genomes(genomes, config):\r\n\r\n # lists to hold the players, the genomes, and the neural net associated with that genome and player\r\n neural_nets = []\r\n genome = []\r\n players = []\r\n\r\n # this tracks the user beating the AI\r\n win = False\r\n\r\n # initialize neural nets and genomes\r\n for _, g in genomes:\r\n g.fitness = 0\r\n nn = neat.nn.FeedForwardNetwork.create(g, config)\r\n neural_nets.append(nn)\r\n bot = Player((800, 1540), Color(255, 80, 80))\r\n bot.isBot = True\r\n players.append(bot)\r\n genome.append(g)\r\n\r\n # initialize the game\r\n player = Player((800, 1540), Color(255, 255, 255))\r\n screen = pygame.display.set_mode(SCREEN.size)\r\n clock = pygame.time.Clock()\r\n\r\n # Set background\r\n local_dir = os.path.dirname(__file__)\r\n background_path = os.path.join(local_dir, 'background.png')\r\n background = pygame.image.load(background_path).convert()\r\n screen.blit(background, [0,0])\r\n\r\n # Set game objects\r\n objects = pygame.sprite.Group()\r\n tiles = []\r\n camera = Camera(SCREEN)\r\n\r\n # Build world\r\n world = World(player, players, tiles, objects)\r\n\r\n # Set up keys\r\n left = right = space = up = False\r\n left2 = right2 = space2 = up2 = False\r\n\r\n # GAME LOOP\r\n run_simulation = True\r\n while run_simulation and len(players) > 0:\r\n clock.tick(60)\r\n \r\n for e in pygame.event.get():\r\n if e.type == QUIT: \r\n run_simulation = False\r\n pygame.quit()\r\n sys.exit()\r\n if e.type == KEYDOWN and e.key == K_LEFT:\r\n left = True\r\n if e.type == KEYDOWN and e.key == K_RIGHT:\r\n right = True\r\n if e.type == KEYDOWN and e.key == K_SPACE:\r\n space = True\r\n if e.type == KEYDOWN and e.key == K_UP:\r\n up = True\r\n\r\n if e.type == KEYUP and e.key == K_RIGHT:\r\n right = False\r\n if e.type == KEYUP and e.key == K_LEFT:\r\n left = False\r\n if e.type == KEYUP and e.key == K_SPACE:\r\n space = False\r\n if e.type == KEYUP and e.key == K_UP:\r\n up = False\r\n \r\n screen.blit(background, [0,0])\r\n camera.update(player)\r\n\r\n player.move(left, right, space, up, world)\r\n \r\n \r\n # SET UP AI PLAYER\r\n '''\r\n -----------------------------------------------------------------------------------------------------------\r\n '''\r\n for i, p in enumerate(players):\r\n x = players[i].rect.x\r\n y = players[i].rect.y\r\n from_goal = dist(p.goalX - x, p.goalY - y)\r\n\r\n output = neural_nets[players.index(p)].activate((x, y, from_goal))\r\n\r\n # set values to allow the computer to jump\r\n if output[1] > 0.5:\r\n space2 = True\r\n up2 = True\r\n\r\n # blocks to choose character's movement left and right\r\n if output[0] > 0.5:\r\n left2 = False\r\n right2 = True\r\n else:\r\n left2 = True\r\n right2 = False\r\n\r\n\r\n p.move(left2, right2, space2, up2, world)\r\n\r\n # get new position\r\n x2 = players[i].rect.x\r\n y2 = players[i].rect.y\r\n new_from_goal = dist(p.goalX - x2, p.goalY - y2)\r\n\r\n # find if we backtracked from our cur_goal\r\n if new_from_goal < from_goal:\r\n genome[players.index(p)].fitness -= 2\r\n\r\n # see if we are any closer to our goal\r\n if new_from_goal > from_goal:\r\n genome[players.index(p)].fitness += 2\r\n\r\n # did we achieve the goal?\r\n if new_from_goal > from_goal == 0:\r\n # if we achieved the goal, we will take this instance out and its fitness will increase\r\n genome[players.index(p)].fitness += 15\r\n neural_nets.pop(players.index(p))\r\n genome.pop(players.index(p))\r\n players.pop(players.index(p))\r\n else:\r\n # decrease exist counter for this player\r\n p.life_counter -= 1\r\n\r\n if p.life_counter < 0:\r\n # remove this player and neural net\r\n genome[players.index(p)].fitness -= 5 # remove some fitness for not making it to the goal\r\n genome[players.index(p)].fitness += 0.002 * new_from_goal # add some fitness back proportional to how close it got to the goal\r\n neural_nets.pop(players.index(p))\r\n genome.pop(players.index(p))\r\n players.pop(players.index(p))\r\n '''\r\n -----------------------------------------------------------------------------------------------------------\r\n '''\r\n\r\n # reset the winner, so it doesn't mess up the loop's dependencies\r\n if player.win and player.isBot:\r\n player.win = False # reset win\r\n\r\n # case for human landing on the correct goal obstacle\r\n if player.win and not player.isBot:\r\n win = True\r\n\r\n # increment user score and end the iteration once you make it to the end\r\n if win:\r\n players.clear()\r\n\r\n # at the end of the loop make sure everything updates constantly\r\n for obj in world.objects:\r\n screen.blit(obj.image, camera.apply(obj))\r\n\r\n # add the scoreboard\r\n scoreboard(game_elements.player_score, game_elements.ai_score)\r\n\r\n pygame.display.update()\r\n\r\n\r\n# implements the run method according to the neat configuration\r\ndef run(config_file):\r\n\r\n config = neat.config.Config(neat.DefaultGenome, neat.DefaultReproduction,\r\n neat.DefaultSpeciesSet, neat.DefaultStagnation,\r\n config_file)\r\n\r\n # creates the population to run the top-level simulation\r\n p = neat.Population(config)\r\n\r\n # output statistics to console\r\n p.add_reporter(neat.StdOutReporter(True))\r\n stats = neat.StatisticsReporter()\r\n p.add_reporter(stats)\r\n\r\n # run the simulation\r\n num_generations = 100\r\n\r\n\r\n # eventually change this to be called in main -- the menu will launch the rest\r\n # commented out, but left for testing and logging purposes\r\n # game_menu()\r\n results = p.run(eval_genomes, num_generations)\r\n\r\n # show stats\r\n print(results)\r\n \r\n\r\nif __name__ == \"__main__\":\r\n # set file paths to respective difficulty levels\r\n local_dir = os.path.dirname(__file__)\r\n config_path = os.path.join(local_dir, 'config-neat.txt')\r\n config_easy = os.path.join(local_dir, 'config-neat-easy.txt')\r\n config_med = os.path.join(local_dir, 'config-neat-medium.txt')\r\n config_hard = os.path.join(local_dir, 'config-neat-hard.txt')\r\n\r\n game_menu()", "sub_path": "new_main.py", "file_name": "new_main.py", "file_ext": "py", "file_size_in_byte": 17492, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.init", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.font.init", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 24, "usage_type": "attribute"}, {"api_name": "game_elements.ai_score", "line_number": 26, "usage_type": "attribute"}, {"api_name": "game_elements.player_score", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 63, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 93, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 95, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 158, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 158, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 176, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 176, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 177, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 178, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 191, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 191, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 200, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 200, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 201, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 202, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 219, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 219, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 224, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 224, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 226, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 226, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 229, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 229, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 235, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 248, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 248, "usage_type": "attribute"}, {"api_name": "neat.nn.FeedForwardNetwork.create", "line_number": 276, "usage_type": "call"}, {"api_name": "neat.nn", "line_number": 276, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 285, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 285, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 286, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 286, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path", "line_number": 289, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 290, "usage_type": "call"}, {"api_name": "os.path", "line_number": 290, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 291, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 291, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 295, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 295, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 311, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 311, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 314, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 315, "usage_type": "call"}, {"api_name": "math.hypot", "line_number": 347, "usage_type": "call"}, {"api_name": "math.hypot", "line_number": 370, "usage_type": "call"}, {"api_name": "game_elements.player_score", "line_number": 419, "usage_type": "attribute"}, {"api_name": "game_elements.ai_score", "line_number": 419, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 421, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 421, "usage_type": "attribute"}, {"api_name": "neat.config.Config", "line_number": 427, "usage_type": "call"}, {"api_name": "neat.config", "line_number": 427, "usage_type": "attribute"}, {"api_name": "neat.DefaultGenome", "line_number": 427, "usage_type": "attribute"}, {"api_name": "neat.DefaultReproduction", "line_number": 427, "usage_type": "attribute"}, {"api_name": "neat.DefaultSpeciesSet", "line_number": 428, "usage_type": "attribute"}, {"api_name": "neat.DefaultStagnation", "line_number": 428, "usage_type": "attribute"}, {"api_name": "neat.Population", "line_number": 432, "usage_type": "call"}, {"api_name": "neat.StdOutReporter", "line_number": 435, "usage_type": "call"}, {"api_name": "neat.StatisticsReporter", "line_number": 436, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 454, "usage_type": "call"}, {"api_name": "os.path", "line_number": 454, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 455, "usage_type": "call"}, {"api_name": "os.path", "line_number": 455, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 456, "usage_type": "call"}, {"api_name": "os.path", "line_number": 456, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 457, "usage_type": "call"}, {"api_name": "os.path", "line_number": 457, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 458, "usage_type": "call"}, {"api_name": "os.path", "line_number": 458, "usage_type": "attribute"}]} +{"seq_id": "66860708", "text": "import os\r\nimport shutil\r\nimport zlib\r\n\r\nimport requests\r\n\r\nfrom mblib.systems import *\r\n\r\n\r\ndef get_icon(exe, filecache_root):\r\n if not EXTRACT_ICONS:\r\n return \"icons/none.png\"\r\n if os.path.exists(filecache_root + \"/extract\"):\r\n shutil.rmtree(filecache_root + \"/extract\")\r\n print(\"extracting\", exe.encode(\"utf8\"))\r\n os.system(\r\n 'tools\\\\ResourcesExtract.exe /Source \"%s\" /DestFolder \"%s\" /ExtractIcons 1 /ExtractCursors 0 /FileExistMode 1 /OpenDestFolder 0'\r\n % (exe, (filecache_root + \"/extract\").replace(\"/\", os.path.sep))\r\n )\r\n for f in os.listdir(filecache_root + \"/extract\"):\r\n if \"MAINICON\" in f:\r\n return filecache_root + \"/extract/\" + f\r\n for f in os.listdir(filecache_root + \"/extract\"):\r\n if \".ico\" in f:\r\n return filecache_root + \"/extract/\" + f\r\n return \"icons/none.png\"\r\n\r\n\r\ncrcmap = {}\r\n# f = open(\"gbaroms.dat\")\r\n# lines = f.read().split(\"\\n\")\r\n# f.close()\r\nfor l in []: # lines:\r\n if l.strip():\r\n fields = l.split(\";\")\r\n crc = fields[8].lstrip(\"0\")\r\n num = fields[0]\r\n crcmap[crc] = num\r\n\r\n\r\ndef read_crc(filename):\r\n return \"%X\" % (zlib.crc32(open(filename, \"rb\").read()) & 0xFFFFFFFF)\r\n\r\n\r\ndef get_gba(gba):\r\n if not os.path.exists(\"gba\"):\r\n return \"icons/none.png\"\r\n if os.path.exists(\"extract\"):\r\n shutil.rmtree(\"extract\")\r\n os.mkdir(\"extract\")\r\n print(\"extracting\", gba)\r\n # get crc\r\n crc = read_crc(gba)\r\n print(crc)\r\n # get rom number\r\n num = crcmap[crc]\r\n print(num)\r\n # get icon path\r\n r = requests.get(\"http://www.emuparadise.me/GBA/boxart/%s.jpg\" % num)\r\n f = open(\"extract/icon.png\", \"wb\")\r\n f.write(r.content)\r\n f.close()\r\n return \"extract/icon.png\"\r\n", "sub_path": "mblib/resources/extract_icons.py", "file_name": "extract_icons.py", "file_ext": "py", "file_size_in_byte": 1766, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 14, "usage_type": "call"}, {"api_name": "os.system", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 23, "usage_type": "call"}, {"api_name": "zlib.crc32", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 49, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 50, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "600349527", "text": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\nimport bsplineWithOptim as bspline\r\nfrom matplotlib import cm\r\n\r\nclass bsplineSurface(object):\r\n def __init__(self, uKnots, vKnots, points, degree):\r\n self.uBasis = bspline.BSplineBaseFuncs(uKnots, degree)\r\n self.vBasis = bspline.BSplineBaseFuncs(vKnots, degree)\r\n self.points_ = points\r\n self.degree_ = degree\r\n\r\n def getUKnotsExtent(self): return self.uBasis.getKnotsExtent()\r\n def getVKnotsExtent(self): return self.vBasis.getKnotsExtent()\r\n def getUKnotsBegin(self): return self.uBasis.getKnots()[0]\r\n def getVKnotsBegin(self): return self.vBasis.getKnots()[0]\r\n\r\n def computePoint(self, u, v):\r\n uSpanBack = self.uBasis.findSpan(u)\r\n uBases = self.uBasis.computeBase(uSpanBack, u)\r\n vSpanBack = self.vBasis.findSpan(v)\r\n vBases = self.vBasis.computeBase(vSpanBack, v)\r\n res = 0.\r\n uStart = uSpanBack-self.degree_\r\n vStart = vSpanBack-self.degree_\r\n for i in range(uStart, uStart+self.degree_+1):\r\n for j in range(vStart, vStart+self.degree_+1):\r\n point = self.points_[i][j]\r\n res += uBases[i-uStart] * vBases[j-vStart] * point\r\n return res\r\n\r\ndef fill(evaluator, uCount, vCount):\r\n uBegin = evaluator.getUKnotsBegin()\r\n vBegin = evaluator.getVKnotsBegin()\r\n uStep = evaluator.getUKnotsExtent() / uCount;\r\n vStep = evaluator.getVKnotsExtent() / vCount;\r\n xx, yy = np.meshgrid(np.empty(uCount+1), np.empty(vCount+1))\r\n zz = np.empty(shape=xx.shape)\r\n for i in range(0, uCount + 1):\r\n for j in range(0, vCount + 1):\r\n u = uBegin + i * uStep\r\n v = vBegin + j * vStep\r\n p = evaluator.computePoint(u, v)\r\n xx[i,j] = p[0]\r\n yy[i,j] = p[1]\r\n zz[i,j] = p[2]\r\n return xx, yy, zz\r\n\r\ndef hillsData():\r\n uKnots = np.array([0,0,0,0,1,2,3,4,4,4,4])\r\n vKnots = np.copy(uKnots)\r\n side = 7\r\n points = np.empty(shape=(side,side,3))\r\n for i in range(0,side):\r\n for j in range(0,side):\r\n points[i][j] = np.array([i,j,0])\r\n points[side//2][1][2] = 1\r\n points[side//2][side-2][2] = 1.5\r\n points[1][side//2][2] = 2\r\n points[side-2][side//2][2] = 2.5\r\n return uKnots, vKnots, points, 3\r\n\r\ndef demo():\r\n evaluator = bsplineSurface(*hillsData())\r\n xx, yy, zz = fill(evaluator, 30, 30)\r\n fig = plt.figure()\r\n ax = fig.add_subplot(111, projection='3d')\r\n ax.plot_surface(xx, yy, zz, facecolors=cm.Greens(zz / zz.max()))\r\n plt.show()\r\n", "sub_path": "PythonPlayground/bsplineSurface.py", "file_name": "bsplineSurface.py", "file_ext": "py", "file_size_in_byte": 2617, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "bsplineWithOptim.BSplineBaseFuncs", "line_number": 9, "usage_type": "call"}, {"api_name": "bsplineWithOptim.BSplineBaseFuncs", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.cm.Greens", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "238194848", "text": "#\n# This file is part of BDC-Collectors.\n# Copyright (C) 2020 INPE.\n#\n# BDC-Collectors is free software; you can redistribute it and/or modify it\n# under the terms of the MIT License; see LICENSE file for more details.\n#\n\n\"\"\"Base definitions for USGS catalog.\"\"\"\n\nfrom pathlib import Path\n\nfrom bdc_catalog.models import Collection\nfrom flask import current_app\n\nfrom ..base import BaseCollection\nfrom .parser import LandsatScene\n\n\nclass BaseLandsat(BaseCollection):\n \"\"\"Define base Landsat Collection.\"\"\"\n\n parser_class = LandsatScene\n\n assets = [\n 'MTL.txt', 'ANG.txt', 'radsat_qa.tif',\n 'sr_aerosol.tif', 'pixel_qa.tif',\n 'sensor_azimuth_band4.tif', 'sensor_zenith_band4.tif',\n 'solar_azimuth_band4.tif', 'solar_zenith_band4.tif'\n ]\n\n def get_files(self, collection, path=None, prefix=None):\n \"\"\"List all files from Landsat.\"\"\"\n # TODO: Use parameter path instead\n if path is None:\n path = self.path(collection, prefix)\n\n path = Path(path)\n\n output = dict()\n scene_id = self.parser.scene_id\n\n internal_bands = self.bands or []\n\n for f in path.iterdir():\n if f.is_file() and f.suffix.lower() == '.tif':\n band_name = f.stem.replace(f'{scene_id}_', '')\n\n if (band_name.startswith('sr_') and band_name != 'sr_aerosol') or band_name == 'Fmask4' or \\\n band_name.startswith('nbar_') or \\\n any(filter(lambda band_ext: band_name in band_ext, internal_bands)):\n output[band_name] = f\n\n return output\n\n def path(self, collection: Collection, prefix=None) -> Path:\n \"\"\"Retrieve the relative path to the Collection on Brazil Data Cube cluster.\"\"\"\n if prefix is None:\n prefix = current_app.config.get('DATA_DIR')\n\n sensing_date = self.parser.sensing_date()\n\n year_month = sensing_date.strftime('%Y-%m')\n\n scene_path = Path(prefix or '') / 'Repository/Archive' / collection.name / year_month / self.parser.tile_id()\n\n return scene_path\n\n def compressed_file(self, collection, prefix=None):\n \"\"\"Retrieve path to the compressed scene .zip.\"\"\"\n if prefix is None:\n prefix = current_app.config.get('DATA_DIR')\n\n year = self.parser.sensing_date().strftime('%Y')\n\n base = Path(prefix or '')\n\n version = 'v{0:03d}'.format(collection.version)\n\n scene_id = self.parser.scene_id\n\n tile_id = self.parser.tile_id()\n\n path, row = tile_id[:3], tile_id[-3:]\n\n scene_path = base / 'Repository/Archive' / collection.name / version / path / row / year / scene_id\n\n return scene_path / f'{scene_id}.tar.gz'\n\n def get_assets(self, collection, path=None, prefix=None) -> dict:\n \"\"\"Retrieve the map of MTL and ANG assets of Landsat product.\"\"\"\n if path is None:\n path = self.path(collection, prefix=prefix)\n\n path = Path(path)\n\n output = dict()\n\n for p in path.glob('*'):\n for asset in self.assets:\n if p.name.endswith(asset):\n output[asset.split('.')[0]] = str(p)\n break\n\n return output\n", "sub_path": "bdc_collectors/usgs/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 3237, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "base.BaseCollection", "line_number": 20, "usage_type": "name"}, {"api_name": "parser.LandsatScene", "line_number": 23, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 38, "usage_type": "call"}, {"api_name": "bdc_catalog.models.Collection", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.current_app.config.get", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 59, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 65, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.current_app.config.get", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 72, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 76, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 95, "usage_type": "call"}]} +{"seq_id": "112602237", "text": "# -*- coding: UTF-8 -*-\n# Copyright 2015 Mirantis, Inc.\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 os\nfrom StringIO import StringIO\nimport yaml\n\nfrom jinja2 import Template, Environment, meta\n\nfrom solar.core import provider\nfrom solar.core import resource\nfrom solar.core import signals\nfrom solar.events.api import add_event\nfrom solar.events.controls import React, Dep\n\n\ndef create(name, base_path, args=None, virtual_resource=None):\n args = args or {}\n if isinstance(base_path, provider.BaseProvider):\n base_path = base_path.directory\n\n if not os.path.exists(base_path):\n raise Exception(\n 'Base resource does not exist: {0}'.format(base_path)\n )\n\n if is_virtual(base_path):\n template = _compile_file(name, base_path, args)\n yaml_template = yaml.load(StringIO(template))\n rs = create_virtual_resource(name, yaml_template)\n else:\n r = create_resource(name,\n base_path,\n args=args,\n virtual_resource=virtual_resource)\n rs = [r]\n\n return rs\n\n\ndef create_resource(name, base_path, args=None, virtual_resource=None):\n args = args or {}\n if isinstance(base_path, provider.BaseProvider):\n base_path = base_path.directory\n\n # List args init with empty list. Elements will be added later\n args = {key: (value if not isinstance(value, list) else []) for key, value in args.items()}\n r = resource.Resource(\n name, base_path, args=args, tags=[], virtual_resource=virtual_resource\n )\n return r\n\n\ndef create_virtual_resource(vr_name, template):\n template_resources = template['resources']\n template_events = template.get('events', {})\n\n created_resources = create_resources(template_resources)\n events = parse_events(template_events)\n for event in events:\n add_event(event)\n return created_resources\n\n\ndef _compile_file(name, path, kwargs):\n with open(path) as f:\n content = f.read()\n\n inputs = get_inputs(content)\n template = _get_template(name, content, kwargs, inputs)\n with open('/tmp/compiled', 'w') as c:\n c.write(template)\n return template\n\n\ndef get_inputs(content):\n env = Environment(trim_blocks=True, lstrip_blocks=True)\n jinja_globals = env.globals.keys()\n ast = env.parse(content)\n return meta.find_undeclared_variables(ast) - set(jinja_globals)\n\n\ndef _get_template(name, content, kwargs, inputs):\n missing = []\n for input in inputs:\n if input not in kwargs:\n missing.append(input)\n if missing:\n raise Exception('[{0}] Validation error. Missing data in input: {1}'.format(name, missing))\n template = Template(content, trim_blocks=True, lstrip_blocks=True)\n template = template.render(str=str, zip=zip, **kwargs)\n return template\n\n\ndef is_virtual(path):\n return os.path.isfile(path)\n\n\ndef create_resources(resources):\n created_resources = []\n cwd = os.getcwd()\n for r in resources:\n resource_name = r['id']\n base_path = os.path.join(cwd, r['from'])\n args = r['values']\n new_resources = create(resource_name, base_path, args)\n created_resources += new_resources\n\n if not is_virtual(base_path):\n add_connections(resource_name, args)\n return created_resources\n\n\ndef parse_events(events):\n parsed_events = []\n for event in events:\n event_type = event['type']\n parent, parent_action = event['parent_action'].split('.')\n child, child_action = event['depend_action'].split('.')\n state = event['state']\n if event_type == Dep.etype:\n event = Dep(parent, parent_action, state, child, child_action)\n elif event_type == React.etype:\n event = React(parent, parent_action, state, child, child_action)\n else:\n raise Exception('Invalid event type: {0}'.format(event_type))\n parsed_events.append(event)\n return parsed_events\n\n\ndef add_connections(resource_name, args):\n connections = []\n for receiver_input, arg in args.items():\n if isinstance(arg, list):\n for item in arg:\n c = parse_connection(resource_name, receiver_input, item)\n connections.append(c)\n else:\n c = parse_connection(resource_name, receiver_input, arg)\n connections.append(c)\n\n connections = [c for c in connections if c is not None]\n for c in connections:\n parent = resource.load(c['parent'])\n child = resource.load(c['child'])\n events = c['events']\n mapping = {c['parent_input'] : c['child_input']}\n signals.connect(parent, child, mapping, events)\n\n\ndef parse_connection(receiver, receiver_input, element):\n if isinstance(element, basestring) and '::' in element:\n emitter, src = element.split('::', 1)\n try:\n src, events = src.split('::')\n if events == 'NO_EVENTS':\n events = False\n except ValueError:\n events = None\n return {'child': receiver,\n 'child_input': receiver_input,\n 'parent' : emitter,\n 'parent_input': src,\n 'events' : events\n }\n", "sub_path": "solar/solar/core/resource/virtual_resource.py", "file_name": "virtual_resource.py", "file_ext": "py", "file_size_in_byte": 5791, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "solar.core.provider.BaseProvider", "line_number": 31, "usage_type": "attribute"}, {"api_name": "solar.core.provider", "line_number": 31, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 41, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 41, "usage_type": "call"}, {"api_name": "solar.core.provider.BaseProvider", "line_number": 55, "usage_type": "attribute"}, {"api_name": "solar.core.provider", "line_number": 55, "usage_type": "name"}, {"api_name": "solar.core.resource.Resource", "line_number": 60, "usage_type": "call"}, {"api_name": "solar.core.resource", "line_number": 60, "usage_type": "name"}, {"api_name": "solar.events.api.add_event", "line_number": 73, "usage_type": "call"}, {"api_name": "jinja2.Environment", "line_number": 89, "usage_type": "call"}, {"api_name": "jinja2.meta.find_undeclared_variables", "line_number": 92, "usage_type": "call"}, {"api_name": "jinja2.meta", "line_number": 92, "usage_type": "name"}, {"api_name": "jinja2.Template", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "solar.events.controls.Dep.etype", "line_number": 133, "usage_type": "attribute"}, {"api_name": "solar.events.controls.Dep", "line_number": 133, "usage_type": "name"}, {"api_name": "solar.events.controls.Dep", "line_number": 134, "usage_type": "call"}, {"api_name": "solar.events.controls.React.etype", "line_number": 135, "usage_type": "attribute"}, {"api_name": "solar.events.controls.React", "line_number": 135, "usage_type": "name"}, {"api_name": "solar.events.controls.React", "line_number": 136, "usage_type": "call"}, {"api_name": "solar.core.resource.load", "line_number": 156, "usage_type": "call"}, {"api_name": "solar.core.resource", "line_number": 156, "usage_type": "name"}, {"api_name": "solar.core.resource.load", "line_number": 157, "usage_type": "call"}, {"api_name": "solar.core.resource", "line_number": 157, "usage_type": "name"}, {"api_name": "solar.core.signals.connect", "line_number": 160, "usage_type": "call"}, {"api_name": "solar.core.signals", "line_number": 160, "usage_type": "name"}]} +{"seq_id": "178498371", "text": "import pytest\n\nfrom astropy import units as u\n\nfrom panoptes.utils import current_time\nfrom panoptes.utils import serializers\nfrom panoptes.utils import error\n\n\n@pytest.fixture(scope='function')\ndef obj():\n return {\n \"name\": \"Generic PANOPTES Unit\",\n \"pan_id\": \"PAN000\",\n \"location\": {\n \"name\": \"Mauna Loa Observatory\",\n \"latitude\": 19.54 * u.degree, # Astropy unit\n \"longitude\": \"-155.58 deg\", # String unit\n \"elevation\": \"3400.0 m\",\n \"horizon\": 30 * u.degree,\n \"flat_horizon\": -6 * u.degree,\n \"focus_horizon\": -12 * u.degree,\n \"observe_horizon\": -18 * u.degree,\n \"timezone\": \"US/Hawaii\",\n \"gmt_offset\": -600,\n },\n \"directories\": {\n \"base\": \"/var/panoptes\",\n \"images\": \"images\",\n \"data\": \"data\",\n \"resources\": \"POCS/resources/\",\n \"targets\": \"POCS/resources/targets\",\n \"mounts\": \"POCS/resources/mounts\",\n },\n \"db\": {\n \"name\": \"panoptes\",\n \"type\": \"file\"\n },\n \"empty\": {},\n \"current_time\": current_time(),\n \"bool\": True,\n \"exception\": TypeError,\n \"panoptes_exception\": error.InvalidObservation\n }\n\n\ndef test_roundtrip_json(obj):\n config_str = serializers.to_json(obj)\n config = serializers.from_json(config_str)\n assert config['name'] == obj['name']\n assert config['location']['latitude'] == obj['location']['latitude']\n\n\ndef test_roundtrip_yaml(obj):\n config_str = serializers.to_yaml(obj)\n config = serializers.from_yaml(config_str)\n assert config['name'] == obj['name']\n assert config['location']['latitude'] == obj['location']['latitude']\n", "sub_path": "panoptes/utils/tests/test_serializers.py", "file_name": "test_serializers.py", "file_ext": "py", "file_size_in_byte": 1775, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "astropy.units.degree", "line_number": 17, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 17, "usage_type": "name"}, {"api_name": "astropy.units.degree", "line_number": 20, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 20, "usage_type": "name"}, {"api_name": "astropy.units.degree", "line_number": 21, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 21, "usage_type": "name"}, {"api_name": "astropy.units.degree", "line_number": 22, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 22, "usage_type": "name"}, {"api_name": "astropy.units.degree", "line_number": 23, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 23, "usage_type": "name"}, {"api_name": "panoptes.utils.current_time", "line_number": 40, "usage_type": "call"}, {"api_name": "panoptes.utils.error.InvalidObservation", "line_number": 43, "usage_type": "attribute"}, {"api_name": "panoptes.utils.error", "line_number": 43, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 10, "usage_type": "call"}, {"api_name": "panoptes.utils.serializers.to_json", "line_number": 48, "usage_type": "call"}, {"api_name": "panoptes.utils.serializers", "line_number": 48, "usage_type": "name"}, {"api_name": "panoptes.utils.serializers.from_json", "line_number": 49, "usage_type": "call"}, {"api_name": "panoptes.utils.serializers", "line_number": 49, "usage_type": "name"}, {"api_name": "panoptes.utils.serializers.to_yaml", "line_number": 55, "usage_type": "call"}, {"api_name": "panoptes.utils.serializers", "line_number": 55, "usage_type": "name"}, {"api_name": "panoptes.utils.serializers.from_yaml", "line_number": 56, "usage_type": "call"}, {"api_name": "panoptes.utils.serializers", "line_number": 56, "usage_type": "name"}]} +{"seq_id": "344337343", "text": "\"\"\"\nproblem formalization:\n\nyou want to put an advertisement poster over a block of buildings. the block is made of several buildings with different\nheights. you can use as many (consecutive) building as you would like, as long as the height of the poster doesn't\nexceed the height of the buildings. find the largest possible area you can use for your poster.\nnote - the width of every building is 1.\n\nCreate an EFFICIENT algorithm to perform the given task.\n\n-----------------------------------------------------------------------------------------------------------------------\n\nExample:\n\ninput: heights = [1, 2, 3, 4]\noutput: 6\nexplanation - the poster will be hang from the second building to the last one (width 3) with height 2 (which is the\nmaximal possible height that does not exceed any of the building heights). notice that this is not the only way to\nachieve this value, yet it's the maximal one.\n\n-----------------------------------------------------------------------------------------------------------------------\n\n\nLimitations:\n\ntime - 0.2 seconds\n\n-----------------------------------------------------------------------------------------------------------------------\n\nTesting:\n\nAfter implementing your solution, test it with our given input by 'CheckSolution' file.\nYou have a total of 10 tests:\n- tests 1-5 are visible to you, and you can access the input using 'get_input' method from utils.Test.\n- tests 6-10 are not visible to you, and need to pass them without knowing the input.\nIt is assured to you that all input is legal and fits the solution signature.\n\n-----------------------------------------------------------------------------------------------------------------------\n\nDocumentation:\n\nAfter passing all tests, write a doc in Confluence describing your solution.\nIn the doc, analyze the runtime of the algorithm you used.\n\n\"\"\"\n\nfrom typing import List\nimport numpy as np\n\n\ndef poster_solution(heights: List[float]) -> float:\n \"\"\" Finds the biggest possible poster area\n\n :param heights: the height of every building, according to their position\n :return: the biggest possible poster area\n \"\"\"\n\n \"\"\"\n idea:\n most naive solution will be to check every possible set of buildings - and it takes O(n^3) time - not implemented.\n \n A better approach will be based on the idea of divide and conquer. we can notice than given a set of buildings, the\n width of the poster will be the amount of buildings, and the height will be the minimal of the buildings' heights.\n so, we know that if the building with the minimal height is in position i, then the maximal area is one of:\n 1. the minimal height times the total amount of buildings\n 2. the biggest area that can be achieved from buildings 0,...,i-1\n 3. the biggest area that can be achieved from buildings i+1,...,n-1\n using this method we create a solution in an O(nlogn) runtime, implemented below.\n \n yet, this solution will not be enough for our last tests, and we would like to find linear-time solution. our\n solution will be based over dynamic programming.\n as we saw before, the height of the optimal poster will be the height of the shortest building within the chosen\n building set. we can change the way we look over this observation: we can find, for every building, the max set of\n buildings containing it with it as the shortest building, and find the poster area over those buildings. our output\n should be the maximum value of all of these areas.\n so, we would like to find, for every building, the largest consecutive set of buildings containing it such that it's\n the shortest building in the set. it can be done by finding the first building on it's left that is shorter than\n our building, and the same to the right, and use the group in between those buildings. the search for 'the first\n building on -some side- that is shorter than specific building can be performed dynamically to all buildings at \n once.\n how can we do it in linear time?\n 1. we initialize an empty stack.\n 2. we iterate our buildings from left to right, every iteration would like to push a new building to the stack.\n 3. the stack will maintain an important invariant: every value in the stack is larger than all the values below it.\n it means that we will indeed push the new building into the stack, only if it's height is bigger that the building\n currently at the top of the stack. if it's bigger, we keep popping building out of the top until the new building\n will be bigger than the building in the top, and then we push it.\n 4. if there are no new buildings, we just pop all the stack out.\n \n now, let's assume we just pushed a building x into the stack, as long as we get buildings higher than x, it is\n assured that x will remain in the stack. but what will happen when we encounter the first building on the right that\n is smaller than x? note that all the current buildings above x must be higher than x, so the new building will be\n shorter from them as well. so, we will pop all buildings above x and x itself. it means that we will pop a building\n out of the stack only when the currently tested building is the first one on ot's right that's shorter that it!\n what about the left side?\n notice that when we pushed x into the stack, we popped all buildings the x is shorter from. it means that the\n building just below x in the stack will be the first on it's left that x is not shorter from! with this observation,\n we can find for every building the maximal set of groups where it's the shortest one, and get the maximal poster\n area out of these possibilities.\n \"\"\"\n\n # # divide and conquer approach:\n # def max_poster_area(heights):\n # if len(heights) == 0:\n # return 0\n # else:\n # min_idx = np.argmin(heights)\n # return np.max([max_poster_area(heights[:min_idx]),\n # max_poster_area(heights[min_idx + 1:]),\n # len(heights) * heights[min_idx]])\n #\n # return max_poster_area(heights)\n\n # dynamic programming approach\n stack = []\n max_area = 0\n n = len(heights)\n for i in range(n):\n\n if not stack or heights[stack[-1]] <= heights[i]:\n stack.append(i)\n\n else:\n while stack and heights[stack[-1]] > heights[i]:\n popped_idx = stack.pop()\n width = i - (stack[-1] + 1) if stack else i\n max_area = max(max_area, heights[popped_idx] * width)\n stack.append(i)\n\n while stack:\n popped_idx = stack.pop()\n width = n - (stack[-1] + 1) if stack else n\n max_area = max(max_area, heights[popped_idx] * width)\n\n return max_area\n", "sub_path": "3 - Algo/Algo - Common Ground/Programming Exercises/problems/poster/PosterSolved.py", "file_name": "PosterSolved.py", "file_ext": "py", "file_size_in_byte": 6787, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.List", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "460198133", "text": "from bs4 import BeautifulSoup\nimport urllib.request, urllib.parse, requests, time\n\nposturl = 'https://gall.dcinside.com/mgallery/board/lists/?id=[gid]&s_type=search_subject_memo&s_keyword=[keyword]'\ncmturl = 'https://gall.dcinside.com/mgallery/board/lists/?id=[gid]&s_type=search_comment&s_keyword=[keyword]'\n\n# 정식 갤러리\n# posturl = 'https://gall.dcinside.com/board/lists?id=[gid]&s_type=search_subject_memo&s_keyword=[keyword]'\n# cmturl = 'https://gall.dcinside.com/board/lists?id=[gid]&s_type=search_comment&s_keyword=[keyword]'\n\n# 미니 갤러리\n# posturl = 'https://gall.dcinside.com/mini/board/lists/?id=[gid]&s_type=search_subject_memo&s_keyword=[keyword]'\n# cmturl = 'https://gall.dcinside.com/mini/board/lists/?id=[gid]&s_type=search_comment&s_keyword=[keyword]'\n\n# (중요!!! 정식갤 또는 미니갤의 경우에는 위 URL 주석을 해제하여 알맞게 설정하세요!!!)\n\n\n\n\n# 유저 에이전트 값\nhdr = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.131 Safari/537.36',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n 'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3', 'Accept-Encoding': 'none',\n 'Accept-Language': 'en-US,en;q=0.8', 'Connection': 'keep-alive'} \n\n# 텔레그램 봇 요청\ndef sendTelegramMsg(APIKey, chatID, text):\n r = requests.get(\"https://api.telegram.org/bot\" + APIKey + \"/sendMessage?chat_id=\"\n + chatID + \"&text=\" + text + \"&parse_mode=Markdown\")\n return r\n\n\n# ================= 사용 전 직접 설정해 주어야 하는 부분 =================\n\n# 텔레그램 설정\nTelAPI = \"123456789:aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\" # 텔레그램 봇키\nTelChan = \"@channelid\" # 주소\n\n# tip. 비공개 채널에다 알림을 받고 싶은 경우\n# 우선 공개 상태에서 봇을 채널에 추가 & 관리자로 설정한 뒤\n# 채널에 아무 메시지나 보낸 후 해당 메시지의 링크를 복사하여\n# https://t.me/c/[이 부분의 숫자]/123 를 복사하고\n# 앞에 -100을 붙여 TelChan = \"[여기에]\" 넣으면\n# 비공개 채널에서도 알림 전송이 가능합니다.\n\n# 갤러리 설정\n# (중요!!! 정식갤 또는 미니갤의 경우에는 위 URL 주석을 해제하여 알맞게 설정하세요!!!)\ngallid = 'galleryid'\n\n# 검색 키워드 채우기 (줄바꿈으로 구분합니다. 권장 키워드수는 1~10개입니다)\nkw = '''관심\n키워드를\n여기에\n입력하세요'''\nkeywords = {}\n\nfor k in kw.split('\\n'):\n keywords[k] = [0,0]\n\n# 무시닉네임 (본인이 쓰는 닉네임을 입력하세요)\npassnick = '''본인의\n닉네임을\n여기에\n입력하세요'''.split('\\n')\n\n\nupdTime = 300 # 업데이트 주기 (초)\n\n# 약 1초 간격으로 모든 키워드를 순서대로 검색한 뒤 위 주기값만큼 대기합니다.\n# 검색 키워드가 10개라면 글 댓글 -> 총 2번 검색하므로 한번에 20번 요청을 보냅니다.\n# 그러니 되도록이면 1분 이상 간격으로 설정해 주세요 (너무 잦으면 서버로의 접근이 잠시 막힙니다)\n\n# ========================================================================\n\n# 시간 표시 형식\ntType = \"%Y-%m-%d %H:%M:%S\"\nprint (\"========DCBELL 설정 값========\")\nprint (\"Telegram 채널ID: \" + TelChan)\nprint (\"업데이트 간격: \" + str(updTime) + \"초\")\nprint (\"==============================\")\n\n# 전송 기록 리스트 (중복전송 방지용)\npost_hist = []\ncomm_hist = []\n\n\nwhile(1):\n\n try:\n \n for g in keywords.items():\n \n kw = g[0] # 검색 키워드\n\n # 0 = 게시글 1 = 댓글\n for i in range(2):\n\n if (i==0):\n print(\"[\" + time.strftime(tType) + \"] \" + kw + \" 글 조회 시작...\")\n link = posturl\n else:\n print(\"[\" + time.strftime(tType) + \"] \" + kw + \" 댓글 조회 시작...\")\n link = cmturl\n\n time.sleep(0.5)\n\n prev_postnum = g[1][i] # 마지막 알림 게시글 번호\n link = link.replace('[gid]', gallid).replace('[keyword]', urllib.parse.quote(kw))\n\n req = urllib.request.Request(link, headers = hdr)\n html = urllib.request.urlopen(req).read()\n soup = BeautifulSoup(html, \"html.parser\")\n\n if (i==0): # 게시글 검색\n link = soup.find_all(\"tr\", { \"class\" : \"ub-content us-post\"})\n else: # 게시댓글 검색\n link = soup.find_all(\"tr\", { \"class\" : \"search\"})\n\n for m in link:\n\n tmp = m.find(\"td\", { \"class\" : \"gall_tit ub-word\"})\n\n # 글(댓글) 제목\n if \"\" not in str(tmp):\n\n if (i==0): # 게시글\n title = tmp.a.text \n postnum = m.find(\"td\", { \"class\" : \"gall_num\"}).text # 게시글 번호\n postlink = 'https://gall.dcinside.com' + m.a['href']\n tmp = m.find(\"td\", { \"class\" : \"gall_writer ub-writer\"}) # 게시글 작성자 (유동은 IP)\n name = tmp.find(\"em\").text\n ip = tmp.find(\"span\", { \"class\" : \"ip\"})\n\n else: # 게시댓글\n title = m.a.text\n tmp = m.a['href']\n postnum = tmp[tmp.find('fcno=')+5:tmp.find('&fpno')]\n postlink = 'https://gall.dcinside.com' + m.a['href']\n name = m.find('span', {'class' : 'nickname'}).text\n ip = m.find(\"span\", { \"class\" : \"ip\"})\n\n if ip is not None: ip = ip.text\n else: ip = \"(고닉)\"\n\n # 아래에 원하는 조건문 넣어도됨\n if (int(postnum) > int(prev_postnum) and not name in passnick):\n\n print()\n\n if (i==0):\n print (\"======새 글이 있습니다!=========\")\n print (\"│글번호: \" + postnum)\n print (\"│글제목: \" + title)\n else:\n print (\"======새 댓글이 있습니다!=======\")\n print (\"│댓글번호: \" + postnum)\n print (\"│댓글내용: \" + title)\n\n print (\"│닉네임(아이피): \" + name + ip )\n print (\"│URL: \" + postlink)\n \n # 처음에는 보내지않기 (재가동때 알림이 중복으로 가지 않도록)\n if prev_postnum == 0:\n print('│(최초 요청이므로 푸시를 보내지 않습니다)')\n\n elif ((i==0 and postnum in post_hist) or (i==1 and postnum in comm_hist)):\n print('│(이미 보낸 요청이므로 푸시를 보내지 않습니다)')\n \n else:\n print (\"│푸시 보내는 중...\")\n\n telmsg = \"*\" + gallid + \" 갤러리 \"\n \n if (i==0):\n telmsg += \"새 '\" + kw + \"' 키워드 글\"\n else:\n telmsg += \"새 '\" + kw + \"' 키워드 댓글\"\n\n telmsg += \"*\\n\" + title.replace(kw,'_'+kw+'_') + \" - \" + name + ip + \"\\n\" + \"[글 링크](\" + urllib.parse.quote(postlink) + \")\"\n\n print(sendTelegramMsg(TelAPI, TelChan, telmsg))\n print (\"│보내기 완료\")\n \n keywords[kw][i] = postnum\n \n print (\"===========작업 끝=============\\n\")\n break\n\n elif (name in passnick):\n print(\"제외 닉네임이므로 무시합니다.\", name)\n\n if keywords[kw][i] == 0:\n print('검색 결과가 없습니다. 검색되면 알림을 보냅니다.')\n keywords[kw][i] = -1\n\n\n time.sleep(1)\n\n # 오류발생시 무시하고 반복 (서버가 오류가 좀 잦음)\n except Exception as ex: print(\"[\" + time.strftime(tType) + \"] 오류 발생! 무시후 다시 시도합니다.\", ex)\n\n print(\"[\" + time.strftime(tType) + \"] 대기중... (\" + str(updTime) + \"초)\")\n time.sleep(updTime)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 8921, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 99, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 102, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 105, "usage_type": "call"}, {"api_name": "urllib.request.parse.quote", "line_number": 108, "usage_type": "call"}, {"api_name": "urllib.request.parse", "line_number": 108, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 108, "usage_type": "name"}, {"api_name": "urllib.request.request.Request", "line_number": 110, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 110, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 110, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 111, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 111, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 111, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 112, "usage_type": "call"}, {"api_name": "urllib.request.parse.quote", "line_number": 179, "usage_type": "call"}, {"api_name": "urllib.request.parse", "line_number": 179, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 179, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 197, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 200, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 202, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 203, "usage_type": "call"}]} +{"seq_id": "600423489", "text": "import datetime\n\nfrom django.urls import reverse\nfrom django.test import TestCase\nfrom django.utils import timezone\n\nfrom .models import Post\n\n\ndef create_post(title, text, days):\n time = timezone.now() + datetime.timedelta(days=days)\n return Post.objects.create(title=title, text=text, published_date=time)\n\n\nclass PostIndexViewTests(TestCase):\n def test_no_posts(self):\n response = self.client.get(reverse('blog:index'))\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, \"No posts are available.\")\n self.assertQuerysetEqual(response.context['latest_posts_list'], [])\n\n def test_past_post(self):\n create_post(title=\"Past post.\", text=\"I'm from the past.\", days=-30)\n response = self.client.get(reverse('blog:index'))\n self.assertQuerysetEqual(response.context['latest_posts_list'],\n [''])\n\n def test_future_post(self):\n create_post(title=\"Future post.\", text=\"I'm from the future.\", days=30)\n response = self.client.get(reverse('blog:index'))\n self.assertContains(response, \"No posts are available.\")\n self.assertQuerysetEqual(response.context['latest_posts_list'], [])\n\n def test_two_past_posts(self):\n create_post(title='Post 1', text='past', days=-30)\n create_post(title='Post 2', text='past too', days=-5)\n response = self.client.get(reverse('blog:index'))\n self.assertQuerysetEqual(response.context['latest_posts_list'],\n ['',\n ''])\n\n\nclass PostDetailViewTests(TestCase):\n def test_future_post(self):\n future_post = create_post('Future', \"I'm from future.\", 5)\n url = reverse('blog:post_info', args=(future_post.id, ))\n response = self.client.get(url)\n self.assertEqual(response.status_code, 404)\n\n def test_past_post(self):\n past_post = create_post('Past', \"I'm from past.\", -5)\n url = reverse('blog:post_info', args=(past_post.id, ))\n response = self.client.get(url)\n self.assertContains(response, past_post.title)\n\n\nclass PostTests(TestCase):\n\n def test_was_published_recently_with_future_post(self):\n time = timezone.now() + datetime.timedelta(days=30)\n future_post = Post()\n future_post.published_date = time\n\n self.assertIs(future_post.was_published_recently(), False)\n\n def test_was_published_recently_with_old_post(self):\n time = timezone.now() - datetime.timedelta(days=1, seconds=1)\n old_post = Post()\n old_post.published_date = time\n\n self.assertIs(old_post.was_published_recently(), False)\n\n def test_was_published_recently_with_recent_post(self):\n time = timezone.now() - datetime.timedelta(hours=23, minutes=59, seconds=59)\n recent_post = Post()\n recent_post.published_date = time\n\n self.assertIs(recent_post.was_published_recently(), True)\n\n\n", "sub_path": "django-blog/blog/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 3010, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.utils.timezone.now", "line_number": 11, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 11, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Post.objects.create", "line_number": 12, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 12, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 37, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 43, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 46, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 52, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 57, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 60, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 60, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 60, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 61, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 67, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 67, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 67, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 68, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 74, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 74, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 74, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "177378527", "text": "import csv\r\nimport pandas as pd\r\nimport json\r\nimport os\r\nimport bcrypt\r\nimport sqlite3\r\nfrom flask import Flask, render_template, request, session, redirect, url_for, flash\r\n\r\n\r\napp = Flask(__name__)\r\napp.secret_key = os.urandom(24)\r\n\r\n\r\n# define a function to check if a password matches a hash\r\ndef check_password(result, password):\r\n # use bcrypt to check if the password matches the hash\r\n if result and bcrypt.checkpw(password.encode('utf-8'), result[1]):\r\n return True\r\n else:\r\n return False\r\n\r\n\r\n@app.route('/login', methods=['GET', 'POST'])\r\ndef login():\r\n if request.method == 'POST':\r\n # get the user's input (e.g. from a form)\r\n username = request.form['username']\r\n password = request.form['password']\r\n\r\n # connect to the database\r\n conn = sqlite3.connect('users.db')\r\n c = conn.cursor()\r\n\r\n # retrieve the user's record from the database\r\n c.execute(\"SELECT id, password FROM users WHERE username = ?\", (username,))\r\n result = c.fetchone()\r\n\r\n # close the database connection\r\n conn.close()\r\n\r\n if check_password(result, password):\r\n # log the user in and set session variables\r\n session['user_id'] = result[0]\r\n session['username'] = username\r\n\r\n return redirect(url_for('home'))\r\n\r\n else:\r\n # display an error message\r\n flash('Invalid login')\r\n return render_template('login.html')\r\n else:\r\n return render_template('login.html')\r\n\r\n\r\n@app.route(\"/\")\r\ndef home():\r\n \"\"\"\r\n Renvoie la page d'accueil (index.html).\r\n \"\"\"\r\n user_id = session.get('user_id')\r\n username = session.get('username')\r\n if user_id is None:\r\n return redirect(url_for('login'))\r\n return render_template(\"index.html\")\r\n\r\n\r\n@app.route(\"/ajout\")\r\ndef ajout():\r\n \"\"\"\r\n Renvoie la page pour ajouter un nouveau parc (ajout.html).\r\n \"\"\"\r\n # Vérifier si l'utilisateur est connecté\r\n user_id = session.get('user_id')\r\n if user_id is None:\r\n return redirect(url_for('login'))\r\n return render_template(\"ajout.html\")\r\n\r\n\r\n@app.route(\"/modification\")\r\ndef modification():\r\n \"\"\"\r\n Renvoie la page pour modifier les informations d'un parc (modification.html).\r\n Charge également les données du fichier CSV dans un dictionnaire et renvoie\r\n une liste JSON pour être utilisée dans le formulaire.\r\n \"\"\"\r\n # Vérifier si l'utilisateur est connecté\r\n user_id = session.get('user_id')\r\n if user_id is None:\r\n return redirect(url_for('login'))\r\n\r\n # Ouvrir le fichier CSV et lire les données\r\n df = pd.read_csv(\"data.csv\", delimiter=';', encoding=\"windows-1252\")\r\n options = list(df['nomParc'].unique())\r\n jsonfiles = json.loads(df.to_json(orient='records'))\r\n return render_template(\"modification.html\", options=options, df=jsonfiles)\r\n\r\n\r\n@app.route(\"/add\", methods=[\"POST\"])\r\ndef add():\r\n \"\"\"\r\n Ajoute les données du formulaire dans le fichier CSV.\r\n Renvoie un message de confirmation.\r\n \"\"\"\r\n # Vérifier si l'utilisateur est connecté\r\n user_id = session.get('user_id')\r\n if user_id is None:\r\n return redirect(url_for('login'))\r\n\r\n # Get the uploaded file from the form data\r\n geojson_file = request.files['geometry']\r\n\r\n # Check if the geometry file is empty\r\n if geojson_file.filename == '':\r\n geojson_data = {'type': 'FeatureCollection', 'features': []}\r\n else:\r\n # Read the contents of the file\r\n dataGeojson = geojson_file.read()\r\n\r\n # Convert the file content to a JSON object\r\n geojson_data = json.loads(dataGeojson)\r\n\r\n # Extract the geometries from the JSON object\r\n features = geojson_data['features']\r\n geometries = [feature['geometry'] for feature in features]\r\n\r\n # Récupérer les données du formulaire\r\n data = [\r\n request.form.get('nomParc'),\r\n request.form.get('typeParc'),\r\n request.form.get('superficie'),\r\n request.form.get('dateCreation'),\r\n request.form.get('arrondissement'),\r\n request.form.get('dateOfficialisation'),\r\n request.form.get('Amenagement_1'),\r\n request.form.get('Amenagement_2'),\r\n request.form.get('Amenagement_3'),\r\n request.form.get('Amenagement_4'),\r\n request.form.get('Amenagement_5'),\r\n request.form.get('Amenagement_6'),\r\n request.form.get('Amenagement_7'),\r\n request.form.get('Amenagement_8'),\r\n request.form.get('Amenagement_9'),\r\n request.form.get('Amenagement_10'),\r\n geometries[0],\r\n request.form.get('nom'),\r\n request.form.get('dateNaissance'),\r\n request.form.get('dateDeces'),\r\n request.form.get('profession'),\r\n request.form.get('employeur'),\r\n request.form.get('lieuEtudes'),\r\n request.form.get('nbreEnfants')\r\n ]\r\n # Écrire les données dans le fichier CSV\r\n with open(\"data.csv\", \"a\", newline=\"\") as csvfile:\r\n writer = csv.writer(csvfile, delimiter=';')\r\n # Écrire l'en-tête s'il n'y a pas de données dans le fichier\r\n if csvfile.tell() == 0:\r\n header = [\r\n 'nomParc',\r\n 'typeParc',\r\n 'superficie',\r\n 'dateCreation',\r\n 'arrondissement',\r\n 'dateOfficialisation',\r\n 'Amenagement_1',\r\n 'Amenagement_2',\r\n 'Amenagement_3',\r\n 'Amenagement_4',\r\n 'Amenagement_5',\r\n 'Amenagement_6',\r\n 'Amenagement_7',\r\n 'Amenagement_8',\r\n 'Amenagement_9',\r\n 'Amenagement_10',\r\n 'geometry',\r\n 'nom',\r\n 'dateNaissance',\r\n 'dateDeces',\r\n 'profession',\r\n 'employeur',\r\n 'lieuEtudes',\r\n 'nbreEnfants'\r\n ]\r\n writer.writerow(header)\r\n # Écrire les données du formulaire dans une nouvelle ligne\r\n writer.writerow(data)\r\n return \"Nouveau parc ajouté avec succès !\"\r\n\r\n\r\n@app.route(\"/update\", methods=[\"POST\"])\r\ndef update():\r\n \"\"\"\r\n Modifie les données d'un parc dans le fichier CSV en utilisant les données\r\n du formulaire. Renvoie un message de confirmation.\r\n \"\"\"\r\n # Vérifier si l'utilisateur est connecté\r\n if 'username' not in session:\r\n return redirect(url_for('login'))\r\n\r\n\r\n # Récupérer les données du formulaire\r\n nomParc = request.form.get('nomParc')\r\n typeParc = request.form.get('typeParc')\r\n superficie = request.form.get('superficie')\r\n dateCreation = request.form.get('dateCreation')\r\n arrondissement = request.form.get('arrondissement')\r\n dateOfficialisation = request.form.get('dateOfficialisation')\r\n Amenagement_1 = request.form.get('Amenagement_1')\r\n Amenagement_2 = request.form.get('Amenagement_2')\r\n Amenagement_3 = request.form.get('Amenagement_3')\r\n Amenagement_4 = request.form.get('Amenagement_4')\r\n Amenagement_5 = request.form.get('Amenagement_5')\r\n Amenagement_6 = request.form.get('Amenagement_6')\r\n Amenagement_7 = request.form.get('Amenagement_7')\r\n Amenagement_8 = request.form.get('Amenagement_8')\r\n Amenagement_9 = request.form.get('Amenagement_9')\r\n Amenagement_10 = request.form.get('Amenagement_10')\r\n\r\n # Get the uploaded file from the form data\r\n geojson_file = request.files['geometry']\r\n\r\n # Check if the geometry file is empty\r\n if geojson_file.filename == '':\r\n geojson_data = {'type': 'FeatureCollection', 'features': []}\r\n else:\r\n # Read the contents of the file\r\n dataGeojson = geojson_file.read()\r\n\r\n # Convert the file content to a JSON object\r\n geojson_data = json.loads(dataGeojson)\r\n\r\n # Extract the geometries from the JSON object\r\n features = geojson_data['features']\r\n geometries = [feature['geometry'] for feature in features]\r\n\r\n nom = request.form.get('nom')\r\n dateNaissance = request.form.get('dateNaissance')\r\n dateDeces = request.form.get('dateDeces')\r\n profession = request.form.get('profession')\r\n employeur = request.form.get('employeur')\r\n lieuEtudes = request.form.get('lieuEtudes')\r\n nbreEnfants = request.form.get('nbreEnfants')\r\n # Charger les données CSV dans un dictionnaire + on prend une liste rows\r\n data = {}\r\n rows = []\r\n with open(\"data.csv\", \"r\") as csvfile:\r\n reader = csv.DictReader(csvfile, delimiter=';')\r\n # Lire les en-têtes des colonnes\r\n header = reader.fieldnames\r\n for row in reader:\r\n # On définit notre clé de recherche comme \"nomParc\" et on parcourt les lignes une à une\r\n key = row[\"nomParc\"]\r\n data[key] = row\r\n # S'il existe déjà le nom du parc dans le CSV, on modifie les infos avec ce qui vient du formulaire puis on\r\n # ajoute la ligne à la liste\r\n if key == nomParc:\r\n row[\"typeParc\"] = typeParc\r\n row[\"superficie\"] = superficie\r\n row[\"dateCreation\"] = dateCreation\r\n row[\"arrondissement\"] = arrondissement\r\n row[\"dateOfficialisation\"] = dateOfficialisation\r\n row[\"Amenagement_1\"] = Amenagement_1\r\n row[\"Amenagement_2\"] = Amenagement_2\r\n row[\"Amenagement_3\"] = Amenagement_3\r\n row[\"Amenagement_4\"] = Amenagement_4\r\n row[\"Amenagement_5\"] = Amenagement_5\r\n row[\"Amenagement_6\"] = Amenagement_6\r\n row[\"Amenagement_7\"] = Amenagement_7\r\n row[\"Amenagement_8\"] = Amenagement_8\r\n row[\"Amenagement_9\"] = Amenagement_9\r\n row[\"Amenagement_10\"] = Amenagement_10\r\n row[\"geometry\"] = geometries\r\n row[\"nom\"] = nom\r\n row[\"dateNaissance\"] = dateNaissance\r\n row[\"dateDeces\"] = dateDeces\r\n row[\"profession\"] = profession\r\n row[\"employeur\"] = employeur\r\n row[\"lieuEtudes\"] = lieuEtudes\r\n row[\"nbreEnfants\"] = nbreEnfants\r\n rows.append(row)\r\n # Sinon on ajoute la ligne à la liste tel quel pour conserver les autres infos\r\n else:\r\n rows.append(row)\r\n # Écrire les lignes mises à jour dans un nouveau fichier CSV\r\n with open(\"data.csv\", \"w\", newline=\"\") as csvfile:\r\n writer = csv.DictWriter(csvfile, delimiter=';', fieldnames=header)\r\n # Écrire les en-têtes des colonnes\r\n writer.writeheader()\r\n # # Écrire les nouvelles lignes dans le fichier\r\n writer.writerows(rows)\r\n return \"Données de parc modifiées avec succès !\"\r\n\r\n\r\nif __name__ == \"__main__\":\r\n app.run(debug=True)\r\n\r\n\r\n", "sub_path": "APP_Donnees_liees/FORMULAIRE/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 10906, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 10, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 11, "usage_type": "call"}, {"api_name": "bcrypt.checkpw", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 88, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 90, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 93, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 106, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 106, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 111, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 111, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 121, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 129, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 129, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 130, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 130, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 130, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 131, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 131, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 131, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 132, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 132, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 132, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 133, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 133, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 133, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 134, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 134, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 134, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 135, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 135, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 136, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 136, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 137, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 137, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 137, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 138, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 138, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 138, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 139, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 139, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 139, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 140, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 140, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 140, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 141, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 141, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 142, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 142, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 143, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 143, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 143, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 144, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 144, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 146, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 146, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 146, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 147, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 147, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 147, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 148, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 148, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 148, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 149, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 149, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 149, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 150, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 150, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 150, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 151, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 151, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 152, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 152, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 156, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 198, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 199, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 199, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 203, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 203, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 203, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 204, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 204, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 204, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 205, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 205, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 205, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 206, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 206, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 206, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 207, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 207, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 207, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 208, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 208, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 208, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 209, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 209, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 209, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 210, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 210, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 210, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 211, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 211, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 211, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 212, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 212, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 212, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 213, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 213, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 213, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 214, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 214, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 214, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 215, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 215, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 215, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 216, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 216, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 216, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 217, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 217, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 217, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 218, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 218, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 218, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 221, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 221, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 231, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 237, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 237, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 237, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 238, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 238, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 238, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 239, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 239, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 239, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 240, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 240, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 240, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 241, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 241, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 241, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 242, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 242, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 242, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 243, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 243, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 243, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 248, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 287, "usage_type": "call"}]} +{"seq_id": "654254566", "text": "import random\r\nimport plotly.figure_factory as ff\r\nimport plotly.graph_objects as go\r\nimport statistics\r\nimport pandas as pd \r\nimport csv \r\nimport random\r\ndf=pd.read_csv(\"medium_data.csv\")\r\ndata=df[\"reading_time\"].tolist()\r\ndef randomsetofmeans(counter):\r\n dataset=[]\r\n for i in range(0, counter):\r\n index=random.randint(0,len(data)-1)\r\n val=data[index]\r\n dataset.append(val)\r\n mean=statistics.mean(dataset)\r\n return mean \r\ndef showfig(meanlist):\r\n mean=statistics.mean(meanlist)\r\n fig=ff.create_distplot([meanlist],[\"meanlist\"],show_hist=False)\r\n fig.add_trace(go.Scatter(x=[mean,mean],y=[0,10],mode=\"lines\",name=\"mean\"))\r\n fig.show()\r\n\r\ndef main():\r\n meanlist=[]\r\n for i in range(0,1000):\r\n setofmeans=randomsetofmeans(25)\r\n meanlist.append(setofmeans)\r\n showfig(meanlist)\r\n mean=statistics.mean(meanlist)\r\n print(\"sampling mean: \",mean)\r\n sd=statistics.stdev(meanlist)\r\n print(\"sampling deviation: \", sd)\r\nmain()\r\npopulationmean=statistics.mean(data)\r\nprint(\"population mean: \", populationmean)\r\npopulationsd=statistics.stdev(data)\r\nprint(\"population deviation: \", populationsd)", "sub_path": "articles.py", "file_name": "articles.py", "file_ext": "py", "file_size_in_byte": 1161, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 13, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 16, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 19, "usage_type": "call"}, {"api_name": "plotly.figure_factory.create_distplot", "line_number": 20, "usage_type": "call"}, {"api_name": "plotly.figure_factory", "line_number": 20, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 21, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 21, "usage_type": "name"}, {"api_name": "statistics.mean", "line_number": 30, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 32, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 35, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "288512113", "text": "from logging import basicConfig, getLogger, INFO\nfrom connect_to_ledger import create_qldb_driver\nimport collections\nimport json\nfrom pyion2json import ion_cursor_to_json\nfrom sampledata.sample_data import get_value_from_documentid, delete_document, print_result\nfrom constants import Constants\nfrom register_person import get_scentityid_from_personid\n\n\nlogger = getLogger(__name__)\nbasicConfig(level=INFO)\n\ndef person_is_superadmin(transaction_executor,person_id):\n is_superadmin = get_value_from_documentid(transaction_executor,Constants.PERSON_TABLE_NAME,person_id,\"isSuperAdmin\")\n if is_superadmin == [1]:\n logger.info(\"Authorized!\")\n return True\n else:\n logger.info('Not Authorized!')\n return False\n\ndef mcg_request_exist(transaction_executor, request_id):\n\n query = 'SELECT * FROM {} as m by RequestId WHERE RequestId = ? '.format(Constants.SUPERADMIN_REQUEST_TABLE_NAME)\n cursor = transaction_executor.execute_statement(query, request_id)\n\n try:\n return_body = ion_cursor_to_json(cursor)\n if len(return_body) > 0:\n return return_body[0]\n else:\n raise Exception(\"AAAAAAA\")\n except StopIteration:\n logger.info('Request not Found')\n return False\n \n\ndef mcg_request_already_approved(transaction_executor, request_id):\n query = 'SELECT m.isAccepted from {} as m by id where id = ?'.format(Constants.SUPERADMIN_REQUEST_TABLE_NAME)\n cursor = transaction_executor.execute_statement(query,request_id)\n approval_status = list(map(lambda x: x.get('isAccepted'), cursor))\n \n logger.info(\"approval status : {}\".format(approval_status))\n\n if approval_status == [0]:\n logger.info(\" not approved\")\n return False\n else:\n logger.info(\"approved\")\n return True\n\ndef get_every_request(transaction_executor, person_id):\n \n if person_is_superadmin(transaction_executor, person_id):\n query = 'SELECT * FROM {} BY RequestId'.format(Constants.SUPERADMIN_REQUEST_TABLE_NAME)\n \n try:\n cursor = transaction_executor.execute_statement(query)\n requests = ion_cursor_to_json(cursor)\n \n return{\n 'statusCode': 200,\n 'body': requests\n }\n \n except StopIteration:\n return{\n 'statusCode': 400,\n 'body': \" No request found\"\n }\n else:\n return{\n 'statusCode': 400,\n 'body': \"Only SuperAdmin can View all Requets\"\n }\n \n\ndef get_self_mcg_request(transaction_executor,event):\n \n try:\n request_id = event[\"RequestId\"]\n person_id = event[\"PersonId\"] \n \n sender_person_id = get_value_from_documentid(transaction_executor,Constants.SUPERADMIN_REQUEST_TABLE_NAME,request_id,\"SenderPersonId\")\n sender_scentity_id = get_scentityid_from_personid(transaction_executor, sender_person_id[0])\n \n \n actual_scentity_id = get_scentityid_from_personid(transaction_executor, person_id)\n \n\n if actual_scentity_id == sender_scentity_id:\n \n return mcg_request_exist(transaction_executor,request_id)\n else:\n return_statement = \"Access denied. Check RequestId or PersonId\"\n return{\n 'statusCode': 400,\n 'body': return_statement\n }\n \n except Exception:\n return_statement = 'Error fetching your request'\n return{\n 'statusCode': 400,\n 'body': return_statement\n }\n\ndef update_approval_status(transaction_executor,request_id,status):\n request_type = get_value_from_documentid(transaction_executor,Constants.SUPERADMIN_REQUEST_TABLE_NAME,request_id,\"RequestType\")\n request_type = request_type[0]\n document_id = get_value_from_documentid(transaction_executor,Constants.SUPERADMIN_REQUEST_TABLE_NAME,request_id,\"DocumentId\")\n logger.info(request_type)\n logger.info(document_id)\n\n if request_type == 1:\n table_name = Constants.SCENTITY_TABLE_NAME\n\n else:\n table_name = Constants.PRODUCT_TABLE_NAME\n\n if status == \"false\":\n \n # delete this if statement if you want to keep person even if SCEntitiy is deleted\n if request_type == 1:\n person_ids = get_value_from_documentid(transaction_executor, table_name,document_id[0],'PersonIds')\n logger.info(person_ids)\n person_ids = person_ids[0]\n logger.info('person_ids are :{}'.format(person_ids))\n delete_document(transaction_executor, Constants.PERSON_TABLE_NAME,person_ids) \n return_statement_1 = 'Following person ids were deleted : person id:{}'.format(person_ids)\n \n\n # id must be in list so request_id was converted to ['request_id']\n delete_document(transaction_executor,table_name,document_id)\n delete_document(transaction_executor,Constants.SUPERADMIN_REQUEST_TABLE_NAME, [request_id]) \n \n return_statement_2 = 'and following documents were deleted : product or scentity id: {} and request id:{}'.format(document_id,request_id)\n \n return{\n 'statusCode': 200,\n 'body': return_statement1+return_statement_2\n }\n \n else:\n update_statement = \" UPDATE {} AS j BY id SET j.isApprovedBySuperAdmin = true WHERE id = ?\".format(table_name)\n\n document_id = document_id[0]\n cursor = transaction_executor.execute_statement(update_statement, document_id)\n try:\n next(cursor)\n print(\"Approval Status Updated!\")\n except StopIteration:\n print(\"Status was not updated!\")\n\ndef accept_request_to_approve_company_or_product(transaction_executor, event):\n request_id = event[\"RequestId\"]\n person_id = event[\"PersonId\"] \n request = mcg_request_exist(transaction_executor, request_id)\n print(request)\n if request:\n if person_is_superadmin(transaction_executor,person_id):\n if mcg_request_already_approved(transaction_executor, request_id):\n return_statement = \"Request already approved : {}\".format(request_id)\n return{\n 'statusCode': 400,\n 'body': return_statement\n }\n else:\n update_approval_status(transaction_executor,request_id,True)\n \n #Update Request status in MCG table\n update_statement = \" UPDATE {} AS j BY id SET j.isAccepted = true WHERE id = ?\".format(Constants.SUPERADMIN_REQUEST_TABLE_NAME)\n try:\n cursor = transaction_executor.execute_statement(update_statement, request_id)\n result_body = ion_cursor_to_json(cursor)\n request.update({\"isAccepted\":True})\n return{\n 'statusCode': 200,\n 'body': request\n }\n \n except StopIteration:\n return_statement = \"Request couldn't be accepted!\"\n return{\n 'statusCode': 400,\n 'body': return_statement\n }\n else:\n return_statement = (\"Access denied -- only MCG\")\n return{\n 'statusCode': 400,\n 'body': return_statement}\n else:\n return_statement = \"Any request with request id : {} doesn't exist.\".format(request_id)\n return{\n 'statusCode': 400,\n 'body': return_statement\n }\n\n###################################################################################################################################################################################################\n\ndef get_all_mcg_requests(event):\n try:\n with create_qldb_driver() as driver:\n person_id = event[\"PersonId\"] \n \n return(driver.execute_lambda(lambda executor: get_every_request(executor,person_id)))\n except Exception:\n return_statement = 'Error fetching all the requests'\n \n return{\n 'statusCode': 400,\n 'body': return_statement\n }\n \n \n\ndef accept_mcg_request(event):\n try:\n with create_qldb_driver() as driver:\n\n return driver.execute_lambda (lambda executor: accept_request_to_approve_company_or_product(executor,event))\n except Exception:\n return_statement = \"Error accepting the request.\"\n return{\n 'statusCode': 400,\n 'body': return_statement\n }\n\ndef get_your_mcg_request(event):\n try:\n with create_qldb_driver() as driver:\n \n return(driver.execute_lambda(lambda executor: get_self_mcg_request(executor,event)))\n except Exception:\n return_statement = \"Error finding Request.\"\n return{\n 'statusCode': 400,\n 'body': return_statement\n }\n\n\n\n ", "sub_path": "lambda/qldb/accept_requests_for_admin.py", "file_name": "accept_requests_for_admin.py", "file_ext": "py", "file_size_in_byte": 9094, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 12, "usage_type": "name"}, {"api_name": "sampledata.sample_data.get_value_from_documentid", "line_number": 15, "usage_type": "call"}, {"api_name": "constants.Constants.PERSON_TABLE_NAME", "line_number": 15, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 15, "usage_type": "name"}, {"api_name": "constants.Constants.SUPERADMIN_REQUEST_TABLE_NAME", "line_number": 25, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 25, "usage_type": "name"}, {"api_name": "pyion2json.ion_cursor_to_json", "line_number": 29, "usage_type": "call"}, {"api_name": "constants.Constants.SUPERADMIN_REQUEST_TABLE_NAME", "line_number": 40, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 40, "usage_type": "name"}, {"api_name": "constants.Constants.SUPERADMIN_REQUEST_TABLE_NAME", "line_number": 56, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 56, "usage_type": "name"}, {"api_name": "pyion2json.ion_cursor_to_json", "line_number": 60, "usage_type": "call"}, {"api_name": "sampledata.sample_data.get_value_from_documentid", "line_number": 85, "usage_type": "call"}, {"api_name": "constants.Constants.SUPERADMIN_REQUEST_TABLE_NAME", "line_number": 85, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 85, "usage_type": "name"}, {"api_name": "register_person.get_scentityid_from_personid", "line_number": 86, "usage_type": "call"}, {"api_name": "register_person.get_scentityid_from_personid", "line_number": 89, "usage_type": "call"}, {"api_name": "sampledata.sample_data.get_value_from_documentid", "line_number": 110, "usage_type": "call"}, {"api_name": "constants.Constants.SUPERADMIN_REQUEST_TABLE_NAME", "line_number": 110, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 110, "usage_type": "name"}, {"api_name": "sampledata.sample_data.get_value_from_documentid", "line_number": 112, "usage_type": "call"}, {"api_name": "constants.Constants.SUPERADMIN_REQUEST_TABLE_NAME", "line_number": 112, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 112, "usage_type": "name"}, {"api_name": "constants.Constants.SCENTITY_TABLE_NAME", "line_number": 117, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 117, "usage_type": "name"}, {"api_name": "constants.Constants.PRODUCT_TABLE_NAME", "line_number": 120, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 120, "usage_type": "name"}, {"api_name": "sampledata.sample_data.get_value_from_documentid", "line_number": 126, "usage_type": "call"}, {"api_name": "sampledata.sample_data.delete_document", "line_number": 130, "usage_type": "call"}, {"api_name": "constants.Constants.PERSON_TABLE_NAME", "line_number": 130, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 130, "usage_type": "name"}, {"api_name": "sampledata.sample_data.delete_document", "line_number": 135, "usage_type": "call"}, {"api_name": "sampledata.sample_data.delete_document", "line_number": 136, "usage_type": "call"}, {"api_name": "constants.Constants.SUPERADMIN_REQUEST_TABLE_NAME", "line_number": 136, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 136, "usage_type": "name"}, {"api_name": "constants.Constants.SUPERADMIN_REQUEST_TABLE_NAME", "line_number": 173, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 173, "usage_type": "name"}, {"api_name": "pyion2json.ion_cursor_to_json", "line_number": 176, "usage_type": "call"}, {"api_name": "connect_to_ledger.create_qldb_driver", "line_number": 205, "usage_type": "call"}, {"api_name": "connect_to_ledger.create_qldb_driver", "line_number": 221, "usage_type": "call"}, {"api_name": "connect_to_ledger.create_qldb_driver", "line_number": 233, "usage_type": "call"}]} +{"seq_id": "447691577", "text": "# coding=utf8\n# author=PlatinumGod\n# created on 2016/11/14\n\nimport threading\nimport logging\nimport mysql.connector\nimport pybloom\nimport re\nfrom Extracting_keywords import Exractkeyword\n\nbloomfilter = pybloom.BloomFilter(10000000)\n\n\nclass DBConnector(threading.Thread):\n\n def __init__(self, basicinfo, storequeue):\n \"\"\"\n 数据库连接器，作为一个单独的线程存在，从storequeue中取出数据存储进数据库\n :param basicinfo: 作为存储线程的标识符，目前指代authorid，以后可能会加入用户标识\n :param storequeue:从中取出数据放回数据库\n \"\"\"\n super(DBConnector, self).__init__()\n self.basicinfo = basicinfo\n self.storequeue = storequeue\n self.cnx = mysql.connector.connect(\n user='root', database='zhihu', password='wangZ686868')\n # self.cnx = mysql.connector.connect(user='root', database='zhihu')\n self.ew = Exractkeyword()\n self.cursor = self.cnx.cursor()\n self.setDaemon(True)\n self.start()\n\n def run(self):\n while True:\n while not self.storequeue.empty():\n # 只要storequeue非空，就从中取出二元组进行进一步出力\n data, name = self.storequeue.get()\n # 封装好的处理函数，以name作为标识符进行处理\n presql = self.getpresql(data, name)\n # 确保发生异常不至于使线程退出\n if presql is None:\n continue\n for sql, sqldata in presql:\n self.cursor.execute(sql, sqldata)\n self.cnx.commit()\n logging.warning(\"SQL execute success! store %s\" % name)\n\n def getpresql(self, data, name):\n \"\"\"\n 数据格式化函数，将从storequeue中取出的data,name二元组按照name进行数据清洗，并将其存储到数据库中\n :param data: 原始数据\n :param name: 数据标识符\n :return: 返回一个包含有多条sql语句的数组\n \"\"\"\n authorsql = answersql = activiessql = questionssql = ''\n authorid = ''\n authorname = ''\n homepage = ''\n agree = ''\n thanks = ''\n answers = ''\n questions = ''\n title = ''\n createdtime = 0\n questionid = ''\n upvotes = ''\n answerid = ''\n excerpt = ''\n content = ''\n activitytype = ''\n if name == 'resultfragment':\n \"\"\"\n +-------+-----------+-----------+-----------+------------+-------+-------+-------+---------+\n |index |authorID |authorName |homepage |icon |agree |thanks |answers|questions|\n +-------+-----------+-----------+-----------+------------+-------+-------+-------+---------+\n |int(11)|varchar(45)|varchar(45)|varchar(45)|varchar(100)|int(11)|int(11)|int(11)|int(11) |\n +-------+-----------+-----------+-----------+------------+-------+-------+-------+---------+\n \"\"\"\n authorpresql = []\n authorsql = (\n \"INSERT authorinfo \"\n \"(authorid, authorname, homepage, icon, agree, thanks, answers, questions)\"\n \"VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\")\n try:\n authorid = data['authorid']\n if str(authorid) in bloomfilter:\n return\n else:\n bloomfilter.add(str(authorid))\n authorname = data['authorname']\n homepage = 'https://www.zhihu.com' + data['home'][0]\n icon = data['icon']\n agree = data['agree']\n thanks = data['thanks']\n answers = data['answer']\n questions = data['questions']\n except ValueError:\n raise ValueError(\"Capture wrong happened...\")\n try:\n authordata = (\n authorid[0],\n authorname[0],\n homepage,\n icon[0],\n agree[0],\n thanks[0],\n answers[0],\n questions[0])\n for info in authordata:\n if info is None:\n info = '0'\n authorpresql.append((authorsql, authordata))\n return authorpresql\n except:\n logging.error(\"presql failed while handling resultfragment...\")\n return None\n\n elif name == 'answers':\n \"\"\"\n +-------+-----------+------------+-----------+-----------+-------+---------+------------+---------+\n |index |authorID |title |createdtime|questionid |upvotes|answerid |excerpt |content |\n +-------+-----------+------------+-----------+-----------+---------+-------+------------+---------+\n |int(11)|varchar(45)|varchar(200)|int(11) |int(11) |int(11)|int(11) |varchar(500)|longtext |\n +-------+-----------+------------+-----------+-----------+---------+-------+------------+---------+\n \"\"\"\n answerspresql = []\n for answer in data:\n answersql = (\n \"INSERT INTO answersinfo \"\n \"(authorid, title, createdtime, questionid, upvotes, answerid, excerpt, content, tags) \"\n \"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)\")\n try:\n authorid = answer['author']['id']\n title = answer['question']['title']\n createdtime = answer['created_time']\n questionid = answer['question']['id']\n answerid = answer['id']\n except ValueError:\n logging.error(\n \"capture failed! no such key in keys\", answer)\n continue\n try:\n upvotes = answer['voteup_count']\n excerpt = answer['excerpt']\n content = answer['content']\n tags = self.ew.getSegPar(content,8)\n except:\n pass\n answerdata = (\n authorid,\n title,\n createdtime,\n questionid,\n upvotes,\n answerid,\n excerpt,\n content,\n\t\t\t\t\ttags)\n answerspresql.append((answersql, answerdata))\n return answerspresql\n\n elif name == 'questions':\n \"\"\"\n +-------+-----------+------------+------------+-----------+----------+\n |index |authorID |title |tags |createdtime|questionid|\n +-------+-----------+------------+------------+-----------+----------+\n |int(11)|varchar(45)|varchar(200)|varchar(200)|int(11) |int(11) |\n +-------+-----------+------------+------------+-----------+----------+\n \"\"\"\n questionspresql = []\n for question in data:\n questionssql = (\n 'INSERT INTO questions '\n '(authorid, title, createdtime, questionid) '\n 'VALUES (%s, %s, %s, %s)')\n try:\n authorid = self.basicinfo\n title = question['title']\n createdtime = question['created']\n questionid = question['id']\n except ValueError:\n logging.error(\n \"capture failed! no such key in keys\", question)\n continue\n except:\n pass\n questionsdata = (authorid, title, createdtime, questionid)\n questionspresql.append((questionssql, questionsdata))\n return questionspresql\n\n elif name == 'activities':\n \"\"\"\n +-------+-----------+------------+------------+-----------+-----------+\n |index |authorID |actiontext |questionid |answerid |createdtime|\n +-------+-----------+------------+------------+-----------+-----------+\n |int(11)|varchar(45)|varchar(200)|varchar(200)|int(11) |int(11) |\n +-------+-----------+------------+------------+-----------+-----------+\n \"\"\"\n activiespresql = []\n for activies in data:\n activiessql = (\n 'INSERT INTO activities '\n '(authorid, verb, questionid, answerid, createdtime) '\n 'VALUES (%s, %s, %s, %s, %s)')\n try:\n authorid = activies['actor']['id']\n createdtime = activies['created_time']\n verb = activies['verb']\n if verb == \"QUESTION_FOLLOW\":\n questionid = activies['target']['id']\n elif verb == \"MEMBER_COLLECT_ANSWER\" or verb == \"ANSWER_CREATE\" or verb == \"ANSWER_VOTE_UP\":\n answerid = activies['target']['id']\n questionid = activies['target']['question']['id']\n activiesdata = (authorid, verb, questionid, answerid, createdtime)\n activiespresql.append((activiessql, activiesdata))\n except:\n pass\n return activiespresql\n return\n\nclass DBConnectorweibo(threading.Thread):\n\n def __init__(self, basicinfo, storequeue):\n \"\"\"\n 数据库连接器，作为一个单独的线程存在，从storequeue中取出数据存储进数据库\n :param basicinfo: 作为存储线程的标识符，目前指代authorid，以后可能会加入用户标识\n :param storequeue:从中取出数据放回数据库\n \"\"\"\n super(DBConnectorweibo, self).__init__()\n self.basicinfo = basicinfo\n self.storequeue = storequeue\n self.cnx = mysql.connector.connect(\n user='root', database='sina', password='wangZ686868')\n # self.cnx = mysql.connector.connect(user='root', database='zhihu')\n self.cursor = self.cnx.cursor()\n self.setDaemon(True)\n self.start()\n\n def run(self):\n while True:\n while not self.storequeue.empty():\n # 只要storequeue非空，就从中取出二元组进行进一步出力\n data, name = self.storequeue.get()\n # 封装好的处理函数，以name作为标识符进行处理\n presql = self.getpresql(data, name)\n # 确保发生异常不至于使线程退出\n if presql is None and presql != []:\n continue\n for sql, sqldata in presql:\n self.cursor.execute(sql, sqldata)\n self.cnx.commit()\n logging.warning(\"SQL execute success! store %s\" % name)\n\n def getpresql(self, data, name):\n if name == 'resultfragment':\n userpresql = []\n usersql = (\n \"INSERT authorinfo \"\n \"(authorid, authorname, homepage, icon, followees, followers, weibonum, description)\"\n \"VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\")\n try:\n userid = data['userid'][0]\n if str(userid) in bloomfilter:\n return\n else:\n bloomfilter.add(str(userid))\n username = data['username'][0]\n homepage = 'http://weibo.com/u/' + userid\n icon = data['icon'][0].replace('\\/', '/')\n followees = data['followees'][0]\n followers = data['followers'][0]\n weibonum = data['weibonum'][0]\n description = data['description'][0]\n except ValueError:\n raise ValueError(\"Capture wrong happened...\")\n try:\n userdata = (userid, username, homepage, icon, followees, followers, weibonum, description)\n for info in userdata:\n if info is None:\n info = '0'\n userpresql.append((usersql, userdata))\n return userpresql\n except:\n logging.error(\"presql failed while handling resultfragment...\")\n return None\n\n elif name == 'weiboinfo':\n weibopresql = []\n weibosql = (\n \"INSERT weiboinfo \"\n \"(authorid, content, cncontent, createdtime, type, transmit, comment, agree, tags)\"\n \"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)\")\n p_userid = re.compile(r'\\\\n(.*?)<\\\\/div>\\\\n')\n p_createdtime = re.compile(r'date=\\\\\"(\\d+)\\\\\"')\n p_weibotype = 1\n thisinfo = re.compile(r'<\\\\/em>(\\d+)<\\\\/em><\\\\/span>')\n cn_content = re.compile('[\\xc2-\\xdf][\\x80-\\xbf]|[\\xe0-\\xef][\\x80-\\xbf][\\x80-\\xbf]|[\\xf0-\\xf4][\\x80-\\xbf][\\x80-\\xbf][\\x80-\\xbf]')\n cnutf8 = re.compile('[\\u4e00-\\u9fa5]')\n try:\n for weibo in data:\n userid = p_userid.findall(weibo)[0]\n content = p_content.findall(weibo)[0]\n try:\n cncontent = cn_content.findall(content)\n if cncontent == []:\n content = content.decode('unicode_escape')\n content = content.replace('\\/', '/')\n cncontent = re.sub('[A-Za-z0-9_<>\\\\:=\\(\\)\\?;&#\"\\*\\.@]', '', content)\n except:\n cncontent = cnutf8.findall(content)\n cnstr = ''.join(cncontent)\n createdtime = int(p_createdtime.findall(weibo)[0])/1000\n weibotype = p_weibotype\n info = thisinfo.findall(weibo)\n if len(info) >= 5:\n transmit = info[2]\n comment = info[3]\n agree = info[4]\n elif len(info) == 3:\n transmit = info[0]\n comment = info[1]\n agree = info[2]\n try:\n tags = self.ew.getSegPar(cnstr,8)\n except:\n tags = ''\n weibodata = (userid, content, cnstr, createdtime, weibotype, transmit, comment, agree, tags)\n weibopresql.append((weibosql, weibodata))\n return weibopresql\n except:\n logging.error(\"presql failed while handling weiboinfo...\")\n return weibopresql\n\nif __name__ == '__main__':\n import Queue\n import json\n import time\n\n store = Queue.Queue()\n with open('data.json', 'r') as f:\n a = json.load(f)\n answer = a['data']\n\n store.put((answer, 'answers'))\n mydbconnector = DBConnector('TuringDon', store)\n time.sleep(30)\n", "sub_path": "SocialMediaAnalysis/spyder/dbconnector.py", "file_name": "dbconnector.py", "file_ext": "py", "file_size_in_byte": 15272, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pybloom.BloomFilter", "line_number": 12, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 15, "usage_type": "attribute"}, {"api_name": "mysql.connector.connector.connect", "line_number": 26, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 26, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 26, "usage_type": "name"}, {"api_name": "Extracting_keywords.Exractkeyword", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 116, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 140, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 183, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 222, "usage_type": "attribute"}, {"api_name": "mysql.connector.connector.connect", "line_number": 233, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 233, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 233, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 253, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 285, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 294, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 295, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 296, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 298, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 299, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 300, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 310, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 333, "usage_type": "call"}, {"api_name": "Queue.Queue", "line_number": 341, "usage_type": "call"}, {"api_name": "json.load", "line_number": 343, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 348, "usage_type": "call"}]} +{"seq_id": "493118681", "text": "# -*- coding: utf-8 -*-\n# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl.html)\nfrom copy import deepcopy\n\nfrom openerp.addons.connector.connector import ConnectorEnvironment\nfrom openerp.addons.connector.checkpoint import checkpoint\n\nimport logging\n_logger = logging.getLogger(__name__)\n\n\ndef get_environment(session, binding_model_name, backend_id):\n \"\"\" Create an environment to work with. \"\"\"\n backend_record = session.env['getresponse.backend'].browse(backend_id)\n\n # Get a connector environment for the given model\n con_env = ConnectorEnvironment(backend_record, session, binding_model_name)\n\n # Change the language based on the backend setting and return the env\n backend_lang = backend_record.default_lang_id\n lang_code = backend_lang.code if backend_lang and backend_lang.code else 'de_DE'\n if lang_code == session.context.get('lang'):\n return con_env\n else:\n _logger.warning(\"Changing lang code for getresponse connector env to %s\" % lang_code)\n with con_env.session.change_context(lang=lang_code):\n return con_env\n\n\ndef add_checkpoint(session, binding_model_name, record_id, backend_id):\n \"\"\" Add a row in the model ``connector.checkpoint`` for a record,\n meaning it has to be reviewed by a user.\n :param session: current session\n :type session: :class:`openerp.addons.connector.session.ConnectorSession`\n :param model_name: name of the model of the record to be reviewed\n :type model_name: str\n :param record_id: ID of the record to be reviewed\n :type record_id: int\n :param backend_id: ID of the GetResponse Backend\n :type backend_id: int\n \"\"\"\n return checkpoint.add_checkpoint(session, binding_model_name, record_id, 'getresponse.backend', backend_id)\n\n\ndef skipp_export_by_context(context, skipp_only_bind_model=None, skipp_only_bind_record_id=None):\n if skipp_only_bind_record_id:\n assert skipp_only_bind_model, \"skipp_only_bind_model is given but skipp_only_bind_record_id is missing!\"\n\n context = context if context else {}\n\n if 'connector_no_export' not in context:\n connector_no_export = 'not_in_context'\n else:\n connector_no_export = context['connector_no_export']\n\n # Not found in context\n if connector_no_export == 'not_in_context':\n skipp_export = False\n\n # Skipp export for all models and all records\n elif connector_no_export is True:\n if skipp_only_bind_model or skipp_only_bind_record_id:\n _logger.debug(\"connector_no_export is True but skipp_only_bind_model '%s' or skipp_only_bind_record_id\"\n \" '%s' are set! \" % (skipp_only_bind_model, skipp_only_bind_record_id))\n skipp_export = True\n\n # Skipp exports only if the model and the record id are in 'connector_no_export'\n elif skipp_only_bind_record_id:\n skipp_ids = connector_no_export.get(skipp_only_bind_model, [])\n skipp_ids = skipp_ids if isinstance(skipp_ids, list) else [skipp_ids]\n skipp_export = True if skipp_only_bind_record_id in skipp_ids else False\n\n # Skipp exports only if the model is in 'connector_no_export'\n elif skipp_only_bind_model:\n skipp_export = True if skipp_only_bind_model in connector_no_export else False\n\n # Unexpected value type for 'connector_no_export'\n else:\n raise TypeError(\"'connector_no_export' (%s) must be of type 'True' or 'dict'!\" % connector_no_export)\n\n if skipp_export:\n _logger.info(\"SKIPP EXPORT OR TASK FOR BINDING RECORD ('%s', '%s') \"\n \"because of 'connector_no_export' (%s) in context!\"\n \"\" % (skipp_only_bind_model, skipp_only_bind_record_id, connector_no_export)\n )\n\n return skipp_export\n\n\ndef cmp_payloads(payload_a, payload_b):\n \"\"\" Compares two GetResponse payloads \"\"\"\n payloads = {'a': deepcopy(payload_a), 'b': deepcopy(payload_b)}\n\n # WARNING: We have to convert the tags-list and custom-field-list to dicts because python cmp()\n # would compare the position of list items also and not just if the same items are in the lists!\n for key, payload in payloads.iteritems():\n assert isinstance(payload, dict), 'The payload to compare must be of type dict! %s' % payload\n # prep_compare_date = deepcopy(compare_data)\n if 'customFieldValues' in payload:\n payload['customFieldValues'] = {f['customFieldId']: f['value'] for f in payload['customFieldValues']}\n if 'tags' in payload:\n payload['tags'] = {t['tagId']: True for t in payload['tags']}\n\n result = cmp(payloads['a'], payloads['b'])\n return result\n", "sub_path": "addons-own/fso_con_getresponse/models/helper_connector.py", "file_name": "helper_connector.py", "file_ext": "py", "file_size_in_byte": 4651, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "openerp.addons.connector.connector.ConnectorEnvironment", "line_number": 17, "usage_type": "call"}, {"api_name": "openerp.addons.connector.checkpoint.checkpoint.add_checkpoint", "line_number": 42, "usage_type": "call"}, {"api_name": "openerp.addons.connector.checkpoint.checkpoint", "line_number": 42, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "640865178", "text": "# -*- coding: utf-8 -*-\n\"\"\"Models helper\n\nThese are helper functions for models.\n\n\"\"\"\n\nimport torch.optim as optim\nimport torch.nn as nn\n\nfrom configs.supported_info import SUPPORTED_OPTIMIZER, SUPPORTED_CRITERION\n\n\ndef get_optimizer(cfg: object, network: object) -> object:\n \"\"\"Get optimizer function\n\n This is function to get optimizer.\n\n Args:\n cfg: Config of optimizer.\n network: Network of model.\n\n Returns:\n Optimizer object.\n\n Raises:\n NotImplementedError: If the optimizer you want to use is not suppoeted.\n\n \"\"\"\n \n optimizer_name = cfg.name\n\n if not optimizer_name:\n return None\n\n if optimizer_name not in SUPPORTED_OPTIMIZER:\n raise NotImplementedError('The optimizer is not supported.')\n\n if optimizer_name == \"adam\":\n return optim.Adam(network.parameters(),\n lr=cfg.lr,\n weight_decay=cfg.decay)\n\n\ndef get_criterion(cfg: object) -> object:\n \"\"\"Get criterion function\n\n This is function to get criterion.\n\n Args:\n cfg: Config of criterion.\n\n Returns:\n Criterion object.\n\n Raises:\n NotImplementedError: If the criterion you want to use is not suppoeted.\n\n \"\"\"\n \n criterion_name = cfg.name\n\n if not criterion_name:\n return None\n\n if criterion_name not in SUPPORTED_CRITERION:\n raise NotImplementedError('The loss function is not supported.')\n\n if criterion_name == \"cross_entropy\":\n return nn.CrossEntropyLoss()\n\n elif criterion_name == \"nll_loss\":\n return nn.NLLLoss()", "sub_path": "models/helper.py", "file_name": "helper.py", "file_ext": "py", "file_size_in_byte": 1594, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "configs.supported_info.SUPPORTED_OPTIMIZER", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 40, "usage_type": "name"}, {"api_name": "configs.supported_info.SUPPORTED_CRITERION", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.NLLLoss", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}]} +{"seq_id": "268995702", "text": "import gym\nimport h5py\nimport tensorflow as tf\nimport numpy as np \nfrom RL_brain_PP_noreduct import PolicyGradient\nimport matplotlib.pyplot as plt\nimport os\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\nDISPLAY_REWARD_THRESHOLD = 400 # renders environment if total episode reward is greater then this threshold\nRENDER = False # rendering wastes time\nEPISODE_n = 40000\n\nrewards = []\n\nenv = gym.make('Pong-v0')\nenv.seed(1) # reproducible, general Policy gradient has high variance\nenv = env.unwrapped\n\ndef prepro(observation):\n observation = observation[35:195]\n observation = observation[::2, ::2, 0]\n observation[observation==144] = 0\n observation[observation != 0] = 1\n \n return observation.astype(np.float).ravel()\n\n\nobservation_space = prepro(env.reset())\n\nRL = PolicyGradient(\n n_actions=env.action_space.n,\n n_features=observation_space.shape[0],\n learning_rate=0.02,\n reward_decay=0.99,\n output_graph=True,\n)\ntimes = 0\nfor i_episode in range(EPISODE_n):\n\n observation = env.reset()\n \n while True:\n observation = prepro(observation)\n if RENDER: env.render()\n #print(observation.shape)\n action = RL.choose_action(observation)\n \n observation_, reward, done, info = env.step(action)\n #print (reward)\n \n RL.store_transition(observation, action, reward)\n \n if done:\n ep_rs_sum = sum(RL.ep_rs)\n\n if 'running_reward' not in globals():\n running_reward = ep_rs_sum\n else:\n running_reward = running_reward * 0.99 + ep_rs_sum * 0.01\n \n rewards.append(running_reward) \n\n if running_reward > DISPLAY_REWARD_THRESHOLD: RENDER = True # rendering\n print(\"episode:\", i_episode, \" reward:\", int(running_reward))\n\n vt = RL.learn()\n \n if i_episode%10 == 9:\n with open('RL_brain_reward/reward_log_noreward'+str(EPISODE_n)+'e.txt','a') as f:\n f.write(\"episode:{:4d}, average reward:{:+.5f}\\n\".format(i_episode+1, sum(rewards[-30:])/30))\n '''if i_episode == 0:\n plt.plot(vt) # plot the episode vt\n plt.xlabel('episode steps')\n plt.ylabel('normalized state-action value')\n plt.show()'''\n '''init = tf.global_variables_initializer()\n saver = tf.train.Saver()\n with tf.Session() as sess:\n sess.run(init)\n save_path = saver.save(sess, \"RL_brain_model/episode\"+str(i_episode+2)+\".ckpt\")'''\n break\n\n observation = observation_\n \n #save model\n '''if i_episode%10==9:\n #RL.save('RL_brain_model/episode'+str(i_episode+1)+\".h9\")\n init = tf.global_variables_initializer()\n saver = tf.train.Saver()\n with tf.Session() as sess:\n sess.run(init)\n save_path = saver.save(sess, \"RL_brain_model/episode\"+str(i_episode+2)+\".ckpt\")'''\n", "sub_path": "hw4/hw4-1/hw4_1/run_Pong_v0.py", "file_name": "run_Pong_v0.py", "file_ext": "py", "file_size_in_byte": 3063, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.environ", "line_number": 9, "usage_type": "attribute"}, {"api_name": "gym.make", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 27, "usage_type": "attribute"}, {"api_name": "RL_brain_PP_noreduct.PolicyGradient", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "177131862", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nML of Tc by SVM\r\n\r\nCreated on Tue Jul 24 13:35:37 2018\r\n\r\n@author: Akitaka\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom time import time\r\nfrom matplotlib import pyplot as plt\r\nfrom sklearn.model_selection import GridSearchCV\r\nfrom sklearn.model_selection import ShuffleSplit\r\nfrom sklearn.svm import SVR\r\nfrom pymatgen import periodic_table, Composition\r\nfrom sklearn.pipeline import Pipeline\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom my_library import print_gscv_score\r\nfrom my_library import print_score\r\n\r\ndef read_xy_csv(name): \r\n data = np.array(pd.read_csv(filepath_or_buffer=name,\r\n index_col=0, header=0, sep=','))[:,:]\r\n y = data[:,0]\r\n X = data[:,1:]\r\n return X, y\r\n\r\nprint(__doc__)\r\n\r\nstart = time()\r\n\r\nprint('')\r\nprint('read train & test data from csv file')\r\nprint('')\r\ntrain_file = 'tc_train.csv'\r\nX_train, y_train = read_xy_csv(train_file)\r\ntest_file = 'tc_test.csv'\r\nX_test, y_test = read_xy_csv(test_file)\r\n\r\n# print statistics of database\r\nltest=True\r\nif(ltest):\r\n data = pd.read_csv(filepath_or_buffer='tc_train.csv',\r\n index_col=0, header=0, sep=',')\r\n data.drop('Z2', axis=1, inplace=True)\r\n print(data.describe())\r\n\r\nltest=True\r\nif(ltest):\r\n # range_c = 2**np.arange( -5, 10, dtype=float)\r\n # range_e = 2**np.arange( -10, 0, dtype=float)\r\n # range_g = 2**np.arange( -20, 10, dtype=float)\r\n range_c = 2**np.arange( 10, 11, dtype=float)\r\n range_e = 2**np.arange( -1, 1, dtype=float)\r\n range_g = 2**np.arange( 10, 11, dtype=float)\r\n \r\n print()\r\n print('Search range')\r\n print('c = ', range_c[0], ' ... ',range_c[len(range_c)-1])\r\n print('e = ', range_e[0], ' ... ',range_e[len(range_e)-1])\r\n print('g = ', range_g[0], ' ... ',range_g[len(range_g)-1])\r\n print()\r\n \r\n # Set the parameters by cross-validation\r\n pipe = Pipeline([\r\n ('scaler', StandardScaler()),\r\n ('svr', SVR())\r\n ])\r\n \r\n param_grid = [\r\n {'svr__kernel': ['rbf'], 'svr__gamma': range_g,\r\n 'svr__C': range_c,'svr__epsilon': range_e},\r\n ]\r\n \r\n cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\r\n \r\n score='neg_mean_absolute_error'\r\n \r\n gscv = GridSearchCV(pipe, param_grid, cv=cv, scoring=score)\r\n gscv.fit(X_train, y_train)\r\n print_gscv_score(gscv)\r\n \r\n y_pred = gscv.predict(X_train)\r\n print('train data: ',end=\"\")\r\n print_score(y_train, y_pred)\r\n \r\n \r\n # yy-plot (train)\r\n y_pred = gscv.predict(X_train)\r\n plt.figure(figsize=(9,4))\r\n plt.subplot(1,2,1)\r\n plt.title(\"yy-plot (train)\")\r\n plt.scatter(y_train, y_pred)\r\n max_y = np.max(np.array([y_train, y_pred]))\r\n min_y = np.min(np.array([y_train, y_pred]))\r\n ylowlim = min_y - 0.05 * (max_y - min_y)\r\n yupplim = max_y + 0.05 * (max_y - min_y)\r\n plt.plot([ylowlim, yupplim],\r\n [ylowlim, yupplim],'k-')\r\n plt.ylim( ylowlim, yupplim)\r\n plt.xlim( ylowlim, yupplim)\r\n plt.xlabel(\"y_observed\")\r\n plt.ylabel(\"y_predicted\")\r\n \r\n # Check: error follows a normal distribution?\r\n # ref:\r\n # http://univprof.com/archives/16-07-20-4857140.html\r\n plt.subplot(1,2,2)\r\n y_pred = gscv.predict(X_train)\r\n error = np.array(y_pred-y_train)\r\n plt.hist(error)\r\n plt.title(\"Gaussian? (train)\")\r\n plt.xlabel('prediction error')\r\n plt.ylabel('Frequency')\r\n plt.tight_layout()\r\n plt.show()\r\n \r\n # step 3. predict\r\n y_pred = gscv.predict(X_test)\r\n\r\n data = []\r\n output = 'test2.csv'\r\n for i in range(len(X_test)):\r\n if(y_pred[i]> 100):\r\n satom1 = periodic_table.get_el_sp(int(X_test[i][0])) \r\n satom2 = periodic_table.get_el_sp(int(X_test[i][1])) \r\n natom1 = int(X_test[i][2])\r\n natom2 = int(X_test[i][3])\r\n str_mat = str(satom1) + str(natom1) + str(satom2) + str(natom2)\r\n formula = Composition(str_mat).reduced_formula\r\n p = int(X_test[i][4])\r\n tc = int(y_pred[i])\r\n temp = (formula, p, tc)\r\n data.append(temp)\r\n properties=['formula','P', 'Tc' ]\r\n df = pd.DataFrame(data, columns=properties)\r\n df.sort_values('Tc', ascending=False, inplace=True)\r\n df.to_csv(output)\r\n print('Predicted Tc is written in file {}'.format(output))\r\n \r\n print('{:.2f} seconds '.format(time() - start))\r\n \r\n", "sub_path": "0724/test2_Tc_SVM.py", "file_name": "test2_Tc_SVM.py", "file_ext": "py", "file_size_in_byte": 4503, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "time.time", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 67, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.svm.SVR", "line_number": 69, "usage_type": "call"}, {"api_name": "sklearn.model_selection.ShuffleSplit", "line_number": 77, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 81, "usage_type": "call"}, {"api_name": "my_library.print_gscv_score", "line_number": 83, "usage_type": "call"}, {"api_name": "my_library.print_score", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "pymatgen.periodic_table.get_el_sp", "line_number": 127, "usage_type": "call"}, {"api_name": "pymatgen.periodic_table", "line_number": 127, "usage_type": "name"}, {"api_name": "pymatgen.periodic_table.get_el_sp", "line_number": 128, "usage_type": "call"}, {"api_name": "pymatgen.periodic_table", "line_number": 128, "usage_type": "name"}, {"api_name": "pymatgen.Composition", "line_number": 132, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 138, "usage_type": "call"}, {"api_name": "time.time", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "624149640", "text": "from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n #example /shedule\n url(r'^$', views.index, name='index'),\n #example /shedule/train2/\n url(r'^train(?P[0-9]+)/$', views.detail, name='detail'),\n #example /shedule/new-train/\n url(r'^new-train/$', views.new_train, name='new_train'),\n url(r'^new-train/result/$', views.result, name='result'),\n url(r'^weeks-schedule/$', views.weeks_schedule, name='weeks_schedule'),\n url(r'^select-route/$', views.select_route, name='select_route'),\n url(r'^select-route/change-data/$', views.change_data, name='change_data'),\n url(r'^select-route/change-data/change-results/$', views.change_results, name='change_results'),\n url(r'^weeks-schedule/view-routes/$', views.view_routes, name='view_routes'),\n url(r'^all-route/$', views.all_route, name='all_route'),\n\n]\n", "sub_path": "trains_schedule/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 866, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "410011199", "text": "from PIL import Image\nimport face_recognition\nimport cv2\nimport os\nfrom imutils import paths\n\n\nimagePaths = list(paths.list_images(\"Dataset_Raw\"))\nfor (i, imagePath) in enumerate(imagePaths):\n print(\"[INFO] processing image {}/{}\".format(i + 1,\n len(imagePaths)))\n face = imagePath.split(os.path.sep)[-1]\n name = imagePath.split(os.path.sep)[-2]\n print(\"name ={}, face={}\".format(name,face))\n image = cv2.imread(imagePath)\n rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n face_locations = face_recognition.face_locations(rgb,model=\"hog\")\n for face_location in face_locations:\n top, right, bottom, left = face_location\n face_image = image[top:bottom, left:right]\n im2 = face_image\n height, width = im2.shape[:2]\n max_height = 50000\n max_width = 50000\n if max_height < height or max_width < width:\n # get scaling factor\n scaling_factor = max_height / float(height)\n if max_width / float(width) < scaling_factor:\n scaling_factor = max_width / float(width)\n # resize image\n thumbnail = cv2.resize(im2, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)\n else:\n thumbnail = im2\n path = \"Dataset_OnlyFace/\"+name\n print(path)\n if not os.path.exists(path):\n os.makedirs(path)\n cv2.imwrite(path+\"/\"+face, thumbnail);\n\n", "sub_path": "GotFace.py", "file_name": "GotFace.py", "file_ext": "py", "file_size_in_byte": 1478, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "imutils.paths.list_images", "line_number": 8, "usage_type": "call"}, {"api_name": "imutils.paths", "line_number": 8, "usage_type": "name"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 16, "usage_type": "attribute"}, {"api_name": "face_recognition.face_locations", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "550410355", "text": "import time\nfrom typing import Dict, Tuple, Callable\n\nfrom utils import deprecated, fetch_category_list, fetch_article_images, fetch_articles, log\nfrom utils.database import get_row_count\nfrom utils.parsers import parse_attributes, parse_maximum_integer, parse_integer, parse_boolean, clean_links, \\\n parse_loot, \\\n parse_min_max, parse_loot_statistics\n\ncreatures = []\n\n\ndef fetch_creature_list():\n start_time = time.time()\n print(\"Fetching creature list... \")\n fetch_category_list(\"Category:Creatures\", creatures)\n print(f\"\\t{len(creatures):,} found\")\n\n for d in deprecated:\n if d in creatures:\n creatures.remove(d)\n print(f\"\\t{len(creatures):,} after removing deprecated articles.\")\n print(f\"\\tDone in {time.time()-start_time:.3f} seconds\")\n\n\ndef fetch_creature(con):\n print(\"Fetching creatures information...\")\n start_time = time.time()\n exception_count = 0\n attribute_map = {\n \"name\": (\"title\", lambda x: x),\n \"actualname\": (\"name\", lambda x: x),\n \"hp\": (\"hitpoints\", lambda x: parse_integer(x, None)),\n \"exp\": (\"experience\", lambda x: parse_integer(x, None)),\n \"maxdmg\": (\"max_damage\", lambda x: parse_maximum_integer(x)),\n \"summon\": (\"summon\", lambda x: parse_integer(x)),\n \"convince\": (\"convince\", lambda x: parse_integer(x)),\n \"illusionable\": (\"illusionable\", lambda x: parse_boolean(x)),\n \"pushable\": (\"pushable\", lambda x: parse_boolean(x)),\n \"senseinvis\": (\"see_invisible\", lambda x: parse_boolean(x)),\n \"paraimmune\": (\"paralysable\", lambda x: parse_boolean(x)),\n \"isboss\": (\"boss\", lambda x: parse_boolean(x)),\n \"physicalDmgMod\": (\"physical\", lambda x: parse_integer(x)),\n \"earthDmgMod\": (\"earth\", lambda x: parse_integer(x)),\n \"fireDmgMod\": (\"fire\", lambda x: parse_integer(x)),\n \"iceDmgMod\": (\"ice\", lambda x: parse_integer(x)),\n \"energyDmgMod\": (\"energy\", lambda x: parse_integer(x)),\n \"deathDmgMod\": (\"death\", lambda x: parse_integer(x)),\n \"holyDmgMod\": (\"holy\", lambda x: parse_integer(x)),\n \"drownDmgMod\": (\"drown\", lambda x: parse_integer(x)),\n \"hpDrainDmgMod\": (\"hpdrain\", lambda x: parse_integer(x)),\n \"abilities\": (\"abilities\", lambda x: clean_links(x)),\n \"implemented\": (\"version\", lambda x: x)\n } # type: Dict[str, Tuple[str, Callable]]\n c = con.cursor()\n for article_id, article in fetch_articles(creatures):\n try:\n content = article[\"revisions\"][0][\"*\"]\n if \"{{Infobox Creature\" not in content:\n # Skipping page without Infoboxes\n continue\n creature = parse_attributes(content)\n columns = []\n values = []\n if \"actualname\" not in creature:\n creature[\"actualname\"] = creature[\"name\"]\n for attribute, value in creature.items():\n if attribute not in attribute_map:\n continue\n column, func = attribute_map[attribute]\n columns.append(column)\n values.append(func(value))\n c.execute(f\"INSERT INTO creatures({','.join(columns)}) VALUES({','.join(['?']*len(values))})\", values)\n creature_id = c.lastrowid\n # Add loot from creature's article\n if \"loot\" in creature:\n loot = parse_loot(creature[\"loot\"])\n loot_items = []\n for item in loot:\n c.execute(\"SELECT id FROM items WHERE title = ?\", (item[1],))\n result = c.fetchone()\n if result is None:\n continue\n item_id = result[0]\n if not item[0]:\n _min, _max = 0, 1\n else:\n _min, _max = parse_min_max(item[0])\n loot_items.append((creature_id, item_id, _min, _max))\n c.executemany(f\"INSERT INTO creatures_drops(creature_id, item_id, min, max) VALUES(?,?,?,?)\",\n loot_items)\n except Exception:\n log.exception(f\"Unknown exception found for {article['title']}\")\n exception_count += 1\n continue\n con.commit()\n c.close()\n rows = get_row_count(con, \"creatures\")\n drops_rows = get_row_count(con, \"creatures_drops\")\n print(f\"\\t{rows:,} entries added to table\")\n if exception_count:\n print(f\"\\t{exception_count:,} exceptions found, check errors.log for more information.\")\n print(f\"\\t{drops_rows:,} item drops added.\")\n print(f\"\\tDone in {time.time()-start_time:.3f} seconds.\")\n\n\ndef fetch_drop_statistics(con):\n print(\"Fetching creature loot statistics...\")\n start_time = time.time()\n added = 0\n c = con.cursor()\n for article_id, article in fetch_articles([f\"Loot Statistics:{c}\" for c in creatures]):\n if \"missing\" in article:\n continue\n content = article[\"revisions\"][0][\"*\"]\n creature_name = article[\"title\"].replace(\"Loot Statistics:\", \"\")\n c.execute(\"SELECT id from creatures WHERE title LIKE ?\", (creature_name,))\n result = c.fetchone()\n if result is None:\n # This could happen if a creature's article was deleted but its Loot Statistics weren't\n continue\n creature_id = result[0]\n # Most loot statistics contain stats for older version,we only care about the latest version\n try:\n start = content.index(\"Loot2\")\n end = content.index(\"}}\", start)\n content = content[start:end]\n except ValueError:\n # Article contains no loot\n continue\n kills, loot_stats = parse_loot_statistics(content)\n loot_items = []\n for item, times, amount in loot_stats:\n c.execute(\"SELECT id FROM items WHERE title LIKE ?\", (item,))\n result = c.fetchone()\n if result is None:\n continue\n item_id = result[0]\n percentage = min(int(times) / kills * 100, 100)\n _min, _max = parse_min_max(amount)\n loot_items.append((creature_id, item_id, percentage, _min, _max))\n # We delete any duplicate record that was added from the creature's article's loot if it exists\n c.execute(\"DELETE FROM creatures_drops WHERE creature_id = ? AND item_id = ?\", (creature_id, item_id))\n c.executemany(f\"INSERT INTO creatures_drops(creature_id, item_id, chance, min, max) VALUES(?,?,?,?,?)\",\n loot_items)\n added += c.rowcount\n con.commit()\n c.close()\n print(f\"\\t{added:,} entries added or modified.\")\n print(f\"\\tDone in {time.time()-start_time:.3f} seconds.\")\n\n\ndef fetch_creature_images(con):\n print(\"Fetching creature images...\")\n start_time = time.time()\n fetch_count, cache_count, missing_count, failed_count = fetch_article_images(con, creatures, \"creatures\")\n print(f\"\\tFetched {fetch_count:,} images, loaded {cache_count:,} from cache.\")\n print(f\"\\t{missing_count:,} without image.\")\n if failed_count > 0:\n print(f\"\\t{failed_count:,} images failed fetching.\")\n print(f\"\\tDone in {time.time()-start_time:.3f} seconds.\")\n", "sub_path": "utils/creatures.py", "file_name": "creatures.py", "file_ext": "py", "file_size_in_byte": 7256, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.time", "line_number": 14, "usage_type": "call"}, {"api_name": "utils.fetch_category_list", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.deprecated", "line_number": 19, "usage_type": "name"}, {"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}, {"api_name": "utils.parsers.parse_integer", "line_number": 33, "usage_type": "call"}, {"api_name": "utils.parsers.parse_integer", "line_number": 34, "usage_type": "call"}, {"api_name": "utils.parsers.parse_maximum_integer", "line_number": 35, "usage_type": "call"}, {"api_name": "utils.parsers.parse_integer", "line_number": 36, "usage_type": "call"}, {"api_name": "utils.parsers.parse_integer", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.parsers.parse_boolean", "line_number": 38, "usage_type": "call"}, {"api_name": "utils.parsers.parse_boolean", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.parsers.parse_boolean", "line_number": 40, "usage_type": "call"}, {"api_name": "utils.parsers.parse_boolean", "line_number": 41, "usage_type": "call"}, {"api_name": "utils.parsers.parse_boolean", "line_number": 42, "usage_type": "call"}, {"api_name": "utils.parsers.parse_integer", "line_number": 43, "usage_type": "call"}, {"api_name": "utils.parsers.parse_integer", "line_number": 44, "usage_type": "call"}, {"api_name": "utils.parsers.parse_integer", "line_number": 45, "usage_type": "call"}, {"api_name": "utils.parsers.parse_integer", "line_number": 46, "usage_type": "call"}, {"api_name": "utils.parsers.parse_integer", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.parsers.parse_integer", "line_number": 48, "usage_type": "call"}, {"api_name": "utils.parsers.parse_integer", "line_number": 49, "usage_type": "call"}, {"api_name": "utils.parsers.parse_integer", "line_number": 50, "usage_type": "call"}, {"api_name": "utils.parsers.parse_integer", "line_number": 51, "usage_type": "call"}, {"api_name": "utils.parsers.clean_links", "line_number": 52, "usage_type": "call"}, {"api_name": "utils.fetch_articles", "line_number": 56, "usage_type": "call"}, {"api_name": "utils.parsers.parse_attributes", "line_number": 62, "usage_type": "call"}, {"api_name": "utils.parsers.parse_loot", "line_number": 77, "usage_type": "call"}, {"api_name": "utils.parsers.parse_min_max", "line_number": 88, "usage_type": "call"}, {"api_name": "utils.log.exception", "line_number": 93, "usage_type": "call"}, {"api_name": "utils.log", "line_number": 93, "usage_type": "name"}, {"api_name": "utils.database.get_row_count", "line_number": 98, "usage_type": "call"}, {"api_name": "utils.database.get_row_count", "line_number": 99, "usage_type": "call"}, {"api_name": "time.time", "line_number": 104, "usage_type": "call"}, {"api_name": "time.time", "line_number": 109, "usage_type": "call"}, {"api_name": "utils.fetch_articles", "line_number": 112, "usage_type": "call"}, {"api_name": "utils.parsers.parse_loot_statistics", "line_number": 131, "usage_type": "call"}, {"api_name": "utils.parsers.parse_min_max", "line_number": 140, "usage_type": "call"}, {"api_name": "time.time", "line_number": 150, "usage_type": "call"}, {"api_name": "time.time", "line_number": 155, "usage_type": "call"}, {"api_name": "utils.fetch_article_images", "line_number": 156, "usage_type": "call"}, {"api_name": "time.time", "line_number": 161, "usage_type": "call"}]} +{"seq_id": "437392099", "text": "import matplotlib.pyplot as plt\n# import matplotlib.colors\n\nx=[2,4,6,1,8]\ny=[1,3,4,6,7]\n\nplt.plot(x,y, color='green',linestyle='dashed', linewidth = 3,marker='o', markerfacecolor='blue', markersize=12)\n# setting x and y axis range\nplt.ylim(1,8)\nplt.xlim(1,8)\n\nplt.xlabel(\"x-axis\")\nplt.ylabel(\"y-axis\")\nplt.show()\n", "sub_path": "customisationOfPlot.py", "file_name": "customisationOfPlot.py", "file_ext": "py", "file_size_in_byte": 313, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "91236121", "text": "# type: ignore\nimport logging\nimport colorlog\nimport pytest\n\n\nlogger = logging.getLogger(__name__)\nhandler = colorlog.StreamHandler()\nhandler.setFormatter(\n colorlog.ColoredFormatter(\n \"(%(asctime)s) [%(log_color)s%(levelname)s] | %(name)s | %(message)s [%(threadName)-10s]\"\n )\n)\n\n# get root logger\nlogger = logging.getLogger()\nlogger.handlers = []\nlogger.addHandler(handler)\nlogger.setLevel(logging.DEBUG)\n\n\n# flake8 plugin is way too verbose\ndef pytest_configure(config):\n logging.getLogger(\"flake8\").setLevel(logging.WARN)\n logging.getLogger(\"bandit\").setLevel(logging.WARN)\n logging.getLogger(\"blib2to3\").setLevel(logging.WARN)\n\n\n@pytest.fixture\ndef Gst(): # noqa\n import gi\n\n gi.require_version(\"Gst\", \"1.0\")\n from gi.repository import Gst as GstCls\n\n GstCls.init(None)\n return GstCls\n\n\n@pytest.fixture\ndef player(Gst):\n from mixtape.players import AsyncPlayer\n\n return AsyncPlayer\n", "sub_path": "tests/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 931, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "colorlog.StreamHandler", "line_number": 8, "usage_type": "call"}, {"api_name": "colorlog.ColoredFormatter", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 19, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.WARN", "line_number": 24, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.WARN", "line_number": 25, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.WARN", "line_number": 26, "usage_type": "attribute"}, {"api_name": "gi.require_version", "line_number": 33, "usage_type": "call"}, {"api_name": "gi.repository.Gst.init", "line_number": 36, "usage_type": "call"}, {"api_name": "gi.repository.Gst", "line_number": 36, "usage_type": "name"}, {"api_name": "gi.repository.Gst", "line_number": 37, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 29, "usage_type": "attribute"}, {"api_name": "mixtape.players.AsyncPlayer", "line_number": 44, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 40, "usage_type": "attribute"}]} +{"seq_id": "456756498", "text": "def BACF_optimized(params):\n \"\"\"\n This function implements the background-aware correlation filter visual object tracker.\n :param params: dict: contains the following keys: 'video_path', 't_features', 't_global', 'search_area_shape',\n 'search_area_scale', 'filter_max_area', 'learning_rate', 'output_sigma_factor', 'interpolate_response',\n 'newton_iterations', 'number_of_scales', 'scale_step', 'wsize', 'init_pos', 's_frames', 'no_fram', 'seq_st_frame',\n 'seq_en_frame', 'admm_iterations', 'admm_lambda', 'visualization'\n :return: dict: results containing bounding box position in the key 'res' and the frame rate in the key 'fps'\n \"\"\"\n import math as m\n import numpy as np\n import cv2\n import time\n import matplotlib.pyplot as plt\n import matplotlib.patches as patches\n\n from resizeDFT2 import resizeDFT2\n from get_pixels import get_pixels\n from get_features import get_features\n from resp_newton import resp_newton\n from get_subwindow_no_window import get_subwindow_no_window\n\n # Setting parameters\n search_area_scale = params['search_area_scale'] # size of training/detection area proportional to the target size\n output_sigma_factor = params['output_sigma_factor']\n learning_rate = params['learning_rate']\n filter_max_area = params['filter_max_area']\n nScales = params['number_of_scales'] # number of scale resolutions to check\n scale_step = params['scale_step']\n interpolate_response = params['interpolate_response']\n\n features = params['t_features']\n video_path = params['video_path']\n s_frames = params['s_frames']\n pos = np.floor(params['init_pos']) # initial centre-point (y by x) of target bounding box\n target_sz = np.floor(params['wsize']) # initial height and height of target bounding box\n\n visualization = params['visualization']\n num_frames = params['no_fram']\n init_target_sz = target_sz\n\n # Set the features ratio to the feature-cell size\n featureRatio = params['t_global']['cell_size']\n search_area = np.prod(init_target_sz / featureRatio * search_area_scale)\n\n # when the number of cells are small, choose a smaller cell size\n if 'cell_selection_thresh' in params['t_global']:\n if search_area < params['t_global']['cell_selection_thresh'] * filter_max_area:\n params['t_global']['cell_size'] = min(featureRatio,\n max(1, np.ceil(m.sqrt(np.prod(init_target_sz * search_area_scale) /\n (params['t_global']['cell_selection_thresh'] *\n filter_max_area)))))\n featureRatio = params['t_global']['cell_size']\n search_area = np.prod(init_target_sz / featureRatio * search_area_scale)\n\n global_feat_params = params['t_global']\n\n if search_area > filter_max_area:\n currentScaleFactor = m.sqrt(search_area / filter_max_area)\n else:\n currentScaleFactor = 1.0\n\n # target size at the initial scale\n base_target_sz = target_sz / currentScaleFactor\n\n # window size, taking padding into account\n if params['search_area_shape'] == 'proportional':\n sz = np.floor(base_target_sz * search_area_scale) # proportional area, same aspect ratio as the target\n elif params['search_area_shape'] == 'square':\n sz = np.tile(m.sqrt(np.prod(base_target_sz * search_area_scale)), [1, 2]) # ignores target aspect ratio\n elif params['search_area_shape'] == 'fix_padding':\n sz = base_target_sz + m.sqrt(np.prod(base_target_sz * search_area_scale) +\n (base_target_sz[0] - base_target_sz[1]) / 4) - \\\n sum(base_target_sz) / 2 # const padding\n else:\n raise ValueError('Unknown \"search_area_shape\". Must be \"proportional\", \"square\", or \"fix_padding\".')\n\n # set the size to exactly match the cell size\n sz = np.round(sz[0] / featureRatio) * featureRatio\n use_sz = np.floor(sz / featureRatio)\n\n # construct the label function- correlation output, 2D gaussian function, with a peak located upon the target\n # np.roll acts a circular shift operator. This is used to compute all possible patches in the entire frame\n output_sigma = m.sqrt(np.prod(np.floor(base_target_sz / featureRatio))) * output_sigma_factor\n rg = np.roll(np.arange(-1 * np.floor((use_sz[0] - 1) / 2), np.ceil((use_sz[0] - 1)/2) + 1),\n int(-1 * np.floor((use_sz[0] - 1) / 2)))\n cg = np.roll(np.arange(-1 * np.floor((use_sz[1] - 1) / 2), np.ceil((use_sz[1] - 1) / 2) + 1),\n int(-1 * np.floor((use_sz[1] - 1) / 2)))\n [rs, cs] = np.meshgrid(rg, cg)\n rs = rs.T\n cs = cs.T\n # y is the desired correlation response at each point within the size of the filter\n y = np.exp(-0.5 * ((np.power(rs, 2) + np.power(cs, 2)) / np.power(output_sigma, 2)))\n yf = np.fft.fft2(y) # fast fourier transform of y\n\n if interpolate_response == 1:\n interp_sz = use_sz * featureRatio\n else:\n interp_sz = use_sz\n\n # construct cosine window\n term1 = np.array([np.hanning(use_sz[0])])\n term2 = np.array([np.hanning(use_sz[1])])\n cos_window = np.matmul(term1.T, term2)\n cos_window = cos_window.astype('float32')\n\n # Calculate feature dimension\n try:\n im = cv2.imread(video_path + '/img/' + s_frames[0])\n im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n except:\n try:\n im = cv2.imread(s_frames[0])\n im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n except:\n im = cv2.imread(video_path + '/' + s_frames[0])\n im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n\n # Check to see if it is a color image or grayscale\n if im.shape[2] == 3:\n if np.all(np.equal(im[:,:,0], im[:,:,1])):\n colorImage = False\n else:\n colorImage = True\n else:\n colorImage = False\n \n if im.shape[2] > 1 and colorImage is False:\n im = im[:,:,0]\n im = im.reshape([im.shape[0], im.shape[1], 1])\n\n # create scale factors to check for object at various scale resolutions\n if nScales > 0:\n scale_exp = np.arange(-1 * np.floor((nScales - 1) / 2), np.ceil((nScales - 1) / 2) + 1)\n scaleFactors = scale_step ** scale_exp\n min_scale_factor = scale_step ** np.ceil(m.log(max(np.divide(5, sz))) / m.log(scale_step))\n max_scale_factor = scale_step ** np.floor(m.log(min(np.divide([im.shape[0],\n im.shape[1]],\n base_target_sz))) / m.log(scale_step))\n\n if interpolate_response >= 3:\n # pre-computes the grid that is used for score optimization\n ky = np.roll(np.arange(-1 * np.floor((use_sz[0] - 1) / 2), np.ceil((use_sz[0] - 1) / 2) + 1),\n int(-1 * np.floor((use_sz[0] - 1) / 2)))\n kx = np.roll(np.arange(-1 * np.floor((use_sz[1] - 1) / 2), np.ceil((use_sz[1] - 1) / 2) + 1),\n int(-1 * np.floor((use_sz[1] - 1) / 2))) # --> SAME AS MATLAB\n kx = kx.T\n newton_iterations = params['newton_iterations']\n\n # initialize the projection matrix (x,y,h,w)\n rect_position = np.zeros([num_frames, 4])\n\n # allocate memory for multi-scale tracking\n multires_pixel_template = np.zeros([int(sz[0]), int(sz[1]), im.shape[2], nScales], dtype=np.uint8)\n small_filter_sz = np.floor(base_target_sz / featureRatio)\n start_time = time.time()\n\n loop_frame = 0\n for frame in range(0, len(s_frames)):\n try:\n im = cv2.imread(video_path + '/img/' + s_frames[frame])\n im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n except:\n try:\n im = cv2.imread(s_frames[frame])\n im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n except:\n im = cv2.imread(video_path + '/' + s_frames[frame])\n im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n\n if im.shape[2] > 1 and colorImage is False:\n im = im[:, :, 0]\n im = im.reshape([im.shape[0], im.shape[1], 1])\n\n # do not estimate translation and scaling on the first frame, since we are just initializing the tracker\n if frame > 0:\n\n # The filter is applied on multiple resolutions of the search area. This is used to determine any changes in\n # scale of the target object\n for scale_ind in range(0, nScales):\n multires_pixel_template[:, :, :, scale_ind] = \\\n get_pixels(im, pos, np.round(sz * currentScaleFactor * scaleFactors[scale_ind]), sz)\n\n feat_term2, _ = get_features(multires_pixel_template, features, global_feat_params, None)\n xtf = np.zeros(feat_term2.shape, dtype=complex)\n for p in range(0, feat_term2.shape[2]):\n for n in range(0, feat_term2.shape[3]):\n xtf[:,:,p,n] = np.fft.fft2(np.multiply(feat_term2[:,:,p,n], cos_window[:, :]))\n responsef = np.sum(np.multiply(np.conj(g_f)[:, :, :, None], xtf), axis=2)\n\n # if we undersampled features, we want to interpolate the response to have the same size as the image patch\n if interpolate_response == 2:\n # use dynamic interp size\n interp_sz = np.floor(y.shape * featureRatio * currentScaleFactor)\n\n responsef_padded = resizeDFT2(responsef, interp_sz)\n\n # Get the response in the spatial domain\n response = np.zeros(responsef_padded.shape)\n for n in range(0, responsef.shape[2]):\n response[:,:,n] = np.real(np.fft.ifft2(responsef_padded[:,:,n])) # MAY HAVE AN ISSUE HERE NOT BEING SYMMETRIC -- therefore added real --> SAME AS MATLAB :D :)\n\n # find maximum peak\n if interpolate_response == 3:\n raise ValueError('Invalid parameter value for \"interpolate_response\"')\n elif interpolate_response == 4:\n [disp_row, disp_col, sind] = resp_newton(response, responsef_padded, newton_iterations, ky, kx, use_sz)\n\n # Check if the target has completely gone off the frame\n if np.isnan(disp_row) or np.isnan(disp_col):\n break\n\n # calculate translation vector\n if interpolate_response == 0 or 3 or 4:\n translation_vec = np.round(np.array([disp_row, disp_col]) * featureRatio *\n currentScaleFactor * scaleFactors[sind])\n elif interpolate_response == 1:\n translation_vec = np.round([disp_row, disp_col] * currentScaleFactor * scaleFactors[sind])\n elif interpolate_response == 2:\n translation_vec = np.round([disp_row, disp_col] * scaleFactors[sind])\n\n # set the scale\n currentScaleFactor = currentScaleFactor * scaleFactors[sind]\n\n # adjust to make sure we are not too large or too small\n if currentScaleFactor < min_scale_factor:\n currentScaleFactor = min_scale_factor\n elif currentScaleFactor > max_scale_factor:\n currentScaleFactor = max_scale_factor\n\n # update position\n old_pos = pos\n pos = pos + translation_vec\n\n # extract training sample image region\n pixels = get_pixels(im, pos, np.round(sz*currentScaleFactor), sz)\n\n # extract features and perform windowing\n feat_term, _ = get_features(pixels, features, global_feat_params, None)\n xf = np.zeros([feat_term.shape[1], feat_term.shape[1], feat_term.shape[2]], dtype=complex)\n for n in range(0, feat_term.shape[2]):\n xf[:, :, n] = np.fft.fft2(np.multiply(feat_term[:, :, n, 0], cos_window[:, :]))\n if frame == 0:\n model_xf = xf\n else:\n model_xf = ((1 - learning_rate) * model_xf) + (learning_rate * xf)\n\n g_f = np.zeros(xf.shape)\n g_f = g_f.astype('float32')\n h_f = g_f\n l_f = g_f\n\n # parameters from the original paper\n mu = 1\n betha = 10\n mumax = 10000\n i = 1\n\n T = np.prod(use_sz)\n S_xx = np.sum(np.multiply(model_xf.conj(), model_xf), axis=2)\n params['admm_iterations'] = 2\n\n while i <= params['admm_iterations']:\n # Solve for G\n B = S_xx + (T * mu)\n S_lx = np.sum(np.multiply(model_xf.conj(), l_f), axis=2)\n S_hx = np.sum(np.multiply(model_xf.conj(), h_f), axis=2)\n\n # equation (10) in original paper\n g_f = (((1 / (T * mu)) * np.multiply(yf[:, :, None], model_xf)) - ((1 / mu) * l_f) + h_f) - \\\n np.divide((((1 / (T * mu)) * np.multiply(model_xf, np.multiply(S_xx, yf)[:, :, None])) -\n ((1 / mu) * np.multiply(model_xf, S_lx[:, :, None])) +\n (np.multiply(model_xf, S_hx[:, :, None]))), B[:, :, None])\n\n # solve for H\n # Equation (6) in original paper\n h = np.zeros([g_f.shape[0], g_f.shape[1], g_f.shape[2]], dtype=complex)\n for n in range(0, g_f.shape[2]):\n h[:, :, n] = (T / ((mu * T) + params['admm_lambda'])) * np.fft.ifft2((mu * g_f[:, :, n]) + l_f[:, :, n])\n\n [sx, sy, h] = get_subwindow_no_window(h, np.floor(use_sz / 2), small_filter_sz)\n t = np.zeros([int(use_sz[0]), int(use_sz[1]), h.shape[2]], dtype=complex)\n t[int(sx[0]):int(sx[-1])+1, int(sy[0]):int(sy[-1])+1, :] = h\n\n h_f = np.zeros([t.shape[1], t.shape[1], t.shape[2]], dtype=complex)\n for n in range(0, t.shape[2]):\n h_f[:, :, n] = np.fft.fft2(t[:, :, n])\n\n # update L\n l_f = l_f + (mu * (g_f - h_f))\n\n # update mu- betha = 10\n mu = min(betha * mu, mumax)\n i = i + 1\n\n target_sz = np.floor(base_target_sz * currentScaleFactor)\n\n # save position and calculate FPS\n rect_position[loop_frame, :] = np.concatenate((pos[1::-1] - np.floor(target_sz[1::-1] / 2), target_sz[1::-1]))\n\n elapsed = time.time() - start_time\n\n # visualization\n if visualization == 1:\n rect_position_vis = np.concatenate((pos[1::-1] - (target_sz[1::-1] / 2), target_sz[1::-1]))\n im_to_show = im / 255\n if im_to_show.shape[2] == 1:\n im_to_show = np.tile(im_to_show, [1, 1, 3]) # if grayscale, ensure it plots image in grayscale\n if frame == 0:\n fig, ax = plt.subplots(1, num='Tracking')\n ax.imshow(im_to_show)\n rect = patches.Rectangle(rect_position_vis[0:2], rect_position_vis[2], rect_position_vis[3],\n linewidth=3, edgecolor='g', facecolor='none')\n ax.add_patch(rect)\n ax.annotate(str(frame + 1), [10, 10], color='c')\n ax.axis('off') # remove axis values\n ax.axis('image') # scale image appropriately\n ax.set_position([0, 0, 1, 1])\n fig.show()\n fig.canvas.draw()\n else:\n ax.clear()\n resp_sz = np.round(sz * currentScaleFactor * scaleFactors[scale_ind])\n xs = np.floor(old_pos[1]) + (np.arange(1, resp_sz[1] + 1)) - np.floor(resp_sz[1] / 2)\n ys = np.floor(old_pos[0]) + (np.arange(1, resp_sz[0] + 1)) - np.floor(resp_sz[0] / 2)\n sc_ind = np.floor((nScales - 1) / 2)\n\n ax.imshow(im_to_show)\n ax.axis('off') # remove axis values\n ax.axis('image') # scale image appropriately\n ax.set_position([0, 0, 1, 1])\n im_overlay = np.fft.fftshift(response[:,:,int(sc_ind)])\n ax.imshow(im_overlay, extent=[xs[0], xs[-1], ys[0], ys[-1]],\n alpha=0.2, cmap='hsv')\n rect.remove() # remove previous rectangle each time\n rect = patches.Rectangle(rect_position_vis[0:2], rect_position_vis[2], rect_position_vis[3],\n linewidth=3, edgecolor='g', facecolor='none')\n ax.add_patch(rect)\n frame_str = '# Frame: ' + str(loop_frame + 1) + ' / ' + str(num_frames)\n FPS_str = 'FPS: ' + str(1 / (elapsed / loop_frame))\n ax.annotate(frame_str, [20, 30], color='r', backgroundcolor='w')\n ax.annotate(FPS_str, [20, 60], color='r', backgroundcolor='w', fontsize=16)\n fig.show()\n fig.canvas.draw() # used to update the plot in real-time\n\n loop_frame += 1\n\n # Save results\n fps = loop_frame / elapsed\n results = {'res': rect_position, 'fps': fps}\n\n return results\n", "sub_path": "BACF_optimized.py", "file_name": "BACF_optimized.py", "file_ext": "py", "file_size_in_byte": 16864, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.floor", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 50, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 54, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 70, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 70, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 80, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.fft.fft2", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.hanning", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.hanning", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 104, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 110, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 113, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 114, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 114, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 117, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.all", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 136, "usage_type": "call"}, {"api_name": "math.log", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 137, "usage_type": "call"}, {"api_name": "math.log", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 137, "usage_type": "call"}, {"api_name": "math.log", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 154, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 155, "usage_type": "call"}, {"api_name": "time.time", "line_number": 156, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 161, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 162, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 162, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 165, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 166, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 166, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 168, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 169, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 169, "usage_type": "attribute"}, {"api_name": "get_pixels.get_pixels", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 182, "usage_type": "call"}, {"api_name": "get_features.get_features", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.fft.fft2", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 188, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 194, "usage_type": "call"}, {"api_name": "resizeDFT2.resizeDFT2", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.fft.ifft2", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 201, "usage_type": "attribute"}, {"api_name": "resp_newton.resp_newton", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 220, "usage_type": "call"}, {"api_name": "get_pixels.get_pixels", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 236, "usage_type": "call"}, {"api_name": "get_features.get_features", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.fft.fft2", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 242, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.fft.ifft2", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 279, "usage_type": "attribute"}, {"api_name": "get_subwindow_no_window.get_subwindow_no_window", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.fft.fft2", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 287, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 299, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 299, "usage_type": "call"}, {"api_name": "time.time", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 308, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 310, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 310, "usage_type": "name"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 312, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 312, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.fft.fftshift", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 332, "usage_type": "attribute"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 336, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 336, "usage_type": "name"}]} +{"seq_id": "454998334", "text": "#### this code adds the background text files\n#### run: python3 stitchEBeamFiles.py \nimport os\nimport sys\nimport time\nimport pprint\nimport math\nfrom ROOT import *\nfrom collections import OrderedDict\nimport argparse\n\n\ndef main():\n \n \n parser = argparse.ArgumentParser(description='Code to get all BX track info')\n parser.add_argument('-out', action=\"store\", dest=\"outFile\", type=str, default=\"ePlusLaserBkgNewSamplesJan262021_AllBX_trackInfoClean.txt\")\n parser.add_argument('-in', action=\"store\", dest=\"inDir\", type=str, default=\"NewSamplesEBeamOnlyFilesJan262021\")\n parser.add_argument('-bx', action=\"store\", dest=\"nbx\", type=int, default=1.0)\n parser.add_argument('-ident', action=\"store\", dest=\"identifier\", type=str, default=\"ePlusLaserBkgNewSamplesJan262021\")\n args = parser.parse_args()\n \n \n outFile = open(args.outFile, \"w\")\n outFile.write(\"### bxNumber << pdg << track_id << det_id << xx << yy << eneg << ev_weight << vtx_x << vtx_y << vtx_z\\n\")\n inputDir = args.inDir\n \n identifierFileName = args.identifier\n \n bxCounter = 0\n for bx in xrange(1, args.nbx+1):\n if(bx%20==0):print(\"bx processed: \", bx)\n bkgFileName = open(inputDir+\"/\"+identifierFileName+\"_DividedByBX\"+str(bx)+\"_trackInfoClean.txt\")\n bxCounter += 1\n ### write the bkg as it is\n for lines in bkgFileName.readlines():\n if '#' in lines:\n continue\n lines = lines.rstrip()\n eachWord = lines.split()\n modifiedLine = [str(bxCounter)] + eachWord[1:]\n writeLine = ' '.join([elem for elem in modifiedLine])\n outFile.write(writeLine+\"\\n\")\n \n outFile.close()\n \nif __name__==\"__main__\":\n start = time.time()\n main()\n print(\"--- The time taken: \", time.time() - start, \" s\")\n", "sub_path": "stitchEBeamFiles.py", "file_name": "stitchEBeamFiles.py", "file_ext": "py", "file_size_in_byte": 1876, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "time.time", "line_number": 48, "usage_type": "call"}, {"api_name": "time.time", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "489308984", "text": "from scipy.integrate import quadrature\nfrom scipy import linspace\nfrom numpy import array, empty_like, ones_like, empty, where, argmax\nfrom scipy import sign, pi\n\n\ndef m_p(t, t_0 =0., m_p0 = 100):\n \"\"\" t time, m_p0 the initial weight of the rocket propellant\"\"\" \n if t sma_threshold[1] and momentum.loc[date] > momentum_threshold[1])\\\n or (bb_ratio.loc[date] > bbratio_threshold[1]):\n if current_holding == 0:\n df_trades.loc[date] = -1000\n current_holding -= 1000\n elif current_holding == 1000:\n df_trades.loc[date] = -2000\n current_holding -= 2000\n elif current_holding == -1000:\n df_trades.loc[date] = 0\n\n # Buy the stock i.e. LONG\n elif (sma.loc[date] < sma_threshold[0] and momentum.loc[date] < momentum_threshold[0])\\\n or (bb_ratio.loc[date] < bbratio_threshold[0]):\n if current_holding == 0:\n df_trades.loc[date] = 1000\n current_holding += 1000\n elif current_holding == -1000:\n df_trades.loc[date] = 2000\n current_holding += 2000\n elif current_holding == 1000:\n df_trades.loc[date] = 0\n\n else:\n df_trades.loc[date] = 0\n\n date_last = date\n\n return df_trades\n\n\ndef author():\n return 'dmehta32'\n\n\ndef test_code():\n # In Sample - Portfolio\n df_trades = testPolicy()\n portvals = ms.compute_portvals(df_trades, start_val=100000, commission=9.95, impact=0.005)\n if isinstance(portvals, pd.DataFrame):\n portvals = portvals[\n portvals.columns[0]] # just get the first column\n else:\n \"warning, code did not return a DataFrame\"\n\n # Benchmark Portfolio\n df_benchmark = pd.DataFrame(index=df_trades.index, columns=[\"JPM\"])\n df_benchmark.loc[df_trades.index] = 0\n df_benchmark.loc[df_trades.index[0]] = 1000 # Buying 1000 shares of JPM\n portvals_benchmark = ms.compute_portvals(df_benchmark, start_val=100000, commission=9.95, impact=0.005)\n\n # Normalize Portfolio and Benchmark Portfolio\n portvals_norm = portvals / portvals.iloc[0]\n portvals_benchmark = portvals_benchmark / portvals_benchmark.iloc[0]\n\n # Generate Plot - In Sample\n figure, axis = plt.subplots()\n portvals_norm.plot(ax=axis, color='r')\n portvals_benchmark.plot(ax=axis, color='g')\n plt.title(\"Comparison of Manual Strategy Portfolio vs Benchmark\")\n plt.legend([\"Manual Strategy\", \"Benchmark\"])\n plt.xlabel(\"Date\")\n plt.ylabel(\"Normalized Portfolio Value\")\n for date, trade in df_trades.iterrows():\n if trade[\"JPM\"] < 0:\n plt.axvline(x=date, color='k')\n elif trade[\"JPM\"] > 0:\n plt.axvline(x=date, color='b')\n plt.savefig(\"ManualStrategy-InSample.png\")\n # plt.show()\n\n # Out Sample - Portfolio\n symbol = \"JPM\"\n sd = dt.datetime(2010, 1, 1)\n ed = dt.datetime(2011, 12, 31)\n df_trades_os = testPolicy(symbol, sd, ed)\n portvals_os = ms.compute_portvals(df_trades_os, start_val=100000, commission=9.95, impact=0.005)\n\n # Benchmark Portfolio - Out Sample\n df_benchmark_os = pd.DataFrame(index=df_trades_os.index, columns=[\"JPM\"])\n df_benchmark_os.loc[df_trades_os.index] = 0\n df_benchmark_os.loc[df_trades_os.index[0]] = 1000 # Buying 1000 shares of JPM\n portvals_benchmark_os = ms.compute_portvals(df_benchmark_os, start_val=100000, commission=9.95, impact=0.005)\n\n # Normalize Portfolio and Benchmark Portfolio\n portvals_norm_os = portvals_os / portvals_os.iloc[0]\n portvals_benchmark_os = portvals_benchmark_os / portvals_benchmark_os.iloc[0]\n\n # Generate Plot - Out Sample\n figure, axis = plt.subplots()\n portvals_norm_os.plot(ax=axis, color='r')\n portvals_benchmark_os.plot(ax=axis, color='g')\n plt.title(\"Comparison of Manual Strategy Portfolio vs Benchmark - Out of Sample\")\n plt.legend([\"Manual Strategy\", \"Benchmark\"])\n plt.xlabel(\"Date\")\n plt.ylabel(\"Normalized Portfolio Value\")\n plt.savefig(\"ManualStrategy-OutSample.png\")\n # plt.show()\n\n # Display Portfolio Stats\n portvals_os = portvals_os[portvals_os.columns[0]]\n\n # In - Sample Stats\n cum_ret, avg_daily_ret, std_daily_ret, sharpe_ratio = ms.compute_portfolio_stats(portvals)\n # print(cumulative_return, avg_daily_ret, std_daily_ret, sharpe_ratio)\n cum_ret_bench, avg_daily_ret_bench, std_daily_ret_bench, sharpe_ratio_bench = \\\n ms.compute_portfolio_stats(portvals_benchmark[portvals_benchmark.columns[0]])\n\n print(f\"Sharpe Ratio of Fund (In-Sample): {sharpe_ratio}\")\n print(f\"Sharpe Ratio of Benchmark (In-Sample): {sharpe_ratio_bench}\")\n print()\n print(f\"Cumulative Return of Fund (In-Sample): {cum_ret}\")\n print(f\"Cumulative Return of Benchmark (In-Sample): {cum_ret_bench}\")\n print()\n print(f\"Standard Deviation of Fund (In-Sample): {std_daily_ret}\")\n print(f\"Standard Deviation of Benchmark (In-Sample): {std_daily_ret_bench}\")\n print()\n print(f\"Average Daily Return of Fund (In-Sample): {avg_daily_ret}\")\n print(f\"Average Daily Return of Benchmark (In-Sample): {avg_daily_ret_bench}\")\n print()\n print(f\"Final Portfolio Value: {portvals[-1]}\")\n print()\n\n # Out-Sample Stats\n cum_ret, avg_daily_ret, std_daily_ret, sharpe_ratio = ms.compute_portfolio_stats(portvals_os)\n cum_ret_bench, avg_daily_ret_bench, std_daily_ret_bench, sharpe_ratio_bench = \\\n ms.compute_portfolio_stats(portvals_benchmark[portvals_benchmark_os.columns[0]])\n\n print(f\"Sharpe Ratio of Fund (Out-Sample): {sharpe_ratio}\")\n print(f\"Sharpe Ratio of Benchmark (Out-Sample): {sharpe_ratio_bench}\")\n print()\n print(f\"Cumulative Return of Fund (Out-Sample): {cum_ret}\")\n print(f\"Cumulative Return of Benchmark (Out-Sample): {cum_ret_bench}\")\n print()\n print(f\"Standard Deviation of Fund (Out-Sample): {std_daily_ret}\")\n print(f\"Standard Deviation of Benchmark (Out-Sample): {std_daily_ret_bench}\")\n print()\n print(f\"Average Daily Return of Fund (Out-Sample): {avg_daily_ret}\")\n print(f\"Average Daily Return of Benchmark (Out-Sample): {avg_daily_ret_bench}\")\n print()\n print(f\"Final Portfolio Value: {portvals_os[-1]}\")\n\n\nif __name__ == \"__main__\":\n test_code()", "sub_path": "ML4T_2019Fall/manual_strategy/ManualStrategy.py", "file_name": "ManualStrategy.py", "file_ext": "py", "file_size_in_byte": 7641, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 18, "usage_type": "call"}, {"api_name": "util.get_data", "line_number": 20, "usage_type": "call"}, {"api_name": "indicators.calculate_sma", "line_number": 26, "usage_type": "call"}, {"api_name": "indicators.calculate_bb", "line_number": 27, "usage_type": "call"}, {"api_name": "indicators.calculate_momentum", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 42, "usage_type": "call"}, {"api_name": "marketsimcode.compute_portvals", "line_number": 86, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 87, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 94, "usage_type": "call"}, {"api_name": "marketsimcode.compute_portvals", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 121, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 122, "usage_type": "call"}, {"api_name": "marketsimcode.compute_portvals", "line_number": 124, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 127, "usage_type": "call"}, {"api_name": "marketsimcode.compute_portvals", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "marketsimcode.compute_portfolio_stats", "line_number": 151, "usage_type": "call"}, {"api_name": "marketsimcode.compute_portfolio_stats", "line_number": 154, "usage_type": "call"}, {"api_name": "marketsimcode.compute_portfolio_stats", "line_number": 172, "usage_type": "call"}, {"api_name": "marketsimcode.compute_portfolio_stats", "line_number": 174, "usage_type": "call"}]} +{"seq_id": "599550781", "text": "from handlers.base import BaseHandler\nfrom config import logging\nfrom Tmall.TmallDeivces import D712_devices, D910_devices, C1004_devices\nfrom service.device_connection import TCP_CONNECTION\nfrom service.device_control import DeviceController, DeviceNotExistException\nfrom models.smartroom import WifiDevice\nfrom tornado import auth\nimport random\nimport string\nimport re\nimport time\n\n\nclass OAuth2Home(BaseHandler, auth.OAuth2Mixin):\n \"\"\"\n 天猫精灵获取授权\n \"\"\"\n\n def set_default_header(self):\n # 后面的*可以换成ip地址，意为允许访问的地址\n self.set_header('Access-Control-Allow-Origin', '*')\n self.set_header('Access-Control-Allow-Headers', 'x-requested-with')\n self.set_header('Access-Control-Allow-Methods', 'POST, GET, PUT, DELETE')\n\n _OAUTH_AUTHORIZE_URL = 'https://ciel.pub/OAuth2Home'\n\n # https://xxx.com/auth/authorize?redirect_uri=https%3A%2F%2Fopen.bot.tmall.com%2Foauth%2Fcallback%3FskillId%3D11111111%26token%3DXXXXXXXXXX&client_id=XXXXXXXXX&response_type=code&state=111\n # 验证天猫上精灵填写的client_id,\n async def get(self):\n if self.get_arguments('code'):\n # self.write({'code': self.get_argument('code'), 'url':self.get_argument('redirect_uri')})\n url = self.get_argument('redirect_uri') + \"&code=\" + self.get_argument(\n 'code') + \"&state=\" + self.get_argument('state')\n self.redirect(url=url)\n else:\n client_id = self.get_arguments('client_id')[0]\n print(client_id,'---------------------------------')\n # if client_id == 'D712':\n # code = ''.join(random.sample(string.ascii_letters + string.digits, 40))+\n # code = 'D712'\n self.authorize_redirect(\n redirect_uri=self.get_argument('redirect_uri'),\n client_id=self.get_argument('client_id'),\n # scope=['profile', 'email'],\n response_type=self.get_argument('response_type'),\n extra_params={'state': self.get_argument('state'), 'code': client_id},\n # extra_params={'approval_prompt': 'auto'}\n )\n\n\nclass AccessTokenURL(BaseHandler):\n \"\"\"\n 天猫精灵获取access token\n \"\"\"\n\n def set_default_header(self):\n # 后面的*可以换成ip地址，意为允许访问的地址\n self.set_header('Access-Control-Allow-Origin', '*')\n self.set_header('Access-Control-Allow-Headers', 'x-requested-with')\n self.set_header('Access-Control-Allow-Methods', 'POST, GET, PUT, DELETE')\n\n async def post(self):\n grant_type = self.get_argument('grant_type')\n\n if grant_type == 'refresh_token':\n # https://XXXXX/token?grant_type=refresh_token&client_id=XXXXX&client_secret=XXXXXX&refresh_token=XXXXXX\n logging.info('refresh_token')\n client_id = self.get_arguments('client_id')\n # 验证refresh_token\n # access_token = ''.join(random.sample(string.ascii_letters + string.digits, 40))\n access_token = ''.join(random.sample(string.ascii_letters + string.digits, 40))\n refresh_token = ''.join(random.sample(string.ascii_letters + string.digits, 40))\n # refresh_token = ''.join(random.sample(string.ascii_letters + string.digits, 40))\n data = {\n \"access_token\": access_token,\n \"refresh_token\": refresh_token,\n \"expires_in\": 3600,\n }\n self.write(data)\n print('refresh_token')\n elif grant_type == 'authorization_code':\n # https://XXXXX/token?grant_type=authorization_code&client_id=XXXXX&client_secret=XXXXXX&code=XXXXXXXX&redirect_uri=https%3A%2F%2Fopen.bot.tmall.com%2Foauth%2Fcallback\n code = self.get_argument('code')\n # 验证code的正确性\n # access_token = ''.join(random.sample(string.ascii_letters + string.digits, 40))\n access_token = code\n # refresh_token = ''.join(random.sample(string.ascii_letters + string.digits, 40))\n refresh_token = ''.join(random.sample(string.ascii_letters + string.digits, 40))\n data = {\n \"access_token\": access_token,\n \"refresh_token\": refresh_token,\n \"expires_in\": 3600,\n }\n self.write(data)\n\nclass RevTmCommand(BaseHandler):\n \"\"\"\n 天猫精灵post指令网关\n \"\"\"\n\n def set_default_header(self):\n # 后面的*可以换成ip地址，意为允许访问的地址\n self.set_header('Access-Control-Allow-Origin', '*')\n self.set_header('Access-Control-Allow-Headers', 'x-requested-with')\n self.set_header('Access-Control-Allow-Methods', 'POST, GET, PUT, DELETE')\n\n async def post(self):\n dicts = eval(self.request.body.decode('utf-8'))\n print(dicts)\n if dicts['header']['namespace'] == \"AliGenie.Iot.Device.Discovery\":\n if dicts['payload']['accessToken'] == 'D712':\n # from TmallDevices import devices\n self.write(D712_devices)\n elif dicts['payload']['accessToken'] == 'D910':\n self.write(D910_devices)\n elif dicts['payload']['accessToken'] == 'C1004':\n self.write(C1004_devices)\n elif dicts['header']['namespace'] == \"AliGenie.Iot.Device.Control\":\n name = dicts['header']['name']\n messageId = dicts['header']['messageId']\n deviceType = dicts['payload']['deviceType']\n deviceId = dicts['payload']['deviceId']\n class_number = dicts['payload']['accessToken']\n return_data = {\n \"header\": {\n \"namespace\": \"AliGenie.Iot.Device.Control\",\n \"name\": \"TurnOnResponse\",\n \"messageId\": messageId,\n \"payLoadVersion\": 1\n },\n \"payload\": {\n \"deviceId\": deviceId\n }\n }\n device_name = deviceId[0:-1]\n devices = WifiDevice.select() \\\n .where((WifiDevice.class_number == class_number) & (WifiDevice.device_name == device_name)).execute()\n # print()\n device_controller = DeviceController(device_name, class_number)\n if deviceType in ['fan', 'curtain', 'light']:\n device_list = [device.device_number for device in devices]\n print(device_list)\n if name == 'TurnOn':\n for _ in range(2):\n print('开')\n if deviceType == 'fan':\n await device_controller.turn_fan_on(device_list)\n elif deviceType == 'light':\n await device_controller.turn_lamp_on(device_list)\n elif deviceType == 'curtain':\n await device_controller.turn_curtain_on(device_list)\n time.sleep(0.2)\n elif name == 'TurnOff':\n if deviceType == 'fan':\n print('关')\n await device_controller.turn_fan_off(device_list)\n elif deviceType == 'light':\n await device_controller.turn_lamp_off(device_list)\n elif deviceType == 'curtain':\n await device_controller.turn_curtain_off(device_list)\n return_data['header']['name'] = 'TurnOffResponse'\n elif deviceType == 'aircondition':\n if name == 'TurnOn':\n # for _ in range(2):\n device_controller.turn_on_air()\n await device_controller.send()\n # time.sleep(0.2)\n elif name == 'TurnOff':\n device_controller.turn_off_air()\n await device_controller.send()\n return_data['header']['name'] = 'TurnOffResponse'\n elif name == 'SetTemperature':\n device_controller.setDegree(str(dicts['payload']['value']))\n await device_controller.send()\n return_data['header']['name'] = 'SetTemperatureResponse'\n elif name == 'AdjustUpTemperature':\n # degree = WifiDevice.select\n # print(devices[0].degree)\n # print(type(devices[0].degree))\n # print(devices.degree)\n device_controller.setDegree(str(int(devices[0].degree)+1))\n await device_controller.send()\n return_data['header']['name'] = 'AdjustUpTemperatureResponse'\n elif name == 'AdjustDownTemperature':\n # degree = WifiDevice.select\n # print(devices[0].degree)\n # print(type(devices[0].degree))\n # print(devices.degree)\n device_controller.setDegree(str(int(devices[0].degree)-1))\n await device_controller.send()\n return_data['header']['name'] = 'AdjustDownTemperatureResponse'\n self.write(return_data)\n\n\nclass WebHook(BaseHandler):\n \"\"\"\n 天猫技能接口\n \"\"\"\n\n def set_default_header(self):\n # 后面的*可以换成ip地址，意为允许访问的地址\n self.set_header('Access-Control-Allow-Origin', '*')\n self.set_header('Access-Control-Allow-Headers', 'x-requested-with')\n self.set_header('Access-Control-Allow-Methods', 'POST, GET, PUT, DELETE')\n\n async def post(self):\n return_dict = {\n \"returnCode\": \"0\",\n \"returnErrorSolution\": \"\",\n \"returnMessage\": \"\",\n \"returnValue\": {\n \"reply\": \"好的\",\n \"resultType\": \"RESULT\",\n \"actions\": [\n {\n \"name\": \"audioPlayGenieSource\",\n \"properties\": {\n \"audioGenieId\": \"123\"\n }\n }\n ],\n \"properties\": {},\n \"executeCode\": \"SUCCESS\",\n \"msgInfo\": \"\"\n }\n }\n get_json = self.request.body.decode('utf-8')\n get_json = get_json.replace('true', '1')\n dicts = eval(get_json.replace('false', '0'))\n # print(/)\n # 这里由天猫精灵开发者平台定义请求头\n # headers = str(self.request.headers).lower()\n # headers = eval(headers)\n\n # command['action'] = self.request.headers['action']\n class_number = self.request.headers['class']\n try:\n if dicts['skillName'] == '全部设备':\n devices_ = self.request.headers['devices']\n devices_list = devices_.split('+')\n print(devices_)\n devices = WifiDevice.select() \\\n .where(WifiDevice.class_number == class_number).execute()\n air_list = []\n other_list = []\n for j in devices_list:\n if re.findall('air', j):\n air_list.append(j)\n else:\n other_list.append(j)\n # device_controller = [DeviceController(k, class_number) for k in other_list]\n list_ = [device.device_number for device in devices]\n if self.request.headers['action'] == 'on':\n print('action ==1 ')\n # await device_controller.turn_devices_on(list_)\n for j in air_list:\n await DeviceController(j, class_number).turn_air_on()\n time.sleep(0.3)\n for k in other_list:\n await DeviceController(k, class_number).turn_devices_on(list_)\n # time.sleep(0.3)\n # device_controller_air.turn_on_air()\n # await device_controller_air.send()\n return_dict['returnValue']['reply'] = \"好的，又是元气满满的一天哦。\"\n self.write(return_dict)\n elif self.request.headers['action'] == 'off':\n print('action ==0')\n for k in other_list:\n await DeviceController(k, class_number).turn_devices_off(list_)\n time.sleep(1)\n for j in air_list:\n await DeviceController(j, class_number).turn_air_off()\n time.sleep(1)\n # device_controller_air.turn_off_air()\n # await device_controller_air.send()\n return_dict['returnValue']['reply'] = \"好的，祝您晚上有个好梦哦。\"\n self.write(return_dict)\n elif dicts['skillName'] == '领导来了':\n return_dict['returnValue']['actions'][0]['properties']['audioGenieId'] = \"24976\"\n return_dict['returnValue']['reply'] = '。'\n self.write(return_dict)\n # time.sleep(1)\n if ('lamp' + '+'+class_number in TCP_CONNECTION.keys()) and ('curtain' +'+'+ class_number in TCP_CONNECTION.keys()):\n await TCP_CONNECTION['lamp' +'+'+ class_number].write(\n bytes(str('''{'device_name': 'lamp', 'class': 'D910', 'lamp-1': '1'}'''), encoding='utf-8'))\n await TCP_CONNECTION['curtain' + '+'+class_number].write(\n bytes(str('''{'device_name': 'curtain', 'class': 'D910', 'curtain-1': '1'}'''), encoding='utf-8'))\n\n else:\n return_dict['returnValue']['reply'] = \"设备未连接\"\n self.write(return_dict)\n else:\n return_dict['returnValue']['reply'] = \"未识别意图\"\n # logging.info('非{}'.format(return_dict))\n self.write(return_dict)\n except Exception as e:\n print('出错了{}'.format(e))\n self.write(return_dict)\n\n", "sub_path": "handlers/tmall.py", "file_name": "tmall.py", "file_ext": "py", "file_size_in_byte": 14195, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "handlers.base.BaseHandler", "line_number": 14, "usage_type": "name"}, {"api_name": "tornado.auth.OAuth2Mixin", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tornado.auth", "line_number": 14, "usage_type": "name"}, {"api_name": "handlers.base.BaseHandler", "line_number": 51, "usage_type": "name"}, {"api_name": "config.logging.info", "line_number": 67, "usage_type": "call"}, {"api_name": "config.logging", "line_number": 67, "usage_type": "name"}, {"api_name": "random.sample", "line_number": 71, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 71, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 71, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 72, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 72, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 72, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 88, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 88, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 88, "usage_type": "attribute"}, {"api_name": "handlers.base.BaseHandler", "line_number": 96, "usage_type": "name"}, {"api_name": "Tmall.TmallDeivces.D712_devices", "line_number": 113, "usage_type": "argument"}, {"api_name": "Tmall.TmallDeivces.D910_devices", "line_number": 115, "usage_type": "argument"}, {"api_name": "Tmall.TmallDeivces.C1004_devices", "line_number": 117, "usage_type": "argument"}, {"api_name": "models.smartroom.WifiDevice.select", "line_number": 136, "usage_type": "call"}, {"api_name": "models.smartroom.WifiDevice", "line_number": 136, "usage_type": "name"}, {"api_name": "models.smartroom.WifiDevice.class_number", "line_number": 137, "usage_type": "attribute"}, {"api_name": "models.smartroom.WifiDevice", "line_number": 137, "usage_type": "name"}, {"api_name": "models.smartroom.WifiDevice.device_name", "line_number": 137, "usage_type": "attribute"}, {"api_name": "service.device_control.DeviceController", "line_number": 139, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 152, "usage_type": "call"}, {"api_name": "handlers.base.BaseHandler", "line_number": 195, "usage_type": "name"}, {"api_name": "models.smartroom.WifiDevice.select", "line_number": 242, "usage_type": "call"}, {"api_name": "models.smartroom.WifiDevice", "line_number": 242, "usage_type": "name"}, {"api_name": "models.smartroom.WifiDevice.class_number", "line_number": 243, "usage_type": "attribute"}, {"api_name": "models.smartroom.WifiDevice", "line_number": 243, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 247, "usage_type": "call"}, {"api_name": "service.device_control.DeviceController", "line_number": 257, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 258, "usage_type": "call"}, {"api_name": "service.device_control.DeviceController", "line_number": 260, "usage_type": "call"}, {"api_name": "service.device_control.DeviceController", "line_number": 269, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 270, "usage_type": "call"}, {"api_name": "service.device_control.DeviceController", "line_number": 272, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 273, "usage_type": "call"}, {"api_name": "service.device_connection.TCP_CONNECTION.keys", "line_number": 283, "usage_type": "call"}, {"api_name": "service.device_connection.TCP_CONNECTION", "line_number": 283, "usage_type": "name"}, {"api_name": "service.device_connection.TCP_CONNECTION", "line_number": 284, "usage_type": "name"}, {"api_name": "service.device_connection.TCP_CONNECTION", "line_number": 286, "usage_type": "name"}]} +{"seq_id": "40160406", "text": "from setuptools import setup, find_packages\n\nfrom pip.req import parse_requirements\nfrom pip.download import PipSession\n\nrequirements = [\n str(ir.req)\n for ir in parse_requirements('./requirements.txt', session=PipSession())]\n\nsetup(\n name='ui-component-tags',\n version='1.0.4',\n author='Albert Treat',\n author_email='albert@counsyl.com',\n maintainer='Counsyl',\n maintainer_email='root@counsyl.com',\n url='https://github.counsyl.com/albert/ui_component_tags',\n description='This makes our ui component layer work',\n long_description=open('README.md', 'r').read(),\n include_package_data=True,\n test_suite='runtests.main',\n packages=find_packages(),\n install_requires=requirements,\n zip_safe=False,\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 747, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pip.req.parse_requirements", "line_number": 8, "usage_type": "call"}, {"api_name": "pip.download.PipSession", "line_number": 8, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 10, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "446698595", "text": "'''\n* uses camera to capture image\npress q to capture images\n\n1st image with object and background\n2nd image only background\n\nvary 'th' to improve results \n'''\n\n\nimport cv2\nprint(\"package_imported\")\nimport numpy as np\n\n\n\nv = cv2.VideoCapture(0) # (\"Path Of Video\") For video playback\nv.set(3,300)\nv.set(4,750)\nv.set(10,0)\ni = 1\nwhile i:\n aimg = []\n aimg2 = []\n while True:\n success, img = v.read()\n aimg = np.array(img)\n cv2.imshow(\"img 1\", img)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n # v.release()\n break\n\n while True:\n success, img2 = v.read()\n aimg2 = np.array(img2)\n cv2.imshow(\"img 2\", img2)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n # v.release()\n break\n\n appen = np.zeros_like(aimg2)\n i = 0\n th = 25\n while i < len(aimg):\n k = aimg[i]\n\n for j in range(len(k)):\n\n if (aimg2[i][j][0] - th < aimg[i][j][0] < aimg2[i][j][0] + th) and (\n aimg2[i][j][0] - th < aimg[i][j][1] < aimg2[i][j][1] + th) and (\n aimg2[i][j][0] - th < aimg[i][j][1] < aimg2[i][j][2] +th):\n appen[i][j] = [0, 0, 0]\n\n else:\n appen[i][j] = aimg[i][j]\n i += 1\n cv2.imshow(\"Made\", appen)\n cv2.waitKey(1)\n\n i = 0\ncv2.imshow('Final', appen)\ncv2.waitKey(0)\nv.release()\ncv2.destroyAllWindows()\n", "sub_path": "Background Remover.py", "file_name": "Background Remover.py", "file_ext": "py", "file_size_in_byte": 1412, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.VideoCapture", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "346406955", "text": "#coding: UTF-8\r\nimport math\r\nimport random\r\nimport pandas as pd\r\nimport datetime as dt\r\nimport numpy as np\r\nimport cupy as cp\r\nimport matplotlib.pylab as plt\r\nimport chainer\r\nfrom chainer import cuda, Function, gradient_check, Variable, optimizers, optimizer, serializers, utils, Link, Chain, ChainList\r\nimport chainer.functions as F\r\nimport chainer.links as L\r\nimport csv\r\n\r\n# ハイパーパラメータ\r\nDATA_REPEAT = 100\r\nEPOCH_NUM = 4950\r\nREPEAT_NUM = EPOCH_NUM * 1\r\nBATCH_SIZE = 10\r\nDELAY_SIZE = 5\r\n\r\nin_size = 10\r\nhidden_size = 10\r\nout_size = 4\r\n\r\nclass LSTM(Chain):\r\n def __init__(self, in_size, hidden_size, out_size):\r\n # クラスの初期化\r\n # :param in_size: 入力層のサイズ\r\n # :param hidden_size: 隠れ層のサイズ\r\n # :param out_size: 出力層のサイズ\r\n super(LSTM, self).__init__(\r\n xh = L.Linear(in_size, hidden_size),\r\n hh = L.LSTM(hidden_size, hidden_size),\r\n hy = L.Linear(hidden_size, out_size)\r\n )\r\n \r\n def __call__(self, x, t=None, train=False):\r\n # 順伝播の計算を行う関数\r\n # :param x: 入力値\r\n # :param t: 正解の予測値\r\n # :param train: 学習かどうか\r\n # :return: 計算した損失 or 予測値\r\n x = Variable(x)\r\n if train:\r\n t = Variable(t)\r\n h = self.xh(x)\r\n h = self.hh(h)\r\n y = self.hy(h)\r\n if train:\r\n return F.mean_squared_error(y, t)\r\n else:\r\n return y.data\r\n \r\n def reset(self):\r\n # 勾配の初期化とメモリの初期化\r\n self.cleargrads()\r\n self.hh.reset_state()\r\n \r\n #正規化\r\ndef zscore(x):\r\n xmean = x.mean()\r\n xstd = np.std(x)\r\n\r\n zscore = (x-xmean)/xstd\r\n return zscore\r\n\r\ndef get_data(x,t):\r\n #教師データ\r\n train_x, train_t = [], []\r\n path = \"G:/CarData/short_train.csv\" \r\n csv_file = open(path, \"r\", encoding=\"utf_8\", errors=\"\", newline=\"\\n\" )\r\n f = csv.reader(csv_file, delimiter=\",\", doublequote=True, lineterminator=\"\\r\\n\", quotechar='\"', skipinitialspace=True)\r\n\r\n #教師データ変換\r\n for row in f:\r\n \"\"\"\r\n obj = row[3:12]\r\n del obj[2]\r\n obj[0] = float(obj[0])*float(10.0/36.0)\r\n #\"\"\"\r\n obj = row[1:12]\r\n del obj[4]\r\n obj[2] = float(obj[2])*float(10.0/36.0)\r\n train_x.append(obj)\r\n \"\"\"\r\n obj2 = row[13:]\r\n total_slip = 0\r\n for i in obj2:\r\n total_slip += int(i)\r\n\r\n if(total_slip >= 1):\r\n train_t.append(1.0)\r\n else:\r\n train_t.append(0.0)\r\n #\"\"\"\r\n train_t.append(row[8:12])\r\n\r\n del train_x[len(train_x)-DELAY_SIZE:]\r\n del train_t[0:DELAY_SIZE]\r\n\r\n train_x = np.array(train_x, dtype=\"float32\")\r\n train_t = np.array(train_t, dtype=\"float32\")\r\n\r\n train_x_zscore = zscore(train_x)\r\n\r\n print(train_x)\r\n print(train_x_zscore)\r\n print(train_t)\r\n\r\n return train_x_zscore,train_t\r\n\r\n# 学習\r\n\r\n# モデルの定義\r\nmodel = LSTM(in_size=in_size, hidden_size=hidden_size, out_size=out_size)\r\noptimizer = optimizers.Adam()\r\noptimizer.setup(model)\r\n\r\n# 学習開始\r\nprint(\"Train\")\r\nst = dt.datetime.now()\r\nx,t = [],[]\r\nx,t = get_data(x,t)\r\nfor repeatnum in range(DATA_REPEAT): \r\n if(repeatnum%10 == 0 and repeatnum >= 10):\r\n serializers.save_npz(\"G:/cuda/test1/new_slip_model/\" + str(in_size) + \"_\" + str(hidden_size) + \"_\" + str(out_size) + \"_\" + str(DELAY_SIZE) + \"_\" + str(BATCH_SIZE) + \"_\" + str(REPEAT_NUM) + \"_\" + str(repeatnum) +\"_use_train_slip_model.npz\",model)\r\n \r\n # 乱数生成\r\n index = random.sample(range(EPOCH_NUM), EPOCH_NUM)\r\n for epoch in range(REPEAT_NUM):\r\n loss = 0\r\n total_loss = 0\r\n model.reset() # 勾配とメモリの初期化\r\n for i in range(BATCH_SIZE):\r\n loss += model(x=np.array(x[index[epoch%EPOCH_NUM] + i],dtype=\"float32\").reshape(1,in_size), t=np.array(t[index[epoch%EPOCH_NUM] + i],dtype=\"float32\").reshape(1,out_size), train=True)\r\n loss.backward()\r\n loss.unchain_backward()\r\n total_loss += loss.data\r\n optimizer.update()\r\n if (epoch+1) % 100 == 0:\r\n ed = dt.datetime.now()\r\n print(\"repeatnum:\\t{}\\tepoch:\\t{}\\ttotal loss:\\t{}\\ttime:\\t{}\".format(repeatnum,epoch+1, total_loss, ed-st))\r\n st = dt.datetime.now()\r\n\r\nserializers.save_npz(\"G:/cuda/test1/new_slip_model/\" + str(in_size) + \"_\" + str(hidden_size) + \"_\" + str(out_size) + \"_\" + str(DELAY_SIZE) + \"_\" + str(BATCH_SIZE) + \"_\" + str(REPEAT_NUM) + \"_\" + str(DATA_REPEAT) + \"_use_train_slip_model.npz\",model)\r\n\r\n# 予測\r\n\r\nprint(\"\\nPredict\")\r\npredict = np.empty(0)\r\ntest_x = []\r\ntest_t = []\r\ntest_path = \"G:/CarData/train.csv\" \r\npredict_path = \"G:/cuda/test1/PredictData/Slip/\"\r\np_file_name = str(in_size) + \"_\" + str(hidden_size) + \"_\" + str(out_size) + \"_\" + str(DELAY_SIZE) + \"_\" + str(BATCH_SIZE) + \"_\" + str(REPEAT_NUM) + \"_\" + str(DATA_REPEAT) + \"_use_train_predict.txt\"\r\nt_file_name = str(in_size) + \"_\" + str(hidden_size) + \"_\" + str(out_size) + \"_\" + str(DELAY_SIZE) + \"_\" + str(BATCH_SIZE) + \"_\" + str(REPEAT_NUM) + \"_\" + str(DATA_REPEAT) + \"_use_train_train.txt\"\r\n\r\ncsv_file = open(test_path, \"r\", encoding=\"utf_8\", errors=\"\", newline=\"\\n\" )\r\nf = csv.reader(csv_file, delimiter=\",\", doublequote=True, lineterminator=\"\\r\\n\", quotechar='\"', skipinitialspace=True)\r\nf_t = open(predict_path + t_file_name,\"w\")\r\nf_p = open(predict_path + p_file_name,\"w\")\r\n\r\nfor row in f:\r\n obj = row[3:12]\r\n del obj[2]\r\n obj[0] = float(obj[0])*float(10.0/36.0)\r\n test_x.append(obj)\r\n \"\"\"\r\n obj2 = row[13:]\r\n total_slip = 0\r\n for i in obj2:\r\n total_slip += int(i)\r\n\r\n if(total_slip >= 1):\r\n test_t.append(1.0)\r\n else:\r\n test_t.append(0.0)\r\n #\"\"\"\r\n test_t.append(row[8:12])\r\n\r\ndel test_x[len(test_x) - DELAY_SIZE:]\r\ndel test_t[0:DELAY_SIZE]\r\ntest_x = np.array(test_x, dtype=\"float32\")\r\ntest_t = np.array(test_t, dtype=\"float32\")\r\n\r\ntest_x_zscore = zscore(test_x)\r\nprint(test_x)\r\nprint(test_x_zscore)\r\n\r\nfor x in test_x_zscore:\r\n x=x.reshape(1,in_size)\r\n y = model(x=x,train=False)\r\n predict = np.append(predict, y)\r\n\r\nnp.savetxt(f_p,predict)\r\nnp.savetxt(f_t,test_t)\r\n\r\nN = len(test_t)\r\nplt.plot(range(N), test_t, color=\"red\", label=\"t\")\r\nplt.plot(range(N), predict, color=\"blue\", label=\"y\")\r\nplt.legend(loc=\"upper left\")\r\nplt.show()", "sub_path": "code/python/slip_lstm_2.py", "file_name": "slip_lstm_2.py", "file_ext": "py", "file_size_in_byte": 6455, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "chainer.Chain", "line_number": 26, "usage_type": "name"}, {"api_name": "chainer.links.Linear", "line_number": 33, "usage_type": "call"}, {"api_name": "chainer.links", "line_number": 33, "usage_type": "name"}, {"api_name": "chainer.links.LSTM", "line_number": 34, "usage_type": "call"}, {"api_name": "chainer.links", "line_number": 34, "usage_type": "name"}, {"api_name": "chainer.links.Linear", "line_number": 35, "usage_type": "call"}, {"api_name": "chainer.links", "line_number": 35, "usage_type": "name"}, {"api_name": "chainer.Variable", "line_number": 44, "usage_type": "call"}, {"api_name": "chainer.Variable", "line_number": 46, "usage_type": "call"}, {"api_name": "chainer.functions.mean_squared_error", "line_number": 51, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 51, "usage_type": "name"}, {"api_name": "numpy.std", "line_number": 63, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 103, "usage_type": "call"}, {"api_name": "chainer.optimizer", "line_number": 117, "usage_type": "name"}, {"api_name": "chainer.optimizers.Adam", "line_number": 117, "usage_type": "call"}, {"api_name": "chainer.optimizers", "line_number": 117, "usage_type": "name"}, {"api_name": "chainer.optimizer.setup", "line_number": 118, "usage_type": "call"}, {"api_name": "chainer.optimizer", "line_number": 118, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 122, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 122, "usage_type": "attribute"}, {"api_name": "chainer.serializers.save_npz", "line_number": 127, "usage_type": "call"}, {"api_name": "chainer.serializers", "line_number": 127, "usage_type": "name"}, {"api_name": "random.sample", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 136, "usage_type": "call"}, {"api_name": "chainer.optimizer.update", "line_number": 140, "usage_type": "call"}, {"api_name": "chainer.optimizer", "line_number": 140, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 142, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 142, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 144, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 144, "usage_type": "attribute"}, {"api_name": "chainer.serializers.save_npz", "line_number": 146, "usage_type": "call"}, {"api_name": "chainer.serializers", "line_number": 146, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 151, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pylab.plot", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pylab.plot", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pylab.legend", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pylab.show", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 203, "usage_type": "name"}]} +{"seq_id": "189349084", "text": "'''\nCreated on Sep 3, 2015\n\n@author: zompro\n'''\nimport unittest\nfrom mock.mock import patch, MagicMock\nfrom imp import reload\nfrom django.conf import settings\nfrom marker_visualiser.testutils import mock_settings_configure\nmock_settings_configure(settings)\n\nfrom marker_visualiser import utils\n\n\nclass MarkersUtilsTest(unittest.TestCase):\n @patch('django.conf.settings')\n def setUp(self, mock_settings):\n self.utils = utils.Utils()\n\n @patch('django.contrib.gis.geos.polygon.Polygon')\n def test_generate_bounds_polygon(self, mock_polygon):\n reload(utils)\n s, w, n, e = [0, 0, 1, 1]\n bounds = [s, w, n, e]\n self.utils.generate_bounds_polygon(bounds)\n mock_polygon.assert_called_once_with( ((s, w), (n, w),\n (n, e), (s, e),\n (s, w)) )\n\n def test_generate_random_number(self):\n n = 100\n actual = self.utils.generate_random_number(n)\n assert actual < n and actual > n * -1\n assert type(actual) == float\n\n @patch('marker_visualiser.models.Marker')\n def test_markers_within(self, mock_marker):\n reload(utils)\n polygon = MagicMock()\n group = MagicMock()\n filter_filter = MagicMock()\n markers = MagicMock()\n filter_filter.return_value = markers\n mock_marker.objects.filter.return_value = filter_filter\n\n self.utils.get_markers_within(polygon, group)\n\n mock_marker.objects.filter.assert_called_once_with(location__within=polygon)\n \nif __name__ == \"__main__\":\n unittest.main()", "sub_path": "django_sterna_project/marker_visualiser/tests/test_utils.py", "file_name": "test_utils.py", "file_ext": "py", "file_size_in_byte": 1621, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "marker_visualiser.testutils.mock_settings_configure", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.settings", "line_number": 11, "usage_type": "argument"}, {"api_name": "unittest.TestCase", "line_number": 16, "usage_type": "attribute"}, {"api_name": "marker_visualiser.utils.Utils", "line_number": 19, "usage_type": "call"}, {"api_name": "marker_visualiser.utils", "line_number": 19, "usage_type": "name"}, {"api_name": "mock.mock.patch", "line_number": 17, "usage_type": "call"}, {"api_name": "imp.reload", "line_number": 23, "usage_type": "call"}, {"api_name": "marker_visualiser.utils", "line_number": 23, "usage_type": "argument"}, {"api_name": "mock.mock.patch", "line_number": 21, "usage_type": "call"}, {"api_name": "imp.reload", "line_number": 39, "usage_type": "call"}, {"api_name": "marker_visualiser.utils", "line_number": 39, "usage_type": "argument"}, {"api_name": "mock.mock.MagicMock", "line_number": 40, "usage_type": "call"}, {"api_name": "mock.mock.MagicMock", "line_number": 41, "usage_type": "call"}, {"api_name": "mock.mock.MagicMock", "line_number": 42, "usage_type": "call"}, {"api_name": "mock.mock.MagicMock", "line_number": 43, "usage_type": "call"}, {"api_name": "mock.mock.patch", "line_number": 37, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "47325223", "text": "\"\"\"\n# Problem 1\nBinary Tree Level Order Traversal (https://leetcode.com/problems/binary-tree-level-order-traversal/)\n\n\n\"\"\"\nfrom collections import deque\nclass TreeNode:\n def __init__(self,x):\n self.val=x\n self.left=None\n self.right=None\n\n#BFS Time - O(N), SPACE- O(N/2)\ndef Levelorder_BFS(root: TreeNode):\n if root == None:\n return None\n result=[]\n q = deque()\n q.append(root)\n while q.count()!=0:\n size= len(q)\n temp=[]\n for i in range(size): # loop for the children of current node\n node=q.popleft()\n temp.append(node.val)\n if node.left:\n q.append(node.left)\n if node.right:\n q.append(node.right)\n result.append(temp)\n return result\n\n\n\n\n#DFS Time - O(N), SPACE- O(max(DEPTH)\nfrom collections import deque\nclass Solution:\n def levelOrder(self, root: TreeNode):\n self.stack=[]\n if root==None:\n return\n self.dfs(root, 0)\n return self.stack\n\n def dfs(self, root,level):\n if root==None:\n return\n if len(self.stack)==level: #if length of stack == level then add empty list in stack\n self.stack.append([])\n self.stack[level].append(root.val) #append root in stack at postion equal to level number\n\n self.dfs(root.left,level+1)\n self.dfs(root.right,level+1)\n\n\n\n", "sub_path": "Problem-1.py", "file_name": "Problem-1.py", "file_ext": "py", "file_size_in_byte": 1413, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.deque", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "109903259", "text": "import networkx as nx\nimport random\nimport sys\n\n# Variables for setting up.\nROUNDS = 1000000\nNODE_TARGET = 10000\nENTRY_NODES = 20\nTARGET_EDGES = 50\n\n# Do not change below this line.\ncurrentNodes = 0\ncycle = 0\n\n\ndef neighborsNoEntry(G: nx.Graph, i: int, entries: int):\n return list(filter(lambda n: n > entries - 1, list(G.neighbors(i))))\n\n\ndef getFittest(G: nx.Graph, n: int, entries: int):\n if len(neighborsNoEntry(G, n, entries)) == 0:\n return -1\n\n total = sum(len(neighborsNoEntry(G, i, entries))\n for i in neighborsNoEntry(G, n, entries))\n\n if(total == 0):\n return -1\n\n edgeNeighbors = map(lambda i: (\n i, len(neighborsNoEntry(G, i, entries))/total), neighborsNoEntry(G, n, entries))\n sortedList = sorted(edgeNeighbors, key=lambda i: i[1])\n\n chance = random.random()\n\n for i in range(len(sortedList) - 1):\n if(sortedList[i][1] >= chance):\n return sortedList[i][0]\n\n chance -= sortedList[i][1]\n\n return -1\n\n\nG = nx.Graph()\nfor i in range(0, ENTRY_NODES - 1):\n G.add_node(i)\n if(i != 0):\n G.add_edge(i, i - 1)\n\n if(i == ENTRY_NODES - 1):\n G.add_edge(i, 0)\n\n currentNodes += 1\n\nwhile cycle < ROUNDS:\n # Add a new node with a change of 10%.\n if currentNodes < NODE_TARGET and random.random() <= 0.1:\n G.add_node(currentNodes)\n G.add_edge(currentNodes, random.randint(0, ENTRY_NODES - 1))\n currentNodes += 1\n\n # Add 10 new connections every round\n for i in random.sample(range(0, currentNodes - 1), 10):\n # If the node already has enough connection skip it.\n if len(list(G.neighbors(i))) >= TARGET_EDGES:\n continue\n\n chosen = i\n for i in range(0, 5):\n res = getFittest(G, chosen, ENTRY_NODES)\n if res == -1:\n break\n chosen = res\n\n if chosen == i or chosen in (G.neighbors(i)):\n continue\n\n G.add_edge(i, chosen)\n\n # Report the percentage\n if cycle % 500 == 0:\n sys.stdout.write(\"\\rBuilding graph %s%%\" %\n str(round((cycle / ROUNDS) * 100, 2))\n )\n sys.stdout.flush()\n cycle += 1\n\nsys.stdout.write(\"\\r\\n\")\n\nprint(\"Total nodes: \", len(G.nodes()))\nprint(\"Total edges: \", len(G.edges()))\nprint(\"Average degree: \", sum(i[1] for i in G.degree()) / len(G.nodes))\nprint(\"Average clustering: \", nx.average_clustering(G))\nprint(\"Average shortest path: \", nx.average_shortest_path_length(G))\n", "sub_path": "pathfinder.py", "file_name": "pathfinder.py", "file_ext": "py", "file_size_in_byte": 2344, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "networkx.Graph", "line_number": 16, "usage_type": "attribute"}, {"api_name": "networkx.Graph", "line_number": 20, "usage_type": "attribute"}, {"api_name": "random.random", "line_number": 34, "usage_type": "call"}, {"api_name": "networkx.Graph", "line_number": 45, "usage_type": "call"}, {"api_name": "random.random", "line_number": 58, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 60, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 64, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 83, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 83, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 86, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 86, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 89, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 89, "usage_type": "attribute"}, {"api_name": "networkx.average_clustering", "line_number": 94, "usage_type": "call"}, {"api_name": "networkx.average_shortest_path_length", "line_number": 95, "usage_type": "call"}]} +{"seq_id": "125257443", "text": "#!/usr/bin/env python3\nimport subprocess\nimport click\nfrom os.path import join as _\nimport os\nimport re\nimport json\nimport pathlib\n\n\nBASIC_PATH = pathlib.Path(os.environ.get('MAGENTO_ROOT', '/var/www/html'))\n\n\nclass cd:\n \"\"\"Context manager for changing the current working directory\"\"\"\n def __init__(self, newPath):\n self.newPath = os.path.expanduser(newPath)\n\n def __enter__(self):\n self.savedPath = os.getcwd()\n os.chdir(self.newPath)\n\n def __exit__(self, etype, value, traceback):\n os.chdir(self.savedPath)\n\n\ndef remove_listeners(path):\n with open(path) as phpunit_config:\n data = phpunit_config.read()\n reg = re.compile(\"\", re.S)\n data = re.sub(reg, '', data)\n with open(path, 'w') as phpunit_config:\n phpunit_config.write(data)\n\n\n\ndef install(path):\n \"\"\"\n Install extension(s) to path from path or zip\n \"\"\"\n\n repo_type = 'path'\n\n click.echo(\"Installing from %s\" % path)\n\n with open(path / 'composer.json') as f:\n composer = json.load(f)\n repo_name = re.sub(r'[^a-z0-9_]', '_', composer['name'])\n\n with cd(BASIC_PATH):\n proc = subprocess.Popen(['composer', 'config', 'repositories.' + repo_name, repo_type, path])\n proc.communicate()\n ec1 = proc.returncode\n proc = subprocess.Popen(['composer', 'require', '--prefer-dist', '{e[name]}:{e[version]}'.format(e=composer)])\n proc.communicate()\n ec2 = proc.returncode\n\n if ec1 or ec2:\n raise click.ClickException(\"Failed to install extension\")\n\n result_path = BASIC_PATH / 'vendor' / composer['name']\n\n return result_path\n\n\n@click.group()\ndef cli():\n click.echo(\"Removing phpunit listeners\")\n remove_listeners(BASIC_PATH / 'dev' / 'tests' / 'static' / 'phpunit.xml.dist')\n remove_listeners(BASIC_PATH / 'dev' / 'tests' / 'unit' / 'phpunit.xml.dist')\n\n\n@cli.command()\n@click.option('--severity', default=10, help='Severity level.')\n@click.option('--report', default=\"junit\", help='Report type.', type=click.Choice([\"full\", \"xml\", \"checkstyle\", \"csv\",\n \"json\", \"junit\", \"emacs\", \"source\",\n \"summary\", \"diff\", \"svnblame\", \"gitblame\",\n \"hgblame\", \"notifysend\"]))\n@click.argument('path', type=click.Path(exists=True))\n@click.argument('report_file', type=click.Path(), required=False)\ndef eqp(severity, report, path, report_file):\n \"\"\"Run EQP tests for path\"\"\"\n\n proc = subprocess.Popen([_('/magento-coding-standard', 'vendor/bin/phpcs'), path, '--standard=Magento2',\n '--severity='+str(severity), '--extensions=php,phtml', '--report='+report],\n stdout=subprocess.PIPE\n )\n stdout, stderr = proc.communicate()\n\n if report_file:\n with open(report_file, 'wb') as fp:\n fp.write(stdout)\n else:\n click.echo(stdout)\n exit(proc.returncode)\n\n\n@cli.command()\n@click.option('--report', default=\"junit\", help='Report type.', type=click.Choice([\"junit\"]))\n@click.argument('path', type=click.Path(exists=True))\n@click.argument('report_file', type=click.Path(), required=False)\ndef unit(report, path, report_file):\n \"\"\"Run unit tests for extension at path\"\"\"\n\n path = pathlib.Path(path)\n path = install(path)\n\n options = [\n _(BASIC_PATH, 'vendor/bin/phpunit'),\n '--configuration', _(BASIC_PATH, 'dev/tests/unit/phpunit.xml.dist')\n ]\n\n if report_file:\n options += ['--log-%s' % report, report_file]\n\n proc = subprocess.Popen(options + [_(path, 'Test/Unit')])\n proc.communicate()\n\n if not report_file:\n exit(proc.returncode)\n\n exit(proc.returncode)\n\n\n\n@cli.command()\n@click.option('--report', default=\"junit\", help='Report type.', type=click.Choice([\"junit\"]))\n@click.argument('path', type=click.Path(exists=True))\n@click.argument('report_path', type=click.Path(), required=False)\ndef static(report, path, report_path):\n \"\"\"\n Run static tests against path\n :param report:\n :param path:\n :param report_file:\n :return:\n \"\"\"\n\n path = pathlib.Path(path)\n\n path = install(path)\n\n with open(path / 'composer.json') as f:\n composer = json.load(f)\n\n path_changed_files = BASIC_PATH / 'dev' / 'tests' / 'static' / 'testsuite' / 'Magento' / 'Test' / 'Php' / '_files' / 'whitelist' / 'common.txt'\n\n options = [os.path.join(BASIC_PATH, 'vendor/bin/phpunit'), '--configuration', BASIC_PATH / 'dev/tests/static/phpunit.xml.dist']\n\n output_base = report_path or os.environ.get('RESULTS_DIR', '/results')\n\n with open(os.path.join(BASIC_PATH, 'dev/tests/static/phpunit.xml.dist')) as phpunit_config:\n suites = {}\n reg = re.compile(']+name=\"([^\"]+)\"')\n for line in phpunit_config:\n try:\n suite = re.search(reg, line).groups()[0]\n suites[suite.lower().replace(' ', '_')] = suite\n except (IndexError, AttributeError):\n pass\n\n # Collect php iles\n with open(path_changed_files, 'w') as fp:\n for root, dirs, files in os.walk(path):\n\n fp.writelines([os.path.relpath(os.path.abspath(os.path.join(root, f)), BASIC_PATH) + '\\n' for f in files if os.path.splitext(f)[1] in (\n '.php',\n '.phtml'\n )])\n\n exit_code = 0\n for fname, name in suites.items():\n\n outfile = os.path.join(output_base, fname + '.xml')\n\n if re.search(re.compile('integrity'), fname):\n continue\n\n args = options + ['--testsuite=%s' % name]\n args += ['--log-%s' % report, outfile]\n proc = subprocess.Popen(args)\n proc.communicate()\n\n # Remove copy-paste results from report\n with open(os.path.join(output_base, fname + '.xml')) as f:\n data = f.read()\n data = re.sub(']+name=\"testCopyPaste\".+', '', data,\n flags=re.MULTILINE and re.DOTALL)\n\n with open(outfile, 'w') as f:\n f.write(data)\n\n exit_code = proc.returncode or exit_code\n\n exit(exit_code)\n\n@cli.command()\n@click.argument('path', type=click.Path(exists=True))\ndef validate_m2_package(path):\n \"\"\"\n Test marketplace package\n :param path:\n :return:\n \"\"\"\n #proc = subprocess.Popen(['php', '-f', '/usr/local/bin/validate_m2_package.php', path])\n #proc.communicate()\n #exit(proc.returncode)\n\nif __name__ == '__main__':\n cli()\n", "sub_path": "latest/m2test.py", "file_name": "m2test.py", "file_ext": "py", "file_size_in_byte": 6674, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pathlib.Path", "line_number": 11, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 11, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 20, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 21, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 24, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 30, "usage_type": "call"}, {"api_name": "re.S", "line_number": 30, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 31, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 44, "usage_type": "call"}, {"api_name": "json.load", "line_number": 47, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 48, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 51, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 54, "usage_type": "call"}, {"api_name": "click.ClickException", "line_number": 59, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 68, "usage_type": "call"}, {"api_name": "click.group", "line_number": 66, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 86, "usage_type": "attribute"}, {"api_name": "click.echo", "line_number": 94, "usage_type": "call"}, {"api_name": "click.option", "line_number": 74, "usage_type": "call"}, {"api_name": "click.option", "line_number": 75, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 75, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 79, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 79, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 80, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 80, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "click.option", "line_number": 99, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 99, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 100, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 100, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 101, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 101, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 139, "usage_type": "call"}, {"api_name": "json.load", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 150, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 150, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 154, "usage_type": "call"}, {"api_name": "re.search", "line_number": 157, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path.relpath", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 174, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 176, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 176, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 187, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 188, "usage_type": "attribute"}, {"api_name": "re.DOTALL", "line_number": 188, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 127, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 127, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 128, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 128, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 129, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 129, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 198, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 198, "usage_type": "call"}]} +{"seq_id": "269064946", "text": "from django.contrib.auth import get_user_model\nUser = get_user_model()\n\n\nclass FacebookBackend:\n def authenticate(self, user_info, token=None):\n try:\n user = User.objects.get(facebook_id=user_info[\"id\"])\n return user\n except:\n user = User.objects.create_user(user_info)\n return user\n\n def get_user(self, user_id):\n try:\n return User.objects.get(pk=user_id)\n except:\n return None", "sub_path": "hackathon/member/facebook_backends.py", "file_name": "facebook_backends.py", "file_ext": "py", "file_size_in_byte": 466, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 2, "usage_type": "call"}]} +{"seq_id": "623206454", "text": "\r\n#Eugene Liu\r\n#9/18/2017\r\n\r\n\"\"\"\r\nSECTION 1:==================================================================\r\nThis part of your program is in charge of setting up the screen for the game\r\n============================================================================\r\n\"\"\"\r\n#Imports for pygame\r\nimport pygame, sys, math, boid\r\n#Initialize PyGame\r\npygame.init()\r\n\r\n#Screen Setup\r\nscreen = pygame.display.set_mode((900, 700))\r\npygame.display.set_caption(\"Boids\")\r\n\r\n#manage how fast the screen updates \r\nclock = pygame.time.Clock()\r\n\r\n\"\"\"\r\nSECTION 2:=================================================================\r\nThis part of your program is for making objects and variables to use in your\r\ngame. This includes score, background images, characters, etc\r\n===========================================================================\r\n\"\"\"\r\n#Create variables\r\nquit = False\r\n\r\n#Groups to hold all sprites\r\nboidGroup = pygame.sprite.Group()\r\n\r\n\"\"\"\r\n============================= Game Loop ================================\r\n\"\"\"\r\nwhile not quit:\r\n clock.tick(30)\r\n\r\n # All events (mouse and keyboard)\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n quit = True\r\n\r\n if event.type == pygame.MOUSEBUTTONDOWN:\r\n boidGroup.add(boid.Boid(\"boid.png\", pygame.mouse.get_pos()[0], pygame.mouse.get_pos()[1]))\r\n\r\n #Clear the screen\r\n screen.fill((255,255,255))\r\n\r\n #update sprites\r\n for eachBoid in range(len(boidGroup.sprites())):\r\n list = boidGroup.sprites()\r\n list.pop(eachBoid)\r\n boidGroup.sprites()[eachBoid].update(screen, list)\r\n\r\n #Draw sprites\r\n boidGroup.draw(screen)\r\n\r\n #Display the next screen\r\n pygame.display.flip()\r\n\r\n#Close the window and quit.\r\npygame.display.quit()\r\nsys.exit()\r\npygame.quit()", "sub_path": "boidMain.py", "file_name": "boidMain.py", "file_ext": "py", "file_size_in_byte": 1803, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.init", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 45, "usage_type": "attribute"}, {"api_name": "boid.Boid", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.mouse.get_pos", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.display.quit", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 64, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 65, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "140525446", "text": "import tkinter as tk\nimport tkinter.ttk as ttk\nimport tkcalendar as tkc\n\ndef main():\n root = tk.Tk()\n style = ttk.Style(root)\n style.theme_use('clam')\n\n test_frame = ttk.Frame(root)\n test_frame.pack(side='top', fill='both', expand=True)\n\n test_date_entry = tkc.DateEntry(test_frame, locale='en_US')\n test_date_entry.pack(padx=10, pady=10)\n test_date_entry._top_cal.overrideredirect(False)\n root.after(100, test_date_entry.drop_down)\n\n root.mainloop()\n\nif __name__ == '__main__':\n main()", "sub_path": "frontend/practice.py", "file_name": "practice.py", "file_ext": "py", "file_size_in_byte": 519, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tkinter.Tk", "line_number": 6, "usage_type": "call"}, {"api_name": "tkinter.ttk.Style", "line_number": 7, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 7, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 10, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 10, "usage_type": "name"}, {"api_name": "tkcalendar.DateEntry", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "295548101", "text": "from django.db import models\n\nfrom account.conf import settings\n\n\nclass TimeZoneField(models.CharField):\n \n __metaclass__ = models.SubfieldBase\n \n def __init__(self, *args, **kwargs):\n defaults = {\n \"max_length\": 100,\n \"default\": \"\",\n \"choices\": settings.ACCOUNT_TIMEZONES,\n \"blank\": True,\n }\n defaults.update(kwargs)\n return super(TimeZoneField, self).__init__(*args, **defaults)\n\ntry:\n from south.modelsinspector import add_introspection_rules\n rules = [\n (\n (TimeZoneField,),\n [],\n {\n \"max_length\": [\"max_length\", {\"default\": 100}],\n \"default\": [\"default\", {\"default\": \"\"}],\n \"choices\": [\"choices\", {\"default\": settings.ACCOUNT_TIMEZONES}],\n \"blank\": [\"blank\", {\"default\": True}],\n },\n )\n ]\n add_introspection_rules(rules, [\"^account\\.fields\\.TimeZoneField\"])\nexcept ImportError:\n pass\n", "sub_path": "account/fields.py", "file_name": "fields.py", "file_ext": "py", "file_size_in_byte": 970, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.db.models.CharField", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.SubfieldBase", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "account.conf.settings.ACCOUNT_TIMEZONES", "line_number": 14, "usage_type": "attribute"}, {"api_name": "account.conf.settings", "line_number": 14, "usage_type": "name"}, {"api_name": "account.conf.settings.ACCOUNT_TIMEZONES", "line_number": 29, "usage_type": "attribute"}, {"api_name": "account.conf.settings", "line_number": 29, "usage_type": "name"}, {"api_name": "south.modelsinspector.add_introspection_rules", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "257574896", "text": "# -*- coding: utf-8 -*-\n#\n# File: package.py\n#\n# Copyright (c) 2010 by Webtide (C)2010\n# Generator: ArchGenXML Version 2.5\n# http://plone.org/products/archgenxml\n#\n# GNU General Public License (GPL)\n#\n\n__author__ = \"\"\"Mike Metcalfe , Jurgen Blignaut \"\"\"\n__docformat__ = 'plaintext'\n\nfrom AccessControl import ClassSecurityInfo\nfrom Products.Archetypes.atapi import *\nfrom zope.interface import implements\nimport interfaces\n\nfrom Products.CMFDynamicViewFTI.browserdefault import BrowserDefaultMixin\n\nfrom Products.eventslist.config import *\n\n# additional imports from tagged value 'import'\nfrom Products.FinanceFields.MoneyField import MoneyField\nfrom Products.FinanceFields.MoneyWidget import MoneyWidget\n\n##code-section module-header #fill in your manual code here\n##/code-section module-header\n\nschema = Schema((\n\n MoneyField(\n name='unitPrice',\n widget=MoneyWidget(\n label=\"Unit Cost Price\",\n label_msgid='eventslist_label_unitPrice',\n i18n_domain='eventslist',\n ),\n required=1,\n ),\n MoneyField(\n name='earlyBirdRebate',\n widget=MoneyWidget(\n label=\"Early Bird Rebate\",\n label_msgid='eventslist_label_earlyBirdRebate',\n i18n_domain='eventslist',\n ),\n ),\n MoneyField(\n name='lateComerPenalty',\n widget=MoneyWidget(\n label=\"Late Registration Penalty\",\n label_msgid='eventslist_label_lateComerPenalty',\n i18n_domain='eventslist',\n ),\n ),\n MoneyField(\n name='cancellationRebate',\n widget=MoneyWidget(\n label=\"Cancellation Rebate\",\n label_msgid='eventslist_label_cancellationRebate',\n i18n_domain='eventslist',\n ),\n ),\n\n),\n)\n\n##code-section after-local-schema #fill in your manual code here\n##/code-section after-local-schema\n\nPackage_schema = BaseSchema.copy() + \\\n schema.copy()\n\n##code-section after-schema #fill in your manual code here\n##/code-section after-schema\n\nclass Package(BaseContent, BrowserDefaultMixin):\n \"\"\"\n \"\"\"\n security = ClassSecurityInfo()\n\n implements(interfaces.IPackage)\n\n meta_type = 'Package'\n _at_rename_after_creation = True\n\n schema = Package_schema\n\n ##code-section class-header #fill in your manual code here\n ##/code-section class-header\n\n # Methods\n\n\nregisterType(Package, PROJECTNAME)\n# end of class Package\n\n##code-section module-footer #fill in your manual code here\n##/code-section module-footer\n\n", "sub_path": "content/package.py", "file_name": "package.py", "file_ext": "py", "file_size_in_byte": 2566, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "Products.FinanceFields.MoneyField.MoneyField", "line_number": 33, "usage_type": "call"}, {"api_name": "Products.FinanceFields.MoneyWidget.MoneyWidget", "line_number": 35, "usage_type": "call"}, {"api_name": "Products.FinanceFields.MoneyField.MoneyField", "line_number": 42, "usage_type": "call"}, {"api_name": "Products.FinanceFields.MoneyWidget.MoneyWidget", "line_number": 44, "usage_type": "call"}, {"api_name": "Products.FinanceFields.MoneyField.MoneyField", "line_number": 50, "usage_type": "call"}, {"api_name": "Products.FinanceFields.MoneyWidget.MoneyWidget", "line_number": 52, "usage_type": "call"}, {"api_name": "Products.FinanceFields.MoneyField.MoneyField", "line_number": 58, "usage_type": "call"}, {"api_name": "Products.FinanceFields.MoneyWidget.MoneyWidget", "line_number": 60, "usage_type": "call"}, {"api_name": "Products.CMFDynamicViewFTI.browserdefault.BrowserDefaultMixin", "line_number": 79, "usage_type": "name"}, {"api_name": "AccessControl.ClassSecurityInfo", "line_number": 82, "usage_type": "call"}, {"api_name": "zope.interface.implements", "line_number": 84, "usage_type": "call"}, {"api_name": "interfaces.IPackage", "line_number": 84, "usage_type": "attribute"}]} +{"seq_id": "187337166", "text": "import csv\nimport re\nfrom cassandra.cluster import Cluster\nfrom tqdm import tqdm\n\nBATCH_SIZE = 50\nMISSING_VALUE = 'null'\n\n\ndef get_session():\n cluster = Cluster(['localhost'])\n session = cluster.connect()\n session.set_keyspace('longen_zhao_td')\n return session\n\n\ndef read_file_gen(fname):\n dateparser = re.compile(\n '(?P\\d+)-(?P\\d+)-(?P\\d+) (?P\\d+):(?P\\d+)'\n )\n\n dial = csv.excel\n dial.delimiter = ','\n with open(fname) as csv_file:\n reader = csv.DictReader(csv_file, dialect=dial)\n for row in reader:\n\n match_valid = dateparser.match(row['valid'])\n\n if not match_valid:\n continue\n valid = match_valid.groupdict()\n data = dict(row)\n\n # set time`\n data['year'] = int(valid['year'])\n data['month'] = int(valid['month'])\n data['day'] = int(valid['day'])\n data['hour'] = int(valid['hour'])\n data['minute'] = int(valid['minute'])\n\n # set season\n if data['month'] in (1, 2, 3):\n data['season'] = 1\n elif data['month'] in (4, 5, 6):\n data['season'] = 2\n elif data['month'] in (7, 8, 9):\n data['season'] = 3\n else:\n data['season'] = 4\n\n # set missing values as null\n for col in data:\n if data[col] in ('M', 'T'):\n data[col] = MISSING_VALUE\n\n yield (data)\n\n\ndef get_insert_row_script(row: dict):\n insert_script = f'''\n insert into longen_zhao_td.project_question_1(\n\n station ,\n\n valid , \n year , \n month ,\n day ,\n hour ,\n minute , \n season ,\n\n lon ,\n lat ,\n\n tmpf ,\n dwpf ,\n relh ,\n drct ,\n sknt ,\n p01i ,\n alti ,\n mslp ,\n vsby ,\n gust ,\n skyc1 ,\n skyc2 ,\n skyc3 ,\n skyc4 ,\n skyl1 ,\n skyl2 ,\n skyl3 ,\n skyl4 ,\n wxcodes ,\n ice_accretion_1hr ,\n ice_accretion_3hr ,\n ice_accretion_6hr ,\n peak_wind_gust ,\n peak_wind_drct ,\n peak_wind_time ,\n feel ,\n metar \n ) values (\n \\'{row['station']}\\',\n\n \\'{row['valid']}\\', \n {row['year']}, \n {row['month']},\n {row['day']} ,\n {row['hour']} ,\n {row['minute']} , \n {row['season']} ,\n {row['lon']} ,\n {row['lat']} ,\n {row['tmpf']} ,\n {row['dwpf']} ,\n {row['relh']} ,\n {row['drct']} ,\n {row['sknt']} ,\n {row['p01i']} ,\n {row['alti']} ,\n {row['mslp']} ,\n {row['vsby']} ,\n {row['gust']} ,\n \\'{row['skyc1']}\\' ,\n \\'{row['skyc2']}\\' ,\n \\'{row['skyc3']}\\' ,\n \\'{row['skyc4']}\\' ,\n {row['skyl1']} ,\n {row['skyl2']} ,\n {row['skyl3']} ,\n {row['skyl4']} ,\n \\'{row['wxcodes']}\\' ,\n {row['ice_accretion_1hr']} ,\n {row['ice_accretion_3hr']} ,\n {row['ice_accretion_6hr']} ,\n {row['peak_wind_gust']} ,\n {row['peak_wind_drct']} ,\n \\'{row['peak_wind_time']}\\' ,\n {row['feel']} ,\n \\'{row['metar']}\\'\n )\n\n '''\n\n return insert_script\n\n\ndef insert_batch(batch, session):\n insert_batch_script = 'BEGIN BATCH\\n'\n for row in batch:\n insert_batch_script += get_insert_row_script(row) + '\\n'\n insert_batch_script += 'APPLY BATCH'\n\n session.execute(insert_batch_script)\n\n\ndef question_1_insert_data_with_batch(fname):\n data_generator = read_file_gen(fname)\n batch_count = 0\n batch = []\n session = get_session()\n\n for row in tqdm(data_generator):\n batch.append(row)\n if batch_count % BATCH_SIZE == 0:\n insert_batch(batch, session)\n batch.clear()\n\n\nif __name__ == '__main__':\n question_1_insert_data_with_batch('data/asos.txt')\n print('insert question 1 data done ')", "sub_path": "question_1_insert_data.py", "file_name": "question_1_insert_data.py", "file_ext": "py", "file_size_in_byte": 4370, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cassandra.cluster.Cluster", "line_number": 11, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 18, "usage_type": "call"}, {"api_name": "csv.excel", "line_number": 22, "usage_type": "attribute"}, {"api_name": "csv.DictReader", "line_number": 25, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 165, "usage_type": "call"}]} +{"seq_id": "24692167", "text": "import logging\n\n# the docker-dependency is special as it's only used with pytest --docker or\n# if you manually start bitcoind --docker\n# If this fails, you need to pip install the test-requirements\nimport docker\nimport os\nfrom .node_controller import Btcd_conn, NodeController\nimport signal\nimport time\n\nlogger = logging.getLogger(__name__)\n\n\nclass BitcoindDockerController(NodeController):\n \"\"\"A class specifically controlling a docker-based bitcoind-container\"\"\"\n\n def __init__(self, rpcport=18443, docker_tag=\"latest\"):\n self.btcd_container = None\n super().__init__(\"bitcoin\", rpcport=rpcport)\n self.docker_tag = docker_tag\n\n if self.detect_bitcoind_container(rpcport) != None:\n rpcconn, btcd_container = self.detect_bitcoind_container(rpcport)\n logger.debug(\"Detected old container ... deleting it\")\n btcd_container.stop()\n btcd_container.remove()\n\n def start_bitcoind(\n self,\n cleanup_at_exit=False,\n cleanup_hard=False,\n datadir=None,\n log_stdout=False,\n extra_args=[],\n timeout=60,\n ):\n self.start_node(\n cleanup_at_exit,\n cleanup_hard,\n datadir,\n log_stdout,\n extra_args,\n timeout,\n )\n\n def _start_node(\n self,\n cleanup_at_exit,\n cleanup_hard=False,\n datadir=None,\n log_stdout=None,\n extra_args=[],\n ):\n if datadir != None:\n # ignored\n pass\n bitcoind_path = self.construct_node_cmd(self.rpcconn, extra_args=extra_args)\n dclient = docker.from_env()\n logger.debug(\"Running (in docker): {}\".format(bitcoind_path))\n ports = {\n \"{}/tcp\".format(self.rpcconn.rpcport - 1): self.rpcconn.rpcport - 1,\n \"{}/tcp\".format(self.rpcconn.rpcport): self.rpcconn.rpcport,\n }\n logger.debug(\"portmapping: {}\".format(ports))\n image = dclient.images.get(\n \"registry.gitlab.com/cryptoadvance/specter-desktop/python-bitcoind:{}\".format(\n self.docker_tag\n )\n )\n self.btcd_container = dclient.containers.run(\n image,\n bitcoind_path,\n ports=ports,\n detach=True,\n )\n\n def cleanup_docker_bitcoind(*args):\n logger.info(\"Cleaning up bitcoind-docker-container\")\n self.btcd_container.stop()\n self.btcd_container.remove()\n\n if cleanup_at_exit:\n logger.debug(\n \"Register function cleanup_docker_bitcoind for SIGINT and SIGTERM\"\n )\n # This is for CTRL-C --> SIGINT\n signal.signal(signal.SIGINT, cleanup_docker_bitcoind)\n # This is for kill $pid --> SIGTERM\n signal.signal(signal.SIGTERM, cleanup_docker_bitcoind)\n\n logger.debug(\n \"Waiting for container {} to come up\".format(self.btcd_container.id)\n )\n self.wait_for_container()\n rpcconn, _ = self.detect_bitcoind_container(self.rpcconn.rpcport)\n if rpcconn == None:\n raise Exception(\n \"Couldn't find container or it died already. Check the logs!\"\n )\n else:\n self.rpcconn = rpcconn\n\n logger.info(\"Started docker bitcoind\")\n\n return\n\n def stop_bitcoind(self):\n if self.btcd_container != None:\n self.btcd_container.reload()\n if self.btcd_container.status == \"running\":\n _, container = self.detect_bitcoind_container(self.rpcconn.rpcport)\n if container == self.btcd_container:\n self.btcd_container.stop()\n logger.info(\"Stopped btcd_container {}\".format(self.btcd_container))\n self.btcd_container.remove()\n return\n raise Exception(\"Ambigious Container running\")\n\n def stop_node(self):\n self.stop_bitcoind()\n\n def check_existing(self):\n \"\"\"Checks whether self.btcd_container is up2date and not ambigious\"\"\"\n if self.btcd_container != None:\n self.btcd_container.reload()\n if self.btcd_container.status == \"running\":\n rpcconn, container = self.detect_bitcoind_container(\n self.rpcconn.rpcport\n )\n if container == self.btcd_container:\n return rpcconn\n raise Exception(\"Ambigious Container running\")\n return None\n\n @staticmethod\n def search_bitcoind_container(all=False):\n \"\"\"returns a list of containers which are running bitcoind\"\"\"\n d_client = docker.from_env()\n return [\n c\n for c in d_client.containers.list(all)\n if (c.attrs[\"Config\"].get(\"Cmd\") or [\"\"])[0] == \"bitcoind\"\n ]\n\n @staticmethod\n def detect_bitcoind_container(with_rpcport):\n \"\"\"checks all the containers for a bitcoind one, parses the arguments and initializes\n the object accordingly\n returns rpcconn, btcd_container\n \"\"\"\n d_client = docker.from_env()\n potential_btcd_containers = BitcoindDockerController.search_bitcoind_container()\n if len(potential_btcd_containers) == 0:\n logger.debug(\n \"could not detect container. Candidates: {}\".format(\n d_client.containers.list()\n )\n )\n all_candidates = BitcoindDockerController.search_bitcoind_container(\n all=True\n )\n logger.debug(\n \"could not detect container. All Candidates: {}\".format(all_candidates)\n )\n if len(all_candidates) > 0:\n logger.debug(\"100 chars of logs of first candidate\")\n logger.debug(all_candidates[0].logs()[0:100])\n return None\n for btcd_container in potential_btcd_containers:\n rpcport = int(\n [\n arg\n for arg in btcd_container.attrs[\"Config\"][\"Cmd\"]\n if \"rpcport\" in arg\n ][0].split(\"=\")[1]\n )\n if rpcport != with_rpcport:\n logger.debug(\n \"checking port {} against searched port {}\".format(\n type(rpcport), type(with_rpcport)\n )\n )\n continue\n rpcpassword = [\n arg\n for arg in btcd_container.attrs[\"Config\"][\"Cmd\"]\n if \"rpcpassword\" in arg\n ][0].split(\"=\")[1]\n rpcuser = [\n arg for arg in btcd_container.attrs[\"Config\"][\"Cmd\"] if \"rpcuser\" in arg\n ][0].split(\"=\")[1]\n if \"CI\" in os.environ: # this is a predefined variable in gitlab\n # This works on Linux (direct docker) and gitlab-CI but not on MAC\n ipaddress = btcd_container.attrs[\"NetworkSettings\"][\"IPAddress\"]\n else:\n # This works on most machines but not on gitlab-CI\n ipaddress = \"127.0.0.1\"\n rpcconn = Btcd_conn(\n rpcuser=rpcuser,\n rpcpassword=rpcpassword,\n rpcport=rpcport,\n ipaddress=ipaddress,\n )\n logger.info(\"detected container {}\".format(btcd_container.id))\n return rpcconn, btcd_container\n logger.debug(\"No matching container found\")\n return None\n\n def wait_for_container(self):\n \"\"\"waits for the docker-container to come up. Times out after 10 seconds\"\"\"\n i = 0\n while True:\n ip_address = self.btcd_container.attrs[\"NetworkSettings\"][\"IPAddress\"]\n if ip_address.startswith(\"172\"):\n self.rpcconn.ipaddress = ip_address\n break\n self.btcd_container.reload()\n time.sleep(0.5)\n i = i + 1\n if i > 20:\n raise Exception(\"Timeout while starting bitcoind-docker-container!\")\n\n def version(self):\n \"\"\"Returns the version of bitcoind, e.g. \"v0.19.1\" \"\"\"\n version = self.get_rpc().getnetworkinfo()[\"subversion\"]\n version = version.replace(\"/\", \"\").replace(\"Satoshi:\", \"v\")\n return version\n", "sub_path": "src/cryptoadvance/specter/process_controller/bitcoind_docker_controller.py", "file_name": "bitcoind_docker_controller.py", "file_ext": "py", "file_size_in_byte": 8323, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "node_controller.NodeController", "line_number": 15, "usage_type": "name"}, {"api_name": "docker.from_env", "line_number": 59, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 88, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 88, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 90, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 90, "usage_type": "attribute"}, {"api_name": "docker.from_env", "line_number": 139, "usage_type": "call"}, {"api_name": "docker.from_env", "line_number": 152, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 193, "usage_type": "attribute"}, {"api_name": "node_controller.Btcd_conn", "line_number": 199, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 219, "usage_type": "call"}]} +{"seq_id": "163630007", "text": "# -*- coding: utf-8 -*-\nimport pandas as pd \nimport numpy as np \nimport matplotlib.pyplot as plt\n\nfig = plt.figure()\n\ndata = pd.read_csv(\"EDG_2016.csv\",sep=',') \ndf = pd.DataFrame(data)\n\n\"\"\"Item 3 a:Provinica a selecccionar Chimborazo pro mi segundo apellido Cinlin\nitem 1 Edad Promedio de Muerte. y número de casos por rango de edad (el rango en múltiplos de 5: 0 a 5, 6 a 10, etc.)\ndf.set_index(\"prov_insc\", inplace=True)\nprom_edad_muerte = df.loc['Chimborazo','edad'].mean()\ncasos_edad = df.loc['Chimborazo','edad'].count()\nprint(\"Edad promedio de muerte en Chimborazo\", prom_edad_muerte)\nrango_edad = 0\nrango_edad_graf = []\nmedia_graf = []\ngraf = fig.add_subplot(111)\nfor edad in range(0, (df.loc['Chimborazo','edad'].max() +1), 5):\n rango_edad+=5\n etiqueta = f\"{rango_edad} : {edad}\"\n rango_edad_graf.append(etiqueta)\n media_graf.append(df.loc['Chimborazo','edad'].between(edad,rango_edad).mean())\n print(\"Rango edad:\",etiqueta,\"media de muerte: \", df.loc['Chimborazo','edad'].between(edad,rango_edad).mean())\ngraf.bar(rango_edad_graf, media_graf, width=0.5)\nplt.show()\nprint((df.groupby('nac_fall')).groups)\n\"\"\"\n\n\"\"\"Item 3 b: acionalidad del Paciente y número de Fallecidos por cada nacionalidad\ndf.set_index(\"nac_fall\", inplace=True)\ngrouped = df.groupby('nac_fall')\ntipos_nacionalidad = []\nnumero_fallecidos = []\ngraf = fig.add_subplot(111)\nfor name,group in grouped:\n tipos_nacionalidad.append(name)\n \nfor nacionalidad in tipos_nacionalidad:\n numero_fallecidos.append(df.loc[nacionalidad,'edad'].count())\n print(nacionalidad,\"=\",df.loc[nacionalidad,'edad'].count() )\ngraf.bar(tipos_nacionalidad,numero_fallecidos,width=0.5)\nplt.show()\n\n\"\"\"\n\n\"\"\" Item 3 c: Número de casos por Mes de fallecimiento\ndf.set_index(\"mes_fall\", inplace=True)\ngrouped = df.groupby('mes_fall')\nmes_fallecimiento = []\nnumero_fallecidos = []\ngraf = fig.add_subplot(111)\nfor name,group in grouped:\n mes_fallecimiento.append(name)\n \nfor mes in mes_fallecimiento:\n numero_fallecidos.append(df.loc[mes,'edad'].count())\n print(mes,\"número de casos=\",df.loc[mes,'edad'].count())\n \ngraf.bar(mes_fallecimiento, numero_fallecidos, width=0.5)\nplt.show()\n\"\"\"\n\"\"\" Item 3 d: Número de casos por Estado Civil\"\"\"\n\ndf.set_index(\"est_civil\", inplace=True)\ndf = df.replace(\" \",\"prueba\")\ngrouped = df.groupby('est_civil')\nest_civil_fallecimiento = []\nnumero_casos = []\ngraf = fig.add_subplot(111)\nfor name,group in grouped:\n est_civil_fallecimiento.append(name)\n\nfor tipos_est in est_civil_fallecimiento:\n numero_casos.append(df.loc[tipos_est,'edad'].count())\n print(tipos_est,\"número de casos=\",df.loc[tipos_est,'edad'].count())\ngraf.bar(est_civil_fallecimiento,numero_casos, width=0.5)\nplt.show()\n\n", "sub_path": "sem2.py", "file_name": "sem2.py", "file_ext": "py", "file_size_in_byte": 2720, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}]} +{"seq_id": "342350965", "text": "import scipy\nimport math\nimport numpy as np\nimport statsmodels.api as sm\n\n# Gets rid of some warnings in output text\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n\ndef importDataFunc(inputFilePath):\n\twith open(r\"%s\" % (inputFilePath), 'r') as dataFile:\n\t\txList, yList = [], []\n\t\tline = dataFile.readline()\n\t\twhile line:\n\t\t\ttempList = line.strip().split(',')\n\t\t\tnoString = True\n\t\t\tfor i, j in enumerate(tempList):\n\t\t\t\ttry:\n\t\t\t\t\ttempList[i] = float(j)\n\t\t\t\texcept(ValueError):\n\t\t\t\t\tnoString = False\n\t\t\tif noString: # handles column headings in 1st line of csv\n\t\t\t\txList.append(tempList[0])\n\t\t\t\tyList.append(tempList[1])\n\t\t\tline = dataFile.readline()\n\t\t# turns data into format of xList & yList\n\tmaxStress = float(max(yList))\n\n\treturn xList, yList, maxStress\n\n\ndef elasticModListGenerate(xInputlist, yInputList, elasticModStart):\n\t\"\"\"removes values in data list that are less than specified value, to remove\n\terror from beginning of graph when finding elastic modulus\"\"\"\n\tlen1 = len(xInputlist)\n\txList = [x for x in xInputlist if x > elasticModStart]\n\tdelta = len1 - len(xList)\n\tyList = yInputList[delta: len(yInputList) + 1: 1]\n\n\treturn xList, yList, delta\n\n\ndef findElasticMod(\n\txList, yList, elasticModFindingStep, rSquaredMin,\n\tnumOfStepsAfterSquaredMinIsHit, elasticModBacktrackValue):\n\t\"\"\" finds elastic modulus by going along initial straight line and stopping\n\twhen best fit line deviates from straight line, then it goes back a few steps\n\tand takes gradient\"\"\"\n\trSquaredMinHit = 0\n\t# ^stores how many times r_squared value is above thresholdvalue\n\tbreakValue = 0 # index where best fit line no longer fits sufficiently well\n\tfor i in range(0, len(xList) - 1, elasticModFindingStep):\n\t\tslope, intercept, r_value, p_value, std_error = scipy.stats.linregress(\n\t\t\txList[0: i + 1: 1], yList[0: i + 1: 1])\n\t\t# ^applies linear regression on current slice of xlist, ylist\n\t\tr_squared = r_value ** 2\n\t\tif r_squared > rSquaredMin:\n\t\t\trSquaredMinHit += 1\n\t\tif (\n\t\t\trSquaredMinHit > numOfStepsAfterSquaredMinIsHit and\n\t\t\tr_squared < rSquaredMin):\n\t\t\tbreakValue = i\n\t\t\tbreak\n\tfinalxList = xList[0: breakValue - elasticModBacktrackValue: 1]\n\tfinalyList = yList[0: breakValue - elasticModBacktrackValue: 1]\n\tslope, intercept, r_value, p_value, std_error = scipy.stats.linregress(\n\t\tfinalxList, finalyList)\n\n\treturn slope, intercept, breakValue\n\n\ndef makeStraightLine(\n\tstrainValue, deltaIndex, maxStress, inputxList, inputyList,\n\tyieldStressAccuracy, yieldStressFindingStep, inputSlope, inputyIntercept):\n\t\"\"\"Generates x, y coordinates that make up straight line which is used for\n\tfinding stress at certain strain, using elastic mod slope based offset\"\"\"\n\t# y = mx + c\n\tif strainValue > max(inputxList):\n\t\tprint(\n\t\t\t\"\"\"WARNING: Selected strain value is outside range of strain values\n\t\t\trecorded, so following stress value will not be correct.\"\"\")\n\txIntercept = (-1 * inputyIntercept) / inputSlope\n\tnewLinexIntercept = xIntercept + strainValue\n\tnewLineyIntercept = -1 * inputSlope * newLinexIntercept\n\n\tnewLinexList = []\n\tnewLineyList = []\n\tif strainValue == 0.2:\n\t\t\"\"\" uses a lower max stress value for creating line in\n\t\tcase of 0.2 % yield stress, to speed up program\"\"\"\n\t\tprovVal = 2 * inputyList[deltaIndex]\n\t\tif provVal < maxStress:\n\t\t\tmaxStress = int(2 * inputyList[deltaIndex])\n\tfor yValue in range(\n\t\tmath.floor(inputyList[deltaIndex]), int(maxStress * yieldStressAccuracy),\n\t\tyieldStressFindingStep):\n\t\t\"\"\"# produces straight line; range function can only step as an integer;\n\t\tstarting at deltaIndex means straight line starts at point where\n\t\t'straightness' of initial line stops\"\"\"\n\t\tyValue = yValue / yieldStressAccuracy\n\t\txValue = (yValue - newLineyIntercept) / inputSlope\n\t\tnewLinexList.append(xValue)\n\t\tnewLineyList.append(yValue)\n\n\treturn newLinexList, newLineyList\n\n\ndef createCutDownLists(\n\tinputSlope, lowElasticModulus, inputxList, inputyList, strainValue,\n\thighElasticModCuttingRange):\n\t\"\"\"Takes only the relevant section of the input curve, so finding\n\tintersection point is faster\"\"\"\n\tif inputSlope > lowElasticModulus:\n\t\tif strainValue >= (max(inputxList) - highElasticModCuttingRange - 1):\n\t\t\t\"\"\" prevents issues where lots of identical strain values near end mess up\n\t\t\tindexing (since .index() takes lowest index)\"\"\"\n\t\t\tcutDownxList = (\n\t\t\t\t[x for x in inputxList if x > (strainValue - highElasticModCuttingRange)])\n\t\t\tcutLowList = []\n\t\t\tfor i in inputxList:\n\t\t\t\tif i not in cutDownxList:\n\t\t\t\t\tcutLowList.append(i)\n\t\t\t\telse:\n\t\t\t\t\tbreak\n\t\t\tnumBelow = len(cutLowList)\n\t\t\tstartingIndex = numBelow\n\t\t\tendingIndex = startingIndex + len(cutDownxList) + 1\n\t\t\tcutDownyList = inputyList[startingIndex: endingIndex - 1: 1]\n\n\t\telse:\n\t\t\tcutDownxList = (\n\t\t\t\t[x for x in inputxList if\n\t\t\t\t\tx > (strainValue - highElasticModCuttingRange) and\n\t\t\t\t\tx < (strainValue + highElasticModCuttingRange)])\n\t\t\tcutLowList = []\n\t\t\tfor i in inputxList:\n\t\t\t\tif i not in cutDownxList:\n\t\t\t\t\tcutLowList.append(i)\n\t\t\t\telse:\n\t\t\t\t\tbreak\n\t\t\tnumBelow = len(cutLowList)\n\t\t\tstartingIndex = numBelow\n\t\t\tendingIndex = startingIndex + len(cutDownxList) + 1\n\t\t\tcutDownyList = inputyList[startingIndex: endingIndex - 1: 1]\n\n\t\treturn cutDownxList, cutDownyList\n\n\telse:\n\t\treturn inputxList, inputyList\n\n\ndef findIntersection(newLinexList, newLineyList, inputxList, inputyList):\n\t\"\"\"After preprocessing is complete, goes about finding intersection of\n\tstraight line and orginal data curve by finding\tpoint on striaght line\n\tthat is closest to a point on data curve (brute force)\"\"\"\n\tmainDiffList = []\n\tmainDiffListDataIndexes = []\n\tfor i, k in enumerate(newLinexList):\n\t\t\"\"\"finds point on data curve that each i is closest to\n\t\tand stores in mainDiffList\"\"\"\n\t\tsubDiffList = []\n\t\tfor j, m in enumerate(inputxList):\n\t\t\txDiff = abs(m - k)\n\t\t\tyDiff = abs(inputyList[j] - newLineyList[i])\n\t\t\tsumDiff = xDiff + yDiff\n\t\t\tsubDiffList.append(sumDiff)\n\t\tsubMinDiff = min(subDiffList)\n\t\tsubMinDiffIndex = subDiffList.index(subMinDiff)\n\t\t# ^index in main data list is stored in mainDiffListDataIndexes\n\t\tmainDiffList.append(subMinDiff)\n\t\tmainDiffListDataIndexes.append(subMinDiffIndex)\n\tglobalMinimumDifference = min(mainDiffList)\n\tglobalMinimumDifferenceIndex = mainDiffList.index(globalMinimumDifference)\n\tdataCurveIndexyieldPoint = (\n\t\tmainDiffListDataIndexes[globalMinimumDifferenceIndex])\n\n\treturn (\n\t\tinputxList[dataCurveIndexyieldPoint], inputyList[dataCurveIndexyieldPoint])\n\n\ndef findStressAtCertainStrain(\n\tinputxList, inputyList, inputSlope, inputyIntercept, strainValue, maxStress,\n\tdeltaIndex, yieldStressAccuracy, yieldStressFindingStep, lowElasticModulus,\n\thighElasticModCuttingRange):\n\t\"\"\" finds stress at certain strain (sloped up from that strain)\"\"\"\n\n\tnewLinexList, newLineyList = makeStraightLine(\n\t\tstrainValue, deltaIndex, maxStress, inputxList, inputyList,\n\t\tyieldStressAccuracy, yieldStressFindingStep, inputSlope, inputyIntercept)\n\n\tinputxList, inputyList = createCutDownLists(\n\t\tinputSlope, lowElasticModulus, inputxList, inputyList, strainValue,\n\t\thighElasticModCuttingRange)\n\n\tyieldStrain, yieldStress = findIntersection(\n\t\tnewLinexList, newLineyList, inputxList, inputyList)\n\n\treturn yieldStress\n\n\ndef findMaxStress(xInputlist, yInputList):\n\tmaxStress = max(yInputList)\n\tmaxStressIndex = yInputList.index(maxStress)\n\tcorrespondingStrain = xInputlist[maxStressIndex]\n\n\treturn maxStress\n\n\ndef findAreaUnderCurve(xList, yList):\n\tarea = np.trapz(yList, xList)\n\n\treturn area\n\n\ndef trimData(yieldStress, plateauRegionDefiningFactor, xList, yList):\n\t\"\"\"Trims data so ~only plateau region is considered,\n\tto improve processing time. Specifically, it cuts off data before yield point\n\tand after end of plateau region (based on multiple of yield stress)\"\"\"\n\tplateauEndingStressValue = yieldStress * plateauRegionDefiningFactor\n\tcutIndexStart = yList.index(yieldStress)\n\txListTrimmed = xList[cutIndexStart: len(xList): 1]\n\tyListTrimmed = yList[cutIndexStart: len(yList): 1]\n\ttempyList = []\n\tfor element in yListTrimmed:\n\t\tif element < plateauEndingStressValue:\n\t\t\ttempyList.append(element)\n\t\telse:\n\t\t\tbreak\n\tyListTrimmed = tempyList\n\txListTrimmed = xListTrimmed[0: len(yListTrimmed): 1]\n\n\treturn xListTrimmed, yListTrimmed\n\n\ndef generateSlopeList(xListTrimmed, yListTrimmed, plateauAnalyseSegmentLength):\n\t\"\"\" stores gradient values over selected\n\tinterval (plateauAnalyseSegmentLength) in slopeList\"\"\"\n\tslopeList = []\n\tfor i in range(plateauAnalyseSegmentLength, len(xListTrimmed)):\n\t\tcurrentxList = xListTrimmed[i - plateauAnalyseSegmentLength: i + 1: 1]\n\t\tcurrentyList = yListTrimmed[i - plateauAnalyseSegmentLength: i + 1: 1]\n\t\tslope, intercept, r_value, p_value, std_error = scipy.stats.linregress(\n\t\t\tcurrentxList, currentyList)\n\t\tslopeList.append(slope)\n\n\treturn slopeList\n\n\ndef findPeaksAndDips(\n\tslopeList, plateauAnalyseSegmentLength, xListTrimmed, yListTrimmed):\n\t\"\"\"Find x, y (strain, stress) cooridnates of peaks and dips\"\"\"\n\tpeakIndexesList = []\n\t# ^stores indexes (wrt trimmed x,y lists) of points\twhere peaks occur\n\tdipIndexesList = []\n\t# ^stores indexes (wrt trimmed x,y lists) of points where dips occur\n\tfor i in range(0, len(slopeList) - 1):\n\t\tif slopeList[i] < 0 and slopeList[i + 1] >= 0: # i.e. sign change\n\t\t\tdipIndexesList.append(i + int((plateauAnalyseSegmentLength / 2)))\n\t\telif slopeList[i] >= 0 and slopeList[i + 1] < 0: # i.e. sign change\n\t\t\tpeakIndexesList.append(i + int((plateauAnalyseSegmentLength / 2)))\n\t\telse:\n\t\t\tpass\n\t\"\"\" These 4 lists store x & y values for peaks and dips found,\n\tready for further analysis\"\"\"\n\tpeakxValues = [xListTrimmed[a] for a in peakIndexesList]\n\tpeakyValues = [yListTrimmed[a] for a in peakIndexesList]\n\tdipxValues = [xListTrimmed[a] for a in dipIndexesList]\n\tdipyValues = [yListTrimmed[a] for a in dipIndexesList]\n\n\tif len(peakxValues) != len(dipyValues):\n\t\treturnStringList.append(\n\t\t\t\"\"\"ATTENTION: NUMBER OF PEAKS AND DIPS DO NOT MATCH.\n\t\t\tTHIS WILL RESULT IN CODE ERROR.\\n\"\"\")\n\n\treturn peakxValues, peakyValues, dipxValues, dipyValues\n\n\ndef generateReturnStringList(\n\tdipxValues, dipyValues, peakxValues, peakyValues, outputDecimalPlaces):\n\treturnStringList = []\n\tnumDips = str(len(dipxValues))\n\treturnStringList.append(\n\t\t\"There are %s dips in the plateau region:\\n\\n\" % (numDips))\n\tdeltaStressList = []\n\tdeltaStrainList = []\n\n\tfor i, j in enumerate(peakxValues):\n\t\tdeltaY = peakyValues[i] - dipyValues[i]\n\t\tdeltaX = dipxValues[i] - peakxValues[i]\n\n\t\treturnStringList.append(\n\t\t\t\"Difference in stress between peak {} and dip {} is {} MPa\\n\".format(\n\t\t\t\tstr(i), str(i), str(round(deltaY, outputDecimalPlaces))))\n\t\tdeltaStressList.append(str(round(deltaY, outputDecimalPlaces)))\n\t\tdeltaStrainList.append(str(round(deltaX, outputDecimalPlaces)))\n\n\treturn returnStringList, numDips, deltaStressList, deltaStrainList\n\n\ndef analysePlateau(\n\txList, yList, yieldStress, plateauRegionDefiningFactor,\n\tplateauAnalyseSegmentLength, outputDecimalPlaces):\n\t\"\"\"Analyses dips and peaks in plateau region of compression curve\"\"\"\n\n\txListTrimmed, yListTrimmed = trimData(\n\t\tyieldStress, plateauRegionDefiningFactor, xList, yList)\n\n\tslopeList = generateSlopeList(\n\t\txListTrimmed, yListTrimmed, plateauAnalyseSegmentLength)\n\n\tpeakxValues, peakyValues, dipxValues, dipyValues = findPeaksAndDips(\n\t\tslopeList, plateauAnalyseSegmentLength, xListTrimmed, yListTrimmed)\n\n\treturnStringList, numDips, delStresses, delStrains = generateReturnStringList(\n\t\tdipxValues, dipyValues, peakxValues, peakyValues, outputDecimalPlaces)\n\n\treturn returnStringList, numDips, delStresses, delStrains\n\n\ndef findBreakingStress(yList, outputDecimalPlaces):\n\tvalue = round(yList[-1], outputDecimalPlaces)\n\tstring = \"Sample breaking stress is %s MPa.\\n\\n\" % (str(value))\n\n\treturn string, value", "sub_path": "analysisFunctions.py", "file_name": "analysisFunctions.py", "file_ext": "py", "file_size_in_byte": 11604, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "warnings.filterwarnings", "line_number": 8, "usage_type": "call"}, {"api_name": "scipy.stats.linregress", "line_number": 54, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 54, "usage_type": "attribute"}, {"api_name": "scipy.stats.linregress", "line_number": 67, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 67, "usage_type": "attribute"}, {"api_name": "math.floor", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 211, "usage_type": "call"}, {"api_name": "scipy.stats.linregress", "line_number": 243, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 243, "usage_type": "attribute"}]} +{"seq_id": "241169643", "text": "\"\"\"\n Qt model for collection\n\"\"\"\n\nfrom pathlib import Path\n\nimport requests\nfrom PySide2.QtCore import Qt, QAbstractTableModel, QModelIndex, QSize\nfrom PySide2.QtGui import QPixmap\n\nclass CollectionModel(QAbstractTableModel):\n\n INDEX_TITLE = 0\n INDEX_ARTIST = 1\n INDEX_COVER = 2\n INDEX_YEAR = 3\n INDEX_LABEL = 4\n INDEX_THUMB = 5\n\n def __init__(self, collection, parent=None):\n super(CollectionModel, self).__init__(parent)\n self.collection = collection\n self.sort_order = Qt.AscendingOrder\n self.sort_column = 1\n\n def rowCount(self, parent=QModelIndex()):\n del parent\n return len(self.collection.releases)\n\n def columnCount(self, parent=QModelIndex()):\n del parent\n #cover, title, artist\n return 3\n\n def data(self, index, role=Qt.DisplayRole):\n if role == Qt.DisplayRole:\n if index.column() == 1:\n return self.itemdata(index.row(), CollectionModel.INDEX_TITLE)\n if index.column() == 2:\n return self.itemdata(index.row(), CollectionModel.INDEX_ARTIST)\n if role == Qt.DecorationRole:\n if index.column() == 0:\n return self.itemdata(index.row(), CollectionModel.INDEX_THUMB)\n if role == Qt.SizeHintRole:\n if index.column() == 0:\n return QSize(100, 100)\n return None\n\n def headerData(self, section, orientation, role=Qt.DisplayRole):\n if orientation == Qt.Horizontal:\n if role == Qt.DisplayRole:\n if section == 0:\n return None\n if section == 1:\n return 'Title'\n if section == 2:\n return 'Artist'\n return None\n\n def itemdata(self, datarow, index):\n item = self.collection.releases[datarow]\n if item is None:\n return None\n if index == CollectionModel.INDEX_TITLE:\n return item.title\n if index == CollectionModel.INDEX_ARTIST:\n return item.artists[0].name\n if index == CollectionModel.INDEX_COVER:\n return load_cover(item.id, item.coverUrl)\n if index == CollectionModel.INDEX_YEAR:\n return item.year\n if index == CollectionModel.INDEX_LABEL:\n return item.labels[0].name\n if index == CollectionModel.INDEX_THUMB:\n return load_thumb(item.id, item.thumbUrl)\n return None\n\n def sort(self, column, order=Qt.AscendingOrder):\n reverse = (order != Qt.AscendingOrder)\n if column == 1:\n self.collection.releases = sorted(self.collection.releases, key=lambda release: release.title, reverse=reverse)\n self.dataChanged.emit(self.createIndex(0, 0), self.createIndex(self.rowCount()-1, self.columnCount()-1))\n self.sort_order = order\n self.sort_column = 1\n if column == 2:\n self.collection.releases = sorted(self.collection.releases, key=lambda release: release.artists[0].name, reverse=reverse)\n self.dataChanged.emit(self.createIndex(0, 0), self.createIndex(self.rowCount()-1, self.columnCount()-1))\n self.sort_order = order\n self.sort_column = 1\n\ndef load_thumb(release_id, url):\n name = 'cache/thumb_{}.jpg'.format(release_id)\n path = Path(name)\n if not path.is_file():\n with open(name, mode='wb') as thumb:\n img_data = requests.get(url).content\n thumb.write(img_data)\n return QPixmap(name)\n\ndef load_cover(release_id, url):\n name = 'cache/cover_{}.jpg'.format(release_id)\n path = Path(name)\n if not path.is_file():\n with open(name, mode='wb') as cover:\n img_data = requests.get(url).content\n cover.write(img_data)\n return QPixmap(name)\n", "sub_path": "collection_model.py", "file_name": "collection_model.py", "file_ext": "py", "file_size_in_byte": 3814, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PySide2.QtCore.QAbstractTableModel", "line_number": 11, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.AscendingOrder", "line_number": 23, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 23, "usage_type": "name"}, {"api_name": "PySide2.QtCore.QModelIndex", "line_number": 26, "usage_type": "call"}, {"api_name": "PySide2.QtCore.QModelIndex", "line_number": 30, "usage_type": "call"}, {"api_name": "PySide2.QtCore.Qt.DisplayRole", "line_number": 35, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 35, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.DisplayRole", "line_number": 36, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 36, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.DecorationRole", "line_number": 41, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 41, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.SizeHintRole", "line_number": 44, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 44, "usage_type": "name"}, {"api_name": "PySide2.QtCore.QSize", "line_number": 46, "usage_type": "call"}, {"api_name": "PySide2.QtCore.Qt.DisplayRole", "line_number": 49, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 49, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.Horizontal", "line_number": 50, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 50, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.DisplayRole", "line_number": 51, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 51, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.AscendingOrder", "line_number": 78, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 78, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.AscendingOrder", "line_number": 79, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 79, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 93, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 96, "usage_type": "call"}, {"api_name": "PySide2.QtGui.QPixmap", "line_number": 98, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 102, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 105, "usage_type": "call"}, {"api_name": "PySide2.QtGui.QPixmap", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "42091230", "text": "#!/usr/bin/env python\nimport os\nimport sys\n\nfrom django.core.exceptions import ImproperlyConfigured\n\n\ndef get_env_variable(var_name):\n try:\n return os.environ[var_name]\n except KeyError:\n error_msg = \"Set the {} environment variable\".format(var_name)\n raise ImproperlyConfigured(error_msg)\n\n\nif __name__ == \"__main__\":\n DJANGO_EXECUTION_ENVIRONMENT = get_env_variable('DJANGO_EXECUTION_ENVIRONMENT')\n if DJANGO_EXECUTION_ENVIRONMENT == \"TEST\":\n os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"{{ project_name }}.settings.test\")\n elif DJANGO_EXECUTION_ENVIRONMENT == 'PRODUCTION':\n os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"{{ project_name }}.settings.production\")\n elif DJANGO_EXECUTION_ENVIRONMENT == 'LOCAL':\n os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"{{ project_name }}.settings.local\")\n else:\n os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"{{ project_name }}.settings.local\")\n try:\n from django.core.management import execute_from_command_line\n except ImportError as exc:\n raise ImportError(\n \"Couldn't import Django. Are you sure it's installed and \"\n \"available on your PYTHONPATH environment variable? Did you \"\n \"forget to activate a virtual environment?\"\n ) from exc\n execute_from_command_line(sys.argv)\n", "sub_path": "manage.py", "file_name": "manage.py", "file_ext": "py", "file_size_in_byte": 1364, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.environ", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 13, "usage_type": "call"}, {"api_name": "os.environ.setdefault", "line_number": 19, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.environ.setdefault", "line_number": 21, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.environ.setdefault", "line_number": 23, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.environ.setdefault", "line_number": 25, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.core.management.execute_from_command_line", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 34, "usage_type": "attribute"}]} +{"seq_id": "267186451", "text": "import os\nfrom datetime import datetime\nimport calendar\n\n\ndef loadReportFor(date: datetime):\n if date.year == 2019 and date.month > 1:\n # there wont be any data for this yet anyway\n return\n cmd = f\"python3 main.py --year {date.year} --month {date.month} --day {date.day} --period daily\"\n print(\"Loading report for date: \"+str(date)+\" with command: \"+cmd)\n os.system(cmd)\n\nfor year in [2018, 2019]:\n for month in range(1, 13):\n _, daysinmonth = calendar.monthrange(year, month)\n for day in range(1, daysinmonth):\n loadReportFor(datetime(year=year, month=month, day=day))\n", "sub_path": "example_daily_report_all_dates.py", "file_name": "example_daily_report_all_dates.py", "file_ext": "py", "file_size_in_byte": 624, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime", "line_number": 6, "usage_type": "name"}, {"api_name": "os.system", "line_number": 12, "usage_type": "call"}, {"api_name": "calendar.monthrange", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "128047102", "text": "from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.index, name='index' ),\n url(r'^login$', views.login, name='login' ),\n url(r'^register$', views.register, name='register' ),\n url(r'^logout$', views.logout, name='logout' ),\n url(r'^showusers/(?P\\d*)$', views.showusers, name='showusers' ),\n url(r'^edituser/(?P\\d+)$', views.edituser, name='edituser' ),\n url(r'^updateuser/(?P\\d+)$', views.updateuser, name='updateuser' ),\n url(r'^deleteuser/(?P\\d+)$', views.deleteuser, name='deleteuser' ),\n url(r'^del_user_prompt/(?P\\d+)$', views.del_user_prompt, name='del_user_prompt' ),\n url(r'^keep_user/(?P\\d+)$', views.keep_user, name='keep_user' ),\n]\n", "sub_path": "apps/login_reg/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 734, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "459950972", "text": "from ipaddress import ip_address\nfrom lib.format_data_host import resolve_host_data\n\nclass Format_data(object):\n \"\"\"docstring for Format_data\"\"\"\n def __init__(self):\n super(Format_data, self).__init__()\n\n def format_host_data(self, data, host_to_as):\n # 将原始的数据格式转换为规范的格式\n\n body_txt = data.get('body')\n resolve_data = resolve_host_data(body_txt)\n mess_code = data.get('mess_code')\n\n try:\n mess = {\n 'strid': host_to_as.get(data.get('ip')),\n 'ctime': data.get('ctime'),\n 'gtime': data.get('gtime'),\n 'type': data.get('type'),\n 'ip': data.get('ip'),\n 'intip': int(ip_address(data.get('ip'))),\n 'mess_code': mess_code,\n }\n\n if mess_code == 1001:\n mess.update(resolve_data.get_cpu_run_data())\n mess.update(resolve_data.get_loadavg())\n return mess\n\n elif mess_code == 1002:\n mess.update(body_txt.get('meminfo'))\n mess['mem_used_rate'] = resolve_data.get_mem_rate()\n mess['swap_used_rate'] = resolve_data.get_swap_rate()\n return mess\n\n elif mess_code == 1003:\n mess['ss_status'] = str(body_txt.get('ss_status'))\n mess['socket_status'] = str(resolve_data.get_socket_status())\n mess['tcp_link_status'] = str(body_txt.get('tcp_link_status'))\n mess['network_interface_info'] = str(resolve_data.get_netinterface_info())\n return mess\n\n elif mess_code == 1004:\n partition, disk_size = resolve_data.get_disk_space_info()\n mess['partition'] = str(partition)\n mess.update(disk_size)\n\n iodevice, iostatus = resolve_data.get_iostatus()\n mess['iodevice'] = str(iodevice)\n mess.update(iostatus)\n return mess\n else:\n return False\n\n except Exception as e:\n raise\n\n def format_webService_data(self,data):\n body = data.get('body')\n new_data = []\n status_info = \"\"\n\n for i in body:\n status = i.get('status')\n if status == 200:\n status = 0\n status_info = \"ok\"\n elif status == 9:\n status_info = \"timeout\"\n else:\n status_info = \"error\"\n\n key = \"monitor:\"+ str(data.get(\"mess_code\")) + \":\" + i.get('name')\n value = {\n \"type\": data.get(\"type\"),\n \"name\": i.get('name'),\n \"url\": i.get('url'),\n \"status\": status,\n \"status_info\": status_info,\n \"ctime\": data.get(\"ctime\")\n }\n new_data.append([key, value])\n return new_data\n", "sub_path": "src/lib/format_data.py", "file_name": "format_data.py", "file_ext": "py", "file_size_in_byte": 2946, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "lib.format_data_host.resolve_host_data", "line_number": 13, "usage_type": "call"}, {"api_name": "ipaddress.ip_address", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "319645359", "text": "import torch\nimport warnings\n\nfrom BBTB import samplers\nfrom BBTB.utils.dist import get_world_size\nfrom BBTB.data.pr_dataloader import PitchBaseballDataset, PitchBaseballDatasetCollate\nfrom BBTB.data.ar_dataloader import ActionBaseballDataset, ActionBaseballDatasetCollate\nfrom BBTB.data.multi_ac_dataloader import MultiActionBaseballDataset, MultiActionBaseballDatasetCollate\n\n\ndef BaseballDatasetLoader(args, dargs, start_iter, dataset='', mode=''):\n num_gpus = get_world_size()\n is_distributed = True if num_gpus > 1 else False\n\n images_per_batch = args.batch_size\n assert (\n images_per_batch % num_gpus == 0\n ), \"SOLVER.IMS_PER_BATCH ({}) must be divisible by the number of GPUs ({}) used.\".format(\n images_per_batch, num_gpus)\n images_per_gpu = images_per_batch // num_gpus\n\n if mode == 'train':\n shuffle = args.shuffle\n else:\n shuffle = False\n\n if dataset == 'pitch':\n dataset = PitchBaseballDataset(dargs, mode=mode)\n collate_fn = PitchBaseballDatasetCollate\n elif dataset == 'action':\n dataset = ActionBaseballDataset(dargs, 96, mode=mode)\n collate_fn = ActionBaseballDatasetCollate\n elif dataset == 'multi-action':\n dataset = MultiActionBaseballDataset(dargs, 96, mode=mode)\n collate_fn = MultiActionBaseballDatasetCollate\n else:\n warnings.warn(\"dataset specification must be checked\")\n\n sampler = make_data_sampler(dataset, shuffle, is_distributed)\n batch_sampler = make_batch_data_sampler(\n dataset, sampler, images_per_gpu, args.max_iter, start_iter\n )\n\n data_loader = torch.utils.data.DataLoader(\n dataset=dataset,\n num_workers=args.num_workers,\n batch_sampler=batch_sampler,\n collate_fn=collate_fn\n )\n\n return data_loader, dataset\n\ndef make_batch_data_sampler(\n dataset, sampler, images_per_batch, num_iters=None, start_iter=0\n):\n batch_sampler = torch.utils.data.sampler.BatchSampler(\n sampler, images_per_batch, drop_last=False\n )\n if num_iters is not None:\n batch_sampler = samplers.IterationBasedBatchSampler(\n batch_sampler, num_iters, start_iter\n )\n return batch_sampler\n\ndef make_data_sampler(dataset, shuffle, distributed):\n if distributed:\n return samplers.DistributedSampler(dataset, shuffle=shuffle)\n if shuffle:\n sampler = torch.utils.data.sampler.RandomSampler(dataset)\n else:\n sampler = torch.utils.data.sampler.SequentialSampler(dataset)\n return sampler\n", "sub_path": "BBTB/data/dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 2433, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "BBTB.utils.dist.get_world_size", "line_number": 12, "usage_type": "call"}, {"api_name": "BBTB.data.pr_dataloader.PitchBaseballDataset", "line_number": 28, "usage_type": "call"}, {"api_name": "BBTB.data.pr_dataloader.PitchBaseballDatasetCollate", "line_number": 29, "usage_type": "name"}, {"api_name": "BBTB.data.ar_dataloader.ActionBaseballDataset", "line_number": 31, "usage_type": "call"}, {"api_name": "BBTB.data.ar_dataloader.ActionBaseballDatasetCollate", "line_number": 32, "usage_type": "name"}, {"api_name": "BBTB.data.multi_ac_dataloader.MultiActionBaseballDataset", "line_number": 34, "usage_type": "call"}, {"api_name": "BBTB.data.multi_ac_dataloader.MultiActionBaseballDatasetCollate", "line_number": 35, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torch.utils.data.sampler.BatchSampler", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 56, "usage_type": "attribute"}, {"api_name": "BBTB.samplers.IterationBasedBatchSampler", "line_number": 60, "usage_type": "call"}, {"api_name": "BBTB.samplers", "line_number": 60, "usage_type": "name"}, {"api_name": "BBTB.samplers.DistributedSampler", "line_number": 67, "usage_type": "call"}, {"api_name": "BBTB.samplers", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.utils.data.sampler.RandomSampler", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.utils.data.sampler.SequentialSampler", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 71, "usage_type": "attribute"}]} +{"seq_id": "114617568", "text": "############################ Serial ########################## \n######### final version 1 ##############\nimport serial\nimport time\nimport threading\nimport PID\nimport numpy as np\nfrom time import sleep\nimport cv2 as cv\n\n\nclass LEGOPrinter:\n ser = []\n A_encoder_num = 0\n B_encoder_num = 0\n C_encoder_num = 0\n D_encoder_num = 0\n threadLock = threading.Lock()\n error_count = 0\n d_count = 0\n\n def __init__(self):\n # serial port parameter initialtzation\n self.ser = serial.Serial(\n port='/dev/ttyS0',\n baudrate=9600,\n parity=serial.PARITY_NONE,\n stopbits=serial.STOPBITS_ONE,\n bytesize=serial.EIGHTBITS,\n timeout=0.05\n )\n self.PID_A = PID.PID(1.4, 1.0, 0)\n self.PID_B = PID.PID(2, 0.0, 0.0)\n self.PID_C = PID.PID(0.6, 1.0, 0.01)\n self.PID_D = PID.PID(3, 1, 0.1)\n self.A_last_out = 0\n self.B_last_out = 0\n self.C_last_out = 0\n self.D_last_out = 0\n self.A_target_angle = 0\n self.B_target_angle = 0\n self.C_target_angle = 0\n self.D_target_angle = 0\n self.A_current_angle = 0\n self.B_current_angle = 0\n self.C_current_angle = 0\n self.D_current_angle = 0\n self.next = False\n self.A_ready = False\n self.B_ready = False\n self.C_ready = False\n self.D_ready = False\n self.init_done = False\n\n # motion part\n # [important]Note here A_motor not use PID,if need change this\n def go_angle(self, a_angle, b_angle, c_angle, d_angle):\n if self.read_data():\n if abs(self.A_current_angle + a_angle) > 2.0:\n # A_out = self.PID_A.PID_Driver(a_angle, -self.A_current_angle)\n A_out = 8\n else:\n A_out = 0\n self.A_ready = True\n if self.A_ready:\n A_out = 0\n if abs(self.B_current_angle + b_angle) > 5.0:\n B_out = self.PID_B.PID_Driver(b_angle, -self.B_current_angle)\n else:\n B_out = 0\n self.B_ready = True\n if self.B_ready:\n B_out = 0\n if abs(self.C_current_angle + c_angle) > 1.0:\n C_out = self.PID_C.PID_Driver(c_angle, -self.C_current_angle)\n else:\n C_out = 0\n self.C_ready = True\n if self.C_ready:\n C_out = 0\n if abs(self.D_current_angle + d_angle) > 5.0:\n D_out = self.PID_D.PID_Driver(d_angle, -self.D_current_angle)\n else:\n D_out = 0\n self.D_ready = True\n if self.D_ready:\n D_out = 0\n if self.A_ready and self.B_ready and self.C_ready and self.D_ready:\n self.next = True\n self.A_ready = False\n self.B_ready = False\n self.C_ready = False\n self.D_ready = False\n else:\n A_out = self.A_last_out\n B_out = self.B_last_out\n C_out = self.C_last_out\n D_out = self.D_last_out\n self.send_out(A_out, B_out, C_out, D_out)\n\n # init the position\n def run_init(self):\n send = \"A0B-15C-20D0T3000EN\"\n self.ser.write(send.encode())\n self.ser.readline()\n print(\"init...\")\n time.sleep(3)\n send = \"A0B0C0D0T3000EN\"\n self.ser.write(send.encode())\n self.ser.readline()\n time.sleep(0.02)\n self.A_target_angle = 0\n self.B_target_angle = 0\n self.C_target_angle = 0\n self.D_target_angle = 0\n self.A_current_angle = 0\n self.B_current_angle = 0\n self.C_current_angle = 0\n self.D_current_angle = 0\n self.next = False\n self.A_ready = False\n self.B_ready = False\n self.C_ready = False\n self.D_ready = False\n self.init_done = True\n\n # read four motor angle from sensor\n def read_data(self):\n self.threadLock.acquire()\n # readData = self.ser.readline()\n # sleep(0.05)\n readData = self.ser.readline()\n self.threadLock.release()\n if len(readData) > 5:\n readData = str(readData.decode())\n # print(readData)\n if readData[0] == 'A':\n temp = readData.split('A')\n readData = temp[1]\n temp = readData.split('B')\n # if self.D_last_out:\n # self.D_current_angle = self.D_current_angle-abs(int(temp[0]))*abs(self.D_last_out)/self.D_last_out\n self.D_current_angle = self.D_current_angle - (int(temp[0]))\n readData = temp[1]\n temp = readData.split('C')\n # if self.C_last_out:\n # self.C_current_angle = self.C_current_angle-abs(int(temp[0]))*abs(self.C_last_out)/self.C_last_out\n self.C_current_angle = self.C_current_angle - (int(temp[0]))\n readData = temp[1]\n temp = readData.split('D')\n # if self.B_last_out:\n # self.B_current_angle = self.B_current_angle-abs(int(temp[0]))*abs(self.B_last_out)/self.B_last_out\n self.B_current_angle = self.B_current_angle - (int(temp[0]))\n readData = temp[1]\n temp = readData.split('E')\n # if self.A_last_out:\n # self.A_current_angle = self.A_current_angle-abs(int(temp[0]))*abs(self.A_last_out)/self.A_last_out\n self.A_current_angle = self.A_current_angle - (int(temp[0]))\n return 1\n return 0\n\n # send message\n def send_out(self, A_out, B_out, C_out, D_out):\n send = \"A\" + str(A_out) + \"B\" + str(B_out) + \"C\" + str(C_out) + \"D\" + str(D_out) + \"T40EN\"\n # print(send)\n self.A_last_out = A_out\n self.B_last_out = B_out\n self.C_last_out = C_out\n self.D_last_out = D_out\n self.ser.write(send.encode())\n\n # the lift motor action (lift motor is motorB)\n def lift_motion(self, value):\n self.B_target_angle = value\n b_angle = self.B_target_angle\n if self.read_data():\n if abs(self.B_current_angle + b_angle) > 5.0:\n B_out = self.PID_B.PID_Driver(b_angle, -self.B_current_angle)\n else:\n B_out = 0\n self.B_ready = True\n if self.B_ready:\n B_out = 0\n if self.B_ready:\n self.next = True\n self.A_ready = False\n self.B_ready = False\n self.C_ready = False\n self.D_ready = False\n A_out = 0\n C_out = 0\n D_out = 0\n else:\n A_out = 0\n B_out = self.B_last_out\n C_out = 0\n D_out = 0\n self.send_out(A_out, B_out, C_out, D_out)\n\n # the value of lift up\n def lift_up(self):\n self.lift_motion(100)\n\n # the value of lift down\n def lift_down(self):\n self.lift_motion(300)\n\n # change the point position to motor angle\n def x_position(self, x_point):\n self.C_target_angle = x_point\n\n # change the point position to motor angle\n def y_position(self, y_point):\n self.A_target_angle = y_point / 1.3\n\n # end action\n def end(self):\n send = \"A20B-20C-20D0T3000EN\"\n self.ser.write(send.encode())\n self.ser.readline()\n time.sleep(3)\n print(\"Draw Done!\")\n\n # main print process\n def point_transfer(self, points):\n x_range_min = 20\n # x_range_max = 660\n y_range_min = 10\n if not self.init_done:\n self.run_init()\n point_x_max = 0\n point_y_max = 0\n point_x_min = 1000\n point_y_min = 1000\n # find range\n for point in points:\n if point[0] > point_x_max:\n point_x_max = point[0]\n if point[0] < point_x_min:\n point_x_min = point[0]\n if point[1] > point_y_max:\n point_y_max = point[1]\n if point[1] < point_y_min:\n point_y_min = point[1]\n\n # zoom all point and move to (0,0)\n zoom_x = 500 / (point_x_max - point_x_min)\n # zoom_y = 500 / (point_y_max - point_y_min)\n # if zoom_x < zoom_y:\n # points = np.array(points) / zoom_y - point_y_min\n # else:\n points = (np.array(points)) * zoom_x\n img = np.zeros((700, 700, 3), np.uint8)\n for i in range(len(points)):\n points[i][0] = points[i][0] - point_x_min * zoom_x + x_range_min\n points[i][1] = points[i][1] - point_y_min * zoom_x + y_range_min\n cv.circle(img, (int(points[i][0]), int(points[i][1])), 1, (0, i, 255 - i), 4)\n cv.namedWindow(\"image\")\n cv.imshow('image', img)\n cv.waitKey(1000)\n print(points, point_x_min, zoom_x)\n count_point = 0\n print(\"<<<<<<<>>>>>>\")\n\n # print loop\n for point in points:\n print(point)\n complete_count = count_point / 106.0 * 100.0\n print(\"Compelete:\" + str(complete_count) + \"%\")\n # x,y move action\n while not self.next:\n self.x_position(point[0])\n self.y_position(point[1])\n self.go_angle(self.A_target_angle, self.B_target_angle, self.C_target_angle, self.D_target_angle)\n sleep(0.02)\n self.next = False\n\n # lift action\n while not self.next:\n self.lift_down()\n # self.go_angle(self.A_target_angle, self.B_target_angle, self.C_target_angle, self.D_target_angle)\n sleep(0.02)\n self.next = False\n while not self.next:\n self.lift_up()\n # self.go_angle(self.A_target_angle, self.B_target_angle, self.C_target_angle, self.D_target_angle)\n sleep(0.02)\n self.next = False\n count_point = count_point + 1\n self.end()\n\n\n# change txt to numpy\ndef open_points(name):\n all_points = []\n flag = False\n with open(name, 'r') as file_to_read:\n first = True\n while True:\n lines = file_to_read.readline()\n if not lines:\n break\n pass\n if not first:\n x_tmp, y_tmp = [float(i) for i in lines.split()]\n all_points.append([x_tmp, y_tmp])\n if first:\n if lines == \"A\\n\":\n flag = True\n first = False\n else:\n flag = False\n break\n pass\n\n if flag:\n data = np.array(all_points)\n point = data[np.lexsort(data.T)]\n else:\n point = [[-1, -1]]\n return point, flag\n\n\nif __name__ == \"__main__\":\n LP = LEGOPrinter()\n c_time = time.time()\n filename = 'myfile.txt'\n while True:\n raw_point, flag = open_points(filename)\n # raw_point = [[1,10],[100,50],[200,100],[300,200],[1,10],[100,50],[200,100],[300,200]]\n if flag:\n print(\"start print\")\n print(raw_point)\n LP.point_transfer(raw_point)\n # c_time = time.time() - c_time\n # print(\"take time:\" + str(c_time))\n else:\n print(\"wait\")\n sleep(1)\n", "sub_path": "legoCoreC.py", "file_name": "legoCoreC.py", "file_ext": "py", "file_size_in_byte": 11441, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "threading.Lock", "line_number": 18, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 24, "usage_type": "call"}, {"api_name": "serial.PARITY_NONE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "serial.STOPBITS_ONE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "serial.EIGHTBITS", "line_number": 29, "usage_type": "attribute"}, {"api_name": "PID.PID", "line_number": 32, "usage_type": "call"}, {"api_name": "PID.PID", "line_number": 33, "usage_type": "call"}, {"api_name": "PID.PID", "line_number": 34, "usage_type": "call"}, {"api_name": "PID.PID", "line_number": 35, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 107, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 111, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 253, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 257, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 258, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 259, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 260, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 275, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 282, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.lexsort", "line_number": 318, "usage_type": "call"}, {"api_name": "time.time", "line_number": 326, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 339, "usage_type": "call"}]} +{"seq_id": "574629722", "text": "import csv\nimport nltk\nfrom nltk.corpus import stopwords\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom collections import Counter\nfrom sklearn import metrics\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport re\n\n\nfrom nltk.stem.snowball import SnowballStemmer\nporter = SnowballStemmer(\"english\", ignore_stopwords=True)\n\n#nltk.download('stopwords')\n\n####################################################################################\n\n####################################################################################\n\n\ndef load_data(filename, num_annotate=1,shift=True):\n ###\n # Inputs:\n # # filename: (string: csv file name)\n # # num_annotate: (integer: 1 or 2) determines if one or two sets of labels are present\n # # shift: tells if the 1st line of .csv file is heading\n # Outputs:\n # #Comments, labels and brandname\n # \n ###\n reader = csv.reader(open(filename, 'r'), delimiter= \",\")\n Tweets,Labels1,Labels2,brand = read_output(reader,num_annotate,shift)\n \n if num_annotate == 2:\n return Tweets,Labels1,Labels2,brand\n else:\n return Tweets,Labels1,brand\n \n####################################################################################\n\n####################################################################################\n\ndef tokenizer(data):\n return data.split()\n\ndef tokenizer_porter(text):\n return [porter.stem(word) for word in text.split()]\n \n####################################################################################\n\n####################################################################################\n\ndef category_tokenization(Tweets, Labels,all_stopwords,porter,stop_words=False,tokenize_porter=False):\n tokens = {\n \"all\":[],\n \"tok_vp\" : [],\n \"tok_p\" : [],\n \"tok_ne\" : [],\n \"tok_n\" : [],\n \"tok_vn\" : [],\n \"tok_q\":[],\n }\n count=0\n for t in Tweets:\n if tokenize_porter:\n tok_temp = tokenizer_porter(t,porter)#word_tokenize(t)\n else:\n tok_temp = tokenizer(t)\n if stop_words:\n tok_temp = [word for word in tok_temp if not word in all_stopwords]\n tokens[\"all\"] += tok_temp\n if Labels[count]=='Very Positive':\n tokens[\"tok_vp\"] += tok_temp\n elif Labels[count]=='Positive':\n tokens[\"tok_p\"] += tok_temp\n elif Labels[count]=='Neutral':\n tokens[\"tok_ne\"] += tok_temp\n elif Labels[count]=='Negative':\n tokens[\"tok_n\"] += tok_temp\n elif Labels[count]=='Very Negative':\n tokens[\"tok_vn\"] += tok_temp\n elif Labels[count]=='Query':\n tokens[\"tok_q\"] += tok_temp\n else:\n pass\n count+=1\n return tokens\n\n####################################################################################\n\n####################################################################################\n\ndef most_common(tokens):\n c= Counter(tokens[\"all\"])\n c_vp= Counter(tokens[\"tok_vp\"])\n c_p= Counter(tokens[\"tok_p\"])\n c_ne= Counter(tokens[\"tok_ne\"])\n c_n= Counter(tokens[\"tok_n\"])\n c_vn= Counter(tokens[\"tok_vn\"])\n c_q= Counter(tokens[\"tok_q\"])\n\n print(\"Most common tokens in all vocabulary: \\n\",c.most_common(10),'\\n')\n print(\"Most common tokens in VeryPositive: \\n\",c_vp.most_common(10),'\\n')\n print(\"Most common tokens in Positive: \\n\",c_p.most_common(10),'\\n')\n print(\"Most common tokens in Neutral: \\n\",c_ne.most_common(10),'\\n')\n print(\"Most common tokens in Negative: \\n\",c_n.most_common(10),'\\n')\n print(\"Most common tokens in VeryNegative: \\n\",c_vn.most_common(10),'\\n')\n print(\"Most common tokens in Query: \\n\",c_q.most_common(10),'\\n')\n return c,c_vp,c_p,c_ne,c_n,c_vn,c_q\n \n####################################################################################\n\n####################################################################################\n\ndef read_output(reader,num_annotate,shift):\n Labels2=[]\n Tweets=[]\n Labels1=[]\n brand=[]\n num_tweets=0\n for line in reader:\n count=0\n for field in line:\n count +=1\n if count==1:\n brand.append(field)\n elif(count==2):\n Tweets.append(field)\n elif (count==3):\n Labels1.append(field)\n elif (count==4) and num_annotate==2:\n Labels2.append(field)\n \n num_tweets+=1\n if num_tweets>0 and shift==True:\n brand = brand[1:np.size(Tweets)]\n Tweets = Tweets[1:np.size(Tweets)]\n Labels1 = Labels1[1:np.size(Labels1)]\n if num_annotate==2:\n Labels2 = Labels2[1:np.size(Labels2)]\n return Tweets,Labels1,Labels2,brand\n \n####################################################################################\n\n####################################################################################\n\n\ndef store_data(filename,Tweets_cleaned,Labels,brand):\n # Storing to csv File\n count = np.size(Labels)\n with open(filename, 'w', newline='') as file:\n writer = csv.writer(file)\n writer.writerow(['Brand','Comments', 'Labels'])\n for c in range(count):\n writer.writerow([brand[c],Tweets_cleaned[c], Labels[c]])\n file.close()\n\n####################################################################################\n\n####################################################################################\n\ndef category_reduction(cm):\n\n c11= cm[0][0]+cm[0][1]+cm[1][0]+cm[1][1]\n c12= cm[0][2]+cm[0][3]+cm[1][2]+cm[1][3]\n c13= cm[0][4]+cm[0][5]+cm[1][4]+cm[1][5]\n\n\n c21= cm[2][0]+cm[2][1]+cm[3][0]+cm[3][1]\n c22= cm[2][2]+cm[2][3]+cm[3][2]+cm[3][3]\n c23= cm[2][4]+cm[2][5]+cm[3][4]+cm[3][5]\n\n c31= cm[4][0]+cm[4][1]+cm[5][0]+cm[5][1]\n c32= cm[4][2]+cm[4][3]+cm[5][2]+cm[5][3]\n c33= cm[4][4]+cm[4][5]+cm[5][4]+cm[5][5]\n\n\n c = [[c11, c12,c13],\n [c21, c22,c23],\n [c31, c32,c33]]\n\n fig = plt.figure(figsize=(20, 8))\n ax = fig.add_subplot(121)\n cax = ax.matshow(c,cmap=plt.get_cmap('Blues'))\n plt.title('Confusion matrix of predictions')\n\n N2= np.shape(c)[0]\n thresh=900\n for i in range(N2):\n for j in range(N2):\n plt.text(j, i, \"{:,}\".format(c[i][j]),\n horizontalalignment=\"center\",\n color=\"White\" if c[i][j] > thresh else \"black\")\n\n labels=['Positive', 'Neutral','Negative']\n plt.colorbar(cax)\n ax.set_xticklabels([''] + labels)\n ax.set_yticklabels([''] + labels)\n plt.xlabel('predictions')\n plt.ylabel('actual labels')\n plt.show()\n \n accuracy = (c11+c22+c33)/np.sum(c)\n print('Accuracy : ', accuracy*100)\n p_o = np.trace(np.asarray(c))\n \n #observed_agreement_rate\n observed_agreement_rate = p_o/np.sum(c)\n # random_chance_agreement_rate\n p_e = np.sum(np.sum(c,0)*np.sum(c,1))/np.sum(c) \n\n kappa = (p_o - p_e)/(np.sum(c)-p_e)\n print('Observed Agreement :', p_o)\n print(\"Random Chance Agreement :\", p_e)\n print(\"Kappa :\", kappa)\n \n return c, accuracy\n\n####################################################################################\n\n####################################################################################\n \ndef plot_results(preds,labels,y_test):\n\n\n cm = metrics.confusion_matrix(y_test, preds, labels)\n conf_mat = cm\n\n \n fig = plt.figure(figsize=(8, 6))\n ax = fig.add_subplot(111)\n cax = ax.matshow(conf_mat,cmap=plt.get_cmap('Greens'))\n plt.title('Confusion matrix of predictions')\n \n thresh = 0.001#maxcm/0.1\n \n \n label =np.unique(y_test,return_counts=False)\n\n N = np.size(labels);\n for i in range(N):\n for j in range(N):\n plt.text(j, i, \"{:,}\".format(conf_mat[i][j]),\n horizontalalignment=\"center\",\n color=\"black\" if conf_mat[i][j] > thresh else \"black\")\n\n plt.colorbar(cax)\n ax.set_xticklabels([''] + labels)\n ax.set_yticklabels([''] + labels)\n plt.xlabel('predictions')\n plt.ylabel('actual labels')\n plt.show()\n return conf_mat\n\n####################################################################################\n\n####################################################################################\n\ndef clean_data(Tweets,brand,all_stopwords):\n count = 0\n\n Tweets_cleaned = []\n\n # rules for elements being eliminated from the tweets\n r_at = r'@[A-Za-z0-9_]+' # Removing @ from all tweets\n r_hash = r'#[A-Za-z0-9_]+' # Removing hash tags\n r_rt = r'RT ' # Removing RT i.e. if the tweet is a retweet\n r_emoji = '[^a-zA-Z]' # Removing emoji and replacing with space\n #r_brandtag=r'#'+brand.lower()\n vectorizer = TfidfVectorizer()\n tokens=[]\n r_se = r'[:]]'\n r_se2 = r'[=)]'\n r_se3 = r'[:-D]'\n r_se4 = r'[:D]'\n r_se5 = r'[=D]'\n r_se6 = r'[:)]'\n\n r_sae2 = r'[:(]'\n r_sae3 = r'[:[]'\n r_sae4 = r'[=(]'\n \n r_ae = r'[>:(]'\n r_ae2 = r'[>:(]'\n \n r_le = r'[(y)]'\n r_le2 = r'[(Y)]'\n i=0\n for t in Tweets:\n \n r_brandtag=r'#'+brand[i].lower()\n clean_tweets = re.sub(r'|'.join((r_at, r_rt)),'',t) \n \n clean_tweets = re.sub('https?:[A-Za-z0-9./]+','URL',clean_tweets)\n #clean_tweets = re.sub(r_brandtag,'Brandtag ',clean_tweets)\n #clean_tweets = re.sub(r''+brand,'Brandtag ',clean_tweets)\n #clean_tweets = re.sub(r'|'.join((r_se,r_se2,r_se3,r_se4,r_se5,r_se6)),'happy',clean_tweets)\n #clean_tweets = re.sub(r'|'.join((r_sae2,r_sae3,r_sae4)),'sad',clean_tweets)\n #clean_tweets = re.sub(r'|'.join((r_ae,r_ae2)),'angry',clean_tweets)\n #clean_tweets = re.sub(r'|'.join((r_le,r_le2)),'like',clean_tweets)\n #clean_tweets = re.sub(r'[<3]','love',clean_tweets)\n clean_tweets = re.sub(r_hash, ' ', clean_tweets)\n clean_tweets = re.sub(r_emoji, ' ', clean_tweets)\n clean_tweets = re.sub('[\\s ]+', ' ',clean_tweets)\n clean_tweets = clean_tweets.lower()\n clean_tweets = re.sub(r_brandtag,'Brandtag ',clean_tweets)\n i+=1\n \n\n Tweets_cleaned.append(clean_tweets)\n tokens+=tokenizer_porter(clean_tweets)\n tokens = [word for word in tokens if not word in all_stopwords]\n\n\n vectorizer.fit_transform(Tweets_cleaned)\n\n #print(vectorizer.idf_)\n #print(vectorizer.vocabulary_)\n return Tweets_cleaned#, tokens\n\n\n####################################################################################\n\n####################################################################################\n\ndef count_labels(Labels1, Labels2):\n \n cm ={\"\":0,\n \"Very Negative\" : 0,\n \"Negative\": 0,\n \"Neutral\" : 0,\n \"Positive\" : 0,\n \"Very Positive\" : 0,\n \"Query\":0,\n \n \"Very NegativeNeutral\" :0,\n \"Very NegativePositive\" : 0,\n \"Very NegativeVery Positive\" :0,\n \"Very NegativeNegative\" : 0,\n \"Very NegativeQuery\" : 0,\n \n \"NegativeNeutral\" :0,\n \"NegativePositive\" : 0,\n \"NegativeVery Positive\" :0,\n \"NegativeVery Negative\" : 0,\n \"NegativeQuery\" : 0,\n \n \"NeutralVery Negative\" :0,\n \"NeutralNegative\" : 0,\n \"NeutralVery Positive\" :0,\n \"NeutralPositive\" : 0,\n \"NeutralQuery\" : 0,\n \n \"PositiveNeutral\" :0,\n \"PositiveNegative\" : 0,\n \"PositiveVery Positive\" :0,\n \"PositiveVery Negative\" : 0,\n \"PositiveQuery\" : 0,\n \n \"Very PositiveNeutral\" :0,\n \"Very PositiveNegative\" : 0,\n \"Very PositivePositive\" :0,\n \"Very PositiveVery Negative\" : 0,\n \"Very PositiveQuery\" : 0,\n \n \"QueryVery Positive\" :0,\n \"QueryNegative\" : 0,\n \"QueryPositive\" :0,\n \"QueryVery Negative\" : 0,\n \"QueryNeutral\" : 0,\n \n }\n try: \n assert np.size(Labels1)==np.size(Labels2)\n except:\n print('Labels don\\'t have same size')\n return cm\n N = np.size(Labels1)\n for i in range(N):\n if Labels1[i]==Labels2[i]:\n try:\n cm[Labels1[i]] += 1\n except:\n pass\n #print(Labels1[i])\n else:\n #if Labels1[i]!='Query' and Labels2[i]!='Query':\n cm[Labels1[i]+Labels2[i]] +=1\n conf_mat =[\n [cm[\"Very Negative\"], cm[\"Very NegativeNegative\"], \n cm[\"Very NegativeNeutral\"], cm[\"Very NegativeQuery\"], cm[\"Very NegativePositive\"],\n cm[\"Very NegativeVery Positive\"]],\n \n [cm[\"NegativeVery Negative\"], cm[\"Negative\"], \n cm[\"NegativeNeutral\"],cm[\"NegativeQuery\"], cm[\"NegativePositive\"],\n cm[\"NegativeVery Positive\"]],\n \n [cm[\"NeutralVery Negative\"], cm[\"NeutralNegative\"], \n cm[\"Neutral\"],cm[\"NeutralQuery\"], cm[\"NeutralPositive\"],\n cm[\"NeutralVery Positive\"]],\n \n [cm[\"QueryVery Negative\"], cm[\"QueryNegative\"], \n cm[\"QueryNeutral\"], cm[\"Query\"] , cm[\"QueryPositive\"],\n cm[\"QueryVery Positive\"]],\n \n [cm[\"PositiveVery Negative\"], cm[\"PositiveNegative\"], \n cm[\"PositiveNeutral\"],cm[\"PositiveQuery\"], cm[\"Positive\"],\n cm[\"PositiveVery Positive\"]],\n \n [cm[\"Very PositiveVery Negative\"], cm[\"Very PositiveNegative\"], \n cm[\"Very PositiveNeutral\"],cm[\"Very PositiveQuery\"], \n cm[\"Very PositivePositive\"],cm[\"Very Positive\"]]\n \n \n ]\n \n labels = ['VeryPositive', 'Positive', 'Neutral','Query', 'Negative', 'VeryNegative']\n \n fig = plt.figure(figsize=(8, 6))\n ax = fig.add_subplot(111)\n cax = ax.matshow(conf_mat,cmap=plt.get_cmap('Greens'))\n plt.title('Confusion matrix of labelled annotations')\n \n maxcm=max(max(conf_mat))\n thresh = maxcm/0.1\n \n \n #label =np.unique(Labels1,return_counts=False)\n N = np.size(labels);\n for i in range(N):\n for j in range(N):\n plt.text(j, i, \"{:,}\".format(conf_mat[i][j]),\n horizontalalignment=\"center\",\n color=\"white\" if conf_mat[i][j] > thresh else \"black\")\n\n\n plt.colorbar(cax)\n ax.set_xticklabels([''] + labels)\n ax.set_yticklabels([''] + labels)\n plt.xlabel('Annotator 2')\n plt.ylabel('Annotator 1')\n plt.show()\n #observed_agreement\n p_o = np.trace(np.asarray(conf_mat))\n #observed_agreement_rate\n observed_agreement_rate = p_o/np.sum(conf_mat)\n # random_chance_agreement_rate\n p_e = np.sum(np.sum(conf_mat,0)*np.sum(conf_mat,1))/np.sum(conf_mat) \n \n kappa = (p_o - p_e)/(np.sum(conf_mat)-p_e)\n print('Observed Agreement :', p_o)\n print(\"Random Chance Agreement :\", p_e)\n print(\"Kappa :\", kappa)\n #label, count =np.unique(Labels,return_counts=True)\n #cm =[count[5],count[3],count[2],count[1],count[4]]\n return cm\n \n####################################################################################\n\n####################################################################################\n\ndef return_stopwords(lists=[],append=True):\n all_stopwords = stopwords.words(\"english\")\n if append:\n all_stopwords.append('brandtag')\n all_stopwords.append('url')\n all_stopwords.append('season')\n all_stopwords.append('series')\n all_stopwords.append('review')\n all_stopwords.append('seri')\n all_stopwords.append('ki')\n all_stopwords.append('ha')\n all_stopwords.append('ka')\n all_stopwords.append('ke')\n all_stopwords.append('hai')\n all_stopwords.append('k')\n all_stopwords.append('ko')\n all_stopwords.append('hain')\n all_stopwords.append('ho')\n all_stopwords.append('se')\n all_stopwords.append('ye')\n all_stopwords.append('bhi')\n all_stopwords.append('mein')\n all_stopwords.append('koi')\n all_stopwords.append('kia')\n all_stopwords.append('b')\n all_stopwords.append('ya')\n all_stopwords.append('yeh')\n all_stopwords.append('ab')\n all_stopwords.append('hi')\n all_stopwords.append('aur')\n all_stopwords.append('hy')\n all_stopwords.append('kya')\n all_stopwords.append('h')\n all_stopwords.append('is')\n all_stopwords.append('he')\n all_stopwords.append('hi')\n all_stopwords.append('to')\n all_stopwords.append('and')\n all_stopwords.append('or')\n all_stopwords.append('i')\n all_stopwords.append('me')\n if lists!=[]:\n for i in range(np.size(lists)):\n all_stopwords.append(lists[i])\n return all_stopwords", "sub_path": "HelperFunctions2.py", "file_name": "HelperFunctions2.py", "file_ext": "py", "file_size_in_byte": 16828, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "nltk.stem.snowball.SnowballStemmer", "line_number": 13, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 32, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 95, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 96, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 97, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 98, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 99, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 100, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 151, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.trace", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 213, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 227, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 231, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 233, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 234, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 253, "usage_type": "name"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 271, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 293, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 295, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 303, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 304, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 305, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 379, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 420, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 420, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 422, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 422, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 423, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 423, "usage_type": "name"}, {"api_name": "numpy.size", "line_number": 430, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 433, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 433, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 438, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 438, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 441, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 441, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 442, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 442, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 443, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 443, "usage_type": "name"}, {"api_name": "numpy.trace", "line_number": 445, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 445, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 447, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 449, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 451, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 464, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 464, "usage_type": "name"}, {"api_name": "numpy.size", "line_number": 505, "usage_type": "call"}]} +{"seq_id": "293527893", "text": "\nimport os.path\nfrom importlib import import_module\n\ndef enumerate_plugins(package_path, namespace, class_, attributes={}):\n\n \"\"\"Import plugins of type `class` located at `dirpath` into the\n `namespace` that starts with `module_prefix`. If `dirpath` represents a\n filepath then it is converted into its containing directory. The\n `attributes` dictionary allows one to set extra fields for all imported\n plugins. Using `as_dict` a dictionary based on the module name is\n returned.\"\"\"\n\n try:\n dirpath = import_module(package_path).__file__\n except ImportError as e:\n raise ImportError(\n f\"Unable to import plugins from package path: {package_path}. {e}\"\n )\n if os.path.isfile(dirpath):\n dirpath = os.path.dirname(dirpath)\n\n for fname in os.listdir(dirpath):\n if fname.endswith(\".py\") and not fname.startswith(\"__init__\"):\n module_name, _ = os.path.splitext(fname)\n try:\n import_module(f\"{package_path}.{module_name}\")\n except ImportError as e:\n raise ImportError(\n \"Unable to load the Cuckoo plugin at %s: %s. Please \"\n \"review its contents and/or validity!\" % (fname, e)\n )\n\n subclasses = class_.__subclasses__()[:]\n\n plugins = []\n while subclasses:\n subclass = subclasses.pop(0)\n\n # Include subclasses of this subclass (there are some subclasses, e.g.,\n # Libvirt machineries such as KVM. KVM<-Libvirt<-Machinery\n subclasses.extend(subclass.__subclasses__())\n\n # Check whether this subclass belongs to the module namespace that\n # we are currently importing. It should be noted that parent and child\n # namespaces should fail the following if-statement.\n if package_path != \".\".join(subclass.__module__.split(\".\")[:-1]):\n continue\n\n namespace[subclass.__name__] = subclass\n for key, value in attributes.items():\n setattr(subclass, key, value)\n\n plugins.append(subclass)\n\n return sorted(plugins, key=lambda x: x.__name__.lower())\n", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2132, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "importlib.import_module", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.path.isfile", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 20, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 21, "usage_type": "name"}, {"api_name": "os.path.listdir", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.path.splitext", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 25, "usage_type": "name"}, {"api_name": "importlib.import_module", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "499970286", "text": "from collections import Counter \nfrom string import ascii_lowercase\nfrom itertools import product, cycle\n\nD_frequency = { \\\n 'a' : .08167, 'b' : .01492, \\\n 'c' : .02782, 'd' : .04253, \\\n 'e' : .12702, 'f' : .02228, \\\n 'g' : .02015, 'h' : .06094, \\\n 'i' : .06966, 'j' : .00153, \\\n 'k' : .00772, 'l' : .04025, \\\n 'm' : .02406, 'n' : .06749, \\\n 'o' : .07507, 'p' : .01929, \\\n 'q' : .00095, 'r' : .05987, \\\n 's' : .06327, 't' : .09056, \\\n 'u' : .02758, 'v' : .00978, \\\n 'w' : .02360, 'x' : .00150, \\\n 'y' : .01974, 'z' : .00074 }\n \ndef euclidean_distance_frequency_letter(i_C) :\n DD = {k : v/sum(i_C.values()) for (k, v) in i_C.items()}\n for k in set(D_frequency.keys()) - set(i_C.keys()) :\n DD[k] = 0\n return sum((D_frequency[k] - DD[k])**2 for k in D_frequency.keys())\n\na = \"\"\nwith open(\"projectEuler_i059.txt\", \"r\") as f :\n\tfor line in f :\n\t\ta += line\n\na = [int(i) for i in a.split(',')\t]\n\n\nl = []\nfor X in product(ascii_lowercase, repeat = 3) :\n temp_C = Counter(''.join(chr(c ^ ord(x)).lower() for (c, x) in zip(a, cycle(X)) if chr(c ^ ord(x)).isalpha()))\n l.append((X, euclidean_distance_frequency_letter(temp_C)))\n\n\nl = sorted(l, key = lambda t : t[1])\ns_result = ''.join(chr(c ^ ord(x)) for (c, x) in zip(a, cycle(l[0][0])))\n\n# print results for safety sake\n# print(l[0][0])\n# print(s_result)\n \nprint(sum(ord(c) for c in s_result))\n", "sub_path": "projectEuler_p059.py", "file_name": "projectEuler_p059.py", "file_ext": "py", "file_size_in_byte": 1385, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "itertools.product", "line_number": 35, "usage_type": "call"}, {"api_name": "string.ascii_lowercase", "line_number": 35, "usage_type": "argument"}, {"api_name": "collections.Counter", "line_number": 36, "usage_type": "call"}, {"api_name": "itertools.cycle", "line_number": 36, "usage_type": "call"}, {"api_name": "itertools.cycle", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "210469804", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n \nimport boto.kinesis,datetime,time\n \nconnection = boto.kinesis.connect_to_region('us-east-1')\nstream_name = 'nakanishi-kinesis-test'\n \nstream = connection.describe_stream(stream_name)\nshards = stream['StreamDescription']['Shards'][0]['ShardId']\n \nkinesis_iterator = connection.get_shard_iterator(stream_name,shards,'LATEST') \n \nnext_iterator = None\nwhile True:\n if next_iterator is None:\n next_iterator = kinesis_iterator['ShardIterator']\n else:\n next_iterator = responce['NextShardIterator']\n \n responce = None\n responce = connection.get_records(next_iterator,limit=1)\n print(responce['Records'])\n time.sleep(1)", "sub_path": "python_kinesis_test/getdata.py", "file_name": "getdata.py", "file_ext": "py", "file_size_in_byte": 687, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "boto.kinesis.kinesis.connect_to_region", "line_number": 6, "usage_type": "call"}, {"api_name": "boto.kinesis.kinesis", "line_number": 6, "usage_type": "attribute"}, {"api_name": "boto.kinesis", "line_number": 6, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "337181920", "text": "'''\nALARM CLOCK\n\nA simple clock where it plays a sound after X\nnumber of minutes/seconds or at a particular time\n'''\n\nimport pygame\nimport time\n\ndef playSound():\n\tpygame.mixer.init()\n\tpygame.mixer.music.load(\"Assets/Mp3-alarm-clock.mp3\")\n\tpygame.mixer.music.play()\n\twhile pygame.mixer.music.get_busy() == True:\n\t continue\n\nseconds = raw_input(\"Enter seconds for alarm: \")\nseconds = int(seconds)\n\nwhile (seconds >= 0):\n\tprint(seconds)\n\tseconds -= 1\n\ttime.sleep(1)\n\nplaySound()\n\n\n\n\n\n\n\n", "sub_path": "Python/alarm_clock.py", "file_name": "alarm_clock.py", "file_ext": "py", "file_size_in_byte": 486, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.mixer.init", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.get_busy", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 15, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "508536490", "text": "from django.shortcuts import render, HttpResponseRedirect\nfrom django.urls import reverse\nfrom django.contrib import messages\nfrom products.models import Productwithimage, Variation\n\nfrom .models import Cart, CartItem\n\ndef view(request):\n\ttry:\n\t\tthe_id =request.session['cart_id']\n\texcept:\n\t\tthe_id =None\n\tif the_id:\n\t\tcart = Cart.objects.get(id=the_id)\n\t\tnew_total = 0.00\n\t\tfor item in cart.cartitem_set.all():\n\t\t\tline_total = float(item.product.price) * item.quantity\n\t\t\tnew_total += line_total\n\n\t\trequest.session['items_total']= cart.cartitem_set.count()\n\t\tcart.total = new_total\n\t\tcart.save()\n\t\tcontext ={\"cart\":cart}\n\telse :\n\t\tempty_message =\"Your cart is currently empty!\"\n\t\tcontext ={\"empty\": True, \"empty_message\": empty_message}\n\t\t\n\ttemplate =\"carts/view.html\" \n\treturn render(request, template, context)\n\ndef remove_from_cart(request, id):\n\ttry:\n\t\tthe_id =request.session['cart_id']\n\t\tcart = Cart.objects.get(id=the_id)\n\texcept:\n\t\treturn HttpResponseRedirect(reverse(\"cart\"))\n\n\tcartitem = CartItem.objects.get(id=id)\n\tcartitem.delete()\n\t#cartitem.cart = None\n\t#cartitem.save()\n\n\treturn HttpResponseRedirect(reverse(\"cart\"))\n\t\t\n\n\ndef add_to_cart(request, slug):\n\trequest.session.set_expiry(30000)\n\n\n\n\ttry:\n\t\tthe_id = request.session['cart_id']\n\texcept:\n\t\tnew_cart = Cart()\n\t\tnew_cart.save()\n\t\trequest.session['cart_id'] = new_cart.id\n\t\tthe_id =new_cart.id\n\n\tcart = Cart.objects.get(id=the_id)\n\n\ttry:\n\t\tproduct = Productwithimage.objects.get(slug=slug)\n\texcept Productwithimage.DoesNotExist:\n\t\tpass\n\texcept:\n\t\tpass\n\n\tproduct_var = [] #product_variation\n\tif request.method == \"POST\":\n\t\tqty = request.POST['qty']\n\n\t\tfor item in request.POST:\n\t\t\tkey = item\n\t\t\tval = request.POST[key] \n\t\t\ttry: \n\t\t\t\tv = Variation.objects.get(product=product, category__iexact=key, title__iexact=val)\n\t\t\t\tproduct_var.append(v)\n\t\t\texcept: \n\t\t\t\tpass\n\t\tcart_item = CartItem.objects.create(cart=cart, product=product)\n\t\tif len(product_var) > 0:\n\t\t\tcart_item.variations.add(*product_var)\n\t\tcart_item.quantity = qty\n\t\tcart_item.save()\n\t\t#success message (possible)\n\n\t\n\t\n\n\t\t\n\t\treturn HttpResponseRedirect(reverse(\"cart\"))\n\n\treturn HttpResponseRedirect(reverse(\"cart\"))\n\n\n\n\n\t\n\n\n", "sub_path": "carts/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2157, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "models.Cart.objects.get", "line_number": 14, "usage_type": "call"}, {"api_name": "models.Cart.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.Cart", "line_number": 14, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 29, "usage_type": "call"}, {"api_name": "models.Cart.objects.get", "line_number": 34, "usage_type": "call"}, {"api_name": "models.Cart.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.Cart", "line_number": 34, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 36, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 36, "usage_type": "call"}, {"api_name": "models.CartItem.objects.get", "line_number": 38, "usage_type": "call"}, {"api_name": "models.CartItem.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.CartItem", "line_number": 38, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 43, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 43, "usage_type": "call"}, {"api_name": "models.Cart", "line_number": 55, "usage_type": "call"}, {"api_name": "models.Cart.objects.get", "line_number": 60, "usage_type": "call"}, {"api_name": "models.Cart.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "models.Cart", "line_number": 60, "usage_type": "name"}, {"api_name": "products.models.Productwithimage.objects.get", "line_number": 63, "usage_type": "call"}, {"api_name": "products.models.Productwithimage.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "products.models.Productwithimage", "line_number": 63, "usage_type": "name"}, {"api_name": "products.models.Productwithimage.DoesNotExist", "line_number": 64, "usage_type": "attribute"}, {"api_name": "products.models.Productwithimage", "line_number": 64, "usage_type": "name"}, {"api_name": "products.models.Variation.objects.get", "line_number": 77, "usage_type": "call"}, {"api_name": "products.models.Variation.objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "products.models.Variation", "line_number": 77, "usage_type": "name"}, {"api_name": "models.CartItem.objects.create", "line_number": 81, "usage_type": "call"}, {"api_name": "models.CartItem.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "models.CartItem", "line_number": 81, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 92, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 92, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 94, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "390115856", "text": "#!/usr/bin/env python3\n# coding=utf-8\n\nfrom functools import wraps\nfrom utilities import MetaSingleton\n\n\nclass App(metaclass=MetaSingleton):\n \"\"\"\n A Class for storing and managing components of an application.\n It allows to store and read configuration data permanently in a configuration file.\n Beside that it can work as a central hub for managing the aplication flow.\n \"\"\"\n def __init__(self, conf_component, db_component, storage_component, view_component):\n self._conf = conf_component\n self._db = db_component\n self._storage = storage_component\n self._view = view_component\n\n def load_conf(self, path=None):\n if path:\n self._conf.set_source(path=path)\n self._conf.load()\n elif self.is_ready(\"conf\"):\n self._conf.load()\n else:\n raise FileNotFoundError(\"path not set hence config can't load\")\n # TODO: check if any components can be set initially with loaded config file\n\n def is_ready(self, component):\n attr = \"_\" + component\n try:\n result = self.__dict__[attr].is_valid()\n except KeyError:\n raise KeyError(\"No component by the name of '\" + component + \"'.\")\n else:\n return result\n\n def setup_database(self, file=\":memory:\", user=None, password=None, url=None, db=None):\n if user and password and url and db:\n self._db.set(user=user, password=password, url=url, db=db)\n else:\n self._db.set(file=file)\n\n def setup_storage(self, location=None):\n self._storage.set(location=location)\n\n def setup_view(self, **kwargs):\n self._view.set(**kwargs)\n\n def start(self):\n # TODO: Implement App.start() when all components are finished\n pass\n\n\nclass Component:\n \"\"\"\n Base class for all components used by App class. It serves both: providing a common\n interface and internal manageing functionality.\n \"\"\"\n def __init__(self):\n self._state_postive = dict()\n self._state_negative = dict()\n self._states_alternate_positive = dict()\n self._states_alternate_negative = dict()\n\n @classmethod\n def dependent(cls, func):\n @wraps(func)\n def wrapper(self, *args, **kwargs):\n if self.is_valid():\n return func(self, *args, **kwargs)\n else:\n raise ValueError(\"{} object is not in a valid state.\".format(cls))\n return wrapper\n\n def valid_state(self, member, value, alternate=False):\n if not alternate:\n self._state_postive[member] = value\n else:\n self._states_alternate_positive.update({member: value})\n return value\n\n def invalid_state(self, member, value, alternate=False):\n if not alternate:\n self._state_negative[member] = value\n else:\n self._states_alternate_negative.update({member: value})\n return value\n\n def is_valid(self):\n for member in self._state_postive:\n if self._state_postive[member] != self.__dict__[member]:\n return False\n for member in self._state_negative:\n if self._state_negative[member] == self.__dict__[member]:\n return False\n if len(self._states_alternate_positive) > 0:\n false_state = 0\n for member, value in self._states_alternate_positive.items():\n if value != self.__dict__[member]:\n false_state += 1\n if false_state == len(self._states_alternate_positive):\n return False\n if len(self._states_alternate_negative) > 0:\n false_state = 0\n for member, value in self._states_alternate_negative.items():\n if value == self.__dict__[member]:\n false_state += 1\n if false_state == len(self._states_alternate_negative):\n return False\n return True\n", "sub_path": "diary/application.py", "file_name": "application.py", "file_ext": "py", "file_size_in_byte": 3977, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "utilities.MetaSingleton", "line_number": 8, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "546224427", "text": "from books_authors_app.models import Book, Author\nfrom django.shortcuts import redirect, render, HttpResponse\n\n\ndef index(request):\n context = {\"saludo\": \"Hola \"}\n return render(request, \"index.html\", context)\n\n\ndef author(request):\n books = Book.objects.all()\n authors = Author.objects.all()\n context = {\"books\": books, \"authors\": authors}\n return render(request, \"authors.html\", context)\n\n\ndef book(request):\n books = Book.objects.all()\n authors = Author.objects.all()\n context = {\"saludo\": \"Hola libros\", \"books\": books, \"authors\": authors}\n return render(request, \"books.html\", context)\n\n\ndef add_authors(request):\n a_nombre = request.POST[\"nombre\"]\n a_apellido = request.POST[\"apellido\"]\n a_notas = request.POST[\"notas\"]\n Author.objects.create(first_name=a_nombre, last_name=a_apellido, notes=a_notas)\n return redirect(\"/author\")\n\n\ndef add_books(request):\n b_titulo = request.POST[\"titulo\"]\n b_desc = request.POST[\"descripcion\"]\n Book.objects.create(title=b_titulo, desc=b_desc)\n return redirect(\"/book\")\n\n\ndef author_view(request, a_id):\n authors = Author.objects.get(id=a_id)\n books = Book.objects.all()\n context = {\"authors\": authors, \"books\": books}\n return render(request, \"authorview.html\", context)\n\n\ndef book_view(request, b_id):\n authors = Author.objects.all()\n books = Book.objects.get(id=b_id)\n context = {\"authors\": authors, \"books\": books}\n return render(request, \"bookview.html\", context)\n\n\ndef add_book_to_author(request):\n b_id = int(request.POST[\"book_id\"])\n a_id = int(request.POST[\"author_id\"])\n author = Author.objects.get(id=a_id)\n book = Book.objects.get(id=b_id)\n author.books.add(book)\n return redirect(request.META.get(\"HTTP_REFERER\"))\n\n\ndef add_author_to_book(request):\n b_id = int(request.POST[\"book_id\"])\n a_id = int(request.POST[\"author_id\"])\n author = Author.objects.get(id=a_id)\n book = Book.objects.get(id=b_id)\n book.authors.add(author)\n return redirect(request.META.get(\"HTTP_REFERER\"))\n", "sub_path": "books_authors_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2043, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.shortcuts.render", "line_number": 7, "usage_type": "call"}, {"api_name": "books_authors_app.models.Book.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "books_authors_app.models.Book.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Book", "line_number": 11, "usage_type": "name"}, {"api_name": "books_authors_app.models.Author.objects.all", "line_number": 12, "usage_type": "call"}, {"api_name": "books_authors_app.models.Author.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Author", "line_number": 12, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "books_authors_app.models.Book.objects.all", "line_number": 18, "usage_type": "call"}, {"api_name": "books_authors_app.models.Book.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Book", "line_number": 18, "usage_type": "name"}, {"api_name": "books_authors_app.models.Author.objects.all", "line_number": 19, "usage_type": "call"}, {"api_name": "books_authors_app.models.Author.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Author", "line_number": 19, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 21, "usage_type": "call"}, {"api_name": "books_authors_app.models.Author.objects.create", "line_number": 28, "usage_type": "call"}, {"api_name": "books_authors_app.models.Author.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Author", "line_number": 28, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 29, "usage_type": "call"}, {"api_name": "books_authors_app.models.Book.objects.create", "line_number": 35, "usage_type": "call"}, {"api_name": "books_authors_app.models.Book.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Book", "line_number": 35, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 36, "usage_type": "call"}, {"api_name": "books_authors_app.models.Author.objects.get", "line_number": 40, "usage_type": "call"}, {"api_name": "books_authors_app.models.Author.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Author", "line_number": 40, "usage_type": "name"}, {"api_name": "books_authors_app.models.Book.objects.all", "line_number": 41, "usage_type": "call"}, {"api_name": "books_authors_app.models.Book.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Book", "line_number": 41, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 43, "usage_type": "call"}, {"api_name": "books_authors_app.models.Author.objects.all", "line_number": 47, "usage_type": "call"}, {"api_name": "books_authors_app.models.Author.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Author", "line_number": 47, "usage_type": "name"}, {"api_name": "books_authors_app.models.Book.objects.get", "line_number": 48, "usage_type": "call"}, {"api_name": "books_authors_app.models.Book.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Book", "line_number": 48, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 50, "usage_type": "call"}, {"api_name": "books_authors_app.models.Author.objects.get", "line_number": 56, "usage_type": "call"}, {"api_name": "books_authors_app.models.Author.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Author", "line_number": 56, "usage_type": "name"}, {"api_name": "books_authors_app.models.Book.objects.get", "line_number": 57, "usage_type": "call"}, {"api_name": "books_authors_app.models.Book.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Book", "line_number": 57, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 59, "usage_type": "call"}, {"api_name": "books_authors_app.models.Author.objects.get", "line_number": 65, "usage_type": "call"}, {"api_name": "books_authors_app.models.Author.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Author", "line_number": 65, "usage_type": "name"}, {"api_name": "books_authors_app.models.Book.objects.get", "line_number": 66, "usage_type": "call"}, {"api_name": "books_authors_app.models.Book.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "books_authors_app.models.Book", "line_number": 66, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "143963483", "text": "import xlwt\nimport traceback\nfrom PyQt5.QtCore import QDate\n\nfrom Common_Function import *\nfrom history import Ui_MainWindow\n773\nGet_data_num=[]\n#All_name = ['TYPEOF', 'TXM', 'WUPIN_NAME', 'CDT', 'WUPIN_OK', 'WUPIN_NG', 'COMMENTS', 'QTY', 'INOUT', 'STATUS','USERNAME', 'USERBUM', 'USERPHONE', 'STATION', 'ENG_NAME']\nAll_name = [\"操作时间\",\"名称\",\"条形码\",\"物品全称\",\"数量\",\"出入库类型\",\"出入库方式\",\"所用站点\",\"更换人\",\"领用课别\",\"联系��式\",\"ENG人员\",\"ok编码\",\"ng编码\",\"备注\"]\nNew_All_name=['时间','人员选择','名称','机种','系统','物品全称','条形码','数量','出入库类型','出入库方式','更换人','联系方式','更换人工号','所用站点','备注']\n\n\nclass Thing_History(Ui_MainWindow,Farther,No_Main_Window,QMainWindow):\n def __init__(self):\n super().__init__()\n self.setupUi(self)\n self.Seek_sql_data()\n self.setWindowFlags(QtCore.Qt.WindowMinimizeButtonHint)\n self.center()\n self.com_lines.addItems(Read_load_info())\n self.set_check()\n\n self.action_to_lend.triggered.connect(self.to_lend)\n self.action_lend.triggered.connect(self.lend)\n\n\n #一个返回函数\n def return_mainwindow(self):\n print('this is thing history seek')\n\n\n\n def clear(self):\n self.tableWidget.clear()\n if self.History_chioce.currentText()=='旧记录':\n self.tableWidget.setHorizontalHeaderLabels(All_name)\n else:\n self.tableWidget.setHorizontalHeaderLabels(New_All_name)\n\n def Choice_operation(self,Choice):\n try:\n\n start_datetime = self.dat_start.text() + ' ' + self.tim_start.text() + ':000'\n fiannly_datetime = self.dat_finally.text() + ' ' + self.tim_finally.text() + ':000'\n if self.History_chioce.currentText() == '旧记录':\n while_num=15\n sql_order = \"select CDT,TYPEOF,TXM,WUPIN_NAME,QTY,INOUT,STATUS,STATION,USERNAME,USERBUM,USERPHONE,ENG_NAME,WUPIN_OK,WUPIN_NG,COMMENTS from NC_KUCUN_INOUT where CDT between '%s' and '%s' %s order by CDT desc\" % (start_datetime, fiannly_datetime,Choice)\n elif self.History_chioce.currentText() == '新记录':\n while_num=15\n sql_order = \"select CDT,worker_choice_wire,TYPEOF,machine_name,systems,wire_name,wire_barcode,inout_num,INOUT,STATUS,worker_name,worker_num,worker_phone,STATION,comments from New_History where CDT between '%s' and '%s' %s order by CDT desc\" % (start_datetime, fiannly_datetime, Choice)\n\n print(\"HISTORY:\",sql_order)\n get_sql_alldata = Execute_Sql_Get_Date(sql_order)\n #print(get_sql_alldata)\n Get_data_num.append(len(get_sql_alldata))\n\n self.tableWidget.setRowCount(len(get_sql_alldata)) #设置有多少行\n\n for j in range(len(get_sql_alldata)):\n for i in range(while_num):\n #print(get_sql_alldata[j][i])\n try:\n data = QTableWidgetItem(str(get_sql_alldata[j][i])) # 转换后可插入表格\n self.tableWidget.setItem(j, i, data)#version(),Name\n except Exception:\n traceback.print_exc()\n\n except Exception:\n self.Message_two('条件不全或无此条件搜索结果')\n print_exc()\n\n\n\n def seek(self):\n try:\n Get_data_num.clear()\n self.tableWidget.clear()\n\n #设置列表的标头\n if self.History_chioce.currentText() == '旧记录':\n self.tableWidget.setHorizontalHeaderLabels(All_name)\n else:\n self.tableWidget.setHorizontalHeaderLabels(New_All_name)\n\n\n\n print('选中的线体', self.Get_Line_Check_Name(self.com_class))\n print('选中的操作', self.Get_Line_Check_Name(self.com_lines))\n\n #得到标记的选择：\n get_status_data=self.Get_Line_Check_Name(self.com_class)\n\n get_line_data=self.Get_Line_Check_Name(self.com_lines)\n\n get_thing_class_data=self.Get_Line_Check_Name(self.com_thing_class)\n\n\n\n if len(get_status_data) == 1:\n get_status_data=str(get_status_data).replace(',','')\n\n\n if len(get_line_data) == 1:\n get_line_data = str(get_line_data).replace(',', '')\n\n if len(get_thing_class_data) == 1:\n get_thing_class_data = str(get_thing_class_data).replace(',', '')\n\n if self.com_thing_class.currentText()=='全选':\n thing_class_chioce=\" and TYPEOF not in ('')\"\n\n elif self.com_thing_class.currentText()=='其他':\n thing_class_chioce=\" and TYPEOF not in ('測試線', '電源線', '驅動板', 'FFC', 'Sensor', 'INV_電源線')\"\n\n else:\n thing_class_chioce = ' and TYPEOF in ' + str(get_thing_class_data)\n\n\n\n ggg='and STATUS in ' + str(get_status_data) + ' and STATION in ' + str(get_line_data) + thing_class_chioce\n\n\n print('555555555',ggg)\n\n self.Choice_operation(ggg)\n\n\n # if self.com_class.currentText()=='进出品查询':\n # self.Choice_operation('')\n # elif self.com_class.currentText()=='ng品查询':\n # self.Choice_operation(\"and (STATUS='NG品更換' or STATUS='NG退料')\")\n # elif self.com_class.currentText()=='借用记录':\n # self.Choice_operation(\"and STATUS='外單位借用'\")\n # elif self.com_class.currentText() == 'ng出库':\n # self.Choice_operation(\"and STATUS='NG出庫'\")\n # elif self.com_class.currentText() == 'ng报废':\n # self.Choice_operation(\"and STATUS='報廢出庫'\")\n # elif self.com_class.currentText()=='盤點記錄':\n # self.Choice_operation(\"and INOUT='盤點'\")\n #\n\n\n except Exception:\n traceback.print_exc()\n\n\n\n\n def Seek_sql_data(self):\n self.dat_finally.setDate(QDate.currentDate())\n self.dat_start.setDate(QDate.currentDate())\n self.dat_start.setCalendarPopup(True)\n self.dat_finally.setCalendarPopup(True)\n\n\n def set_check(self):\n for j in range(3,len(Read_load_info())):\n self.com_lines.model().item(j).setCheckState(QtCore.Qt.Unchecked)\n\n for j in range(3,self.com_class.count()):\n self.com_class.model().item(j).setCheckState(QtCore.Qt.Unchecked)\n\n for j in range(3,self.com_thing_class.count()):\n self.com_thing_class.model().item(j).setCheckState(QtCore.Qt.Unchecked)\n\n def Get_Line_Index(self,i):\n try:\n\n sender=self.sender()\n count_line=self.com_lines.count()\n print('129',count_line)\n #这是一个复选框的使用\n #self.com_lines.addItems(['gggg'])\n print(i)\n\n if i not in [0,1,2]:\n if sender.model().item(i).checkState() == QtCore.Qt.Unchecked:\n sender.model().item(i).setCheckState(QtCore.Qt.Checked)\n else:\n sender.model().item(i).setCheckState(QtCore.Qt.Unchecked)\n\n\n print('选中的线体',self.Get_Line_Check_Name(sender))\n\n if sender.itemText(i)=='全选':\n for j in range(3,sender.count()):\n sender.model().item(j).setCheckState(QtCore.Qt.Checked)\n\n elif sender.itemText(i)=='清除':\n for j in range(3,sender.count()):\n sender.model().item(j).setCheckState(QtCore.Qt.Unchecked)\n\n self.statusBar().showMessage(str(list(self.Get_Line_Check_Name(self.com_lines)))+str(list(self.Get_Line_Check_Name(self.com_class))))\n\n\n except Exception:\n print_exc()\n\n\n def Get_Line_Check_Name(self,sender):\n\n Line_Name=[]\n for i in range(3,sender.count()):\n if sender.model().item(i).checkState() == QtCore.Qt.Checked:\n Line_Name.append(sender.itemText(i))\n\n print('187',Line_Name)\n print(tuple(Line_Name))\n\n return tuple(Line_Name)\n\n\n def to_lend(self):\n try:\n # delete_sql_order=\"delete Serson_TableTEST\"\n # file_name=self.to_lend_function(delete_sql_order,'Serson_TableTEST',5)\n # print('38',file_name)\n pass\n except Exception:\n print_exc()\n\n\n def lend(self):\n save_file_name=self.lend_function(self.tableWidget)\n print('44',save_file_name)\n\n\n\nif __name__==\"__main__\":\n app = QtWidgets.QApplication(argv)\n MainWindow = QtWidgets.QMainWindow()\n ui = Thing_History()\n\n ui.show()\n exit(app.exec_())\n", "sub_path": "history_monther.py", "file_name": "history_monther.py", "file_ext": "py", "file_size_in_byte": 8746, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "history.Ui_MainWindow", "line_number": 14, "usage_type": "name"}, {"api_name": "traceback.print_exc", "line_number": 67, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 145, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate.currentDate", "line_number": 151, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 151, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate.currentDate", "line_number": 152, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 152, "usage_type": "name"}]} +{"seq_id": "207583685", "text": "import numpy as np\nfrom scipy.optimize import minimize, root_scalar\nfrom math import atan\nfrom time import time\n\nD = [92,119,148,171,198,238,284]\nY = [63,192,313,357,509,540,641]\nn = len(D)\n\ninitialTime = time()\n\nlastPosition = n-1\nmidPosition = int(n/2)\nfirstPosition = 0\n\ndef initialGuessFunction(a):\n return (Y[firstPosition]-Y[midPosition])/(Y[firstPosition]-Y[lastPosition])*(atan(a/D[firstPosition])-atan(a/D[lastPosition])) - atan(a/D[firstPosition]) + atan(a/D[midPosition])\n\ninitialSolution = root_scalar(initialGuessFunction, x0=100, bracket=[1, 500])\n\ndef squaredErrorFunction(x):\n a = x[0]\n b = x[1]\n c = x[2]\n error = 0\n for i in range(n):\n error += (D[i] - (a/np.tan(b*Y[i]+c)))**2\n return error\n\naMin = initialSolution.root-50\naMax = initialSolution.root+50\na = initialSolution.root\nb = (atan(a/D[firstPosition]) - atan(a/D[midPosition]))/(Y[firstPosition]-Y[midPosition])\nc = atan(a/D[firstPosition])-b*Y[firstPosition]\n\nfinalSolution = [a, b, c]\nfinalSolutionError = squaredErrorFunction([a, b, c])\na = aMin\nwhile a < aMax:\n x0 = np.array([a, b, c])\n minimizeSolution = minimize(squaredErrorFunction, x0, bounds=[(aMin, aMax), (-1, 0), (0.1, 2)])\n if minimizeSolution.success:\n if minimizeSolution.fun < finalSolutionError:\n finalSolution = minimizeSolution.x\n finalSolutionError = minimizeSolution.fun\n if int(100*a) % 50 == 0:\n print(\"\\n\", round(a-aMin, 2), \"/\", round(aMax-aMin, 2))\n print(\"error: \", round(minimizeSolution.fun, 6))\n print(\"minimum:\", round(finalSolutionError, 6))\n print(minimizeSolution.x)\n a += 0.01\nprint(\"finalSolution:\")\nprint(finalSolution)\nfinalTime = time()\nprint(\"time: {}\".format(finalTime-initialTime))", "sub_path": "calcDistanceParams.py", "file_name": "calcDistanceParams.py", "file_ext": "py", "file_size_in_byte": 1769, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.time", "line_number": 10, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 17, "usage_type": "call"}, {"api_name": "scipy.optimize.root_scalar", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.tan", "line_number": 27, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 33, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize", "line_number": 41, "usage_type": "call"}, {"api_name": "time.time", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "338412662", "text": "__version__ = '0.6.1'\nimport logging\n\nlogging.basicConfig(format=('%(levelname)s: [%(asctime)s] %(name)s'\n ' - %(message)s'),\n level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S')\n\n\nlogger = logging.getLogger('gilda')\n\n\nfrom .api import *\n", "sub_path": "gilda/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 281, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 4, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 6, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "286587652", "text": "from itertools import groupby\nfrom operator import itemgetter\nfrom pathlib import Path\nfrom typing import Iterable\nfrom typing import Optional\nfrom typing import Union\nfrom uuid import uuid4\n\nfrom async_lru import alru_cache\nfrom asyncpg import Connection\nfrom asyncpg import ForeignKeyViolationError\nfrom asyncpg import UniqueViolationError\nfrom asyncpg import create_pool\nfrom asyncpg.pool import Pool\nfrom dateutil.parser import parse\nfrom ujson import dumps\n\nfrom app.config import config\nfrom app.domain.appinsights import APPINSIGHTS_EVENT\nfrom app.domain.appinsights import APPINSIGHTS_EXCEPTION\nfrom app.domain.appinsights import APPINSIGHTS_LOG\nfrom app.domain.exceptions import DuplicateClient\nfrom app.domain.exceptions import UnknownClient\n\nDatabase = Union[Connection, Pool]\n\n\n@alru_cache(maxsize=1)\nasync def _get_db_pool() -> Pool:\n return await create_pool(\n min_size=int(config.DATABASE_OPTIONS.get('pool_min_size') or '1'),\n max_size=int(config.DATABASE_OPTIONS.get('pool_max_size') or '2'),\n database=config.DATABASE_URL.path[1:],\n user=config.DATABASE_URL.username,\n password=config.DATABASE_URL.password,\n host=config.DATABASE_URL.hostname,\n port=config.DATABASE_URL.port,\n ssl=config.DATABASE_OPTIONS.get('ssl') == 'True',\n )\n\n\nasync def _insert_events(db: Database, telemetries: Iterable[dict]):\n await db.executemany('''\n INSERT INTO events (\n client,\n created_at,\n name,\n properties\n ) VALUES (\n $1::UUID,\n $2,\n $3,\n $4\n )\n ''', [(\n telemetry['iKey'],\n parse(telemetry['time']),\n telemetry['data']['baseData']['name'],\n dumps(telemetry['data']['baseData'].get('properties', {})),\n ) for telemetry in telemetries])\n\n\nasync def _insert_logs(db: Database, telemetries: Iterable[dict]):\n await db.executemany('''\n INSERT INTO logs (\n client,\n created_at,\n message,\n severity\n ) VALUES (\n $1::UUID,\n $2,\n $3,\n $4\n )\n ''', [(\n telemetry['iKey'],\n parse(telemetry['time']),\n telemetry['data']['baseData']['message'],\n telemetry['data']['baseData']['severityLevel']\n ) for telemetry in telemetries])\n\n\nasync def _insert_exceptions(db: Database, telemetries: Iterable[dict]):\n await db.executemany('''\n INSERT INTO exceptions (\n client,\n created_at,\n exceptions\n ) VALUES (\n $1::UUID,\n $2,\n $3\n )\n ''', [(\n telemetry['iKey'],\n parse(telemetry['time']),\n dumps(telemetry['data']['baseData'].get('exceptions', []))\n ) for telemetry in telemetries])\n\n\nasync def create():\n db = await _get_db_pool()\n\n schema_file = Path(__file__).parent / 'postgres.sql'\n with schema_file.open(encoding='utf-8') as fobj:\n schema = fobj.read()\n\n for statement in schema.split(';'):\n if statement.strip():\n await db.execute(statement)\n\n\nasync def register(client: Optional[str] = None) -> str:\n client = client or str(uuid4())\n\n db = await _get_db_pool()\n\n try:\n await db.execute('INSERT INTO clients (client) VALUES ($1)', client)\n except UniqueViolationError:\n raise DuplicateClient()\n\n return client\n\n\nasync def ingest(telemetries: Iterable[dict]):\n pool = await _get_db_pool()\n\n async with pool.acquire() as db:\n async with db.transaction():\n try:\n for event_type, group in groupby(telemetries, itemgetter('name')):\n if event_type == APPINSIGHTS_EVENT:\n await _insert_events(db, group)\n elif event_type == APPINSIGHTS_LOG:\n await _insert_logs(db, group)\n elif event_type == APPINSIGHTS_EXCEPTION:\n await _insert_exceptions(db, group)\n else:\n raise NotImplementedError(event_type)\n except ForeignKeyViolationError:\n raise UnknownClient()\n", "sub_path": "app/database/postgres.py", "file_name": "postgres.py", "file_ext": "py", "file_size_in_byte": 4205, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.Union", "line_number": 25, "usage_type": "name"}, {"api_name": "asyncpg.Connection", "line_number": 25, "usage_type": "name"}, {"api_name": "asyncpg.pool.Pool", "line_number": 25, "usage_type": "name"}, {"api_name": "asyncpg.create_pool", "line_number": 30, "usage_type": "call"}, {"api_name": "app.config.config.DATABASE_OPTIONS.get", "line_number": 31, "usage_type": "call"}, {"api_name": "app.config.config.DATABASE_OPTIONS", "line_number": 31, "usage_type": "attribute"}, {"api_name": "app.config.config", "line_number": 31, "usage_type": "name"}, {"api_name": "app.config.config.DATABASE_OPTIONS.get", "line_number": 32, "usage_type": "call"}, {"api_name": "app.config.config.DATABASE_OPTIONS", "line_number": 32, "usage_type": "attribute"}, {"api_name": "app.config.config", "line_number": 32, "usage_type": "name"}, {"api_name": "app.config.config.DATABASE_URL", "line_number": 33, "usage_type": "attribute"}, {"api_name": "app.config.config", "line_number": 33, "usage_type": "name"}, {"api_name": "app.config.config.DATABASE_URL", "line_number": 34, "usage_type": "attribute"}, {"api_name": "app.config.config", "line_number": 34, "usage_type": "name"}, {"api_name": "app.config.config.DATABASE_URL", "line_number": 35, "usage_type": "attribute"}, {"api_name": "app.config.config", "line_number": 35, "usage_type": "name"}, {"api_name": "app.config.config.DATABASE_URL", "line_number": 36, "usage_type": "attribute"}, {"api_name": "app.config.config", "line_number": 36, "usage_type": "name"}, {"api_name": "app.config.config.DATABASE_URL", "line_number": 37, "usage_type": "attribute"}, {"api_name": "app.config.config", "line_number": 37, "usage_type": "name"}, {"api_name": "app.config.config.DATABASE_OPTIONS.get", "line_number": 38, "usage_type": "call"}, {"api_name": "app.config.config.DATABASE_OPTIONS", "line_number": 38, "usage_type": "attribute"}, {"api_name": "app.config.config", "line_number": 38, "usage_type": "name"}, {"api_name": "async_lru.alru_cache", "line_number": 28, "usage_type": "call"}, {"api_name": "asyncpg.pool.Pool", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 42, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 57, "usage_type": "call"}, {"api_name": "ujson.dumps", "line_number": 59, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 63, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 78, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 84, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 97, "usage_type": "call"}, {"api_name": "ujson.dumps", "line_number": 98, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 105, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 114, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 115, "usage_type": "call"}, {"api_name": "asyncpg.UniqueViolationError", "line_number": 121, "usage_type": "name"}, {"api_name": "app.domain.exceptions.DuplicateClient", "line_number": 122, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 127, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 133, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 133, "usage_type": "call"}, {"api_name": "app.domain.appinsights.APPINSIGHTS_EVENT", "line_number": 134, "usage_type": "name"}, {"api_name": "app.domain.appinsights.APPINSIGHTS_LOG", "line_number": 136, "usage_type": "name"}, {"api_name": "app.domain.appinsights.APPINSIGHTS_EXCEPTION", "line_number": 138, "usage_type": "name"}, {"api_name": "asyncpg.ForeignKeyViolationError", "line_number": 142, "usage_type": "name"}, {"api_name": "app.domain.exceptions.UnknownClient", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "370641929", "text": "import holter_monitor_errors as hme\nimport holter_monitor_constants as hmc\nimport numpy as np\nimport lvm_read as lr\nimport nptdms as npt\nimport os.path\nimport logging\nlog = logging.getLogger(\"hm_logger\")\n\n\ndef read_tdms(filename=\"ecg.tdms\", folder=\"data/\",\n sample_rate=hmc.SAMPLE_RATE,\n group_name=\"GroupName\",\n channel_name=\"ChName\"):\n \"\"\" reads ecg data from an LabView TDMS (.tdms) file\n\n :param filename: name of tdms file\n :param folder: folder where data files are kept\n :param sample_rate: sampling rate of the tdms file\n :param group_name: group name of the channel to read from in the TDMS file\n :param channel_name: name of the channel to read from in the group\n :return: time data array, ecg data array\n \"\"\"\n extension = os.path.splitext(filename)[1]\n if extension != \".tdms\":\n message = filename + \" was not a TDMS file\"\n log.error(message)\n raise hme.InvalidFormatError(message)\n\n file = npt.TdmsFile(file_path(folder, filename))\n ecg = file.object(group_name, channel_name).data\n num_samples = len(ecg)\n num_seconds = num_samples / sample_rate\n time = np.linspace(0, num_seconds, num_samples)\n return time, ecg\n\n\ndef read_lvm(filename=\"ecg.lvm\", folder=\"data/\"):\n \"\"\" reads ecg data from an LabView (.lvm) file\n\n :param filename: name of lvm file\n :param folder: folder where data files are kept\n :return: time data array, ecg data array\n \"\"\"\n\n extension = os.path.splitext(filename)[1]\n if extension != \".lvm\":\n message = filename + \" was not a LabView file\"\n log.error(message)\n raise hme.InvalidFormatError(message)\n data = lr.read(file_path(folder, filename))\n\n if data[\"Segments\"] != 1:\n message = \"multiple segments detected in \" + filename\n log.error(message)\n raise hme.InvalidFormatError(message)\n\n arr = data[0]['data']\n ecg = arr[:, 1]\n time = arr[:, 0]\n return time, ecg\n\n\ndef read_bin(filename=\"ecg.npy\", folder=\"data/\"):\n \"\"\" reads ecg data from a NumPy (.npy) binary file\n\n :param filename: name of binary file\n :param folder: folder where data files are kept\n :return: time data array, ecg data array\n \"\"\"\n\n extension = os.path.splitext(filename)[1]\n if extension != \".npy\":\n message = filename + \" was not a NumPy binary file\"\n log.error(message)\n raise hme.InvalidFormatError(message)\n data = np.load(file_path(folder, filename))\n time = data[:, 0]\n ecg = data[:, 1]\n return time, ecg\n\n\ndef read_txt(filename=\"ecg.txt\", folder=\"data/\",\n sample_rate=hmc.SAMPLE_RATE):\n \"\"\" reads ecg data from a text file generated using the memory system\n \n :param filename: name of .txt file\n :param folder: folder where data files are kept\n :param sample_rate: sampling rate of the data\n :return: time data array, ecg data array\n \"\"\"\n\n extension = os.path.splitext(filename)[1]\n if extension != \".txt\":\n message = filename + \" was not a .txt file\"\n log.error(message)\n raise hme.InvalidFormatError(message)\n with open(file_path(folder, filename)) as f:\n lines = f.read().splitlines()\n ecg = np.array([int(i) for i in lines])\n num_samples = len(ecg)\n num_seconds = num_samples / sample_rate\n time = np.linspace(0, num_seconds, num_samples)\n return time, ecg\n\n\ndef read_data(data_filename=\"ecg.lvm\",\n folder=\"data/\"):\n \"\"\" Read data from a file\n\n :param data_filename: name of npy or lvm file\n :param folder: folder where data files are kept\n :return: time data array, ecg data array\n \"\"\"\n\n extension = os.path.splitext(data_filename)[1]\n if extension == \".lvm\":\n time, ecg = read_lvm(data_filename, folder)\n elif extension == \".npy\":\n time, ecg = read_bin(data_filename, folder)\n elif extension == \".tdms\":\n time, ecg = read_tdms(data_filename, folder)\n elif extension == \".txt\":\n time, ecg = read_txt(data_filename, folder)\n else:\n message = extension + \" files are not supported yet\"\n log.error(message)\n raise hme.InvalidFormatError(message)\n\n log.debug(\"successfully read and constructed ecg data from \" +\n data_filename)\n\n return time.astype(\"float32\"), ecg.astype(\"float32\")\n\n\ndef file_path(folder, filename):\n \"\"\" returns the complete path to the file by concatenating the folder\n\n :param folder: name of folder\n :param filename: name of file\n :return: relative path of file\n \"\"\"\n folder_path = \"./\" if len(folder) == 0 else folder\n return folder_path + filename\n\n\ndef save_binary(data, input_filename, output_filename, folder=\"data/\"):\n input_extension = os.path.splitext(input_filename)[1]\n if input_extension == \".npy\":\n message = \"input file was already a NumPy binary file\"\n log.error(message)\n raise hme.InvalidFormatError(message)\n output_extension = os.path.splitext(output_filename)[1]\n if output_extension != \".npy\":\n message = \"output file is not a NumPy binary file\"\n log.error(message)\n raise hme.InvalidFormatError(message)\n output_file = open(file_path(folder, output_filename), 'wb')\n np.save(output_file, data)\n output_file.close()\n", "sub_path": "input_reader.py", "file_name": "input_reader.py", "file_ext": "py", "file_size_in_byte": 5319, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "holter_monitor_constants.SAMPLE_RATE", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.path.splitext", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 24, "usage_type": "name"}, {"api_name": "holter_monitor_errors.InvalidFormatError", "line_number": 28, "usage_type": "call"}, {"api_name": "nptdms.TdmsFile", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.path.splitext", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 46, "usage_type": "name"}, {"api_name": "holter_monitor_errors.InvalidFormatError", "line_number": 50, "usage_type": "call"}, {"api_name": "lvm_read.read", "line_number": 51, "usage_type": "call"}, {"api_name": "holter_monitor_errors.InvalidFormatError", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.path.splitext", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 72, "usage_type": "name"}, {"api_name": "holter_monitor_errors.InvalidFormatError", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 77, "usage_type": "call"}, {"api_name": "holter_monitor_constants.SAMPLE_RATE", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.path.splitext", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 93, "usage_type": "name"}, {"api_name": "holter_monitor_errors.InvalidFormatError", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.path.splitext", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 116, "usage_type": "name"}, {"api_name": "holter_monitor_errors.InvalidFormatError", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path.path.splitext", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 148, "usage_type": "name"}, {"api_name": "holter_monitor_errors.InvalidFormatError", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path.path.splitext", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 153, "usage_type": "name"}, {"api_name": "holter_monitor_errors.InvalidFormatError", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 159, "usage_type": "call"}]} +{"seq_id": "353690728", "text": "import json\nimport os\nimport random\nimport sys\nfrom math import sqrt, degrees, asin\n\nimport pygame\nimport global_variables as gb\n\nimport obj\n\n\nclass cd:\n \"\"\"Context manager for changing the current working directory\"\"\"\n\n def __init__(self, newPath):\n self.newPath = os.getcwd() + newPath\n\n def __enter__(self):\n self.savedPath = os.getcwd()\n os.chdir(self.newPath)\n\n def __exit__(self, etype, value, traceback):\n os.chdir(self.savedPath)\n\n\ndef resource_path(relative_path):\n \"\"\" Get absolute path to resource, works for dev and for PyInstaller \"\"\"\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\".\")\n\n return os.path.join(base_path, relative_path)\n\n\ndef load_image(name, color_key=None):\n fullname = os.path.join('res', name)\n\n try:\n image = pygame.image.load(resource_path(fullname)).convert()\n except Exception as e:\n print('failed to load image %s. stack:' % fullname, e)\n image = pygame.image.load(resource_path('res/missing.png')).convert()\n\n if color_key is not None and not 0:\n if color_key == -1:\n color_key = image.get_at((0, 0))\n\n image.set_colorkey(color_key)\n else:\n image = image.convert_alpha()\n return image\n\n\ndef load_level(name):\n try:\n if name == 'barrens':\n from levels import barrens as lvl\n if name == 'start':\n from levels import start as lvl\n if name == 'old_house':\n from levels import old_house as lvl\n if name == 'hub':\n from levels import hub as lvl\n print(name)\n return lvl\n except Exception as e:\n print('stack:', e)\n raise ImportError\n\n\ndef generate_level(lvl):\n new_player, x, y = None, None, None\n fx, fy = lvl.playerpos\n\n gb.tile_width, gb.tile_height = lvl.tile_size\n map = lvl.tile_map\n for iy in range(len(map)):\n for ix in range(len(map[iy])):\n cur_cntstr = lvl.tile_images_links[map[iy][ix]][0]\n if cur_cntstr is obj.Emptiness:\n if 0 < iy < len(map) - 1 and 0 < ix < len(map[iy]) - 1:\n obj.Emptiness(ix, iy, map)\n else:\n img = (lvl.tile_images_links[map[iy][ix]][1][0])\n color_key = -1\n if type(img) is tuple:\n color_key = img[1]\n img = img[0]\n if type(img) is list:\n img = random.choice(img)\n if len(lvl.tile_images_links[map[iy][ix]][1]) == 1:\n cur_cntstr(ix, iy, load_image(img))\n if len(lvl.tile_images_links[map[iy][ix]][1]) > 1:\n cur_cntstr(ix, iy, load_image(img, color_key),\n *lvl.tile_images_links[map[iy][ix]][1][1:])\n\n map = lvl.items_map\n for iy in range(len(map)):\n for ix in range(len(map[iy])):\n if map[iy][ix] and lvl.items_on_ground_links[map[iy][ix]]:\n cur_cntstr = lvl.items_on_ground_links[map[iy][ix]][0]\n\n img_link = lvl.items_on_ground_links[map[iy][ix]][1][0]\n color_key = None\n\n if type(img_link) is tuple:\n color_key = img_link[1]\n img_link = img_link[0]\n\n if type(img_link) is list:\n img_link = random.choice(img_link)\n print(img_link, 'rand9')\n\n if type(img_link) is tuple:\n color_key = img_link[1]\n img_link = img_link[0]\n\n if len(lvl.items_on_ground_links[map[iy][ix]][1]) == 1:\n cur_cntstr(ix, iy, load_image(img_link, color_key))\n if len(lvl.items_on_ground_links[map[iy][ix]][1]) > 1:\n cur_cntstr(ix, iy, load_image(img_link, color_key),\n *lvl.items_on_ground_links[map[iy][ix]][1][1:])\n gb.cur_lvl.init()\n new_player = obj.Player(fx, fy, lvl.bulb)\n gb.current_music_theme = lvl.music_theme\n pygame.mixer.music.stop()\n return new_player, x, y\n\n\ndef lvl_change(lvl):\n fade_out()\n gb.all_sprites.empty()\n gb.cur_lvl = load_level(lvl)\n gb.player, level_x, level_y = generate_level(gb.cur_lvl)\n\n\ndef open_eyes_animation():\n for i in range(58):\n cur_img = pygame.transform.scale(load_image('player/wake_up/wake ' + str(i) + '.png'), gb.size)\n gb.screen.blit(cur_img, (0, 0))\n pygame.display.flip()\n gb.clock.tick(15)\n\n\ndef door_move():\n fade_out()\n walk_in()\n\n\ndef fade_out():\n for i in range(255):\n alpha = max(i, 0)\n alpha_surf = pygame.Surface(gb.size)\n alpha_surf.fill((255, 255, 255, alpha))\n gb.screen.blit(alpha_surf, (0, 0), special_flags=pygame.BLEND_RGBA_MULT)\n pygame.display.flip()\n\n\ndef walk_in():\n moving_right = [\n load_image('player\\\\' + gb.player.bulb + '\\walking_right\\walk_' + str(i) + '.png', -1) for i in range(1, 7)]\n counter_for_movements = 0\n for i in range(36):\n alpha_surf = pygame.Surface(gb.size)\n alpha_surf.fill((0, 0, 0, 0))\n alpha_surf.blit(moving_right[counter_for_movements], (gb.width // 2 - 36, gb.height // 2 - 48))\n counter_for_movements += 1\n if counter_for_movements == 6:\n counter_for_movements = 0\n gb.screen.blit(alpha_surf, (0, 0))\n pygame.display.flip()\n gb.clock.tick(15)\n fade_out()\n\n\ndef draw_inv():\n img = load_image('invertory.png', -1).convert_alpha()\n sc = pygame.Surface(gb.size, pygame.SRCALPHA)\n\n sc.blit(img, (0, 10))\n x, y = 0, 0\n start_pos = 100, 100\n rigth_collum = 400\n for i in range(min(16, len(gb.invertory))):\n cur_item = gb.invertory[i]\n x = i % 2 * rigth_collum\n y = i // 2 * 60\n\n if type(cur_item.image) is tuple:\n cur_item.image = load_image(*cur_item.image)\n if type(cur_item.image) is str:\n cur_item.image = load_image(cur_item.image, -1)\n sc.blit(cur_item.image, (start_pos[0] + x, start_pos[1] + y))\n\n font = pygame.font.Font(None, 50)\n text = font.render(str(cur_item.name), 1, (167, 95, 12))\n sc.blit(text, (start_pos[0] + x + 70, start_pos[1] + y))\n\n gb.screen.blit(sc, (0, 10))\n\n\ndef pick_up_bulb_animation():\n frames = [\n pygame.transform.scale(load_image(r'pickup_bulb_animation\\b' + str(i) + '.png', (0, 0, 0)), gb.size) for i in\n range(0, 10)]\n counter_for_movements = 0\n for i in range(199):\n alpha_surf = pygame.Surface(gb.size)\n # alpha_surf.fill((0, 0, 0, 0))\n try:\n alpha_surf.blit(frames[counter_for_movements], (0, 0))\n except:\n alpha_surf.blit(frames[-1], (0, 0))\n gb.screen.blit(alpha_surf, (0, 0))\n\n if i % 10 == 0 and counter_for_movements < 9:\n if counter_for_movements < 3:\n fade_out()\n counter_for_movements += 1\n pygame.display.flip()\n gb.clock.tick(30)\n fade_out()\n\n\ndef pick_up_event():\n gb.intractable_group.update(\"pick_up\")\n\n\ndef draw_msg():\n sc = load_image('msg.png', -1).convert_alpha()\n msg, face = gb.msg_query[0]\n font = pygame.font.Font(None, 30)\n msgs = msg.msg.split('\\n')\n for line in range(len(msgs)):\n text = font.render(str(msgs[line]), 1, (255, 255, 255))\n sc.blit(text, (30, 25 + 35 * line))\n\n sc.blit(face, (710, 5))\n\n gb.screen.blit(sc, (100, 565))\n\n\ndef face(person, mood='idle'):\n if mood == 'idle':\n imgs = [load_image('faces\\\\' + person + '\\\\' + person + str(i) + '.png', -2) for i in range(6)]\n return pygame.transform.scale(random.choice(imgs), (150, 150))\n else:\n return pygame.transform.scale(load_image('faces\\\\' + person + '\\\\' + person + '_' + mood + '.png', -2),\n (150, 150))\n\n\ndef make_color_darker(color, percentage):\n r, g, b = color\n percentage *= 0.001\n\n\ndef darken():\n alpha = 70\n alpha_surf = pygame.Surface(gb.size)\n alpha_surf.fill((alpha, alpha, alpha, 255))\n gb.screen.blit(alpha_surf, (0, 0), special_flags=pygame.BLEND_RGBA_MULT)\n\n\ndef distance_between_points(pos1, pos2):\n x1, y1 = pos1\n x2, y2 = pos2\n return sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)\n\n\ndef angle_between_points(pos1, pos2):\n x1, y1 = pos1\n x2, y2 = pos2\n return round(degrees(asin((y2 - y1) / distance_between_points(pos1, pos2))))\n\n\ndef direction_to_star_at(pos1, pos2):\n #\n #\n ang = angle_between_points(pos1, pos2)\n if 45 >= ang >= 135:\n return 1\n if 135 >= ang >= 225:\n return 2\n if 225 > ang > 315:\n return 3\n if 315 > ang or ang < 45:\n return 4\n\n\ndef mod(x):\n return x if x > 0 else -x\n\n\ndef play_sound(sound):\n pygame.mixer.Sound('res/sounds/' + sound + '.wav').play()\n\n\ndef play_music(song=None):\n try:\n pygame.mixer.music.stop()\n if song:\n file = song\n else:\n file = random.choice(os.listdir('res/music/'))\n pygame.mixer.music.load('music/' + file)\n pygame.mixer.music.play()\n pygame.mixer.music.set_volume(0.3)\n except Exception as e:\n print('failed to load music queue. stack:', e)\n\n\ndef debug_hud():\n data = \"\"\"\n vX:%d X_tile:%f \n vY:%d Y_tile:%f\n vX_act:%d\n vY_act:%d sprites:%s\n start X:%d, Y:%d\n \"\"\" % (\n gb.velocity_x, gb.playerpos_x / gb.tile_width, gb.velocity_y, gb.playerpos_y / gb.tile_height,\n gb.velocity_x_actual,\n gb.velocity_y_actual, str(gb.all_sprites), gb.playerpos_x, gb.playerpos_y)\n font = pygame.font.Font(None, 30)\n msgs = data.split('\\n')\n for line in range(len(msgs)):\n text = font.render(str(msgs[line]), 1, (255, 0, 0))\n gb.screen.blit(text, (10, 10 + 35 * line))\n\n\ndef digit_password_ask(password):\n \"\"\"\n :param password: str\n :return: bool\n \"\"\"\n nums = num1, num2, num3, num4 = [0, 0, 0, 0]\n cur = 0\n\n font = pygame.font.Font(None, 60)\n\n while True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n exit(0)\n elif event.type == pygame.KEYDOWN:\n if event.key == gb.right:\n cur += 1\n if cur == 4:\n cur = 0\n if event.key == gb.left:\n cur -= 1\n if cur == -1:\n cur = 3\n if event.key == gb.up:\n nums[cur] += 1\n if nums[cur] == 10:\n nums[cur] = 0\n if event.key == gb.down:\n nums[cur] -= 1\n if nums[cur] == -1:\n nums[cur] = 9\n if event.key == gb.action:\n return ''.join([str(i) for i in nums]) == password\n\n string_rendered = pygame.Surface((600, 100))\n\n for i in range(4):\n str1 = font.render(str(nums[i]), 1, pygame.Color('yellow' if cur == i else 'white'))\n string_rendered.blit(str1, (i * 40, 0))\n gb.screen.fill((0, 0, 0))\n gb.screen.blit(string_rendered, (gb.width // 2 - 90, gb.height // 2 - 30))\n pygame.display.flip()\n gb.clock.tick(gb.FPS)\n\n\ndef light_bulb_install():\n frames = [\n pygame.transform.scale(load_image(r'stand_animation/stand ' + str(i) + '.png', (0, 0, 0)), gb.size) for i in\n range(0, 4)]\n counter_for_movements = 0\n fade_out()\n for i in range(150):\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n gb.running = False\n\n alpha_surf = pygame.Surface(gb.size)\n # alpha_surf.fill((0, 0, 0, 0))\n try:\n frame = 0\n if counter_for_movements > 2:\n frame = 1\n if counter_for_movements > 4:\n frame = 2\n if counter_for_movements > 6:\n frame = 3\n\n alpha_surf.blit(frames[frame], (0, 0))\n except:\n alpha_surf.blit(frames[-1], (0, 0))\n gb.screen.blit(alpha_surf, (0, 0))\n\n if i % 10 == 0 and counter_for_movements < 8:\n counter_for_movements += 1\n pygame.display.flip()\n gb.clock.tick(30)\n fade_out()\n\n\ndef ask_nap():\n orig_frame = pygame.Surface(gb.size)\n orig_frame.blit(gb.screen, (0, 0))\n var_ques = ['It\\'s a bed. It\\'s soft and comfortable.\\nMay i take a little nap?',\n 'aw...I\\'m quite tired of walking.\\nIs it alright to take a little nap before we go?',\n 'aw...%s, I\\'m quite tired of walking.\\nIs it alright to take a little nap before we go?' % gb.player_name,\n ]\n var_yes = ['yeah sure.',\n 'Yeah it\\'s alright.',\n 'Sure, have a good night!'\n ]\n var_no = ['I think, we have what to do know.',\n 'Not now, maybe a little bit later?',\n 'uhhh...no.']\n msg, face1, face2 = random.choice(var_ques), face('niko', 'yawn'), face('niko')\n\n yes = random.choice(var_yes)\n no = random.choice(var_no)\n\n is_yes = False\n\n font = pygame.font.Font(None, 30)\n font2 = pygame.font.Font(None, 25)\n selection = load_image('selection.png')\n counter = 0\n\n while True:\n\n if counter < 50:\n counter += 1\n fface = face1\n else:\n fface = face2\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n gb.running = False\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_SPACE:\n return is_yes\n if event.key == gb.up or event.key == gb.down:\n is_yes = not is_yes\n\n frame = pygame.Surface(gb.size)\n frame.blit(orig_frame, (0, 0))\n sc = load_image('msg.png', -1).convert_alpha()\n\n msgs = msg.split('\\n')\n for line in range(len(msgs)):\n text = font.render(str(msgs[line]), 1, (255, 255, 255))\n sc.blit(text, (30, 25 + 35 * line))\n yellow = (255, 241, 35)\n white = (255, 255, 255)\n\n sc.blit(selection, (25, 80 if is_yes else 105))\n\n yes_txt = font2.render(yes, 1, yellow if is_yes else white)\n no_txt = font2.render(no, 1, yellow if not is_yes else white)\n sc.blit(yes_txt, (30, 85))\n sc.blit(no_txt, (30, 110))\n\n sc.blit(fface, (710, 5))\n\n frame.blit(sc, (100, 565))\n\n gb.screen.blit(frame, (0, 0))\n pygame.display.flip()\n gb.clock.tick(60)\n\n\ndef save():\n file_entry = open('res/data.txt', 'w')\n data = [gb.cur_lvl.name, (gb.playerpos_x, gb.playerpos_y)]\n json_file = json.dumps(data)\n file_entry.write(json_file)\n\n\ndef load():\n file_entry = open('res/data.txt', 'r')\n try:\n data = json.loads(file_entry.read())\n cur_lvl_name, player_pos = data\n gb.from_save = True\n return cur_lvl_name, player_pos\n except Exception as e:\n gb.from_save = False\n print('failed to load save, stack:', e)\n return None\n\n\ndef broken_bulb():\n frames = [\n pygame.transform.scale(load_image(r'player/broken/a ' + str(i) + '.png', (0, 0, 0)), gb.size) for i in\n range(0, 8)]\n counter_for_movements = 0\n fade_out()\n for i in range(199):\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n gb.running = False\n\n alpha_surf = pygame.Surface(gb.size)\n # alpha_surf.fill((0, 0, 0, 0))\n if counter_for_movements < 5:\n frame = 0\n if 5 <= counter_for_movements < 13:\n frame = counter_for_movements - 5\n if 13 <= counter_for_movements < 15:\n frame = -1\n try:\n alpha_surf.blit(frames[frame], (0, 0))\n except:\n alpha_surf.blit(frames[-1], (0, 0))\n gb.screen.blit(alpha_surf, (0, 0))\n\n if i % 10 == 0 and counter_for_movements < 15:\n counter_for_movements += 1\n pygame.display.flip()\n gb.clock.tick(30)\n fade_out()\n gb.from_save = False\n exit(0)\n\n\ndef load_animation():\n frames = [\n pygame.transform.scale(load_image(r'first/' + str(i) + '.png', (0, 0, 0)), gb.size) for i in\n range(1, 12)]\n counter_for_movements = 0\n for i in range(199):\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n gb.running = False\n\n alpha_surf = pygame.Surface(gb.size)\n # alpha_surf.fill((0, 0, 0, 0))\n try:\n alpha_surf.blit(frames[counter_for_movements], (0, 0))\n except:\n alpha_surf.blit(frames[-1], (0, 0))\n gb.screen.blit(alpha_surf, (0, 0))\n\n if i % 10 == 0 and counter_for_movements < 13:\n counter_for_movements += 1\n pygame.display.flip()\n gb.clock.tick(30)\n fade_out()\n", "sub_path": "utility_functions.py", "file_name": "utility_functions.py", "file_ext": "py", "file_size_in_byte": 17045, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.getcwd", "line_number": 17, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 20, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 21, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 24, "usage_type": "call"}, {"api_name": "sys._MEIPASS", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 45, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 68, "usage_type": "name"}, {"api_name": "levels.hub.playerpos", "line_number": 76, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 76, "usage_type": "name"}, {"api_name": "global_variables.tile_width", "line_number": 78, "usage_type": "attribute"}, {"api_name": "global_variables.tile_height", "line_number": 78, "usage_type": "attribute"}, {"api_name": "levels.hub.tile_size", "line_number": 78, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 78, "usage_type": "name"}, {"api_name": "levels.hub.tile_map", "line_number": 79, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 79, "usage_type": "name"}, {"api_name": "levels.hub.tile_images_links", "line_number": 82, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 82, "usage_type": "name"}, {"api_name": "obj.Emptiness", "line_number": 83, "usage_type": "attribute"}, {"api_name": "obj.Emptiness", "line_number": 85, "usage_type": "call"}, {"api_name": "levels.hub.tile_images_links", "line_number": 87, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 87, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 93, "usage_type": "call"}, {"api_name": "levels.hub.tile_images_links", "line_number": 94, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 94, "usage_type": "name"}, {"api_name": "levels.hub.tile_images_links", "line_number": 96, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 96, "usage_type": "name"}, {"api_name": "levels.hub.tile_images_links", "line_number": 98, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 98, "usage_type": "name"}, {"api_name": "levels.hub.items_map", "line_number": 100, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 100, "usage_type": "name"}, {"api_name": "levels.hub.items_on_ground_links", "line_number": 103, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 103, "usage_type": "name"}, {"api_name": "levels.hub.items_on_ground_links", "line_number": 104, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 104, "usage_type": "name"}, {"api_name": "levels.hub.items_on_ground_links", "line_number": 106, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 106, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 114, "usage_type": "call"}, {"api_name": "levels.hub.items_on_ground_links", "line_number": 121, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 121, "usage_type": "name"}, {"api_name": "levels.hub.items_on_ground_links", "line_number": 123, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 123, "usage_type": "name"}, {"api_name": "levels.hub.items_on_ground_links", "line_number": 125, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 125, "usage_type": "name"}, {"api_name": "global_variables.cur_lvl.init", "line_number": 126, "usage_type": "call"}, {"api_name": "global_variables.cur_lvl", "line_number": 126, "usage_type": "attribute"}, {"api_name": "obj.Player", "line_number": 127, "usage_type": "call"}, {"api_name": "levels.hub.bulb", "line_number": 127, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 127, "usage_type": "name"}, {"api_name": "global_variables.current_music_theme", "line_number": 128, "usage_type": "attribute"}, {"api_name": "levels.hub.music_theme", "line_number": 128, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 128, "usage_type": "name"}, {"api_name": "pygame.mixer.music.stop", "line_number": 129, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 129, "usage_type": "attribute"}, {"api_name": "global_variables.all_sprites.empty", "line_number": 135, "usage_type": "call"}, {"api_name": "global_variables.all_sprites", "line_number": 135, "usage_type": "attribute"}, {"api_name": "global_variables.cur_lvl", "line_number": 136, "usage_type": "attribute"}, {"api_name": "levels.hub", "line_number": 136, "usage_type": "argument"}, {"api_name": "global_variables.player", "line_number": 137, "usage_type": "attribute"}, {"api_name": "global_variables.cur_lvl", "line_number": 137, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 142, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 142, "usage_type": "attribute"}, {"api_name": "global_variables.size", "line_number": 142, "usage_type": "attribute"}, {"api_name": "global_variables.screen.blit", "line_number": 143, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 144, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 144, "usage_type": "attribute"}, {"api_name": "global_variables.clock.tick", "line_number": 145, "usage_type": "call"}, {"api_name": "global_variables.clock", "line_number": 145, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 156, "usage_type": "call"}, {"api_name": "global_variables.size", "line_number": 156, "usage_type": "attribute"}, {"api_name": "global_variables.screen.blit", "line_number": 158, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 158, "usage_type": "attribute"}, {"api_name": "pygame.BLEND_RGBA_MULT", "line_number": 158, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 159, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 159, "usage_type": "attribute"}, {"api_name": "global_variables.player", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 167, "usage_type": "call"}, {"api_name": "global_variables.size", "line_number": 167, "usage_type": "attribute"}, {"api_name": "global_variables.width", "line_number": 169, "usage_type": "attribute"}, {"api_name": "global_variables.height", "line_number": 169, "usage_type": "attribute"}, {"api_name": "global_variables.screen.blit", "line_number": 173, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 173, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 174, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 174, "usage_type": "attribute"}, {"api_name": "global_variables.clock.tick", "line_number": 175, "usage_type": "call"}, {"api_name": "global_variables.clock", "line_number": 175, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 181, "usage_type": "call"}, {"api_name": "global_variables.size", "line_number": 181, "usage_type": "attribute"}, {"api_name": "pygame.SRCALPHA", "line_number": 181, "usage_type": "attribute"}, {"api_name": "global_variables.invertory", "line_number": 187, "usage_type": "attribute"}, {"api_name": "global_variables.invertory", "line_number": 188, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 198, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 198, "usage_type": "attribute"}, {"api_name": "global_variables.screen.blit", "line_number": 202, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 202, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 207, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 207, "usage_type": "attribute"}, {"api_name": "global_variables.size", "line_number": 207, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 211, "usage_type": "call"}, {"api_name": "global_variables.size", "line_number": 211, "usage_type": "attribute"}, {"api_name": "global_variables.screen.blit", "line_number": 217, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 217, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 223, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 223, "usage_type": "attribute"}, {"api_name": "global_variables.clock.tick", "line_number": 224, "usage_type": "call"}, {"api_name": "global_variables.clock", "line_number": 224, "usage_type": "attribute"}, {"api_name": "global_variables.intractable_group.update", "line_number": 229, "usage_type": "call"}, {"api_name": "global_variables.intractable_group", "line_number": 229, "usage_type": "attribute"}, {"api_name": "global_variables.msg_query", "line_number": 234, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 235, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 235, "usage_type": "attribute"}, {"api_name": "global_variables.screen.blit", "line_number": 243, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 243, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 249, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 249, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 249, "usage_type": "call"}, {"api_name": "pygame.transform.scale", "line_number": 251, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 251, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 262, "usage_type": "call"}, {"api_name": "global_variables.size", "line_number": 262, "usage_type": "attribute"}, {"api_name": "global_variables.screen.blit", "line_number": 264, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 264, "usage_type": "attribute"}, {"api_name": "pygame.BLEND_RGBA_MULT", "line_number": 264, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 270, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 276, "usage_type": "call"}, {"api_name": "math.asin", "line_number": 276, "usage_type": "call"}, {"api_name": "pygame.mixer.Sound", "line_number": 298, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 298, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.stop", "line_number": 303, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 303, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 307, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 307, "usage_type": "call"}, {"api_name": "pygame.mixer.music.load", "line_number": 308, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 308, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 309, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 309, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.set_volume", "line_number": 310, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 310, "usage_type": "attribute"}, {"api_name": "global_variables.velocity_x", "line_number": 323, "usage_type": "attribute"}, {"api_name": "global_variables.playerpos_x", "line_number": 323, "usage_type": "attribute"}, {"api_name": "global_variables.tile_width", "line_number": 323, "usage_type": "attribute"}, {"api_name": "global_variables.velocity_y", "line_number": 323, "usage_type": "attribute"}, {"api_name": "global_variables.playerpos_y", "line_number": 323, "usage_type": "attribute"}, {"api_name": "global_variables.tile_height", "line_number": 323, "usage_type": "attribute"}, {"api_name": "global_variables.velocity_x_actual", "line_number": 324, "usage_type": "attribute"}, {"api_name": "global_variables.velocity_y_actual", "line_number": 325, "usage_type": "attribute"}, {"api_name": "global_variables.all_sprites", "line_number": 325, "usage_type": "attribute"}, {"api_name": "global_variables.playerpos_x", "line_number": 325, "usage_type": "attribute"}, {"api_name": "global_variables.playerpos_y", "line_number": 325, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 326, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 326, "usage_type": "attribute"}, {"api_name": "global_variables.screen.blit", "line_number": 330, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 330, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 341, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 341, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 344, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 344, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 345, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 347, "usage_type": "attribute"}, {"api_name": "global_variables.right", "line_number": 348, "usage_type": "attribute"}, {"api_name": "global_variables.left", "line_number": 352, "usage_type": "attribute"}, {"api_name": "global_variables.up", "line_number": 356, "usage_type": "attribute"}, {"api_name": "global_variables.down", "line_number": 360, "usage_type": "attribute"}, {"api_name": "global_variables.action", "line_number": 364, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 367, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 370, "usage_type": "call"}, {"api_name": "global_variables.screen.fill", "line_number": 372, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 372, "usage_type": "attribute"}, {"api_name": "global_variables.screen.blit", "line_number": 373, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 373, "usage_type": "attribute"}, {"api_name": "global_variables.width", "line_number": 373, "usage_type": "attribute"}, {"api_name": "global_variables.height", "line_number": 373, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 374, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 374, "usage_type": "attribute"}, {"api_name": "global_variables.clock.tick", "line_number": 375, "usage_type": "call"}, {"api_name": "global_variables.clock", "line_number": 375, "usage_type": "attribute"}, {"api_name": "global_variables.FPS", "line_number": 375, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 380, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 380, "usage_type": "attribute"}, {"api_name": "global_variables.size", "line_number": 380, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 385, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 385, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 386, "usage_type": "attribute"}, {"api_name": "global_variables.running", "line_number": 387, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 389, "usage_type": "call"}, {"api_name": "global_variables.size", "line_number": 389, "usage_type": "attribute"}, {"api_name": "global_variables.screen.blit", "line_number": 403, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 403, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 407, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 407, "usage_type": "attribute"}, {"api_name": "global_variables.clock.tick", "line_number": 408, "usage_type": "call"}, {"api_name": "global_variables.clock", "line_number": 408, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 413, "usage_type": "call"}, {"api_name": "global_variables.size", "line_number": 413, "usage_type": "attribute"}, {"api_name": "global_variables.screen", "line_number": 414, "usage_type": "attribute"}, {"api_name": "global_variables.player_name", "line_number": 417, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 426, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 428, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 429, "usage_type": "call"}, {"api_name": "pygame.font.Font", "line_number": 433, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 433, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 434, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 434, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 446, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 446, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 447, "usage_type": "attribute"}, {"api_name": "global_variables.running", "line_number": 448, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 449, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 450, "usage_type": "attribute"}, {"api_name": "global_variables.up", "line_number": 452, "usage_type": "attribute"}, {"api_name": "global_variables.down", "line_number": 452, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 455, "usage_type": "call"}, {"api_name": "global_variables.size", "line_number": 455, "usage_type": "attribute"}, {"api_name": "global_variables.screen.blit", "line_number": 477, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 477, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 478, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 478, "usage_type": "attribute"}, {"api_name": "global_variables.clock.tick", "line_number": 479, "usage_type": "call"}, {"api_name": "global_variables.clock", "line_number": 479, "usage_type": "attribute"}, {"api_name": "global_variables.cur_lvl", "line_number": 484, "usage_type": "attribute"}, {"api_name": "global_variables.playerpos_x", "line_number": 484, "usage_type": "attribute"}, {"api_name": "global_variables.playerpos_y", "line_number": 484, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 485, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 492, "usage_type": "call"}, {"api_name": "global_variables.from_save", "line_number": 494, "usage_type": "attribute"}, {"api_name": "global_variables.from_save", "line_number": 497, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 504, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 504, "usage_type": "attribute"}, {"api_name": "global_variables.size", "line_number": 504, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 509, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 509, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 510, "usage_type": "attribute"}, {"api_name": "global_variables.running", "line_number": 511, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 513, "usage_type": "call"}, {"api_name": "global_variables.size", "line_number": 513, "usage_type": "attribute"}, {"api_name": "global_variables.screen.blit", "line_number": 525, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 525, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 529, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 529, "usage_type": "attribute"}, {"api_name": "global_variables.clock.tick", "line_number": 530, "usage_type": "call"}, {"api_name": "global_variables.clock", "line_number": 530, "usage_type": "attribute"}, {"api_name": "global_variables.from_save", "line_number": 532, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 538, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 538, "usage_type": "attribute"}, {"api_name": "global_variables.size", "line_number": 538, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 542, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 542, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 543, "usage_type": "attribute"}, {"api_name": "global_variables.running", "line_number": 544, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 546, "usage_type": "call"}, {"api_name": "global_variables.size", "line_number": 546, "usage_type": "attribute"}, {"api_name": "global_variables.screen.blit", "line_number": 552, "usage_type": "call"}, {"api_name": "global_variables.screen", "line_number": 552, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 556, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 556, "usage_type": "attribute"}, {"api_name": "global_variables.clock.tick", "line_number": 557, "usage_type": "call"}, {"api_name": "global_variables.clock", "line_number": 557, "usage_type": "attribute"}]} +{"seq_id": "145121248", "text": "import numpy as np\nnp.set_printoptions(threshold=np.inf, linewidth=300)\nimport pandas as pd\nimport time\nfrom PIL import Image\n\nclass Map_Obj():\n path_cell = (1,2,3,4)\n wall_cell = -1\n starting_point = 'S'\n goal_point = 'G'\n def __init__(self, task=1):\n self.start_pos, self.goal_pos, self.end_goal_pos, self.path_to_map = self.fill_critical_positions(task)\n self.int_map, self.str_map = self.read_map(self.path_to_map)\n self.tmp_cell_value = self.get_cell_value(self.goal_pos)\n self.set_cell_value(self.start_pos, ' S ')\n self.set_cell_value(self.goal_pos, ' G ')\n self.tick_counter = 0\n #self.set_start_pos_str_marker(start_pos, self.str_map)\n #self.set_goal_pos_str_marker(goal_pos, self.str_map)\n\n def read_map(self, path):\n \"\"\"\n Reads maps specified in path from file, converts them to a numpy array and a string array. Then replaces\n specific values in the string array with predefined values more suitable for printing.\n :param path: Path to .csv maps\n :return: the integer map and string map\n \"\"\"\n # Read map from provided csv file\n df = pd.read_csv(path, index_col=None, header=None)#,error_bad_lines=False)\n # Convert pandas dataframe to numpy array\n data = df.values\n # Convert numpy array to string to make it more human readable\n data_str = data.astype(str)\n # Replace numeric values with more human readable symbols\n data_str[data_str == '-1'] = ' # '\n data_str[data_str == '1'] = ' . '\n data_str[data_str == '2'] = ' , '\n data_str[data_str == '3'] = ' : '\n data_str[data_str == '4'] = ' ; '\n return data, data_str\n\n def fill_critical_positions(self, task):\n \"\"\"\n Fills the important positions for the current task. Given the task, the path to the correct map is set, and the\n start, goal and eventual end_goal positions are set.\n :param task: The task we are currently solving\n :return: Start position, Initial goal position, End goal position, path to map for current task.\n \"\"\"\n if task == 1:\n start_pos = [27, 18]\n goal_pos = [40, 32]\n end_goal_pos = goal_pos\n path_to_map = 'Samfundet_map_1.csv'\n elif task == 2:\n start_pos = [40, 32]\n goal_pos = [8, 5]\n end_goal_pos = goal_pos\n path_to_map = 'Samfundet_map_1.csv'\n elif task == 3:\n start_pos = [28, 32]\n goal_pos = [6, 32]\n end_goal_pos = goal_pos\n path_to_map = 'Samfundet_map_2.csv'\n elif task == 4:\n start_pos = [28, 32]\n goal_pos = [6, 32]\n end_goal_pos = goal_pos\n path_to_map = 'Samfundet_map_Edgar_full.csv'\n elif task == 5:\n start_pos = [14, 18]\n goal_pos = [6, 36]\n end_goal_pos = [6, 7]\n path_to_map = 'Samfundet_map_2.csv'\n\n\n return start_pos, goal_pos, end_goal_pos, path_to_map\n\n def get_cell_value(self, pos):\n return self.int_map[pos[0], pos[1]]\n\n def get_goal_pos(self):\n return self.goal_pos\n\n def get_start_pos(self):\n return self.start_pos\n\n def get_end_goal_pos(self):\n return self.end_goal_pos\n\n def get_maps(self):\n # Return the map in both int and string format\n return self.int_map, self.str_map\n\n def move_goal_pos(self, pos):\n \"\"\"\n Moves the goal position towards end_goal position. Moves the current goal position and replaces its previous\n position with the previous values for correct printing.\n :param pos: position to move current_goal to\n :return: nothing.\n \"\"\"\n tmp_val = self.tmp_cell_value\n tmp_pos = self.goal_pos\n self.tmp_cell_value = self.get_cell_value(pos)\n self.goal_pos = [pos[0], pos[1]]\n self.replace_map_values(tmp_pos, tmp_val, self.goal_pos)\n\n def set_cell_value(self, pos, value, str_map = True):\n if str_map:\n self.str_map[pos[0], pos[1]] = value\n else:\n self.int_map[pos[0], pos[1]] = value\n\n def print_map(self, map_to_print):\n # For every column in provided map, print it\n for column in map_to_print:\n print(column)\n\n\n def pick_move(self):\n \"\"\"\n A function used for moving the goal position. It moves the current goal position towards the end_goal position.\n :return: Next coordinates for the goal position.\n \"\"\"\n if self.goal_pos[0] < self.end_goal_pos[0]:\n return [self.goal_pos[0]+1, self.goal_pos[1]]\n elif self.goal_pos[0] > self.end_goal_pos[0]:\n return [self.goal_pos[0]-1, self.goal_pos[1]]\n elif self.goal_pos[1] < self.end_goal_pos[1]:\n return [self.goal_pos[0], self.goal_pos[1]+1]\n else:\n return [self.goal_pos[0], self.goal_pos[1]-1]\n\n def replace_map_values(self, pos, value, goal_pos):\n \"\"\"\n Replaces the values in the two maps at the coordinates provided with the values provided.\n :param pos: coordinates for where we want to change the values\n :param value: the value we want to change to\n :param goal_pos: The coordinate of the current goal\n :return: nothing.\n \"\"\"\n if value == 1:\n str_value = ' . '\n elif value == 2:\n str_value = ' , '\n elif value == 3:\n str_value = ' : '\n elif value == 4:\n str_value = ' ; '\n else:\n str_value = str(value)\n self.int_map[pos[0]][pos[1]] = value\n self.str_map[pos[0]][pos[1]] = str_value\n self.str_map[goal_pos[0], goal_pos[1]] = ' G '\n\n\n def tick(self):\n \"\"\"\n Moves the current goal position every 4th call if current goal position is not already at the end_goal position.\n :return: current goal position\n \"\"\"\n # For every 4th call, actually do something\n if self.tick_counter % 4 == 0:\n # The end_goal_pos is not set\n if self.end_goal_pos is None:\n return self.goal_pos\n # The current goal is at the end_goal\n elif self.end_goal_pos == self.goal_pos:\n return self.goal_pos\n else:\n # Move current goal position\n move = self.pick_move()\n self.move_goal_pos(move)\n #print(self.goal_pos)\n self.tick_counter +=1\n\n return self.goal_pos\n\n\n def set_start_pos_str_marker(self, start_pos, map):\n # Attempt to set the start position on the map\n if self.int_map[start_pos[0]][start_pos[1]] == -1:\n self.print_map(self.str_map)\n print('The selected start position, '+str(start_pos) + ' is not a valid position on the current map.')\n exit()\n else:\n map[start_pos[0]][start_pos[1]] = ' S '\n\n def set_goal_pos_str_marker(self, goal_pos, map):\n # Attempt to set the goal position on the map\n if self.int_map[goal_pos[0]][goal_pos[1]] == -1:\n self.print_map(self.str_map)\n print('The selected goal position, '+ str(goal_pos) + ' is not a valid position on the current map.')\n exit()\n else:\n map[goal_pos[0]][goal_pos[1]] = ' G '\n\n def show_map(self, map=None):\n \"\"\"\n A function used to draw the map as an image and show it.\n :param map: map to use\n :return: nothing.\n \"\"\"\n # If a map is provided, set the goal and start positions\n if map is not None:\n self.set_start_pos_str_marker(self.start_pos, map)\n self.set_goal_pos_str_marker(self.goal_pos, map)\n # If no map is provided, use string_map\n else:\n map = self.str_map\n\n # Define width and height of image\n width = map.shape[1]\n height = map.shape[0]\n # Define scale of the image\n scale = 20\n # Create an all-yellow image\n image = Image.new('RGB', (width * scale, height * scale), (255, 255, 0))\n # Load image\n pixels = image.load()\n\n # Define what colors to give to different values of the string map (undefined values will remain yellow, this is\n # how the yellow path is painted)\n colors = {' # ': (255, 0, 0), ' . ': (215, 215, 215), ' , ': (166, 166, 166), ' : ': (96, 96, 96),\n ' ; ': (36, 36, 36), ' S ': (255, 0, 255), ' G ': (0, 128, 255)}\n # Go through image and set pixel color for every position\n for y in range(height):\n for x in range(width):\n if map[y][x] not in colors: continue\n for i in range(scale):\n for j in range(scale):\n pixels[x * scale + i, y * scale + j] = colors[map[y][x]]\n # Show image\n image.show()\n\nclass search_node:\n \"\"\"\n • state - an object describing a state of the search process\n • g - cost of getting to this node\n • h - estimated cost to goal\n • f - estimated total cost of a solution path going through this node; f = g + h\n • status - open or closed\n • parent - pointer to best parent node\n • kids - list of all successor nodes, whether or not this node is currently their best parent.\n \"\"\"\n def __init__(self, state=None, g=None, h=None, f=None, status=None, parent=None, children=None):\n self.state = state\n self.g = g\n self.h = h\n self.f = f\n self.status = status\n self.parent = parent\n if children:\n self.children = children\n else:\n self.children = []\n def __str__(self):\n return \"state is %s, parent.state is %s\" % (self.state, self.parent)\n\ndef initialize_node(state0):\n n0 = search_node(state=state0)\n n0.g = 0\n n0.h = euclidean(state0)\n n0.f = n0.g + n0.h\n return n0\n\ndef euclidean(state):\n map, pos = state\n goal = map.get_goal_pos()\n dist = ((goal[0]-pos[0])**2+(goal[1]-pos[1])**2)**0.5\n #print(' ')\n #print('pos', pos)\n #print('goal', goal)\n #print('dist', dist)\n #print(' ')\n return dist\n\"\"\"\ndef euclidean(state):\n map, pos = state\n goal = map.get_goal_pos()\n return abs(goal[0] - pos[0]) + abs(goal[1] - pos[1])\"\"\"\n\ndef isGoal(state):\n map, (x, y) = state\n return tuple(map.get_goal_pos()) == (x, y)\n\ndef reconstruct_path(node, path):\n if node.parent is None:\n return path\n path.append(node.parent)\n return reconstruct_path(node.parent,path)#+[node]\n\ndef generate_all_successors(X):\n map, (x, y) = X.state\n (i,j) = map.int_map.shape\n neigbours = [(1,0), (-1,0), (0,1), (0,-1)]#, (1,1), (-1,-1), (1,-1),(-1,1)]\n for x_dir, y_dir in neigbours:\n new_x = x + x_dir\n new_y = y + y_dir\n if (new_x < 0) or (new_y < 0) or (new_x >= i) or (new_y >= j):\n continue\n if map.get_cell_value((new_x, new_y)) != Map_Obj.wall_cell:\n #print('[new_x, new_y]', [new_x, new_y])\n yield (map, [new_x, new_y])\n\ndef initialize_child_node(parent, node): #works\n child = search_node(state=node, parent=parent)\n child.g = parent.g + 1 # Since 1 is the cost of moving from one point to another\n child.h = euclidean(child.state)\n child.f = child.g + child.h\n return child\n\ndef attach_and_eval(parent, child):\n child.parent = parent\n child.g = parent.g + 1 # Since cost of moving is 1\n child.h = euclidean(child.state)\n child.f = child.g + child.h\n\ndef sort_list(list):\n sorted_list = sorted(list, key=lambda x: x.f)\n return sorted_list\n\ndef propagate_path_improvements(node):\n for child in node.children:\n if node.g + 1 < child.g:\n child.parent = node\n child.g = node.g + 1\n print('child.state', child.state)\n print('child.h', child.h)\n child.f = child.g + child.h\n #propagate_path_improvements(child)\n\ndef best_first_search(state):\n closed = []\n open = []\n closed_state = []\n open_state = []\n node0 = initialize_node(state)\n open.append(node0)\n open_state.append(node0.state)\n visited_nodes = {}\n map, (x, y) = node0.state\n key = x*100+y # The unique identifier for the node\n visited_nodes[key] = node0\n\n while open: # AGENDA-loop\n X = open.pop()\n open_state.pop() # This changes something\n print('X.state', X.state)\n #print('X.g', X.g)\n #print('X.h', X.h)\n #print('X.f', X.f)\n closed.append(X)\n closed_state.append(X.state)\n\n # If we're in target area\n if isGoal(X.state):\n path = []\n print('Found goal')\n print('open', open)\n reconstruct_path(X, path)\n return path\n children = generate_all_successors(X)\n\n for child in children:\n map, (x,y) = child\n key = x*100+y\n #print('(x,y)', (x,y))\n\n # Making child into node\n #print('visited_nodes.keys()', visited_nodes.keys())\n if key in visited_nodes.keys():\n #print('hello')\n child_node = visited_nodes[key]\n #print('this child:', child_node)\n else:\n #print('sup')\n child_node = initialize_child_node(X, child)\n visited_nodes[key] = child_node\n\n (X.children).append(child_node)\n #print('child_node.f', child_node.f)\n #print('X.f + 1', X.f + 1)\n\n # Checks if this is a new step\n if child_node.state not in open_state and child_node.state not in closed_state:\n attach_and_eval(X, child_node)\n open.append(child_node)\n open_state.append(child_node.state)\n open = sort_list(open)\n\n # Checks if this is a shorter way to get to this node, if we've already been here\n elif X.g + 1 < child_node.g:\n #print('Attaching!')\n attach_and_eval(X, child_node)\n #print('its children', X.children)\n #print('yo',child_node)\n if child_node.state in closed_state:\n propagate_path_improvements(child_node)\n\n #for item in open:\n #print('item.f', item.f)\n #print('item.state', item.state)\n\ndef main():\n map_obj = Map_Obj(task=2)\n state0 = map_obj, map_obj.get_start_pos()\n path = best_first_search(state0)\n for node in path:\n map, pos = node.state\n print(pos)\n map_obj.set_cell_value(pos, \"o\")#, str_map = True)\n map_obj.show_map()\n input()\n\nmain()\n", "sub_path": "Assignment2/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 14798, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.set_printoptions", "line_number": 2, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 2, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 218, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 218, "usage_type": "name"}]} +{"seq_id": "153731512", "text": "import requests\n\n\n# files = {'imagen': open(\"stars2.jpg\", \"rb\")} #para cargar la imagen\nfiles = {'imagen': open(\"aurora.mp4\", \"rb\")} #para cargar un video\n\npyload = {'region': 1}\nurl = \"http://127.0.0.1:8000/radioastronomia/album-imagenes\"\nr = requests.post(url, data=pyload, files=files)\nprint(r.text)\nprint(r.status_code)\nr.close()", "sub_path": "Modulos_aplicaciones/radioastronomia/subsistema_imagenes/test_API.py", "file_name": "test_API.py", "file_ext": "py", "file_size_in_byte": 333, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.post", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "282629579", "text": "from django import forms\nfrom django.core.validators import MaxValueValidator, MinValueValidator\n\nfrom questionario.models import Questionario, Pergunta, Resposta\n\n\nclass RespostaForm(forms.ModelForm):\n resposta = forms.IntegerField(\n error_messages={\n 'required': 'Este campo é obrigatório!'},\n widget=forms.NumberInput(\n attrs={\n 'class': 'form-control',\n 'placeholder': 'Nota de 0 a 10',\n }\n ),\n validators=[\n MaxValueValidator(10),\n MinValueValidator(0)\n ],\n )\n\n class Meta():\n model = Resposta\n fields = ('resposta',)\n\n def __init__(self, n, *args, **kwargs):\n super(RespostaForm, self).__init__(*args, **kwargs)\n for i in range(1, n):\n self.fields[\"resposta%d\" % i] = forms.IntegerField(\n error_messages={\n 'required': 'Este campo é obrigatório!'},\n widget=forms.NumberInput(\n attrs={\n 'class': 'form-control',\n 'placeholder': 'Nota de 0 a 10',\n }\n ),\n validators=[\n MaxValueValidator(10),\n MinValueValidator(0)\n ],\n )\n\n def clean(self):\n all_clean_data = super().clean()\n resposta = all_clean_data['resposta']\n\n if resposta < 0 or resposta > 10:\n raise forms.ValidationError(\n \"O número tem que estar no intervalo de 0 e 10!\"\n )\n\n# def save(self, id_pergunta, id_user, pontos):\n# print(id_pergunta)\n# print(pontos)\n# self.usuario = id_user\n# self.pergunta = id_pergunta\n# self.pontos = pontos\n# super().save()\n", "sub_path": "src/questionario/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1833, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.forms.ModelForm", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 7, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 8, "usage_type": "name"}, {"api_name": "django.forms.NumberInput", "line_number": 11, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 11, "usage_type": "name"}, {"api_name": "django.core.validators.MaxValueValidator", "line_number": 18, "usage_type": "call"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 19, "usage_type": "call"}, {"api_name": "questionario.models.Resposta", "line_number": 24, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 30, "usage_type": "name"}, {"api_name": "django.forms.NumberInput", "line_number": 33, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 33, "usage_type": "name"}, {"api_name": "django.core.validators.MaxValueValidator", "line_number": 40, "usage_type": "call"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 41, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 50, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 50, "usage_type": "name"}]} +{"seq_id": "111876518", "text": "import os\nimport re\nimport json\nimport hmac\nimport hashlib\nfrom decimal import Decimal\nfrom datetime import datetime\nfrom collections import deque\nfrom requests.auth import AuthBase\n\nclass Auth(AuthBase):\n def __init__(self, key, secret, nonce):\n self.key = key\n self.secret = secret\n self.nonce = nonce\n def __call__(self, r):\n r.body += '&nonce=%d' % self.nonce\n r.headers['Key'] = self.key\n r.headers['Sign'] = hmac.new(self.secret, r.body.encode('utf-8'), hashlib.sha512).hexdigest()\n return r\n\ndef get_nonce(nonce_file_path):\n with open(nonce_file_path, 'r+') as f:\n nonce = json.load(f)\n current_nonce = int(nonce['nonce'])\n next_nonce = {'nonce':current_nonce + 1}\n formatted_json = json.dumps(next_nonce, indent=4, sort_keys=True)\n f.seek(0)\n f.write(formatted_json)\n f.close()\n return current_nonce\n\ndef get_key(exchange):\n credentials_file = os.path.dirname(os.path.abspath(__file__)) + '/credentials.json'\n with open(credentials_file, 'r') as f:\n credentials = json.load(f)\n f.close()\n return credentials[exchange]['key']\n\ndef get_secret(exchange):\n credentials_file = os.path.dirname(os.path.abspath(__file__)) + '/credentials.json'\n with open(credentials_file, 'r') as f:\n credentials = json.load(f)\n f.close()\n return credentials[exchange]['secret']\n\ndef is_float(element):\n try:\n float(element)\n return True\n except ValueError:\n return False\n\ndef non_numeric_filter(s, filler=' '):\n \"\"\" replaces all non-numeric characters in string s with whitespace \"\"\"\n return re.sub(r'\\D', filler, s)\n\ndef swap(x, y, lst):\n \"\"\" swap first instance of x with first instance of y in lst \"\"\"\n x_index, y_index = lst.index(x), lst.index(y)\n lst[x_index], lst[y_index] = lst[y_index], lst[x_index]\n return lst\n\ndef lr_fill(seq, max_length, filler, front=False):\n \"\"\"\n Fills contents of seq with filler until len(seq) == max_length-1\n Extends seq backward by default unless front is True\n \"\"\"\n if len(seq) < max_length:\n fill = [filler] * (max_length - len(seq))\n if front:\n if type(seq) is str:\n seq = ''.join(fill) + seq\n if type(seq) is list:\n deq = deque(seq)\n deq.extendleft(fill)\n seq = list(deque(deq))\n else:\n if type(seq) is str:\n seq += ''.join(fill)\n if type(seq) is list:\n seq += fill\n return seq\n\ndef date_to_timestamp(calendar_date):\n \"\"\"\n Takes date as elements delimited by non-numbers in format yyyy-mm-dd and returns time since epoch as integer\n Produces correct values as long as date elements are separated by non-numeric characters, e.g. 1990/5-31 1990*5!31\n \"\"\"\n date_ws = non_numeric_filter(calendar_date)\n date_lst = [int(i) for i in date_ws.split(' ') if len(i) > 0]\n if len(date_lst) == 3:\n for d in date_lst:\n if d > 31:\n date_lst = swap(d, date_lst[0], date_lst)\n elif 12 < d < 28:\n date_lst = swap(d, date_lst[2], date_lst)\n\n d = lr_fill(date_lst, 6, 0)\n return (datetime(d[0], d[1], d[2], d[3], d[4], d[5]) - datetime(1970,1,1)).total_seconds()\n\ndef date_to_unix_timestamp(date):\n if is_float(date):\n return float(date)\n else:\n return date_to_timestamp(date)\n\ndef truncate_decimal(d, n):\n if '.' in str(d):\n pre_split, post_split = str(d).split('.', 1)\n d = Decimal(('.'.join((pre_split, post_split[:n]))))\n return d", "sub_path": "_apitools.py", "file_name": "_apitools.py", "file_ext": "py", "file_size_in_byte": 3630, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.auth.AuthBase", "line_number": 11, "usage_type": "name"}, {"api_name": "hmac.new", "line_number": 19, "usage_type": "call"}, {"api_name": "hashlib.sha512", "line_number": 19, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 24, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 34, "usage_type": "call"}, {"api_name": "json.load", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 41, "usage_type": "call"}, {"api_name": "json.load", "line_number": 43, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 56, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 75, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 100, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 111, "usage_type": "call"}]} +{"seq_id": "170152603", "text": "from game_components.ship import Ship\nimport pygame as pg\nfrom pygame.sprite import Sprite\n\n\nclass ShipIndicator(Sprite):\n \"\"\"A class that serves as indicator for the remaining ships before the game is over.\"\"\"\n\n def __init__(self, game):\n super().__init__()\n\n self.ship = Ship(game)\n\n self.screen = game.screen\n self.screen_rect = self.screen.get_rect()\n\n self.image = self.ship.img\n self.image_width, self.image_height = 12, 20\n self.image = pg.transform.scale(self.image, (self.image_width, self.image_height))\n self.rect = self.image.get_rect()\n", "sub_path": "game_components/ship_indicator.py", "file_name": "ship_indicator.py", "file_ext": "py", "file_size_in_byte": 611, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.sprite.Sprite", "line_number": 6, "usage_type": "name"}, {"api_name": "game_components.ship.Ship", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.transform.scale", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 19, "usage_type": "attribute"}]} +{"seq_id": "217346304", "text": "from flask import Flask, render_template, request\nfrom flask_paginate import Pagination, get_page_args\nimport pymongo\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef entry_point():\n return render_template('home.html')\n\n\n@app.route('/search_results')\ndef search_results():\n connect_url = 'mongodb://127.0.0.1:27017/'\n\n client = pymongo.MongoClient(connect_url, connect=False)\n\n db = client.results\n\n search_string = request.args.get('search')\n\n query = db.search_results.find(\n {'$text': {'$search': search_string, '$caseSensitive': False}})\n\n search_result = []\n\n for doc in query:\n exist = False\n for result in search_result:\n if result['title'] == doc['title'] or result['url'] == doc['url']:\n exist = True\n break\n\n if exist == False:\n search_result.append(doc)\n\n page, per_page, offset = get_page_args(page_parameter='page',\n per_page_parameter='per_page')\n\n total = len(search_result)\n\n pagination = Pagination(page=page, per_page=per_page, total=total,\n css_framework='bootstrap4')\n\n return render_template('search.html',\n search_result=search_result[offset:offset+per_page],\n page=page,\n per_page=per_page,\n pagination=pagination,\n search_string=search_string\n )\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n\n\n# python app/app.py\n", "sub_path": "app/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1582, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 10, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask_paginate.get_page_args", "line_number": 38, "usage_type": "call"}, {"api_name": "flask_paginate.Pagination", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "93524464", "text": "import pytest\nimport requests\nimport json\nimport os\n\nfrom dotenv import find_dotenv, load_dotenv\nfrom app import create_app\nfrom entity.item import Item\n\n@pytest.fixture\ndef client():\n file_path = find_dotenv('.env.test')\n load_dotenv(file_path, override=True)\n\n # Create the new app.\n test_app = create_app()\n\n test_app.config['LOGIN_DISABLED'] = True\n \n # Use the app to create a test_client that can be used in our tests.\n with test_app.test_client() as client:\n yield client\n\ndef stub_get_db_collection():\n return\n\ndef stub_get_all_items(collection):\n return [\n Item(\n 'test-to-do-item-id',\n 'Test To Do item Title',\n 'To Do',\n 'Test To Do item Description',\n '2020-07-30T12:52:06.278Z'\n ),\n Item(\n 'test-doing-item-id',\n 'Test Doing item Title',\n 'Doing',\n 'Test Doing item Description',\n '2020-07-30T12:52:06.278Z'\n ),\n Item(\n 'test-done-item-id',\n 'Test Done item Title',\n 'Done',\n 'Test Done item Description',\n '2020-07-30T12:52:06.278Z'\n )\n ]\n\ndef test_app(monkeypatch, client):\n monkeypatch.setattr(\n 'app.get_db_collection',\n stub_get_db_collection\n )\n monkeypatch.setattr(\n 'app.get_all_items',\n stub_get_all_items\n )\n\n response = client.get('/')\n \n content = str(response.data)\n print (content)\n assert response.status_code == 200 \n assert 'Test To Do item Title' in content\n assert 'Test Doing item Title' in content\n assert 'Test Done item Title' in content", "sub_path": "tests_integration/test_app_integration.py", "file_name": "test_app_integration.py", "file_ext": "py", "file_size_in_byte": 1684, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "dotenv.find_dotenv", "line_number": 12, "usage_type": "call"}, {"api_name": "dotenv.load_dotenv", "line_number": 13, "usage_type": "call"}, {"api_name": "app.create_app", "line_number": 16, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 10, "usage_type": "attribute"}, {"api_name": "entity.item.Item", "line_number": 29, "usage_type": "call"}, {"api_name": "entity.item.Item", "line_number": 36, "usage_type": "call"}, {"api_name": "entity.item.Item", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "209299188", "text": "import dlib, os, cv2\nimport numpy as np\nimport pandas as pd\n\n\nclass FaceRecognizer:\n\n def __init__(self, shape_dat='', face_dat=''):\n # define model path.\n if shape_dat == '':\n shape_dat = 'libs/sh n ape_predictor_68_face_landmarks.dat'\n if face_dat == '':\n face_dat = 'libs/dlib_face_recognition_resnet_model_v1.dat'\n\n # initialize objects.\n self.face_cascade = cv2.CascadeClassifier('libs/haarcascade_frontalface_default.xml')\n\n self.detector = dlib.get_frontal_face_detector()\n self.predictor = dlib.shape_predictor(shape_dat)\n self.recognizer = dlib.face_recognition_model_v1(face_dat)\n self.orientation_str = ['front', 'left', 'right', '3', '4', '5', '6']\n\n self.description_filename = 'description.xlsx'\n self.users = {}\n\n def face_detect_cv(self, color_image):\n gray = cv2.cvtColor(color_image, cv2.COLOR_RGB2GRAY)\n rects = self.face_cascade.detectMultiScale(gray, minSize=(150, 150))\n faces = []\n for (x, y, w, h) in rects:\n face = {}\n face['d'] = dlib.rectangle(left=int(x), top=int(y), right=int(x + w), bottom=int(y + h))\n face['p1'] = (x, y)\n face['p2'] = (x + w, y + h)\n face['w'] = w\n face['h'] = h\n face['score'] = 0\n face['orientation'] = 0\n face['orientation_str'] = self.orientation_str[0]\n face['shape'] = None\n face['description'] = None\n face['display_name'] = 'unknown'\n face['distance'] = 0\n faces.append(face)\n return faces\n\n # Step 1.\n def face_detect(self, color_image, multi_detect=0):\n # face detect\n faces = []\n dets, scores, orientations = self.detector.run(color_image, multi_detect)\n for i, d in enumerate(dets):\n x1 = d.left()\n y1 = d.top()\n x2 = d.right()\n y2 = d.bottom()\n face = {}\n face['d'] = d\n face['p1'] = (x1, y1)\n face['p2'] = (x2, y2)\n face['w'] = int(x2 - x1)\n face['h'] = int(y2 - y1)\n face['score'] = scores[i]\n face['orientation'] = orientations[i]\n face['orientation_str'] = self.orientation_str[int(orientations[i])]\n face['shape'] = None\n face['description'] = None\n face['display_name'] = 'unknown'\n face['distance'] = 0\n faces.append(face)\n return faces\n\n # Step 2.\n def face_shape(self, color_image, face):\n # predict face shape\n shape = self.predictor(color_image, face['d'])\n face['shape'] = shape\n return face\n\n def faces_shape(self, color_image, faces):\n for face in faces:\n face = self.face_shape(color_image, face)\n return faces\n\n # Step 3.\n def face_description(self, color_image, face):\n # recognize face description\n if face['shape'] == None:\n face = self.face_shape(color_image, face)\n description = self.recognizer.compute_face_descriptor(color_image, face['shape'])\n v = np.array(description)\n face['description'] = v\n return face\n\n def faces_description(self, color_image, faces):\n for face in faces:\n face = self.face_description(color_image, face)\n return faces\n\n def draw_faces(self, color_image, faces, c=(0, 255, 0), w=2):\n image = color_image.copy()\n for face in faces:\n cv2.rectangle(image, face['p1'], face['p2'], c, w)\n return image\n\n def draw_shape(self, color_image, faces, c=(255, 0, 0), w=2, r=3):\n image = color_image.copy()\n for face in faces:\n shape = face['shape']\n for i in range(68):\n cv2.circle(image, (shape.part(i).x, shape.part(i).y), r, c, w)\n return image\n\n def calc_128D_by_path(self, path, export=False):\n for _, _, filenames in os.walk(path):\n descriptions = []\n\n for filename in filenames:\n if filename[-4:] != '.jpg': continue\n\n image = cv2.imread(\"%s/%s\" % (path, filename), cv2.IMREAD_COLOR)\n color_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n faces = self.face_detect(color_image)\n for face in faces:\n face = self.face_shape(color_image, face)\n face = self.face_description(color_image, face)\n descriptions.append(face['description'])\n\n desc = np.average(descriptions, axis=0)\n\n if export:\n writer = pd.ExcelWriter(path + os.sep + self.description_filename, engine='xlsxwriter')\n data = pd.DataFrame(desc)\n data.to_excel(writer, '128D', float_format='%.9f')\n writer.save()\n\n return desc\n return None\n\n def load_users(self, path):\n for _, folders, _ in os.walk(path):\n for folder in folders:\n filepath = \"%s/%s/%s\" % (path, folder, self.description_filename)\n if os.path.exists(filepath):\n data = pd.read_excel(filepath)\n desc = data.iloc[:, 1].values.tolist()\n desc = np.array(desc).reshape(1, -1)[0]\n username = folder\n self.users[username] = desc\n\n def recognize(self, color_image, multi_detect=0, threshold=0.4):\n faces = self.face_detect(color_image, multi_detect)\n for face in faces:\n face = self.face_shape(color_image, face)\n face = self.face_description(color_image, face)\n desc = face['description']\n\n dists = {}\n for key, val in self.users.items():\n dist = np.sqrt(np.sum(np.square(val - desc)))\n dists[key] = dist\n\n dists_sorted = sorted(dists.items(), key=lambda d: d[1])\n print(dists_sorted)\n\n if len(dists_sorted) > 0:\n if dists_sorted[0][1] < threshold:\n face['display_name'] = dists_sorted[0][0]\n face['distance'] = dists_sorted[0][1]\n return faces\n\n\nfr = FaceRecognizer(\"./lib/shape_predictor_68_face_landmarks.dat\", \"lib/dlib_face_recognition_resnet_model_v1.dat\")\n", "sub_path": "Face_recognition/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 6371, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 16, "usage_type": "call"}, {"api_name": "dlib.get_frontal_face_detector", "line_number": 18, "usage_type": "call"}, {"api_name": "dlib.shape_predictor", "line_number": 19, "usage_type": "call"}, {"api_name": "dlib.face_recognition_model_v1", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 27, "usage_type": "attribute"}, {"api_name": "dlib.rectangle", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 91, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 103, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 111, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 121, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 121, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 122, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.average", "line_number": 129, "usage_type": "call"}, {"api_name": "pandas.ExcelWriter", "line_number": 132, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 133, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 160, "usage_type": "call"}]} +{"seq_id": "96222388", "text": "import argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--message\", \"-m\", help=\"Le message a envoyer\")\nparser.add_argument(\"-read\", action=\"store_true\")\nargs = parser.parse_args()\n\nprint(args.message)\nprint(args.read)\n\nif args.read:\n import simple_queue_read\nelse:\n message = args.message\n import simple_queue_publish as pub\n pub.sendMessage(message)", "sub_path": "CloudAMQP/queue_publish_read.py", "file_name": "queue_publish_read.py", "file_ext": "py", "file_size_in_byte": 379, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 3, "usage_type": "call"}, {"api_name": "simple_queue_publish.sendMessage", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "595650971", "text": "import pymongo\nimport json\nfrom pymongo import MongoClient\n\nclient = MongoClient(\"mongodb+srv://sam9977:codered1234@cluster0.bsjir.mongodb.net/kpi_proj?retryWrites=true&w=majority\", ssl=True,ssl_cert_reqs='CERT_NONE')\ndb = client[\"kpi_proj\"]\ncollection_project = db[\"project\"]\n\nwith open('data/db.json') as json_file:\n data = json.load(json_file)\n\n# populate project collection\n\n# for project in data[\"projects\"]:\n \n# entry = {\n# \"id\": project[\"id\"],\n# \"name\": project[\"projectName\"],\n# \"type_work\": project[\"typeOfWork\"],\n# \"start_date\": project[\"startDate\"],\n# \"end_date\": project[\"endDate\"],\n# \"budget\": project[\"budget\"],\n# \"actual\": project[\"actual\"],\n# \"timecards\": project[\"timecardIds\"],\n# \"photo_id\": project[\"photoIds\"],\n# \"location\": project[\"location\"]\n# }\n\n# collection_project.insert_one(entry)\n\n#----------------------------------------------------------------------------------------------------------\n\n# populate budget collection\n\ncollection_budget = db[\"budget\"]\n\n# populates budgets for all projects\n\n# for project in data[\"projects\"]:\n\n# for budgets in project[\"budget\"]:\n \n \n# entry = {\n# \"id\": project[\"id\"],\n# \"name\": project[\"projectName\"],\n# \"date\": budgets[\"date\"],\n# \"budget\": budgets,\n# \"actual\": None,\n# \"totals\": None,\n# }\n\n# collection_budget.insert_one(entry)\n\n\n# populates actuals for the dates and projectNames that match\n\n# for project in data[\"projects\"]:\n\n# for budgets in project[\"actual\"]:\n \n# query = { \"date\": budgets[\"date\"], \"name\": project[\"projectName\"] }\n\n# newvalues_actual = { \"$set\": { \"actual\": budgets } }\n\n# collection_budget.update_one(query, newvalues_actual)\n\n# # deletes entries with no actuals\n\n# delete_query = {\"actual\": None}\n# collection_budget.delete_many(delete_query)\n\n# calculate totals\n\n# for doc in collection_budget.find():\n# query = { \"date\": doc[\"date\"], \"name\": doc[\"name\"] }\n# total_dict = {\n# \"total_labor\": doc[\"budget\"][\"labor\"] - doc[\"actual\"][\"labor\"],\n# \"total_equipment\":doc[\"budget\"][\"equipment\"] - doc[\"actual\"][\"equipment\"],\n# \"total_material\": doc[\"budget\"][\"material\"] - doc[\"actual\"][\"material\"]\n# }\n# new_values_total = { \"$set\": { \"totals\": total_dict } }\n# collection_budget.update_one(query, new_values_total)\n\nresult = {\n}\n\nfor doc in collection_budget.find():\n if doc[\"name\"] not in result:\n result[doc[\"name\"]] = [0,0,0]\n else:\n result[doc[\"name\"]][0] += doc[\"totals\"][\"total_labor\"]\n result[doc[\"name\"]][1] += doc[\"totals\"][\"total_equipment\"]\n result[doc[\"name\"]][2] += doc[\"totals\"][\"total_material\"]\n\nfor entry in result:\n for element in range(len(result[entry])):\n result[entry][element] = \"{:.2f}\".format(result[entry][element])\n\n\n\n\n\n\n\n\n\n", "sub_path": "data_init.py", "file_name": "data_init.py", "file_ext": "py", "file_size_in_byte": 2967, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pymongo.MongoClient", "line_number": 5, "usage_type": "call"}, {"api_name": "json.load", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "150842823", "text": "#!/usr/bin/env python3\n\n# IPP: 2. projekt - source_parser.py\n# autor: Jan Šulavík (xsulav01)\n\n\nimport xml.etree.ElementTree as ET\nimport sys\nimport re\n\nfrom .instruction import Instruction\n\n\nclass SourceParser:\n def __init__(self, srcFile, instruction_loader):\n self.srcFile = srcFile\n self.instruction_list = instruction_loader\n self.instruction_order = 0\n self.instruction_order_arr = []\n\n try:\n tree = ET.parse(self.srcFile)\n self.program = tree.getroot()\n except:\n sys.stderr.write(\"Bad formatting of XML file!\")\n exit(31)\n\n\n def checkXMLformat(self):\n if self.program.tag != 'program':\n sys.stderr.write(\"Bad root element!\\n\")\n exit(32)\n\n for attribute in self.program.attrib:\n if attribute not in ['language', 'name', 'description']:\n sys.stderr.write(\"Wrong program element attributes!\\n\")\n exit(32)\n\n # check if file is empty, or if it has any unwanted text\n if self.program.text is None:\n pass\n elif not self.program.text.isspace():\n sys.stderr.write(\"Excess text in program!\")\n exit(32)\n\n if self.program.attrib['language'].upper() != \"IPPCODE20\":\n sys.stderr.write(\"Wrong value of language attribute!\")\n exit(32)\n\n for instruction in self.program:\n if instruction.tag != 'instruction':\n sys.stderr.write(\"Wrong instruction element!\\n\")\n exit(32)\n if 'opcode' not in instruction.attrib or 'order' not in instruction.attrib:\n sys.stderr.write(\"Wrong instruction attributes!\\n\")\n exit(32)\n\n if instruction.text:\n if not instruction.text.isspace():\n sys.stderr.write(\"Excess text in instruction!\")\n exit(32)\n\n for attribute in instruction.attrib:\n if attribute not in ['opcode', 'order']:\n sys.stderr.write(\"Wrong attribute of instruction\")\n exit(32)\n\n try:\n int(instruction.attrib['order'])\n except ValueError:\n sys.stderr.write(\"Attribute order of instruction is not int!\\n\")\n exit(32)\n\n if int(instruction.attrib['order']) < 0:\n sys.stderr.write(\"Wrong value of attribute order!\\n\")\n exit(32)\n else:\n if int(instruction.attrib['order']) in self.instruction_order_arr:\n sys.stderr.write(\"Duplicit instruction order!\\n\")\n exit(32)\n else:\n self.instruction_order_arr.append(int(instruction.attrib['order']))\n\n argNumber = 0\n for argument in instruction:\n\n for attribute in argument.attrib:\n if attribute not in ['type']:\n sys.stderr.write(\"Wrong attribute of argument\")\n exit(32)\n\n if len(argument):\n sys.stderr.write(\"Argument cannot have own elements\")\n exit(32)\n\n argNumber = argNumber + 1\n if argument.tag != 'arg1' and argument.tag != 'arg2' and argument.tag != 'arg3':\n sys.stderr.write(\"Wrong argument number!\\n\")\n exit(32)\n if 'type' not in argument.attrib:\n sys.stderr.write(\"Wrong argument attribute!\\n\")\n exit(32)\n if argument.attrib['type'] not in ['bool', 'string', 'int', 'var', 'label', 'nil', 'type']:\n sys.stderr.write(\"Wrong argument type!\\n\")\n exit(32)\n\n # checking if argument tags have proper names in relation to argument number\n if argNumber == 1:\n for argument in instruction:\n if argument.tag != \"arg1\":\n sys.stderr.write(\"Wrong argument number!\")\n exit(32)\n elif argNumber == 2:\n for argument in instruction:\n if argument.tag != \"arg2\" and argument.tag != \"arg1\":\n sys.stderr.write(\"Wrong argument number!\")\n exit(32)\n elif argNumber == 3:\n for argument in instruction:\n if argument.tag != \"arg3\" and argument.tag != \"arg2\" and argument.tag != \"arg1\":\n sys.stderr.write(\"Wrong argument number!\")\n exit(32)\n\n def checkXMLsyntax(self):\n for instruction in self.program:\n argument_array = []\n for argument in instruction:\n argument_array.append(argument)\n\n # sorting the argument array, so the arguments come in expected order\n argument_array.sort(key=lambda x: x.tag)\n opcode = instruction.attrib['opcode'].upper()\n\n if opcode in [\"MOVE\", \"NOT\", \"STRLEN\", \"TYPE\", \"INT2CHAR\"]:\n if len(argument_array) != 2:\n sys.stderr.write(\"wrong syntax\\n\")\n exit(32)\n\n argument_array[0] = self.check_var(argument_array[0])\n argument_array[1] = self.check_symbol(argument_array[1])\n\n self.instruction_order = int(instruction.attrib['order'])\n i = Instruction(opcode.upper(), self.instruction_order, arg1=argument_array[0], arg2=argument_array[1])\n self.instruction_list.add_to_list(i)\n\n elif opcode in [\"CREATEFRAME\", \"PUSHFRAME\", \"POPFRAME\", \"BREAK\", \"RETURN\"]:\n if len(argument_array) != 0:\n sys.stderr.write(\"wrong syntax\\n\")\n exit(32)\n\n self.instruction_order = int(instruction.attrib['order'])\n i = Instruction(opcode.upper(), self.instruction_order)\n self.instruction_list.add_to_list(i)\n\n elif opcode in [\"DEFVAR\", \"POPS\"]:\n if len(argument_array) != 1:\n sys.stderr.write(\"wrong syntax\\n\")\n exit(32)\n\n argument_array[0] = self.check_var(argument_array[0])\n\n self.instruction_order = int(instruction.attrib['order'])\n i = Instruction(opcode.upper(), self.instruction_order, arg1=argument_array[0])\n self.instruction_list.add_to_list(i)\n\n elif opcode in [\"LABEL\", \"JUMP\", \"CALL\"]:\n if len(argument_array) != 1:\n sys.stderr.write(\"wrong syntax\\n\")\n exit(32)\n\n argument_array[0] = self.check_label(argument_array[0])\n\n self.instruction_order = int(instruction.attrib['order'])\n i = Instruction(opcode.upper(), self.instruction_order, arg1=argument_array[0])\n self.instruction_list.add_to_list(i)\n\n elif opcode in [\"WRITE\", \"EXIT\", \"DPRINT\", \"PUSHS\"]:\n if len(argument_array) != 1:\n sys.stderr.write(\"wrong syntax\\n\")\n exit(32)\n\n argument_array[0] = self.check_symbol(argument_array[0])\n\n self.instruction_order = int(instruction.attrib['order'])\n i = Instruction(opcode.upper(), self.instruction_order, arg1=argument_array[0])\n self.instruction_list.add_to_list(i)\n\n elif opcode in [\"ADD\", \"SUB\", \"MUL\", \"IDIV\", \"LT\", \"GT\", \"EQ\", \"AND\", \"OR\", \"CONCAT\",\n \"GETCHAR\", \"SETCHAR\", \"STRI2INT\"]:\n if len(argument_array) != 3:\n sys.stderr.write(\"wrong syntax\\n\")\n exit(32)\n\n argument_array[0] = self.check_var(argument_array[0])\n argument_array[1] = self.check_symbol(argument_array[1])\n argument_array[2] = self.check_symbol(argument_array[2])\n\n self.instruction_order = int(instruction.attrib['order'])\n i = Instruction(opcode.upper(), self.instruction_order, arg1=argument_array[0], arg2=argument_array[1],\n arg3=argument_array[2])\n self.instruction_list.add_to_list(i)\n\n elif opcode in [\"READ\"]:\n if len(argument_array) != 2:\n sys.stderr.write(\"wrong syntax\\n\")\n exit(32)\n\n argument_array[0] = self.check_var(argument_array[0])\n argument_array[1] = self.check_type(argument_array[1])\n\n self.instruction_order = int(instruction.attrib['order'])\n i = Instruction(opcode.upper(), self.instruction_order, arg1=argument_array[0], arg2=argument_array[1])\n self.instruction_list.add_to_list(i)\n\n elif opcode in [\"JUMPIFEQ\", \"JUMPIFNEQ\"]:\n if len(argument_array) != 3:\n sys.stderr.write(\"wrong syntax\\n\")\n exit(32)\n\n argument_array[0] = self.check_label(argument_array[0])\n argument_array[1] = self.check_symbol(argument_array[1])\n argument_array[2] = self.check_symbol(argument_array[2])\n\n self.instruction_order = int(instruction.attrib['order'])\n i = Instruction(opcode.upper(), self.instruction_order, arg1=argument_array[0], arg2=argument_array[1], arg3=argument_array[2])\n self.instruction_list.add_to_list(i)\n\n else:\n sys.stderr.write(\"Unknown instruction {}!\\n\".format(opcode))\n exit(32)\n\n def check_var(self, var):\n var_value = var.text\n\n pattern = re.compile(r'^(GF|LF|TF)@[A-Za-z|0-9|_|\\-|$|&|%|*|!|?]+$')\n if pattern.search(var_value) is None:\n sys.stderr.writeint(\"wrong variable syntax\\n\")\n exit(32)\n return var\n\n def check_label(self, label):\n label_value = label.text\n\n pattern = re.compile(r'^[A-Za-z]|[0-9]|_|-|$|&|%|\\*|!|\\?]+$')\n if pattern.search(label_value) is None:\n sys.stderr.write(\"wrong label syntax\\n\")\n exit(32)\n return label\n\n def check_type(self, type):\n type_value = type.text\n\n pattern = re.compile(r'^(int|string|bool|nil)$')\n if pattern.search(type_value) is None:\n sys.stderr.write(\"wrong label syntax\\n\")\n exit(32)\n return type\n\n def check_symbol(self, symbol):\n if symbol.attrib['type'] == \"label\":\n symbol = self.check_label(symbol)\n elif symbol.attrib['type'] == \"var\":\n symbol = self.check_var(symbol)\n else:\n symbol = self.check_value(symbol)\n return symbol\n\n def check_value(self, symbol):\n if symbol.attrib['type'] not in [\"string\", \"int\", \"bool\", \"nil\"]:\n sys.stderr.write(\"Wrong value type.\")\n exit(32)\n if symbol.attrib['type'] == \"string\":\n if symbol.text:\n if \"#\" in symbol.text or \" \" in symbol.text:\n sys.stderr.write(\"The '#' and whitespace symbols not supported in string type.\")\n exit(32)\n\n if isinstance(symbol.text, str):\n value = symbol.text\n index = 0\n for character in value:\n if character == \"\\\\\":\n esc_numbers = []\n\n # esc_numbers.append(character)\n esc_numbers.append(value[index + 1])\n esc_numbers.append(value[index + 2])\n esc_numbers.append(value[index + 3])\n\n value = value.replace(value[index + 1], \"\", 1)\n value = value.replace(value[index + 1], \"\", 1)\n value = value.replace(value[index + 1], \"\", 1)\n\n esc_sequence = int(\"\".join(esc_numbers))\n char = chr(esc_sequence)\n\n value = value.replace(value[index], char)\n symbol.text = value\n index += 1\n else:\n symbol.text = \"\"\n elif symbol.attrib['type'] == \"nil\":\n if symbol.text != \"nil\":\n sys.stderr.write(\"Wrong value of type nil\")\n exit(32)\n return symbol", "sub_path": "IPP/proj2/interpret_classes/source_parser.py", "file_name": "source_parser.py", "file_ext": "py", "file_size_in_byte": 12463, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "xml.etree.ElementTree.parse", "line_number": 22, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 22, "usage_type": "name"}, {"api_name": "sys.stderr.write", "line_number": 25, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 31, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 36, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 43, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 47, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 47, "usage_type": "attribute"}, {"api_name": "instruction.tag", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 52, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 52, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 54, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 55, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 55, "usage_type": "attribute"}, {"api_name": "instruction.text", "line_number": 58, "usage_type": "attribute"}, {"api_name": "instruction.text.isspace", "line_number": 59, "usage_type": "call"}, {"api_name": "instruction.text", "line_number": 59, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 60, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 60, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 63, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 65, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 65, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 69, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 71, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 71, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 74, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 75, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 75, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 78, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 79, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 79, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 82, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 89, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 89, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 93, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 93, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 98, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 98, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 101, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 101, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 104, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 104, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 111, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 111, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 116, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 116, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 121, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 121, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 132, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 136, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 136, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 142, "usage_type": "attribute"}, {"api_name": "instruction.Instruction", "line_number": 143, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 148, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 148, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 151, "usage_type": "attribute"}, {"api_name": "instruction.Instruction", "line_number": 152, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 157, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 157, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 162, "usage_type": "attribute"}, {"api_name": "instruction.Instruction", "line_number": 163, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 168, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 168, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 173, "usage_type": "attribute"}, {"api_name": "instruction.Instruction", "line_number": 174, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 179, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 179, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 184, "usage_type": "attribute"}, {"api_name": "instruction.Instruction", "line_number": 185, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 191, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 191, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 198, "usage_type": "attribute"}, {"api_name": "instruction.Instruction", "line_number": 199, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 205, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 205, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 211, "usage_type": "attribute"}, {"api_name": "instruction.Instruction", "line_number": 212, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 217, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 217, "usage_type": "attribute"}, {"api_name": "instruction.attrib", "line_number": 224, "usage_type": "attribute"}, {"api_name": "instruction.Instruction", "line_number": 225, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 229, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 229, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 235, "usage_type": "call"}, {"api_name": "sys.stderr.writeint", "line_number": 237, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 237, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 244, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 246, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 246, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 253, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 255, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 255, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 270, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 270, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 275, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 275, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 304, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 304, "usage_type": "attribute"}]} +{"seq_id": "324840268", "text": "\"\"\"Util functions for Model Evaluation pipelines.\"\"\"\n\nimport os\nimport pathlib\nfrom typing import Any, Dict, List, Tuple\n\n\ndef get_sdk_pipeline_and_parameters(\n project: str,\n location: str,\n root_dir: str,\n model_name: str,\n target_column_name: str,\n prediction_type: str,\n batch_predict_gcs_source_uris: List[str],\n batch_predict_instances_format: str,\n batch_predict_machine_type: str = 'n1-standard-16',\n batch_predict_starting_replica_count: int = 25,\n batch_predict_max_replica_count: int = 25,\n class_names: List[str] = ['0', '1'],\n dataflow_machine_type: str = 'n1-standard-4',\n dataflow_max_num_workers: int = 25,\n dataflow_disk_size_gb: int = 50,\n encryption_spec_key_name: str = '') -> Tuple[str, Dict[str, Any]]:\n \"\"\"Get the evaluation sdk pipeline and parameters.\n\n Args:\n project: The GCP project that runs the pipeline components.\n location: The GCP region that runs the pipeline components.\n root_dir: The root GCS directory for the pipeline components.\n model_name: The Vertex model resource name to be imported and used for batch\n prediction.\n target_column_name: The target column name.\n prediction_type: The type of prediction the Model is to produce.\n \"classification\" or \"regression\".\n batch_predict_gcs_source_uris: Google Cloud Storage URI(-s) to your\n instances to run batch prediction on. May contain wildcards. For more\n information on wildcards, see\n https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames. For\n more details about this input config, see\n https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.\n batch_predict_instances_format: The format in which instances are given,\n must be one of the Model's supportedInputStorageFormats. If not set,\n default to \"jsonl\". For more details about this input config, see\n https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.\n batch_predict_machine_type: The type of machine for running batch prediction\n on dedicated resources. If the Model supports DEDICATED_RESOURCES this\n config may be provided (and the job will use these resources). If the\n Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.\n For more details about the BatchDedicatedResources, see\n https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#BatchDedicatedResources.\n For more details about the machine spec, see\n https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec\n batch_predict_starting_replica_count: The number of machine replicas used at\n the start of the batch operation. If not set, Vertex AI decides starting\n number, not greater than `max_replica_count`. Only used if `machine_type`\n is set.\n batch_predict_max_replica_count: The maximum number of machine replicas the\n batch operation may be scaled to. Only used if `machine_type` is set.\n Default is 10.\n class_names: The list of class names that the ground truth can be, in the\n same order they appear in the batch predict predictions output.\n dataflow_machine_type: The dataflow machine type for evaluation components.\n dataflow_max_num_workers: The max number of Dataflow workers for evaluation\n components.\n dataflow_disk_size_gb: Dataflow worker's disk size in GB for evaluation\n components.\n encryption_spec_key_name: The KMS key name.\n\n Returns:\n Tuple of pipeline_definiton_path and parameter_values.\n \"\"\"\n if prediction_type == 'regression':\n prediction_score_column = 'prediction.value'\n prediction_label_column = ''\n elif prediction_type == 'classification':\n prediction_score_column = 'prediction.scores'\n prediction_label_column = 'prediction.classes'\n\n parameter_values = {\n 'project':\n project,\n 'location':\n location,\n 'root_dir':\n root_dir,\n 'model_name':\n model_name,\n 'target_column_name':\n target_column_name,\n 'prediction_type':\n prediction_type,\n 'class_names':\n class_names,\n 'batch_predict_gcs_source_uris':\n batch_predict_gcs_source_uris,\n 'batch_predict_instances_format':\n batch_predict_instances_format,\n 'batch_predict_machine_type':\n batch_predict_machine_type,\n 'batch_predict_starting_replica_count':\n batch_predict_starting_replica_count,\n 'batch_predict_max_replica_count':\n batch_predict_max_replica_count,\n 'prediction_score_column':\n prediction_score_column,\n 'prediction_label_column':\n prediction_label_column,\n 'dataflow_machine_type':\n dataflow_machine_type,\n 'dataflow_max_num_workers':\n dataflow_max_num_workers,\n 'dataflow_disk_size_gb':\n dataflow_disk_size_gb,\n 'encryption_spec_key_name':\n encryption_spec_key_name,\n }\n\n pipeline_definition_path = os.path.join(\n pathlib.Path(__file__).parent.resolve(), 'templates', 'sdk_pipeline.json')\n return pipeline_definition_path, parameter_values\n", "sub_path": "components/google-cloud/google_cloud_pipeline_components/experimental/evaluation/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 5258, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.List", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 20, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 120, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "602189746", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.fftpack import fft\nfrom scipy.signal import blackman\nfrom numpy import hamming\nfrom numpy import hanning\nfrom plot import plot_old\nimport csv\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"path\", default='dane.txt')\nargs = parser.parse_args()\n\nt=[]\ndane=[]\n\nwith open(args.path, 'r') as csvfile:\n\tplots = csv.reader(csvfile, delimiter=',')\n\tfor row in plots:\n\t\ttry:\n\t\t\tt.append(int(row[0]))\n\t\t\tdane.append(float(row[1]))\n\t\texcept:\n\t\t\tpass\n\n#make x table start from zero\t\nt0 = t[0]\nfor i in range(len(t)):\n\tt[i] = (t[i]-t0)\n\n#czestotliwosc sygnalu 100Hz\nF=100\n#ilosc probek\nN=len(t)/2\n#sredni okres probkowania\nokres_ns= (t[-1])/float(len(t))\nT=okres_ns/(10.0**9)\nprint(okres_ns, T)\n\nx=np.linspace(0.0,T*N, N)\ny = 1.5*np.sin(F*2.0*np.pi*x)\n#generujemy szum na poziomie < 5us\nnoise = np.random.rand(N)/200000\nx_noised = x + noise\ny_noised = 1.5*np.sin(F*2.0*np.pi*x_noised)\n\n#FFT\nw = hanning(N)\n\nyf_noised = fft(y_noised*w)\nyf = fft(y*w)\nxf = np.linspace(0.0, 1.0/(2.0*T), N/2)\n\nfig, axes = plt.subplots(3,1)\naxes[0].set_title(u'Sygnał idealny z rozrzutem czasu próbkowania')\naxes[0].plot(x_noised[1:1000],y_noised[1:1000], marker='.')\n\naxes[1].set_title(u'FFT sygnału idealnego z rozrzutem czasu próbkowania')\naxes[1].semilogy(xf[1:N//2], 2.0/N * np.abs(yf_noised[1:N//2]), '-b')\n\naxes[2].set_title(u'FFT sygnału idealnego')\naxes[2].semilogy(xf[1:N//2], 2.0/N * np.abs(yf[1:N//2]), '-g')\n\nfig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\n\n# dla danych z przetwornika\n\ndane = dane[:N]\nt = t[:N]\n\nfig, axes = plt.subplots(3,1)\naxes[0].set_title(u'Sygnał spróbkowany z przetwornika MAX1202')\naxes[0].plot(t[1:1000],dane[1:1000],'-r', marker='.')\n\ndane_fourier = fft(dane*hanning(len(dane)))\ntf = np.linspace(0.0, 1.0/(2.0*T), N/2)\n\naxes[1].set_title(u'FFT sygnału spróbkowanego z przetwornika MAX1202')\n#plt.plot(xf_noised, yf_noised)\naxes[1].semilogy(tf[1:N//2], 2.0/N * np.abs(dane_fourier[1:N//2]), '-b')\naxes[2].set_title(u'FFT sygnału idealnego')\naxes[2].semilogy(xf[1:N//2], 2.0/N * np.abs(yf[1:N//2]), '-g')\n\nfig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\n\n#plt.savefig('sin_fft_max1202.png', bbox_inches='tight')\n\nfig2 = plot_old(t, dane)\nplt.show()\n", "sub_path": "programy/plots/noise.py", "file_name": "noise.py", "file_ext": "py", "file_size_in_byte": 2294, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.hanning", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.fftpack.fft", "line_number": 53, "usage_type": "call"}, {"api_name": "scipy.fftpack.fft", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "scipy.fftpack.fft", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.hanning", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 85, "usage_type": "call"}, {"api_name": "plot.plot_old", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}]} +{"seq_id": "590128501", "text": "import praw\nimport pickledb\nfrom time import sleep\nfrom psaw import PushshiftAPI\nfrom config import *\n\n\nclass C:\n W, G, R, P, Y, C = '\\033[0m', '\\033[92m', '\\033[91m', '\\033[95m', '\\033[93m', '\\033[36m'\n\n\nprint(f\"\"\"{C.Y}\n╦═╗╔═╗╔╦╗╔╦╗╦╔╦╗ ╔═╗╔═╗╦\n╠╦╝║╣ ║║ ║║║ ║ ╠╣ ╠═╝║\n╩╚═╚═╝═╩╝═╩╝╩ ╩ ╚ ╩ ╩ v1.0\n{C.W}\"\"\")\n\nflairs = pickledb.load('data/flairs.db', False)\nmessaged = pickledb.load('data/messaged.db', False)\ninvited = pickledb.load('data/invited.db', False)\n\n\nreddit = praw.Reddit(client_id=client_id,\n client_secret=client_secret,\n user_agent='Flair finder/pokerer/invitererer (by /u/impshum)',\n username=reddit_user,\n password=reddit_pass)\n\napi = PushshiftAPI()\n\n\ndef gather():\n submissions = api.search_submissions(\n subreddit=target_sub, aggs='author+author_flair_css_class')\n comments = api.search_comments(\n subreddit=target_sub, aggs='author+author_flair_css_class')\n\n for switch in [submissions, comments]:\n for post in switch:\n author = post.author\n flair = post.author_flair_text\n if flair in target_flairs:\n if not flairs.exists(author):\n print(f'{C.G}Added {author} {flair}{C.W}')\n flairs.set(author, flair)\n flairs.dump()\n else:\n print(f'{C.R}Exists {author} {flair}{C.W}')\n\n\ndef message():\n for author in flairs.getall():\n if not messaged.exists(author):\n flair = flairs.get(author)\n messaged.set(author, flair)\n messaged.dump()\n if not test_mode:\n reddit.redditor(author).message(message_title, message_text)\n print(author, flair)\n sleep(100)\n\n\ndef invite():\n for author in flairs.getall():\n if not invited.exists(author):\n flair = flairs.get(author)\n invited.set(author, flair)\n invited.dump()\n if not test_mode:\n reddit.subreddit(secret_sub).contributor.add(author)\n print(f'{C.G} Invited {author} {flair}{C.W}')\n sleep(100)\n\n\ndef get_submission_count(user, api):\n submissions = api.search_comments(\n subreddit=target_sub, author=user, aggs='subreddit')\n for x in submissions:\n return x['subreddit'][0]['doc_count']\n\n\ndef get_comment_count(user, api):\n comments = api.search_submissions(\n subreddit=target_sub, author=user, aggs='subreddit')\n for x in comments:\n return x['subreddit'][0]['doc_count']\n\n\ndef check_count():\n removal = []\n for author in flairs.getall():\n s_count = get_submission_count(author, api)\n c_count = get_comment_count(author, api)\n if s_count + c_count >= total_posts:\n print(f'{C.G}{author} survived the cut{C.W}')\n else:\n removal.append(author)\n print(f'{C.R}{author} has been cut{C.W}')\n return removal\n\n\ndef clean():\n removal_list = check_count()\n for x in removal_list:\n flairs.rem(x)\n flairs.dump()\n\n\ndef main():\n if gather_users:\n gather()\n if clean_users:\n clean()\n if message_users:\n message()\n if invite_users:\n invite()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "once.py", "file_name": "once.py", "file_ext": "py", "file_size_in_byte": 3433, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pickledb.load", "line_number": 18, "usage_type": "call"}, {"api_name": "pickledb.load", "line_number": 19, "usage_type": "call"}, {"api_name": "pickledb.load", "line_number": 20, "usage_type": "call"}, {"api_name": "praw.Reddit", "line_number": 23, "usage_type": "call"}, {"api_name": "psaw.PushshiftAPI", "line_number": 29, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 60, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "155995955", "text": "import pandas as pd\nfrom io import open\nfrom bs4 import BeautifulSoup\n\ndef replace_space(text):\n text = \" \".join(text.split())\n return text\n\n\ndef parse_html(file_name_csv):\n dataset = []\n df = pd.read_csv(file_name_csv,encoding=\"utf-8\")\n a = df.values\n for row in a:\n col2 = BeautifulSoup(row[1]).text\n col2 = replace_space(col2)\n col3 = u\"\"+str(row[0])\n #col3 = replace_space(col3)\n dataset.append([col2,col3])\n return dataset\n\n\ndef check_similar2(elm,data):\n ques = elm[0]\n for elm2 in data:\n ques2 = elm2[0]\n if ques2 == ques:\n print(\"duplicate sentence \")\n return True\n return False\n\n\ndef remove_duplicate(data):\n direct_list = []\n for elm in data:\n if check_similar2(elm,direct_list):\n continue\n else:\n direct_list.append(elm)\n return direct_list\n\n\ndef write_to_file(direct_list,file_name):\n with open(file_name,\"w\",encoding=\"utf-8\") as f_w:\n for i,line in enumerate(direct_list):\n line = u\"--->\".join(line)\n if(len(line) > 1000):\n continue\n f_w.write(line)\n f_w.write(u\"\\n\")\n print(\"Write sen \" + str(i))\n f_w.close()\n\ndef process_data(csv_file,data_file):\n data = parse_html(csv_file)\n nomed_data = remove_duplicate(data)\n write_to_file(nomed_data,data_file)\n\nif __name__ == \"__main__\":\n process_data(\"H2472_DATA/phat_trien_ky_nang.csv\",\"phat_trien_ky_nang/raw.txt\")", "sub_path": "data/process_raw_data.py", "file_name": "process_raw_data.py", "file_ext": "py", "file_size_in_byte": 1518, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 15, "usage_type": "call"}, {"api_name": "io.open", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "544279893", "text": "# -*- coding: utf8 -*-\nfrom django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom settings import MEDIA_ROOT\nfrom main.views import IndexPageView\n\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n url(r'^admin/', include(admin.site.urls)),\n url(u'^media/(?P.*)$', 'django.views.static.serve',\n {'document_root': MEDIA_ROOT}),\n url(r'^$', IndexPageView.as_view(), ),\n url(r'^auth/', include('authentication.urls')),\n url(r'^users/', include('main.urls')),\n)", "sub_path": "linksbank/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 586, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 7, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.patterns", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "settings.MEDIA_ROOT", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "main.views.IndexPageView.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "main.views.IndexPageView", "line_number": 14, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "627689681", "text": "import requests\r\nfrom bs4 import BeautifulSoup as bs\r\n\r\ns = requests.session()\r\nheaders = {\r\n 'User-Agent': \"\"\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36\r\"\"\",\r\n 'Origin': 'https://www.oschina.net', \r\n 'Referer': 'https://www.oschina.net/home/login', \r\n 'Host': 'www.oschina.net', \r\n}\r\n\r\ns.get('https://www.oschina.net/home/login',headers=headers)\r\nlogin_data = {'email':'pstare@163.com','pwd':'0fab769c542749d2e7fa66923b8ebb7b43723385','verifyCode':'','save_login':'0'}\r\n\r\nr=s.post('https://www.oschina.net/home/login',login_data,headers=headers)\r\n#r = s.get('http://www.oschina.net',headers=headers)\r\n#soup = bs(r.content)\r\n#print(r.content.decode('utf8'))\r\nprint(r.text)\r\n", "sub_path": "oschina_login.py", "file_name": "oschina_login.py", "file_ext": "py", "file_size_in_byte": 756, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.session", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "434461344", "text": "\"\"\"\n Contains the model architecture for predicting a cropland data layer within a time series of SAR images.\n\"\"\"\nfrom keras.layers import (\n Activation, BatchNormalization, Bidirectional, ConvLSTM2D, Dropout, Input,\n Layer, Reshape, TimeDistributed, UpSampling2D\n)\nfrom keras.layers.convolutional import Conv2D, Conv2DTranspose\nfrom keras.layers.merge import concatenate\nfrom keras.layers.pooling import MaxPooling2D\nfrom keras.layers import Lambda, LSTM\nfrom keras.metrics import MeanIoU\nfrom keras.losses import (\n BinaryCrossentropy, CategoricalCrossentropy, SparseCategoricalCrossentropy\n)\nfrom keras.models import Model\nfrom keras.optimizers import Adam\nfrom keras.optimizers.schedules import ExponentialDecay\nfrom tensorflow.python.framework.ops import disable_eager_execution\n\nfrom src.config import CROP_CLASSES, N_CHANNELS, NUM_FILTERS\nfrom src.config import NETWORK_DEMS as dems\nfrom src.config import TIME_STEPS\nfrom src.model.architecture.dice_loss import jaccard_distance_loss, dice_coefficient_loss, cosh_dice_coefficient_loss\n\n\"\"\" Cropland Data Time Series version of U-net model used in masked.py \"\"\"\n\ndef conv2d_block(\n input_tensor: Input,\n num_filters: int,\n kernel_size: int = 3,\n batchnorm: bool = True,\n depth: int = 3,\n activation: bool = True,\n) -> Layer:\n \"\"\" Function to add 2 convolutional layers with the parameters\n passed to it \"\"\"\n \n # first conv layer\n \n x = Conv2D(filters=num_filters, kernel_size=(kernel_size, kernel_size), padding='same',)(input_tensor)\n if batchnorm:\n x = BatchNormalization()(x)\n if activation:\n x = Activation('relu')(x)\n\n # second conv layer\n\n x = Conv2D(filters=num_filters, kernel_size=(kernel_size, kernel_size), padding='same',)(x)\n if batchnorm:\n x = BatchNormalization()(x)\n if activation:\n x = Activation('relu')(x)\n\n return x\n\n\"\"\" Cropland Data Time Series version of U-net model used in masked.py \"\"\"\n\ndef deconv2d_block_time_dist(\n input_tensor: Input,\n concat_layer: Input,\n dropout: int,\n num_filters: int,\n kernel_size: int = 3,\n batchnorm: bool = True,\n return_last_sequence: bool = True,\n activation=True,\n \n) -> Layer:\n \"\"\" Function to add 2 convolutional layers with the parameters\n passed to it \"\"\"\n # x = Conv2DTranspose(\n # num_filters * 1, (3, 3), strides=(2, 2), padding='same'\n # )(input_tensor)\n x = UpSampling2D(\n size=(2, 2)\n )(input_tensor)\n # x = Conv2D(filters=num_filters, kernel_size=(kernel_size, kernel_size), padding='same',)(input_tensor)\n x = concatenate([x, concat_layer], axis=-1)\n x = conv2d_block(x, num_filters, kernel_size=3, batchnorm=batchnorm, activation=activation)\n\n return x\n\n\"\"\" Cropland Data Time Series version of U-net model used in masked.py \"\"\"\n\ndef create_cdl_model_masked(\n model_name: str,\n num_filters: int = NUM_FILTERS,\n time_steps: int = TIME_STEPS,\n dropout: float = 0.5,\n batchnorm: bool = True\n) -> Model:\n \"\"\" Function to define the Time Distributed UNET Model \"\"\"\n\n \"\"\"Requires stack of Sequential SAR data (with vh vv channels stacked), where each image is a different timestep\"\"\"\n inputs = Input(shape=(None, None, TIME_STEPS*N_CHANNELS), batch_size=None)\n c1 = conv2d_block(\n inputs, num_filters * 1, kernel_size=3, batchnorm=batchnorm\n )\n\n p1 = MaxPooling2D((2, 2))(c1)\n p1 = Dropout(dropout)(p1)\n\n c2 = conv2d_block(p1, num_filters * 2, kernel_size=3, batchnorm=batchnorm)\n p2 = MaxPooling2D((2, 2))(c2)\n p2 = Dropout(dropout)(p2)\n\n c3 = conv2d_block(p2, num_filters * 4, kernel_size=3, batchnorm=batchnorm)\n p3 = MaxPooling2D((2, 2))(c3)\n p3 = Dropout(dropout)(p3)\n\n\n c4 = conv2d_block(p3, num_filters * 8, kernel_size=3, batchnorm=batchnorm)\n p4 = MaxPooling2D((2, 2))(c4)\n p4 = Dropout(dropout)(p4)\n\n c5 = conv2d_block(p4, num_filters * 16, kernel_size=3, batchnorm=batchnorm)\n p5 = MaxPooling2D((2, 2))(c5)\n p5 = Dropout(dropout)(p5)\n\n c6 = conv2d_block(p5, num_filters * 32, kernel_size=3, batchnorm=batchnorm)\n p6 = MaxPooling2D((2, 2))(c6)\n p6 = Dropout(dropout)(p6)\n\n # c7 = conv2d_block(p6, num_filters * 64, kernel_size=3, batchnorm=batchnorm)\n # p7 = MaxPooling2D((2, 2))(c7)\n # p7 = Dropout(dropout)(p7)\n\n # c8 = conv2d_block(p7, num_filters * 128, kernel_size=3, batchnorm=batchnorm)\n # p8 = MaxPooling2D((2, 2))(c8)\n # p8 = Dropout(dropout)(p8) \n # middle_clstm = ConvLSTM2D(filters=num_filters * 4, kernel_size=3, activation=\"tanh\", padding='same', return_sequences=True)\n # middle_bidirection = Bidirectional(middle_clstm)(p3)\n middle = conv2d_block(p6, num_filters * 32, kernel_size=3)\n\n # Expanding dims\n # uv = deconv2d_block_time_dist(middle, num_filters=num_filters*128, dropout=dropout, kernel_size=3, batchnorm=batchnorm, concat_layer=c8, activation=True)\n # uw = deconv2d_block_time_dist(uv, num_filters=num_filters*64, dropout=dropout, kernel_size=3, batchnorm=batchnorm, concat_layer=c7, activation=True)\n uy = deconv2d_block_time_dist(middle, num_filters=num_filters*32, dropout=dropout, kernel_size=3, batchnorm=batchnorm, concat_layer=c6, activation=True)\n uz = deconv2d_block_time_dist(uy, num_filters=num_filters*16, dropout=dropout, kernel_size=3, batchnorm=batchnorm, concat_layer=c5, activation=True)\n u = deconv2d_block_time_dist(uz, num_filters=num_filters*8, dropout=dropout, kernel_size=3, batchnorm=batchnorm, concat_layer=c4, activation=True)\n u1 = deconv2d_block_time_dist(u, num_filters=num_filters*4, dropout=dropout, kernel_size=3, batchnorm=batchnorm, concat_layer=c3, activation=True)\n u2 = deconv2d_block_time_dist(u1, num_filters=num_filters*2, dropout=dropout, kernel_size=3, batchnorm=batchnorm, concat_layer=c2, activation=True)\n u3 = deconv2d_block_time_dist(u2, num_filters=num_filters, dropout=dropout, kernel_size=3, batchnorm=batchnorm, concat_layer=c1, activation=True)\n \n # classifier (forward-backwards convlstm)\n # final_conv_forward = ConvLSTM2D(filters=num_filters, kernel_size=3, activation=\"tanh\", padding='same', return_sequences=False)(u3)\n # final_conv_backwards = ConvLSTM2D(filters=num_filters, kernel_size=3, activation=\"tanh\", padding='same', return_sequences=False)\n # final_bidirectional = Bidirectional(final_conv_forward)(u3)\n\n final = Conv2D(filters=1, kernel_size=1, activation=\"sigmoid\", padding='same')(u3)\n # final = ConvLSTM2D(filters=1, kernel_size=1, activation=\"sigmoid\", padding='same', return_sequences=False)(final_bidirecitonal)\n # final_conv_locality = feature_locality(inputs, final, num_filters, batchnorm, dropout)\n model = Model(inputs=inputs, outputs=[final])\n\n model.__asf_model_name = model_name\n\n lr_schedule = ExponentialDecay(1e-3, decay_steps=2000, decay_rate=0.96, staircase=True)\n # Adam(lr=1e-3)\n # dice_coefficient_loss\n #[BinaryCrossentropy(from_logits=False), cosh_dice_coefficient_loss]\n model.compile(\n loss=jaccard_distance_loss, optimizer=Adam(learning_rate=lr_schedule), metrics=['accuracy', MeanIoU(num_classes=2) ]\n )\n\n return model\n\n# get first, middle, and last layer, and compare with the output of the first model\ndef feature_locality(original_input, raw_ouput, num_filters, batchnorm, dropout):\n # inputs = Input(shape=(None, None, N_CHANNELS*time_steps), batch_size=None)\n beg = Lambda(lambda x: x [:, :, :2])(original_input)\n # mid = Lambda(lambda x: x [:, :, x.shape[-1]//2: 2+ (x.shape[-1]//2)])(original_input)\n last = Lambda(lambda x: x [:, :, -2:])(original_input)\n\n beg_mid = []\n beg_mid.append(Lambda(lambda x: x [:, :, :2])(original_input))\n beg_mid.append(Lambda(lambda x: x [:, :, -2:])(original_input))\n # beg_mid = concatenate([beg, last], axis=-1)\n # beg_mid_last = concatenate([beg_mid, last], axis=-1)\n\n # lstm_layer = LSTM()\n c1 = conv2d_block(\n beg_mid, num_filters, kernel_size=3, batchnorm=batchnorm\n )\n\n p1 = MaxPooling2D((2, 2))(c1)\n p1 = Dropout(dropout)(p1)\n\n c2 = conv2d_block(p1, num_filters * 2, kernel_size=3, batchnorm=batchnorm)\n p2 = MaxPooling2D((2, 2))(c2)\n p2 = Dropout(dropout)(p2)\n\n c3 = conv2d_block(p2, num_filters * 4, kernel_size=3, batchnorm=batchnorm)\n p3 = MaxPooling2D((2, 2))(c3)\n p3 = Dropout(dropout)(p3)\n\n middle = conv2d_block(p3, num_filters * 4, kernel_size=3)\n\n u1 = deconv2d_block_time_dist(middle, num_filters=num_filters*4, dropout=dropout, kernel_size=3, batchnorm=batchnorm, concat_layer=c3, activation=True)\n u2 = deconv2d_block_time_dist(u1, num_filters=num_filters*2, dropout=dropout, kernel_size=3, batchnorm=batchnorm, concat_layer=c2, activation=True)\n u3 = deconv2d_block_time_dist(u2, num_filters=num_filters, dropout=dropout, kernel_size=3, batchnorm=batchnorm, concat_layer=c1, activation=True)\n\n final_conv_local = Conv2D(filters=1, kernel_size=1, activation=\"softmax\", padding='same')(u3)\n\n final_concat = concatenate([raw_ouput, final_conv_local])\n\n final_conv = Conv2D(filters=1, kernel_size=1, activation=\"sigmoid\", padding='same')(final_concat)\n\n return final_conv\n", "sub_path": "src/model/architecture/crop_masked.py", "file_name": "crop_masked.py", "file_ext": "py", "file_size_in_byte": 9159, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "keras.layers.Input", "line_number": 29, "usage_type": "name"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers.Layer", "line_number": 35, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 60, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 61, "usage_type": "name"}, {"api_name": "keras.layers.UpSampling2D", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers.merge.concatenate", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.layers.Layer", "line_number": 69, "usage_type": "name"}, {"api_name": "src.config.NUM_FILTERS", "line_number": 88, "usage_type": "name"}, {"api_name": "src.config.TIME_STEPS", "line_number": 89, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 96, "usage_type": "call"}, {"api_name": "src.config.TIME_STEPS", "line_number": 96, "usage_type": "name"}, {"api_name": "src.config.N_CHANNELS", "line_number": 96, "usage_type": "name"}, {"api_name": "keras.layers.pooling.MaxPooling2D", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.layers.pooling.MaxPooling2D", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 106, "usage_type": "call"}, {"api_name": "keras.layers.pooling.MaxPooling2D", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 110, "usage_type": "call"}, {"api_name": "keras.layers.pooling.MaxPooling2D", "line_number": 114, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 115, "usage_type": "call"}, {"api_name": "keras.layers.pooling.MaxPooling2D", "line_number": 118, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 119, "usage_type": "call"}, {"api_name": "keras.layers.pooling.MaxPooling2D", "line_number": 122, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 123, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 151, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 154, "usage_type": "call"}, {"api_name": "keras.optimizers.schedules.ExponentialDecay", "line_number": 158, "usage_type": "call"}, {"api_name": "src.model.architecture.dice_loss.jaccard_distance_loss", "line_number": 163, "usage_type": "name"}, {"api_name": "keras.optimizers.Adam", "line_number": 163, "usage_type": "call"}, {"api_name": "keras.metrics.MeanIoU", "line_number": 163, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 92, "usage_type": "name"}, {"api_name": "keras.layers.Lambda", "line_number": 171, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 173, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 176, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 177, "usage_type": "call"}, {"api_name": "keras.layers.pooling.MaxPooling2D", "line_number": 186, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 187, "usage_type": "call"}, {"api_name": "keras.layers.pooling.MaxPooling2D", "line_number": 190, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 191, "usage_type": "call"}, {"api_name": "keras.layers.pooling.MaxPooling2D", "line_number": 194, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 195, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 203, "usage_type": "call"}, {"api_name": "keras.layers.merge.concatenate", "line_number": 205, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 207, "usage_type": "call"}]} +{"seq_id": "100594775", "text": "#!/usr/bin/env python3\nfrom flask import Flask, jsonify, make_response, abort, url_for, request\nfrom user import User\nfrom apidb import logger, check, init\nfrom telegramapi import Telegram\nimport threading\nimport random\nimport time\nimport os\nimport sys\nimport json\nimport requests\n\napp = Flask(__name__)\n\n\n@app.route('/status', methods=['GET'])\ndef get_status():\n return 'Up and running', 201\n\ndef insert_into_influxdb(bot):\n data = '{} value=1'.format('bot' if bot else 'human')\n url = 'https://influxdb.territoriolinux.es/write?db=telegraf'\n headers = {'Content-type': 'application/octet-stream'}\n try:\n res = requests.post(url=url, data=data, headers=headers)\n except Exception:\n logger('Can\\'t write in inbluxdb')\n\ndef wait_for_new_user(member, chat_id, result):\n time.sleep(int(os.environ['COURTESY_TIME']))\n user = User.get_user(member['id'])\n logger(user)\n logger(json.dumps(result))\n if user.get_timestamp() > 0:\n user.set_timestamp(0)\n user.set_is_bot(True)\n user.save()\n telegram = Telegram(os.environ['TELEGRAM_API_TOKEN'])\n telegram.kick_chat_member(chat_id, member['id'])\n telegram.delete_message(chat_id, result['result']['message_id'])\n insert_into_influxdb(True)\n\n@app.route('/webhook/', methods=['GET', 'POST'])\ndef get_webhook(webhook):\n logger(webhook)\n if os.environ['WEBHOOK'] != webhook:\n return 'KO', 404\n try:\n if request.method == 'GET' or not request.json:\n return 'OK', 200\n except Exception:\n return 'OK', 200\n telegram = Telegram(os.environ['TELEGRAM_API_TOKEN'])\n logger(request.json)\n payload = request.json\n if 'message' in payload and 'new_chat_member' in payload['message']:\n logger('New member')\n member = payload['message']['new_chat_member']\n chat_id = payload['message']['chat']['id']\n user = User.get_user(member['id'])\n if user:\n logger(user)\n delta = int(time.time()) - int(user.get_timestamp())\n if (user.get_timestamp() > 0 and delta > int(os.environ['COURTESY_TIME'])) \\\n or user.get_is_bot():\n user.set_timestamp(0)\n user.set_is_bot(True)\n user.save()\n telegram.kick_chat_member(chat_id, member['id'])\n insert_into_influxdb(True)\n else:\n User.insert_user(member, chat_id)\n rows = []\n buttons = []\n buttons.append({'text': '🐸',\n 'callback_data': 'ko|{}'.format(member['id'])})\n buttons.append({'text': '🤖',\n 'callback_data': 'ko|{}'.format(member['id'])})\n buttons.append({'text': '🐵',\n 'callback_data': 'ko|{}'.format(member['id'])})\n buttons.append({'text': '🐱',\n 'callback_data': 'ko|{}'.format(member['id'])})\n random.shuffle(buttons)\n rows.append(buttons)\n buttons = []\n buttons.append({'text': '🐼',\n 'callback_data': 'ko|{}'.format(member['id'])})\n buttons.append({'text': '🦝', \n 'callback_data': 'ko|{}'.format(member['id'])})\n buttons.append({'text': '🐧',\n 'callback_data': 'ok|{}'.format(member['id'])})\n buttons.append({'text': '🐮',\n 'callback_data': 'ko|{}'.format(member['id'])})\n random.shuffle(buttons)\n rows.append(buttons)\n random.shuffle(rows)\n inline_keyboard = {'inline_keyboard': rows}\n mention = \"{}\".format(\n member['id'],\n member['first_name'] if member['first_name'] else 'amig@')\n result = telegram.send_message(\n chat_id,\n 'Hola {}, selecciona el pingüino, en menos de {} segundos'.format(\n mention, os.environ['COURTESY_TIME']),\n json.dumps(inline_keyboard))\n t1 = threading.Thread(target=wait_for_new_user, args=(member,\n chat_id, result))\n t1.start()\n elif 'callback_query' in payload:\n member = payload['callback_query']['from']\n message_id = payload['callback_query']['message']['message_id']\n chat_id = payload['callback_query']['message']['chat']['id']\n result, member_id = payload['callback_query']['data'].split('|')\n logger('Result: {}, Id: {}'.format(result, member_id))\n if int(member_id) == int(member['id']):\n user = User.get_user(member['id'])\n if not user:\n user = User.insert_user(member, chat_id)\n if user and user.get_timestamp() > 0:\n user.set_timestamp(0)\n user.set_is_bot(result == 'ko')\n user.save()\n logger('Chat id: {}, Message id: {}'.format(chat_id, message_id))\n telegram.delete_message(chat_id, message_id)\n if result == 'ko':\n telegram.kick_chat_member(chat_id, member['id'])\n insert_into_influxdb(result == 'ko')\n elif 'message' in payload:\n from_id = payload['message']['from']['id']\n chat_id = payload['message']['chat']['id']\n message_id = payload['message']['message_id']\n if User.get_bots(from_id):\n telegram.delete_message(chat_id, message_id)\n return 'OK', 201\n\n\n@app.errorhandler(404)\ndef not_found(error):\n return make_response(jsonify({'error': 'Not found'}), 404)\n\n\nif __name__ == '__main__':\n if not check('SELECT * FROM USERS'):\n init()\n app.run(debug=True, host='0.0.0.0')\n", "sub_path": "src/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 5844, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 26, "usage_type": "call"}, {"api_name": "apidb.logger", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 31, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 31, "usage_type": "attribute"}, {"api_name": "user.User.get_user", "line_number": 32, "usage_type": "call"}, {"api_name": "user.User", "line_number": 32, "usage_type": "name"}, {"api_name": "apidb.logger", "line_number": 33, "usage_type": "call"}, {"api_name": "apidb.logger", "line_number": 34, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 34, "usage_type": "call"}, {"api_name": "user.get_timestamp", "line_number": 35, "usage_type": "call"}, {"api_name": "user.set_timestamp", "line_number": 36, "usage_type": "call"}, {"api_name": "user.set_is_bot", "line_number": 37, "usage_type": "call"}, {"api_name": "user.save", "line_number": 38, "usage_type": "call"}, {"api_name": "telegramapi.Telegram", "line_number": 39, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 39, "usage_type": "attribute"}, {"api_name": "apidb.logger", "line_number": 46, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 47, "usage_type": "attribute"}, {"api_name": "flask.request.method", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 50, "usage_type": "attribute"}, {"api_name": "telegramapi.Telegram", "line_number": 54, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 54, "usage_type": "attribute"}, {"api_name": "apidb.logger", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "apidb.logger", "line_number": 58, "usage_type": "call"}, {"api_name": "user.User.get_user", "line_number": 61, "usage_type": "call"}, {"api_name": "user.User", "line_number": 61, "usage_type": "name"}, {"api_name": "apidb.logger", "line_number": 63, "usage_type": "call"}, {"api_name": "time.time", "line_number": 64, "usage_type": "call"}, {"api_name": "user.get_timestamp", "line_number": 64, "usage_type": "call"}, {"api_name": "user.get_timestamp", "line_number": 65, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 65, "usage_type": "attribute"}, {"api_name": "user.get_is_bot", "line_number": 66, "usage_type": "call"}, {"api_name": "user.set_timestamp", "line_number": 67, "usage_type": "call"}, {"api_name": "user.set_is_bot", "line_number": 68, "usage_type": "call"}, {"api_name": "user.save", "line_number": 69, "usage_type": "call"}, {"api_name": "user.User.insert_user", "line_number": 73, "usage_type": "call"}, {"api_name": "user.User", "line_number": 73, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 84, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 95, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 97, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 105, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 106, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 107, "usage_type": "call"}, {"api_name": "apidb.logger", "line_number": 115, "usage_type": "call"}, {"api_name": "user.User.get_user", "line_number": 117, "usage_type": "call"}, {"api_name": "user.User", "line_number": 117, "usage_type": "name"}, {"api_name": "user.User.insert_user", "line_number": 119, "usage_type": "call"}, {"api_name": "user.User", "line_number": 119, "usage_type": "name"}, {"api_name": "user.get_timestamp", "line_number": 120, "usage_type": "call"}, {"api_name": "user.set_timestamp", "line_number": 121, "usage_type": "call"}, {"api_name": "user.set_is_bot", "line_number": 122, "usage_type": "call"}, {"api_name": "user.save", "line_number": 123, "usage_type": "call"}, {"api_name": "apidb.logger", "line_number": 124, "usage_type": "call"}, {"api_name": "user.User.get_bots", "line_number": 133, "usage_type": "call"}, {"api_name": "user.User", "line_number": 133, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 140, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 140, "usage_type": "call"}, {"api_name": "apidb.check", "line_number": 144, "usage_type": "call"}, {"api_name": "apidb.init", "line_number": 145, "usage_type": "call"}]} +{"seq_id": "247053812", "text": "import datetime\nimport csv\nimport os\n\n\nclass CasEntity(object):\n __measurement_conditions = {}\n __results = {}\n __general_information = {}\n __data = {}\n __uv = {}\n objDatetime = None\n\n def __init__(self, fname):\n isdfile = open(fname, 'rt', encoding='utf-8', errors='ignore')\n valid = self.__map_data(isdfile)\n isdfile.close()\n\n if valid:\n # print(fname + ' is valid file')\n datatable = self.get_dict_to_list(self.__data)\n self.__set_additional_data(datatable)\n self.__set_uv_dict(datatable)\n self.objDatetime = self.__parse_objdt(\n self.__general_information['Date'] + ' ' + self.__general_information['Time'])\n\n def __map_data(self, file):\n line = file.readline()\n linetype = 0\n\n if line.strip() != '[Curve Information]':\n return False\n\n while line:\n line = line.strip()\n if line == '[Measurement Conditions]':\n linetype = 1\n elif line == '[Results]':\n linetype = 2\n elif line == '[General Information]':\n linetype = 3\n elif line == 'Data':\n linetype = 4\n else:\n # try:\n if line.find('=') != -1:\n strKey, strValue = line.split('=')\n key = strKey.strip()\n strValue = strValue.strip()\n endidx = strKey.find('[')\n if endidx != -1:\n key = key[:endidx].strip()\n try:\n value = float(strValue)\n except ValueError:\n value = strValue\n\n elif line.find('\\t') != -1:\n strKey, strValue = line.split('\\t')\n key = float(strKey.strip())\n value = float(strValue.strip())\n else:\n line = file.readline()\n continue\n # except ValueError:\n # line = file.readline()\n # continue\n\n # print(key, value)\n\n if linetype == 1:\n self.__measurement_conditions[key] = value\n\n elif linetype == 2:\n self.__results[key] = value\n\n elif linetype == 3:\n self.__general_information[key] = value\n\n elif linetype == 4:\n self.__data[float(key)] = value\n\n else: # type == 0\n pass\n\n line = file.readline()\n return True\n\n def __set_additional_data(self, datatable):\n bird_vis = self.get_ird(datatable, 380, 780)\n bird_sw = self.get_ird(datatable, 380, 480)\n bird_mw = self.get_ird(datatable, 480, 560)\n bird_lw = self.get_ird(datatable, 560, 780)\n bird_narrow = self.get_ird(datatable, 446, 477)\n\n if bird_vis == 0:\n self.__results['swr'] = 0\n self.__results['mwr'] = 0\n self.__results['lwr'] = 0\n self.__results['narr'] = 0\n else:\n self.__results['swr'] = bird_sw / bird_vis\n self.__results['mwr'] = bird_mw / bird_vis\n self.__results['lwr'] = bird_lw / bird_vis\n self.__results['narr'] = bird_narrow / bird_vis\n\n def __set_uv_dict(self, datatable):\n # calc bb ird of uv\n # self.__uv['uv_general_info'] = {\n # 'unit': 'W/m2',\n # }\n # self.__uv['integration_range'] = {\n # 'tuv': [280, 400],\n # 'uva': [315, 400],\n # 'uvb': [280, 315],\n # 'euv': [280, 400],\n # 'euva': [315, 400],\n # 'euvb': [280, 315],\n # 'duv': [280, 400],\n # }\n self.__uv['tuv'] = self.get_ird(datatable, 280, 400)\n self.__uv['uva'] = self.get_ird(datatable, 315, 400)\n self.__uv['uvb'] = self.get_ird(datatable, 280, 315)\n self.__uv['euv'] = self.get_ird(datatable, 280, 400, weight_func='ery')\n self.__uv['euva'] = self.get_ird(datatable, 315, 400, weight_func='ery')\n self.__uv['euvb'] = self.get_ird(datatable, 280, 315, weight_func='ery')\n self.__uv['uvi'] = self.__uv['euv'] * 40\n self.__uv['duv'] = self.get_ird(datatable, 280, 400, weight_func='vitd')\n\n if self.__uv['euv'] == 0:\n self.__uv['euva_ratio'] = 0\n self.__uv['euvb_ratio'] = 0\n else:\n self.__uv['euva_ratio'] = self.__uv['euva'] / self.__uv['euv']\n self.__uv['euvb_ratio'] = self.__uv['euvb'] / self.__uv['euv']\n\n self.__uv['hazard_uv'] = self.get_ird(datatable, 200, 400, weight_func='uv_hazard', alg='trapezoid')\n\n def __parse_objdt(self, strdt):\n return datetime.datetime.strptime(strdt, '%m/%d/%Y %I:%M:%S %p')\n\n def get_datetime(self, tostr=False):\n if tostr:\n return self.objDatetime.strftime('%Y-%m-%d %H:%M:%S')\n else:\n return self.objDatetime\n\n def get_json(self, *args):\n import json\n dict_json = {}\n\n if 'measurement conditions' in args:\n dict_json['measurement conditions'] = self.__measurement_conditions\n if 'results' in args:\n dict_json['results'] = self.__results\n if 'general information' in args:\n dict_json['general information'] = self.__general_information\n if 'data' in args:\n dict_json['data'] = self.__data\n if 'uv' in args:\n dict_json['uv'] = self.__uv\n\n return json.dumps(dict_json, indent=4)\n\n def get_dict(self, item='all'):\n if item == 'measurement conditions':\n return self.__measurement_conditions\n elif item == 'results':\n return self.__results\n elif item == 'general information':\n return self.__general_information\n elif item == 'data':\n return self.__data\n elif item == 'uv':\n return self.__uv\n\n elif item == 'all':\n return {\n 'measurement conditions': self.__measurement_conditions,\n 'results': self.__results,\n 'general information': self.__general_information,\n 'data': self.__data,\n 'uv': self.__uv\n }\n\n else:\n return None\n\n @staticmethod\n def get_dict_to_list(dict_src, kv=True):\n retlist = []\n for key in dict_src.keys():\n if kv:\n retlist.append([key, dict_src[key]])\n else:\n retlist.append(dict_src[key])\n return retlist\n\n def get_element(self, item=None):\n keyset_mc = self.__measurement_conditions.keys()\n keyset_re = self.__results.keys()\n keyset_gi = self.__general_information.keys()\n keyset_da = self.__data.keys()\n keyset_uv = self.__uv.keys()\n\n if item in keyset_mc:\n return self.__measurement_conditions[item]\n elif item in keyset_re:\n return self.__results[item]\n elif item in keyset_gi:\n return self.__general_information[item]\n elif item in keyset_da:\n return self.__data[item]\n elif item in keyset_uv:\n return self.__uv[item]\n else:\n return None\n\n @staticmethod\n def search(dirname):\n # dirname 디렉토리 내의 모든 파일과 디렉토리 이름을 리스트로 반환함\n filelist = []\n filenames = os.listdir(dirname)\n for filename in filenames:\n full_filename = os.path.join(dirname, filename)\n filelist.append(full_filename)\n return filelist\n\n @staticmethod\n def get_ird(table, range_val1, range_val2, weight_func='none', alg='rect'):\n # 분광 데이터 테이블로부터 특정 범위에 대한 광파장 복사량(broadband irradiance)을\n # float 단일값으로 반환 (광파장복사량 == 적산 값)\n # range_val1, range_val2 : 적분구간 시작, 끝 값\n # table : get_dict_to_list()로부터 생성된 분광 데이터 테이블(list)\n # weight_func : 가중함수 선택, 홍반가중함수('ery'), 비타민 d 가중함수('vitd'), 없음('none')\n # alg : 적분 알고리즘 선택, 기본값('rect')은 직사각형 공식, 'trapezoid' 로 설정하면 사다리꼴 공식 적용\n\n broadband_ird = 0\n\n # for i in range(len(table) - 1):\n index = 0\n\n for i in range(len(table) - 2):\n wll = float(table[i][0])\n irdl = float(table[i][1])\n wlr = float(table[i + 1][0])\n irdr = float(table[i + 1][1])\n\n if irdl < 0 or irdr < 0: # filter\n # print(str(wll) + '\\t0.0')\n continue\n\n if weight_func == 'ery':\n from nldc_entity.ref_func import erythemal_action_spectrum as eryf\n weightl = eryf(wll)\n weightr = eryf(wlr)\n elif weight_func == 'vitd':\n from nldc_entity.ref_func import vitd_weight_func_interpolated as vitdf\n weightl = vitdf(wll)\n weightr = vitdf(wlr)\n elif weight_func == 'uv_hazard':\n from nldc_entity.ref_func import actinic_uv_weight_func as actuvf\n weightl = actuvf(wll)\n weightr = actuvf(wlr)\n else:\n weightl = 1\n weightr = 1\n\n if range_val1 <= wll < range_val2:\n try:\n # calculate weighted integration\n if alg == 'trapezoid':\n e = 0.5 * (wlr - wll) * (irdl * weightl + irdr * weightr)\n else: # alg == 'rect'\n # print(str(wll) + '\\t' + str(irdl*weightl))\n e = (wlr - wll) * (irdl * weightl)\n except TypeError:\n print('exception!')\n break\n\n broadband_ird += e\n else:\n pass\n\n return broadband_ird\n\n @staticmethod\n def get_specirrad_table(fname):\n # CAS파일(fname=FILExxxxx.ISD)의 분광(SPECtral IRRADinace) 데이터 테이블 반환\n # ex) [[wl, ird], ... , []]\n\n file = open(fname, 'rt', encoding='utf-8', errors='ignore')\n line = file.readline()\n table = []\n valid = False\n\n while line:\n if valid:\n try:\n table.append(line.strip().split())\n except IndexError:\n break\n if line.strip() == 'Data':\n valid = True\n line = file.readline()\n\n return table\n\n def get_attrib_fast(self, fname, attrib):\n # CAS파일(fname=FILExxxxx.ISD)의 조도 값을 float 단일 값으로 반환\n # select attrib\n category = '[Results]'\n key = ''\n\n if attrib == 'inttime':\n key = 'IntegrationTime [ms]'\n elif attrib == 'illum':\n key = 'Photometric [lx]'\n elif attrib == 'uva':\n key = 'UVA [W/m²]'\n elif attrib == 'uvb':\n key = 'UVB [W/m²]'\n elif attrib == 'uvc':\n key = 'UVC [W/m²]'\n elif attrib == 'vis':\n key = 'VIS [W/m²]'\n elif attrib == 'cct':\n key = 'CCT [K]'\n elif attrib == 'colorcoord_x':\n key = 'ColorCoordinates/x'\n elif attrib == 'colorcoord_y':\n key = 'ColorCoordinates/y'\n elif attrib == 'colorcoord_z':\n key = 'ColorCoordinates/z'\n elif attrib == 'colorcoord_u':\n key = 'ColorCoordinates/u'\n elif attrib == 'peakwl':\n key = 'PeakWavelength [nm]'\n elif attrib == 'cri':\n key = 'CRI'\n elif attrib == 'cdi':\n key = 'CDI'\n elif attrib == 'date':\n category = '[General Information]'\n key = 'Date'\n elif attrib == 'time':\n category = '[General Information]'\n key = 'Time'\n\n file = open(fname, 'rt', encoding='utf-8', errors='ignore')\n line = file.readline()\n valid = False\n value = 0.0\n\n while line:\n if valid:\n # print(line)\n try:\n linesplit = line.split('=')\n if linesplit[0] == key:\n value = linesplit[1].strip()\n except IndexError as e:\n break\n if line == '\\n\\r\\n':\n break\n if line.strip() == category:\n valid = True\n line = file.readline()\n\n return value\n\n def log_csv(self, rootdir):\n # .ISD파일들이 들어있는 디렉토리명을 루트 디렉토리로 적어줘야함\n # 생성될 csv 파일은 rootdir에 저장됨\n outputFileName = '_output_jake.csv'\n\n # 루트 디렉토리의 파일명리스트 가져오기\n filelist = self.search(rootdir)\n\n # csv 파일 출력 설정\n outfile = open(rootdir + '/' + outputFileName, 'w', encoding='utf-8', newline='')\n csv_writer = csv.writer(outfile)\n # csv 첫줄에 컬럼명 적어주기\n csv_writer.writerow(['time', 'illum', 'tuv', 'uva', 'uvb', 'euva', 'euvb', 'uvi'])\n\n # 각 파일별 데이터 추출하기\n for fname in filelist:\n datatable = self.get_specirrad_table(fname)\n hazard_uv = self.get_ird(datatable, 200, 400, weight_func='uv_hazard')\n uva = self.get_ird(datatable, 315, 400)\n uvb = self.get_ird(datatable, 280, 315)\n euva = self.get_ird(datatable, 315, 400, weight_func='ery')\n euvb = self.get_ird(datatable, 280, 315, weight_func='ery')\n uvi = 40 * self.get_ird(datatable, 280, 400, weight_func='ery')\n\n # filter\n # if euvb + euva == 0:\n # ratio = 0\n # else:\n # ratio = euvb / (euvb + euva)\n\n # ccx = get_result_attrib(fname, 'colorcoord_x')\n # ccy = get_result_attrib(fname, 'colorcoord_y')\n # ccz = get_result_attrib(fname, 'colorcoord_z')\n # ccu = get_result_attrib(fname, 'colorcoord_u')\n # cct = get_result_attrib(fname, 'cct')\n # cri = get_result_attrib(fname, 'cri')\n # ccdtemp = get_CCDTemp(fname)\n\n # if bbirrad != 0:\n # swr = get_bbird(380, 480, datatable) / bbirrad\n # narrow = get_bbird(446, 477, datatable) / bbirrad\n # else:\n # swr = 0\n # narrow = 0\n\n # 콘솔에 출력\n # print('time:', timestamp, ', euva:', euva, ', euvb:', euvb, ', ratio:', ratio, ', illum:', illum)\n\n # csv write\n csv_writer.writerow([hazard_uv, uva, uvb, euva, euvb, uvi])\n\n outfile.close()\n\n\nif __name__ == '__main__':\n rootdir = 'D:/Desktop'\n outputFileName = 'Data.txt'\n\n flist = CasEntity.search(rootdir)\n outfile = open(rootdir + '/' + outputFileName, 'w', encoding='utf-8', newline='')\n csv_writer = csv.writer(outfile)\n\n csv_writer.writerow(['tuv', 'uva', 'uvb', 'euv', 'euva', 'euvb',\n 'uvi', 'duv', 'euva_ratio', 'euvb_ratio', 'hazard_uv'])\n\n for fname in flist:\n # print('>>' + fname)\n entity = CasEntity(fname)\n csv_writer.writerow(CasEntity.get_dict_to_list(entity.get_dict('uv'), kv=False))\n\n outfile.close()\n", "sub_path": "ActionSpectrumVerification/nldc_entity/cas_entity.py", "file_name": "cas_entity.py", "file_ext": "py", "file_size_in_byte": 15620, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 140, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 140, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 163, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 225, "usage_type": "call"}, {"api_name": "os.path", "line_number": 225, "usage_type": "attribute"}, {"api_name": "nldc_entity.ref_func.erythemal_action_spectrum", "line_number": 255, "usage_type": "call"}, {"api_name": "nldc_entity.ref_func.erythemal_action_spectrum", "line_number": 256, "usage_type": "call"}, {"api_name": "nldc_entity.ref_func.vitd_weight_func_interpolated", "line_number": 259, "usage_type": "call"}, {"api_name": "nldc_entity.ref_func.vitd_weight_func_interpolated", "line_number": 260, "usage_type": "call"}, {"api_name": "nldc_entity.ref_func.actinic_uv_weight_func", "line_number": 263, "usage_type": "call"}, {"api_name": "nldc_entity.ref_func.actinic_uv_weight_func", "line_number": 264, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 382, "usage_type": "call"}, {"api_name": "{'json': 'json', 'eryf': 'nldc_entity.ref_func.erythemal_action_spectrum', 'vitdf': 'nldc_entity.ref_func.vitd_weight_func_interpolated', 'actuvf': 'nldc_entity.ref_func.actinic_uv_weight_func'}.search", "line_number": 430, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 432, "usage_type": "call"}, {"api_name": "{'json': 'json', 'eryf': 'nldc_entity.ref_func.erythemal_action_spectrum', 'vitdf': 'nldc_entity.ref_func.vitd_weight_func_interpolated', 'actuvf': 'nldc_entity.ref_func.actinic_uv_weight_func'}", "line_number": 439, "usage_type": "call"}, {"api_name": "{'json': 'json', 'eryf': 'nldc_entity.ref_func.erythemal_action_spectrum', 'vitdf': 'nldc_entity.ref_func.vitd_weight_func_interpolated', 'actuvf': 'nldc_entity.ref_func.actinic_uv_weight_func'}.get_dict_to_list", "line_number": 440, "usage_type": "call"}]} +{"seq_id": "147813064", "text": "# -*- coding: utf-8 -*-\nfrom docxtpl import DocxTemplate, RichText\nfrom struct import unpack\nfrom socket import AF_INET, inet_pton\nimport wx\n#import wx\nimport pygeoip\nimport sqlite3\nimport time\nimport msvcrt\nimport datetime\nimport cx_Oracle\nimport ipcheck\nfrom datetime import date \n\n\n\ntpl=DocxTemplate('sample/event_report.docx')\n\ndef geoipcounty(county_code):\n\tconn = sqlite3.connect(\"county.db\") \n\t# Connection 으로부터 Cursor 생성\n\tcur = conn.cursor() \n\tsql = \"select c_name from county where c_code='\" + county_code +\"'\"\n\tcur.execute(sql) \n\t# 데이타 Fetch\n\trows = cur.fetchall()\n\tfor row in rows:\n\t\tcounty = row\n\t##rows = c.fetchall()\n\t##print( rows )\n\t##for row in rows:\n\t##\tprint(row)\n \n\t## Connection 닫기\n\tconn.close()\t\n\t#print(county)\n\t\n\t#return county\n\ndef today_type(type):\t\n\t#e = datetime.datetime.now()\n\t# 함수설명 넣는곳 \n\t#\n\tnow = time.localtime()\n\t#now = time.localtime(\t)\n\t#s = \"%04d-%02d-%02d %02d:%02d:%02d\" % (now.tm_year, now.tm_mon, now.tm_mday, now.tm_hour, now.tm_min, now.tm_sec)\n\t#s = \"%04d/%02d/%02d%02d\" % (now.tm_year, now.tm_mon, now.tm_mday,now.tm_hour) # Ԣ࠹OރУ \n\tif(type==1): \t\t\n\t\ts = \"%04d/%02d/%02d\" % (now.tm_year, now.tm_mon, now.tm_mday) # Ԣ/࠹/O\n\tif(type==2):\n\t\ts = \"%04d-%02d-%02d\" % (now.tm_year, now.tm_mon, now.tm_mday) \n\tif(type==3):\n\t\ts = \"%04d%02d-%02d-%02d\" % (now.tm_year, now.tm_mon, now.tm_mday,now.tm_sec) \n\tif(type==4):\n\t\ts = \"%04d%02d%02d\" % (now.tm_year, now.tm_mon, now.tm_mday) \n\tif(type==5):\n\t\ts = \"%02d\" % (now.tm_hour)\n\t##print ( s )\n\treturn s\n\t#print ( e )\n\t#s\n\n\ndef geoip(ip):\n\t#if len(sys.argv) is 1:\n\t#\tprint (\"옵션을 주지 않고 이 스크립트를 실행하셨군요\")\n\t\n\t#print (\"옵션 개수: %d\" % (len(sys.argv) - 1))\n\t#print ( \"\\n< 옵션 목록 >\")\n\n\t#for i in range(len(sys.argv)):\n\t#\tprint (\"sys.argv[%d] = '%s'\" % (i, sys.argv[i]))\n\tprint( ip )\n\t\t\n\tgi = pygeoip.GeoIP('GeoIP.dat')\n\t\n\t#print ( gi.country_code_by_name('sys.argv[1]'))\n\t# 'US' 출력\n\t#print ( sys.argv[1] )\n\t#county = \n\tcounty_code=gi.country_code_by_addr( ip )\n\t#print (county_code.rstrip('\\n'))\n\tconn = sqlite3.connect(\"county.db\") \n\t# Connection 으로부터 Cursor 생성\n\tcur = conn.cursor() \n\tsql = \"select c_name from county where c_code='\" + county_code +\"'\"\n\tcur.execute(sql) \n\t# 데이타 Fetch\n\trows = cur.fetchall()\n\t#print ( \"rows:rows )\n\tfor row in rows:\n\t\tcounty = row[0].rstrip('\\n')\n\t\t#print('국가코드')\n\t\t#print( county)\n\t#if( ip == '172.18.96.193'):county='내부'\n\t#if( ip == '172.18.59.112'):county='내부'\n\t#if( ip == '172.18.134.90'):county='내부'\n\t#county=geoipcounty(county)\n\t#ounty=county[1]\n\t#print (county.split('\\n'))\n\t#return row\n\t\n\treturn county\n\t# 'US' 출력\n\t#print ( gi.country_name_by_name('google.com'))\n\t# 'United States' 출력\n\t#print (gi.country_name_by_addr('64.233.161.99'))\n\n\t# 'United States' 출력\n\n\t#def main\n\ndef lookup(ip):\n\tf = unpack('!I',inet_pton(AF_INET,ip))[0]\n\tprivate = (\n\t\t[ 2130706432, 4278190080 ], # 127.0.0.0, 255.0.0.0 http://tools.ietf.org/html/rfc3330\n\t\t[ 3232235520, 4294901760 ], # 192.168.0.0, 255.255.0.0 http://tools.ietf.org/html/rfc1918\n\t\t[ 2886729728, 4293918720 ], # 172.16.0.0, 255.240.0.0 http://tools.ietf.org/html/rfc1918\n\t\t[ 167772160, 4278190080 ], # 10.0.0.0, 255.0.0.0 http://tools.ietf.org/html/rfc1918\n\t) \n\tfor net in private:\n\t\tif (f & net[1]) == net[0]:\n\t\t\treturn True\n\treturn False\t\n\nconn = sqlite3.connect('esm_event.db')\nconn.text_factory = str\nc = conn.cursor()\n#curr = conn.cursor()\n#sql = \"select * from esm_event;\"\nc.execute('''PRAGMA encoding = \"UTF-8\";''')\n#sql =\".schema salespeople\"\n#c.execute('''create table esm_event (id INTEGER PRIMARY KEY AUTOINCREMENT, mgr_time TEXT,title TEXT, slocation TEXT, ext1 TEXT,src_info TEXT,src_port TEXT,to_attack_info TEXT,to_attack_port TEXT,alt_level TEXT,org_alert_level TEXT,info_status TEXT)''')\n#print ( sql )\n\nfor row in c.execute('SELECT * FROM esm_event where src_info=\\'50.63.197.204\\''):\n\tprint (\"row[0]/id:\",row[0])\n\tprint (\"row[1]/mgr_time:\",row[1]) #mgr_time 탐지 시간 \n\tprint (\"row[2]/title:\",row[2]) # title 인스던트명 \n\tprint (\"row[3]/slocation:\",row[3]) # slocation 탐지장비\n\tprint (\"row[4]/ext1:\",row[4]) # ext1\n\tprint (\"row[5]/src_info:\",row[5]) # src_info\n\tprint (\"row[6]/src_port:\",row[6]) # src_port \n\tprint (\"row[7]/to_attack_info:\",row[7]) # to_attack_info\n\tprint (\"row[8]/to_attack_port:\",row[8]) # to_attack_port \n\tprint (\"row[9]/alt_level:\",row[9]) # alt_level\n\tprint (\"row[10]/org_alert_level:\",row[10]) # org_alert_level\n\tprint (\"row[11]/info_status :\",row[11]) # info_status \n\tprint (\"row[12]/agent_name:\",row[12]) # agent_name \n\t#print (\"row[12]:\",row[12])\n\t#print (\"row[13]:\",row[13])\n\t#print (\"row[14]:\",row[14])\n\t#print (\"row[2]\n\t#print (\"row[2]\n\t#row[5] src_ip\n\t#row[6] src_port \n\tdate = today_type(2) # today_type{} 함수 type 값 설명 : 1 2016/08/16, 2 2016-08-16 \n\t#print ( date )\n\t#print( lookup(str(row[5])))\n\tif ( True == lookup(str(row[5]))):\n\t\tcounty='내부'\n\tif ( False == lookup(str(row[5]))):\n\t\tcounty=geoip(str(row[5]))\n\t\n\tprint( county )\n\t#print(county)\n\tpayload = { \n\t\t'company_name' : 'KoreaIT', \n\t\t'date' : date, \n\t\t'event_name' : row[2],\n\t\t'detect_unit' : row[12],\n\t\t'Detection_type': '차단',\n\t\t'detect_time' : row[1], #탐지시간\n\t\t'src_ip': row[5],\n\t\t'dst_ip': row[7],\n\t\t'event_detect_list':'공격상세내역',\n\t\t'whois' : county, \n\t\t'target_port' : row[0], \n\t\t'customer_Recommendations':'권고사항', \n\t\t'event_Explanation':'이벤트 설명'\n\t\t#'monthly_avg_count' : 2225000\n\t}\n\tprint ( payload )\n\ttpl.render(payload)\n\t\n\tfilename=\"report/이벤트_탐지_보고서\" + today_type(4)+ \"_\" + today_type(5)+\".docx\"\n\tprint ( filename )\n\ttpl.save(filename) #''' ''''''\n\t\nconn.commit()\n\n#county=geoip('201.101.176.31')\n#print (county.rstrip(\"\\n\"))\n#conn.close()\n\n\n\n", "sub_path": "08_regex/cellbg.py", "file_name": "cellbg.py", "file_ext": "py", "file_size_in_byte": 5800, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "docxtpl.DocxTemplate", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 21, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 45, "usage_type": "call"}, {"api_name": "pygeoip.GeoIP", "line_number": 76, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 84, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 115, "usage_type": "call"}, {"api_name": "socket.inet_pton", "line_number": 115, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 115, "usage_type": "argument"}, {"api_name": "sqlite3.connect", "line_number": 127, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 158, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 170, "usage_type": "name"}]} +{"seq_id": "387744297", "text": "from pathlib import Path\n\nimport numpy as np\n\n\ndef idx_converter(raw_id_file, in_id_file, embedding_file):\n raw_ids = []\n with open(raw_id_file) as f:\n for line in f.readlines():\n raw_ids.append(line.strip())\n\n in_ids = np.fromfile(in_id_file, dtype=int)\n raw_in_dict = dict(zip(raw_ids, in_ids))\n\n num = len(raw_ids)\n embeddings = np.fromfile(embedding_file, np.float32).reshape((num, -1))\n\n raw_emb_dict = []\n for rid in raw_ids:\n iid = raw_in_dict.get(rid)\n emb = embeddings[iid]\n raw_emb_dict.append(np.append(rid, emb))\n\n return raw_emb_dict\n\n\ndef convert_to_tsv(output_dir=None):\n temp = output_dir if output_dir != None else None\n output_dir = \"./output_dir\" if output_dir == None else output_dir\n\n nodes_raw_id_file = Path(output_dir) / Path(\"node_mapping.txt\")\n nodes_in_id_file = Path(output_dir) / Path(\"node_mapping.bin\")\n nodes_embedding_file = Path(\"./training_data/marius/embeddings/embeddings.bin\")\n node_embs = np.array(idx_converter(nodes_raw_id_file, nodes_in_id_file, nodes_embedding_file))\n\n rels_raw_id_file = Path(output_dir) / Path(\"rel_mapping.txt\")\n rels_in_id_file = Path(output_dir) / Path(\"rel_mapping.bin\")\n lhs_rels_embedding_file = Path(\"./training_data/marius/relations/lhs_relations.bin\")\n lhs_rel_embs = np.array(idx_converter(rels_raw_id_file, rels_in_id_file, lhs_rels_embedding_file))\n\n rhs_rels_embedding_file = Path(\"./training_data/marius/relations/rhs_relations.bin\")\n rhs_rel_embs = np.array(idx_converter(rels_raw_id_file, rels_in_id_file, rhs_rels_embedding_file))\n\n if temp != None:\n np.savetxt((Path(\"./training_data/node_embedding.tsv\")), node_embs, fmt=\"%f\", delimiter='\\t', newline='\\n')\n np.savetxt((Path(\"./training_data/edge_lhs_embedding.tsv\")), lhs_rel_embs, fmt=\"%f\", delimiter='\\t',\n newline='\\n')\n np.savetxt((Path(\"./training_data/edge_rhs_embedding.tsv\")), rhs_rel_embs, fmt=\"%f\", delimiter='\\t',\n newline='\\n')\n\n return node_embs, lhs_rel_embs, rhs_rel_embs\n else:\n return node_embs, lhs_rel_embs, rhs_rel_embs\n\n\ndef main():\n n_emb, lhs_emb, rhs_emb = convert_to_tsv()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "src/python/tools/postprocess.py", "file_name": "postprocess.py", "file_ext": "py", "file_size_in_byte": 2252, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.fromfile", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 22, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 31, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 32, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 36, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 37, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 45, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 46, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 48, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "470132072", "text": "# BOXPLOT ALF TAB\nimport pandas as pd\nimport numpy as np\nimport matplotlib, os\nmatplotlib.use('Agg')\nfrom matplotlib import pyplot as plt\nfrom matplotlib import ticker\nimport matplotlib.lines as mlines\nimport matplotlib.patches as mpatches\n\n\n# update rcParams:\nfrom matplotlib import rcParams\nrcParams['xtick.direction'] = 'out'\nrcParams['ytick.direction'] = 'out'\n\n# futures:\nobs = '/atlas_scratch/malindgren/ak_landcarbon_duffy/Fire_Acreage_Thoman.csv'\nobs_df = pd.read_csv( obs, sep=',', index_col=0 )\nobs_df = obs_df.loc[ 1950:2015, : ]\nobs_df = obs_df.reset_index()\nobs_df['km'] = (obs_df.Acreage * 0.00404686)\n\ntab = '/atlas_scratch/malindgren/ak_landcarbon_duffy/cccma_cgcm3_1_sresa1b/total_area_burned/alfresco_totalareaburned_Alaska_cccma_cgcm3_1_sresa1b_landcarbon_ak_1900_2100.csv'\ndf = pd.read_csv( tab, sep=',', index_col=0 )\ndf = df.loc[ 1950:2015, : ]\n\n# RAW AND DIRTY MPL\ndfd = df.T.to_dict( orient='list' )\ndat = [ np.array(dfd[i]) for i in df.T.columns ]\nfigsize = (14, 9)\n\n# Create a figure instance\nfig = plt.figure( 1, figsize=figsize )\n# Create an axes instance\nax = fig.add_subplot( 111 )\n\n# setup spines\nax.spines[ \"top\" ].set_visible( True ) \nax.spines[ \"bottom\" ].set_visible( True )\nax.spines[ \"right\" ].set_visible( True )\nax.spines[ \"left\" ].set_visible( True )\n\n# box configs\nboxprops = dict( linestyle='-', linewidth=0.6, color='black' )\nwhiskerprops = dict( linestyle='-', linewidth=0.6, color='black' )\ncapprops = dict( linestyle='-', linewidth=0.6, color='black' )\nmedianprops = dict( linestyle='-', linewidth=0.6, color='DarkBlue' )\n\n# plot it using base matplotlib's boxplot function... -- Full range \nwhis = [5,95] # 'range'\nbp = plt.boxplot( dat, notch=True, whis=whis, showfliers=False, \\\n\t\t\tboxprops=boxprops, whiskerprops=whiskerprops, \\\n\t\t\tcapprops=capprops, medianprops=medianprops, patch_artist=True )\n\n## change outline color, fill color and linewidth of the boxes\nfor box in bp['boxes']:\n # change outline color\n # box.set( color='#7570b3', linewidth=2)\n # change fill color\n box.set( facecolor='lightgrey' )\n\n# # overplot with black 5-95 percentiles \n# whis = [5,95]\n# whiskerprops = dict( linestyle='-', linewidth=0.5, color='black' )\n# capprops = dict( linestyle='', linewidth=0.5, color='black' )\n# plt.boxplot( dat, notch=True, whis=whis, showfliers=False, \\\n# \t\t\tboxprops=boxprops, whiskerprops=whiskerprops, \\\n# \t\t\tcapprops=capprops, medianprops=medianprops, )\n\n\n# ax, bp = df.T.plot.box( ax=ax, return_type='both', grid=False, figsize=figsize, whis=(5,95), widths=0.75, showfliers=False, sym='', rot=45, notch=True, color=color )\nmarkersize = 60 # default:20\nplt.scatter( range(1,len(obs_df.Year.tolist())+1), obs_df.km.tolist(), zorder=10, marker='*', s=markersize, color='DarkRed' )\n\nax.get_xaxis().tick_bottom()\nax.get_yaxis().tick_left()\n\nplt.xlabel( 'Year' )\nplt.ylabel( 'Area Burned (km2)' )\n\n# TITLE and stuff.\nnreps = len( df.columns )\ndomain = 'Alaska Statewide'\n\n# plot_title = 'ALFRESCO Annual Area Burned 1950-2015 \\n %s - %s Replicates ' \\\n# \t\t% ( domain, nreps )\n# plt.title( plot_title )\n\n# here is the really really hacky way to set the darn xaxis labels in the non-standard way we \n# would like. Where we have the first and last years present regardless of interval.\nyears = df.index.tolist() # all labels\n# # set the labels with the years\nax.xaxis.set_ticklabels( years )\n\n# plt.setp( ax.get_xticklabels(), visible=False ) # turn all labels off\n# locs = ax.get_xticklabels()[::5] # list the ones to turn on\n# last = ax.get_xticklabels()[-1]\n# locs.append( last )\n# plt.setp( locs, visible=True, rotation='vertical' ) # set every 5th to on\n\n# # # A NEW WAY TO DEAL WITH TICKS\n# minor_locator = AutoMinorLocator()\n# ax.xaxis.set_minor_locator( minor_locator )\n\n# majorLocator = ticker.MultipleLocator( 5 )\n# # majorFormatter = ticker.FormatStrFormatter( '%d' )\nminorLocator = ticker.MultipleLocator( 1 )\n# ax.xaxis.set_major_locator( majorLocator )\nax.xaxis.set_minor_locator( minorLocator )\n\n# ax.xaxis.set_major_formatter( majorFormatter )\n# for the minor ticks, use no labels; default NullFormatter\n\n\n# # # END A NEW WAY TO DEAL WITH TICKS\n\n# # ********\n# majorLocator = ticker.MultipleLocator(5)\n# majorFormatter = ticker.FormatStrFormatter('%d')\n# minorLocator = ticker.MultipleLocator(1)\n\n# labels = years[::5]\n# labels.append( 2009 )\n\n# majorLocator = ticker.FixedLocator( labels[::5] )\n# # minorLocator = ticker.FixedLocator(np.linspace(19,41,23))\n\n# ax.xaxis.set_major_locator(majorLocator)\n# ax.xaxis.set_major_formatter(majorFormatter)\n# ax.xaxis.set_minor_locator(minorLocator)\n\n\n# ********\n# FROM plot.py\n\nn = 5 # every n ticks... from the existing set of all\nticks = ax.xaxis.get_ticklocs()\nticklocs = ticks[::n]\nticklocs = np.append( ticklocs, ticks[-1] )\nax.xaxis.set_ticks( ticklocs )\n# ticks.append( ticks[-1]-1 ) # add back in the last year -- 2009\nticklabels = [ years[i-1] for i in ticklocs ]\n# ticklabels = [ l.get_text() for l in ax.xaxis.get_ticklabels() ][::n]\nticklabels.append( years[-1] )\n\n# update the size of the ticks\nax.tick_params( size=8 )\n\n# ax.xaxis.set_ticks( )\nax.xaxis.set_ticklabels( ticklabels )\nax.set_xlabel( 'Year', fontsize=13 )\nax.set_ylabel( 'Area Burned (' + '$\\mathregular{km^2}$' + ')', fontsize=13 )\n\n# we need a dynamic way to set the yaxis lims. Le\nbegin, end = ax.get_ylim()\nax.set_ylim( 0, 30000 )\n\n# make the ticks be comma-style for thousands\nax.get_yaxis().set_major_formatter(\n matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n\n# Update the size of the tick labels\nplt.setp( ax.get_xticklabels(), fontsize=12 )\nplt.setp( ax.get_yticklabels(), fontsize=12 )\n\n# LEGEND IT UP!\n# whiskers = mlines.Line2D( [], [], color='black', markersize=15, marker='', label='data range (whiskers)' )\n# median = mlines.Line2D( [], [], color='DarkBlue', markersize=15, marker='', label='median line' )\n# obs = plt.Line2D((0,1),(0,0), color='DarkRed', marker='*', linestyle='', markeredgecolor='DarkRed', label='observed' )\n# plt.legend( handles=[obs], numpoints=1, fontsize='x-small' ) # they didnt want a legend\n\n# save it out\n# plt.savefig( '/atlas_scratch/malindgren/ak_landcarbon_duffy/alfresco_aab_boxplot_LandCarbon_AK_1950_2015_v2.png', dpi=600 )\nplt.savefig( '/workspace/UA/malindgren/alfresco_aab_boxplot_LandCarbon_AK_1950_2015_v3.png', dpi=600 )\nplt.close()\n\n", "sub_path": "alfresco_postprocessing/_aab_boxplot.py", "file_name": "_aab_boxplot.py", "file_ext": "py", "file_size_in_byte": 6327, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.use", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.rcParams", "line_number": 15, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.boxplot", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 108, "usage_type": "name"}, {"api_name": "numpy.append", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.ticker.FuncFormatter", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 161, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}]} +{"seq_id": "380443822", "text": "import scrapy\nfrom scrapy.utils.project import get_project_settings\n\n\ndef round_robin(arr):\n to_yield_idx = 0\n length = len(arr)\n while True:\n yield arr[to_yield_idx % length]\n to_yield_idx += 1\n\n\nclass PhishSpider(scrapy.Spider):\n name = \"phish\"\n handle_httpstatus_list = [200, 302]\n\n def __init__(self, *args, **kwargs):\n super(PhishSpider, self).__init__(*args, **kwargs)\n self.settings = get_project_settings()\n print(kwargs)\n self.urls = kwargs['urls_objects']\n self.redirect_counter = 0\n self.url_number = 0\n if self.settings['PROXY_LIST']:\n self.proxy_iter = round_robin(self.settings['PROXY_LIST'])\n\n def start_requests(self):\n for url in self.urls:\n request = scrapy.Request(url=url.url, callback=self.parse)\n if self.settings['PROXY_LIST']:\n request.meta['proxy'] = next(self.proxy_iter)\n yield request\n\n def parse(self, response):\n if response.status == 200:\n self.url_number += 1\n yield {\n 'response': response,\n 'url_number': self.url_number,\n 'redirect_count': self.redirect_counter\n }\n self.redirect_counter = 0\n if response.status == 302:\n self.redirect_counter += 1\n", "sub_path": "urls_algo/phish_spider.py", "file_name": "phish_spider.py", "file_ext": "py", "file_size_in_byte": 1350, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "scrapy.Spider", "line_number": 13, "usage_type": "attribute"}, {"api_name": "scrapy.utils.project.get_project_settings", "line_number": 19, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "322510458", "text": "# https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/\r\n\r\nimport cv2\r\n\r\nimg = cv2.imread('d:\\\\srk_1.jpg')\r\nheight = img.shape[0]\r\nwidth = img.shape[1]\r\n\r\nfor row in range(0, width):\r\n\tfor column in range(0, height):\r\n\t\tprint(img[column][row])\r\n\r\n\r\n", "sub_path": "12 - CV2_Image.read.print.py", "file_name": "12 - CV2_Image.read.print.py", "file_ext": "py", "file_size_in_byte": 291, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.imread", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "392327263", "text": "from database.DB import DB\nfrom flask import Flask, render_template, json, request, jsonify\nimport psycopg2 \nfrom psycopg2.sql import SQL, Composable, Identifier, Literal\nfrom psycopg2 import Error\nfrom psycopg2 import sql\nimport decimal\n\n\n\n\nclass DB_usuario(DB):\n\n def get2(self,tipo, valor):\n\n try:\n query = \"SELECT * FROM usuario WHERE {0} = '{1}'\".format(tipo, valor)\n \n print(self.cursor.mogrify(query)) \n self.cursor.execute(query)\n resp = self.cursor.fetchone()\n\n print(resp)\n\n if resp:\n\n columnas = self.cursor.description\n resp = self.querydictdecimal(resp,columnas)\n data = resp[0]\n \n for atributo in data:\n if (data[atributo] == None):\n data[atributo] = ''\n\n return data \n \n else:\n return resp\n\n except Exception:\n return jsonify({'error':'Error: Hubo un problema con el servidor'})\n\n def add (self, data):\n \n try:\n \n keys = data.keys()\n columns = ','.join(keys)\n values = ','.join(['%({})s'.format(k) for k in keys])\n\n query = 'INSERT INTO usuario ({0}) VALUES ({1}) RETURNING us_codigo'.format(columns, values)\n \n print(self.cursor.mogrify(query, data)) \n self.cursor.execute(query,data)\n self.connection.commit()\n \n id_creado = self.cursor.fetchone()[0]\n\n return id_creado\n\n except Exception:\n print(Exception)\n return jsonify({'error':'Error: Hubo un problema con el servidor'})\n\n def update2 (self, id_cl_em ,tipo , data):\n\n try:\n\n datamod = dict(data)\n dataol = self.get2(id_cl_em,tipo)\n \n\n for atributo in data:\n if (data[atributo] == dataol[atributo]):\n datamod.pop(atributo)\n \n \n if (not datamod): return ({'invalido':'Ningun dato fue actualizado'}) \n \n for key in datamod.keys():\n if (datamod[key] == '' or datamod[key] == ' '): datamod[key] = None\n\n keys = datamod.keys()\n values = ','.join(['{} = %({})s'.format(k, k) for k in keys])\n \n query = 'UPDATE usuario SET {0} WHERE {1} = {2}'.format(values,tipo,id_cl_em)\n\n print(self.cursor.mogrify(query,datamod)) \n self.cursor.execute(query,datamod)\n self.connection.commit()\n \n return ({'mensaje':'Usuario modificado satisfactoriamente'}) \n \n\n except Exception:\n return ({'error':'Error: Hubo un problema con el servidor'}) \n\n def delete(self, id_cl_em,tipo):\n\n try:\n query = 'DELETE FROM usuario WHERE {0} = {1}'.format(tipo, id_cl_em)\n \n print(self.cursor.mogrify(query)) \n self.cursor.execute(query)\n self.connection.commit() \n\n return jsonify({'mensaje':'eliminado satisfactoriamente'}) \n\n except Exception:\n return jsonify({'error':'Error: Hubo un problema con el servidor'})\n\n def verif(self,atributo,valor):\n \n try:\n \n if type(valor) == str:\n self.cursor.execute (\"SELECT * FROM usuario WHERE {0} = '{1}'\".format (atributo,valor))\n else:\n self.cursor.execute (\"SELECT * FROM usuario WHERE {0} = '{1}'\".format (atributo,valor))\n \n obj = self.cursor.fetchone() \n\n if obj is None: \n return None\n else:\n return 1\n\n \n except Exception:\n return 2\n \n\n ", "sub_path": "main/final/database/DB_usuario.py", "file_name": "DB_usuario.py", "file_ext": "py", "file_size_in_byte": 3866, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "database.DB.DB", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "171345617", "text": "from old_model.polar_code import PolarCode as OldPolarCode\nfrom polar_coding.polar_code import PolarCode\nfrom unittest import TestCase\nfrom utils import (\n gen_messages,\n transmit_bpsk_awgn,\n)\n\n\nclass TestRedesignedPolarCode(TestCase):\n \"\"\"Tests for non-shortened polar codes port from old model.\"\"\"\n\n @classmethod\n def setUpClass(cls):\n cls.N = 1024\n cls.K = 512\n cls.design_snr = 2.0\n cls.old_code = OldPolarCode(cls.N, cls.K, cls.design_snr)\n cls.new_code = PolarCode(cls.N, cls.K, design_snr=cls.design_snr)\n\n def setUp(self):\n self.message = gen_messages(self.K)\n\n def test_same_masks(self):\n \"\"\"Check same codes have same masks.\"\"\"\n self.assertEqual(\n str(self.old_code.polarcode_mask),\n str(self.new_code.polar_mask)\n )\n\n def test_precoding(self):\n \"\"\"Check pre-coding works the same way\"\"\"\n self.assertEqual(\n str(self.old_code.precode(self.message)),\n str(self.new_code._precode(self.message))\n )\n\n def test_encoding(self):\n \"\"\"Check systematic encoding works the same way\"\"\"\n self.assertEqual(\n str(self.old_code.encode(self.message, issystematic=True)),\n str(self.new_code.encode(self.message))\n )\n\n def test_sc_decoding_systematic_code(self):\n \"\"\"Check SC decoding of systematically encoded message works\n the same way.\n \"\"\"\n encoded = self.new_code.encode(self.message)\n transmitted = transmit_bpsk_awgn(encoded)\n old_decoded = self.old_code.sc_decode(transmitted, issystematic=True)\n new_decoded = self.new_code.sc_decode(transmitted)\n self.assertEqual(str(old_decoded), str(new_decoded))\n\n def test_sc_list_decoding_systematic_code(self):\n \"\"\"\"\"\"\n encoded = self.new_code.encode(self.message)\n transmitted = transmit_bpsk_awgn(encoded)\n old_decoded = self.old_code.sclist_decoder(\n transmitted,\n issystematic=True\n )\n new_decoded = self.new_code.sc_list_decode(transmitted)\n self.assertEqual(str(old_decoded), str(new_decoded))\n", "sub_path": "tests/test_polar_code.py", "file_name": "test_polar_code.py", "file_ext": "py", "file_size_in_byte": 2174, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "name"}, {"api_name": "old_model.polar_code.PolarCode", "line_number": 18, "usage_type": "call"}, {"api_name": "polar_coding.polar_code.PolarCode", "line_number": 19, "usage_type": "call"}, {"api_name": "utils.gen_messages", "line_number": 22, "usage_type": "call"}, {"api_name": "utils.transmit_bpsk_awgn", "line_number": 50, "usage_type": "call"}, {"api_name": "utils.transmit_bpsk_awgn", "line_number": 58, "usage_type": "call"}]} +{"seq_id": "38097820", "text": "\"\"\"\n Sample Controller File\n\n A Controller should be in charge of responding to a request.\n Load models to interact with the database and load views to render them to the client.\n\n Create a controller using this template\n\"\"\"\nfrom functools import wraps\nfrom system.core.controller import *\nfrom flask import Flask\nfrom flask.ext.jsonpify import jsonify\n\nclass Quotes(Controller):\n def __init__(self, action):\n super(Quotes, self).__init__(action)\n\n self.load_model('Quote')\n self.db = self._app.db\n \n def index(self):\n return self.load_view('quotes/index.html')\n\n @support_jsonp\n def index_json(self, methods=['GET']):\n quotes = self.models['Quote'].all()\n return jsonify(quotes=quotes)\n\n def support_jsonp(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n callback = request.args.get('callback', False)\n if callback:\n content = str(callback) + '(' + str(f(*args,**kwargs).data) + ')'\n return current_app.response_class(content, mimetype='application/javascript')\n else:\n return f(*args, **kwargs)\n return decorated_function\n\n", "sub_path": "Quotes/Pylot/app/controllers/Quotes.py", "file_name": "Quotes.py", "file_ext": "py", "file_size_in_byte": 1204, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.ext.jsonpify.jsonify", "line_number": 27, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "265059408", "text": "'''\n\nLIF Neuron using Brian2\n\nReferences\n==========\nhttps://github.com/returns-null/BIMLClass/blob/464e5ae2a04574c2b38130b3a67c6fc84040bd2c/psych268_bilm/code/brian2_lif.py\n\n# Post -> Pre\n\n'''\n\n\nimport brian2 as b2\nimport numpy as np\nimport time\nimport matplotlib.pyplot as plt\nimport mnist_images as mnist\n\n## Global Parameters ##\n\nms = b2.ms\nmV = b2.mV\nHz = b2.Hz\namp = b2.amp\n\n# Neuron Parameters\nCm = 50 * b2.pF\ngl = 1e-9 * b2.siemens\ntaus = 5 * ms\nsigma = 3 / b2.sqrt(ms) * mV\nVt = 10 * mV\nVr = 0 * mV\n\n# STDP params\ntau_pre = tau_post = 20 * ms\napre = 0.01e-12\napost = -apre * tau_pre / tau_post * 1.05\n\n\n'''\ntau_pre = tau_post = 20 * ms\nw_max = 0.06\nA_pre = 0.3\nA_post = 0.6 #-A_pre * tau_pre / tau_post * 1.05\n\nEr = -60. * mV\n\n# Excitatory Layer\nV_reset_exc = -65. * mV\nV_thresh_e = -52. * mV\nrefrac_e = 5. * ms\ntau_exec = 50 * ms\nN_exec = 784\n\n# Inhibitory Layer\nV_reset_inh = -60. * mV\nV_thresh_inh = -40. * mV\nrefrac_i = 2. * ms\ntau_inh = 20 * ms\nN_inh = 1\n\n'''\n\n########################\n\n\ndef elapsed(sec):\n\n '''\n\n This function returns the elapsed time\n\n '''\n\n if sec < 60:\n return str(round(sec)) + ' secs'\n\n elif sec < 3600:\n return str((sec)) + ' mins'\n\n else:\n return str(round(sec / 3600)) + ' hrs'\n\n\n\n\ndef drawBinImage(img):\n\n '''\n\n This function prints the given\n image matrix for better display of\n the data\n\n '''\n for row in img.tolist():\n\n #print(row)\n for r in row:\n if r == 1:\n print('*' , end = ' ')\n else:\n print(' ' , end = '')\n\n print()\n\n\n print()\n\n\nif __name__ == '__main__':\n\n start_time = time.time()\n\n\n zero = np.matrix([\n 0, 1, 1, 1, 0,\n 1, 0, 0, 0, 1,\n 1, 0, 0, 0, 1,\n 1, 0, 0, 0, 1,\n 1, 0, 0, 0, 1,\n 0, 1, 1, 1, 0\n ])\n\n one = np.matrix([\n 0, 1, 1, 0, 0,\n 0, 0, 1, 0, 0,\n 0, 0, 1, 0, 0,\n 0, 0, 1, 0, 0,\n 0, 0, 1, 0, 0,\n 0, 1, 1, 1, 0\n ]) \n\n\n two = np.matrix([\n 0, 1, 1, 0, 0,\n 1, 0, 0, 1, 0,\n 0, 0, 0, 1, 0,\n 0, 1, 1, 0, 0,\n 1, 0, 0, 0, 0,\n 1, 1, 1, 1, 1,\n ])\n\n data = [zero]\n\n data = [d.reshape(6 , 5) for d in data]\n\n num_samples , neurons = data[0].T.shape\n \n\n print('Num samples: ' , num_samples , ' neurons: ' , neurons)\n\n eqs_lif = '''\n\n dv/dt = -gl * v/Cm + i_syn/Cm + sigma * xi : volt (unless refractory)\n di_syn/dt = -i_syn/taus : amp\n \n\n '''\n \n Poi_inp = b2.PoissonGroup(10 , rates = 30 * Hz , name = 'Poisson_Input')\n\n P = b2.NeuronGroup(1 ,\n eqs_lif ,\n threshold = 'v > Vt' ,\n reset = 'v = Vr' ,\n method = 'euler' ,\n refractory = 5 * ms ,\n name = 'LIF')\n\n S = b2.Synapses(Poi_inp , P ,\n '''\n w : 1\n dx/dt = -x / tau_pre : 1\n dy/dt = -y / tau_post : 1 \n\n ''' ,\n on_pre = '''\n\n i_syn += w * amp\n x += apre\n w += y\n\n ''' ,\n on_post = '''\n\n y += apost\n w += x\n \n ''' ,\n method = 'linear' , \n name = 'STDP_Syn')\n\n S.connect()\n\n S.w = '(rand() - 0.5) * 1e-9'\n\n state_mon = b2.StateMonitor(S , variables = True ,\n record = True ,\n name = 'State_mon'\n )\n\n\n poi_spk = b2.SpikeMonitor(Poi_inp)\n lif_spk = b2.SpikeMonitor(P)\n\n b2.run(1 * b2.second , report = 'text')\n\n print('No of Poisson Spikes : {}'.format(poi_spk.num_spikes))\n print('No of LIF Spikes : {}'.format(lif_spk.num_spikes))\n print('Poisson Spike array: {}'.format(poi_spk.count))\n print('LIF Spike array: {}'.format(lif_spk.count))\n print()\n\n print('Poisson Spike times: ' , poi_spk.t[ : ])\n print('LIF Spike times: ' , lif_spk.t[ : ])\n print() \n\n\n elapsed_time = elapsed(time.time() - start_time)\n print('Elapsed time: ' , elapsed_time)\n\n\n plt.figure(figsize = (6 , 4))\n plt.subplot(211)\n plt.plot(state_mon.t/ms , state_mon.x[0] , label = 'pre')\n plt.plot(state_mon.t/ms , state_mon.y[0] , label = 'post')\n #plt.tight_layout()\n plt.legend(loc = 'best')\n plt.show()\n #for i in poi_spk\n \n \n \n \n", "sub_path": "lif_bio_model.py", "file_name": "lif_bio_model.py", "file_ext": "py", "file_size_in_byte": 4565, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "brian2.ms", "line_number": 22, "usage_type": "attribute"}, {"api_name": "brian2.mV", "line_number": 23, "usage_type": "attribute"}, {"api_name": "brian2.Hz", "line_number": 24, "usage_type": "attribute"}, {"api_name": "brian2.amp", "line_number": 25, "usage_type": "attribute"}, {"api_name": "brian2.pF", "line_number": 28, "usage_type": "attribute"}, {"api_name": "brian2.siemens", "line_number": 29, "usage_type": "attribute"}, {"api_name": "brian2.sqrt", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 136, "usage_type": "call"}, {"api_name": "brian2.PoissonGroup", "line_number": 162, "usage_type": "call"}, {"api_name": "brian2.NeuronGroup", "line_number": 164, "usage_type": "call"}, {"api_name": "brian2.Synapses", "line_number": 172, "usage_type": "call"}, {"api_name": "brian2.StateMonitor", "line_number": 199, "usage_type": "call"}, {"api_name": "brian2.SpikeMonitor", "line_number": 205, "usage_type": "call"}, {"api_name": "brian2.SpikeMonitor", "line_number": 206, "usage_type": "call"}, {"api_name": "brian2.run", "line_number": 208, "usage_type": "call"}, {"api_name": "brian2.second", "line_number": 208, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 230, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 231, "usage_type": "name"}]} +{"seq_id": "116633122", "text": "# Copyright 2014 Netflix, 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.. module: security_monkey.auditor\n :platform: Unix\n :synopsis: This class is subclassed to add audit rules.\n\n.. version:: $$VERSION$$\n.. moduleauthor:: Patrick Kelley \n\n\"\"\"\n\nimport datastore\n\nfrom security_monkey import app, db\nfrom security_monkey.watcher import ChangeItem\nfrom security_monkey.common.jinja import get_jinja_env\nfrom security_monkey.datastore import User, AuditorSettings, Item, ItemAudit, Technology\nfrom security_monkey.common.utils.utils import send_email\n\nfrom sqlalchemy import and_\n\nclass Auditor(object):\n \"\"\"\n This class (and subclasses really) run a number of rules against the configurations\n and look for any violations. These violations are saved with the object and a report\n is made available via the Web UI and through email.\n \"\"\"\n index = None # Should be overridden\n i_am_singular = None # Should be overridden\n i_am_plural = None # Should be overridden\n\n def __init__(self, accounts=None, debug=False):\n self.datastore = datastore.Datastore()\n self.accounts = accounts\n self.debug = debug\n self.items = []\n self.team_emails = app.config.get('SECURITY_TEAM_EMAIL')\n self.emails = []\n self.emails.extend(self.team_emails)\n\n for account in self.accounts:\n users = User.query.filter(User.daily_audit_email==True).filter(User.accounts.any(name=account)).all()\n\n self.emails.extend([user.email for user in users])\n\n def add_issue(self, score, issue, item, notes=None):\n \"\"\"\n Adds a new issue to an item, if not already reported.\n :return: The new issue\n \"\"\"\n\n if notes and len(notes) > 512:\n notes = notes[0:512]\n\n for existing_issue in item.audit_issues:\n if existing_issue.issue == issue:\n if existing_issue.notes == notes:\n if existing_issue.score == score:\n app.logger.debug(\n \"Not adding issue because it was already found:{}/{}/{}/{}\\n\\t{} -- {}\"\n .format(item.index, item.region, item.account, item.name, issue, notes))\n return existing_issue\n\n app.logger.debug(\"Adding issue: {}/{}/{}/{}\\n\\t{} -- {}\"\n .format(item.index, item.region, item.account, item.name, issue, notes))\n new_issue = datastore.ItemAudit(score=score,\n issue=issue,\n notes=notes,\n justified=False,\n justified_user_id=None,\n justified_date=None,\n justification=None)\n\n item.audit_issues.append(new_issue)\n return new_issue\n\n def prep_for_audit(self):\n \"\"\"\n To be overridden by child classes who\n need a way to prepare for the next run.\n \"\"\"\n pass\n\n def audit_these_objects(self, items):\n \"\"\"\n Only inspect the given items.\n \"\"\"\n app.logger.debug(\"Asked to audit {} Objects\".format(len(items)))\n self.prep_for_audit()\n methods = [getattr(self, method_name) for method_name in dir(self) if method_name.find(\"check_\") == 0]\n app.logger.debug(\"methods: {}\".format(methods))\n for item in items:\n for method in methods:\n method(item)\n self.items = items\n\n def audit_all_objects(self):\n \"\"\"\n Read all items from the database and inspect them all.\n \"\"\"\n self.items = self.read_previous_items()\n self.audit_these_objects(self.items)\n\n def read_previous_items(self):\n \"\"\"\n Pulls the last-recorded configuration from the database.\n :return: List of all items for the given technology and the given account.\n \"\"\"\n prev_list = []\n for account in self.accounts:\n prev = self.datastore.get_all_ctype_filtered(tech=self.index, account=account, include_inactive=False)\n # Returns a map of {Item: ItemRevision}\n for item in prev:\n item_revision = prev[item]\n new_item = ChangeItem(index=self.index,\n region=item.region,\n account=item.account.name,\n name=item.name,\n new_config=item_revision.config)\n new_item.audit_issues.extend(item.issues)\n new_item.audit_issues = []\n new_item.db_item = item\n prev_list.append(new_item)\n return prev_list\n\n def save_issues(self):\n \"\"\"\n Save all new issues. Delete all fixed issues.\n \"\"\"\n app.logger.debug(\"\\n\\nSaving Issues.\")\n for item in self.items:\n if not hasattr(item, 'db_item'):\n item.db_item = self.datastore._get_item(item.index, item.region, item.account, item.name)\n\n existing_issues = item.db_item.issues\n new_issues = item.audit_issues\n\n # Add new issues\n old_scored = [(\"{} -- {}\".format(old_issue.issue, old_issue.notes), old_issue.score) for old_issue in existing_issues]\n for new_issue in new_issues:\n nk = \"{} -- {}\".format(new_issue.issue, new_issue.notes)\n if (nk, new_issue.score) not in old_scored:\n app.logger.debug(\"Saving NEW issue {}\".format(nk))\n item.found_new_issue = True\n item.confirmed_new_issues.append(new_issue)\n item.db_item.issues.append(new_issue)\n db.session.add(item.db_item)\n db.session.add(new_issue)\n else:\n for issue in existing_issues:\n if issue.issue == new_issue.issue and issue.notes == new_issue.notes and issue.score == new_issue.score:\n item.confirmed_existing_issues.append(issue)\n break\n key = \"{}/{}/{}/{}\".format(item.index, item.region, item.account, item.name)\n app.logger.debug(\"Issue was previously found. Not overwriting.\\n\\t{}\\n\\t{}\".format(key, nk))\n\n # Delete old issues\n new_scored = [(\"{} -- {}\".format(new_issue.issue, new_issue.notes), new_issue.score) for new_issue in new_issues]\n for old_issue in existing_issues:\n ok = \"{} -- {}\".format(old_issue.issue, old_issue.notes)\n if (ok, old_issue.score) not in new_scored:\n app.logger.debug(\"Deleting FIXED issue {}\".format(ok))\n item.confirmed_fixed_issues.append(old_issue)\n db.session.delete(old_issue)\n\n db.session.commit()\n self._create_auditor_settings()\n\n def email_report(self, report):\n \"\"\"\n Given a report, send an email using SES.\n \"\"\"\n if not report:\n app.logger.info(\"No Audit issues. Not sending audit email.\")\n return\n\n subject = \"Security Monkey {} Auditor Report\".format(self.i_am_singular)\n send_email(subject=subject, recipients=self.emails, html=report)\n\n def create_report(self):\n \"\"\"\n Using a Jinja template (jinja_audit_email.html), create a report that can be emailed.\n :return: HTML - The output of the rendered template.\n \"\"\"\n jenv = get_jinja_env()\n template = jenv.get_template('jinja_audit_email.html')\n # This template expects a list of items that have been sorted by total score in\n # decending order.\n for item in self.items:\n item.totalscore = 0\n for issue in item.audit_issues:\n item.totalscore = item.totalscore + issue.score\n sorted_list = sorted(self.items, key=lambda item: item.totalscore)\n sorted_list.reverse()\n report_list = []\n for item in sorted_list:\n if item.totalscore > 0:\n report_list.append(item)\n else:\n break\n if len(report_list) > 0:\n return template.render({'items': report_list})\n else:\n return False\n\n def _create_auditor_settings(self):\n \"\"\"\n Checks to see if an AuditorSettings entry exists for each issue.\n If it does not, one will be created with disabled set to false.\n \"\"\"\n app.logger.debug(\"Creating/Assigning Auditor Settings in account {} and tech {}\".format(self.accounts, self.index))\n\n query = ItemAudit.query\n query = query.join((Item, Item.id == ItemAudit.item_id))\n query = query.join((Technology, Technology.id == Item.tech_id))\n query = query.filter(Technology.name == self.index)\n issues = query.filter(ItemAudit.auditor_setting_id == None).all()\n\n for issue in issues:\n self._set_auditor_setting_for_issue(issue)\n\n db.session.commit()\n app.logger.debug(\"Done Creating/Assigning Auditor Settings in account {} and tech {}\".format(self.accounts, self.index))\n\n def _set_auditor_setting_for_issue(self, issue):\n\n auditor_setting = AuditorSettings.query.filter(\n and_(\n AuditorSettings.tech_id == issue.item.tech_id,\n AuditorSettings.account_id == issue.item.account_id,\n AuditorSettings.issue_text == issue.issue\n )\n ).first()\n\n if auditor_setting:\n auditor_setting.issues.append(issue)\n db.session.add(auditor_setting)\n return auditor_setting\n\n auditor_setting = AuditorSettings(\n tech_id=issue.item.tech_id,\n account_id=issue.item.account_id,\n disabled=False,\n issue_text=issue.issue\n )\n\n auditor_setting.issues.append(issue)\n db.session.add(auditor_setting)\n db.session.commit()\n db.session.refresh(auditor_setting)\n\n app.logger.debug(\"Created AuditorSetting: {} - {} - {}\".format(\n issue.issue,\n self.index,\n issue.item.account.name))\n\n return auditor_setting", "sub_path": "security_monkey/auditor.py", "file_name": "auditor.py", "file_ext": "py", "file_size_in_byte": 10911, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datastore.Datastore", "line_number": 45, "usage_type": "call"}, {"api_name": "security_monkey.app.config.get", "line_number": 49, "usage_type": "call"}, {"api_name": "security_monkey.app.config", "line_number": 49, "usage_type": "attribute"}, {"api_name": "security_monkey.app", "line_number": 49, "usage_type": "name"}, {"api_name": "security_monkey.datastore.User.query.filter", "line_number": 54, "usage_type": "call"}, {"api_name": "security_monkey.datastore.User.query", "line_number": 54, "usage_type": "attribute"}, {"api_name": "security_monkey.datastore.User", "line_number": 54, "usage_type": "name"}, {"api_name": "security_monkey.datastore.User.daily_audit_email", "line_number": 54, "usage_type": "attribute"}, {"api_name": "security_monkey.datastore.User.accounts.any", "line_number": 54, "usage_type": "call"}, {"api_name": "security_monkey.datastore.User.accounts", "line_number": 54, "usage_type": "attribute"}, {"api_name": "security_monkey.app.logger.debug", "line_number": 71, "usage_type": "call"}, {"api_name": "security_monkey.app.logger", "line_number": 71, "usage_type": "attribute"}, {"api_name": "security_monkey.app", "line_number": 71, "usage_type": "name"}, {"api_name": "security_monkey.app.logger.debug", "line_number": 76, "usage_type": "call"}, {"api_name": "security_monkey.app.logger", "line_number": 76, "usage_type": "attribute"}, {"api_name": "security_monkey.app", "line_number": 76, "usage_type": "name"}, {"api_name": "datastore.ItemAudit", "line_number": 78, "usage_type": "call"}, {"api_name": "security_monkey.app.logger.debug", "line_number": 100, "usage_type": "call"}, {"api_name": "security_monkey.app.logger", "line_number": 100, "usage_type": "attribute"}, {"api_name": "security_monkey.app", "line_number": 100, "usage_type": "name"}, {"api_name": "security_monkey.app.logger.debug", "line_number": 103, "usage_type": "call"}, {"api_name": "security_monkey.app.logger", "line_number": 103, "usage_type": "attribute"}, {"api_name": "security_monkey.app", "line_number": 103, "usage_type": "name"}, {"api_name": "security_monkey.watcher.ChangeItem", "line_number": 127, "usage_type": "call"}, {"api_name": "security_monkey.app.logger.debug", "line_number": 142, "usage_type": "call"}, {"api_name": "security_monkey.app.logger", "line_number": 142, "usage_type": "attribute"}, {"api_name": "security_monkey.app", "line_number": 142, "usage_type": "name"}, {"api_name": "security_monkey.app.logger.debug", "line_number": 155, "usage_type": "call"}, {"api_name": "security_monkey.app.logger", "line_number": 155, "usage_type": "attribute"}, {"api_name": "security_monkey.app", "line_number": 155, "usage_type": "name"}, {"api_name": "security_monkey.db.session.add", "line_number": 159, "usage_type": "call"}, {"api_name": "security_monkey.db.session", "line_number": 159, "usage_type": "attribute"}, {"api_name": "security_monkey.db", "line_number": 159, "usage_type": "name"}, {"api_name": "security_monkey.db.session.add", "line_number": 160, "usage_type": "call"}, {"api_name": "security_monkey.db.session", "line_number": 160, "usage_type": "attribute"}, {"api_name": "security_monkey.db", "line_number": 160, "usage_type": "name"}, {"api_name": "security_monkey.app.logger.debug", "line_number": 167, "usage_type": "call"}, {"api_name": "security_monkey.app.logger", "line_number": 167, "usage_type": "attribute"}, {"api_name": "security_monkey.app", "line_number": 167, "usage_type": "name"}, {"api_name": "security_monkey.app.logger.debug", "line_number": 174, "usage_type": "call"}, {"api_name": "security_monkey.app.logger", "line_number": 174, "usage_type": "attribute"}, {"api_name": "security_monkey.app", "line_number": 174, "usage_type": "name"}, {"api_name": "security_monkey.db.session.delete", "line_number": 176, "usage_type": "call"}, {"api_name": "security_monkey.db.session", "line_number": 176, "usage_type": "attribute"}, {"api_name": "security_monkey.db", "line_number": 176, "usage_type": "name"}, {"api_name": "security_monkey.db.session.commit", "line_number": 178, "usage_type": "call"}, {"api_name": "security_monkey.db.session", "line_number": 178, "usage_type": "attribute"}, {"api_name": "security_monkey.db", "line_number": 178, "usage_type": "name"}, {"api_name": "security_monkey.app.logger.info", "line_number": 186, "usage_type": "call"}, {"api_name": "security_monkey.app.logger", "line_number": 186, "usage_type": "attribute"}, {"api_name": "security_monkey.app", "line_number": 186, "usage_type": "name"}, {"api_name": "security_monkey.common.utils.utils.send_email", "line_number": 190, "usage_type": "call"}, {"api_name": "security_monkey.common.jinja.get_jinja_env", "line_number": 197, "usage_type": "call"}, {"api_name": "security_monkey.app.logger.debug", "line_number": 223, "usage_type": "call"}, {"api_name": "security_monkey.app.logger", "line_number": 223, "usage_type": "attribute"}, {"api_name": "security_monkey.app", "line_number": 223, "usage_type": "name"}, {"api_name": "security_monkey.datastore.ItemAudit.query", "line_number": 225, "usage_type": "attribute"}, {"api_name": "security_monkey.datastore.ItemAudit", "line_number": 225, "usage_type": "name"}, {"api_name": "security_monkey.datastore.Item", "line_number": 226, "usage_type": "name"}, {"api_name": "security_monkey.datastore.Item.id", "line_number": 226, "usage_type": "attribute"}, {"api_name": "security_monkey.datastore.ItemAudit.item_id", "line_number": 226, "usage_type": "attribute"}, {"api_name": "security_monkey.datastore.ItemAudit", "line_number": 226, "usage_type": "name"}, {"api_name": "security_monkey.datastore.Technology", "line_number": 227, "usage_type": "name"}, {"api_name": "security_monkey.datastore.Technology.id", "line_number": 227, "usage_type": "attribute"}, {"api_name": "security_monkey.datastore.Item.tech_id", "line_number": 227, "usage_type": "attribute"}, {"api_name": "security_monkey.datastore.Item", "line_number": 227, "usage_type": "name"}, {"api_name": "security_monkey.datastore.Technology.name", "line_number": 228, "usage_type": "attribute"}, {"api_name": "security_monkey.datastore.Technology", "line_number": 228, "usage_type": "name"}, {"api_name": "security_monkey.datastore.ItemAudit.auditor_setting_id", "line_number": 229, "usage_type": "attribute"}, {"api_name": "security_monkey.datastore.ItemAudit", "line_number": 229, "usage_type": "name"}, {"api_name": "security_monkey.db.session.commit", "line_number": 234, "usage_type": "call"}, {"api_name": "security_monkey.db.session", "line_number": 234, "usage_type": "attribute"}, {"api_name": "security_monkey.db", "line_number": 234, "usage_type": "name"}, {"api_name": "security_monkey.app.logger.debug", "line_number": 235, "usage_type": "call"}, {"api_name": "security_monkey.app.logger", "line_number": 235, "usage_type": "attribute"}, {"api_name": "security_monkey.app", "line_number": 235, "usage_type": "name"}, {"api_name": "security_monkey.datastore.AuditorSettings.query.filter", "line_number": 239, "usage_type": "call"}, {"api_name": "security_monkey.datastore.AuditorSettings.query", "line_number": 239, "usage_type": "attribute"}, {"api_name": "security_monkey.datastore.AuditorSettings", "line_number": 239, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 240, "usage_type": "call"}, {"api_name": "security_monkey.datastore.AuditorSettings.tech_id", "line_number": 241, "usage_type": "attribute"}, {"api_name": "security_monkey.datastore.AuditorSettings", "line_number": 241, "usage_type": "name"}, {"api_name": "security_monkey.datastore.AuditorSettings.account_id", "line_number": 242, "usage_type": "attribute"}, {"api_name": "security_monkey.datastore.AuditorSettings", "line_number": 242, "usage_type": "name"}, {"api_name": "security_monkey.datastore.AuditorSettings.issue_text", "line_number": 243, "usage_type": "attribute"}, {"api_name": "security_monkey.datastore.AuditorSettings", "line_number": 243, "usage_type": "name"}, {"api_name": "security_monkey.db.session.add", "line_number": 249, "usage_type": "call"}, {"api_name": "security_monkey.db.session", "line_number": 249, "usage_type": "attribute"}, {"api_name": "security_monkey.db", "line_number": 249, "usage_type": "name"}, {"api_name": "security_monkey.datastore.AuditorSettings", "line_number": 252, "usage_type": "call"}, {"api_name": "security_monkey.db.session.add", "line_number": 260, "usage_type": "call"}, {"api_name": "security_monkey.db.session", "line_number": 260, "usage_type": "attribute"}, {"api_name": "security_monkey.db", "line_number": 260, "usage_type": "name"}, {"api_name": "security_monkey.db.session.commit", "line_number": 261, "usage_type": "call"}, {"api_name": "security_monkey.db.session", "line_number": 261, "usage_type": "attribute"}, {"api_name": "security_monkey.db", "line_number": 261, "usage_type": "name"}, {"api_name": "security_monkey.db.session.refresh", "line_number": 262, "usage_type": "call"}, {"api_name": "security_monkey.db.session", "line_number": 262, "usage_type": "attribute"}, {"api_name": "security_monkey.db", "line_number": 262, "usage_type": "name"}, {"api_name": "security_monkey.app.logger.debug", "line_number": 264, "usage_type": "call"}, {"api_name": "security_monkey.app.logger", "line_number": 264, "usage_type": "attribute"}, {"api_name": "security_monkey.app", "line_number": 264, "usage_type": "name"}]} +{"seq_id": "579265665", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__version__ = '0.0.1'\n__date__ = '10 Feb, 2017'\n__author__ = 'jinxie@rdamicro.com'\n\nimport sys\nimport time\n\nCOMPORT = 'COM4'\n\nclass AtCommand(object):\n\n def __init__(self, port):\n self.comport = port\n self.timeout = 3\n self.com = None\n self.data = None\n self.atcommand = None\n\n def __enter__(self):\n self.open(self.comport, self.timeout)\n return self\n\n def __exit__(self, typeof, value, tbx):\n self.close()\n \n # Connect to the specified AT COM Port with a required Timeout\n def open(self, port, timeout=10):\n self.comport = port\n self.timeout = timeout\n try:\n import serial\n except ImportError:\n raise Exception('Not able to find PySerial installation or may be is not installed.')\n\n self.com = serial.Serial(self.comport, 115200, timeout=10)\n\n # Send an AT Command, store output data and response in a tuple and return it\n def send(self, atcommand, TIMEWAITING=.3, resp=True):\n self.com.flushInput()\n self.com.flushOutput()\n \n self.atcommand = atcommand\n self.com.write((self.atcommand + \"\\r\\n\").encode())\n if resp:\n time.sleep(TIMEWAITING)\n return self.read()\n else:\n return None\n\n # Read the output data and response from DUT\n def read(self):\n for _ in range(10): # try 10 times in 1s interval\n size = self.com.inWaiting()\n if size > 0:\n break\n time.sleep(1)\n else:\n return (self.atcommand.strip(), None)\n raw = self.com.read(size).decode('utf-8')\n self.data = list(l for l in raw.split('\\r\\n') if l.strip())\n if self.data[0].upper().startswith(\"AT\"):\n return (self.data[0].strip(), self.data[1:])\n else:\n return (self.atcommand, self.data)\n\n # Clear the Input and Output buffer, and close the serial connection.\n def close(self):\n self.com.flushInput()\n self.com.flushOutput()\n self.com.close()\n\nclass AtTest(object):\n\t\n def __init__(self, port):\n self.port = port\n\n def __enter__(self):\n return self\n\n def __exit__(self, typeof, value, tbc):\n pass\n\n # Send AT command to DUT\n def to(self, cmd, TIMESLEEP=.3, resp=True):\n with AtCommand(self.port) as atc:\n cmd.encode()\n return atc.send(cmd, TIMESLEEP, resp)\n \n # Execute cmd and verify its response \n def executeCmd(self, cmd, TIMEOUT=.3, *expctedCmdBuf):\n cmdResp = self.to(cmd, TIMEOUT)\n nCmdRespList = len(cmdResp[1])\n if cmdResp[1][nCmdRespList - 1] == \"OK\":\n print('>>> ')\n print('>>> AT cmd sent : ' + cmd)\n i = 0\n for buf in expctedCmdBuf:\n if buf == cmdResp[1][i]:\n print('>>> Unsolicited : ' + buf)\n i += 1\n else:\n print('>>> Actual unsolicited : ' + cmdResp[1][i])\n print('>>> Expected unsolicted : ' + buf)\n raise Exception('!!! Incorrect unsolicited code !!!');\n print('>>> AT cmd resp : ' + cmdResp[1][nCmdRespList - 1])\n return True\n\n print('>>> AT cmd resp : ' + cmdResp[1][nCmdRespList - 1])\n raise Exception('!!! Failed to execute the test case !!!')\n\nif __name__ == '__main__':\n pass\n", "sub_path": "ziguangzhanrui/8955-auto test scripts/full functions stability test/comm/at.py", "file_name": "at.py", "file_ext": "py", "file_size_in_byte": 3485, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "serial.Serial", "line_number": 38, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "55829581", "text": "# Modules\nimport requests\nfrom datetime import datetime, timedelta\n\n# User Modules\nimport settings\n\ndef is_target(item):\n corpID = item['victim']['corporationID']\n allyID = item['victim']['allianceID']\n ssID = item['solarSystemID']\n if corpID in settings.CORPORATION_ID or allyID in settings.ALLIANCE_ID and ssID in settings.SOLARSYSTEM_ID:\n return True\n else:\n return False\n\ndef post_to_discord(item, webhook = settings.DISCORD_WEBHOOK_URL):\n eve_time = item['killTime']\n jp_time = datetime.strptime(eve_time, '%Y-%m-%d %H:%M:%S') + timedelta(hours=9)\n character = item['victim']['characterName']\n url = f'https://zkillboard.com/kill/{item[\"killID\"]}'\n comment = f'���牲者: {character} 日本時間: {jp_time} {url}'\n requests.post(webhook, data = json.dumps({'content': comment}), headers = {'Content-Type': 'application/json'})\n", "sub_path": "notification.py", "file_name": "notification.py", "file_ext": "py", "file_size_in_byte": 850, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "settings.CORPORATION_ID", "line_number": 12, "usage_type": "attribute"}, {"api_name": "settings.ALLIANCE_ID", "line_number": 12, "usage_type": "attribute"}, {"api_name": "settings.SOLARSYSTEM_ID", "line_number": 12, "usage_type": "attribute"}, {"api_name": "settings.DISCORD_WEBHOOK_URL", "line_number": 17, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "146082234", "text": "# #\n# 1 Track ID. All rows with the same ID belong to the same path.\n# 2 xmin. The top left x-coordinate of the bounding box.\n# 3 ymin. The top left y-coordinate of the bounding box.\n# 4 xmax. The bottom right x-coordinate of the bounding box.\n# 5 ymax. The bottom right y-coordinate of the bounding box.\n# 6 frame. The frame that this annotation represents.\n# 7 lost. If 1, the annotation is outside of the view screen.\n# 8 occluded. If 1, the annotation is occluded.\n# 9 generated. If 1, the annotation was automatically interpolated.\n# 10 label. The label for this annotation, enclosed in quotation marks.\n# #\n\nimport cv2\nimport numpy as np\n\n\nclass AnnotateFrames:\n\n x_max = 1500\n y_max = 1100\n classes = ['Biker', 'Pedestrian', 'Cart', 'Skater', 'Car', 'Bus']\n img = np.zeros((x_max, y_max, 3), np.uint8)\n annotation = []\n label = []\n\n def __init__(self, image):\n self.img = image\n\n def load_data(self):\n annotation_path = './annotations/bookstore/video0/annotations.txt'\n self.annotation = np.loadtxt(annotation_path, usecols=(0, 1, 2, 3, 4, 5, 6, 7, 8),\n dtype=np.int)\n self.label = np.loadtxt(annotation_path, usecols=(9,) ,\n dtype=np.str)\n\n def draw_on_img(self, x1, y1, x2, y2, label):\n color = {'\"Biker\"':(0, 255, 255), '\"Pedestrian\"': (255, 0, 0), '\"Cart\"': (0, 0, 255), '\"Skater\"': (255, 255, 255),\n '\"Car\"': (100, 100, 255), '\"Bus\"': (255, 100, 100)}\n cv2.rectangle(self.img, (x1, y1), (x2, y2), color[label], 2)\n\n fontface = cv2.FONT_HERSHEY_SIMPLEX\n fontscale = .3\n fontcolor = (255, 255, 255)\n cv2.putText(self.img, str(label), (x1, y1), fontface, fontscale, fontcolor)\n\n def show_image(self):\n while True:\n cv2.namedWindow(\"Output Image\", cv2.WINDOW_NORMAL)\n cv2.imshow(\"Output Image\", self.img)\n key = cv2.waitKey(10)\n if key == 27:\n break\n\n def annotate(self, frame_number):\n # self.img = np.zeros((self.x_max, self.y_max, 3), np.uint8)\n mask = self.annotation[:, 5] == frame_number\n data = self.annotation[mask]\n label = self.label[mask]\n\n if len(label) == 0:\n print('No points found')\n\n print(\"Annotating\")\n for cls in self.classes:\n cls = '\"' + cls + '\"'\n index_arr = np.where(label == cls)\n for index in index_arr[0]:\n self.draw_on_img(data[index,1], data[index,2], data[index,3], data[index,4], cls)\n\n # self.show_image()\n print('Writing to file.')\n cv2.imwrite(\"annotated_image_\" + str(frame_number) + \".png\", self.img)\n\n\nframe = cv2.imread('/home/dl-box/Arghya/joseph/data/StanfordDroneDataset/from_vigl_server/sdd/JPEGImages/bookstore_video0_5130.jpg')\nannotator = AnnotateFrames(frame)\nannotator.load_data()\nannotator.annotate(5130)\n# 10008\n\n", "sub_path": "annotation.py", "file_name": "annotation.py", "file_ext": "py", "file_size_in_byte": 3015, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.str", "line_number": 35, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.WINDOW_NORMAL", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "394060169", "text": "import sys\nimport socket\nimport time\nsys.path.append(\"/data/CODE/blindpad-next/BPexercises\")\n\nfrom BPexercises.Common.AppConnector import AppConnector\nfrom BPexercises.Common import primitives as pt\nfrom BPexercises.Common.keyThread import KeyThread\n\nimport pyttsx3\n\n\ndef onBPevents(type, event):\n\n msg = ''\n if type == 0:\n if event == 'SL':\n msg = pt.move(-1, 0)\n elif event == 'SR':\n msg = pt.move(1, 0)\n elif event == 'SU':\n msg = pt.move(0, -1)\n elif event == 'SD':\n msg = pt.move(0, 1)\n elif event == 'ST':\n msg = pt.move(0, 1)\n elif event == 'DT':\n msg = \"rotate(90, *fig);\"\n elif event == 'LP':\n msg = pt.move(0, 1)\n elif type == 1:\n if event == '1':\n msg = pt.move(1, 0)\n elif event == '2':\n msg = pt.move(0, -1)\n elif event == '3':\n msg = pt.move(0, 1)\n elif event == '4':\n pass\n elif event == '5':\n msg = pt.move(-1, 0)\n elif event == '6':\n pass\n elif event == '7':\n pass\n elif type == 2:\n if event == 'L':\n msg = pt.move(-1, 0)\n elif event == 'R':\n msg = pt.move(1, 0)\n elif event == 'U':\n msg = pt.move(0, -1)\n elif event == 'D':\n msg = pt.move(0, 1)\n\n if pd_socket is not None and msg is not '':\n pd_socket.send(bytes(msg, 'utf-8'))\n time.sleep(0.1)\n print(event)\n\n\nif __name__ == \"__main__\":\n\n delay = 0.75\n connect_APP = True\n connect_PD = True\n\n # BLINDPAD APP\n bp_c = None\n if connect_APP is True:\n # bp_address = '10.245.71.73'\n bp_address = '192.168.43.251'\n bp_port = 4001\n bp_c = AppConnector(bp_address, bp_port, onBPevents)\n\n # PADDRAW APP\n pd_socket = None\n if connect_PD is True:\n pd_address = '10.245.72.26'\n pd_address = 'localhost'\n pd_port = 12345\n pd_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n pd_socket.connect((pd_address, pd_port))\n pd_socket.send(bytes('clear();', 'utf-8'))\n\n # draw figure\n msg = pt.draw_horz_line(3, 4, 7, 1, \"fig\")\n pd_socket.send(bytes(msg, 'utf-8'))\n time.sleep(1)\n\n engine = pyttsx3.init()\n engine.say('voglio andare a casa')\n engine.runAndWait()\n\n # kt = KeyThread(onBPevents)\n # pd_socket.send(bytes(pt.move(1, 0), 'utf-8'))\n # time.sleep(delay)\n #\n # pd_socket.send(bytes(pt.move(1, 0), 'utf-8'))\n # time.sleep(delay)\n #\n # pd_socket.send(bytes(pt.move(0, 1), 'utf-8'))\n # time.sleep(delay)\n #\n # pd_socket.send(bytes(pt.move(0, 1), 'utf-8'))\n # time.sleep(delay)\n #\n # pd_socket.send(bytes(pt.move(-1, 0), 'utf-8'))\n # time.sleep(delay)\n #\n # pd_socket.send(bytes(pt.move(-1, 0), 'utf-8'))\n # time.sleep(delay)\n #\n # pd_socket.send(bytes(pt.move(0, -1), 'utf-8'))\n # time.sleep(delay)\n #\n # pd_socket.send(bytes(pt.move(0, -1), 'utf-8'))\n # time.sleep(delay)\n\nprint('finished')", "sub_path": "BPexercises/Tactris/accessory_code/prova_comm_bpApp.py", "file_name": "prova_comm_bpApp.py", "file_ext": "py", "file_size_in_byte": 3117, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 18, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 18, "usage_type": "name"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 20, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 20, "usage_type": "name"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 22, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 22, "usage_type": "name"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 24, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 24, "usage_type": "name"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 26, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 26, "usage_type": "name"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 30, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 30, "usage_type": "name"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 33, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 33, "usage_type": "name"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 35, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 35, "usage_type": "name"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 37, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 37, "usage_type": "name"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 41, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 41, "usage_type": "name"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 48, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 48, "usage_type": "name"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 50, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 50, "usage_type": "name"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 52, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 52, "usage_type": "name"}, {"api_name": "BPexercises.Common.primitives.move", "line_number": 54, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 54, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "BPexercises.Common.AppConnector.AppConnector", "line_number": 74, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 82, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 82, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 82, "usage_type": "attribute"}, {"api_name": "BPexercises.Common.primitives.draw_horz_line", "line_number": 87, "usage_type": "call"}, {"api_name": "BPexercises.Common.primitives", "line_number": 87, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 89, "usage_type": "call"}, {"api_name": "pyttsx3.init", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "260038316", "text": "# Copyright 2010 New Relic, 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\nimport logging\n\nfrom collections import namedtuple\n\nfrom newrelic.packages import six\n\nfrom newrelic.core.attribute_filter import (DST_ALL, DST_ERROR_COLLECTOR,\n DST_TRANSACTION_TRACER, DST_TRANSACTION_EVENTS, DST_SPAN_EVENTS,\n DST_TRANSACTION_SEGMENTS)\n\n\n_logger = logging.getLogger(__name__)\n\n_Attribute = namedtuple('_Attribute',\n ['name', 'value', 'destinations'])\n\n# The following destinations are created here, never changed, and only\n# used in create_agent_attributes. It is placed at the module level here\n# as an optimization.\n\n# All agent attributes go to transaction traces and error traces by default.\n\n_DESTINATIONS = (DST_ERROR_COLLECTOR |\n DST_TRANSACTION_TRACER |\n DST_TRANSACTION_SEGMENTS)\n_DESTINATIONS_WITH_EVENTS = (_DESTINATIONS |\n DST_TRANSACTION_EVENTS |\n DST_SPAN_EVENTS)\n\n# The following subset goes to transaction events by default.\n\n_TRANSACTION_EVENT_DEFAULT_ATTRIBUTES = set((\n 'host.displayName',\n 'request.method',\n 'request.headers.contentType',\n 'request.headers.contentLength',\n 'request.uri',\n 'response.status',\n 'request.headers.accept',\n 'response.headers.contentLength',\n 'response.headers.contentType',\n 'request.headers.host',\n 'request.headers.userAgent',\n 'message.queueName',\n 'message.routingKey',\n 'http.url',\n 'http.statusCode',\n 'aws.requestId',\n 'aws.operation',\n 'aws.lambda.arn',\n 'aws.lambda.coldStart',\n 'aws.lambda.eventSource.arn',\n 'db.instance',\n 'db.operation',\n 'db.statement',\n 'error.class',\n 'error.message',\n 'error.expected',\n 'peer.hostname',\n 'peer.address',\n 'graphql.field.name',\n 'graphql.field.parentType',\n 'graphql.field.path',\n 'graphql.field.returnType',\n 'graphql.operation.name',\n 'graphql.operation.type',\n 'graphql.operation.query',\n))\n\nMAX_NUM_USER_ATTRIBUTES = 128\nMAX_ATTRIBUTE_LENGTH = 255\nMAX_64_BIT_INT = 2 ** 63 - 1\n\n\nclass NameTooLongException(Exception):\n pass\n\n\nclass NameIsNotStringException(Exception):\n pass\n\n\nclass IntTooLargeException(Exception):\n pass\n\n\nclass CastingFailureException(Exception):\n pass\n\n\nclass Attribute(_Attribute):\n\n def __repr__(self):\n return \"Attribute(name=%r, value=%r, destinations=%r)\" % (\n self.name, self.value, bin(self.destinations))\n\n\ndef create_attributes(attr_dict, destinations, attribute_filter):\n attributes = []\n\n for k, v in attr_dict.items():\n dest = attribute_filter.apply(k, destinations)\n attributes.append(Attribute(k, v, dest))\n\n return attributes\n\n\ndef create_agent_attributes(attr_dict, attribute_filter):\n attributes = []\n\n for k, v in attr_dict.items():\n if v is None:\n continue\n\n if k in _TRANSACTION_EVENT_DEFAULT_ATTRIBUTES:\n dest = attribute_filter.apply(k, _DESTINATIONS_WITH_EVENTS)\n else:\n dest = attribute_filter.apply(k, _DESTINATIONS)\n\n attributes.append(Attribute(k, v, dest))\n\n return attributes\n\n\ndef resolve_user_attributes(\n attr_dict, attribute_filter, target_destination, attr_class=dict):\n u_attrs = attr_class()\n\n for attr_name, attr_value in attr_dict.items():\n if attr_value is None:\n continue\n\n dest = attribute_filter.apply(attr_name, DST_ALL)\n\n if dest & target_destination:\n u_attrs[attr_name] = attr_value\n\n return u_attrs\n\n\ndef resolve_agent_attributes(\n attr_dict, attribute_filter, target_destination, attr_class=dict):\n a_attrs = attr_class()\n\n for attr_name, attr_value in attr_dict.items():\n if attr_value is None:\n continue\n\n if attr_name in _TRANSACTION_EVENT_DEFAULT_ATTRIBUTES:\n dest = attribute_filter.apply(attr_name, _DESTINATIONS_WITH_EVENTS)\n else:\n dest = attribute_filter.apply(attr_name, _DESTINATIONS)\n\n if dest & target_destination:\n a_attrs[attr_name] = attr_value\n\n return a_attrs\n\n\ndef create_user_attributes(attr_dict, attribute_filter):\n destinations = DST_ALL\n return create_attributes(attr_dict, destinations, attribute_filter)\n\n\ndef truncate(\n text, maxsize=MAX_ATTRIBUTE_LENGTH, encoding='utf-8', ending=None):\n\n # Truncate text so that it's byte representation\n # is no longer than maxsize bytes.\n\n # If text is unicode (Python 2 or 3), return unicode.\n # If text is a Python 2 string, return str.\n\n if isinstance(text, six.text_type):\n truncated = _truncate_unicode(text, maxsize, encoding)\n else:\n truncated = _truncate_bytes(text, maxsize)\n ending = ending and ending.encode(encoding)\n\n if ending and truncated != text:\n truncated = truncated[:-len(ending)] + ending\n\n return truncated\n\n\ndef _truncate_unicode(u, maxsize, encoding='utf-8'):\n encoded = u.encode(encoding)[:maxsize]\n return encoded.decode(encoding, 'ignore')\n\n\ndef _truncate_bytes(s, maxsize):\n return s[:maxsize]\n\n\ndef check_name_length(name, max_length=MAX_ATTRIBUTE_LENGTH, encoding='utf-8'):\n trunc_name = truncate(name, max_length, encoding)\n if name != trunc_name:\n raise NameTooLongException()\n\n\ndef check_name_is_string(name):\n if not isinstance(name, (six.text_type, six.binary_type)):\n raise NameIsNotStringException()\n\n\ndef check_max_int(value, max_int=MAX_64_BIT_INT):\n if isinstance(value, six.integer_types) and value > max_int:\n raise IntTooLargeException()\n\n\ndef process_user_attribute(\n name, value, max_length=MAX_ATTRIBUTE_LENGTH, ending=None):\n\n # Perform all necessary checks on a potential attribute.\n #\n # Returns:\n # (name, value) if attribute is OK.\n # (NONE, NONE) if attribute isn't.\n #\n # If any of these checks fail, they will raise an exception, so we\n # log a message, and return (None, None).\n\n FAILED_RESULT = (None, None)\n\n try:\n check_name_is_string(name)\n check_name_length(name)\n check_max_int(value)\n\n value = sanitize(value)\n\n except NameIsNotStringException:\n _logger.debug('Attribute name must be a string. Dropping '\n 'attribute: %r=%r', name, value)\n return FAILED_RESULT\n\n except NameTooLongException:\n _logger.debug('Attribute name exceeds maximum length. Dropping '\n 'attribute: %r=%r', name, value)\n return FAILED_RESULT\n\n except IntTooLargeException:\n _logger.debug('Attribute value exceeds maximum integer value. '\n 'Dropping attribute: %r=%r', name, value)\n return FAILED_RESULT\n\n except CastingFailureException:\n _logger.debug('Attribute value cannot be cast to a string. '\n 'Dropping attribute: %r=%r', name, value)\n return FAILED_RESULT\n\n else:\n\n # Check length after casting\n\n valid_types_text = (six.text_type, six.binary_type)\n\n if isinstance(value, valid_types_text):\n trunc_value = truncate(value, maxsize=max_length, ending=ending)\n if value != trunc_value:\n _logger.debug('Attribute value exceeds maximum length '\n '(%r bytes). Truncating value: %r=%r.',\n max_length, name, trunc_value)\n\n value = trunc_value\n\n return (name, value)\n\n\ndef sanitize(value):\n\n # Return value unchanged, if it's a valid type that is supported by\n # Insights. Otherwise, convert value to a string.\n #\n # Raise CastingFailureException, if str(value) somehow fails.\n\n valid_value_types = (six.text_type, six.binary_type, bool, float,\n six.integer_types)\n\n if not isinstance(value, valid_value_types):\n original = value\n\n try:\n value = str(value)\n except Exception:\n raise CastingFailureException()\n else:\n _logger.debug('Attribute value is of type: %r. Casting %r to '\n 'string: %s', type(original), original, value)\n\n return value\n", "sub_path": "newrelic/core/attribute.py", "file_name": "attribute.py", "file_ext": "py", "file_size_in_byte": 8802, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 26, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 28, "usage_type": "call"}, {"api_name": "newrelic.core.attribute_filter.DST_ERROR_COLLECTOR", "line_number": 37, "usage_type": "name"}, {"api_name": "newrelic.core.attribute_filter.DST_TRANSACTION_TRACER", "line_number": 38, "usage_type": "name"}, {"api_name": "newrelic.core.attribute_filter.DST_TRANSACTION_SEGMENTS", "line_number": 39, "usage_type": "name"}, {"api_name": "newrelic.core.attribute_filter.DST_TRANSACTION_EVENTS", "line_number": 41, "usage_type": "name"}, {"api_name": "newrelic.core.attribute_filter.DST_SPAN_EVENTS", "line_number": 42, "usage_type": "name"}, {"api_name": "newrelic.core.attribute_filter.DST_ALL", "line_number": 147, "usage_type": "argument"}, {"api_name": "newrelic.core.attribute_filter.DST_ALL", "line_number": 175, "usage_type": "name"}, {"api_name": "newrelic.packages.six.text_type", "line_number": 188, "usage_type": "attribute"}, {"api_name": "newrelic.packages.six", "line_number": 188, "usage_type": "name"}, {"api_name": "newrelic.packages.six.text_type", "line_number": 216, "usage_type": "attribute"}, {"api_name": "newrelic.packages.six", "line_number": 216, "usage_type": "name"}, {"api_name": "newrelic.packages.six.binary_type", "line_number": 216, "usage_type": "attribute"}, {"api_name": "newrelic.packages.six.integer_types", "line_number": 221, "usage_type": "attribute"}, {"api_name": "newrelic.packages.six", "line_number": 221, "usage_type": "name"}, {"api_name": "newrelic.packages.six.text_type", "line_number": 270, "usage_type": "attribute"}, {"api_name": "newrelic.packages.six", "line_number": 270, "usage_type": "name"}, {"api_name": "newrelic.packages.six.binary_type", "line_number": 270, "usage_type": "attribute"}, {"api_name": "newrelic.packages.six.text_type", "line_number": 291, "usage_type": "attribute"}, {"api_name": "newrelic.packages.six", "line_number": 291, "usage_type": "name"}, {"api_name": "newrelic.packages.six.binary_type", "line_number": 291, "usage_type": "attribute"}, {"api_name": "newrelic.packages.six.integer_types", "line_number": 292, "usage_type": "attribute"}, {"api_name": "newrelic.packages.six", "line_number": 292, "usage_type": "name"}]} +{"seq_id": "330345116", "text": "# Modul Test\n# 05.08.2020\n# Sahin MERSIN\n# Mese Bilisim\n# https://www.mesebilisim.com\n# sunucudan versiyon bilgisi ceksin\n# yeni ve eski dosyayi degistirir\n# bir liste alsin, neler degisti ise icinde yazsin, ona gore yeni dosyalari indirsin,\n# eski dosya isimlerini degistirsin ve indirdiklerini yeni dosya olarak kaydetsin\n\nimport os\nimport time\n\nimport requests\n\n\ndef dosya_indir():\n print(\"dosya_indir start\")\n versiyon = \"https://www.mesebilisim.com/media/v/tkinter_example/ModulTest.py\"\n r = requests.get(versiyon, allow_redirects=True)\n open('ModulTest.py', 'wb').write(r.content)\n\n # from subprocess import call\n # call([\"python\", \"ModulTest.py\"])\n\n print(\"dosya_indir end\")\n return True\n\ndef eski_bak_sil():\n print(\"eski_bak_sil\")\n if os.path.exists('bak_ModulTest.py'):\n os.remove(\"bak_ModulTest.py\")\n return True\n return False\n\ndef eski_dosya_isim_degistir():\n print(\"eski_dosya_isim_degistir start\")\n eski_bak_sil()\n os.rename(\"ModulTest.py\", \"bak_ModulTest.py\")\n dosya_indir()\n print(\"eski_dosya_isim_degistir end\")\n return True\n\ndef yeni_dosya():\n time.sleep(5)\n print(\"yeni_dosya start\")\n eski_dosya_isim_degistir()\n print(\"yeni_dosya end\")\n return True\n\n\n\n", "sub_path": "tkinter_example/update.py", "file_name": "update.py", "file_ext": "py", "file_size_in_byte": 1251, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.get", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 32, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 39, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "561640387", "text": "# -*- coding: utf-8 -*-\n\nfrom abc import abstractproperty\nimport itertools as it\n\nimport numpy as np\nimport sympy as sy\n\nfrom pyfr.bases.base import BaseBasis\nfrom pyfr.quadrules import BaseLineQuadRule, get_quadrule\nfrom pyfr.syutil import lagrange_basis\nfrom pyfr.util import ndrange, lazyprop\n\n\ndef cart_prod_points(points, ndim):\n \"\"\"Performs a cartesian product extension of *points* into *ndim*\n\n For idiosyncratic reason the counting order of indices is from\n first to last, i.e, it is the first index that counts quickest,\n followed by the second index and so on.\n\n **Example**\n >>> cart_prod_points([-1, 0, 1], 2)\n array([[-1., -1.],\n [ 0., -1.],\n [ 1., -1.],\n [-1., 0.],\n [ 0., 0.],\n [ 1., 0.],\n [-1., 1.],\n [ 0., 1.],\n [ 1., 1.]])\n \"\"\"\n npoints = len(points)\n\n cprodpts = np.empty((npoints,)*ndim + (ndim,), dtype=np.object)\n for i,ax in enumerate(np.ix_(*(points,)*ndim)):\n # -i-1 ensures we count first-to-last\n cprodpts[...,-i-1] = ax\n\n # Compact into an array of ndim component tuples\n return cprodpts.reshape(-1, ndim)\n\n\ndef nodal_basis(points, dims, compact=True):\n \"\"\"Generates a nodal basis for *points* over *dims*\n\n .. note::\n This function adcfg the same first-to-last counting order as\n :func:`cart_prod_points` with the first index varying quickest.\n\n **Example**\n >>> import sympy as sy\n >>> nb = nodal_basis([-1, 1], sy.symbols('p q'))\n >>> nb[0]\n (-p/2 + 1/2)*(-q/2 + 1/2)\n >>> nb[0].subs(dict(p=-1, q=-1))\n 1\n >>> nb[0].subs(dict(p=1, q=-1))\n 0\n \"\"\"\n p = list(points)\n\n # Evaluate the basis function in terms of each dimension\n basis = [lagrange_basis(p, d) for d in reversed(dims)]\n\n # Take the cartesian product of these and multiply the resulting tuples\n cpbasis = np.array([np.prod(b) for b in it.product(*basis)])\n\n return cpbasis if compact else cpbasis.reshape((len(p),)*len(dims))\n\n\n_quad_map_rots_np = np.array([[[ 1, 0], [ 0, 1]],\n [[ 0, 1], [-1, 0]],\n [[-1, 0], [ 0, -1]],\n [[ 0, -1], [ 1, 0]]])\n\n\ndef quad_map_edge(fpts):\n mfpts = np.empty((4,) + fpts.shape, dtype=fpts.dtype)\n\n for i, frot in enumerate(_quad_map_rots_np):\n mfpts[i,...] = np.dot(fpts, frot)\n\n return mfpts\n\n\n# Cube map face rotation scheme to go from face 1 -> 0..5\n_cube_map_rots = np.array([\n [[-1, 0, 0], [ 0, 0, 1], [ 0, 1, 0]], # 1 -> 0\n [[ 1, 0, 0], [ 0, 1, 0], [ 0, 0, 1]], # 1 -> 1 (ident)\n [[ 0, 1, 0], [-1, 0, 0], [ 0, 0, 1]], # 1 -> 2\n [[-1, 0, 0], [ 0, -1, 0], [ 0, 0, 1]], # 1 -> 3\n [[ 0, -1, 0], [ 1, 0, 0], [ 0, 0, 1]], # 1 -> 4\n [[ 1, 0, 0], [ 0, 0, -1], [ 0, 1, 0]]]) # 1 -> 5\n\n\ndef quad_map_face(fpts):\n \"\"\"Given a matrix of points (p,q,r) corresponding to face one of\n `the cube' this method maps these points onto the remaining faces\n\n On a cube parameterized by (p,q,r) -> (-1,-1,-1) × (1,1,1) face one\n is defined by (-1,-1,-1) × (1,-1,1).\"\"\"\n mfpts = np.empty((6,) + fpts.shape, dtype=fpts.dtype)\n\n for i, frot in enumerate(_cube_map_rots):\n mfpts[i,...] = np.dot(fpts, frot)\n\n return mfpts\n\n\nclass TensorProdBasis(object):\n # List of face numbers paired according to their normal dimension\n # e.g, [(a, b), ...] where a, b are the faces whose normal points\n # in -p and p, respectively\n _fpairs = None\n\n # List of opposite face numbers\n _flipb = None\n\n def __init__(self, *args, **kwargs):\n super(TensorProdBasis, self).__init__(*args, **kwargs)\n\n if self.nspts:\n # Root the number of shape points to get the # in each dim\n self._nsptsord = sy.S(self.nspts)**(sy.S(1)/self.ndims)\n\n if not self._nsptsord.is_Number:\n raise ValueError('Invalid number of shape points for {} dims'\n .format(self.ndims))\n\n @classmethod\n def std_ele(cls, sptord):\n esqr = get_quadrule(BaseLineQuadRule, 'equi-spaced', sptord + 1)\n return cart_prod_points(esqr.points, cls.ndims)\n\n @lazyprop\n def _pts1d(self):\n rule = self._cfg.get('solver-elements-' + self.name, 'soln-pts')\n return get_quadrule(BaseLineQuadRule, rule, self._order + 1).points\n\n def _vcjh_fn(self, sym):\n k = self._order\n eta = self._cfg.get('solver-elements-' + self.name, 'vcjh-eta')\n\n # Expand shorthand forms of eta for common schemes\n etacommon = dict(dg='0', sd='k/(k+1)', hu='(k+1)/k')\n eta_k = sy.S(etacommon.get(eta, eta), locals=dict(k=k))\n\n lkm1, lk, lkp1 = [sy.legendre_poly(m, sym) for m in [k - 1, k, k + 1]]\n return (sy.S(1)/2 * (lk + (eta_k*lkm1 + lkp1)/(1 + eta_k)))\n\n @lazyprop\n def upts(self):\n return cart_prod_points(self._pts1d, self.ndims)\n\n @lazyprop\n def ubasis(self):\n return nodal_basis(self._pts1d, self._dims)\n\n @lazyprop\n def fbasis(self):\n # Get the 1D points\n pts1d = self._pts1d\n\n # Dummy symbol\n _x = sy.Symbol('_x')\n\n # Get the derivative of the 1D correction function\n diffg = self._vcjh_fn(_x).diff()\n\n # Allocate space for the flux points basis\n fbasis = np.empty([2*self.ndims] + [len(pts1d)]*(self.ndims - 1),\n dtype=np.object)\n\n # Pair up opposite faces with their associated (normal) dimension\n for (fl, fr), sym in zip(self._fpairs, self._dims):\n nbdims = [d for d in self._dims if d is not sym]\n fbasis[(fl, fr),...] = nodal_basis(pts1d, nbdims, compact=False)\n\n fbasis[fl,...] *= diffg.subs(_x, -sym)\n fbasis[fr,...] *= diffg.subs(_x, sym)\n\n # Some faces have flux points that count backwards; for\n # these faces we must reverse the basis\n fbasis[self._flipb] = fbasis[self._flipb,...,::-1]\n\n return fbasis.ravel()\n\n @property\n def facefpts(self):\n kn = (self._order + 1)**(self.ndims - 1)\n return [list(xrange(i*kn, (i + 1)*kn)) for i in xrange(2*self.ndims)]\n\n @lazyprop\n def spts1d(self):\n esqr = get_quadrule(BaseLineQuadRule, 'equi-spaced', self._nsptsord)\n return esqr.points\n\n @lazyprop\n def spts(self):\n return cart_prod_points(self.spts1d, self.ndims)\n\n @lazyprop\n def sbasis(self):\n return nodal_basis(self.spts1d, self._dims)\n\n @property\n def nupts(self):\n return (self._order + 1)**self.ndims\n\n\nclass QuadBasis(TensorProdBasis, BaseBasis):\n name = 'quad'\n ndims = 2\n\n _fpairs = [(3, 1), (0, 2)]\n _flipb = [2, 3]\n\n @lazyprop\n def fpts(self):\n # Get the 1D points\n pts1d = self._pts1d\n\n # Edge zero has points (q,-1)\n ezeropts = np.empty((len(pts1d), 2), dtype=np.object)\n ezeropts[:,0] = pts1d\n ezeropts[:,1] = -1\n\n # Quad map edge zero to get the full set\n return quad_map_edge(ezeropts).reshape(-1, 2)\n\n @lazyprop\n def norm_fpts(self):\n # Normals for edge zero are (0,-1)\n ezeronorms = np.zeros((self._order + 1, 2), dtype=np.int)\n ezeronorms[:,1] = -1\n\n # Edge map\n return quad_map_edge(ezeronorms).reshape(-1, 2)\n\n\nclass HexBasis(TensorProdBasis, BaseBasis):\n name = 'hex'\n ndims = 3\n\n _fpairs = [(4, 2), (1, 3), (0, 5)]\n _flipb = [0, 3, 4]\n\n @lazyprop\n def fpts(self):\n # Get the 1D points\n pts1d = self._pts1d\n\n # Perform a 2D extension to get the (p,r) points of face one\n pts2d = cart_prod_points(pts1d, 2)\n\n # 3D points are just (p,-1,r) for face one\n fonepts = np.empty((len(pts2d), 3), dtype=np.object)\n fonepts[...,(0,2)] = pts2d\n fonepts[...,1] = -1\n\n # Cube map face one to get faces zero through five\n return quad_map_face(fonepts).reshape(-1, 3)\n\n @lazyprop\n def norm_fpts(self):\n # Normals for face one are (0,-1,0)\n fonenorms = np.zeros([self._order + 1]*2 + [3], dtype=np.int)\n fonenorms[...,1] = -1\n\n # Cube map to get the remaining face normals\n return quad_map_face(fonenorms).reshape(-1, 3)\n", "sub_path": "pyfr/bases/tensorprod.py", "file_name": "tensorprod.py", "file_ext": "py", "file_size_in_byte": 8249, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.empty", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.object", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.ix_", "line_number": 37, "usage_type": "call"}, {"api_name": "pyfr.syutil.lagrange_basis", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 68, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 107, "usage_type": "call"}, {"api_name": "sympy.S", "line_number": 126, "usage_type": "call"}, {"api_name": "pyfr.quadrules.get_quadrule", "line_number": 134, "usage_type": "call"}, {"api_name": "pyfr.quadrules.BaseLineQuadRule", "line_number": 134, "usage_type": "argument"}, {"api_name": "pyfr.quadrules.get_quadrule", "line_number": 140, "usage_type": "call"}, {"api_name": "pyfr.quadrules.BaseLineQuadRule", "line_number": 140, "usage_type": "argument"}, {"api_name": "pyfr.util.lazyprop", "line_number": 137, "usage_type": "name"}, {"api_name": "sympy.S", "line_number": 148, "usage_type": "call"}, {"api_name": "sympy.legendre_poly", "line_number": 150, "usage_type": "call"}, {"api_name": "sympy.S", "line_number": 151, "usage_type": "call"}, {"api_name": "pyfr.util.lazyprop", "line_number": 153, "usage_type": "name"}, {"api_name": "pyfr.util.lazyprop", "line_number": 157, "usage_type": "name"}, {"api_name": "sympy.Symbol", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.object", "line_number": 174, "usage_type": "attribute"}, {"api_name": "pyfr.util.lazyprop", "line_number": 161, "usage_type": "name"}, {"api_name": "pyfr.quadrules.get_quadrule", "line_number": 197, "usage_type": "call"}, {"api_name": "pyfr.quadrules.BaseLineQuadRule", "line_number": 197, "usage_type": "argument"}, {"api_name": "pyfr.util.lazyprop", "line_number": 195, "usage_type": "name"}, {"api_name": "pyfr.util.lazyprop", "line_number": 200, "usage_type": "name"}, {"api_name": "pyfr.util.lazyprop", "line_number": 204, "usage_type": "name"}, {"api_name": "pyfr.bases.base.BaseBasis", "line_number": 213, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.object", "line_number": 226, "usage_type": "attribute"}, {"api_name": "pyfr.util.lazyprop", "line_number": 220, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 236, "usage_type": "attribute"}, {"api_name": "pyfr.util.lazyprop", "line_number": 233, "usage_type": "name"}, {"api_name": "pyfr.bases.base.BaseBasis", "line_number": 243, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.object", "line_number": 259, "usage_type": "attribute"}, {"api_name": "pyfr.util.lazyprop", "line_number": 250, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 269, "usage_type": "attribute"}, {"api_name": "pyfr.util.lazyprop", "line_number": 266, "usage_type": "name"}]} +{"seq_id": "590143376", "text": "# import\n\n# from\nfrom flask import jsonify\nfrom flask import request\nfrom flask import Flask\nfrom tamtamapi import Bot\n\n# settings\nbot = BotHandler('token_from_prime_bot')\napp = Flask(__name__)\n\n\n@app.route('/', methods=['GET'])\ndef index():\n return 'Hello!'\n\n\n@app.route('/', methods=['POST'])\ndef main():\n updates = request.get_json()\n if updates:\n message = bot.get_message_text(updates)\n chat_id = bot.get_chat_id(updates)\n mid = bot.get_mid(updates)\n if message == '/hello':\n bot.reply_message(chat_id, 'Hello!', mid)\n return jsonify(updates)\n\n\nif __name__ == '__main__':\n app.run()\n\n", "sub_path": "flask_example.py", "file_name": "flask_example.py", "file_ext": "py", "file_size_in_byte": 643, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "313964969", "text": "import numpy as np\r\nimport time\r\nfrom matplotlib import pyplot as plt\r\nfrom data.data_utils import load_dataset\r\n\r\n__author__ = \"Mackenzie Clark\"\r\n__date__ = \"Feb. 12th 2019\"\r\n\r\npossible_datasets = ['mauna_loa', 'rosenbrock', 'pumadyn32nm', 'iris', 'mnist_small']\r\n\r\ndef load_data(dataset):\r\n '''\r\n param dataset: str, the name of the dataset to be loaded for this iteration of the model\r\n '''\r\n if dataset not in possible_datasets:\r\n return 0,0,0,0,0,0\r\n elif dataset == 'rosenbrock':\r\n x_train, x_valid, x_test, y_train, y_valid, y_test = load_dataset('rosenbrock', n_train=1000, d=2)\r\n else:\r\n x_train, x_valid, x_test, y_train, y_valid, y_test = load_dataset(str(dataset))\r\n return x_train, x_valid, x_test, y_train, y_valid, y_test\r\n\r\ndef rmse(y_estimates, y_valid):\r\n '''\r\n calculate the root mean squared error between the estimated y values and \r\n the actual y values\r\n\r\n param y_estimates: list of lists, estimated y values by k-NN algorithm\r\n param y_valid: list of lists, actual y values\r\n return: float, the root mean squared error of the k-NN prediction \r\n '''\r\n return np.sqrt(np.average(np.abs(y_estimates-y_valid)**2))\r\n\r\ndef svd_regression(dataset):\r\n '''\r\n compute the FULL singular value decomposition of the matrix \r\n of x_train_valid values\r\n\r\n param dataset: str, dataset name, must be a part of possible_datasets\r\n '''\r\n x_train, x_valid, x_test, y_train, y_valid, y_test = load_data(dataset)\r\n start = time.time()\r\n\r\n x_train_valid = np.vstack([x_train, x_valid])\r\n y_train_valid = np.vstack([y_train, y_valid])\r\n X = np.ones((len(x_train_valid), len(x_train_valid[0])+1))\r\n X[:, 1:] = x_train_valid\r\n\r\n # compute the matrices for SVD\r\n U, s, vh = np.linalg.svd(X, full_matrices=True)\r\n Sigma = np.diag(s)\r\n zero_m = np.zeros([len(x_train_valid)-len(Sigma), len(Sigma)])\r\n\r\n # concatenate the singular values to 0 matrix to be the same \r\n # dimension as x_train_valid \r\n S_full = np.vstack([Sigma, zero_m])\r\n # determining the weights \r\n w = np.dot(vh.T, np.dot(np.linalg.pinv(S_full), np.dot(U.T, y_train_valid)))\r\n\r\n # copy over the values of x_test\r\n X_test = np.ones((len(x_test), len(x_test[0]) + 1))\r\n X_test[:, 1:] = x_test\r\n y_pred = np.dot(X_test, w)\r\n\r\n error = rmse(y_test, y_pred)\r\n end = time.time()\r\n\r\n if dataset == \"mauna_loa\":\r\n # only make plot for 1D mauna loa set as a test\r\n plt.figure(1)\r\n plt.plot(x_test, y_test, '-b', label='Actual')\r\n plt.plot(x_test, y_pred, '-r', label='Prediction')\r\n plt.title('SVD predictions for Mauna Loa dataset')\r\n plt.xlabel('x test')\r\n plt.ylabel('y')\r\n plt.legend(loc='upper right')\r\n plt.savefig('mauna_loa_svd.png')\r\n\r\n return (end-start, error)\r\n\r\ndef svd_classification(dataset):\r\n '''\r\n compute the one of k binary classification for the classification\r\n datasets using linear regression/SVD\r\n\r\n param dataset: str, dataset name, must be a part of possible_datasets\r\n '''\r\n x_train, x_valid, x_test, y_train, y_valid, y_test = load_data(dataset)\r\n start = time.time()\r\n\r\n x_train_valid = np.vstack([x_train, x_valid])\r\n y_train_valid = np.vstack([y_train, y_valid])\r\n\r\n # add column of ones to X to account for w0\r\n X = np.ones([len(x_train_valid), len(x_train_valid[0]) + 1])\r\n X[:, 1:] = x_train_valid\r\n\r\n U, s, vh = np.linalg.svd(X)\r\n\r\n # calculate the sigma matrix\r\n Sigma = np.diag(s)\r\n zero_m = np.zeros([len(x_train_valid) - len(Sigma), len(Sigma)])\r\n S_full = np.vstack([Sigma, zero_m])\r\n\r\n # calculate weights for the SVD to get y predictions\r\n w = np.dot(vh.T, np.dot(np.linalg.pinv(S_full), np.dot(U.T, y_train_valid)))\r\n\r\n # copy over the values of x_test\r\n X_test = np.ones((len(x_test), len(x_test[0]) + 1))\r\n X_test[:, 1:] = x_test\r\n\r\n # find the maximum values of the predictions and the test,\r\n # to compare and calculate accuracy\r\n y_pred = np.argmax(np.dot(X_test, w), axis=1)\r\n y_test = np.argmax(1 * y_test, axis=1)\r\n\r\n # count the number of correct classifications\r\n result = (y_pred == y_test).sum() / len(y_test)\r\n\r\n end = time.time()\r\n return (end-start, result) \r\n\r\nif __name__ == \"__main__\":\r\n # 1. regression\r\n for set_i in possible_datasets[0:1]:\r\n timer, error = svd_regression(set_i)\r\n print('Dataset: ' + str(set_i) + ' ran in: ' + str(timer))\r\n print('RMSE: ' + str(error))\r\n\r\n # 2. classification\r\n for set_i in possible_datasets[3:3]:\r\n timer, error = svd_classification(set_i)\r\n print('Dataset: ' + str(set_i) + ' ran in: ' + str(timer))\r\n print('RMSE: ' + str(error))", "sub_path": "knn_and_linear_regression/svd.py", "file_name": "svd.py", "file_ext": "py", "file_size_in_byte": 4758, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "data.data_utils.load_dataset", "line_number": 18, "usage_type": "call"}, {"api_name": "data.data_utils.load_dataset", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 32, "usage_type": "call"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.linalg.svd", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 63, "usage_type": "call"}, {"api_name": "time.time", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "time.time", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.linalg.svd", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 115, "usage_type": "call"}, {"api_name": "time.time", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "307199155", "text": "from netCDF4 import Dataset\nimport dataAttributeL1\nimport dataAttributeL2\nimport dataAttributeL2Samd\nimport numpy as np\n\ndef defineAttr(prefix):\n \n if prefix == 'tripex_3fr_L1_mom':\n dataAttribute = dataAttributeL1 \n\n elif prefix == 'tripex_3fr_L2_mom':\n dataAttribute = dataAttributeL2 \n\n elif prefix == 'tripex_joy_tricr00_l2_any_v00':\n dataAttribute = dataAttributeL2Samd\n\n return dataAttribute\n\ndef createNetCdf(outPutFilePath, prefix):\n\n dataAttribute = defineAttr(prefix)\n\n try:\n rootgrpOut = Dataset(outPutFilePath, 'a', format='NETCDF4')\n \n except:\n rootgrpOut = Dataset(outPutFilePath, 'w', format='NETCDF4')\n\n rootgrpOut = dataAttribute.globalAttributes(rootgrpOut)\n\n return rootgrpOut\n\ndef createNvDimension(rootgrpOut, prefix):\n \n dataAttribute = defineAttr(prefix)\n \n try:\n #rootgrpOut.createDimension('nv',2)\n rootgrpOut.createDimension('nv',4)\n nv = rootgrpOut.createVariable('nv',np.float32,('nv',))\n #nv[:] = np.array([0,1])\n nv[:] = np.array([0,1,2,3])\n return nv\n \n except:\n return None\n\ndef createTimeDimension(rootgrpOut, timeRef, prefix):\n\n dataAttribute = defineAttr(prefix)\n\n try:\n rootgrpOut.createDimension('time', None)\n time_ref = rootgrpOut.createVariable('time', np.float64, ('time',))\n time_ref[:] = timeRef\n time_ref = dataAttribute.timeAttributes(time_ref)\n return time_ref\n\n except:\n return None\n\n\ndef createRangeDimension(rootgrpOut, rangeRef, prefix):\n\n dataAttribute = defineAttr(prefix)\n\n try:\n rootgrpOut.createDimension('range', len(rangeRef))\n range_ref = rootgrpOut.createVariable('range', np.float32,\n\t\t ('range',))\n range_ref[:] = rangeRef\n range_ref = dataAttribute.rangeAttributes(range_ref)\n return range_ref\n\n except:\n return None\n\n\ndef createVariable(rootgrpOut, variable, varName, \n varNameOutput, radar, prefix,\n dataType):\n\n dataAttribute = defineAttr(prefix)\n\n try:\n \n if dataType == np.uint16: \n var_nearest = rootgrpOut.createVariable(varNameOutput, dataType,\n ('time','range'))\n\n else:\n var_nearest = rootgrpOut.createVariable(varNameOutput, dataType,\n ('time','range'), \n fill_value=np.nan)\n \n var_nearest[:] = variable\n var_nearest = dataAttribute.variableAttribute(var_nearest,\n varName,\n radar)\n return var_nearest\n\n except:\n return None\n\ndef createBndsVariable(rootgrpOut, variable, varNameOutPut, dimName):\n\n try:\n\n var_nearest = rootgrpOut.createVariable(varNameOutPut, np.float32,\n (dimName,'nv'))\n var_nearest[:] = variable\n return var_nearest\t\n \n except IOError:\n return None\n\ndef createDeviation(rootgrpOut, variable, varName, radar, prefix):\n\n dataAttribute = defineAttr(prefix)\n dimension = varName.split('_')[-1]\n varNameOutput = '_'.join([varName, radar])\n \n if varName == 'time':\n varType = np.int64\n \n else: \n varType = np.float32\n\n try:\n \n var_nearest = rootgrpOut.createVariable(varNameOutput, varType,\n (dimension), fill_value=np.nan)\n var_nearest[:] = variable\n var_nearest = dataAttribute.variableAttribute(var_nearest, \n varName, radar)\n return var_nearest\n\n except:\n return None\n\ndef createOneValvariable(rootgrpOut, variable, varName, sensor, prefix):\n\n dataAttribute = defineAttr(prefix)\n\n if varName == 'lat' or \\\n varName == 'lon' or \\\n varName == 'zsl':\n varNameOutput = varName\t\n\n else:\n varNameOutput = '_'.join([varName, sensor]) \n\n try:\n var_nearest = rootgrpOut.createVariable(varNameOutput, np.float32,\n fill_value=np.nan)\n var_nearest[:] = variable\n var_nearest = dataAttribute.variableAttribute(var_nearest,\n varName, sensor)\n return var_nearest\n\n except:\n return None\n \n\n", "sub_path": "tripexPro/writeData.py", "file_name": "writeData.py", "file_ext": "py", "file_size_in_byte": 4374, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "netCDF4.Dataset", "line_number": 25, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 133, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 156, "usage_type": "attribute"}]} +{"seq_id": "351801432", "text": "from sklearn.externals import joblib\nimport numpy as np\nfrom sklearn import decomposition\n\nclf = joblib.load('../model/my_face_rating.pkl')\nfeatures = np.loadtxt('../data/train_features', delimiter=',')\nmy_features = np.loadtxt('../data/my_features', delimiter=',')\nif my_features.ndim == 1: my_features = np.reshape(my_features, (1, -1))\namount = len(my_features)\npca = decomposition.PCA(n_components=20)\npca.fit(features)\n\npredictions = np.zeros([amount,1]);\n\nfor i in range(0, amount):\n features_test = my_features[i, :]\n features_test = pca.transform([features_test])\n predictions[i] = clf.predict(features_test)\n\nprint(predictions)\n", "sub_path": "source/myPredict.py", "file_name": "myPredict.py", "file_ext": "py", "file_size_in_byte": 640, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.externals.joblib.load", "line_number": 5, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 5, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 10, "usage_type": "call"}, {"api_name": "sklearn.decomposition", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "436112078", "text": "#!/usr/bin/env python3\nimport requests, sys, time, urllib.parse, signal\nfrom bs4 import BeautifulSoup\n\ncookie = {\n\t'remember_user_token' : '',\n\t'kktix_session_token_v2': ''\n}\n\ndef getCSRFToken(event):\n\tr = requests.get('https://kktix.com/events/{}/registrations/new'.format(event), cookies = cookie)\n\tsoup = BeautifulSoup(r.text, 'html.parser')\n\tcsrfTag = soup.find('meta', attrs = {'name':\"csrf-token\"})\n\ttry:\n\t\treturn csrfTag['content']\n\texcept Exception as e:\n\t\tprint('getCSRFToken Failed: {}\\n{}'.format(repr(e), r.text))\n\t\tsys.exit(-1)\n\ndef getOrderToken(event, csrfToken, ticketId, ticketQuantity):\n\tcsrfToken = urllib.parse.quote(csrfToken)\n\torderInfo = \"\"\"\n\t\t{\"tickets\":[{\"id\":%s,\"quantity\":%s,\"invitationCodes\":[],\"member_code\":\"\",\"use_qualification_id\":null}],\"currency\":\"TWD\",\"recaptcha\":{},\"agreeTerm\":true}\n\t\"\"\" % (ticketId, ticketQuantity)\n\tr = requests.post('https://queue.kktix.com/queue/{}?authenticity_token={}'.format(event, csrfToken), cookies = cookie, data = orderInfo)\n\ttry:\n\t\treturn r.json()['token']\n\texcept:\n\t\tprint(r.text)\n\t\t#print('getOrderToken Failed: {}\\n{}'.format(repr(e), r.text))\n\t\treturn -1\n\ndef getOrderId(orderToken):\n\tr = requests.get('https://queue.kktix.com/queue/token/{}'.format(orderToken), cookies = cookie)\n\ttry:\n\t\treturn r.json()['to_param']\n\texcept Exception as e:\n\t\tprint('getOrderId Failed: {}\\n{}'.format(repr(e), r.text))\n\t\tsys.exit(-1)\n\ndef signal_handler(signal, frame):\n\tsys.exit()\nsignal.signal(signal.SIGINT, signal_handler)\n\ndef main():\n\tif len(sys.argv) < 5:\n\t\tprint('Usage: python3 oktix.py [Event] [Ticket ID] [Ticket Quantity] [Sleep Seconds]')\n\t\tsys.exit(-1)\n\n\tevent = sys.argv[1]\n\tticketId = sys.argv[2]\n\tticketQuantity = sys.argv[3]\n\tsleepTime = float(sys.argv[4])\n\n\tcsrfToken = getCSRFToken(event)\n\twhile True:\n\t\torderToken = getOrderToken(event, csrfToken, ticketId, ticketQuantity)\n\t\ttime.sleep(sleepTime)\n\t\tif not orderToken is -1:\n\t\t\tbreak\n\torderId = getOrderId(orderToken)\n\n\tprint('https://kktix.com/events/{}/registrations/{}'.format(event, orderId))\n\nif __name__ == '__main__':\n\tmain()\n", "sub_path": "oktix.py", "file_name": "oktix.py", "file_ext": "py", "file_size_in_byte": 2059, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 18, "usage_type": "call"}, {"api_name": "urllib.parse.parse.quote", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 21, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 21, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 25, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 39, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 42, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 43, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 48, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 50, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 52, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 53, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}]} +{"seq_id": "532001904", "text": "import PIL\r\nimport numpy as np\r\n\r\n\r\nnorm_mean = [0.485, 0.456, 0.406]\r\nnorm_std = [0.229, 0.224, 0.225]\r\n\r\n\r\ndef image_loader(loader, image_name):\r\n image = PIL.Image.open(image_name)\r\n image = loader(image).float()\r\n image = image.clone().detach().requires_grad_(True)\r\n image = image.unsqueeze(0)\r\n\r\n return image\r\n\r\n\r\ndef im_convert(tensor):\r\n \"\"\" Display a tensor as an image. \"\"\"\r\n\r\n image = tensor.to(\"cpu\").clone().detach()\r\n image = image.numpy().squeeze()\r\n image = image.transpose(1, 2, 0)\r\n image = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))\r\n image = image.clip(0, 1)\r\n\r\n return image\r\n\r\n\r\ndef convert_image(image_path):\r\n \"\"\" Scales, crops, and normalizes a PIL image for a PyTorch model,\r\n returns an Numpy array\r\n \"\"\"\r\n\r\n # Open the image\r\n from PIL import Image\r\n image = Image.open(image_path)\r\n\r\n # Resize\r\n image = image.resize([256, 256])\r\n\r\n # Crop\r\n left_margin = (image.width - 224) / 2\r\n bottom_margin = (image.height - 224) / 2\r\n right_margin = left_margin + 224\r\n top_margin = bottom_margin + 224\r\n image = image.crop((left_margin, bottom_margin, right_margin,\r\n top_margin))\r\n # Normalize\r\n image = np.array(image) / 255\r\n mean = np.array(norm_mean) # provided mean\r\n std = np.array(norm_std) # provided std\r\n image = (image - mean) / std\r\n\r\n # Move color channels to first dimension as expected by PyTorch\r\n image = image.transpose((2, 0, 1))\r\n return image\r\n", "sub_path": "image_conventer.py", "file_name": "image_conventer.py", "file_ext": "py", "file_size_in_byte": 1549, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PIL.Image.open", "line_number": 10, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 37, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "438617420", "text": "# This is for image preprossesing\n# Needs PIL library to work\n\nfrom PIL import Image\nimport numpy as np\nimport os\nimport random\n\n\n# This function an PIL image object, crops the image and then resizes and returns the resized image\n# either as a vector or a matrix\ndef open_square_image_to_array(file_path, size=250, vec=True):\n with Image.open(file_path) as img:\n #convert to 3 channel if not\n if img.mode != 'RGB':\n img = img.convert(\"RGB\")\n (width, height) = img.size\n if width < height:\n cropped_image = img.crop((0, int((height-width)/2), width, int((height-width)/2 + width)))\n else:\n cropped_image = img.crop((int((width-height)/2), 0, (int(width-height)/2) + height, height))\n rim = np.array(cropped_image.resize((size, size)))\n if not vec:\n return rim/255\n return rim.flatten()/255\n\n\n# This function turns a vector or matrix into an image\ndef array_to_image(arr, size=250, vec = True):\n if vec == False:\n return Image.fromarray((255 * arr).astype(np.uint8))\n return Image.fromarray(np.reshape((255*arr).astype(np.uint8), (size, size, 3)))\n\n\n# Give PetImages path and this function splits the file names into train test and validation sets\n# Returns three lists of list were the first element of the inner list is the label and the second is the file name\ndef load_train_test_val(path_dir):\n cat_file_names = os.listdir(path_dir + '/Cat/')\n dog_file_names = os.listdir(path_dir + '/Dog/')\n random.seed(5) # so we get same splits\n random.shuffle(cat_file_names)\n random.shuffle(dog_file_names)\n train = []\n val = []\n test = []\n for i in range(int(len(cat_file_names) * .6)):\n if cat_file_names[i].split('.')[1] == \"jpg\":\n train = train + [[0, \"/Cat/\" + cat_file_names[i]]]\n for i in range(int(len(dog_file_names) * .6)):\n if dog_file_names[i].split('.')[1] == \"jpg\":\n train = train + [[1, \"/Dog/\" + dog_file_names[i]]]\n for i in range(int(len(cat_file_names) * .6), int(len(cat_file_names) * .8)):\n if cat_file_names[i].split('.')[1] == \"jpg\":\n test = test + [[0, \"/Cat/\" + cat_file_names[i]]]\n for i in range(int(len(dog_file_names) * .6), int(len(dog_file_names) * .8)):\n if dog_file_names[i].split('.')[1] == \"jpg\":\n test = test + [[1, \"/Dog/\" + dog_file_names[i]]]\n for i in range(int(len(cat_file_names) * .8),len(cat_file_names)):\n if cat_file_names[i].split('.')[1] == \"jpg\":\n val = val + [[0, \"/Cat/\" + cat_file_names[i]]]\n for i in range(int(len(dog_file_names) * .8),len(dog_file_names)):\n if dog_file_names[i].split('.')[1] == \"jpg\":\n val = val + [[1, \"/Dog/\" + dog_file_names[i]]]\n return train, test, val\n\n", "sub_path": "image_processing.py", "file_name": "image_processing.py", "file_ext": "py", "file_size_in_byte": 2781, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PIL.Image.open", "line_number": 13, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 31, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 32, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 38, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 39, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 40, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 41, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "523612056", "text": "import json \nimport sys\n#Comma separated Ticker, Date, Open, High, Low, Close, Volume.\nfrom filesInPath import filesInPath\nimport pandas as pd\n\ndef toTuple(line: str):\n line = line.split(\",\")\n name = line[0]\n date = line[1]\n open = float(line[2])\n high = float(line[3])\n low = float(line[4])\n close = float(line[5])\n volume = int(line[6])\n return (name, date,(open, high, low, close, volume))\n\n\ndef getDataFromFile(pathToFiles, fileName, selectedListings, stockPrices: dict):\n f = open(pathToFiles + \"/\" + fileName, \"r\")\n line = \"\"\n lines = []\n while(True):\n line = f.readline()\n if(len(line) == 0):\n break\n lines.append(line.strip())\n f.close()\n\n for line in lines:\n [name, date, priceData] = toTuple(line)\n if(selectedListings == None or name in selectedListings):\n if(name in stockPrices):\n stockPrices[name][date] = priceData\n else:\n stockPrices[name] = {date: priceData}\n\n return stockPrices\n\n\ndef getDataFromFiles(pathToFiles, fileNames, selectedListings):\n stockPrices = {}\n for fileName in fileNames:\n getDataFromFile(pathToFiles, fileName, selectedListings, stockPrices)\n \n return stockPrices\n\ndef getDataOfListings(selectedListings, pathToFiles = \"raw\"):\n\n filenames = filesInPath(pathToFiles)\n\n stockPrices = getDataFromFiles(pathToFiles, filenames, selectedListings)\n\n for key in stockPrices:\n try:\n jsonFile = open(\"data/\"+key+\".json\", \"w+\")\n jsonFile.write(json.dumps(stockPrices[key]))\n except:\n print(\"error with \" + key)\n\n return stockPrices\n\n\n\ndef getDataFileNames():\n return map(lambda fileName:fileName.replace(\".json\",\"\"),\n filesInPath(\"data\"))\n\n\ndef getJsonDataFromFile(fileName):\n f = open(\"data/\" + fileName + \".json\", \"r\") \n return json.load(f)\n\n\n\n", "sub_path": "convertStockToJson.py", "file_name": "convertStockToJson.py", "file_ext": "py", "file_size_in_byte": 1916, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "filesInPath.filesInPath", "line_number": 50, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 57, "usage_type": "call"}, {"api_name": "filesInPath.filesInPath", "line_number": 67, "usage_type": "call"}, {"api_name": "json.load", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "253143186", "text": "from functools import partial\n\nfrom qtpy.QtCore import Qt, QPoint\nfrom qtpy.QtGui import QDoubleValidator, QFontMetrics, QFont\nfrom qtpy.QtWidgets import QHBoxLayout, QLineEdit\n\nfrom .qt_modal import QtPopup\nfrom .qt_range_slider import QHRangeSlider, QVRangeSlider\nfrom .utils import qt_signals_blocked\n\n\nclass LabelEdit(QLineEdit):\n def __init__(self, value='', parent=None, get_pos=None):\n \"\"\"Helper class to position LineEdits above the slider handle\n\n Parameters\n ----------\n value : str, optional\n starting value, by default ''\n parent : QRangeSliderPopup, optional\n required for proper label positioning above handle, by default None\n get_pos : callable, optional\n function that returns the position of the appropriate slider handle\n by default None\n \"\"\"\n super().__init__(value, parent=parent)\n self.fm = QFontMetrics(QFont(\"\", 0))\n self.setObjectName('slice_label')\n self.min_width = 30\n self.max_width = 200\n self.setCursor(Qt.IBeamCursor)\n self.setValidator(QDoubleValidator())\n self.textChanged.connect(self._on_text_changed)\n self._on_text_changed(value)\n\n self.get_pos = get_pos\n if parent is not None:\n self.min_width = 50\n self.slider = parent.slider\n self.setAlignment(Qt.AlignCenter)\n\n def _on_text_changed(self, text):\n # with non mono-spaced fonts, an \"n-digit\" number isn't always the same\n # width... so we convert all numbers to \"n 8s\" before measuring width\n # so as to avoid visual jitter in the width of the label\n width = self.fm.boundingRect('8' * len(text)).width() + 4\n width = max(self.min_width, min(width, self.max_width))\n if width > self.min_width:\n # don't ever make the label smaller ... it causes visual jitter\n self.min_width = width\n self.setFixedWidth(width)\n\n def update_position(self):\n x = self.get_pos() - self.width() / 2\n y = self.slider.handle_radius + 6\n self.move(QPoint(x, -y) + self.slider.pos())\n\n def mouseDoubleClickEvent(self, event):\n self.selectAll()\n\n\nclass QRangeSliderPopup(QtPopup):\n def __init__(self, parent=None, horizontal=True, precision=0, **kwargs):\n \"\"\"A popup window that contains a range slider and linked LineEdits.\n\n Parameters\n ----------\n parent : QWidget, optional\n Will like be an instance of QtLayerControls. Note, providing\n parent can be useful to inherit stylesheets.\n horizontal : bool, optional\n Whether the slider is oriented horizontally, by default True.\n (Vertical orientation has not been tested much)\n precision : int, optional\n Number of decimal values in the labels, by default 0\n **kwargs\n all additional keyword arguments will be passed to the RangeSlider\n \"\"\"\n super().__init__(parent)\n self.precision = precision\n\n # create slider\n self.slider = (\n QHRangeSlider(parent=parent, **kwargs)\n if horizontal\n else QVRangeSlider(parent=parent, **kwargs)\n )\n self.slider.setMinimumHeight(18)\n self.slider.setFocus()\n self.slider.valuesChanged.connect(self._on_values_change)\n self.slider.rangeChanged.connect(self._on_range_change)\n self.slider.resized.connect(self._update_cur_label_positions)\n\n # create \"floating\" min/max value labels\n cmin, cmax = self.slider.values()\n get_min_pos = partial(getattr, self.slider, 'display_min')\n get_max_pos = partial(getattr, self.slider, 'display_max')\n self.curmin_label = LabelEdit(self._numformat(cmin), self, get_min_pos)\n self.curmax_label = LabelEdit(self._numformat(cmax), self, get_max_pos)\n self.curmin_label.editingFinished.connect(self._curmin_label_changed)\n self.curmax_label.editingFinished.connect(self._curmax_label_changed)\n self.curmin_label.setToolTip(\"current minimum contrast limit\")\n self.curmax_label.setToolTip(\"current maximum contrast limit\")\n\n # create range min/max labels (left & right of slider)\n rmin, rmax = self.slider.range()\n self.range_min_label = LabelEdit(self._numformat(rmin))\n self.range_max_label = LabelEdit(self._numformat(rmax))\n self.range_min_label.editingFinished.connect(self._range_label_changed)\n self.range_max_label.editingFinished.connect(self._range_label_changed)\n self.range_min_label.setToolTip(\"minimum contrast range\")\n self.range_max_label.setToolTip(\"maximum contrast range\")\n self.range_min_label.setAlignment(Qt.AlignRight)\n\n # add widgets to layout\n self.layout = QHBoxLayout()\n self.frame.setLayout(self.layout)\n self.frame.setContentsMargins(0, 8, 0, 0)\n self.layout.addWidget(self.range_min_label)\n self.layout.addWidget(self.slider, 50)\n self.layout.addWidget(self.range_max_label)\n\n def _numformat(self, number):\n if round(number) == number:\n return \"{:.{}f}\".format(number, 0)\n else:\n return \"{:.{}f}\".format(number, self.precision)\n\n def _update_cur_label_positions(self):\n self.curmin_label.update_position()\n self.curmax_label.update_position()\n\n def _on_values_change(self, values):\n cmin_, cmax_ = values\n with qt_signals_blocked(self.slider):\n self.curmin_label.setText(self._numformat(cmin_))\n self.curmax_label.setText(self._numformat(cmax_))\n self._update_cur_label_positions()\n\n def _on_range_change(self, values):\n cmin_, cmax_ = values\n with qt_signals_blocked(self.slider):\n self.range_min_label.setText(self._numformat(cmin_))\n self.range_max_label.setText(self._numformat(cmax_))\n # changing range may also change values\n vmin_, vmax_ = self.slider.values()\n self.curmin_label.setText(self._numformat(vmin_))\n self.curmax_label.setText(self._numformat(vmax_))\n\n def _curmin_label_changed(self):\n cmin = float(self.curmin_label.text())\n cmax = float(self.curmax_label.text())\n if cmin > cmax:\n cmin = cmax\n self.slider.setValues((cmin, cmax))\n\n def _curmax_label_changed(self):\n cmin = float(self.curmin_label.text())\n cmax = float(self.curmax_label.text())\n if cmax < cmin:\n cmax = cmin\n self.slider.setValues((cmin, cmax))\n\n def _range_label_changed(self):\n rmin = float(self.range_min_label.text())\n rmax = float(self.range_max_label.text())\n if rmin >= rmax:\n rmax = rmin + 1\n self.slider.setRange((rmin, rmax))\n\n def keyPressEvent(self, event):\n # we override the parent keyPressEvent so that hitting enter does not\n # hide the window... but we do want to lose focus on the lineEdits\n if event.key() in (Qt.Key_Return, Qt.Key_Enter):\n self.slider.setFocus()\n return\n super().keyPressEvent(event)\n", "sub_path": "napari/_qt/qt_range_slider_popup.py", "file_name": "qt_range_slider_popup.py", "file_ext": "py", "file_size_in_byte": 7208, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "qtpy.QtWidgets.QLineEdit", "line_number": 12, "usage_type": "name"}, {"api_name": "qtpy.QtGui.QFontMetrics", "line_number": 27, "usage_type": "call"}, {"api_name": "qtpy.QtGui.QFont", "line_number": 27, "usage_type": "call"}, {"api_name": "qtpy.QtCore.Qt.IBeamCursor", "line_number": 31, "usage_type": "attribute"}, {"api_name": "qtpy.QtCore.Qt", "line_number": 31, "usage_type": "name"}, {"api_name": "qtpy.QtGui.QDoubleValidator", "line_number": 32, "usage_type": "call"}, {"api_name": "qtpy.QtCore.Qt.AlignCenter", "line_number": 40, "usage_type": "attribute"}, {"api_name": "qtpy.QtCore.Qt", "line_number": 40, "usage_type": "name"}, {"api_name": "qtpy.QtCore.QPoint", "line_number": 56, "usage_type": "call"}, {"api_name": "qt_modal.QtPopup", "line_number": 62, "usage_type": "name"}, {"api_name": "qt_range_slider.QHRangeSlider", "line_number": 84, "usage_type": "call"}, {"api_name": "qt_range_slider.QVRangeSlider", "line_number": 86, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 96, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 97, "usage_type": "call"}, {"api_name": "qtpy.QtCore.Qt.AlignRight", "line_number": 113, "usage_type": "attribute"}, {"api_name": "qtpy.QtCore.Qt", "line_number": 113, "usage_type": "name"}, {"api_name": "qtpy.QtWidgets.QHBoxLayout", "line_number": 116, "usage_type": "call"}, {"api_name": "utils.qt_signals_blocked", "line_number": 135, "usage_type": "call"}, {"api_name": "utils.qt_signals_blocked", "line_number": 142, "usage_type": "call"}, {"api_name": "qtpy.QtCore.Qt.Key_Return", "line_number": 174, "usage_type": "attribute"}, {"api_name": "qtpy.QtCore.Qt", "line_number": 174, "usage_type": "name"}, {"api_name": "qtpy.QtCore.Qt.Key_Enter", "line_number": 174, "usage_type": "attribute"}]} +{"seq_id": "557182306", "text": "from stemming.porter2 import stem\nfrom ResultObj import ResultObj\nimport sqlite3\nimport Const\n\n\n\ndef buildQueryR(terms):\n if(len(terms) == 0):\n return [\"\",\"\"]\n elif(len(terms) == 1):\n return ['SELECT id, url, author, email FROM urls WHERE id IN (SELECT url_id FROM term_index WHERE _term_id='+str(terms[0]), ')' ]\n else:\n vals= [' AND url_id IN (SELECT url_id FROM term_index WHERE _term_id='+str(terms[0]), ')']\n subvals = buildQueryR(terms[1:])\n tvals = [subvals[0] + vals[0], subvals[1]+vals[1]]\n return tvals\n\ndef buildSQLQuery(terms):\n vals = buildQueryR(terms)\n return vals[0] + vals[1]\n\ndef execQuery(aSQLQuery, c):\n c.execute(aSQLQuery)\n rows = c.fetchall()\n results = []\n for row in rows:\n results.append(ResultObj(row))\n return results\n\n\ndef queryfix(queryterms):\n output = map(stem, queryterms)\n output = list(set(output))\n return output\n\ndef getPhrases(rawquery):\n phrases = []\n ind = 0\n while(True):\n quotestart = rawquery.find('\"', ind)\n if(quotestart == -1):\n break\n quoteend = rawquery.find('\"', quotestart + 1)\n ind = quoteend + 1\n if(ind == 0):\n break\n quote = rawquery[quotestart+1:quoteend]\n phrases.append(quote.lower())\n return phrases\n\nclass QueryObj():\n\n\n def __init__(self, rawquery):\n self.rawquery = rawquery\n sanquery = Const.sanitize(rawquery).split()\n self.query = queryfix(sanquery)\n self.reorderedterms = []\n\n def getQIDs(self, listterms, c):\n qur = 'SELECT id, idf, _term FROM terms WHERE _term IN(\"'\n for term in listterms:\n qur = qur + term + '\",\"'\n qur = qur[:-2]\n qur = qur + \") ORDER BY idf ASC\"\n c.execute(qur)\n ret = []\n self.idfs = []\n self.reorderedterms = []\n\n rows = c.fetchall()\n for row in rows:\n ret.append(row[0])\n self.idfs.append(row[1])\n self.reorderedterms.append(row[2])\n return ret\n\n \n def execute(self):\n conn = sqlite3.connect(Const.DATABASEFILE)\n c = conn.cursor()\n\n qids = self.getQIDs(self.query, c)\n sqlq = buildSQLQuery(qids)\n \n results = execQuery(sqlq, c)\n \n conn.close()\n \n phrases = getPhrases(self.rawquery)\n\n if(len(phrases) > 0):\n for result in results:\n result.calcTFIDF(self.reorderedterms, qids, self.idfs)\n\n results = sorted(results, key=lambda result: result.rank, reverse=True)\n newresults = []\n ind = 0\n while(len(newresults) < 20 and ind < len(results)):\n if(results[ind].containsPhrases(phrases)):\n newresults.append(results[ind])\n ind = ind + 1\n results = newresults\n\n else:\n for result in results:\n result.calcTFIDF(self.reorderedterms, qids, self.idfs)\n\n\n results = sorted(results, key=lambda result: result.rank, reverse=True)\n \n if(len(results) > 20):\n results = results[:20]\n\n for result in results:\n result.prepare()\n\n return results\n", "sub_path": "searchone/searchapp/QueryObj.py", "file_name": "QueryObj.py", "file_ext": "py", "file_size_in_byte": 3256, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ResultObj.ResultObj", "line_number": 28, "usage_type": "call"}, {"api_name": "stemming.porter2.stem", "line_number": 33, "usage_type": "argument"}, {"api_name": "Const.sanitize", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 81, "usage_type": "call"}, {"api_name": "Const.DATABASEFILE", "line_number": 81, "usage_type": "attribute"}]} +{"seq_id": "136453397", "text": "import os\nfrom argparse import Namespace\nfrom tqdm import tqdm\nimport time\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\nimport sys\n\nimport faiss\nfrom PIL import Image\n\nsys.path.append(\".\")\nsys.path.append(\"..\")\n\nfrom configs import data_configs\nfrom datasets.inference_dataset import InferenceDatasetWithPath, SeqSampler\nfrom options.test_options import TestOptions\nfrom models.psp import pSp\nfrom models.e4e import e4e\nfrom utils.model_utils import ENCODER_TYPES\nfrom utils.common import tensor2im\nfrom utils.inference_utils import get_average_image\nfrom utils.lm_utils import *\n\n\ndef main():\n\n # path setup\n test_opts = TestOptions().parse()\n out_path_results = os.path.join(test_opts.exp_dir, 'inference_results')\n out_path_coupled = os.path.join(test_opts.exp_dir, 'inference_coupled')\n out_path_latents = os.path.join(test_opts.faiss_dir, 'inference_latents')\n os.makedirs(out_path_results, exist_ok=True)\n os.makedirs(out_path_coupled, exist_ok=True)\n os.makedirs(out_path_latents, exist_ok=True)\n\n # update test options with options used during training\n ckpt = torch.load(test_opts.checkpoint_path, map_location='cpu')\n opts = ckpt['opts']\n opts.update(vars(test_opts))\n opts = Namespace(**opts)\n\n # model setup\n net = pSp(opts)\n net.eval()\n net.cuda()\n\n # dataset setup\n print('Loading dataset for {}'.format(opts.dataset_type))\n dataset_args = data_configs.DATASETS[opts.dataset_type]\n transforms_dict = dataset_args['transforms'](opts).get_transforms()\n \n if opts.save_latents:\n dataset = InferenceDatasetWithPath(\n root=opts.data_path,\n transform=transforms_dict['transform_inference'],\n opts=opts\n )\n dataloader = DataLoader(\n dataset,\n batch_size=opts.test_batch_size,\n shuffle=True,\n num_workers=int(opts.test_workers),\n drop_last=True\n )\n else:\n dataset = InferenceDatasetWithPath(\n root=opts.data_path,\n transform=transforms_dict['transform_inference'],\n opts=opts,\n seq_ids=True\n )\n dataloader = DataLoader(\n dataset,\n batch_sampler=SeqSampler(dataset.seq_ids),\n num_workers=int(opts.test_workers)\n )\n \n # n images to generate\n if opts.n_images is None:\n opts.n_images = len(dataset)\n\n # agg\n if opts.agg == 'avg':\n agg = np.mean\n elif opts.agg == 'sum':\n agg = np.sum\n\n # get the image corresponding to the latent average\n avg_image = get_average_image(net, opts)\n\n # inference setup\n global_i = 0\n global_time = []\n batch_input_paths = {}\n\n # read in latents\n if not opts.save_latents:\n index = faiss.read_index(os.path.join(opts.faiss_dir, 'index.bin'))\n lookup_arrays = np.load(os.path.join(opts.faiss_dir, 'lookup_array.npy'), mmap_mode='r')\n with open(os.path.join(opts.faiss_dir, 'im_names.txt')) as f:\n im_names = f.read().split()\n \n # read in lm\n if not opts.save_latents:\n bertjapanese = AutoModelForMaskedLM.from_pretrained('cl-tohoku/bert-base-japanese-char')\n bertjapanesetokenizer = BertJapaneseTokenizer.from_pretrained(\"cl-tohoku/bert-base-japanese-char\")\n\n # pca\n if opts.pca is not None:\n lat_comp = np.load(opts.pca)[\"lat_comp\"]\n first_four_lat_comp = np.squeeze(lat_comp[:4,:,:], axis=1)\n else:\n first_four_lat_comp = None\n\n # setup eval\n if opts.eval_data:\n top1_acc = []\n top5_acc = []\n top10_acc = []\n \n # inference\n for input_batch, input_paths in tqdm(dataloader):\n\n if global_i >= opts.n_images:\n break\n\n with torch.no_grad():\n\n input_cuda = input_batch.cuda().float()\n tic = time.time()\n\n result_batch, result_latents = run_on_batch(input_cuda, net, opts, avg_image)\n\n if opts.save_latents:\n\n latent_array = result_latents.cpu().detach().numpy().astype('float32')\n latents_save_path = os.path.join(out_path_latents, f'{global_i}.npy')\n batch_input_paths[str(global_i)] = list(input_paths)\n with open(latents_save_path, 'wb') as f:\n np.save(f, latent_array)\n \n else:\n \n closest_latents, closet_im_names = run_faiss(\n result_latents, \n index, \n lookup_arrays,\n im_names,\n n_latents=opts.n_latents, \n n_neighbors=opts.n_neighbors,\n verbose=opts.verbose,\n pcomp=first_four_lat_comp,\n agg_func=agg\n )\n\n sequence_ocr_recog_chars = []\n\n for bidx, (clatent, bimgn) in enumerate(zip(closest_latents, closet_im_names)):\n\n closest_input_cuda = torch.from_numpy(clatent).cuda().float()\n result_neighbors, _ = run_on_batch(closest_input_cuda, net, opts, avg_image, just_decode=True)\n\n im_path = input_paths[bidx]\n input_im = tensor2im(input_batch[bidx])\n\n viz_results(input_im, result_neighbors, out_path_coupled, im_path, bimgn, opts)\n\n ocr_recog_chars = [extract_char_from_im_name(imgn) for imgn in bimgn]\n sequence_ocr_recog_chars.append(ocr_recog_chars)\n\n if opts.eval_data:\n top1_acc.append(ocr_recog_chars[0] == extract_char_from_im_name(im_path))\n top5_acc.append(extract_char_from_im_name(im_path) in ocr_recog_chars[:5])\n top10_acc.append(extract_char_from_im_name(im_path) in ocr_recog_chars)\n\n print(input_paths)\n print(sequence_ocr_recog_chars)\n beam_output = beam_search_from_marginal_mlm(\n sequence_ocr_recog_chars, \n bertjapanese, bertjapanesetokenizer, \n beams=opts.n_beams\n )\n print(beam_output)\n print(\"***\\n\\n\\n\")\n\n toc = time.time()\n global_time.append(toc - tic)\n\n global_i += opts.test_batch_size\n\n # faiss index creation\n if opts.save_latents:\n index, lookup_arrays, ord_batch_paths = setup_faiss(\n opts, batch_input_paths, \n n_latents=opts.n_latents, n_imgs=global_i, \n pcomp=first_four_lat_comp,\n agg_func=agg\n )\n faiss.write_index(index, os.path.join(opts.faiss_dir, 'index.bin'))\n with open(os.path.join(opts.faiss_dir, 'lookup_array.npy'), 'wb') as f:\n np.save(f, lookup_arrays)\n with open(os.path.join(opts.faiss_dir, 'im_names.txt'), 'w') as f:\n f.write('\\n'.join(ord_batch_paths))\n\n # create stats\n stats_path = os.path.join(opts.exp_dir, 'stats.txt')\n result_str = 'Runtime {:.4f}+-{:.4f}'.format(np.mean(global_time), np.std(global_time))\n print(result_str)\n with open(stats_path, 'w') as f:\n f.write(result_str)\n if opts.eval_data:\n top1_acc = sum(top1_acc)/len(top1_acc)\n top5_acc = sum(top5_acc)/len(top5_acc)\n top10_acc = sum(top10_acc)/len(top10_acc)\n print(f\"Top-1 accuracy is {top1_acc}\")\n print(f\"Top-5 accuracy is {top5_acc}\")\n print(f\"Top-10 accuracy is {top10_acc}\")\n\n\ndef viz_results(input_im, result_neighbors, out_path_coupled, im_path, bimgn, opts):\n\n # viz results encoded\n res = [np.array(input_im)]\n res = res + [np.array(tensor2im(result_neighbors[i])) for i in range(opts.n_neighbors)]\n res = np.concatenate(res, axis=1)\n Image.fromarray(res).save(os.path.join(out_path_coupled, os.path.basename(im_path)))\n\n # viz results wrt src file\n res = [np.array(input_im)]\n res = res + [np.array(Image.open(i).convert('RGB')) for i in bimgn]\n res = np.concatenate(res, axis=1)\n Image.fromarray(res).save(os.path.join(out_path_coupled, f\"src_im_{os.path.basename(im_path)}\"))\n\n # ocr top1 save\n top_char = extract_char_from_im_name(bimgn[0])\n input_im.save(os.path.join(out_path_coupled, \n f\"top1_{top_char}.png\"))\n\n\ndef setup_faiss(opts, batch_im_paths, n_latents, n_imgs, ldim=512, wplus=10, pcomp=None, agg_func=np.mean):\n\n # create index\n if opts.pca:\n index = faiss.IndexFlatIP(n_latents*pcomp.shape[0])\n elif agg_func is np.mean:\n index = faiss.IndexFlatIP(ldim)\n elif agg_func is np.sum:\n index = faiss.IndexFlatIP(ldim)\n \n all_arrays = np.empty((n_imgs, wplus, ldim), dtype=np.float32)\n all_paths = []\n\n # load index\n root_dir = os.path.join(opts.faiss_dir, 'inference_latents')\n idx = 0\n\n for filename in tqdm(os.listdir(root_dir)):\n\n paths = batch_im_paths[os.path.splitext(filename)[0]]\n all_paths.extend(paths)\n\n saved_latents = np.load(os.path.join(root_dir, filename))\n all_arrays[idx:idx+opts.test_batch_size,:,:] = saved_latents\n\n reshaped_latents = embed_latent(saved_latents, n_latents, agg_func, pcomp)\n faiss.normalize_L2(reshaped_latents)\n index.add(reshaped_latents)\n\n idx += opts.test_batch_size\n\n print(f'Total indices {index.ntotal}')\n\n return index, all_arrays, all_paths\n\n\ndef run_faiss(query_latents, index, all_arrays, all_im_names, n_latents, n_neighbors=5, verbose=True, pcomp=None, agg_func=np.mean):\n \n # search index\n reshaped_query_latents = embed_latent(query_latents, n_latents, agg_func, pcomp)\n D, I = index.search(reshaped_query_latents, n_neighbors)\n if verbose:\n print(I)\n print(D)\n\n # return closest\n closest_indices = np.apply_along_axis(lambda x: x[:n_neighbors], axis=1, arr=I)\n closest_codes = [all_arrays[cidx,:,:] for cidx in closest_indices]\n closest_im_names = [[all_im_names[i] for i in cidx] for cidx in closest_indices]\n\n return closest_codes, closest_im_names\n\n\ndef embed_latent(latents, n_latents, agg_func, pcomp=None):\n\n if torch.is_tensor(latents):\n latents = latents.cpu().detach().numpy() # eg (2, 10, 512)\n\n if pcomp is None:\n\n embedding = np.ascontiguousarray(\n agg_func(latents[:,:n_latents,:], axis=1).reshape((latents.shape[0], -1))\n )\n\n else:\n\n embedding = np.empty((latents.shape[0], pcomp.shape[0], latents.shape[1]))\n\n for i in range(latents.shape[0]):\n\n latent = latents[i,:,:].T\n proj = pcomp @ latent\n embedding[i,:,:] = proj\n\n embedding = np.ascontiguousarray(\n embedding[:,:,:n_latents].reshape((latents.shape[0], -1))\n ).astype('float32')\n\n return embedding\n\n\ndef run_on_batch(inputs, net, opts, avg_image, just_decode=False):\n\n if not just_decode:\n\n y_hat, latent = None, None\n\n for iter in range(opts.n_iters_per_batch):\n\n if iter == 0:\n avg_image_for_batch = avg_image.unsqueeze(0).repeat(inputs.shape[0], 1, 1, 1)\n x_input = torch.cat([inputs, avg_image_for_batch], dim=1)\n else:\n x_input = torch.cat([inputs, y_hat], dim=1)\n\n y_hat, latent = net.forward(x_input,\n latent=latent,\n randomize_noise=False,\n return_latents=True)\n\n else:\n\n y_hat, latent = net.forward(inputs,\n latent=None,\n input_code=True,\n randomize_noise=False,\n return_latents=True)\n\n\n return y_hat, latent\n\n\ndef extract_char_from_im_name(imn):\n return os.path.basename(imn)[0]\n\n\ndef nn_scoring(neighbors):\n neighbors_set = list(set(neighbors))\n scores = [sum([len(neighbors) - idx for idx, x in enumerate(neighbors) if x == s]) for s in neighbors_set]\n return max(neighbors_set, key=lambda x: scores[neighbors_set.index(x)])\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "scripts/ocr_psnp.py", "file_name": "ocr_psnp.py", "file_ext": "py", "file_size_in_byte": 12195, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "options.test_options.TestOptions", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 34, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 35, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 39, "usage_type": "call"}, {"api_name": "argparse.Namespace", "line_number": 42, "usage_type": "call"}, {"api_name": "models.psp.pSp", "line_number": 45, "usage_type": "call"}, {"api_name": "configs.data_configs.DATASETS", "line_number": 51, "usage_type": "attribute"}, {"api_name": "configs.data_configs", "line_number": 51, "usage_type": "name"}, {"api_name": "datasets.inference_dataset.InferenceDatasetWithPath", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 60, "usage_type": "call"}, {"api_name": "datasets.inference_dataset.InferenceDatasetWithPath", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 74, "usage_type": "call"}, {"api_name": "datasets.inference_dataset.SeqSampler", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 88, "usage_type": "attribute"}, {"api_name": "utils.inference_utils.get_average_image", "line_number": 91, "usage_type": "call"}, {"api_name": "faiss.read_index", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 113, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 129, "usage_type": "call"}, {"api_name": "time.time", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 162, "usage_type": "call"}, {"api_name": "utils.common.tensor2im", "line_number": 166, "usage_type": "call"}, {"api_name": "time.time", "line_number": 188, "usage_type": "call"}, {"api_name": "faiss.write_index", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path", "line_number": 201, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path", "line_number": 202, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 203, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path", "line_number": 204, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path", "line_number": 208, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 226, "usage_type": "call"}, {"api_name": "utils.common.tensor2im", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 227, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 228, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 228, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path", "line_number": 228, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 232, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 232, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 232, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 233, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 234, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 234, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 234, "usage_type": "call"}, {"api_name": "os.path", "line_number": 234, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 234, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path", "line_number": 238, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 242, "usage_type": "attribute"}, {"api_name": "faiss.IndexFlatIP", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 247, "usage_type": "attribute"}, {"api_name": "faiss.IndexFlatIP", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 249, "usage_type": "attribute"}, {"api_name": "faiss.IndexFlatIP", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 252, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 256, "usage_type": "call"}, {"api_name": "os.path", "line_number": 256, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 259, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 259, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 261, "usage_type": "call"}, {"api_name": "os.path", "line_number": 261, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path", "line_number": 264, "usage_type": "attribute"}, {"api_name": "faiss.normalize_L2", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 278, "usage_type": "attribute"}, {"api_name": "numpy.apply_along_axis", "line_number": 288, "usage_type": "call"}, {"api_name": "torch.is_tensor", "line_number": 297, "usage_type": "call"}, {"api_name": "numpy.ascontiguousarray", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.ascontiguousarray", "line_number": 316, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 333, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 335, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 355, "usage_type": "call"}, {"api_name": "os.path", "line_number": 355, "usage_type": "attribute"}]} +{"seq_id": "50027311", "text": "import numpy as np\nimport matplotlib.pyplot as plt \ndef rw_path(N=1000, T=1.0):\n\tdelta = T/np.float(N)\n\th=1.0/np.sqrt(np.float(N))\n\tb= np.random.binomial(1,.5, N)\t# bernulli 0,1\n\tomega=2.0*b-1\t\t\t\t\t# bernulli -1,1\n\tXn=h*(omega.cumsum())\t\t\t# bernulli -h,h\n\tXn=np.concatenate(([0], Xn))\n\treturn Xn\n\nN = 1000\nN_paths = 100\nT = 1.0\nh = 1.0/np.sqrt(np.float(N))\nt = np.linspace(0,T,N+1)\nXn = rw_path()\nmu = np.zeros(Xn.shape[0])\nfor i in np.arange(N_paths-1):\n\tplt.plot(t,Xn,'g-',alpha=0.3,lw=1,ms=4,mfc='green')\n\tXn=rw_path()\n\tmu = mu + Xn\nmu = 1.0 / np.float(N_paths) * mu\nplt.plot(t,Xn,'g-',alpha=.8,lw=1,ms=4,mfc='green',label=r'$RW$')\nplt.plot(t,mu,'r-',label=r'$E[X_n]$')\n'''\nplt.plot(t,np.sqrt(t),'-o',ms=2, color='yellow',mec='none',label=r'$\\sigma$')\nplt.plot(t, 2 * np.sqrt(t),'-o',ms=2,mfc='yellow',mec='none')\nplt.plot(t, 3 * np.sqrt(t),'-o',ms=2,mfc='yellow',mec='none')\nplt.plot(t, -1 * np.sqrt(t),'-o',ms=2,mfc='yellow',mec='none')\nplt.plot(t, -2 * np.sqrt(t),'-o',ms=2,mfc='yellow',mec='none')\nplt.plot(t, -3 * np.sqrt(t),'-o',ms=2,mfc='yellow',mec='none')\n'''\nM = np.abs(Xn).max()+h\n#\nplt.xlabel(r'$t_n$')\nplt.ylabel(r'$X_n$')\nplt.title(r'Construccion toerema Kuo')\n\nplt.grid(True)\nplt.legend(shadow=True,loc=0)\nplt.show()\n", "sub_path": "IMAGENES/RW/RW01Distribution.py", "file_name": "RW01Distribution.py", "file_ext": "py", "file_size_in_byte": 1234, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.float", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.random.binomial", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.float", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "94956540", "text": "#!/usr/bin/env python\n\n\"\"\"\n# =============================================================================\n\nCopyright Government of Canada 2015\n\nWritten by: Eric Marinier, Public Health Agency of Canada,\n National Microbiology Laboratory\n\nFunded by the National Micriobiology Laboratory and the Genome Canada / Alberta\n Innovates Bio Solutions project \"Listeria Detection and Surveillance\n using Next Generation Genomics\"\n\nLicensed under the Apache License, Version 2.0 (the \"License\"); you may not use\nthis file except in compliance with the License. You may obtain a copy of the\nLicense at:\n\nhttp://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software distributed\nunder the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\nCONDITIONS OF ANY KIND, either express or implied. See the License for the\nspecific language governing permissions and limitations under the License.\n\n# =============================================================================\n\"\"\"\n\n\"\"\"\n# =============================================================================\n\nAuthor: Eric Marinier\nDate: 17 April 2015\n\nThis script aggregates the k-mers from one or more inclusion files with one or\nmore exclusion files. The output provides a count of the number of distinct\nk-mer observaions per each inclusion and exclusion files.\n\nThe input files must have one k-mer per line and each k-mer must be preceded\nby no spaces or characters. Any characters, including k-mer counts, following\nthe k-mers will be ignored.\n\nIf the delete flag is used, then all input files will be deleted after they\naggregated.\n\nINPUT (one file):\n\nAAAAA\nAAAAC\nAAAAG\nAAAAT\n\nThe output will be in the following format:\n\n[k-mer] [inclusion counts] [exclusion counts]\n\nOUTPUT:\n\nAAAAA 3 1\nAAAAC 1 2\nAAAAG 3 3\nAAAAT 3 0\n\nUSAGE:\n\nscript.py -h\nscript.py -i INCLUSION [...] -e EXCLUSION [...] -o OUTPUT [--delete]\n\nEXAMPLE:\n\nscript.py -i inclusion1.kmers inclusion2.kmers -e exclusion1.kmers -o out.kmers\nscript.py -i inclusion1.kmers -e exclusion1.kmers -o out.kmers --delete\n\n# =============================================================================\n\"\"\"\n\nimport os\nimport argparse\n\n\"\"\"\n# =============================================================================\n\nGLOBALS\n\n# =============================================================================\n\"\"\"\n\n# NAMES\nINCLUSION = \"inclusion\"\nEXCLUSION = \"exclusion\"\nOUTPUT = \"output\"\nDELETE = \"delete\"\n\n# ARGUMENTS\nLONG = \"--\"\n\nINCLUSION_LONG = LONG + INCLUSION\nEXCLUSION_LONG = LONG + EXCLUSION\nOUTPUT_LONG = LONG + OUTPUT\nDELETE_LONG = LONG + DELETE\n\nSHORT = \"-\"\n\nINCLUSION_SHORT = SHORT + \"i\"\nEXCLUSION_SHORT = SHORT + \"e\"\nOUTPUT_SHORT = SHORT + \"o\"\n\n\"\"\"\n# =============================================================================\n\nFIND SMALLEST\n\nPURPOSE:\n Locates the lexicographically smallest element in a list of strings.\n\n The function ignores empty strings and uses a sentinel value to determine\n if all the strings are empty. This sentinel value must always be\n lexicographically larger than all string values.\n\nINPUT:\n [STRING ITERABLE] [strings] - An iterable strings object.\n [STRING] [SENTINEL] - A sentinel value, which must be lexicographically\n larger than all [strings].\n\nRETURN:\n [STRING] [smallest] - The lexicographically next value of all the strings\n or SENTINEL if all strings are empty.\n\n# =============================================================================\n\"\"\"\ndef findSmallest(strings, SENTINEL):\n\n smallest = SENTINEL # the lexicographically smallest observed string\n\n for string in strings:\n if string != \"\" and string < smallest:\n smallest = string\n\n return smallest\n\n\"\"\"\n# =============================================================================\n\nAGGREGATE KMER\n\nPURPOSE:\n Computes the counts of the k-mer, determined by the number of observations\n across all files.\n\n This function assumes: len(kmers) == len(files)\n\n The function considers all the k-mers and compares them with the k-mer\n parameter. When there is a match, the function increases the count and\n advances the file associated with the k-mer.\n\n The kmers parameter corresponds to the heads of all the k-mer files. The\n files must necessarily be read and advanced when there is a k-mer match\n found in a corresponding kmers array. This is because each k-mer in each\n file is only ever observed once.\n\nINPUT:\n [STRING] [kmer] - The k-mer to compare against all other k-mers and score\n when observed.\n [STRING LIST] [kmers] - A list of strings, understood as the head k-mer of\n files.\n [FILE LIST] [files] - A list of open files associated with kmers list.\n It is assumed: len(kmers) == len(files)\n\nRETURN:\n [INT >= 0] [count] - The number of exact k-mer matches found in the list of\n k-mers.\n\n# =============================================================================\n\"\"\"\ndef aggregateKMer(kmer, kmers, files):\n\n count = 0\n\n # iterate over all k-mers\n for i in range(len(kmers)):\n\n # check for empty string\n if kmers[i] != \"\" and kmers[i] == kmer:\n\n count += 1\n\n # advance file\n line = files[i].readline()\n\n # check for end of file and assign next k-mer\n kmers[i] = line.split()[0] if line.split() else line\n\n return count\n\n\"\"\"\n# =============================================================================\n\nAGGREGATE\n\nPURPOSE:\n This function aggregates the k-mers in the inclusion and exclusion\n files and produces a file with the k-mers and their inclusion and\n exclusion counts. These files must contain only distinct and\n lexicographically sorted k-mers.\n\nINPUT:\n [FILE LIST] [inclusionFiles] - The list of open inclusion k-mer files.\n [FILE LIST] [exclusionFiles] - The list of open exclusion k-mer files.\n [FILE] [outputFile] - The file to write the aggregated k-mers.\n\n NOTE: The input files must contain only distinct and lexicographically\n sorted k-mers. These k-mers must appear first on every line and be\n preceded by no spaces or special characters. Any characters following\n the k-mers, including k-mer counts, will be ignored.\n\nPOST:\n The k-mers and their aggregate counts value will be written to the\n [outputFile].\n\n# =============================================================================\n\"\"\"\ndef aggregate(inclusionFiles, exclusionFiles, outputFile):\n\n SENTINEL = \"~\" # sentinel value\n\n inclusionKMers = [] # current k-mer of inclusion files\n exclusionKMers = [] # current k-mer of exclusion files\n\n # initialize k-mers:\n for inclusionFile in inclusionFiles:\n\n line = inclusionFile.readline()\n\n # check for end of file and assign next k-mer\n kmer = line.split()[0] if line.split() else line\n inclusionKMers.append(kmer)\n\n for exclusionFile in exclusionFiles:\n\n line = exclusionFile.readline()\n\n # check for end of file and assign next k-mer\n kmer = line.split()[0] if line.split() else line\n exclusionKMers.append(kmer)\n\n # aggregate values:\n while(1):\n\n kmer = findSmallest((inclusionKMers + exclusionKMers), SENTINEL)\n\n # inclusion aggregation:\n incounts = aggregateKMer(kmer, inclusionKMers, inclusionFiles)\n\n # exclusion aggregation:\n excounts = aggregateKMer(kmer, exclusionKMers, exclusionFiles)\n\n # are all files at end of file?\n if kmer == SENTINEL:\n break\n\n # write aggregated k-mer to output\n outputString = str(kmer) + \" \" + str(incounts) + \" \" + str(excounts)\n outputFile.write(outputString + \"\\n\")\n\n\"\"\"\n# =============================================================================\n\nMAIN\n\n# =============================================================================\n\"\"\"\ndef main():\n\n # description\n parser = argparse.ArgumentParser(\n description='Aggregates one or more inclusion k-mer files \\\n with one or more exclusion files. The number of distinct \\\n inclusion and exclusion k-mer observations per file will be \\\n reported immediately following each k-mer in the output.')\n\n # inclusion k-mers\n parser.add_argument(\n INCLUSION_SHORT,\n INCLUSION_LONG,\n dest=INCLUSION,\n help=\"inclusion k-mer file(s)\",\n type=str, required=True, nargs='+')\n\n # exclusion k-mers\n parser.add_argument(\n EXCLUSION_SHORT,\n EXCLUSION_LONG,\n dest=EXCLUSION,\n help=\"exclusion k-mer file(s)\",\n type=str, required=True, nargs='+')\n\n # output k-mers\n parser.add_argument(\n OUTPUT_SHORT,\n OUTPUT_LONG,\n dest=OUTPUT,\n help=\"output file path\", type=str,\n required=True)\n\n # delete input files\n parser.add_argument(\n DELETE_LONG,\n dest=DELETE,\n help=\"delete input flag\",\n action='store_true', default=False)\n\n args = parser.parse_args()\n\n inclusionLocations = args.inclusion\n exclusionLocations = args.exclusion\n outputLocation = args.output\n delete = args.delete\n\n # open files\n inclusionFiles = []\n exclusionFiles = []\n\n # open inclusion files\n for location in inclusionLocations:\n\n if not os.path.isfile(location):\n raise RuntimeError(\n \"ERROR: Could not open inclusion file: \" +\n str(location) + \"\\n\")\n\n inclusionFiles.append(open(location, 'r'))\n\n # open exclusion files\n for location in exclusionLocations:\n\n if not os.path.isfile(location):\n raise RuntimeError(\n \"ERROR: Could not open exclusion file: \" +\n str(location) + \"\\n\")\n\n exclusionFiles.append(open(location, 'r'))\n\n outputFile = open(outputLocation, 'w')\n\n # aggregate\n aggregate(inclusionFiles, exclusionFiles, outputFile)\n\n # close files\n for inclusion in inclusionFiles:\n\n inclusion.close()\n\n for exclusion in exclusionFiles:\n\n exclusion.close()\n\n outputFile.close()\n\n # delete input files\n if delete:\n\n for filename in inclusionLocations + exclusionLocations:\n\n if os.path.exists(filename):\n os.remove(filename)\n\n\"\"\"\n# =============================================================================\n\nPARSING\n\n# =============================================================================\n\"\"\"\nif __name__ == \"__main__\":\n\n main()\n", "sub_path": "neptune/AggregateKMers.py", "file_name": "AggregateKMers.py", "file_ext": "py", "file_size_in_byte": 10568, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 275, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 326, "usage_type": "call"}, {"api_name": "os.path", "line_number": 326, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 336, "usage_type": "call"}, {"api_name": "os.path", "line_number": 336, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 364, "usage_type": "call"}, {"api_name": "os.path", "line_number": 364, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 365, "usage_type": "call"}]} +{"seq_id": "161540255", "text": "import random\nfrom django.core.management.base import BaseCommand\nfrom django_seed import Seed\nfrom reviews import models as review_models\nfrom users import models as user_models\nfrom rooms import models as room_models\n\n\nclass Command(BaseCommand):\n help = \"This Command creates reviews\"\n\n def add_arguments(self, parser):\n\n parser.add_argument(\"--number\", help=\"how many reviews do you want to create?\")\n\n pass\n\n def handle(self, *args, **options):\n\n number = options.get(\"number\")\n\n if review_models.Review.objects.all().count() >= int(number):\n return None\n\n seeder = Seed.seeder()\n all_users = user_models.User.objects.all()\n all_rooms = room_models.Room.objects.all()\n seeder.add_entity(\n review_models.Review,\n int(number),\n {\n \"review\": seeder.faker.sentence(),\n \"accuracy\": lambda x: random.randint(1, 5),\n \"communication\": lambda x: random.randint(1, 5),\n \"cleanliness\": lambda x: random.randint(1, 5),\n \"location\": lambda x: random.randint(1, 5),\n \"check_in\": lambda x: random.randint(1, 5),\n \"value\": lambda x: random.randint(1, 5),\n \"user\": lambda x: random.choice(all_users),\n \"room\": lambda x: random.choice(all_rooms),\n },\n )\n seeder.execute()\n self.stdout.write(self.style.SUCCESS(f\"reviews {number} is Created !\"))\n", "sub_path": "reviews/management/commands/seed_reviews.py", "file_name": "seed_reviews.py", "file_ext": "py", "file_size_in_byte": 1508, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 9, "usage_type": "name"}, {"api_name": "reviews.models.Review.objects.all", "line_number": 22, "usage_type": "call"}, {"api_name": "reviews.models.Review", "line_number": 22, "usage_type": "attribute"}, {"api_name": "reviews.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django_seed.Seed.seeder", "line_number": 25, "usage_type": "call"}, {"api_name": "django_seed.Seed", "line_number": 25, "usage_type": "name"}, {"api_name": "users.models.User.objects.all", "line_number": 26, "usage_type": "call"}, {"api_name": "users.models.User", "line_number": 26, "usage_type": "attribute"}, {"api_name": "users.models", "line_number": 26, "usage_type": "name"}, {"api_name": "rooms.models.Room.objects.all", "line_number": 27, "usage_type": "call"}, {"api_name": "rooms.models.Room", "line_number": 27, "usage_type": "attribute"}, {"api_name": "rooms.models", "line_number": 27, "usage_type": "name"}, {"api_name": "reviews.models.Review", "line_number": 29, "usage_type": "attribute"}, {"api_name": "reviews.models", "line_number": 29, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 33, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 34, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 35, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 36, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 37, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 38, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 39, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "440324067", "text": "\"\"\"\nEvaluates an agent based on a configurated environment and evaluation.\n\"\"\"\n\nimport torch\nimport numpy as np\nfrom docopt import docopt\nfrom gym import spaces\n\nfrom neroRL.utils.yaml_parser import YamlParser\nfrom neroRL.trainers.PPO.evaluator import Evaluator\nfrom neroRL.environments.wrapper import wrap_environment\nfrom neroRL.trainers.PPO.otc_model import OTCModel\n\ndef main():\n # Docopt command line arguments\n _USAGE = \"\"\"\n Usage:\n evaluate.py [options]\n evaluate.py --help\n\n Options:\n --config= Path of the Config file [default: ./configs/default.yaml].\n --untrained Whether an untrained model should be used [default: False].\n --worker-id= Sets the port for each environment instance [default: 2].\n --run-id= Specifies the tag of the tensorboard summaries [default: default].\n \"\"\"\n options = docopt(_USAGE)\n untrained = options[\"--untrained\"]\n config_path = options[\"--config\"]\n worker_id = int(options[\"--worker-id\"])\n run_id = options[\"--run-id\"]\n\n # Load environment, model, evaluation and training parameters\n configs = YamlParser(config_path).get_config()\n\n # Determine cuda availability\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n # Create dummy environment to retrieve the shapes of the observation and action space for further processing\n print(\"Step 1: Creating dummy environment of type \" + configs[\"environment\"][\"type\"])\n dummy_env = wrap_environment(configs[\"environment\"], worker_id)\n\n visual_observation_space = dummy_env.visual_observation_space\n vector_observation_space = dummy_env.vector_observation_space\n if isinstance(dummy_env.action_space, spaces.Discrete):\n action_space_shape = (dummy_env.action_space.n,)\n else:\n action_space_shape = tuple(dummy_env.action_space.nvec)\n dummy_env.close()\n\n # Build or load model\n if untrained:\n print(\"Step 2: Creating model\")\n model = OTCModel(configs[\"model\"], visual_observation_space,\n vector_observation_space, action_space_shape,\n configs[\"model\"][\"use_recurrent\"],\n configs[\"model\"][\"hidden_state_size\"]).to(device)\n else:\n print(\"Step 2: Loading model from \" + configs[\"model\"][\"model_path\"])\n model = torch.load(configs[\"model\"][\"model_path\"]).to(device)\n model.eval()\n\n # Initialize evaluator\n print(\"Step 3: Initialize evaluator\")\n print(\"Step 3: Number of Workers: \" + str(configs[\"evaluation\"][\"n_workers\"]))\n print(\"Step 3: Seeds: \" + str(configs[\"evaluation\"][\"seeds\"]))\n print(\"Step 3: Number of episodes: \" + str(len(configs[\"evaluation\"][\"seeds\"]) * configs[\"evaluation\"][\"n_workers\"]))\n evaluator = Evaluator(configs[\"evaluation\"], configs[\"environment\"], worker_id, visual_observation_space, vector_observation_space)\n\n # Evaluate\n print(\"Step 4: Run evaluation . . .\")\n eval_duration, raw_episode_results = evaluator.evaluate(model, device)\n episode_result = _process_episode_info(raw_episode_results)\n\n # Print results\n print(\"RESULT: sec={:3} mean reward={:.2f} std={:.2f} mean length={:.1f} std={:.2f}\".format(\n eval_duration, episode_result[\"reward_mean\"], episode_result[\"reward_std\"], episode_result[\"length_mean\"], episode_result[\"length_std\"]))\n\n # Close\n print(\"Step 5: Closing evaluator . . .\")\n evaluator.close()\n\ndef _process_episode_info(episode_info):\n \"\"\"Extracts the mean and std of completed episodes. At minimum the episode length and the collected reward is available.\n \n Arguments:\n episode_info {list} -- List of episode information, each individual item is a dictionary\n\n Returns:\n result {dict} -- Dictionary that contains the mean, std, min and max of all episode infos \n \"\"\"\n result = {}\n if len(episode_info) > 0:\n keys = episode_info[0].keys()\n # Compute mean and std for each information, skip seed\n for key in keys:\n if key == \"seed\":\n continue\n result[key + \"_mean\"] = np.mean([info[key] for info in episode_info])\n result[key + \"_min\"] = np.min([info[key] for info in episode_info])\n result[key + \"_max\"] = np.max([info[key] for info in episode_info])\n result[key + \"_std\"] = np.std([info[key] for info in episode_info])\n return result\n\nif __name__ == \"__main__\":\n main()\n ", "sub_path": "eval.py", "file_name": "eval.py", "file_ext": "py", "file_size_in_byte": 4543, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "docopt.docopt", "line_number": 28, "usage_type": "call"}, {"api_name": "neroRL.utils.yaml_parser.YamlParser", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 38, "usage_type": "attribute"}, {"api_name": "neroRL.environments.wrapper.wrap_environment", "line_number": 42, "usage_type": "call"}, {"api_name": "gym.spaces.Discrete", "line_number": 46, "usage_type": "attribute"}, {"api_name": "gym.spaces", "line_number": 46, "usage_type": "name"}, {"api_name": "neroRL.trainers.PPO.otc_model.OTCModel", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 61, "usage_type": "call"}, {"api_name": "neroRL.trainers.PPO.evaluator.Evaluator", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "232086036", "text": "# *_*coding:utf-8 *_*\nimport sys\n\nfrom PyQt5.QtCore import QDateTime, QDate\nfrom PyQt5.QtWidgets import QWidget, QDateTimeEdit, QPushButton, QVBoxLayout, QApplication\n\n\nclass MainWindow(QWidget):\n\n def __init__(self):\n super().__init__()\n self.InitGUI()\n\n def InitGUI(self):\n\n self.dateTime = QDateTimeEdit(QDateTime.currentDateTime(),self)\n self.dateTime.setDisplayFormat(\"yyyy-MM-dd HH:mm:ss\")\n\n self.dateTime.setMinimumDate(QDate.currentDate().addDays(-365))\n self.dateTime.setMaximumDate(QDate.currentDate().addDays(365))\n self.dateTime.setCalendarPopup(True)\n self.dateTime.dateChanged.connect(self.changeDate)\n self.dateTime.dateTimeChanged.connect(self.changeDateTime)\n self.dateTime.timeChanged.connect(self.changeTime)\n\n self.btn = QPushButton(\"获得最大时间\")\n\n vLayout = QVBoxLayout(self)\n vLayout.addWidget(self.dateTime)\n vLayout.addWidget(self.btn)\n\n self.setLayout(vLayout)\n self.resize(300,100)\n self.setWindowTitle(\"DateTimeEdit 例子\")\n\n def changeDate(self,date):\n print(date.toString(\"yyyy-MM-dd ddddd\"))\n\n def changeDateTime(self,time):\n print(time.toString(\"yyyy-MM-dd HH:mm:ss\"))\n\n def changeTime(self,time):\n print(time.toString(\"HH:mm:ss\"))\n\n\n\n\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n mainWin = MainWindow()\n mainWin.show()\n sys.exit(app.exec_())\n\n\n", "sub_path": "gui/pyqt/qt/widget/QDateTimeEdit01.py", "file_name": "QDateTimeEdit01.py", "file_ext": "py", "file_size_in_byte": 1467, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 8, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDateTimeEdit", "line_number": 16, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDateTime.currentDateTime", "line_number": 16, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDateTime", "line_number": 16, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate.currentDate", "line_number": 19, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 19, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate.currentDate", "line_number": 20, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 20, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 28, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "306219551", "text": "\"\"\"\nCopyright (c) 2015 Red Hat, Inc\nAll rights reserved.\n\nThis software may be modified and distributed under the terms\nof the BSD license. See the LICENSE file for details.\n\"\"\"\n\nfrom __future__ import unicode_literals\n\nimport json\nimport os\n\nfrom atomic_reactor.core import DockerTasker\nfrom atomic_reactor.inner import DockerBuildWorkflow\nfrom atomic_reactor.plugin import PostBuildPluginsRunner, PluginFailedException\nfrom atomic_reactor.util import ImageName\nfrom tests.constants import INPUT_IMAGE, SOURCE\nfrom atomic_reactor.plugins.post_import_image import ImportImagePlugin\n\nfrom osbs.api import OSBS\nfrom flexmock import flexmock\nimport pytest\n\n\nclass X(object):\n image_id = INPUT_IMAGE\n git_dockerfile_path = None\n git_path = None\n base_image = ImageName(repo=\"qwe\", tag=\"asd\")\n\n\ndef prepare():\n \"\"\"\n Boiler-plate test set-up\n \"\"\"\n\n tasker = DockerTasker()\n workflow = DockerBuildWorkflow(SOURCE, \"test-image\")\n setattr(workflow, 'builder', X())\n setattr(workflow.builder, 'image_id', 'asd123')\n setattr(workflow.builder, 'source', X())\n setattr(workflow.builder.source, 'dockerfile_path', None)\n setattr(workflow.builder.source, 'path', None)\n\n flexmock(OSBS)\n\n # No-op implementation until this is implemented in osbs-client\n setattr(OSBS, 'import_image', lambda **kwargs: None)\n\n flexmock(OSBS, import_image=lambda name: None)\n\n runner = PostBuildPluginsRunner(tasker, workflow, [{\n 'name': ImportImagePlugin.key,\n 'args': {\n 'url': '',\n 'verify_ssl': False,\n 'use_auth': False\n }}])\n\n return runner\n\n\ndef must_not_be_called(*_):\n \"\"\"\n Set as implementation for methods than must not be called\n \"\"\"\n\n assert False\n\n\ndef test_bad_setup():\n \"\"\"\n Try all the early-fail paths.\n \"\"\"\n\n runner = prepare()\n\n flexmock(OSBS, import_image=must_not_be_called)\n\n # No build JSON\n with pytest.raises(PluginFailedException):\n runner.run()\n\n # No metadata\n os.environ[\"BUILD\"] = json.dumps({})\n with pytest.raises(PluginFailedException):\n runner.run()\n\n # No imagestream label\n os.environ[\"BUILD\"] = json.dumps({\n \"metadata\": {\n \"labels\": {\n }\n }\n })\n with pytest.raises(PluginFailedException):\n runner.run()\n\n\nclass Collect(object):\n \"\"\"\n Collect the values a method is called with.\n \"\"\"\n\n def __init__(self):\n self.called_with = []\n\n def called(self, *args, **kwargs):\n \"\"\"\n Set this as the implementation for the method to watch.\n \"\"\"\n self.called_with.append((args, kwargs))\n\n def raise_exc(self, *args, **kwargs):\n raise RuntimeError \n\n\ndef test_import_image():\n \"\"\"\n Test action of plugin.\n \"\"\"\n\n runner = prepare()\n\n my_imagestream = 'fedora'\n\n collect = Collect()\n flexmock(OSBS, import_image=collect.called)\n\n # No namespace in metadata\n os.environ[\"BUILD\"] = json.dumps({\n \"metadata\": {\n \"labels\": {\n \"imagestream\": my_imagestream\n }\n }\n })\n runner.run()\n\n # import_image() is called with the correct arguments\n # (no namespace keyword)\n assert collect.called_with == [((my_imagestream,), {})]\n\n # Namespace in metadata\n collect = Collect()\n flexmock(OSBS, import_image=collect.called)\n namespace = 'namespace'\n os.environ[\"BUILD\"] = json.dumps({\n \"metadata\": {\n \"namespace\": namespace,\n \"labels\": {\n \"imagestream\": my_imagestream\n }\n }\n })\n runner.run()\n\n # import_image() is called with the correct arguments\n # (including namespace keyword)\n assert collect.called_with == [((my_imagestream,),\n {'namespace': namespace})]\n\n\ndef test_exception_during_import():\n \"\"\"\n The plugin should fail if the import fails.\n \"\"\"\n\n runner = prepare()\n\n my_imagestream = 'fedora'\n collect = Collect()\n flexmock(OSBS, import_image=collect.raise_exc)\n\n os.environ[\"BUILD\"] = json.dumps({\n \"metadata\": {\n \"labels\": {\n \"imagestream\": my_imagestream\n }\n }\n })\n\n with pytest.raises(PluginFailedException):\n runner.run()\n", "sub_path": "tests/plugins/test_import_image.py", "file_name": "test_import_image.py", "file_ext": "py", "file_size_in_byte": 4309, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tests.constants.INPUT_IMAGE", "line_number": 27, "usage_type": "name"}, {"api_name": "atomic_reactor.util.ImageName", "line_number": 30, "usage_type": "call"}, {"api_name": "atomic_reactor.core.DockerTasker", "line_number": 38, "usage_type": "call"}, {"api_name": "atomic_reactor.inner.DockerBuildWorkflow", "line_number": 39, "usage_type": "call"}, {"api_name": "tests.constants.SOURCE", "line_number": 39, "usage_type": "argument"}, {"api_name": "flexmock.flexmock", "line_number": 46, "usage_type": "call"}, {"api_name": "osbs.api.OSBS", "line_number": 46, "usage_type": "argument"}, {"api_name": "osbs.api.OSBS", "line_number": 49, "usage_type": "argument"}, {"api_name": "flexmock.flexmock", "line_number": 51, "usage_type": "call"}, {"api_name": "osbs.api.OSBS", "line_number": 51, "usage_type": "argument"}, {"api_name": "atomic_reactor.plugin.PostBuildPluginsRunner", "line_number": 53, "usage_type": "call"}, {"api_name": "atomic_reactor.plugins.post_import_image.ImportImagePlugin.key", "line_number": 54, "usage_type": "attribute"}, {"api_name": "atomic_reactor.plugins.post_import_image.ImportImagePlugin", "line_number": 54, "usage_type": "name"}, {"api_name": "flexmock.flexmock", "line_number": 79, "usage_type": "call"}, {"api_name": "osbs.api.OSBS", "line_number": 79, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 82, "usage_type": "call"}, {"api_name": "atomic_reactor.plugin.PluginFailedException", "line_number": 82, "usage_type": "argument"}, {"api_name": "os.environ", "line_number": 86, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 86, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 87, "usage_type": "call"}, {"api_name": "atomic_reactor.plugin.PluginFailedException", "line_number": 87, "usage_type": "argument"}, {"api_name": "os.environ", "line_number": 91, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 91, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 97, "usage_type": "call"}, {"api_name": "atomic_reactor.plugin.PluginFailedException", "line_number": 97, "usage_type": "argument"}, {"api_name": "flexmock.flexmock", "line_number": 129, "usage_type": "call"}, {"api_name": "osbs.api.OSBS", "line_number": 129, "usage_type": "argument"}, {"api_name": "os.environ", "line_number": 132, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 132, "usage_type": "call"}, {"api_name": "flexmock.flexmock", "line_number": 147, "usage_type": "call"}, {"api_name": "osbs.api.OSBS", "line_number": 147, "usage_type": "argument"}, {"api_name": "os.environ", "line_number": 149, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 149, "usage_type": "call"}, {"api_name": "flexmock.flexmock", "line_number": 174, "usage_type": "call"}, {"api_name": "osbs.api.OSBS", "line_number": 174, "usage_type": "argument"}, {"api_name": "os.environ", "line_number": 176, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 176, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 184, "usage_type": "call"}, {"api_name": "atomic_reactor.plugin.PluginFailedException", "line_number": 184, "usage_type": "argument"}]} +{"seq_id": "651578315", "text": "from django.http import Http404\nfrom rest_framework import serializers\n\nfrom shop.controllers.image import ImageController\nfrom shop.dal.image import ImageDAL\nfrom shop.dal.product import ProductDAL\nfrom shop.dal.product_material import ProductMaterialDAL\nfrom shop.models import Product\nfrom shop.tools import are_all_elements_in_list\n\n\nclass ProductController:\n @classmethod\n def get_product_list(cls, requesting_user, category_pk):\n if requesting_user.is_staff:\n return ProductDAL.get_all_or_category_products(category_pk)\n else:\n return ProductDAL.get_available_or_category_products(category_pk)\n\n @classmethod\n def get_product(cls, product_pk, is_staff):\n try:\n if is_staff:\n return ProductDAL.get_any_product_by_pk(product_pk)\n else:\n return ProductDAL.get_available_product_by_pk(product_pk)\n except Product.DoesNotExist:\n raise Http404\n\n @classmethod\n def create_product(cls, category, name, price, description, size, weight, stock, is_available, materials=None,\n images=None):\n product = ProductDAL.insert_product(category, name, price, description, size, weight, stock, is_available)\n if materials is not None:\n cls.add_materials_to_product(product, materials)\n if images is not None:\n ProductDAL.create_images(product, images)\n\n @classmethod\n def add_materials_to_product(cls, product_obj, material_names):\n existing_materials = {material.name: material for material in ProductMaterialDAL.get_all_materials()}\n for material_name in material_names:\n if material_name in existing_materials:\n ProductMaterialDAL.add_products(existing_materials[material_name], [product_obj])\n else:\n new_material = ProductMaterialDAL.insert_material(material_name)\n ProductMaterialDAL.add_products(new_material, [product_obj])\n\n @classmethod\n def update_product(cls, product_pk, category, name, price, description, size, weight, stock, is_available,\n materials=None, images=None, images_to_delete=None):\n product_obj = cls.get_product(product_pk, True)\n cls.update_product_materials(product_obj, materials)\n if images_to_delete is not None:\n cls.process_images_to_delete(product_obj, images_to_delete)\n if images is not None:\n ProductDAL.create_images(product_obj, images)\n ProductDAL.update_product(product_obj, category, name, price, description, size, weight, stock, is_available)\n\n @classmethod\n def update_product_materials(cls, product_obj, new_materials):\n if new_materials is None:\n ProductDAL.delete_all_product_materials(product_obj)\n else:\n current_materials = {material.name: material for material in\n ProductDAL.get_all_product_materials(product_obj)}\n cls.delete_unnecessary_materials(product_obj, current_materials, new_materials)\n cls.add_necessary_materials(product_obj, current_materials, new_materials)\n\n @classmethod\n def delete_unnecessary_materials(cls, product_obj, current_materials, new_materials):\n for current_material_name in current_materials:\n if current_material_name not in new_materials:\n ProductDAL.remove_product_material(product_obj, current_materials[current_material_name])\n\n @classmethod\n def add_necessary_materials(cls, product_obj, current_materials, new_materials):\n materials_to_add = []\n for new_material in new_materials:\n if new_material not in current_materials:\n materials_to_add.append(new_material)\n cls.add_materials_to_product(product_obj, materials_to_add)\n\n @classmethod\n def process_images_to_delete(cls, product_obj, images_pk_to_delete):\n cls.validate_images_pk_to_delete(product_obj, images_pk_to_delete)\n images_to_delete = [ImageController.get_image(image_pk) for image_pk in images_pk_to_delete]\n [ImageDAL.delete_image(image) for image in images_to_delete]\n\n @classmethod\n def validate_images_pk_to_delete(cls, product_obj, images_pk_to_delete):\n existing_images_pk = [obj.pk for obj in ProductDAL.get_all_product_images(product_obj)]\n if not are_all_elements_in_list(images_pk_to_delete, existing_images_pk):\n raise serializers.ValidationError({'images_to_delete': 'Image with such pk doesn\\'t belong to this '\n 'product or doesn\\'t exist!'})\n\n @classmethod\n def delete_product(cls, product_pk):\n ProductDAL.delete_product(cls.get_product(product_pk, True))\n\n @classmethod\n def delete_product_images(cls, product_pk):\n ProductDAL.delete_images(cls.get_product(product_pk, True))\n", "sub_path": "shop/controllers/product.py", "file_name": "product.py", "file_ext": "py", "file_size_in_byte": 4940, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "shop.dal.product.ProductDAL.get_all_or_category_products", "line_number": 16, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 16, "usage_type": "name"}, {"api_name": "shop.dal.product.ProductDAL.get_available_or_category_products", "line_number": 18, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 18, "usage_type": "name"}, {"api_name": "shop.dal.product.ProductDAL.get_any_product_by_pk", "line_number": 24, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 24, "usage_type": "name"}, {"api_name": "shop.dal.product.ProductDAL.get_available_product_by_pk", "line_number": 26, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 26, "usage_type": "name"}, {"api_name": "shop.models.Product.DoesNotExist", "line_number": 27, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 27, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 28, "usage_type": "name"}, {"api_name": "shop.dal.product.ProductDAL.insert_product", "line_number": 33, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 33, "usage_type": "name"}, {"api_name": "shop.dal.product.ProductDAL.create_images", "line_number": 37, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 37, "usage_type": "name"}, {"api_name": "shop.dal.product_material.ProductMaterialDAL.get_all_materials", "line_number": 41, "usage_type": "call"}, {"api_name": "shop.dal.product_material.ProductMaterialDAL", "line_number": 41, "usage_type": "name"}, {"api_name": "shop.dal.product_material.ProductMaterialDAL.add_products", "line_number": 44, "usage_type": "call"}, {"api_name": "shop.dal.product_material.ProductMaterialDAL", "line_number": 44, "usage_type": "name"}, {"api_name": "shop.dal.product_material.ProductMaterialDAL.insert_material", "line_number": 46, "usage_type": "call"}, {"api_name": "shop.dal.product_material.ProductMaterialDAL", "line_number": 46, "usage_type": "name"}, {"api_name": "shop.dal.product_material.ProductMaterialDAL.add_products", "line_number": 47, "usage_type": "call"}, {"api_name": "shop.dal.product_material.ProductMaterialDAL", "line_number": 47, "usage_type": "name"}, {"api_name": "shop.dal.product.ProductDAL.create_images", "line_number": 57, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 57, "usage_type": "name"}, {"api_name": "shop.dal.product.ProductDAL.update_product", "line_number": 58, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 58, "usage_type": "name"}, {"api_name": "shop.dal.product.ProductDAL.delete_all_product_materials", "line_number": 63, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 63, "usage_type": "name"}, {"api_name": "shop.dal.product.ProductDAL.get_all_product_materials", "line_number": 66, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 66, "usage_type": "name"}, {"api_name": "shop.dal.product.ProductDAL.remove_product_material", "line_number": 74, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 74, "usage_type": "name"}, {"api_name": "shop.controllers.image.ImageController.get_image", "line_number": 87, "usage_type": "call"}, {"api_name": "shop.controllers.image.ImageController", "line_number": 87, "usage_type": "name"}, {"api_name": "shop.dal.image.ImageDAL.delete_image", "line_number": 88, "usage_type": "call"}, {"api_name": "shop.dal.image.ImageDAL", "line_number": 88, "usage_type": "name"}, {"api_name": "shop.dal.product.ProductDAL.get_all_product_images", "line_number": 92, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 92, "usage_type": "name"}, {"api_name": "shop.tools.are_all_elements_in_list", "line_number": 93, "usage_type": "call"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 94, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 94, "usage_type": "name"}, {"api_name": "shop.dal.product.ProductDAL.delete_product", "line_number": 99, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 99, "usage_type": "name"}, {"api_name": "shop.dal.product.ProductDAL.delete_images", "line_number": 103, "usage_type": "call"}, {"api_name": "shop.dal.product.ProductDAL", "line_number": 103, "usage_type": "name"}]} +{"seq_id": "138959219", "text": "import numpy as np\nimport bct\nfrom sklearn.externals import joblib\nfrom my_settings import *\nfrom sklearn.cross_validation import (StratifiedKFold, cross_val_score)\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.grid_search import GridSearchCV\n\nsubjects = [\"0008\", \"0009\", \"0010\", \"0012\", \"0014\", \"0015\", \"0016\",\n \"0017\", \"0018\", \"0019\", \"0020\", \"0021\", \"0022\"]\n\ncls_all = []\npln_all = []\n\nscores_all = np.empty([4, 6])\n\nfor subject in subjects:\n cls = np.load(source_folder + \"graph_data/%s_classic_pow_pln.npy\" %\n subject).item()\n\n pln = np.load(source_folder + \"graph_data/%s_plan_pow_pln.npy\" %\n subject).item()\n\n cls_all.append(cls)\n pln_all.append(pln)\n\n\ncls_all_2 = np.asarray(cls_all)\npln_all_2 = np.asarray(pln_all)\n\nfull_matrix = np.concatenate([cls_all_2, pln_all_2], axis=0)\n\nthreshold = np.median(full_matrix[np.nonzero(full_matrix)]) + \\\n np.std(full_matrix[np.nonzero(full_matrix)])\n\ndata_cls_bin = cls_all > threshold\ndata_pln_bin = pln_all > threshold\n\n\nfor k, band in enumerate(bands.keys()):\n data_cls = []\n for j in range(len(cls_all)):\n tmp = cls_all[j][band]\n data_cls.append(np.asarray([bct.strengths_und(g)\n for g in tmp]).mean(axis=0))\n data_pln = []\n for j in range(len(pln_all)):\n tmp = pln_all[j][band]\n data_pln.append(np.asarray([bct.strengths_und(g)\n for g in tmp]).mean(axis=0))\n\n data_cls = np.asarray(data_cls)\n data_pln = np.asarray(data_pln)\n\n full_matrix = np.concatenate([data_cls, data_pln], axis=0)\n\n threshold = np.median(full_matrix[np.nonzero(full_matrix)]) + \\\n np.std(full_matrix[np.nonzero(full_matrix)])\n\n data_cls_bin = data_cls > threshold\n data_pln_bin = data_pln > threshold\n\n\n X = np.vstack([data_cls, data_pln])\n y = np.concatenate([np.zeros(len(data_cls)), np.ones(len(data_pln))])\n\n cv = StratifiedKFold(y, n_folds=6, shuffle=True)\n\n cv_params = {\"learning_rate\": np.arange(0.1, 1.1, 0.1),\n 'n_estimators': np.arange(1, 80, 2)}\n\n grid = GridSearchCV(AdaBoostClassifier(),\n cv_params,\n scoring='accuracy',\n cv=cv,\n n_jobs=4,\n verbose=1)\n grid.fit(X, y)\n ada_cv = grid.best_estimator_\n\n scores = cross_val_score(ada_cv, X, y, cv=cv)\n scores_all[k, :] = scores\n\n # save the classifier\n joblib.dump(\n ada_cv,\n source_folder + \"graph_data/sk_models/path-strength_ada_%s_pln.plk\" % band)\n\nnp.save(source_folder + \"graph_data/path-strength_scores_all_pln.npy\", scores_all)\n", "sub_path": "graph_path-strength_ada_bin.py", "file_name": "graph_path-strength_ada_bin.py", "file_ext": "py", "file_size_in_byte": 2712, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.empty", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 44, "usage_type": "call"}, {"api_name": "bct.strengths_und", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 49, "usage_type": "call"}, {"api_name": "bct.strengths_und", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 65, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.StratifiedKFold", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 70, "usage_type": "call"}, {"api_name": "sklearn.grid_search.GridSearchCV", "line_number": 72, "usage_type": "call"}, {"api_name": "sklearn.ensemble.AdaBoostClassifier", "line_number": 72, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.cross_val_score", "line_number": 81, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 85, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 85, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 89, "usage_type": "call"}]} +{"seq_id": "332619461", "text": "import tempfile\nimport webbrowser\nimport time\nimport os\nimport sys\nimport locale\n\nfrom six.moves import input\n\nfrom ..base_backend import BaseServiceBackend\n\n\nclass BrowserBackend(BaseServiceBackend):\n\n # ***************\n # Submitting Task\n # ***************\n\n def build_task_request(self, data=None, options=None):\n fd, path = tempfile.mkstemp()\n with open(path, 'wb') as out:\n out.write(data)\n os.close(fd)\n url = 'file://' + path\n return {\n 'url': url,\n 'data': None,\n }\n\n def parse_task_response(self, res):\n return {\n 'task_id': res['url'].replace('file://', ''),\n }\n\n # ***************\n # Checking Result\n # ***************\n\n def build_result_request(self, task_id):#, options=None):\n url = 'file://' + task_id\n return {\n 'url': url,\n 'data': None,\n }\n\n def parse_result_response(self, res):\n webbrowser.open(url=res['url'])\n # Wait some time, skip some debug messages\n # which browser could dump to console\n time.sleep(0.5)\n solution = input('Enter solution: ')\n if hasattr(solution, 'decode'):\n solution = solution.decode(sys.stdin.encoding or\n locale.getpreferredencoding(True))\n path = res['url'].replace('file://', '')\n os.unlink(path)\n return {\n 'result': solution,\n }\n", "sub_path": "decaptcher/backend/browser.py", "file_name": "browser.py", "file_ext": "py", "file_size_in_byte": 1484, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "base_backend.BaseServiceBackend", "line_number": 13, "usage_type": "name"}, {"api_name": "tempfile.mkstemp", "line_number": 20, "usage_type": "call"}, {"api_name": "os.close", "line_number": 23, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 50, "usage_type": "call"}, {"api_name": "six.moves.input", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 53, "usage_type": "attribute"}, {"api_name": "locale.getpreferredencoding", "line_number": 54, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "470901265", "text": "# Enttec USB DMX Pro control interface\n# Written by Ryan Smith \n# Based on the API documented in http://www.enttec.com/docs/dmx_usb_pro_api_spec.pdf\n# This library is available under the MIT license found in the LICENCE file\n\nimport sys\nimport serial\n#import serial.tools\n#import serial.tools.list_ports\nimport struct\nimport threading\nimport time\nimport traceback\n\nclass EnttecUsbDmxProWidget:\n# Constructor, destructors and globals\n def __init__(self):\n self.serial = serial.Serial()\n self.debug = {'SerialBuffer':False, 'RXWarning':False}\n self.widget = {'SerialNumber':0x0FFFFFFFF, 'UserParameters':{'FirmwareVersion':[0,0], 'DMXBreak':96, 'DMXMarkAfterBreak':10, 'DMXRate':40}} # Initialize the register. Note that DMXOutBreak and DMXMarkAfterBreak are in 10.67us units\n self.widget_event = {'SerialNumber':threading.Event(), 'UserParameters':threading.Event(), 'ThreadExit':threading.Event()} # Initialize the data requests variable\n self.serial.port = \"\"\n self.dmxRX = {\"status\":0,\"frame\":[]}\n if sys.version_info > (3,0):\n self.py2 = False\n else:\n self.py2 = True\n def setPort(self,port,baud=57600):\n self.serial.port = port\n self.serial.baudrate = baud\n def getPort(self):\n return self.serial.port\n def setDebug(mode,value):\n self.debug[mode] = value\n def getDebug(mode=\"all\"):\n if mode == \"all\":\n return self.debug\n else:\n return self.debug[mode]\n\n# Port control\n def connect(self):\n # Open and connect to the serial port\n self.serial.open()\n self.widget_event['ThreadExit'].clear()\n self.thread_read = threading.Thread(target=self.reader)\n self.thread_read.setDaemon(True)\n self.thread_read.setName(\"EnttecUsbDmxPro Reader on \"+self.serial.port)\n self.thread_read.start()\n def open(self, baud=57600): # \n self.connect(baud)\n def isOpen(self):\n return self.serial.isOpen()\n #def list(self):\n # print(serial.tools.list_ports.comports())\n def disconnect(self):\n # Disconnects from the serial port and closes read thread\n print('Stopping the read thread...')\n self.widget_event['ThreadExit'].set() # Signal the thread to stop\n self.widget_event['ThreadExit'].wait() # Wait for the signal to be cleared\n self.thread_read.join() # Make sure the thread is stopped\n print(\"Closing Enttec USB DMX Pro on\",self.serial.port,\"at\",self.serial.baudrate,\"baud\")\n self.serial.close() # Close the serial port\n print(\"Close successful!\")\n def clearBuffer(self):\n self.serialbuffer = []\n def close(self): \n self.disconnect()\n\n def sendmsg(self,label,message=[]):\n l = len(message)\n lm = l >> 8\n ll = l-(lm << 8)\n if l <= 600:\n if self.isOpen():\n self.serial.write(bytearray([0x7E,label,ll,lm]+message+[0xE7]))\n else:\n sys.stderr.write('TX_ERROR: Malformed message! The message to be send is too long!\\n')\n \n# Serial reading thread (and related)\n def reader(self):\n # loop forever and copy the supported values to their appropriate registers\n rx = b''\n self.serialbuffer = []\n while not self.widget_event['ThreadExit'].is_set():\n try:\n if self.serial.inWaiting() > 0:\n rx += self.serial.read(self.serial.inWaiting()) # Read the buffer into a variable\n for i in rx: # Convert the byte string into something a little more useful\n if self.py2:\n i =struct.unpack('B',i)[0]\n self.serialbuffer += [i]\n rx = b''\n si = 0\n for i in self.serialbuffer: # Find the start byte\n if i != 0x7E:\n si += 1\n else:\n break\n if si > 0: # Remove anything before the start byte\n if self.debug['RXWarning']:\n sys.stderr.write('RX_WARNING: Removing invalid data from buffer\\n')\n self.serialbuffer = self.serialbuffer[si:-1]\n if len(self.serialbuffer) >= 4:\n m_label = self.serialbuffer[1]\n m_size = self.serialbuffer[2] + (self.serialbuffer[3] << 8)\n m_cont = self.serialbuffer[4:4+m_size]\n endbyte_loc=4+m_size\n if endbyte_loc >= len(self.serialbuffer):\n if len(m_cont) == m_size:\n if self.debug['RXWarning']:\n sys.stderr.write('RX_WARNING: No end byte was found, but the message appears to be complete. Message will be parsed.\\n')\n self.parse(m_label,m_cont)\n self.serialbuffer = []\n else:\n if self.debug['RXWarning']:\n sys.stderr.write('RX_WARNING: Recieved incomplete message {0}\\n'.format(self.serialbuffer))\n elif self.serialbuffer[endbyte_loc] != 0xE7:\n if self.debug['RXWarning']:\n sys.stderr.write('RX_WARNING: Malformed message! Expecting an end byte, but did not find one! Found byte {0} at location {2} in self.serialbuffer {1}\\n'.format(self.serialbuffer[4+m_size],self.serialbuffer,endbyte_loc))\n self.serialbuffer = self.serialbuffer[endbyte_loc+1:]\n else:\n self.parse(m_label,m_cont)\n if len(self.serialbuffer) > endbyte_loc:\n self.serialbuffer = self.serialbuffer[endbyte_loc+1:]\n except:\n e = sys.exc_info()\n sys.stderr.write('RX_FAIL: {0}: {1}\\n'.format(e[0],e[1]))\n traceback.print_tb(e[2])\n sys.stderr.write('Data in queue: {0}\\n'.format(self.serialbuffer))\n time.sleep(0.01)\n self.widget_event['ThreadExit'].clear()\n def parse(self,label,message):\n # The message label and remaining data are parsed and values stored in the registers\n if label == 6: # Get recieved DMX frame\n self.dmxRX = {\"status\":message[0],\"frame\":message[1:-1]}\n\n \n# Widget properties and parameters\n \n def getWidgetParameters(self):\n # Writes the message to get the options set in the widget hardware\n # WARNING: Some third-party DMX Pro Compatible devices DO NOT support this operation\n raise NotImplemented(\"The function in EnttecUsbDmxPro is not implemeted yet\")\n\n def setWidgetParameters(self, name, value):\n # Sets the paramter `name` to `value`\n # WARNING: Some third-party DMX Pro Compatible devices DO NOT support this operation\n raise NotImplemented(\"The function setWidgetParameters in EnttecUsbDmxPro is not implemeted yet\")\n \n def getWidgetSerialNumber(self):\n # Returns the serial number of the ENTTEC USB DMX Pro widget\n # WARNING: Some third-party DMX Pro Compatible devices DO NOT support this operation\n raise NotImplemented(\"The function in EnttecUsbDmxPro is not implemeted yet\")\n# DMX\n def sendDMX(self, channels):\n # Sends an array of up to 512 channels\n data = [0] + channels\n while len(data) < 25:\n data += [0]\n self.sendmsg(5,[0]+data)\n \n# RDM\n def getRecievedFrame(self):\n # Returns the last DMX frame recieved from the widget\n return self.dmxRX\n \n def requestRDM(self):\n # Sends an RDM packet on the DMX and changes the direction to input to recieve it\n raise NotImplemented(\"The function requestRDM in EnttecUsbDmxPro is not implemeted yet\")\n \n def requestDmxOnChange(self):\n # Tells the widget to only send the DMX packet to the comptuer if the \n # values have changed on the input port\n raise NotImplemented(\"The function requestDmxOnChange in EnttecUsbDmxPro is not implemeted yet\")\n \n def sendRdmDiscovery(self):\n # Sends an RDM discovery packet\n raise NotImplemented(\"The function sendRdmDiscovery in EnttecUsbDmxPro is not implemeted yet\")\n\n# Exceptions for handling DMX errors\nclass DMXException(Exception):\n def __init__(self,message):\n self.message = message\nclass UsbDmxProException(Exception):\n def __init__(self,message):\n self.message = message\n", "sub_path": "EnttecUsbDmxProWidget.py", "file_name": "EnttecUsbDmxProWidget.py", "file_ext": "py", "file_size_in_byte": 8624, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "serial.Serial", "line_number": 18, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 24, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 78, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 78, "usage_type": "attribute"}, {"api_name": "struct.unpack", "line_number": 91, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 102, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 102, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 112, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 112, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 117, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 117, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 120, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 120, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 127, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 128, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 128, "usage_type": "attribute"}, {"api_name": "traceback.print_tb", "line_number": 129, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 130, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 130, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "281671624", "text": "from django.conf.urls import url\nfrom genie.views import create, index, show, offline\n\napp_name = \"genie\"\n\nurlpatterns = [\n url(r'^$', index, name=\"index\"),\n url(r'^offline/$', offline, name=\"offline\"),\n url(r'^(?P\\d+)/create$', create, name=\"create\"),\n url(r'^(?P\\d+)/show$', show, name=\"show\"),\n]\n", "sub_path": "genie/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 319, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "genie.views.index", "line_number": 7, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "genie.views.offline", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "genie.views.create", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "genie.views.show", "line_number": 10, "usage_type": "argument"}]} +{"seq_id": "148287251", "text": "from django import forms\nfrom django.forms import ModelForm\n\nfrom expense_report.models import Category, ExpenseReport, Employee\n\n\nclass ExpenseCSVUploadForm(forms.Form):\n expense_file = forms.FileField(label='Expense File', required=True)\n\n def clean_expense_file(self):\n cleaned_data = self.cleaned_data['expense_file']\n\n # Basic validation to check for csv file extension.\n if not cleaned_data.name.endswith('.csv'):\n self.add_error('expense_file', 'Please, upload a csv file')\n\n return cleaned_data\n\n\nclass ExpenseReportForm(ModelForm):\n class Meta:\n model = ExpenseReport\n fields = [\n 'date',\n 'category',\n 'expense_description',\n 'pre_tax_amount',\n 'tax_name',\n 'tax_amount',\n 'employee',\n ]\n\n\nclass EmployeeForm(ModelForm):\n class Meta:\n model = Employee\n fields = ['name', 'address']\n\n\nclass CategoryForm(ModelForm):\n class Meta:\n model = Category\n fields = ['name']\n", "sub_path": "se_challenge/expense_report/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1055, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.forms.Form", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 7, "usage_type": "name"}, {"api_name": "django.forms.FileField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 8, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 20, "usage_type": "name"}, {"api_name": "expense_report.models.ExpenseReport", "line_number": 22, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 34, "usage_type": "name"}, {"api_name": "expense_report.models.Employee", "line_number": 36, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 40, "usage_type": "name"}, {"api_name": "expense_report.models.Category", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "486305929", "text": "#!/usr/bin/python3\nimport subprocess\nimport pycparser\nimport os\nimport json\nfrom annotate_JSON import annotate\nfrom clang import clang\n\n# run the gcc command on c file\n# gcc -nostdinc -E -I/home/rg/pycparser-master/utils/fake_libc_include test.c > testPP.c\n\n#Import data from JSON file\nwith open('/home/klee/FYP/clean_UAT/UAT/config_template.json') as f:\n data = json.load(f)\n\n\nfilepath = \"/home/klee/pycparser/utils/fake_libc_include/\"\n\n\nprint_output = []\narray_list = []\n\n# check existing fake headers\nexisting_h = []\nwith open(\"ls.out\", \"r\") as f:\n existing_h = f.readlines()\n existing_h = [line.strip() for line in existing_h]\n\n# run gcc command for each file indicated in JSON\nfor filename, functions in data.items():\n \n ## HANDLE HEADERS ##\n header_files = []\n\n #get header name\n with open(filename, \"r\") as f:\n lines = f.readlines()\n for line in lines:\n if \"#include\" in line:\n #print(\"#include found at line:\" + line)\n if \"<\" in line:\n header = line.split(\"<\")[1].strip()[:-1]\n header_files.append(header)\n elif '\"' in line:\n header = line.split('\"')[1].strip()\n header_files.append(header)\n elif \"'\" in line:\n header = line.split(\"'\")[1].strip()\n header_files.append(header)\n \n for header in header_files:\n if header not in existing_h:\n # create the fake headers\n #fpath = filepath + header\n #print(os.getcwd()) \n with open(os.path.join(filepath, header), \"w\") as f:\n #print(os.getcwd())\n to_write = '#include \"_fake_defines.h\"\\n#include \"_fake_typedefs.h\"\\n'\n f.write(to_write)\n\n ## Comment out SCANF ##\n content = []\n with open(filename, \"r\") as f:\n lines = f.readlines()\n for line in lines:\n if \"scanf\" in line and \"/*\" not in line:\n content.append(\"/*\" + line + \"*/\")\n else:\n content.append(line)\n \n with open(filename, \"w\") as f:\n for line in content:\n f.write(line) \n \n\n\n #run gcc command for file indicated in JSON\n command = \"gcc -nostdinc -E -I/home/klee/pycparser/utils/fake_libc_include \" + filename + \" > \" + filename[:-2] + \"_PP.c\"\n os.system (command)\n \n #extract relevant lines\n ast = pycparser.parse_file(filename[:-2] + \"_PP.c\")\n ast_text = ast.show(showcoord=True, buf=open(filename[:-2] + \"_testout.txt\", \"w+\"))\n ast_lines = []\n with open(filename[:-2]+ \"_testout.txt\", \"r\") as f:\n ast_lines = f.readlines()\n\n\n #start of line number detection\n for i in functions:\n for function, arguments in i.items():\n #filter by function\n #if function == \"main\":\n filtered_lines = []\n for index, line in enumerate(ast_lines):\n if \"FuncDef:\" in line:\n next_line = ast_lines[index + 1]\n if (function + \",\") in next_line:\n # remove the other lines before this line\n filtered_lines = ast_lines[index + 1:]\n break # efficiency\n \n for argument in arguments:\n #filtered = [line for line in ast_lines if argument in line]\n search_decl = \"Decl: \" + argument + \",\"\n decl = []\n #check if argument is an array\n for index, line in enumerate(filtered_lines):\n if search_decl in line:\n decl.append(line)\n #print(argument + \" found at index \" + str(index))\n next_line = filtered_lines[index + 1]\n #print(\"Checking next line after \" + argument + \" declaration... (index: \" + str(index) + \")\")\n #print(next_line)\n if \"ArrayDecl\" in next_line:\n array_list.append(argument)\n\n\n #get line number of the argument\n line = decl[0]\n line_number = line.split(\":\")[2]\n\n \n is_array = False\n if argument in array_list:\n is_array = True\n\n print_output.append([filename, function, argument, is_array ,line_number])\n\nprint(print_output)\n\n## Annotate required C files and produce .bc from C files\nshifted = 1\nunique_files = []\nfor item in print_output:\n if item[0] not in unique_files:\n unique_files.append(item[0])\n shifted = 1\n annotate(item[0], item[2], item[3], shifted + int(item[4]))\n shifted += 1\n\n## Produce .bc from C files\n#unique_files = []\n#for item in print_output:\n #if item[0] not in unique_files:\n #unique_files.append(item[0])\n\nfor file in unique_files:\n print(clang(file[:-2]))\n# klee_command = \"clang -I /home/klee/klee_src/include -emit-llvm -c -O0 -Xclang -disable-O0-optnone \" + file[:-2] + \"_annotated.c\"\n# os.system(klee_command)\n\n\n \n\n", "sub_path": "clean_UAT/UAT/parser_JSON.py", "file_name": "parser_JSON.py", "file_ext": "py", "file_size_in_byte": 5138, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 79, "usage_type": "call"}, {"api_name": "pycparser.parse_file", "line_number": 82, "usage_type": "call"}, {"api_name": "annotate_JSON.annotate", "line_number": 139, "usage_type": "call"}, {"api_name": "clang.clang", "line_number": 149, "usage_type": "call"}]} +{"seq_id": "138150581", "text": "from django.core.urlresolvers import reverse\nfrom django.contrib.auth.models import User\n\nfrom rest_framework.test import APITestCase\nfrom rest_framework import status\nfrom rest_framework.test import APIClient\nfrom .models import Task\n\n\ndef create_user(username='foo', password='abc123456', **kwargs):\n return User.objects.create_user(username=username, password=password, **kwargs)\n\n\ndef create_task(task='foo-bar', **kwargs):\n return Task.objects.create(task=task, **kwargs)\n\n\nclass TaskApiTest(APITestCase):\n\n def setUp(self):\n self.user = create_user()\n self.task = create_task(user=self.user)\n self.url = reverse('task-list')\n self.url_set_is_done = reverse(\n 'task-set-is-done',\n kwargs={'pk': self.task.pk}\n )\n\n def test_no_login(self):\n response = self.client.post(self.url)\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_task_list(self):\n client = APIClient()\n client.credentials(HTTP_AUTHORIZATION='Token ' + self.user.auth_token.key)\n\n response = client.get(self.url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n def test_task_create(self):\n client = APIClient()\n client.credentials(HTTP_AUTHORIZATION='Token ' + self.user.auth_token.key)\n\n self.assertEqual(Task.objects.count(), 1)\n params = {'user': self.user.pk, 'task': 'task test'}\n response = client.post(self.url, params)\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(Task.objects.count(), 2)\n\n def test_set_is_done_true(self):\n client = APIClient()\n client.credentials(HTTP_AUTHORIZATION='Token ' + self.user.auth_token.key)\n\n self.assertFalse(self.task.is_done)\n response = client.put(self.url_set_is_done)\n self.assertEqual(response.status_code, status.HTTP_206_PARTIAL_CONTENT)\n self.assertEqual(response.content, '{\"message\":\"is done modified\"}')\n self.task.refresh_from_db()\n self.assertTrue(self.task.is_done)\n\n def test_set_is_done_false(self):\n client = APIClient()\n client.credentials(HTTP_AUTHORIZATION='Token ' + self.user.auth_token.key)\n\n self.task.is_done = True\n self.task.save()\n\n self.assertTrue(self.task.is_done)\n response = client.put(self.url_set_is_done)\n self.assertEqual(response.status_code, status.HTTP_206_PARTIAL_CONTENT)\n self.assertEqual(response.content, '{\"message\":\"is done modified\"}')\n self.task.refresh_from_db()\n self.assertFalse(self.task.is_done)\n", "sub_path": "corner/apps/tasks/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 2642, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 11, "usage_type": "name"}, {"api_name": "models.Task.objects.create", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Task.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "models.Task", "line_number": 15, "usage_type": "name"}, {"api_name": "rest_framework.test.APITestCase", "line_number": 18, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 23, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 24, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 31, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 31, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 34, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 38, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 38, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Task.objects.count", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Task.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "models.Task", "line_number": 44, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 47, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 47, "usage_type": "name"}, {"api_name": "models.Task.objects.count", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Task.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "models.Task", "line_number": 48, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 51, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_206_PARTIAL_CONTENT", "line_number": 56, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 56, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 62, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_206_PARTIAL_CONTENT", "line_number": 70, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "576113983", "text": "from datetime import datetime\n\ndef current_date_format(date):\n months = (\"Enero\", \"Febrero\", \"Marzo\", \"Abril\", \"Mayo\", \"Junio\", \"Julio\", \"Agosto\", \"Septiembre\", \"Octubre\", \"Noviembre\", \"Diciembre\")\n day = date.day\n month = months[date.month - 1]\n year = date.year\n messsage = \"{} de {} del {}\".format(day, month, year)\n\n return messsage\n\nnow = datetime.now()\nprint(current_date_format(now))\n", "sub_path": "recursos/ejemplo.py", "file_name": "ejemplo.py", "file_ext": "py", "file_size_in_byte": 409, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.now", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "599082750", "text": "import requests\nimport os\nfrom bs4 import BeautifulSoup\nfrom urllib.parse import urljoin\n\npages = [\n 'fest3.html',\n '4fest.html',\n '6fest.html',\n '7fest.html',\n '8fest.html',\n '9fest.html',\n '10fest.html',\n]\n\nfor week in pages:\n out = os.path.join('out/', week)\n url = urljoin('http://www.ccs.neu.edu/home/matthias/4500-f18/', week)\n page = requests.get(url).text\n soup = BeautifulSoup(page)\n\n grade_table = soup.find(class_='maincolumn').table\n\n rows = grade_table.findAll('tr')[1:]\n\n print(week)\n for row in rows:\n group = row.findAll('td')[0].p.text\n links = [a.get('href') for a in row.findAll('a')]\n\n dl_dir = os.path.join(out, group)\n os.makedirs(dl_dir, exist_ok=True)\n\n print('\\t' + group, end=' ')\n for link in links:\n print(link, end=' ')\n content = requests.get(urljoin(url, link)).text\n open(os.path.join(dl_dir, link), 'w').write(content)\n print()\n", "sub_path": "code/scrape.py", "file_name": "scrape.py", "file_ext": "py", "file_size_in_byte": 988, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "urllib.parse.urljoin", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "344985978", "text": "import numpy as np\nfrom keras import backend as K\nimport os\nimport PIL\nfrom yad2k.utils.draw_boxes import draw_boxes\nfrom yad2k.models.keras_yolo import yolo_eval, yolo_head\nfrom matplotlib import pyplot as plt\n\ndef process_data(images, boxes=None):\n '''processes the data'''\n images = [PIL.Image.fromarray(i) for i in images]\n orig_size = np.array([images[0].width, images[0].height])\n orig_size = np.expand_dims(orig_size, axis=0)\n\n # Image preprocessing.\n processed_images = [i.resize((416, 416), PIL.Image.BICUBIC) for i in images]\n processed_images = [np.array(image, dtype=np.float) for image in processed_images]\n processed_images = [image/255. for image in processed_images]\n\n if boxes is not None:\n # Box preprocessing.\n # Original boxes stored as 1D list of class, x_min, y_min, x_max, y_max.\n boxes = [box.reshape((-1, 5)) for box in boxes]\n # Get extents as y_min, x_min, y_max, x_max, class for comparision with\n # model output.\n boxes_extents = [box[:, [2, 1, 4, 3, 0]] for box in boxes]\n\n # Get box parameters as x_center, y_center, box_width, box_height, class.\n boxes_xy = [0.5 * (box[:, 3:5] + box[:, 1:3]) for box in boxes]\n boxes_wh = [box[:, 3:5] - box[:, 1:3] for box in boxes]\n boxes_xy = [boxxy / orig_size for boxxy in boxes_xy]\n boxes_wh = [boxwh / orig_size for boxwh in boxes_wh]\n boxes = [np.concatenate((boxes_xy[i], boxes_wh[i], box[:, 0:1]), axis=1) for i, box in enumerate(boxes)]\n\n # find the max number of boxes\n max_boxes = 0\n for boxz in boxes:\n if boxz.shape[0] > max_boxes:\n max_boxes = boxz.shape[0]\n\n # add zero pad for training\n for i, boxz in enumerate(boxes):\n if boxz.shape[0] < max_boxes:\n zero_padding = np.zeros( (max_boxes-boxz.shape[0], 5), dtype=np.float32)\n boxes[i] = np.vstack((boxz, zero_padding))\n\n return np.array(processed_images), np.array(boxes)\n else:\n return np.array(processed_images)\n\ndef draw(model_body, class_names, anchors, image_data, image_set='val',\n weights_name='trained_stage_3_best.h5', out_path=\"output_images\", save_all=True):\n '''\n Draw bounding boxes on image data\n '''\n if image_set == 'train':\n image_data = np.array([np.expand_dims(image, axis=0)\n for image in image_data[:int(len(image_data)*.9)]])\n elif image_set == 'val':\n image_data = np.array([np.expand_dims(image, axis=0)\n for image in image_data[int(len(image_data)*.9):]])\n elif image_set == 'all':\n image_data = np.array([np.expand_dims(image, axis=0)\n for image in image_data])\n else:\n ValueError(\"draw argument image_set must be 'train', 'val', or 'all'\")\n # model.load_weights(weights_name)\n print(image_data.shape)\n model_body.load_weights(weights_name)\n\n # Create output variables for prediction.\n yolo_outputs = yolo_head(model_body.output, anchors, len(class_names))\n input_image_shape = K.placeholder(shape=(2, ))\n boxes, scores, classes = yolo_eval(\n yolo_outputs, input_image_shape, score_threshold=0.07, iou_threshold=0)\n\n # Run prediction on overfit image.\n sess = K.get_session() # TODO: Remove dependence on Tensorflow session.\n\n if not os.path.exists(out_path):\n os.makedirs(out_path)\n for i in range(len(image_data)):\n out_boxes, out_scores, out_classes = sess.run(\n [boxes, scores, classes],\n feed_dict={\n model_body.input: image_data[i],\n input_image_shape: [image_data.shape[2], image_data.shape[3]],\n K.learning_phase(): 0\n })\n print('Found {} boxes for image.'.format(len(out_boxes)))\n print(out_boxes)\n\n # Plot image with predicted boxes.\n image_with_boxes = draw_boxes(image_data[i][0], out_boxes, out_classes,\n class_names, out_scores)\n \"\"\"\n # Save the image:\n if save_all or (len(out_boxes) > 0):\n image = PIL.Image.fromarray(image_with_boxes)\n image.save(os.path.join(out_path,str(i)+'.png'))\n \"\"\"\n # To display (pauses the program):\n plt.imshow(image_with_boxes, interpolation='nearest')\n plt.show()", "sub_path": "playground.py", "file_name": "playground.py", "file_ext": "py", "file_size_in_byte": 4361, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PIL.Image.fromarray", "line_number": 11, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 13, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.vstack", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 63, "usage_type": "call"}, {"api_name": "yad2k.models.keras_yolo.yolo_head", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.backend.placeholder", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 73, "usage_type": "name"}, {"api_name": "yad2k.models.keras_yolo.yolo_eval", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.backend.get_session", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 78, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.backend.learning_phase", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 88, "usage_type": "name"}, {"api_name": "yad2k.utils.draw_boxes.draw_boxes", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}]} +{"seq_id": "562012259", "text": "from django.shortcuts import render, get_object_or_404\nfrom .models import Post, News, Like, RegistrationMessage\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.views.generic import ListView\nfrom .forms import EmailPostForm, CommentForm, UserRegistrationForm\nfrom django.core.mail import send_mail\nfrom django.contrib.auth.decorators import login_required\n\n\ndef post_list(request):\n object_list = Post.objects.filter(status='published')\n paginator = Paginator(object_list, 3)\n page = request.GET.get('page') # получаем аттрибут page из запроса, указывающий на нужную страницу\n news = News.objects.all()\n news = news[len(news)-1]\n try:\n posts = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer deliver the first page\n posts = paginator.page(1)\n except EmptyPage:\n # If page is out of range deliver last page of results\n posts = paginator.page(paginator.num_pages)\n return render(request,\n 'DjangoChat/post/list.html',\n {'page': page,\n 'posts': posts,\n 'news': news},)\n\n\n@login_required\ndef post_detail(request, year, month, day, post):\n post = get_object_or_404(Post, slug=post,\n status='published',\n publish__year=year,\n publish__month=month,\n publish__day=day)\n comment_objects = post.comments.filter(active=True)\n paginator = Paginator(comment_objects, 5)\n page = request.GET.get('page')\n likes = Like.objects.filter(post = post)\n is_liked = False\n for l in likes:\n if l.author == request.user:\n is_liked = True\n if request.method == 'POST':\n comment_form = CommentForm(data=request.POST)\n if comment_form.is_valid():\n new_comment = comment_form.save(commit=False)\n new_comment.author = request.user\n new_comment.post = post\n new_comment.save()\n else:\n comment_form = CommentForm()\n try:\n comments = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer deliver the first page\n comments = paginator.page(1)\n except EmptyPage:\n # If page is out of range deliver last page of results\n comments = paginator.page(paginator.num_pages)\n return render(request,\n 'DjangoChat/post/detail.html',\n {'post': post,\n 'page': page,\n 'paginator': paginator,\n 'comments': comments,\n 'comment_form': comment_form,\n })\n\n\n@login_required\ndef post_like(request, year, month, day, post):\n post = get_object_or_404(Post, slug=post,\n status='published',\n publish__year=year,\n publish__month=month,\n publish__day=day)\n comment_objects = post.comments.filter(active=True)\n paginator = Paginator(comment_objects, 5)\n page = request.GET.get('page')\n if request.method == 'GET':\n likes = Like.objects.filter(post=post)\n is_liked = True\n for l in likes:\n if l.author == request.user:\n is_liked = False\n post.like_count-=1\n post.save()\n l.delete()\n break\n if is_liked:\n post.like_count += 1\n post.save()\n like = Like()\n like.author = request.user\n like.post = post\n like.save()\n if request.method == 'POST':\n comment_form = CommentForm(data=request.POST)\n if comment_form.is_valid():\n new_comment = comment_form.save(commit=False)\n new_comment.author = request.user\n new_comment.post = post\n new_comment.save()\n else:\n comment_form = CommentForm()\n try:\n comments = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer deliver the first page\n comments = paginator.page(1)\n except EmptyPage:\n # If page is out of range deliver last page of results\n comments = paginator.page(paginator.num_pages)\n\n return render(request,\n 'DjangoChat/post/detail.html',\n {'post': post,\n 'page': page,\n 'paginator': paginator,\n 'comments': comments,\n 'comment_form': comment_form,\n })\n\n\nclass PostListView(ListView):\n queryset = Post.objects.filter(status='published')\n context_object_name = 'posts'\n paginate_by = 3\n template_name = 'DjangoChat/post/list.html'\n\n\n@login_required\ndef post_share(request, post_id):\n # Retrieve post by id\n post = get_object_or_404(Post, id=post_id, status='published')\n sent = False\n if request.method == 'POST':\n # Form was submitted\n form = EmailPostForm(request.POST)\n if form.is_valid():\n # Form fields passed validation\n cd = form.cleaned_data\n post_url = request.build_absolute_uri(post.get_absolute_url())\n subject = '{} ({}) recommends you reading \"{}\"'.format(request.user.username, request.user.email, post.title)\n message = 'Read \"{}\" at {}\\n\\n{}\\'s comments: {}'.format(post.title, post_url, request.user.email, cd['comments'])\n send_mail(subject, message, 'admin@myblog.com', [cd['to']])\n sent = True\n else:\n form = EmailPostForm()\n return render(request, 'DjangoChat/post/share.html', {'post': post,\n 'form': form,\n 'sent': sent})\n\n\ndef register(request):\n if request.method == 'POST':\n user_form = UserRegistrationForm(request.POST)\n if user_form.is_valid():\n # Create a new user object but avoid saving it yet\n new_user = user_form.save(commit=False)\n # Set the chosen password\n new_user.set_password(user_form.cleaned_data['password'])\n # Save the User object\n new_user.save()\n return render(request, 'registration/register_done.html', {'new_user': new_user})\n else:\n user_form = UserRegistrationForm()\n return render(request, 'registration/register.html', {'user_form': user_form})\n\n\ndef register_confirm(request, key):\n print(key)\n message = get_object_or_404(RegistrationMessage, url=key)\n print('MSG: {}'.format(message))\n message.profile.verified = True\n message.profile.save()\n return render(request, 'registration/register_confirm.html')\n", "sub_path": "Solutions/Task3/853506_Karpyza_Andrey/DjangoChat/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6843, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "models.Post.objects.filter", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 11, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 12, "usage_type": "call"}, {"api_name": "models.News.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "models.News.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.News", "line_number": 14, "usage_type": "name"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 18, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 21, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 33, "usage_type": "argument"}, {"api_name": "django.core.paginator.Paginator", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Like.objects.filter", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Like.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "models.Like", "line_number": 41, "usage_type": "name"}, {"api_name": "forms.CommentForm", "line_number": 47, "usage_type": "call"}, {"api_name": "forms.CommentForm", "line_number": 54, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 57, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 60, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 63, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 31, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 75, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 75, "usage_type": "argument"}, {"api_name": "django.core.paginator.Paginator", "line_number": 81, "usage_type": "call"}, {"api_name": "models.Like.objects.filter", "line_number": 84, "usage_type": "call"}, {"api_name": "models.Like.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "models.Like", "line_number": 84, "usage_type": "name"}, {"api_name": "models.Like", "line_number": 96, "usage_type": "call"}, {"api_name": "forms.CommentForm", "line_number": 101, "usage_type": "call"}, {"api_name": "forms.CommentForm", "line_number": 108, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 111, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 114, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 118, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 73, "usage_type": "name"}, {"api_name": "django.views.generic.ListView", "line_number": 128, "usage_type": "name"}, {"api_name": "models.Post.objects.filter", "line_number": 129, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 129, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 129, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 138, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 138, "usage_type": "argument"}, {"api_name": "forms.EmailPostForm", "line_number": 142, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 149, "usage_type": "call"}, {"api_name": "forms.EmailPostForm", "line_number": 152, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 153, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 135, "usage_type": "name"}, {"api_name": "forms.UserRegistrationForm", "line_number": 160, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 168, "usage_type": "call"}, {"api_name": "forms.UserRegistrationForm", "line_number": 170, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 171, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 176, "usage_type": "call"}, {"api_name": "models.RegistrationMessage", "line_number": 176, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 180, "usage_type": "call"}]} +{"seq_id": "395031914", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jan 18 11:12:23 2020\n\n@author: ABucherie\n\"\"\"\n#%%\n# setting up your environment\n\nimport pandas as pd\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport geopandas as gpd\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\n\n#%% Cell to change per country\n \ncountry = 'Kenya' \nct_code='ken'\n\n#Path name to the folder : \npath = my_local_path + '/' + country + '/'\n\n# Read the path to the relevant admin level shape to use for the study\n\n#for Uganda activate the following lines :\n#Admin= path + 'input/Admin/uga_admbnda_adm1_UBOS_v2.shp' # for Uganda\n#Admin_col = 'ADM1_EN' # column name of the Admin name in the shapefile of Uganda\n\n#for Kenya activate the following lines :\nAdmin= path + 'input/Admin/KEN_adm1_mapshaper_corrected.shp' # for Kenya\nAdmin_col = 'name' # column name of the Admin name in the shapefile for Kenya\n\n#for Mali activate the following lines :\n#Admin= path + 'input/Admin/mli_admbnda_adm2_1m_dnct_20190802.shp' # for Mali\n#Admin_col = 'ADM2_FR' # column name of the Admin name in the shapefile for Mali\n\n# sources of the model perforfance results from the previous script V111_glofas\nmodel_performance = path + 'output/Performance_scores/%s_glofas_performance_score.csv' %ct_code\n\n#%% \n# Open the district admin 1 sapefile\ndistrict= gpd.read_file(Admin)\n#open the csv file with performance results of the model\nmodel_perf =pd.read_csv(model_performance)\nmodel_perf =model_perf.dropna()\n# find the best station to use per district based on the lowest FAR values\n \nbest= model_perf.groupby(['district', 'quantile'])['far'].transform(min)==model_perf['far']\nmodel_perf_best= model_perf[best]\n\n# lower case the district column in both file and merge\ndistrict[Admin_col]= district[Admin_col].str.replace(u\"é\", \"e\").str.lower()\nmodel_perf_best['district']= model_perf_best['district'].str.lower()\n\nmerged_perf= district.set_index(Admin_col).join(model_perf_best.set_index('district')) \n\n# create a shapefile out of the uga_affected_area_stations.csv file:\n\nAffArea_station =district.set_index(Admin_col).join(df_dg)\nAffArea_station.to_file(path + 'output/Performance_scores/AffDistrict_%s_v111.shp' %ct_code, index=True)\n\n\n#%% create a figure with the number of flood event recorded per district\nfig, ax=plt.subplots(1,figsize=(10,10))\ndivider = make_axes_locatable(ax)\ncax = divider.append_axes(\"right\", size=\"5%\", pad=0.2)\n\nmerged_perf.plot(ax=ax, color='lightgrey', edgecolor='grey')\nax.set_title('Number of recorded flood event per district', fontsize= 14)\ncmap = cm.get_cmap('jet', 60) # adapt the number if needed\n\nperfdrop= merged_perf.dropna(subset=['nb_event'])\nperfdrop.plot(ax=ax,column='nb_event', legend= True,vmin=1,vmax=60, cmap=cmap, cax=cax) # adapt the vmax number if needed\n\nfig.savefig(path+'output/Performance_scores/Nb_event_district.png')\n\n#%% create 4 maps representing the results of the glofas model performance per district (POD, FAR, POFD and CSI)\nquantiles = ['Q50_pred', 'Q80_pred', 'Q90_pred']\n\nfor quantile in quantiles:\n \n perf_quantile= merged_perf[merged_perf['quantile']== quantile]\n perf_quantile.to_file(path + 'output/Performance_scores/perf_%s_v111_%s.shp' % (ct_code, quantile) )\n \n fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16,16))\n fig.suptitle('Performance results of model v1.1.1 in %s (%s Glofas)' %(country, quantile),fontsize= 22,fontweight= 'bold', x=0.5, y=0.94) \n divider_ax1 = make_axes_locatable(ax1)\n divider_ax2 = make_axes_locatable(ax2)\n divider_ax3 = make_axes_locatable(ax3)\n divider_ax4 = make_axes_locatable(ax4)\n cax1 = divider_ax1.append_axes(\"right\", size=\"5%\", pad=0.2)\n cax2 = divider_ax2.append_axes(\"right\", size=\"5%\", pad=0.2)\n cax3 = divider_ax3.append_axes(\"right\", size=\"5%\", pad=0.2)\n cax4 = divider_ax4.append_axes(\"right\", size=\"5%\", pad=0.2)\n \n ax1.set_title('False Alarm Ratio (FAR)', fontsize= 16)\n merged_perf.plot(ax=ax1, color='lightgrey', edgecolor='grey')\n perf_quantile.plot(ax=ax1,column='far', legend= True, vmin=0,vmax=1, cmap='coolwarm', cax=cax1)\n \n ax2.set_title('Probability of Detection (POD)', fontsize= 16)\n merged_perf.plot(ax=ax2, color='lightgrey', edgecolor='grey')\n perf_quantile.plot(ax=ax2,column='pod', legend= True, vmin=0,vmax=1, cmap='coolwarm_r', cax=cax2)\n \n ax3.set_title('Probability of False Detection (POFD)', fontsize= 16)\n merged_perf.plot(ax=ax3, color='lightgrey', edgecolor='grey')\n perf_quantile.plot(ax=ax3,column='pofd', legend= True, vmin=0,vmax=1, cmap='coolwarm', cax=cax3)\n \n ax4.set_title('Critical Success Index (CSI)', fontsize= 16)\n merged_perf.plot(ax=ax4, color='lightgrey', edgecolor='grey')\n perf_quantile.plot(ax=ax4,column='pod', legend= True, vmin=0,vmax=1, cmap='coolwarm_r', cax=cax4)\n \n\n fig.savefig(path + 'output/Performance_scores/%s_v111_%s.png' % (country, quantile))\n\n\n\n\n\n\n\n", "sub_path": "trigger-model-development/flood/skill-assessment/scripts/IBF_flood_model_performance_visual.py", "file_name": "IBF_flood_model_performance_visual.py", "file_ext": "py", "file_size_in_byte": 4951, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "geopandas.read_file", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "mpl_toolkits.axes_grid1.make_axes_locatable", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.cm.get_cmap", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "mpl_toolkits.axes_grid1.make_axes_locatable", "line_number": 89, "usage_type": "call"}, {"api_name": "mpl_toolkits.axes_grid1.make_axes_locatable", "line_number": 90, "usage_type": "call"}, {"api_name": "mpl_toolkits.axes_grid1.make_axes_locatable", "line_number": 91, "usage_type": "call"}, {"api_name": "mpl_toolkits.axes_grid1.make_axes_locatable", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "433171767", "text": "#!/usr/bin/env python\n\nfrom unittest import TestCase, TestProgram\n\nimport pythonpath0; pythonpath0\nfrom dhcpdconf import main\nimport pythonpath; pythonpath\nfrom x19290 import if3encode, stream_compare, IterReader\n\nfrom os.path import dirname, join, pardir, realpath\nfrom io import BytesIO\nfrom shutil import copyfileobj\n\n_TEST = dirname(realpath(__file__))\n_ROOT = join(_TEST, pardir)\n_DHCPD_HOSTS_SHORT = join(_TEST, r'dhcpd.hosts.short')\n_DHCPD_HOSTS_LONG = join(_TEST, r'dhcpd.hosts.long')\n_DHCP_NESTED_SHORT = join(_TEST, r'dhcpd.nested.short')\n_DHCP_NESTED_LONG = join(_TEST, r'dhcpd.nested.long')\n_FORWARD_NET_DHCPD = join(_TEST, r'forward.net.dhcpd')\n_FORWARD_NET_MASTER = join(_ROOT, r'forward.net')\n_DHCPD_CONF = join(_ROOT, r'dhcpd.conf')\nclass T(TestCase):\n def test0hosts0short(self):\n self._assert(_FORWARD_NET_DHCPD, _DHCPD_HOSTS_SHORT)\n def test0hosts1master(self):\n self._assert(_FORWARD_NET_MASTER, _DHCPD_HOSTS_LONG)\n def test1nest0short(self):\n self._assert(_FORWARD_NET_DHCPD, _DHCP_NESTED_SHORT, _DHCPD_CONF)\n def test1nest1long(self):\n self._assert(_FORWARD_NET_MASTER, _DHCP_NESTED_LONG, _DHCPD_CONF)\n def _assert(self, source, derived, header=None):\n with BytesIO() as b:\n g = main(source, header)\n g = (if3encode(y) for y in g)\n copyfileobj(IterReader(g), b)\n b.seek(0)\n with open('/tmp/a', r'wb') as ostream:\n ostream.write(b.getvalue())\n with open(derived, r'rb') as istream:\n self.assertTrue(stream_compare(b, istream))\n\nif __name__ == '__main__':\n TestProgram()\n", "sub_path": "tests/t00dhcpdconf.py", "file_name": "t00dhcpdconf.py", "file_ext": "py", "file_size_in_byte": 1640, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.pardir", "line_number": 15, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 23, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 33, "usage_type": "call"}, {"api_name": "dhcpdconf.main", "line_number": 34, "usage_type": "call"}, {"api_name": "x19290.if3encode", "line_number": 35, "usage_type": "call"}, {"api_name": "shutil.copyfileobj", "line_number": 36, "usage_type": "call"}, {"api_name": "x19290.IterReader", "line_number": 36, "usage_type": "call"}, {"api_name": "x19290.stream_compare", "line_number": 41, "usage_type": "call"}, {"api_name": "unittest.TestProgram", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "189096868", "text": "from django.urls import path\r\nfrom . import views\r\n\r\napp_name = 'craftDB'\r\nurlpatterns = [\r\n path('', views.index, name='index'),\r\n path('testview/', views.testview, name = 'testview'),\r\n path('items/', views.get_item_names, name= 'items'),\r\n path('recipe/', views.get_recipe_info, name='recipe')\r\n]", "sub_path": "urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 319, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "540254469", "text": "#!/usr/bin/env python\n\nfrom os import listdir\nfrom os.path import isfile, join\n\nimport json\n\nimport web\n\nurls = (\n \"/api/listAllStock\", \"ApiAllStockList\",\n \"/api/getDataByStockName\", \"ApiGetDataByStockName\"\n)\n\napp = web.application(urls, globals())\n\nbase = \"/Users/gaoxinbo/Desktop/prod/data\"\n\n\nclass ApiAllStockList:\n def __init__(self):\n pass\n\n def GET(self):\n files = [f for f in listdir(base) if isfile(join(base, f))]\n\n ret = {'result': files}\n\n web.header('Content-Type', 'application/json')\n return json.dumps(ret)\n\n\nclass ApiGetDataByStockName:\n def GET(self):\n input = web.input()\n\n ret = {'result': None}\n\n name = input[\"name\"]\n l = []\n if len(name) != 0:\n filename = join(base, name)\n f = open(filename, \"r\")\n for line in f:\n obj = {}\n item = line.split()\n obj[\"date\"] = item[0]\n obj[\"open\"] = item[1]\n obj[\"close\"] = item[2]\n obj[\"high\"] = item[3]\n obj[\"low\"] = item[4]\n obj[\"volume\"] = item[5]\n l.append(obj)\n f.close()\n\n l = l[-100:]\n web.header('Content-Type', 'application/json')\n ret['result'] = l\n\n return json.dumps(ret)\n\n\nif __name__ == '__main__':\n app.run()\n", "sub_path": "src/py/web/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "web.application", "line_number": 15, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "web.header", "line_number": 29, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 30, "usage_type": "call"}, {"api_name": "web.input", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "web.header", "line_number": 57, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "387956041", "text": "from django.http import HttpResponse\nfrom django.template import Context, loader\nfrom main.homepage_utils import *\n\ndef index(request):\n\tt = loader.get_template('index.html')\n\tc = Context({\n\t\t'page_id' : '#a-home',\n\t\t'title' : 'profile',\n\t\t'media_path': build_media_url(),\n\t})\n\treturn HttpResponse(t.render(c))\n", "sub_path": "apps/main/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 311, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.template.loader.get_template", "line_number": 6, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 6, "usage_type": "name"}, {"api_name": "django.template.Context", "line_number": 7, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "166809620", "text": "\"\"\"Functions handling the narratives\n\"\"\"\nimport logging\nfrom collections import defaultdict\n\nfrom energy_demand.basic import lookup_tables\n\n\ndef get_all_sectors_of_narratives(narratives):\n \"\"\"Get all defined sectors of all narratives\n\n ARguments\n --------\n narratives : list\n All defined narratives\n\n Returns\n -------\n all_sectors : list\n All sectors\n \"\"\"\n all_sectors = set()\n for narrative in narratives:\n all_sectors.add(narrative['sector'])\n all_sectors = list(all_sectors)\n\n return all_sectors\n\n\ndef get_sector_narrative_and_single_from_multi(sector_to_match, switches):\n \"\"\"Get all switches of a sector if the switches are\n defined specifically for a sector. If the switches are\n not specifically for a sector, return all switches\n\n Arguments\n ----------\n sector_to_match : int\n Sector to find switches\n switches : list\n Switches\n\n Returns\n -------\n switches : list\n Switches of sector\n \"\"\"\n if sector_to_match is True:\n return switches\n else:\n switches_out = []\n\n # Test if multiple switches e.g. per fueltype\n try:\n fueltypes_switched = set([])\n for switch in switches:\n fueltypes_switched.add(switch['fueltype_new'])\n except:\n fueltypes_switched = []\n\n if len(fueltypes_switched) > 1:\n\n for fueltype in fueltypes_switched:\n switches_out_single = []\n for switch in switches:\n if switch['sector'] == sector_to_match and switch['fueltype_new'] == fueltype:\n switches_out_single.append(switch)\n elif not switch['sector'] and switch['fueltype_new'] == fueltype: # Not defined specifically for sectors and append all\n switches_out_single.append(switch)\n else:\n pass\n switches_out.append(switches_out_single)\n else:\n switches_out_single = []\n for switch in switches:\n if switch['sector'] == sector_to_match:\n switches_out_single.append(switch)\n elif not switch['sector']: # Not defined specifically for sectors and append all\n switches_out_single.append(switch)\n else:\n pass\n\n switches_out = [switches_out_single]\n\n return switches_out\n\ndef crit_dim_var(var):\n \"\"\"Check if nested dict or not\n\n Arguments\n ---------\n var : dict\n Dictionary to test wheter single or multidimensional parameter\n\n Returns\n -------\n single_dimension : bool\n True: Single dimension, False: Multidimensional parameter\n \"\"\"\n single_dimension = True\n\n exclude = ['regional_vals_by', 'regional_vals_ey']\n\n # Test if list nor not\n if type(var) is list:\n for list_entry in var:\n for key, value in list_entry.items():\n\n if type(value) is not list:\n if hasattr(value, 'keys') and key not in exclude:\n if len(value.keys()) != 0:\n single_dimension = False\n else:\n pass\n else:\n for key, value in var.items():\n if type(value) is not list:\n if hasattr(value, 'keys') and key not in exclude:\n if len(value.keys()) != 0:\n single_dimension = False\n else:\n if value == []:\n pass\n else:\n single_dimension = False\n\n return single_dimension\n\ndef read_from_narrative(narratives):\n \"\"\"Read from narratives the defined\n value for the last defined timestep\n\n Arguments\n ---------\n narratives : lives\n Narratives\n\n Returns\n -------\n last_value : float\n Value of last defined timestep narrative\n \"\"\"\n last_year = 0\n for narrative in narratives:\n if narrative['end_yr'] > last_year:\n last_value = narrative['value_ey']\n last_year = narrative['end_yr']\n\n return last_value\n\ndef default_narrative(\n end_yr,\n value_by,\n value_ey,\n diffusion_choice='linear',\n sig_midpoint=0,\n sig_steepness=1,\n base_yr=2015,\n regional_specific=True,\n fueltype_replace=0,\n fueltype_new=0,\n ):\n \"\"\"Create a default single narrative with a single timestep\n\n E.g. from value 0.2 in 2015 to value 0.5 in 2050\n\n Arguments\n ----------\n end_yr : int\n End year of narrative\n value_by : float\n Value of start year of narrative\n value_ey : float\n Value at end year of narrative\n diffusion_choice : str, default='linear'\n Wheter linear or sigmoid\n sig_midpoint : float, default=0\n Sigmoid midpoint\n sig_steepness : float, default=1\n Sigmoid steepness\n base_yr : int\n Base year\n regional_specific : bool\n If regional specific or not\n\n Returns\n -------\n container : list\n List with narrative\n \"\"\"\n return [{\n 'base_yr': base_yr,\n 'end_yr': end_yr,\n 'value_by': value_by,\n 'value_ey': value_ey,\n 'diffusion_choice': diffusion_choice,\n 'sig_midpoint': sig_midpoint,\n 'sig_steepness': sig_steepness,\n 'regional_specific': regional_specific,\n 'fueltype_replace': fueltype_replace,\n 'fueltype_new': fueltype_new\n }]\n\ndef autocomplete(parameter_narratives, simulation_base_yr, sub_param_crit):\n \"\"\"\n \"\"\"\n autocomplet_param_narr = defaultdict(dict)\n\n for sub_param_name, narratives_sector in parameter_narratives.items():\n logging.debug(\" ... \" + str(sub_param_name))\n for sector, narratives in narratives_sector.items():\n autocomplet_param_narr[sub_param_name][sector] = []\n\n switches_to_create_narrative = get_sector_narrative_and_single_from_multi(\n sector, narratives)\n\n for switch_to_create_narrative in switches_to_create_narrative:\n # Get all years of switches_to_create_narrative\n all_yrs = []\n for narrative in switch_to_create_narrative:\n all_yrs.append(narrative['end_yr'])\n\n all_yrs.sort()\n\n for year_cnt, year in enumerate(all_yrs):\n for narrative in switch_to_create_narrative:\n if narrative['end_yr'] == year:\n yr_narrative = narrative\n break\n\n # Add missing entries to narrative\n if year_cnt == 0:\n # Update\n yr_narrative['base_yr'] = simulation_base_yr\n yr_narrative['value_by'] = narrative['default_by']\n\n # previous value\n previous_yr = narrative['end_yr']\n previous_value = narrative['value_ey']\n else:\n # Update\n yr_narrative['base_yr'] = previous_yr\n yr_narrative['value_by'] = previous_value\n\n # previous value\n previous_yr = narrative['end_yr']\n previous_value = narrative['value_ey']\n\n autocomplet_param_narr[sub_param_name][sector].append(yr_narrative)\n\n # Remove all dummy sector\n autocomplet_param_narr_new = defaultdict(dict)\n\n for param_name, sector_data in autocomplet_param_narr.items():\n for sector, data in sector_data.items():\n if sector == 'dummy_sector':\n autocomplet_param_narr_new[param_name] = data\n else:\n autocomplet_param_narr_new[param_name][sector] = data\n\n autocomplet_param_narr = dict(autocomplet_param_narr_new)\n\n # If only single dimension parameter, remove dummy mutliparameter name\n if not sub_param_crit:\n try:\n autocomplet_param_narr = autocomplet_param_narr['dummy_single_param']\n except:\n pass\n\n return autocomplet_param_narr\n\ndef read_user_defined_param(\n df,\n simulation_base_yr,\n simulation_end_yr,\n default_streategy_var,\n var_name\n ):\n \"\"\"Read in user defined narrative parameters\n \"\"\"\n parameter_narratives = {}\n single_param_narratives = {}\n\n lookups = lookup_tables.basic_lookups()\n columns = list(df.columns)\n\n if 'enduses' in columns:\n sub_param_crit = True\n else:\n sub_param_crit = False\n\n if len(list(df.columns)) == 1:\n single_dim_param = True\n else:\n single_dim_param = False\n\n if single_dim_param:\n\n # Read single dimension param and create single step narrative\n single_step_narrative = {}\n single_step_narrative['sector'] = 'dummy_sector'\n single_step_narrative['default_value'] = default_streategy_var['default_value']\n single_step_narrative['value_ey'] = float(df[var_name][0])\n single_step_narrative['end_yr'] = simulation_end_yr\n single_step_narrative['base_yr'] = simulation_base_yr\n single_step_narrative['value_by'] = default_streategy_var['default_value']\n single_step_narrative['regional_specific'] = default_streategy_var['regional_specific']\n single_step_narrative['diffusion_choice'] = 'linear'\n single_param_narratives = [single_step_narrative]\n return single_param_narratives\n else:\n # Read multidmensional param\n if sub_param_crit:\n enduses = set(df['enduses'].values)\n for enduse in enduses:\n parameter_narratives[enduse] = {}\n\n # All rows of enduse\n df_enduse = df.loc[df['enduses'] == enduse]\n\n # Get all sectors and years\n sectors = set()\n end_yrs = set()\n\n for i in df_enduse.index:\n try:\n sector = df_enduse.at[i, 'sector']\n sectors.add(sector)\n except:\n pass\n\n try:\n end_yr = int(df_enduse.at[i, 'end_yr'])\n end_yrs.add(end_yr)\n except:\n pass\n if list(sectors) == []:\n\n for end_yr in end_yrs:\n try:\n _ = default_streategy_var[enduse]\n defined_in_model = True\n except KeyError:\n defined_in_model = False\n\n # All entries of this year df_enduse and this fueltype\n df_enduse_sim_yr = df_enduse.loc[df_enduse['end_yr'] == end_yr]\n\n if defined_in_model:\n narrative = {}\n narrative['sector'] = 'dummy_sector'\n narrative['end_yr'] = end_yr\n narrative['sig_midpoint'] = 0\n narrative['sig_steepness'] = 1\n narrative['regional_specific'] = default_streategy_var[enduse]['regional_specific']\n narrative['default_by'] = default_streategy_var[enduse]['default_value']\n\n # Check if more than one entry\n for _index, row in df_enduse_sim_yr.iterrows():\n\n try:\n interpolation_params = row['interpolation_params']\n except KeyError:\n # Generic fuel switch\n interpolation_params = row['param_generic_fuel_switch']\n\n # If more than one switch per enduse\n if interpolation_params in narrative:\n\n # Add narrative and start new one\n try:\n parameter_narratives[enduse][narrative['sector']].append(narrative)\n except KeyError:\n parameter_narratives[enduse][narrative['sector']] = [narrative]\n\n narrative = {}\n narrative['sector'] = 'dummy_sector'\n narrative['end_yr'] = end_yr\n narrative['sig_midpoint'] = 0\n narrative['sig_steepness'] = 1\n narrative['regional_specific'] = default_streategy_var[enduse]['regional_specific']\n narrative['default_by'] = default_streategy_var[enduse]['default_value']\n\n if interpolation_params == 'diffusion_choice':\n int_diffusion_choice = float(row[var_name])\n narrative['diffusion_choice'] = lookups['diffusion_type'][int_diffusion_choice]\n else:\n narrative[interpolation_params] = float(row[var_name])\n else:\n if interpolation_params == 'diffusion_choice':\n int_diffusion_choice = float(row[var_name])\n narrative['diffusion_choice'] = lookups['diffusion_type'][int_diffusion_choice]\n else:\n narrative[interpolation_params] = float(row[var_name])\n\n # Add narrative\n try:\n parameter_narratives[enduse][narrative['sector']].append(narrative)\n except KeyError:\n parameter_narratives[enduse][narrative['sector']] = [narrative]\n else:\n # If setctor specific, this needs to be implemented\n pass\n else:\n sectors = set()\n end_yrs = set()\n\n for i in df.index:\n try:\n sector = df.at[i, 'sector']\n sectors.add(sector)\n except:\n pass\n try:\n end_yr = int(df.at[i, 'end_yr'])\n end_yrs.add(end_yr)\n except:\n pass\n\n if list(sectors) == []:\n parameter_narratives['dummy_single_param'] = {}\n\n for end_yr in end_yrs:\n narrative = {}\n narrative['sector'] = 'dummy_sector'\n narrative['end_yr'] = end_yr\n narrative['sig_midpoint'] = 0\n narrative['sig_steepness'] = 1\n narrative['regional_specific'] = default_streategy_var['regional_specific']\n narrative['default_by'] = default_streategy_var['default_value']\n\n for _index, row in df.iterrows():\n\n interpolation_params = row['interpolation_params']\n\n if interpolation_params == 'diffusion_choice':\n lookups = lookup_tables.basic_lookups()\n int_diffusion_choice = int(row[var_name])\n narrative['diffusion_choice'] = lookups['diffusion_type'][int_diffusion_choice]\n else:\n narrative[interpolation_params] = float(row[var_name])\n\n # Add narrative\n try:\n parameter_narratives['dummy_single_param'][narrative['sector']].append(narrative)\n except (KeyError, AttributeError):\n parameter_narratives['dummy_single_param'][narrative['sector']] = [narrative]\n else:\n pass # Needs to be implemented in case sector specific\n\n # Autocomplete\n parameter_narratives = autocomplete(\n parameter_narratives,\n simulation_base_yr,\n sub_param_crit)\n\n return parameter_narratives\n", "sub_path": "energy_demand/read_write/narrative_related.py", "file_name": "narrative_related.py", "file_ext": "py", "file_size_in_byte": 16514, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.defaultdict", "line_number": 206, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 209, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 251, "usage_type": "call"}, {"api_name": "energy_demand.basic.lookup_tables.basic_lookups", "line_number": 283, "usage_type": "call"}, {"api_name": "energy_demand.basic.lookup_tables", "line_number": 283, "usage_type": "name"}, {"api_name": "energy_demand.basic.lookup_tables.basic_lookups", "line_number": 436, "usage_type": "call"}, {"api_name": "energy_demand.basic.lookup_tables", "line_number": 436, "usage_type": "name"}]} +{"seq_id": "612738940", "text": "from django import forms\n\nfrom pagina_web.models import ApplicationEnrollment, FrequentlyAskedQuestions, Profile\n\n\nclass ApplicationEnrollmentForm(forms.ModelForm):\n class Meta:\n model = ApplicationEnrollment\n fields = [\"avatar\", \"first_name\", \"last_name\", \"email\", \"birth_date\", \"country\", 'county', \"city\",\n \"nationality\", \"study_type\", \"faculty\", \"specialization\", \"year_of_study\", \"motivation\"]\n\n widgets = {\n 'birth_date': forms.DateInput(attrs={'placeholder': 'YYYY-MM-DD'}),\n }\n\n def __init__(self, *args, **kwargs):\n super(ApplicationEnrollmentForm, self).__init__(*args, **kwargs)\n for field_name, field in self.fields.items():\n field.widget.attrs['class'] = 'form-control'\n\n\nclass FrequentlyAskedQuestionsForm(forms.ModelForm):\n class Meta:\n model = FrequentlyAskedQuestions\n fields = [\"question\", \"answer\"]\n\n def __init__(self, *args, **kwargs):\n super(FrequentlyAskedQuestionsForm, self).__init__(*args, **kwargs)\n for field_name, field in self.fields.items():\n field.widget.attrs['class'] = 'form-control'\n\n\nclass UserProfileForm(forms.ModelForm):\n class Meta:\n model = Profile\n fields = [\"study_type\", \"faculty\", \"specialization\", \"year_of_study\", \"country\", \"county\", \"city\",\n \"nationality\"]\n\n def __init__(self, *args, **kwargs):\n super(UserProfileForm, self).__init__(*args, **kwargs)\n for field_name, field in self.fields.items():\n field.widget.attrs['class'] = 'form-control'\n\n\nclass UserPhotoProfileForm(forms.Form):\n avatar = forms.ImageField()\n\n def __init__(self, *args, **kwargs):\n super(UserPhotoProfileForm, self).__init__(*args, **kwargs)\n for field_name, field in self.fields.items():\n field.widget.attrs['class'] = 'form-control'\n", "sub_path": "src/pagina_web/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1884, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.forms.ModelForm", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 6, "usage_type": "name"}, {"api_name": "pagina_web.models.ApplicationEnrollment", "line_number": 8, "usage_type": "name"}, {"api_name": "django.forms.DateInput", "line_number": 13, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 13, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 22, "usage_type": "name"}, {"api_name": "pagina_web.models.FrequentlyAskedQuestions", "line_number": 24, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 33, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 33, "usage_type": "name"}, {"api_name": "pagina_web.models.Profile", "line_number": 35, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 45, "usage_type": "name"}, {"api_name": "django.forms.ImageField", "line_number": 46, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "519647795", "text": "import numpy\nimport pandas\nimport datetime\nimport logging\nLOGGING_LEVEL = logging.INFO\n\nimport performance_statistics\n\n\nclass TradeError(Exception):\n pass\n\n\nclass EquityCalculator(object):\n ''' Calculates EquityCurve from trades and price changes.\n The idea is to keep track of equity changes on trade and on every price\n change separately. '''\n\n def __init__(self, full_curve=None, trades_curve=None, log_level=None):\n self._full_curve = full_curve or EquityCurve()\n self._trades_curve = trades_curve or EquityCurve()\n self._full_curve_merged = EquityCurve(log_level=log_level)\n self._trades_curve_merged = EquityCurve(log_level=log_level)\n self.pos = 0\n self.var = 0\n self.now = None\n self.price = None\n self.log = logging.getLogger(self.__class__.__name__)\n self.log.setLevel(log_level or LOGGING_LEVEL)\n\n def new_price(self, timestamp, price):\n ''' Account for a new price.\n Call this every time an information about tested asset's\n price changes. '''\n self.now = timestamp\n self.price = price\n self._full_curve.add_point(timestamp, self.var + self.pos * price)\n\n def new_trade(self, timestamp, price, volume):\n ''' Account for a new trade.\n Call this every time strategy makes a trade. '''\n self.var -= price * volume\n self.pos += volume\n equity = self.var + self.pos * price\n diff = equity - sum(self._trades_curve._changes)\n if diff != 0:\n self.log.debug('new equity point %s registered on %s', equity,\n timestamp)\n self.log.debug('equity change: %s', diff)\n self._trades_curve.add_point(timestamp, equity)\n self._trades_curve.add_trade(timestamp, price, volume)\n\n def merge(self):\n ''' Record current results and prepare to start calculating equity\n from the scratch. Purpose: to be able to backtest single strategy\n on a whole basket of instruments. '''\n if self.pos != 0:\n raise Exception('Merge requested when position != 0')\n self._full_curve_merged.merge(self._full_curve)\n self._full_curve = EquityCurve()\n self._trades_curve_merged.merge(self._trades_curve)\n self._trades_curve = EquityCurve()\n self.var = 0\n\n @property\n def full_curve(self):\n ''' Return full equity curve (i.e. curve that tracked equity changes on\n every price change, even between the trades). '''\n if not len(self._full_curve) == 0:\n self.merge()\n return self._full_curve_merged\n\n @property\n def trades_curve(self):\n ''' Return trades equity curve (i.e. curve that tracked equity changes\n only on trades. '''\n if not len(self._trades_curve) == 0:\n self.merge()\n return self._trades_curve_merged\n\n\nclass EquityCurve(object):\n ''' Keeps history of equity changes and calculates various performance\n statistics. Optional: keeps track of trades. '''\n\n def __init__(self, log_level=None):\n self._changes = list()\n self._times = list()\n self._cumsum = 0\n self.trades = dict()\n self.log = logging.getLogger(self.__class__.__name__)\n self.log.setLevel(log_level or LOGGING_LEVEL)\n\n def __len__(self):\n return len(self._changes)\n\n def add_change(self, timestamp, equity_change):\n self._changes.append(equity_change)\n self._cumsum += equity_change\n self._times.append(timestamp)\n\n def add_point(self, timestamp, equity):\n self._changes.append(equity - self._cumsum)\n self._cumsum = equity\n self._times.append(timestamp)\n\n def add_trade(self, timestamp, price, volume):\n ''' Add trade. Not used in any computation currently. '''\n if volume != 0:\n if timestamp in self.trades:\n self.log.debug(\"trade with timestamp %s is already present,\"\n \" incrementing timestamp by 1 mcs\" %\n timestamp)\n self.add_trade(timestamp + datetime.timedelta(0, 0, 1),\n price, volume)\n else:\n self.trades[timestamp] = (price, volume)\n else:\n self.log.warning(\"trade with 0 volume: %s %s %s\", timestamp,\n price, volume)\n\n def series(self, mode='equity', frequency=None):\n ''' Pandas TimeSeries object of equity/changes.\n * `mode` determines type, could be \"equity\" for cumulative equity\n dynamic or \"changes\" for time series of changes between neighbour\n equity points.\n * `frequency` is pandas-compatible object for frequency conversions.\n (e.g. \"D\" for daily, \"M\" for monthly, \"5min\" for obvious.) '''\n if not frequency:\n if mode == 'equity':\n return pandas.TimeSeries(data=numpy.cumsum(self._changes),\n index=self._times)\n elif mode == 'changes':\n return pandas.TimeSeries(data=self._changes, index=self._times)\n else:\n ts = pandas.TimeSeries(data=numpy.cumsum(self._changes),\n index=self._times).asfreq(frequency,\n method='ffill')\n ts = ts - ts.shift(1)\n ts = ts[ts != 0]\n if mode == 'changes':\n return ts\n elif mode == 'equity':\n return ts.cumsum()\n raise Exception('Unsupported requirements (probably)')\n\n def __getitem__(self, stat, precision=2):\n ''' Calculate statistic `stat` on equity dynamics '''\n if len(self._changes) == 0:\n raise Exception('Cannot calculate statistics on empty EquityCurve')\n s = stat.lower()\n func = getattr(performance_statistics, stat.lower(), None)\n if func:\n stat = func(numpy.array(self._changes))\n if isinstance(stat, float):\n stat = round(stat, precision)\n return stat\n else:\n raise KeyError('Cannot calculate statistic with name `%s`', stat)\n\n def statistics(self, mode='fast'):\n ''' Calculate all possible statistics, as specified in\n `performance_statistics` '''\n if mode == 'full':\n return dict((k, self[k]) for k in performance_statistics.full)\n if mode == 'fast':\n return dict((k, self[k]) for k in performance_statistics.fast)\n else:\n raise Exception('Unsupported `mode` of statistics request')\n\n def merge(self, curve, overwrite=True):\n ''' Merge two curves. Used for backet testing. Will overwrite self\n unless `overwrite` was set to False.\n Warning: recorded trades will be discarded for self to avoid\n potential confusion. '''\n changes1 = self._changes\n changes2 = curve._changes\n times1 = self._times\n times2 = curve._times\n if len(changes1) == 0:\n if overwrite:\n self._changes = curve._changes\n self._times = curve._times\n self.trades = curve.trades\n return\n else:\n return curve\n if len(changes2) == 0:\n return self * overwrite or None\n i = j = 0\n changes = []\n times = []\n while i < len(changes1) or j < len(changes2):\n if i < len(changes1) and j < len(changes2) and \\\n times1[i] == times2[j]:\n times.append(times1[i])\n changes.append(changes1[i] + changes2[j])\n i += 1\n j += 1\n elif j >= len(changes2) or i < len(changes1) and \\\n times1[i] < times2[j]:\n times.append(times1[i])\n changes.append(changes1[i])\n i += 1\n elif i >= len(changes1) or j < len(changes2) and \\\n times1[i] > times2[j]:\n times.append(times2[j])\n changes.append(changes2[j])\n j += 1\n else:\n raise Exception(\"EquityCurve merge error\")\n trades = self.trades.copy()\n if hasattr(self, 'trades'):\n if len(curve.trades) != 0:\n if len(trades) == 0:\n trades = curve.trades\n elif len(curve.trades) != 0:\n for time in curve.trades.keys():\n if time in self.trades:\n raise Exception(\"EquityCurve merge error: \"\n \"attempting to merge a trade \"\n \"with non-unique timestamp\")\n self.trades.update(curve.trades)\n if overwrite:\n self._changes = changes\n self._times = times\n self.trades = trades\n else:\n eq = EquityCurve()\n eq._changes = changes\n eq._times = times\n eq.trades = trades\n return eq\n", "sub_path": "backtest/equity.py", "file_name": "equity.py", "file_ext": "py", "file_size_in_byte": 9205, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.INFO", "line_number": 5, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 91, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 114, "usage_type": "call"}, {"api_name": "pandas.TimeSeries", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 131, "usage_type": "call"}, {"api_name": "pandas.TimeSeries", "line_number": 134, "usage_type": "call"}, {"api_name": "pandas.TimeSeries", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 154, "usage_type": "call"}, {"api_name": "performance_statistics.full", "line_number": 165, "usage_type": "attribute"}, {"api_name": "performance_statistics.fast", "line_number": 167, "usage_type": "attribute"}]} +{"seq_id": "480221210", "text": "import pymysql\npymysql.install_as_MySQLdb()\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column\nfrom sqlalchemy.types import *\n\nBaseModel = declarative_base()\n\nengine = create_engine('mysql://testuser:test123@192.168.11.78:3306/testdb')\nDBSession = sessionmaker(bind=engine)\nsession = DBSession()\n\nret=session.execute('desc users')\nprint (ret)\n# print ret.fetchall()\nfor i in ret:\n print (i)\n\nclass User(BaseModel):\n __tablename__ = 'users'\n id = Column(Integer, primary_key=True)\n username = Column(String(80), unique=False)\n email = Column(String(320), unique=False)\n password = Column(String(32), nullable=False)\n\n'''\nuser1=User(id=4,username='4',email='4@dd.com',password='123')\nsession.add(user1)\nsession.commit()\n'''\nquery = session.query(User) \n#user = query.get('111')\nusers = query.delete()\n\n #session.delete(i)\n#print (user.email)\n#user.email='111'\n#session.flush()\n#session.delete(user)\nsession.flush()\nsession.commit()\n'''\nquery = session.query(User)\nprint (query) # 只显示sql语句，不会执行查询\nprint (query[0]) # 执行查询\nprint (query.all()) # 执行查询\nprint (query.first()) # 执行查询\nfor user in query: # 执行查询\n print (user.username)\n'''", "sub_path": "src/testsqlalchemy.py", "file_name": "testsqlalchemy.py", "file_ext": "py", "file_size_in_byte": 1323, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pymysql.install_as_MySQLdb", "line_number": 2, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 9, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "60789036", "text": "#!/usr/bin/python\nimport bs4\nimport re\n\nrx_digit = re.compile(r'\\d')\n\ndef separator():\n '''Print a line of dashes.'''\n print('-' * 50)\n\nwith open('../DATA/people.xml') as people_in:\n soup = bs4.BeautifulSoup(people_in, 'lxml')\n \nfor person in soup.findAll('person'):\n print(person.string)\nseparator()\n\nfor tag in soup.findAll(re.compile(r'^s')):\n print(tag)\nseparator()\n\nfor tag in soup.findAll(['street', 'state']):\n print(\"{0}\\n {1}\".format(tag,tag.string))\nseparator()\n\nall_tags = soup.findAll(True)\nprint(\"There were {0} tags\".format(len(all_tags)))\nseparator()\n\n\ndef has_digit(tag):\n 'Return True if specified string contains at least one digit'\n return tag.string != None and rx_digit.search(tag.string)\n\nfor tag in soup.findAll(has_digit):\n print(tag.string)\nseparator()\n\nfor tag in soup.findAll(class_='number'):\n print(tag)\nseparator()\n\nfor tag in soup.findAll(text=re.compile(r'wacker', re.IGNORECASE)):\n print(tag)\nseparator()\n", "sub_path": "EXAMPLES/bs4_filter_people.py", "file_name": "bs4_filter_people.py", "file_ext": "py", "file_size_in_byte": 975, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "re.compile", "line_number": 5, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 12, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 18, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 43, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 43, "usage_type": "attribute"}]} +{"seq_id": "557508737", "text": "# 从sklearn.datasets里导入新闻数据抓取器 fetch_20newsgroups\r\nfrom sklearn.datasets import fetch_20newsgroups\r\n# 使用sklearn.model_selection里的train_test_split模块用于分割数据\r\nfrom sklearn.model_selection import train_test_split\r\n# 从sklearn.feature_extraction.text里导入用于文本特征向量转化模块\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\n# 从sklearn.naive_bayes里导入朴素贝叶斯模型\r\nfrom sklearn.naive_bayes import MultinomialNB\r\n# 从sklearn.metrics里导入classification_report用于详细的分类性能报告\r\nfrom sklearn.metrics import classification_report\r\n\r\n\r\n'''读取数据'''\r\n# 与之前预存的数据不同，fetch_20newsgroups需要即时从互联网下载数据\r\nnews = fetch_20newsgroups(subset='all')\r\n# 查验数据规模和细节\r\nprint(len(news.data))\r\nprint(news.data[0])\r\n# 该数据共有18846条新闻;不同于前面的样例数据,这些文本数据既没有被设定特征,也没有数字化的量度。\r\n# 因此,在交给朴素贝叶斯分类器学习之前,要对数据做进一步的处理。不过在此之前,我们仍然需要对数据进行分割并且随机采样出一部分用于测试。\r\n\r\n'''数据分割'''\r\n# 随机选取75%的数据作为训练样本，其余25%的数据作为测试样本\r\nX_train, X_test, y_train, y_test = train_test_split(news.data, news.target, test_size=0.25, random_state=33)\r\n\r\n'''使用朴素贝叶斯分类器对新闻文本数据进行类别预测'''\r\n# 首先将文本转化为特征向量,然后利用朴素贝叶斯模型从训练数据中估计参数,最后利用这些概率参数对同样转化为特征向量的测试新闻样本进行类别预测。\r\nvec = CountVectorizer()\r\nX_train = vec.fit_transform(X_train)\r\nX_test = vec.transform(X_test)\r\n# 使用默认设置初始化朴素贝叶斯模型\r\nmnb = MultinomialNB()\r\n# 利用训练数据对模型参数进行估计\r\nmnb.fit(X_train, y_train)\r\n# 对测试样本进行类别预测，结果存储在变量y_predict中\r\ny_predict = mnb.predict((X_test))\r\n\r\n'''性能评估'''\r\nprint(\"The accuracy of Naive Bayes Classifier is\", mnb.score(X_test, y_test))\r\nprint(classification_report(y_test, y_predict, target_names=news.target_names))\r\n# 通过输出结果﹐我们获知朴素贝叶斯分类器对4712条新闻文本测试样本分类的准确性约为83.977%,平均精确率、召回率以及F1指标分别为0.86,0.84和0.82。\r\n\r\n", "sub_path": "2.3_朴素贝叶斯（分类）.py", "file_name": "2.3_朴素贝叶斯（分类）.py", "file_ext": "py", "file_size_in_byte": 2439, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.datasets.fetch_20newsgroups", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 28, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.MultinomialNB", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "594601295", "text": "# -*- coding: utf-8 -*-\n\"\"\"Creates plots of the various baseline algorithms in pybaselines for documentation.\n\nManually create the plots for each of the modules in pybaselines rather than\nusing matplotlib's plot directive so that matplotlib does not need to be a\nrequirement for documentation.\n\nCreated on March 28, 2021\n\n@author: Donald Erb\n\n\"\"\"\n\n\nif __name__ == '__main__':\n\n from pathlib import Path\n\n try:\n import matplotlib.pyplot as plt\n except ImportError:\n print('This file requires matplotlib to run')\n raise\n import numpy as np\n\n from pybaselines.morphological import (amormol, imor, mor, mormol, mpls,\n rolling_ball)\n from pybaselines.optimizers import adaptive_minmax, optimize_extended_range\n from pybaselines.polynomial import (imodpoly, loess, modpoly,\n penalized_poly, poly)\n from pybaselines.utils import gaussian\n from pybaselines.whittaker import (airpls, arpls, asls, aspls, drpls,\n iarpls, iasls, psalsa)\n from pybaselines.window import noise_median, snip, swima\n\n x = np.linspace(100, 4200, 1000)\n signal = (\n gaussian(x, 2, 700, 50)\n + gaussian(x, 3, 1200, 150)\n + gaussian(x, 5, 1600, 100)\n + gaussian(x, 4, 2500, 50)\n + gaussian(x, 7, 3300, 100)\n + np.random.default_rng(1).normal(0, 0.2, x.size) # noise\n )\n true_baseline = (\n 10 + 0.001 * x # polynomial baseline\n + gaussian(x, 6, 2000, 2000) # gaussian baseline\n )\n\n y = signal + true_baseline\n non_peak_mask = (x < 600) | ((x > 1900) & (x < 2500)) | ((x > 2600) & (x < 3100)) | (x > 3600)\n\n algorithms = {\n 'whittaker': (\n (asls, (y, 1e7, 0.005)),\n (iasls, (y, x, 1e6, 0.04, 1e-3)),\n (airpls, (y, 1e7)),\n (drpls, (y, 1e8)),\n (arpls, (y, 1e7)),\n (iarpls, (y, 1e6)),\n (aspls, (y, 1e8)),\n (psalsa, (y, 1e7))\n ),\n 'polynomial': (\n (poly, (y, x, 3)),\n (poly, (y, x, 3), {'weights': non_peak_mask}, ', fit only non-peaks'),\n (modpoly, (y, x, 3)),\n (imodpoly, (y, x, 3)),\n (penalized_poly, (y, x, 3), {'threshold': 0.02 * (max(y) - min(y))}),\n (loess, (y, x, 0.6))\n ),\n 'morphological': (\n (mpls, (y, 100, 1e7, 0.002)),\n (mor, (y, 100)),\n (imor, (y, 25)),\n (mormol, (y, 100), {'pad_kwargs': {'extrapolate_window': 50}, 'smooth_half_window': 3}),\n (amormol, (y, 45), {'pad_kwargs': {'extrapolate_window': 50}}),\n (rolling_ball, (y, 125, 100), {'pad_kwargs': {'extrapolate_window': 50}})\n ),\n 'window': (\n (noise_median, (y, 250, 150, 50), {'extrapolate_window': 50}),\n (snip, (y, 40), {'extrapolate_window': 50}),\n (snip, (y, 40, True, 1), {'extrapolate_window': 50}, ', decreasing & smooth'),\n (swima, (y,), {'extrapolate_window': 50})\n ),\n 'optimizers': (\n (optimize_extended_range, (y, x, 'aspls', 'both', 0.25),\n {'pad_kwargs': {'extrapolate_window': 50}}),\n (adaptive_minmax, (y, x), {'constrained_fraction': 0.05}),\n )\n }\n\n # assumes file is in pybaselines/tools\n image_directory = Path(__file__).parent.parent.joinpath('docs/images')\n with plt.rc_context(\n {'interactive': False, 'lines.linewidth': 2.5, 'legend.frameon': False,\n 'figure.figsize': (4.5, 4), 'figure.dpi': 100}\n ):\n for module, func_calls in algorithms.items():\n fig, ax = plt.subplots(num='plot', tight_layout={'pad': 0.15})\n ax.plot(x, y, label='data', lw=1.5)\n ax.plot(x, true_baseline, label='true baseline', lw=4)\n for call in func_calls:\n if len(call) == 2:\n func, args = call\n kwargs = {}\n extra_naming = ''\n elif len(call) == 3:\n func, args, kwargs = call\n extra_naming = ''\n else:\n func, args, kwargs, extra_naming = call\n ax.plot(x, func(*args, **kwargs)[0], '--', label=func.__name__ + extra_naming)\n\n ax.legend()\n ax.set_xticks([])\n ax.set_yticks([])\n ax.set_title(f'pybaselines.{module}')\n fig.savefig(\n image_directory.joinpath(f'{module}.jpg'),\n pil_kwargs={'quality': 90, 'optimize': True, 'progressive': True}\n )\n plt.close(fig)\n\n fig, ax = plt.subplots(num='plot', tight_layout={'pad': 0.15})\n\n bkg_1 = modpoly(y, x, poly_order=3)[0]\n bkg_2 = asls(y, lam=1e7, p=0.01)[0]\n bkg_3 = imor(y, half_window=25)[0]\n bkg_4 = snip(y, max_half_window=40, decreasing=True, smooth_half_window=1)[0]\n\n plt.plot(x, y, label='raw data', lw=1.5)\n plt.plot(x, true_baseline, lw=3, label='true baseline')\n plt.plot(x, bkg_1, '--', label='modpoly')\n plt.plot(x, bkg_2, '--', label='asls')\n plt.plot(x, bkg_3, '--', label='imor')\n plt.plot(x, bkg_4, '--', label='snip')\n\n plt.legend()\n fig.savefig(\n image_directory.joinpath('quickstart.jpg'),\n pil_kwargs={'quality': 90, 'optimize': True, 'progressive': True}\n )\n plt.close(fig)\n", "sub_path": "tools/plots_for_docs.py", "file_name": "plots_for_docs.py", "file_ext": "py", "file_size_in_byte": 5481, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.linspace", "line_number": 36, "usage_type": "call"}, {"api_name": "pybaselines.utils.gaussian", "line_number": 38, "usage_type": "call"}, {"api_name": "pybaselines.utils.gaussian", "line_number": 39, "usage_type": "call"}, {"api_name": "pybaselines.utils.gaussian", "line_number": 40, "usage_type": "call"}, {"api_name": "pybaselines.utils.gaussian", "line_number": 41, "usage_type": "call"}, {"api_name": "pybaselines.utils.gaussian", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.random.default_rng", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pybaselines.utils.gaussian", "line_number": 47, "usage_type": "call"}, {"api_name": "pybaselines.whittaker.asls", "line_number": 55, "usage_type": "name"}, {"api_name": "pybaselines.whittaker.iasls", "line_number": 56, "usage_type": "name"}, {"api_name": "pybaselines.whittaker.airpls", "line_number": 57, "usage_type": "name"}, {"api_name": "pybaselines.whittaker.drpls", "line_number": 58, "usage_type": "name"}, {"api_name": "pybaselines.whittaker.arpls", "line_number": 59, "usage_type": "name"}, {"api_name": "pybaselines.whittaker.iarpls", "line_number": 60, "usage_type": "name"}, {"api_name": "pybaselines.whittaker.aspls", "line_number": 61, "usage_type": "name"}, {"api_name": "pybaselines.whittaker.psalsa", "line_number": 62, "usage_type": "name"}, {"api_name": "pybaselines.polynomial.poly", "line_number": 65, "usage_type": "name"}, {"api_name": "pybaselines.polynomial.poly", "line_number": 66, "usage_type": "name"}, {"api_name": "pybaselines.polynomial.modpoly", "line_number": 67, "usage_type": "name"}, {"api_name": "pybaselines.polynomial.imodpoly", "line_number": 68, "usage_type": "name"}, {"api_name": "pybaselines.polynomial.penalized_poly", "line_number": 69, "usage_type": "name"}, {"api_name": "pybaselines.polynomial.loess", "line_number": 70, "usage_type": "name"}, {"api_name": "pybaselines.morphological.mpls", "line_number": 73, "usage_type": "name"}, {"api_name": "pybaselines.morphological.mor", "line_number": 74, "usage_type": "name"}, {"api_name": "pybaselines.morphological.imor", "line_number": 75, "usage_type": "name"}, {"api_name": "pybaselines.morphological.mormol", "line_number": 76, "usage_type": "name"}, {"api_name": "pybaselines.morphological.amormol", "line_number": 77, "usage_type": "name"}, {"api_name": "pybaselines.morphological.rolling_ball", "line_number": 78, "usage_type": "name"}, {"api_name": "pybaselines.window.noise_median", "line_number": 81, "usage_type": "name"}, {"api_name": "pybaselines.window.snip", "line_number": 82, "usage_type": "name"}, {"api_name": "pybaselines.window.snip", "line_number": 83, "usage_type": "name"}, {"api_name": "pybaselines.window.swima", "line_number": 84, "usage_type": "name"}, {"api_name": "pybaselines.optimizers.optimize_extended_range", "line_number": 87, "usage_type": "name"}, {"api_name": "pybaselines.optimizers.adaptive_minmax", "line_number": 89, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rc_context", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "pybaselines.polynomial.modpoly", "line_number": 127, "usage_type": "call"}, {"api_name": "pybaselines.whittaker.asls", "line_number": 128, "usage_type": "call"}, {"api_name": "pybaselines.morphological.imor", "line_number": 129, "usage_type": "call"}, {"api_name": "pybaselines.window.snip", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}]} +{"seq_id": "403010070", "text": "from __future__ import (absolute_import, division, print_function,\n unicode_literals)\nimport os\nimport re\nimport sys\nimport time\nimport glob\nimport random\nimport logging\nimport ccdproc\nimport numpy as np\nimport numpy.ma as ma\nimport matplotlib\nimport shutil\nimport subprocess\nfrom threading import Timer\n\nmatplotlib.use('Qt4Agg')\nfrom matplotlib import pyplot as plt\nfrom ccdproc import CCDData, ImageFileCollection\nfrom astropy.coordinates import EarthLocation\nfrom astropy.time import Time, TimeDelta\nfrom astropy.stats import sigma_clip\nfrom astroplan import Observer\nfrom astropy import units as u\nfrom astropy.io import fits\nfrom astropy.modeling import (models, fitting, Model)\nfrom scipy import signal\n\nlog_ccd = logging.getLogger('goodmanccd.core')\nlog_spec = logging.getLogger('redspec.core')\n\n\ndef convert_time(in_time):\n \"\"\"Converts time to seconds since epoch\n\n Args:\n in_time (str): time obtained from header's keyword DATE-OBS\n\n Returns:\n time in seconds since epoch\n\n \"\"\"\n return time.mktime(time.strptime(in_time, \"%Y-%m-%dT%H:%M:%S.%f\"))\n\n\ndef fix_duplicated_keywords(night_dir):\n \"\"\"Remove duplicated keywords\n\n There are some cases when the raw data comes with duplicated keywords.\n The origin has not been tracked down. The solution is to identify the\n duplicated keywords and the remove all but one from the end backwards.\n\n Args:\n night_dir (str): The full path for the raw data location\n\n \"\"\"\n log_ccd.debug('Finding duplicated keywords')\n log_ccd.warning('Files headers will be overwritten')\n files = glob.glob(os.path.join(night_dir, '*.fits'))\n # Pick a random file to find duplicated keywords\n random_file = random.choice(files)\n ccd = CCDData.read(random_file, unit=u.adu)\n header = ccd.header\n # Put the duplicated keywords in a list\n multiple_keys = []\n for keyword in header.keys():\n if keyword != '':\n if header.count(keyword) > 1:\n if keyword not in multiple_keys:\n multiple_keys.append(keyword)\n if multiple_keys != []:\n log_ccd.debug('Found {:d} duplicated keyword '\n '{:s}'.format(len(multiple_keys),\n 's' if len(multiple_keys) > 1 else ''))\n\n for image_file in files:\n log_ccd.debug('Processing Image File: {:s}'.format(image_file))\n try:\n ccd = CCDData.read(image_file, unit=u.adu)\n for keyword in multiple_keys:\n while ccd.header.count(keyword) > 1:\n ccd.header.remove(keyword,\n ccd.header.count(keyword) - 1)\n log_ccd.warning('Overwriting file with duplicated keywords removed')\n log_ccd.debug('File %s overwritten', image_file)\n ccd.write(image_file, clobber=True)\n except IOError as error:\n log_ccd.error(error)\n\n\ndef ra_dec_to_deg(right_ascension, declination):\n \"\"\"Converts right ascension and declination to degrees\n\n Args:\n right_ascension (str): Right ascension in the format hh:mm:ss.sss\n declination (str): Declination in the format dd:mm:ss.sss\n\n Returns:\n right_ascension_deg (float): Right ascension in degrees\n declination_deg (float): Declination in degrees\n\n \"\"\"\n right_ascension = right_ascension.split(\":\")\n declination = declination.split(\":\")\n\n # RIGHT ASCENTION conversion\n right_ascension_deg = (float(right_ascension[0])\n + (float(right_ascension[1])\n + (float(right_ascension[2]) / 60.)) / 60.) * \\\n (360. / 24.)\n\n # DECLINATION conversion\n if float(declination[0]) == abs(float(declination[0])):\n sign = 1\n else:\n sign = -1\n declination_deg = sign * (abs(float(declination[0]))\n + (float(declination[1])\n + (float(declination[2]) / 60.)) / 60.)\n return right_ascension_deg, declination_deg\n\n\ndef print_spacers(message):\n \"\"\"Miscellaneous function to print uniform spacers\n\n Prints a spacer of 80 columns with and 3 rows height. The first and last\n rows contains the symbol \"=\" repeated 80 times. The middle row contains the\n message centered and the extremes has one single \"=\" symbol.\n The only functionality of this is aesthetic.\n\n Args:\n message (str): A message to be printed\n\n Returns:\n True (bool): A True value\n\n \"\"\"\n # define the width of the message\n columns = 80\n if len(message) % 2 == 1 and int(columns) % 2 != 1:\n message += \" \"\n\n bar_length = int(columns)\n\n # compose bars top and bottom\n spacer_bar = \"=\" * bar_length\n\n blanks = bar_length - 2\n space_length = int((blanks - len(message)) / 2)\n\n # compose the message\n message_bar = \"=\" + \" \" * space_length + message + \" \" * space_length + \"=\"\n\n print(spacer_bar)\n print(message_bar)\n print(spacer_bar)\n return True\n\n\ndef print_progress(current, total):\n \"\"\"Prints the percentage of a progress\n\n It works for FOR loops, requires to know the full length of the loop.\n Prints to the standard output.\n\n Notes:\n A possible improvement for this is to run it using multithreading\n\n Args:\n current (int): Current value in the range of the loop.\n total (int): The length of the loop.\n\n \"\"\"\n if current == total:\n sys.stdout.write(\"Progress {:.2%}\\n\".format(1.0 * current / total))\n else:\n sys.stdout.write(\"\\rProgress {:.2%}\".format(1.0 * current / total))\n sys.stdout.flush()\n return\n\n\ndef get_twilight_time(date_obs):\n \"\"\"Get end/start time of evening/morning twilight\n\n Notes:\n Taken from David Sanmartim's development\n\n Args:\n date_obs (list): List of all the dates from data.\n\n Returns:\n twilight_evening (str): Evening twilight time in the format\n 'YYYY-MM-DDTHH:MM:SS.SS'\n twilight_morning (str): Morning twilight time in the format\n 'YYYY-MM-DDTHH:MM:SS.SS'\n sun_set_time (str): Sun set time in the format 'YYYY-MM-DDTHH:MM:SS.SS'\n sun_rise_time (str): Sun rise time in the format\n 'YYYY-MM-DDTHH:MM:SS.SS'\n\n \"\"\"\n # observatory(str): Observatory name.\n observatory = 'SOAR Telescope'\n geodetic_location = ['-70d44m01.11s', '-30d14m16.41s', 2748]\n\n # longitude (str): Geographic longitude in string format\n longitude = geodetic_location[0]\n\n # latitude (str): Geographic latitude in string format.\n latitude = geodetic_location[1]\n\n # elevation (int): Geographic elevation in meters above sea level\n elevation = geodetic_location[2]\n\n # timezone (str): Time zone.\n timezone = 'UTC'\n\n # description(str): Observatory description\n description = 'Soar Telescope on Cerro Pachon, Chile'\n\n soar_loc = EarthLocation.from_geodetic(longitude,\n latitude,\n elevation * u.m,\n ellipsoid='WGS84')\n\n soar = Observer(name=observatory,\n location=soar_loc,\n timezone=timezone,\n description=description)\n\n time_first_frame, time_last_frame = Time(min(date_obs)), Time(max(date_obs))\n\n twilight_evening = soar.twilight_evening_astronomical(\n Time(time_first_frame), which='nearest').isot\n\n twilight_morning = soar.twilight_morning_astronomical(\n Time(time_last_frame), which='nearest').isot\n\n sun_set_time = soar.sun_set_time(\n Time(time_first_frame), which='nearest').isot\n\n sun_rise_time = soar.sun_rise_time(\n Time(time_last_frame), which='nearest').isot\n\n log_ccd.debug('Sun Set ' + sun_set_time)\n log_ccd.debug('Sun Rise ' + sun_rise_time)\n\n return twilight_evening, twilight_morning, sun_set_time, sun_rise_time\n\n\ndef image_overscan(ccd, overscan_region, add_keyword=False):\n \"\"\"Apply overscan to data\n\n Uses ccdproc.subtract_overscan to perform the task.\n\n Notes:\n The overscan_region argument uses FITS convention, just like IRAF,\n therefore is 1 based. i.e. it starts in 1 not 0.\n\n Args:\n ccd (object): A ccdproc.CCDData instance\n overscan_region (str): The overscan region in the format '[x1:x2,y1:y2]'\n where x is the spectral axis and y is the spatial axis.\n add_keyword (bool): Tells ccdproc whether to add a keyword or not.\n Default False.\n\n Returns:\n ccd (object): Overscan corrected ccdproc.CCDData instance\n\n \"\"\"\n log_ccd.debug('Applying overscan Correction: {:s}'.format(overscan_region))\n ccd = ccdproc.subtract_overscan(ccd=ccd,\n median=True,\n fits_section=overscan_region,\n add_keyword=add_keyword)\n\n ccd.header.add_history('Applied overscan correction ' + overscan_region)\n return ccd\n\n\ndef image_trim(ccd, trim_section, add_keyword=False):\n \"\"\"Trim image to a given section\n\n Notes:\n The overscan_region argument uses FITS convention, just like IRAF,\n therefore is 1 based. i.e. it starts in 1 not 0.\n\n Args:\n ccd (object): A ccdproc.CCDData instance\n trim_section (str): The trimming section in the format '[x1:x2,y1:y2]'\n where x is the spectral axis and y is the spatial axis.\n add_keyword (bool): Tells ccdproc whether to add a keyword or not.\n Default False.\n\n Returns:\n ccd (object): Trimmed ccdproc.CCDData instance\n\n \"\"\"\n ccd = ccdproc.trim_image(ccd=ccd,\n fits_section=trim_section,\n add_keyword=add_keyword)\n ccd.header.add_history('Trimmed image to ' + trim_section)\n\n return ccd\n\n\ndef get_slit_trim_section(master_flat):\n \"\"\"Find the slit edges to trim all data\n\n Using a master flat, ideally good signal to noise ratio, this function will\n identify the edges of the slit projected into the detector. Having this data\n will allow to reduce the overall processing time and also reduce the\n introduction of artifacts due to non-illuminated regions in the detectors,\n such as NaNs -INF +INF, etc.\n\n Args:\n master_flat (object): A ccdproc.CCDData instance\n\n Returns:\n slit_trim_section (str): Trim section in spatial direction in the format\n [:,slit_lower_limit:slit_higher_limit]\n\n \"\"\"\n x, y = master_flat.data.shape\n\n # Using the middle point to make calculations, usually flats have good\n # illumination already at the middle.\n middle = int(y / 2.)\n ccd_section = master_flat.data[:, middle:middle + 200]\n ccd_section_median = np.median(ccd_section, axis=1)\n spatial_axis = range(len(ccd_section_median))\n\n # set values for initial box model definition\n box_max = np.max(ccd_section_median)\n box_center = len(ccd_section_median) / 2.\n box_width = .75 * len(ccd_section_median)\n\n # box model definition\n box_model = models.Box1D(amplitude=box_max, x_0=box_center, width=box_width)\n\n box_fitter = fitting.SimplexLSQFitter()\n\n fitted_box = box_fitter(box_model, spatial_axis, ccd_section_median)\n\n # the number of pixels that will be removed from the detected edge of the\n # image on each side\n offset = 10\n\n # this defines a preliminary set of slit limit\n l_lim = fitted_box.x_0.value - fitted_box.width.value / 2. + offset\n\n h_lim = fitted_box.x_0.value + fitted_box.width.value / 2. - offset\n\n # Here we force the slit limits within the boundaries of the data (image)\n low_lim = int(np.max([0 + offset, l_lim]))\n\n high_lim = int(np.min([h_lim, len(ccd_section_median) - offset]))\n\n # define the slit trim section as (IRA\n slit_trim_section = '[:,{:d}:{:d}]'.format(low_lim, high_lim)\n\n # debugging plots that have to be manually turned on\n if False:\n manager = plt.get_current_fig_manager()\n manager.window.showMaximized()\n plt.title('Slit Edge Detection')\n plt.plot(box_model(spatial_axis), color='c', label='Initial Box1D')\n plt.plot(fitted_box(spatial_axis), color='k', label='Fitted Box1D')\n plt.plot(ccd_section_median, label='Median Along Disp.')\n # plt.plot(pseudo_derivative, color='g', label='Pseudo Derivative')\n plt.axvline(None, color='r', label='Detected Edges')\n plt.axvline(low_lim, color='r')\n plt.axvline(high_lim, color='r')\n # for peak in peaks:\n # plt.axvline(peak, color='r')\n plt.legend(loc='best')\n plt.show()\n\n # plt.imshow(master_flat.data[low_lim:high_lim, :])\n # plt.axvline(low_lim, color='r')\n # plt.axvline(high_lim, color='r')\n # plt.show()\n\n return slit_trim_section\n\n\ndef dcr_cosmicray_rejection(data_path, in_file, prefix, dcr_par_dir,\n delete=False):\n \"\"\"Runs an external code for cosmic ray rejection\n\n DCR was created by Wojtek Pych and the code can be obtained from\n http://users.camk.edu.pl/pych/DCR/ and is written in C. Contrary to\n ccdproc's LACosmic it actually applies the correction, and also doesn't\n update the mask attribute since it doesn't work with CCDData instances.\n\n The binary takes three positional arguments, they are: 1. input image,\n 2. output image and 3. cosmic rays images. Also it needs that a dcr.par file\n is located in the directory. All this is implemented in this function if\n delete is True it will remove the original image and the cosmic rays image.\n The removal of the original image is absolutely safe when used in the\n context of the goodman pipeline, however if you want to implement it\n somewhere else, be careful.\n\n Notes:\n This function operates an external code therefore it doesn't return\n anything, instead it creates a new image.\n\n Args:\n data_path (str): Data location\n in_file (str): Name of the file to have its cosmic rays removed\n prefix (str): Prefix to add to the file with the cosmic rays removed\n dcr_par_dir (str): Directory of default dcr.par file\n delete (bool): True for deleting the input and cosmic ray file.\n\n \"\"\"\n\n log_ccd.info('Removing cosmic rays using DCR by Wojtek Pych')\n log_ccd.debug('See http://users.camk.edu.pl/pych/DCR/')\n\n # add the prefix for the output file\n out_file = prefix + in_file\n\n # define the name for the cosmic rays file\n cosmic_file = 'cosmic_' + '_'.join(in_file.split('_')[1:])\n\n # define full path for all the files involved\n full_path_in = os.path.join(data_path, in_file)\n full_path_out = os.path.join(data_path, out_file)\n full_path_cosmic = os.path.join(data_path, cosmic_file)\n\n # this is the command for running dcr, all arguments are required\n command = 'dcr {:s} {:s} {:s}'.format(full_path_in,\n full_path_out,\n full_path_cosmic)\n\n log_ccd.debug('DCR command:')\n log_ccd.debug(command)\n # print(command.split(' '))\n\n # get the current working directory to go back to it later in case the\n # the pipeline has not been called from the same data directory.\n cwd = os.getcwd()\n\n # move to the directory were the data is, dcr is expecting a file dcr.par\n os.chdir(data_path)\n\n # check if file dcr.par exists\n while not os.path.isfile('dcr.par'):\n\n log_spec.debug('File dcr.par does not exist. Copying default one.')\n dcr_par_path = os.path.join(dcr_par_dir, 'dcr.par')\n log_ccd.debug('dcr.par full path: {:s}'.format(dcr_par_path))\n if os.path.isfile(dcr_par_path):\n shutil.copy2(dcr_par_path, data_path)\n else:\n log_ccd.error('Could not find dcr.par file')\n else:\n log_ccd.debug('File dcr.par exists.')\n\n # call dcr\n try:\n\n dcr = subprocess.Popen(command.split(),\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n\n except OSError as error:\n log_ccd.error(error)\n sys.exit('Your system can not locate the executable file dcr, try '\n 'moving it to /bin or create a symbolic link\\n\\n\\tcd /bin\\n\\t'\n 'sudo ln -s /full/path/to/dcr')\n\n # return False\n\n # if the process is taking too long to respond, kill it\n # kill_process = lambda process: process.kill()\n def kill_process(process): process.kill()\n\n dcr_timer = Timer(5, kill_process, [dcr])\n try:\n dcr_timer.start()\n stdout, stderr = dcr.communicate()\n finally:\n dcr_timer.cancel()\n\n # wait for dcr to terminate\n # dcr.wait()\n\n # go back to the original directory. Could be the same.\n os.chdir(cwd)\n\n # If no error stderr is an empty string\n if stderr != '':\n log_ccd.error(stderr)\n if 'dcr: not found' in stderr:\n sys.exit('Your system can not locate the executable file dcr, try '\n 'moving it to /bin or create a symbolic link\\n\\n\\tcd /bin\\n\\t'\n 'sudo ln -s /full/path/to/dcr')\n else:\n for output_line in stdout.split('\\n'):\n log_ccd.debug(output_line)\n\n # delete extra files only if the execution ended without error\n if delete and stderr == '' and 'USAGE:' not in stdout:\n try:\n log_ccd.warning('Removing input file: {:s}'.format(full_path_in))\n os.unlink(full_path_in)\n except OSError as error:\n log_ccd.error(error)\n\n try:\n log_ccd.warning(\n 'Removing cosmic rays file: {:s}'.format(full_path_cosmic))\n os.unlink(full_path_cosmic)\n except OSError as error:\n log_ccd.error(error)\n\n\ndef lacosmic_cosmicray_rejection(ccd, mask_only=False):\n \"\"\"Do cosmic ray rejection using ccdproc.LACosmic\n\n This function in fact does not apply any correction, it detects the cosmic\n rays and updates the attribute mask of the ccd object (CCDData instance).\n The attribute mask is used later as a mask for the pixels hit by cosmic rays\n\n Notes:\n OBS: cosmic ray rejection is working pretty well by defining gain = 1.\n It's not working when we use the real gain of the image. In this case\n the sky level changes by a factor equal the gain.\n Function to determine the sigfrac and objlim: y = 0.16 * exptime + 1.2\n\n Args:\n ccd (object): A ccdproc.CCDData instance.\n mask_only (bool): In some cases you may want to obtain the cosmic\n rays mask only.\n\n Returns:\n ccd (object): A CCDData instance with the mask attribute updated or an\n array which corresponds to the mask.\n\n \"\"\"\n if ccd.header['OBSTYPE'] == 'OBJECT':\n value = 0.16 * float(ccd.header['EXPTIME']) + 1.2\n log_ccd.info('Cleaning cosmic rays... ')\n\n ccd = ccdproc.cosmicray_lacosmic(\n ccd,\n sigclip=2.5,\n sigfrac=value,\n objlim=value,\n gain=float(ccd.header['GAIN']),\n readnoise=float(ccd.header['RDNOISE']),\n satlevel=np.inf,\n sepmed=True,\n fsmode='median',\n psfmodel='gaussy',\n verbose=False)\n\n ccd.header.add_history(\"Cosmic rays rejected with LACosmic\")\n log_ccd.info(\"Cosmic rays rejected with LACosmic\")\n if mask_only:\n return ccd.mask\n else:\n return ccd\n else:\n log_ccd.debug('Skipping cosmic ray rejection for image of datatype: '\n '{:s}'.format(ccd.header['OBSTYPE']))\n return ccd\n\n\ndef call_cosmic_rejection(ccd, image_name, out_prefix, red_path,\n dcr_par, keep_files=False, prefix='c', method='dcr'):\n \"\"\"Call for the appropriate cosmic ray rejection method\n\n There are three options when dealing with cosmic ray rejection in this\n pipeline, the first is ``dcr`` which is a program written in C by Wojtek\n Pych (http://users.camk.edu.pl/pych/DCR/) and works very well for\n spectroscopy the only negative aspect is that integration with python was\n difficult and not native.\n\n Args:\n ccd (object): a ccdproc.CCDData instance.\n image_name (str): Science image name.\n out_prefix (str): Partial prefix to be added to the image name. Related\n to previous processes and not cosmic ray rejection.\n red_path (str): Path to reduced data directory.\n dcr_par (str): Path to dcr.par file.\n keep_files (bool): If True, the original file and the cosmic ray mask\n will not be deleted. Default is False.\n prefix (str): Cosmic ray rejection related prefix to be added to image\n name.\n method (str): Method to use for cosmic ray rejection. There are three\n options: dcr, lacosmic and none.\n\n \"\"\"\n\n if method == 'dcr':\n log_ccd.warning('DCR does apply the correction to images if you want '\n 'the mask use --keep-cosmic-files')\n full_path = os.path.join(red_path, out_prefix + image_name)\n ccd.write(full_path, clobber=True)\n log_ccd.info('Saving image: {:s}'.format(full_path))\n\n in_file = out_prefix + image_name\n\n dcr_cosmicray_rejection(data_path=red_path,\n in_file=in_file,\n prefix=prefix,\n dcr_par_dir=dcr_par,\n delete=keep_files)\n\n elif method == 'lacosmic':\n log_ccd.warning('LACosmic does not apply the correction to images '\n 'instead it updates the mask attribute for CCDData '\n 'objects. For saved files the mask is a fits extension')\n\n ccd = lacosmic_cosmicray_rejection(ccd=ccd)\n\n out_prefix = prefix + out_prefix\n full_path = os.path.join(red_path, out_prefix + image_name)\n\n ccd.write(full_path, clobber=True)\n log_ccd.info('Saving image: {:s}'.format(full_path))\n\n elif method == 'none':\n full_path = os.path.join(red_path, out_prefix + image_name)\n log_ccd.warning(\"--cosmic set to 'none'\")\n ccd.write(full_path, clobber=True)\n log_ccd.info('Saving image: {:s}'.format(full_path))\n\n else:\n log_ccd.error('Unrecognized Cosmic Method {:s}'.format(method))\n\n\ndef get_best_flat(flat_name):\n \"\"\"Look for matching master flat\n\n Given a basename for masterflats defined as a combination of key parameters\n extracted from the header of the image that we want to flatfield, this\n function will find the name of the files that matches the base name and then\n will choose the first. Ideally this should go further as to check signal,\n time gap, etc.\n After it identifies the file it will load it using ccdproc.CCDData and\n return it along the filename.\n In case if fails it will return None instead of master_flat and another\n None instead of master_flat_name.\n\n Args:\n flat_name (str): Full path of masterflat basename. Ends in '*.fits' for\n globbing.\n\n Returns:\n master_flat (object): A ccdproc.CCDData instance\n master_flat_name (str): Full path to the chosen masterflat.\n\n \"\"\"\n flat_list = glob.glob(flat_name)\n log_ccd.debug('Flat base name {:s}'.format(flat_name))\n log_ccd.debug('Matching master flats found: {:d}'.format(len(flat_list)))\n if len(flat_list) > 0:\n if len(flat_list) == 1:\n master_flat_name = flat_list[0]\n else:\n master_flat_name = flat_list[0]\n # elif any('dome' in flat for flat in flat_list):\n # master_flat_name =\n\n master_flat = CCDData.read(master_flat_name, unit=u.adu)\n log_ccd.debug('Found suitable master flat: {:s}'.format(master_flat_name))\n return master_flat, master_flat_name\n else:\n log_ccd.error('There is no flat available')\n return None, None\n\n\ndef print_default_args(args):\n \"\"\"Print default values of arguments.\n\n This is mostly helpful for debug but people not familiar with the software\n might find it useful as well\n\n Notes:\n This function is deprecated.\n\n Notes:\n This is not dynamically updated so use with caution\n\n Args:\n args (object): An argparse instance\n\n \"\"\"\n arg_name = {'auto_clean': '--auto-clean',\n 'clean_cosmic': '-c, --cosmic',\n 'debug_mode': '--debug',\n 'flat_normalize': '--flat-normalize',\n 'ignore_bias': '--ignore-bias',\n 'log_to_file': '--log-to-file',\n 'norm_order': '--flat-norm-order',\n 'raw_path': '--raw-path',\n 'red_path': '--red-path',\n 'saturation_limit': '--saturation',\n 'destiny': '-d --proc-path',\n 'interactive_ws': '-i --interactive',\n 'lamp_all_night': '-r --reference-lamp',\n 'lamp_file': '-l --lamp-file',\n 'output_prefix': '-o --output-prefix',\n 'pattern': '-s --search-pattern',\n 'procmode': '-m --proc-mode',\n 'reference_dir': '-R --reference-files',\n 'source': '-p --data-path',\n 'save_plots': '--save-plots',\n 'dcr_par_dir': '--dcr-par-dir'}\n for key in args.__dict__:\n log_ccd.debug('Default value for {:s} is {:s}'.format(\n arg_name[key],\n str(args.__getattribute__(key))))\n\n\ndef normalize_master_flat(master, name, method='simple', order=15):\n \"\"\" Master flat normalization method\n\n This function normalize a master flat in three possible ways:\n *mean*: simply divide the data by its mean\n\n *simple*: Calculates the median along the spatial axis in order to obtain\n the dispersion profile. Then fits a Chebyshev1D model and apply this to all\n the data.\n\n *full*: This is for experimental purposes only because it takes a lot of\n time to process. It will fit a model to each line along the dispersion axis\n and then divide it by the fitted model. I do not recommend this method\n unless you have a good reason as well as a powerful computer..\n\n Args:\n master (object): Master flat. Has to be a ccdproc.CCDData instance\n name (str): Full path of master flat prior to normalization\n method (str): Normalization method, 'mean', 'simple' or 'full'\n order (int): Order of the polinomial to be fitted.\n\n Returns:\n master (object): The normalized master flat. ccdproc.CCDData instance\n\n \"\"\"\n assert isinstance(master, CCDData)\n\n # define new name, base path and full new name\n new_name = 'norm_' + name.split('/')[-1]\n path = '/'.join(name.split('/')[0:-1])\n norm_name = os.path.join(path, new_name)\n\n if method == 'mean':\n log_ccd.debug('Normalizing by mean')\n master.data /= master.data.mean()\n\n master.header.add_history('Flat Normalized by Mean')\n\n elif method == 'simple' or method == 'full':\n log_ccd.debug('Normalizing flat by {:s} model'.format(method))\n\n # Initialize Fitting models and fitter\n model_init = models.Chebyshev1D(degree=order)\n model_fitter = fitting.LevMarLSQFitter()\n\n # get data shape\n x_size, y_size = master.data.shape\n x_axis = range(y_size)\n\n if method == 'simple':\n # get profile along dispersion axis to fit a model to use for\n # normalization\n profile = np.median(master.data, axis=0)\n\n # do the actual fit\n fit = model_fitter(model_init, x_axis, profile)\n\n # convert fit into an array\n fit_array = fit(x_axis)\n\n # pythonic way to divide an array by a vector\n master.data = master.data / fit_array[None, :]\n\n master.header.add_history('Flat Normalized by simple model')\n\n elif method == 'full':\n log_ccd.warning('This part of the code was left here for experimental '\n 'purposes only')\n log_ccd.warning('This procedure takes a lot to process, you might want to'\n 'see other method such as simple or mean.')\n for i in range(x_size):\n fit = model_fitter(model_init, x_axis, master.data[i])\n master.data[i] = master.data[i] / fit(x_axis)\n master.header.add_history('Flat Normalized by full model')\n\n # write normalized flat to a file\n master.write(norm_name, clobber=True)\n\n return master\n\n\ndef get_central_wavelength(grating, grt_ang, cam_ang):\n \"\"\"Calculates the central wavelength for a given spectroscopic mode\n\n The equation used to calculate the central wavelength is the following\n\n\n .. math::\n \\\\lambda_{central} = \\\\frac{1e6}{GRAT}\n \\\\sin\\\\left(\\\\frac{\\\\alpha \\\\pi}{180}\\\\right) +\n \\\\sin\\\\left(\\\\frac{\\\\beta \\\\pi}{180}\\\\right)\n\n\n Args:\n grating (str): Grating frequency as a string. Example '400'.\n grt_ang (str): Grating Angle as a string. Example '12.0'.\n cam_ang (str): Camera Angle as a string. Example '20.0'\n\n Returns:\n central_wavelength (float): Central wavelength as a float value.\n\n \"\"\"\n\n grating_frequency = float(grating)\n alpha = float(grt_ang)\n beta = float(cam_ang) - float(grt_ang)\n\n central_wavelength = (1e6 / grating_frequency) * \\\n (np.sin(alpha * np.pi / 180.) +\n np.sin(beta * np.pi / 180.))\n\n log_spec.debug('Found {:.3f} as central wavelength'.format(central_wavelength))\n\n return central_wavelength\n\n\ndef remove_conflictive_keywords(path, file_list):\n \"\"\"Removes problematic keywords\n\n The blue camera has a set of keywords whose comments contain non-ascii\n characters, in particular the degree symbol. Those keywords are not\n needed in any stage of the data reduction therefore they are removed.\n The data will be overwritten with the keywords removed. The user will\n need to have backups of raw data.\n\n Notes:\n This function solves a problem with old data, new headers are compliant\n with the headers.\n\n Args:\n path (str): Path to the folder containing the files\n file_list (list): List of files to remove keywords\n\n \"\"\"\n log_ccd.debug('Removing conflictive keywords in Blue Camera Headers')\n log_ccd.warning('Files will be overwritten')\n for blue_file in file_list:\n full_path = os.path.join(path, blue_file)\n log_ccd.debug('Processing file {:s}'.format(blue_file))\n try:\n data, header = fits.getdata(full_path,\n header=True,\n ignore_missing_end=True)\n\n keys_to_remove = ['PARAM0',\n 'PARAM61',\n 'PARAM62',\n 'PARAM63',\n 'NAXIS3']\n\n if data.ndim == 3:\n header['NAXIS'] = 2\n data = data[0]\n\n log_ccd.debug('Modified file to be 2D instead of 3D '\n '(problematic)')\n\n for keyword in keys_to_remove:\n header.remove(keyword)\n\n log_ccd.debug('Removed conflictive keyword '\n '{:s}'.format(keyword))\n\n log_ccd.debug('Updated headers')\n\n fits.writeto(full_path,\n data,\n header,\n clobber=True)\n\n except KeyError as error:\n log_ccd.debug(error)\n\n\n# spectroscopy specific functions\n\ndef classify_spectroscopic_data(path, search_pattern):\n \"\"\"Classify data by grouping them as pandas.DataFrame instances\n\n This functions uses ImageFileCollection from ccdproc. First it creates a\n collection of information regarding the images located in *path* that match\n the pattern *search_pattern*\n The information obtained are all keywords listed in the list *keywords*\n The ImageFileCollection is translated into pandas.DataFrame and then is used\n much like an SQL database to select and filter values and in that way put\n them in groups that are pandas.DataFrame instances.\n\n\n Args:\n path (str): Path to data location\n search_pattern (str): Prefix to match files.\n\n Returns:\n data_container (object): Instance of NightDataContainer\n\n \"\"\"\n\n search_path = os.path.join(path, search_pattern + '*.fits')\n\n file_list = glob.glob(search_path)\n\n if file_list == []:\n log_spec.error('No file found using search pattern '\n '\"{:s}\"'.format(search_pattern))\n\n sys.exit('Please use the argument --search-pattern to define the '\n 'common prefix for the files to be processed.')\n\n data_container = NightDataContainer(path=path,\n instrument=str('Red'),\n technique=str('Spectroscopy'))\n\n keywords = ['date',\n 'slit',\n 'date-obs',\n 'obstype',\n 'object',\n 'exptime',\n 'obsra',\n 'obsdec',\n 'grating',\n 'cam_targ',\n 'grt_targ',\n 'filter',\n 'filter2',\n 'gain',\n 'rdnoise']\n\n ifc = ImageFileCollection(path, keywords=keywords, filenames=file_list)\n\n pifc = ifc.summary.to_pandas()\n\n pifc['radeg'] = ''\n pifc['decdeg'] = ''\n for i in pifc.index.tolist():\n radeg, decdeg = ra_dec_to_deg(pifc.obsra.iloc[i], pifc.obsdec.iloc[i])\n\n pifc.iloc[i, pifc.columns.get_loc('radeg')] = '{:.2f}'.format(radeg)\n\n pifc.iloc[i, pifc.columns.get_loc('decdeg')] = '{:.2f}'.format(decdeg)\n # now we can compare using degrees\n\n confs = pifc.groupby(['slit',\n 'radeg',\n 'decdeg',\n 'grating',\n 'cam_targ',\n 'grt_targ',\n 'filter',\n 'filter2',\n 'gain',\n 'rdnoise']).size().reset_index().rename(\n columns={0: 'count'})\n\n for i in confs.index:\n spec_group = pifc[((pifc['slit'] == confs.iloc[i]['slit']) &\n (pifc['radeg'] == confs.iloc[i]['radeg']) &\n (pifc['decdeg'] == confs.iloc[i]['decdeg']) &\n (pifc['grating'] == confs.iloc[i]['grating']) &\n (pifc['cam_targ'] == confs.iloc[i]['cam_targ']) &\n (pifc['grt_targ'] == confs.iloc[i]['grt_targ']) &\n (pifc['filter'] == confs.iloc[i]['filter']) &\n (pifc['filter2'] == confs.iloc[i]['filter2']) &\n (pifc['gain'] == confs.iloc[i]['gain']) &\n (pifc['rdnoise'] == confs.iloc[i]['rdnoise']))]\n\n group_obstype = spec_group.obstype.unique()\n\n if 'COMP' in group_obstype and len(group_obstype) == 1:\n log_spec.debug('Adding COMP group')\n data_container.add_comp_group(comp_group=spec_group)\n elif 'OBJECT' in group_obstype and len(group_obstype) == 1:\n log_spec.debug('Adding OBJECT group')\n data_container.add_object_group(object_group=spec_group)\n else:\n log_spec.debug('Adding OBJECT-COMP group')\n data_container.add_spec_group(spec_group=spec_group)\n\n return data_container\n\n\ndef search_comp_group(object_group, comp_groups):\n \"\"\"Search for a suitable comparison lamp group\n\n In case a science target was observed without comparison lamps, usually\n right before or right after, this function will look for a compatible set\n obtained at a different time or pointing.\n\n Notes:\n This methodology is not recommended for radial velocity studies.\n\n Args:\n object_group (object): A pandas.DataFrame instances containing a group\n of images for a given scientific target.\n comp_groups (list): A list in which every element is a pandas.DataFrame\n that contains information regarding groups of comparison lamps.\n\n Returns:\n\n \"\"\"\n log_spec.debug('Finding a suitable comparison lamp group')\n\n object_confs = object_group.groupby(['slit',\n 'grating',\n 'cam_targ',\n 'grt_targ',\n 'filter',\n 'filter2']\n ).size().reset_index()\n # .rename(columns={0: 'count'})\n\n for comp_group in comp_groups:\n\n if ((comp_group['slit'] == object_confs.iloc[0]['slit']) &\n (comp_group['grating'] == object_confs.iloc[0]['grating']) &\n (comp_group['cam_targ'] == object_confs.iloc[0]['cam_targ']) &\n (comp_group['grt_targ'] == object_confs.iloc[0]['grt_targ']) &\n (comp_group['filter'] == object_confs.iloc[0]['filter']) &\n (comp_group['filter2'] == object_confs.iloc[0]['filter2'])).all():\n log_spec.debug('Found a matching comparison lamp group')\n\n return comp_group\n\n raise NoMatchFound\n\n\ndef spectroscopic_extraction(ccd, extraction,\n comp_list=None,\n nfind=3,\n n_sigma_extract=10,\n plots=False):\n \"\"\"This function does not do the actual extraction but prepares the data\n\n There are several steps involved in a spectroscopic extraction, this\n function manages them.\n\n Args:\n ccd (object): A ccdproc.CCDData Instance\n extraction (str): Extraction type name. _simple_ or _optimal_\n comp_list (list): List of ccdproc.CCDData instances of COMP lamps data\n nfind (int): Maximum number of targets to be returned\n n_sigma_extract (int): Number of sigmas to be used for extraction\n plots (bool): If plots will be shown or not.\n\n Returns:\n extracted (list): List of ccdproc.CCDData instances\n comp_zones (list): List of ccdproc.CCDData instances\n\n Raises:\n NoTargetException (Exception): A NoTargetException if there is no target\n found.\n\n \"\"\"\n\n assert isinstance(ccd, CCDData)\n\n comp_zones = []\n extracted = []\n\n if comp_list is None:\n comp_list = []\n # print(comp_list)\n\n iccd = remove_background_by_median(ccd=ccd)\n\n profile_model = identify_targets(ccd=iccd, nfind=nfind, plots=plots)\n del (iccd)\n\n if profile_model is None:\n log_spec.critical('Target identification FAILED!')\n raise NoTargetException\n else:\n background_image = create_background_image(ccd=ccd,\n profile_model=profile_model,\n nsigma=n_sigma_extract,\n separation=5)\n\n if isinstance(profile_model, Model):\n traces = trace_targets(ccd=ccd, profile=profile_model, plots=plots)\n # extract(ccd=ccd,\n # spatial_profile=profile_model,\n # n_sigma_extract=10,\n # sampling_step=5)\n if 'CompoundModel' in profile_model.__class__.name:\n log_spec.debug(profile_model.submodel_names)\n for m in range(len(profile_model.submodel_names)):\n submodel_name = profile_model.submodel_names[m]\n\n ntrace = traces[m]\n model = profile_model[submodel_name]\n\n zone = get_extraction_zone(\n ccd=ccd,\n extraction=extraction,\n trace=ntrace,\n trace_index=m,\n model=profile_model[submodel_name],\n n_sigma_extract=n_sigma_extract,\n plots=plots)\n\n for comp in comp_list:\n comp_zone = get_extraction_zone(ccd=comp,\n extraction=extraction,\n trace=ntrace,\n trace_index=m,\n zone=zone,\n plots=plots)\n # since a comparison lamp only needs only the relative line\n # center in the dispersion direction, therefore the flux is\n # not important we are only calculating the median along the\n # spatial direction\n comp_zone.data = np.median(comp_zone.data, axis=0)\n comp_zones.append(comp_zone)\n\n background_level = get_background_value(\n background_image=background_image,\n zone=zone)\n\n extracted_ccd = extract(ccd=ccd,\n trace=ntrace,\n spatial_profile=model,\n extraction=extraction,\n zone=zone,\n background_level=background_level,\n sampling_step=10,\n plots=plots)\n extracted.append(extracted_ccd)\n\n # if plots:\n # plt.imshow(nccd.data)\n # plt.show()\n\n else:\n ntrace = traces[0]\n\n zone = get_extraction_zone(\n ccd=ccd,\n extraction=extraction,\n trace=traces[0],\n trace_index=0,\n model=profile_model,\n n_sigma_extract=n_sigma_extract,\n plots=plots)\n\n for comp in comp_list:\n comp_zone = get_extraction_zone(ccd=comp,\n extraction=extraction,\n trace=ntrace,\n trace_index=0,\n zone=zone,\n plots=plots)\n\n # since a comparison lamp only needs the relative line\n # center in the dispersion direction, therefore the flux is not\n # important we are only calculating the median along the spatial\n # direction\n comp_zone.data = np.median(comp_zone.data, axis=0)\n comp_zones.append(comp_zone)\n\n background_level = get_background_value(\n background_image=background_image,\n zone=zone)\n\n extracted_ccd = extract(ccd=ccd,\n trace=ntrace,\n spatial_profile=profile_model,\n extraction=extraction,\n zone=zone,\n background_level=background_level,\n sampling_step=10,\n plots=plots)\n\n extracted.append(extracted_ccd)\n\n # if plots:\n # plt.imshow(nccd.data)\n # plt.show()\n\n # print(extracted)\n # print(comp_zones)\n return extracted, comp_zones\n\n elif profile_model is None:\n log_spec.warning(\"Didn't receive identified targets \"\n \"from {:s}\".format(ccd.header['OFNAME']))\n raise NoTargetException\n else:\n log_spec.error('Got wrong input')\n\n\ndef identify_targets(ccd, nfind=3, plots=False):\n \"\"\"Identify spectroscopic targets in an image\n\n This function collapses the image along the dispersion direction using a\n median, This highlights the spatial features present in a 2D spectrum\n (image), Then does a sigma clip to remove any features in order to fit the\n background level and shape, the fit is a linear function. Once the\n background has been removed it will equal to zero all negative values. It\n will perform a new sigma clipping but this time to determinate the\n background amplitude. Finally it finds all the peaks above the background\n level and pick the n largets ones. n is defined by nfind.\n\n Args:\n ccd (object): a ccdproc.CCDData instance\n nfind (int): Maximum number of targets to be returned\n plots (bool): to show debugging plots\n\n Returns:\n profile_model (object): an astropy.modeling.Model instance, it could be\n a Gaussian1D or CompoundModel (several Gaussian1D). Each of them\n represent a point source spectrum found.\n\n \"\"\"\n if isinstance(ccd, CCDData):\n slit_size = re.sub('[a-zA-Z\"]', '', ccd.header['SLIT'])\n serial_binning = int(ccd.header['CCDSUM'].split()[0])\n # order will be used for finding the peaks later but also as an initial\n # estimate for stddev of gaussian\n order = int(round(float(slit_size) / (0.15 * serial_binning)))\n\n if plots:\n plt.title(ccd.header['OFNAME'])\n plt.imshow(ccd.data, clim=(30, 250))\n plt.xlabel('Dispersion Axis (x)')\n plt.ylabel('Spatial Axis (y)')\n plt.show()\n\n median_profile = np.median(ccd.data, axis=1)\n\n # Fitting Background\n\n # Before doing the fitting we will do a sigma clipping in order to\n # remove any feature.\n clipped_profile = sigma_clip(median_profile, sigma=2, iters=5)\n\n linear_model = models.Linear1D(slope=0,\n intercept=np.median(median_profile))\n\n linear_fitter = fitting.LinearLSQFitter()\n\n # the fitters do not support masked arrays so we need to have a new\n # array without the masked (clipped) elements.\n new_profile = clipped_profile[~clipped_profile.mask]\n\n # also the indexes are different\n new_x_axis = [i for i in range(len(clipped_profile)) if not clipped_profile.mask[i]]\n\n fitted_background = linear_fitter(linear_model, new_x_axis, new_profile)\n\n if plots:\n plt.title('Background Fitting Model Defined')\n plt.plot(median_profile, color='k')\n plt.plot(linear_model(range(ccd.data.shape[0])), color='r')\n plt.show()\n\n plt.title('Background Fitted Model')\n plt.plot(median_profile, color='k')\n plt.plot(fitted_background(range(ccd.data.shape[0])), color='r')\n plt.show()\n\n # Removing Background\n # Remove the background and set negative values to zero\n\n # build an array of the same dimensions of the profile\n background_array = fitted_background(range(len(median_profile)))\n\n background_subtracted = median_profile - background_array\n\n # set negative values to zero\n background_subtracted[background_subtracted < 0] = 0\n\n final_profile = background_subtracted.copy()\n\n # sigma clip and then get some features of the noise.\n clipped_final_profile = sigma_clip(final_profile, sigma=3, iters=3)\n\n # define the propper x-axis\n new_x_axis = [i for i in range(len(clipped_final_profile)) if\n not clipped_final_profile.mask[i]]\n\n clipped_final_profile = clipped_final_profile[~clipped_final_profile.mask]\n\n\n\n background_level = np.abs(np.max(clipped_final_profile) - np.min(clipped_final_profile))\n # print('MEAN: ', np.mean(clipped_final_profile))\n # print('MEDIAN: ', np.median(clipped_final_profile))\n # print('STDDEV: ', np.std(clipped_final_profile))\n # print('RANGE: ', background_level)\n\n # TODO (simon): Add information to plots\n if plots:\n plt.ioff()\n plt.close()\n # if plt.isinteractive():\n # plt.ioff()\n plt.title('Median Along Dispersion Axis (spatial)')\n plt.plot(background_subtracted, label='Background Subtracted Data')\n plt.plot(new_x_axis, clipped_final_profile, color='r', label='Sigma Clip Data')\n\n plt.axhline(background_level, color='m', label='Min-Max Difference')\n # plt.plot(final_profile, color='r')\n # plt.plot(median_profile)\n # plt.plot(background_array)\n plt.legend(loc='best')\n if plt.isinteractive():\n plt.draw()\n plt.pause(5)\n else:\n plt.show()\n\n # Identify targets\n # Now that the profile is flat it should be easier to identify targets.\n\n filtered_profile = np.where(np.abs(\n final_profile > final_profile.min() + 0.03 * final_profile.max()),\n final_profile,\n None)\n\n _upper_limit = final_profile.min() + 0.03 * final_profile.max()\n # print(_upper_limit)\n # print(np.median(final_profile))\n\n # find the peaks\n peaks = signal.argrelmax(filtered_profile, axis=0, order=order)[0]\n\n # find profile values for peaks found\n values = [final_profile[i] for i in peaks]\n\n # sort values and reverse the order so that larger values are first\n sorted_values = np.sort(values)[::-1]\n\n # pick nfind top values\n n_top_values = sorted_values[:nfind]\n # print(n_top_values)\n\n # retrieve the original index (real location) of the peaks\n selected_peaks = []\n for val in n_top_values:\n # TODO (simon): replace the 3 below by a parameter in a conf file.\n # discard peaks smaller than twice the level of background\n if val > 3 * background_level:\n index = np.where(values == val)[0]\n # print(index[0])\n selected_peaks.append(peaks[index[0]])\n else:\n log_spec.debug('Discarding peak: {:.3f}'.format(val))\n\n if plots:\n plt.ioff()\n plt.plot(final_profile)\n plt.axhline(_upper_limit, color='g')\n for peak in selected_peaks:\n plt.axvline(peak, color='r')\n plt.show()\n\n # build the model to return\n fitter = fitting.LevMarLSQFitter()\n best_stddev = None\n\n profile_model = None\n for peak in selected_peaks:\n peak_value = median_profile[peak]\n gaussian = models.Gaussian1D(amplitude=peak_value,\n mean=peak,\n stddev=order).rename(\n 'Gaussian_{:d}'.format(peak))\n\n # fixes mean and amplitude already found, just finding stddev\n gaussian.mean.fixed = True\n gaussian.amplitude.fixed = True\n fitted_gaussian = fitter(gaussian,\n range(len(median_profile)),\n median_profile)\n\n # after being fitted, unfix the parameters and now fix stddev\n fitted_gaussian.mean.fixed = False\n fitted_gaussian.amplitude.fixed = False\n fitted_gaussian.stddev.fixed = True\n\n # manually forcing the use of the best stddev if possitive\n # this disables the pipeline for extended sources\n if best_stddev is None:\n best_stddev = fitted_gaussian.stddev.value\n elif best_stddev < fitted_gaussian.stddev.value:\n fitted_gaussian.stddev.value = best_stddev\n else:\n best_stddev = fitted_gaussian.stddev.value\n if best_stddev < 0:\n best_stddev = None\n\n # print(fitted_gaussian.stddev.value)\n # plt.plot(median_profile, color='b')\n # plt.plot(fitted_gaussian(range(len(median_profile))), color='r')\n # plt.show()\n\n # this ensures the profile returned are valid\n if fitted_gaussian.stddev.value > 0:\n if profile_model is None:\n profile_model = fitted_gaussian\n else:\n profile_model += fitted_gaussian\n if plots:\n plt.plot(median_profile, color='b')\n plt.plot(profile_model(range(len(median_profile))), color='r')\n plt.show()\n\n # plt.imshow(ccd.data, clim=(50, 200), cmap='gray')\n # for peak in selected_peaks:\n # plt.axhline(peak, color='r')\n # plt.show()\n\n if profile_model is None:\n return None\n else:\n return profile_model\n\n\ndef trace(ccd, model, trace_model, fitter, sampling_step, nsigmas=2):\n \"\"\"Find the trace of a spectrum\n\n This function is called by the `trace_targets` targets, the difference is\n that it only takes single models only not CompoundModels so this function\n is called for every single target.\n\n Notes:\n This method forces the trace to go withing a rectangular region of\n center `model.mean.value` and width `2 * nsigmas`, this is for allowing\n the trace of low SNR targets. The assumption is valid since the spectra\n are always well aligned to the detectors's pixel columns. (dispersion\n axis)\n\n Args:\n ccd (object): A ccdproc.CCDData instance, 2D image.\n model (object): An astropy.modeling.Model instance that contains\n information regarding the target to be traced.\n trace_model (object): An astropy.modeling.Model instance, usually a low\n order polynomial.\n fitter (object): An astropy.modeling.fitting.Fitter instance. Will fit\n the sampled points to construct the trace model\n sampling_step (int): Step for sampling the spectrum.\n nsigmas (int): Number of stddev to each side of the mean to be used for\n searching the trace.\n\n Returns:\n An astropy.modeling.Model instance, that defines the trace of the\n spectrum.\n\n \"\"\"\n spatial_length, dispersion_length = ccd.data.shape\n\n sampling_axis = range(0, dispersion_length, sampling_step)\n sample_values = []\n\n model_stddev = model.stddev.value\n model_mean = model.mean.value\n\n sample_center = float(model_mean)\n\n for point in sampling_axis:\n\n lower_limit = int(sample_center - nsigmas * model_stddev)\n upper_limit = int(sample_center + nsigmas * model_stddev)\n\n # print(sample_center, nsigmas, model_stddev, lower_limit, upper_limit)\n\n sample = ccd.data[lower_limit:upper_limit, point:point + sampling_step]\n sample_median = np.median(sample, axis=1)\n\n try:\n sample_peak = np.argmax(sample_median)\n # print(sample_peak + lower_limit)\n except ValueError:\n # plt.plot(model(range(spatial_length)))\n # plt.plot(ccd.data[:,point])\n # plt.show()\n print('Nsigmas ', nsigmas)\n print('Model Stddev ', model_stddev)\n print('sample_center ', sample_center)\n print('sample ', sample)\n print('sample_median ', sample_median)\n print('lower_limit ', lower_limit)\n print('upper_limit ', upper_limit)\n print('point ', point)\n print('point + sampling_step ', point + sampling_step)\n print(spatial_length, dispersion_length)\n sys.exit()\n\n sample_values.append(sample_peak + lower_limit)\n\n if np.abs(sample_peak + lower_limit - model_mean) < nsigmas * model_stddev:\n sample_center = int(sample_peak + lower_limit)\n else:\n # print(np.abs(sample_peak + lower_limit - model_mean), nsigmas * model_stddev)\n sample_center = float(model_mean)\n\n fitted_trace = fitter(trace_model, sampling_axis, sample_values)\n\n if False:\n plt.title(ccd.header['OFNAME'])\n plt.imshow(ccd.data, clim=(30, 200))\n plt.plot(sampling_axis, sample_values, color='y', marker='o')\n plt.axhspan(lower_limit,\n upper_limit,\n alpha=0.4,\n color='g')\n plt.plot(fitted_trace(range(dispersion_length)), color='c')\n # plt.plot(model(range(spatial_length)))\n if plt.isinteractive():\n plt.draw()\n plt.pause(2)\n else:\n plt.show()\n # print(dispersion_length)\n # print(sampling_axis)\n\n # fitted_trace = None\n\n return fitted_trace\n\n\ndef trace_targets(ccd, profile, sampling_step=5, pol_deg=2, plots=True):\n \"\"\"Find the trace of the target's spectrum on the image\n\n This function defines a low order polynomial that trace the location of the\n spectrum. The attributes pol_deg and sampling_step define the polynomial\n degree and the spacing in pixels for the samples. For every sample a\n gaussian model is fitted and the center (mean) is recorded and since\n spectrum traces vary smoothly this value is used as a new center for the\n base model used to fit the spectrum profile.\n\n Notes:\n This doesn't work for extended sources. Also this calls for the function\n trace for doing the actual trace, the difference is that this method is\n at a higher level.\n\n Args:\n ccd (object): Instance of ccdproc.CCDData\n profile (object): Instance of astropy.modeling.Model, contains the\n spatial profile of the 2D spectrum.\n sampling_step (int): Frequency of sampling in pixels\n pol_deg (int): Polynomial degree for fitting the trace\n plots (bool): If True will show plots (debugging)\n\n Returns:\n all_traces (list): List that contains traces that are\n astropy.modeling.Model instance\n\n \"\"\"\n\n # added two assert for debugging purposes\n assert isinstance(ccd, CCDData)\n assert isinstance(profile, Model)\n\n # Initialize model fitter\n model_fitter = fitting.LevMarLSQFitter()\n\n # Initialize the model to fit the traces\n trace_model = models.Polynomial1D(degree=pol_deg)\n\n # List that will contain all the Model instances corresponding to traced\n # targets\n all_traces = None\n\n if 'CompoundModel' in profile.__class__.name:\n log_spec.debug(profile.__class__.name)\n # TODO (simon): evaluate if targets are too close together.\n\n stddev_keys = [key for key in profile._param_names if 'stddev' in key]\n\n mean_keys = [key for key in profile._param_names if 'mean' in key]\n\n stddev_values = [\n profile.__getattribute__(key).value for key in stddev_keys]\n\n mean_values = [\n profile.__getattribute__(key).value for key in mean_keys]\n\n # if len(mean_values) == 1:\n # nsigmas = 20\n # else:\n # # get maximum width\n # for i in range(len(mean_values)-1):\n\n\n for m in range(len(profile.submodel_names)):\n submodel_name = profile.submodel_names[m]\n\n model = profile[submodel_name]\n\n single_trace = trace(ccd=ccd,\n model=model,\n trace_model=trace_model,\n fitter=model_fitter,\n sampling_step=sampling_step)\n\n if all_traces is None:\n all_traces = [single_trace]\n else:\n all_traces.append(single_trace)\n\n return all_traces\n else:\n single_trace = trace(ccd=ccd,\n model=profile,\n trace_model=trace_model,\n fitter=model_fitter,\n sampling_step=sampling_step,\n nsigmas=10)\n return [single_trace]\n\n\ndef get_extraction_zone(ccd,\n extraction=None,\n trace=None,\n trace_index=None,\n model=None,\n n_sigma_extract=None,\n zone=None,\n plots=False):\n \"\"\"Get the minimum rectangular CCD zone that fully contains the spectrum\n\n In this context, *fully contained* means the spectrum plus some region for\n background subtraction.\n\n Notes:\n For Goodman HTS the alignment of the spectrum with the detector lines\n is quite good, that's why this function does not consider the trace.\n Also because the `model` argument is based on the median throughout all\n the detector along the dispersion axis, so if there is a strong\n misalignment it will result in a wider Gaussian Profile\n\n Args:\n ccd (object): A ccdproc.CCDData instance, the image from which the zone\n will be extracted\n extraction (str): Extraction type, `simple` or `optimal`\n trace (object): An astropy.modeling.Model instance that correspond to\n the trace of the spectrum\n trace_index (int): The index number of the spectrum. 0 based.\n model (object): An astropy.modeling.Model instance that was previously\n fitted to the spatial profile.\n n_sigma_extract (int): Total number of sigmas to be extracted.\n plots (bool): If True will show plots, similar to a debugging mode.\n zone (list): Low and high limits to extract\n\n Returns:\n nccd (object): Instance of ccdproc.CCDData that contains only the region\n extracted from the full image. The header is updated with a new HISTORY\n keyword that contain the region of the original image extracted.\n model (object): Instance of astropy.modeling.Model with an updated mean\n to match the new center in pixel units.\n zone (list): Low and high limits of extraction zone\n\n \"\"\"\n # make a copy of the ccd image\n nccd = ccd.copy()\n\n if zone is None and extraction is not None:\n assert (model is not None) and (n_sigma_extract is not None)\n assert isinstance(trace, Model)\n log_spec.debug('Extracting zone centered at: {:.3f}'.format(model.mean.value))\n\n spatial_length, dispersion_length = nccd.data.shape\n\n # get maximum variation in spatial direction\n trace_array = trace(range(dispersion_length))\n trace_inclination = trace_array.max() - trace_array.min()\n log_spec.debug('Trace Min-Max difference: {:.3f}'.format(trace_inclination))\n\n # m_mean = model.mean.value\n m_stddev = model.stddev.value\n extract_width = n_sigma_extract // 2 * m_stddev\n\n low_lim = np.max([0, int(trace_array.min() - extract_width)])\n\n hig_lim = np.min([int(trace_array.max() + extract_width),\n spatial_length])\n\n # in some rare cases the low_lim turns out larger than hig_lim which\n # creates a series of problems regarding extraction, here I just reverse\n # them\n if low_lim > hig_lim:\n low_lim, hig_lim = hig_lim, low_lim\n\n log_spec.debug('Zone: low {:d}, high {:d}'.format(low_lim, hig_lim))\n zone = [low_lim, hig_lim]\n\n # This is to define the APNUM1 Keyword for the header.\n\n apnum_1 = '{:d} {:d} {:d} {:d}'.format(trace_index + 1,\n 1,\n low_lim,\n hig_lim)\n\n nccd.header['APNUM1'] = apnum_1\n\n # this is necessary since we are cutting a piece of the full ccd.\n trace.c0.value -= low_lim\n log_spec.debug('Changing attribute c0 from trace, this is to adjust it to '\n 'the new extraction zone which is smaller that the full CCD.')\n\n log_spec.debug('Changing attribute mean of profile model')\n model.mean.value = extract_width\n\n\n\n nccd.data = nccd.data[low_lim:hig_lim, :]\n if nccd.mask is not None:\n log_spec.debug('Trimming mask')\n nccd.mask = nccd.mask[low_lim:hig_lim, :]\n nccd.header['HISTORY'] = 'Subsection of CCD ' \\\n '[{:d}:{:d}, :]'.format(low_lim, hig_lim)\n\n if plots:\n plt.imshow(ccd.data, clim=(0, 60))\n plt.axhspan(low_lim, hig_lim, color='r', alpha=0.2)\n plt.show()\n\n # return nccd, trace, model, zone\n return zone\n\n else:\n\n low_lim, hig_lim = zone\n\n apnum_1 = '{:d} {:d} {:d} {:d}'.format(trace_index + 1,\n 1,\n low_lim,\n hig_lim)\n\n nccd = ccd.copy()\n nccd.header['APNUM1'] = apnum_1\n\n nccd.data = nccd.data[low_lim:hig_lim, :]\n if nccd.mask is not None:\n log_spec.debug('Trimming mask')\n nccd.mask = nccd.mask[low_lim:hig_lim, :]\n nccd.header['HISTORY'] = 'Subsection of CCD ' \\\n '[{:d}:{:d}, :]'.format(low_lim, hig_lim)\n return nccd\n\n\ndef add_wcs_keys(header):\n \"\"\"Adds generic keyword to the header\n\n Linear wavelength solutions require a set of standard fits keywords. Later\n on they will be updated accordingly\n The main goal of putting them here is to have consistent and nicely ordered\n headers\n\n Notes:\n This does NOT add a WCS solution, just the keywords\n\n Args:\n header (object): New header without WCS entries\n\n Returns:\n header (object): Modified header with added WCS keywords\n\n \"\"\"\n try:\n header['BANDID1'] = 'spectrum - background none, weights none, clean no'\n header['APNUM1'] = '1 1 0 0'\n header['WCSDIM'] = 1\n header['CTYPE1'] = 'LINEAR'\n header['CRVAL1'] = 1\n header['CRPIX1'] = 1\n header['CDELT1'] = 1\n header['CD1_1'] = 1\n header['LTM1_1'] = 1\n header['WAT0_001'] = 'system=equispec'\n header['WAT1_001'] = 'wtype=linear label=Wavelength units=angstroms'\n header['DC-FLAG'] = 0\n header['DCLOG1'] = 'REFSPEC1 = non set'\n return header\n except TypeError as err:\n log_spec.error(\"Can't add wcs keywords to header\")\n log_spec.debug(err)\n\n\ndef remove_background_by_median(ccd, plots=False):\n \"\"\"Remove Background of a ccd spectrum image\n\n Notes:\n This function works well for images without strong sky lines. Or for\n targets embedded in extended sources.\n\n Args:\n ccd (object): A ccdproc.CCDData instance.\n\n Returns:\n ccd (object): The modified ccdproc.CCDData instance.\n\n \"\"\"\n new_ccd = ccd.copy()\n data = ma.masked_invalid(new_ccd.data)\n # x, y = ccd.data.shape\n median = ma.median(data, axis=0)\n\n data -= median\n data.set_fill_value(-np.inf)\n new_ccd.data = data.filled()\n\n # ccd.write('/user/simon/dummy_{:d}.fits'.format(g), clobber=True)\n return new_ccd\n\n\ndef get_background_value(background_image, zone_sep=0.5, zone=None):\n \"\"\"finds background value for each dispersion line\n\n A background image is an image whose spectrum has been masked with zeros,\n the spectrum zone is retrieved by analyzing the pixels values and then two\n background zones are defined at a distance from the edges of the target zone\n defined by zone_sep, the width of the background zone is the same as the\n target's. After validating the background zone they averaged by median along\n the spatial direction. If there are two zones they are averaged as well.\n\n Args:\n background_image (object): ccdproc.CCDData instance. Spectra are masked.\n zone_sep (float): How far the background zone should be from the edges\n of the target zone. Default 0.5.\n zone (list): Alternative you could parse the zone as a list with each\n element a limit. Therefore there should be an even number of\n elements.\n\n Returns:\n A 1D array the same length of the dispersion length.\n\n \"\"\"\n spatial_length, dispersion_length = background_image.data.shape\n if zone is None:\n background_profile = np.median(background_image.data, axis=1)\n\n target_zones_points = np.where(background_profile == 0)[0]\n\n target_zones = [i + 1 for i in range(len(target_zones_points) - 1) \\\n if\n target_zones_points[i + 1] - target_zones_points[i] > 1]\n\n target_zones.append(np.argmin(target_zones_points))\n target_zones.append(len(target_zones_points))\n target_zones.sort()\n\n for i in range(len(target_zones) - 1):\n\n if target_zones[i + 1] - target_zones[i] > 2:\n\n log_spec.debug('Found valid target zone:'\n ' {:d}:{:d}'.format(target_zones[i],\n target_zones[i + 1]))\n\n full_zone = target_zones_points[\n target_zones[i]:target_zones[i + 1]]\n zone = [full_zone[0], full_zone[-1]]\n # zone_width = len(zone)\n\n\n else:\n log_spec.warning('Target Zone is too small')\n\n # here do the evaluation if background zones are valid.\n first_background = None\n second_background = None\n background_median = None\n\n zone_width = zone[1] - zone[0]\n\n # first background zone\n back_first_low = int(zone[0] - (1 + zone_sep) * zone_width)\n back_first_high = int(zone[0] - zone_sep * zone_width)\n if 0 < back_first_low < back_first_high:\n\n first_background = np.median(background_image.data[\n back_first_low:back_first_high,\n :],\n axis=0)\n else:\n log_spec.debug('Zone [{:d}:{:d}] is forbidden'.format(\n back_first_low,\n back_first_high))\n\n # second background zone\n back_second_low = int(zone[-1] + zone_sep * zone_width)\n back_second_high = int(zone[-1] + (1 + zone_sep) * zone_width)\n if back_second_low < back_second_high < spatial_length:\n\n second_background = np.mean(background_image.data[\n back_second_low:back_second_high,\n :],\n axis=0)\n else:\n log_spec.debug('Zone [{:d}:{:d}] is forbidden'.format(\n back_second_low,\n back_second_high))\n\n if first_background is not None and second_background is not None:\n background_mean = np.mean([first_background, second_background], axis=0)\n\n return background_mean\n elif first_background is not None and second_background is None:\n return first_background\n elif first_background is None and second_background is not None:\n return second_background\n else:\n log_spec.error(\n 'Not possible to get a background extraction zone')\n return 0\n\n\ndef create_background_image(ccd, profile_model, nsigma, separation):\n \"\"\"Creates a background-only image\n\n Using a profile model and assuming the spectrum is misaligned only a little\n bit (i.e. a couple of pixels from end to end) with respect to the lines of\n the detector. The number of sigmas determines the width of the zone to be\n masked and the separation is an offset that is added.\n\n Args:\n ccd (object): A ccdproc.CCDData instance.\n profile_model (object): An astropy.modeling.Model instance. Describes\n the spatial profile of the target.\n nsigma (float): Number of sigmas. Used to calculate the width of the\n target zone to be masked.\n separation (float): Additional offset that adds to the width of the\n target zone.\n\n Returns:\n A ccdproc.CCDData instance with the spectrum masked.\n\n \"\"\"\n background_ccd = ccd.copy()\n spatial_length, dispersion_length = background_ccd.data.shape\n target_profiles = []\n if 'CompoundModel' in profile_model.__class__.name:\n log_spec.debug(profile_model.submodel_names)\n for m in range(len(profile_model.submodel_names)):\n submodel_name = profile_model.submodel_names[m]\n\n target_profiles.append(profile_model[submodel_name])\n else:\n target_profiles.append(profile_model)\n\n for target in target_profiles:\n target_mean = target.mean.value\n target_stddev = target.stddev.value\n\n data_low_lim = np.max(\n [0, target_mean - (nsigma / 2. + separation) * target_stddev])\n\n data_high_lim = np.min([spatial_length, int(\n target_mean + (nsigma / 2. + separation) * target_stddev)])\n\n background_ccd.data[data_low_lim:data_high_lim, :] = 0\n\n if False:\n plt.title('Background Image')\n plt.imshow(background_ccd.data, clim=(0, 50))\n plt.show()\n\n return background_ccd\n\n\ndef extract(ccd,\n trace,\n spatial_profile,\n extraction,\n zone,\n background_level=0,\n sampling_step=1,\n plots=False):\n \"\"\"Performs spectrum extraction\n\n This function is designed to perform two types of spectrum extraction, a\n simple sum in the spatial direction and an optimal extraction.\n\n Notes:\n For the beta release the optimal extraction is not implemented.\n\n Args:\n ccd (object): Instance of ccdproc.CCDData containing a 2D spectrum\n trace (object): Instance of astropy.modeling.Model, a low order\n polynomial that defines the trace of the spectrum in the ccd object.\n spatial_profile (object): Instance of astropy.modeling.Model, a Gaussian\n model previously fitted to the spatial profile of the 2D spectrum\n contained in the ccd object.\n extraction (str): Extraction type, can be `simple` or `optimal` for the\n beta release the optimal extraction is not implemented yet.\n zone (list):\n background_level (array):\n sampling_step (int): The optimal extraction needs to sample the spatial\n profile, this value defines the intervals at which get such\n sampling.\n plots (bool): Determines whether display plots or not.\n\n Returns:\n ccd (object): Instance of ccdproc.CCDData containing a 1D spectrum. The\n attribute 'data' is replaced by the 1D array resulted from the\n extraction process.\n\n Raises:\n NotImplementedError: When `extraction` is optimal, this is valid for the\n beta release\n\n \"\"\"\n assert isinstance(ccd, CCDData)\n assert isinstance(trace, Model)\n\n nccd = ccd.copy()\n\n spatial_length, dispersion_length = nccd.data.shape\n\n apnum1 = '{:d} {:d} {:d} {:d}'.format(1, 1, zone[0], zone[1])\n log_spec.debug('APNUM1 Keyword: {:s}'.format(apnum1))\n\n # create variance model\n rdnoise = float(nccd.header['RDNOISE'])\n gain = float(nccd.header['GAIN'])\n log_spec.debug('Original Name {:s}'.format(nccd.header['OFNAME']))\n\n variance_2d = (rdnoise + np.absolute(nccd.data) * gain) / gain\n cr_mask = np.ones(nccd.data.shape, dtype=int)\n # if nccd.mask is None and nccd.header['OBSTYPE'] == 'OBJECT':\n # log_spec.debug('Finding cosmic rays to create mask')\n # cr_mask = cosmicray_rejection(ccd=ccd, mask_only=True)\n # cr_mask = np.log_spec.cal_not(cr_mask).astype(int)\n # elif nccd.mask is None and nccd.header['OBSTYPE'] != 'OBJECT':\n # log_spec.debug('Only OBSTYPE == OBJECT get cosmic ray rejection.')\n # cr_mask = np.ones(nccd.data.shape, dtype=int)\n # else:\n # log_spec.debug('Cosmic ray mask already exists.')\n # cr_mask = np.logical_not(nccd.mask).astype(int)\n\n model_fitter = fitting.LevMarLSQFitter()\n\n # print(spatial_profile.mean.value)\n # print(trace.c0.value)\n\n if isinstance(spatial_profile, models.Gaussian1D):\n amplitude = spatial_profile.amplitude.value\n mean = spatial_profile.mean.value\n stddev = spatial_profile.stddev.value\n new_model = models.Gaussian1D(amplitude=amplitude,\n mean=mean,\n stddev=stddev)\n # print('Fixed ', new_model.mean.fixed)\n else:\n raise NotImplementedError\n log_spec.debug('nccd.data is a masked array: '\n '{:s}'.format(str(np.ma.isMaskedArray(nccd.data))))\n\n nccd.data = np.ma.masked_invalid(nccd.data)\n # print(np.ma.isMaskedArray(nccd.data))\n np.ma.set_fill_value(nccd.data, 0)\n\n if extraction == 'simple':\n\n\n # print(indexes)\n if plots:\n indexes = np.argwhere(cr_mask == 0)\n fig = plt.figure(1)\n ax1 = fig.add_subplot(111)\n for index in indexes:\n x, y = index\n ax1.plot(y, x, marker='o', color='r')\n\n ax1.imshow(nccd.data, interpolation='none')\n if plt.isinteractive():\n plt.draw()\n plt.pause(1)\n else:\n plt.show()\n\n # print(np.ma.isMaskedArray(nccd.data))\n # spectrum zone limit\n low_lim, high_lim = zone\n spectrum_masked = nccd.data * cr_mask\n # plt.imshow(spectrum_masked, clim=(10, 70))\n # plt.show()\n # TODO (simon): Add fractional pixel\n spectrum_sum = np.ma.sum(spectrum_masked[low_lim:high_lim, :], axis=0)\n\n background_sum = np.abs(high_lim - low_lim) * background_level\n\n nccd.data = spectrum_sum - background_sum\n\n nccd.header['APNUM1'] = apnum1\n\n if plots:\n fig = plt.figure()\n fig.canvas.set_window_title('Simple Extraction')\n # ax = fig.add_subplot(111)\n manager = plt.get_current_fig_manager()\n if plt.get_backend() == u'GTK3Agg':\n manager.window.maximize()\n elif plt.get_backend() == u'Qt4Agg':\n manager.window.showMaximized()\n\n plt.title(nccd.header['OBJECT'])\n plt.xlabel('Dispersion Axis (Pixels)')\n plt.ylabel('Intensity (Counts)')\n # plt.plot(simple_sum, label='Simple Sum', color='k', alpha=0.5)\n plt.plot(nccd.data, color='k',\n label='Simple Extracted')\n plt.plot(background_sum, color='r', label='Background')\n plt.plot(spectrum_sum, color='b', label='Spectrum Raw Sum')\n plt.xlim((0, len(nccd.data)))\n plt.legend(loc='best')\n if plt.isinteractive():\n plt.draw()\n plt.pause(1)\n else:\n plt.show()\n\n elif extraction == 'optimal':\n raise NotImplementedError\n # out_spectrum = np.empty(dispersion_length)\n # for i in range(0, dispersion_length, sampling_step):\n # # force the model to follow the trace\n # new_model.mean.value = trace(i)\n #\n # # warn if the difference of the spectrum position in the trace at the\n # # extremes of the sampling range is larger than 1 pixel.\n # if np.abs(trace(i) - trace(i + sampling_step)) > 1:\n # log_spec.warning('Sampling step might be too large')\n #\n # sample = np.median(nccd.data[:, i:i + sampling_step], axis=1)\n # fitted_profile = model_fitter(model=new_model,\n # x=range(len(sample)),\n # y=sample)\n #\n # profile = fitted_profile(range(sample.size))\n #\n # # enforce positivity\n # pos_profile = np.array([np.max([0, x]) for x in profile])\n #\n # # enforce normalization\n # nor_profile = np.array([x / pos_profile.sum() for x in pos_profile])\n #\n # if sampling_step > 1:\n # # TODO (simon): Simplify to Pythonic way\n # right = min((i + sampling_step), dispersion_length)\n #\n # for e in range(i, right, 1):\n # mask = cr_mask[:, e]\n # data = ma.masked_invalid(nccd.data[:, e])\n # # print(ma.isMaskedArray(data))\n # V = variance_2d[:, e]\n # P = nor_profile\n # a = [(P[z] / V[z]) / np.sum(P ** 2 / V) for z in\n # range(P.size)]\n # weights = (nor_profile / variance_2d[:, e]) / np.sum(\n # nor_profile ** 2 / variance_2d[:, e])\n # # print('SUMN ', np.sum(a), np.sum(weights), np.sum(nor_profile), np.sum(P * weights))\n #\n #\n #\n # # if e in range(5, 4001, 500):\n # # plt.plot(nor_profile * data.max()/ nor_profile.max(), label=str(e))\n # # plt.plot(data, label='Data')\n # # plt.legend(loc='best')\n # # plt.show()\n #\n # out_spectrum[e] = np.sum(data * mask * nor_profile)\n # nccd.data = out_spectrum\n # if plots:\n # fig = plt.figure()\n # fig.canvas.set_window_title('Optimal Extraction')\n # # ax = fig.add_subplot(111)\n # manager = plt.get_current_fig_manager()\n #\n # if plt.get_backend() == u'GTK3Agg':\n # manager.window.maximize()\n # elif plt.get_backend() == u'Qt4Agg':\n # manager.window.showMaximized()\n #\n # plt.title(nccd.header['OBJECT'])\n # plt.xlabel('Dispersion Axis (Pixels)')\n # plt.ylabel('Intensity (Counts)')\n # # plt.plot(simple_sum, label='Simple Sum', color='k', alpha=0.5)\n # plt.plot(nccd.data, color='k',\n # label='Optimal Extracted')\n # plt.xlim((0, len(nccd.data)))\n # plt.legend(loc='best')\n # if plt.isinteractive():\n # plt.draw()\n # plt.pause(1)\n # else:\n # plt.show()\n\n # nccd.data = out_spectrum\n return nccd\n\n\n# classes definition\n\nclass NightDataContainer(object):\n \"\"\"This class is designed to be the organized data container. It doesn't\n store image data but list of pandas.DataFrame objects. Also it stores\n critical variables such as sunrise and sunset times.\n\n \"\"\"\n\n def __init__(self, path, instrument, technique):\n \"\"\"Initializes all the variables for the class\n\n Args:\n path (str): Full path to the directory where raw data is located\n instrument (str): `Red` or `Blue` stating whether the data was taken\n using the Red or Blue Goodman Camera.\n technique (str): `Spectroscopy` or `Imaging` stating what kind of\n data was taken.\n \"\"\"\n\n self.full_path = path\n self.instrument = instrument\n self.technique = technique\n self.is_empty = True\n\n \"\"\"For imaging use\"\"\"\n self.bias = None\n self.day_flats = None\n self.dome_flats = None\n self.sky_flats = None\n self.data_groups = None\n\n \"\"\"For spectroscopy use\"\"\"\n\n # comp_groups will store pandas.DataFrame (groups) that contain only\n # OBSTYPE == COMP, they should be requested only when needed, for the\n # science case when for every science target is observed with comparison\n # lamps and quartz (if)\n self.comp_groups = None\n\n # object_groups will store pandas.DataFrame (groups) with only\n # OBSTYPE == OBJECT this is the case when the observer takes comparison\n # lamps only at the beginning or end of the night.\n self.object_groups = None\n\n # spec_groups will store pandas.DataFrame (groups) with a set of OBJECT\n # and COMP, this is usually the case for radial velocity studies.\n self.spec_groups = None\n\n \"\"\"Time reference points\"\"\"\n self.sun_set_time = None\n self.sun_rise_time = None\n self.evening_twilight = None\n self.morning_twilight = None\n\n def add_bias(self, bias_group):\n \"\"\"Adds a bias group\n\n Args:\n bias_group (pandas.DataFrame): Contains a set of keyword values of\n grouped image metadata\n\n \"\"\"\n\n if len(bias_group) < 2:\n if self.technique == 'Imaging':\n\n log_ccd.error('Imaging mode needs BIAS to work properly. '\n 'Go find some.')\n\n else:\n log_ccd.warning('BIAS are needed for optimal results.')\n else:\n if self.bias is None:\n self.bias = [bias_group]\n else:\n self.bias.append(bias_group)\n if self.bias is not None:\n self.is_empty = False\n\n def add_day_flats(self, day_flats):\n \"\"\"\"Adds a daytime flat group\n\n Args:\n day_flats (pandas.DataFrame): Contains a set of keyword values of\n grouped image metadata\n\n \"\"\"\n\n if self.day_flats is None:\n self.day_flats = [day_flats]\n else:\n self.day_flats.append(day_flats)\n if self.day_flats is not None:\n self.is_empty = False\n\n def add_data_group(self, data_group):\n \"\"\"Adds a data group\n\n Args:\n data_group (pandas.DataFrame): Contains a set of keyword values of\n grouped image metadata\n\n \"\"\"\n\n if self.data_groups is None:\n self.data_groups = [data_group]\n else:\n self.data_groups.append(data_group)\n if self.data_groups is not None:\n self.is_empty = False\n\n def add_comp_group(self, comp_group):\n \"\"\"Adds a comp-only group\n\n Args:\n comp_group (pandas.DataFrame): Contains a set of keyword values of\n grouped image metadata\n\n \"\"\"\n\n if self.comp_groups is None:\n self.comp_groups = [comp_group]\n else:\n self.comp_groups.append(comp_group)\n if self.comp_groups is not None:\n self.is_empty = False\n\n def add_object_group(self, object_group):\n \"\"\"Adds a object-only group\n\n Args:\n object_group (pandas.DataFrame): Contains a set of keyword values of\n grouped image metadata\n\n \"\"\"\n\n if self.object_groups is None:\n self.object_groups = [object_group]\n else:\n self.object_groups.append(object_group)\n if self.object_groups is not None:\n self.is_empty = False\n\n def add_spec_group(self, spec_group):\n \"\"\"Adds a data group containing object and comp\n\n Args:\n spec_group (pandas.DataFrame): Contains a set of keyword values of\n grouped image metadata\n\n \"\"\"\n\n if self.spec_groups is None:\n self.spec_groups = [spec_group]\n else:\n self.spec_groups.append(spec_group)\n if self.spec_groups is not None:\n self.is_empty = False\n\n def set_sun_times(self, sun_set, sun_rise):\n \"\"\"Sets values for sunset and sunrise\n\n Args:\n sun_set (str): Sun set time in the format 'YYYY-MM-DDTHH:MM:SS.SS'\n sun_rise (str):Sun rise time in the format 'YYYY-MM-DDTHH:MM:SS.SS'\n\n \"\"\"\n\n self.sun_set_time = sun_set\n self.sun_rise_time = sun_rise\n\n def set_twilight_times(self, evening, morning):\n \"\"\"Sets values for evening and morning twilight\n\n Args:\n evening (str): Evening twilight time in the format\n 'YYYY-MM-DDTHH:MM:SS.SS'\n morning (str): Morning twilight time in the format\n 'YYYY-MM-DDTHH:MM:SS.SS'\n\n \"\"\"\n\n self.evening_twilight = evening\n self.morning_twilight = morning\n\n\nclass NoTargetException(Exception):\n \"\"\"Exception to be raised when no target is identified\"\"\"\n def __init__(self):\n Exception.__init__(self, 'No targets identified.')\n\n\nclass NoMatchFound(Exception):\n def __init__(self):\n Exception.__init__(self, 'Did not find a match')\n\n\nclass NotEnoughLinesDetected(Exception):\n def __init__(self):\n Exception.__init__(self, 'Not enough lines detected.')\n\n\nclass CriticalError(Exception):\n def __init__(self, message):\n Exception.__init__(self, message)\n", "sub_path": "goodman_ccd/core.py", "file_name": "core.py", "file_ext": "py", "file_size_in_byte": 88727, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.use", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 31, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 44, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 44, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 62, "usage_type": "call"}, {"api_name": "ccdproc.CCDData.read", "line_number": 63, "usage_type": "call"}, {"api_name": "ccdproc.CCDData", "line_number": 63, "usage_type": "name"}, {"api_name": "astropy.units.adu", "line_number": 63, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 63, "usage_type": "name"}, {"api_name": "ccdproc.CCDData.read", "line_number": 80, "usage_type": "call"}, {"api_name": "ccdproc.CCDData", "line_number": 80, "usage_type": "name"}, {"api_name": "astropy.units.adu", "line_number": 80, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 80, "usage_type": "name"}, {"api_name": "sys.stdout.write", "line_number": 176, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 176, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 178, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 178, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 179, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 179, "usage_type": "attribute"}, {"api_name": "astropy.coordinates.EarthLocation.from_geodetic", "line_number": 221, "usage_type": "call"}, {"api_name": "astropy.coordinates.EarthLocation", "line_number": 221, "usage_type": "name"}, {"api_name": "astropy.units.m", "line_number": 223, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 223, "usage_type": "name"}, {"api_name": "astroplan.Observer", "line_number": 226, "usage_type": "call"}, {"api_name": "astropy.time.Time", "line_number": 231, "usage_type": "call"}, {"api_name": "astropy.time.Time", "line_number": 234, "usage_type": "call"}, {"api_name": "astropy.time.Time", "line_number": 237, "usage_type": "call"}, {"api_name": "astropy.time.Time", "line_number": 240, "usage_type": "call"}, {"api_name": "astropy.time.Time", "line_number": 243, "usage_type": "call"}, {"api_name": "ccdproc.subtract_overscan", "line_number": 272, "usage_type": "call"}, {"api_name": "ccdproc.trim_image", "line_number": 299, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 330, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 334, "usage_type": "call"}, {"api_name": "astropy.modeling.models.Box1D", "line_number": 339, "usage_type": "call"}, {"api_name": "astropy.modeling.models", "line_number": 339, "usage_type": "name"}, {"api_name": "astropy.modeling.fitting.SimplexLSQFitter", "line_number": 341, "usage_type": "call"}, {"api_name": "astropy.modeling.fitting", "line_number": 341, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 357, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.get_current_fig_manager", "line_number": 364, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 364, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 366, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 366, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 367, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 367, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 368, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 368, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 369, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 369, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 371, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 371, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 372, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 372, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 373, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 373, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 376, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 376, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 377, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 377, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 427, "usage_type": "call"}, {"api_name": "os.path", "line_number": 427, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 428, "usage_type": "call"}, {"api_name": "os.path", "line_number": 428, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 429, "usage_type": "call"}, {"api_name": "os.path", "line_number": 429, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 442, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 445, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 448, "usage_type": "call"}, {"api_name": "os.path", "line_number": 448, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 451, "usage_type": "call"}, {"api_name": "os.path", "line_number": 451, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 453, "usage_type": "call"}, {"api_name": "os.path", "line_number": 453, "usage_type": "attribute"}, {"api_name": "shutil.copy2", "line_number": 454, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 463, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 464, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 465, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 469, "usage_type": "call"}, {"api_name": "threading.Timer", "line_number": 479, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 490, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 496, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 507, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 514, "usage_type": "call"}, {"api_name": "ccdproc.cosmicray_lacosmic", "line_number": 546, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 553, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 600, "usage_type": "call"}, {"api_name": "os.path", "line_number": 600, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 620, "usage_type": "call"}, {"api_name": "os.path", "line_number": 620, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 626, "usage_type": "call"}, {"api_name": "os.path", "line_number": 626, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 657, "usage_type": "call"}, {"api_name": "ccdproc.CCDData.read", "line_number": 668, "usage_type": "call"}, {"api_name": "ccdproc.CCDData", "line_number": 668, "usage_type": "name"}, {"api_name": "astropy.units.adu", "line_number": 668, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 668, "usage_type": "name"}, {"api_name": "ccdproc.CCDData", "line_number": 744, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 749, "usage_type": "call"}, {"api_name": "os.path", "line_number": 749, "usage_type": "attribute"}, {"api_name": "astropy.modeling.models.Chebyshev1D", "line_number": 761, "usage_type": "call"}, {"api_name": "astropy.modeling.models", "line_number": 761, "usage_type": "name"}, {"api_name": "astropy.modeling.fitting.LevMarLSQFitter", "line_number": 762, "usage_type": "call"}, {"api_name": "astropy.modeling.fitting", "line_number": 762, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 771, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 827, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 827, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 828, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 828, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 856, "usage_type": "call"}, {"api_name": "os.path", "line_number": 856, "usage_type": "attribute"}, {"api_name": "astropy.io.fits.getdata", "line_number": 859, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 859, "usage_type": "name"}, {"api_name": "astropy.io.fits.writeto", "line_number": 884, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 884, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 916, "usage_type": "call"}, {"api_name": "os.path", "line_number": 916, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 918, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 924, "usage_type": "call"}, {"api_name": "ccdproc.ImageFileCollection", "line_number": 947, "usage_type": "call"}, {"api_name": "ccdproc.CCDData", "line_number": 1073, "usage_type": "argument"}, {"api_name": "astropy.modeling.Model", "line_number": 1096, "usage_type": "argument"}, {"api_name": "numpy.median", "line_number": 1130, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 1175, "usage_type": "call"}, {"api_name": "ccdproc.CCDData", "line_number": 1232, "usage_type": "argument"}, {"api_name": "re.sub", "line_number": 1233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 1240, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1240, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 1241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 1242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1242, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 1243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1243, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1244, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 1246, "usage_type": "call"}, {"api_name": "astropy.stats.sigma_clip", "line_number": 1252, "usage_type": "call"}, {"api_name": "astropy.modeling.models.Linear1D", "line_number": 1254, "usage_type": "call"}, {"api_name": "astropy.modeling.models", "line_number": 1254, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 1255, "usage_type": "call"}, {"api_name": "astropy.modeling.fitting.LinearLSQFitter", "line_number": 1257, "usage_type": "call"}, {"api_name": "astropy.modeling.fitting", "line_number": 1257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 1269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1269, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1270, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1271, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1271, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1272, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 1274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1274, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1275, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1275, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1276, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1277, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1277, "usage_type": "name"}, {"api_name": "astropy.stats.sigma_clip", "line_number": 1293, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 1303, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 1303, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 1303, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ioff", "line_number": 1311, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1311, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 1312, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1312, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 1315, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1315, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1316, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1316, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1317, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1317, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 1319, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1319, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 1323, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1323, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.isinteractive", "line_number": 1324, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1324, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 1325, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1325, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 1326, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1326, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1328, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1328, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 1333, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 1333, "usage_type": "call"}, {"api_name": "scipy.signal.argrelmax", "line_number": 1343, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 1343, "usage_type": "name"}, {"api_name": "numpy.sort", "line_number": 1349, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 1361, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ioff", "line_number": 1368, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1368, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1369, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1369, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 1370, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1370, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 1372, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1372, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1373, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1373, "usage_type": "name"}, {"api_name": "astropy.modeling.fitting.LevMarLSQFitter", "line_number": 1376, "usage_type": "call"}, {"api_name": "astropy.modeling.fitting", "line_number": 1376, "usage_type": "name"}, {"api_name": "astropy.modeling.models.Gaussian1D", "line_number": 1382, "usage_type": "call"}, {"api_name": "astropy.modeling.models", "line_number": 1382, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1422, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1422, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1423, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1423, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1424, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1424, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 1486, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 1489, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 1505, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 1509, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 1518, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1518, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 1519, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1519, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1520, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1520, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhspan", "line_number": 1521, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1521, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1525, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1525, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.isinteractive", "line_number": 1527, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1527, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 1528, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1528, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 1529, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1529, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1531, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1531, "usage_type": "name"}, {"api_name": "ccdproc.CCDData", "line_number": 1570, "usage_type": "argument"}, {"api_name": "astropy.modeling.Model", "line_number": 1571, "usage_type": "argument"}, {"api_name": "astropy.modeling.fitting.LevMarLSQFitter", "line_number": 1574, "usage_type": "call"}, {"api_name": "astropy.modeling.fitting", "line_number": 1574, "usage_type": "name"}, {"api_name": "astropy.modeling.models.Polynomial1D", "line_number": 1577, "usage_type": "call"}, {"api_name": "astropy.modeling.models", "line_number": 1577, "usage_type": "name"}, {"api_name": "astropy.modeling.Model", "line_number": 1678, "usage_type": "argument"}, {"api_name": "numpy.max", "line_number": 1692, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 1694, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 1733, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1733, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhspan", "line_number": 1734, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1734, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1735, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1735, "usage_type": "name"}, {"api_name": "numpy.ma.masked_invalid", "line_number": 1814, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 1814, "usage_type": "name"}, {"api_name": "numpy.ma.median", "line_number": 1816, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 1816, "usage_type": "name"}, {"api_name": "numpy.inf", "line_number": 1819, "usage_type": "attribute"}, {"api_name": "numpy.median", "line_number": 1850, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 1852, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 1858, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 1891, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 1905, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 1915, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 1965, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 1968, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 1974, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1974, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 1975, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1975, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1976, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1976, "usage_type": "name"}, {"api_name": "ccdproc.CCDData", "line_number": 2023, "usage_type": "argument"}, {"api_name": "astropy.modeling.Model", "line_number": 2024, "usage_type": "argument"}, {"api_name": "numpy.absolute", "line_number": 2038, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 2039, "usage_type": "call"}, {"api_name": "astropy.modeling.fitting.LevMarLSQFitter", "line_number": 2051, "usage_type": "call"}, {"api_name": "astropy.modeling.fitting", "line_number": 2051, "usage_type": "name"}, {"api_name": "astropy.modeling.models.Gaussian1D", "line_number": 2056, "usage_type": "attribute"}, {"api_name": "astropy.modeling.models", "line_number": 2056, "usage_type": "name"}, {"api_name": "astropy.modeling.models.Gaussian1D", "line_number": 2060, "usage_type": "call"}, {"api_name": "astropy.modeling.models", "line_number": 2060, "usage_type": "name"}, {"api_name": "numpy.ma.isMaskedArray", "line_number": 2067, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 2067, "usage_type": "attribute"}, {"api_name": "numpy.ma.masked_invalid", "line_number": 2069, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 2069, "usage_type": "attribute"}, {"api_name": "numpy.ma.set_fill_value", "line_number": 2071, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 2071, "usage_type": "attribute"}, {"api_name": "numpy.argwhere", "line_number": 2078, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 2079, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2079, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.isinteractive", "line_number": 2086, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2086, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 2087, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2087, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 2088, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2088, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 2090, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2090, "usage_type": "name"}, {"api_name": "numpy.ma.sum", "line_number": 2099, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 2099, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 2101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 2108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_current_fig_manager", "line_number": 2111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_backend", "line_number": 2112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_backend", "line_number": 2114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 2117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 2118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 2119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 2121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 2123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 2124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 2125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 2126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.isinteractive", "line_number": 2127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 2128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 2129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 2131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2131, "usage_type": "name"}]} +{"seq_id": "552179132", "text": "#-*- coding:utf-8 -*-\n\nimport gtk\n\nimport gobject\n\nimport logging\n\nimport gtksourceview2 as gtksourceview\n\nfrom grest.query import RestQuery\n\nlogger = logging.getLogger(__name__)\n\nclass RestView(gtk.VBox):\n \n __gsignals__ = {\n 'execute-request': (gobject.SIGNAL_RUN_LAST, gobject.TYPE_NONE, ())\n }\n \n ''' REST Resource view '''\n def __init__(self):\n gtk.VBox.__init__(self)\n \n self.query = RestQuery()\n self.query.connect('response-available', self.__on_response_available)\n \n self.urlentry = gtk.Entry()\n self.urlentry.set_icon_from_stock(1, gtk.STOCK_EXECUTE)\n self.urlentry.set_text('http://www.velib.paris.fr/service/carto')\n self.urlentry.connect('activate', self.__on_urlentry_activated)\n self.urlentry.connect('icon-press', self.__on_urlentry_icon_press)\n \n url_toolitem = gtk.ToolItem()\n url_toolitem.add(self.urlentry)\n url_toolitem.set_tooltip_text(\"The REST resource URL\")\n url_toolitem.set_expand(True)\n \n \n self.methodcombo = gtk.combo_box_new_text()\n self.methodcombo.append_text(\"GET\")\n self.methodcombo.append_text(\"POST\")\n self.methodcombo.append_text(\"PUT\")\n self.methodcombo.append_text(\"DELETE\")\n self.methodcombo.append_text(\"HEAD\")\n self.methodcombo.append_text(\"OPTIONS\")\n self.methodcombo.append_text(\"TRACE\")\n self.methodcombo.set_active(0)\n method_toolitem = gtk.ToolItem()\n method_toolitem.add(self.methodcombo)\n method_toolitem.set_tooltip_text(\"The REST resource method\")\n \n toolbar = gtk.Toolbar()\n toolbar.insert(method_toolitem, -1)\n toolbar.insert(url_toolitem, -1)\n \n self.set_homogeneous(False)\n self.pack_start(toolbar, False)\n \n self.request_form_view = HTTPRequestFormView()\n self.request_form_view.set_query(self.query)\n self.paned = gtk.HPaned()\n self.paned.set_position(200)\n self.paned.pack1(self.request_form_view)\n \n self.responseview = HTTPResponseViewer()\n self.responseview.set_query(self.query)\n self.responseframe = gtk.Frame(\"Response\")\n self.responseframe.add(self.responseview)\n self.responseframe.set_shadow_type(gtk.SHADOW_NONE)\n self.paned.pack2(self.responseframe)\n self.pack_start(self.paned)\n \n self.show_all()\n \n def __on_urlentry_activated(self, entry):\n self.execute_request()\n \n def __on_urlentry_icon_press(self, entry, icon_pos, event):\n self.execute_request()\n \n def __fill_query(self):\n url = self.urlentry.get_text()\n if not (url.startswith('http://') or url.startswith('https://')): \n url = 'http://%s' % url\n \n model = self.methodcombo.get_model()\n active = self.methodcombo.get_active()\n method = model[active][0]\n if active < 0:\n method = \"GET\"\n \n self.query.url = url\n self.query.method = method\n self.request_form_view.fill_query(self.query)\n \n return self.query\n \n \n def execute_request(self):\n self.emit('execute-request')\n self.urlentry.set_icon_from_stock(1, gtk.STOCK_STOP)\n self.__fill_query().request()\n \n def __on_response_available(self, query, response):\n self.urlentry.set_icon_from_stock(1, gtk.STOCK_EXECUTE)\n\n \nclass HTTPRequestFormView(gtk.VBox):\n '''An editable form to build HTTP Requests'''\n def __init__(self):\n gtk.VBox.__init__(self)\n \n self.request = None\n \n self.headerview = EditableKeyValueView()\n frame = gtk.Frame(\"Headers\")\n frame.add(self.headerview)\n self.pack_start(frame)\n \n self.paramview = EditableKeyValueView()\n frame = gtk.Frame(\"Parameters\")\n frame.add(self.paramview)\n self.pack_start(frame)\n \n self.bodyview = gtk.TextView()\n sw = gtk.ScrolledWindow()\n sw.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\n sw.add(self.bodyview)\n frame = gtk.Frame(\"Body\")\n frame.add(sw)\n self.pack_start(frame)\n \n self.rawview = gtk.TextView()\n self.rawview.set_wrap_mode(gtk.WRAP_WORD)\n sw = gtk.ScrolledWindow()\n sw.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\n sw.add(self.rawview)\n frame = gtk.Frame(\"Raw\")\n frame.add(sw)\n self.pack_start(frame)\n \n self.show_all()\n \n def set_query(self, query):\n self.query = query\n query.connect('request-sent', self.__on_request_sent)\n \n def __on_request_sent(self, query):\n buf = gtk.TextBuffer()\n buf.set_text(query.to_string())\n self.rawview.set_buffer(buf)\n \n def set_request(self, request):\n self.request = request\n \n def get_request(self):\n return None\n \n def fill_query(self,query):\n query.parameters = self.paramview.get_values()\n query.headers = self.headerview.get_values()\n \nclass EditableKeyValueView(gtk.ScrolledWindow):\n \n COL_KEY = 0\n COL_VALUE = 1\n \n '''A Key/Value editable treeview from'''\n def __init__(self):\n gtk.ScrolledWindow.__init__(self)\n self.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\n \n self.model = gtk.ListStore(str,str)\n self.treeview = gtk.TreeView(self.model)\n self.add(self.treeview)\n \n self.crKey = gtk.CellRendererText()\n self.crKey.set_property('editable', True)\n self.crKey.connect('edited', self.on_edited, self.COL_KEY)\n self.colKey = gtk.TreeViewColumn('Key', self.crKey)\n self.colKey.add_attribute(self.crKey, 'text', self.COL_KEY)\n self.treeview.append_column(self.colKey)\n \n self.crValue = gtk.CellRendererText()\n self.crValue.set_property('editable', True)\n self.crValue.connect('edited', self.on_edited, self.COL_VALUE)\n self.colValue = gtk.TreeViewColumn('Value', self.crValue)\n self.colValue.add_attribute(self.crValue, 'text', self.COL_VALUE)\n self.treeview.append_column(self.colValue)\n \n self.treeview.connect('button-release-event', self.on_treeview_click)\n \n def on_edited(self, cr, path, new_text, col):\n self.model[path][col] = new_text\n \n def on_treeview_click(self, widget, event):\n if (event.button == 1 and self.treeview.get_path_at_pos(int(event.x), int(event.y)) is None):\n it = self.model.append(('',''))\n path = self.model.get_path(it)\n self.treeview.set_cursor(path, self.colKey, True)\n \n def get_values(self):\n return dict([(row[self.COL_KEY], row[self.COL_VALUE]) for row in self.model])\n \n \n\nclass HTTPResponseViewer(gtk.VBox):\n '''HTTP Response Viewer'''\n def __init__(self):\n gtk.VBox.__init__(self)\n self.query = None\n self.response = None\n \n hbox = gtk.HBox()\n hbox.set_homogeneous(False)\n \n self.notebook = gtk.Notebook()\n self.notebook.set_tab_pos(gtk.POS_BOTTOM)\n\n self.rawview = RawResponseView()\n \n sw = gtk.ScrolledWindow()\n sw.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\n sw.add(self.rawview)\n self.notebook.append_page(sw, gtk.Label(\"Raw\"))\n \n self.formatted_view = FormattedResponseView()\n\n sw = gtk.ScrolledWindow()\n sw.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\n sw.add(self.formatted_view)\n self.notebook.append_page(sw, gtk.Label(\"Body\"))\n \n self.set_homogeneous(False)\n self.pack_start(self.notebook)\n self.show_all()\n \n def set_query(self, query):\n self.query = query\n query.connect('response-available', self.__on_response_available)\n \n def __on_response_available(self, query, response):\n self.set_response(response)\n \n def set_response(self, response):\n self.response = response\n \n self.rawview.set_response(response)\n self.formatted_view.set_response(response)\n \nclass FormattedResponseView(gtksourceview.View):\n def __init__(self):\n gtksourceview.View.__init__(self)\n self.set_wrap_mode(gtk.WRAP_WORD)\n self.set_editable(False)\n \n def set_response(self, response):\n buf = gtksourceview.Buffer()\n self.set_buffer(buf)\n manager = gtksourceview.language_manager_get_default()\n language = manager.get_language('xml')\n buf.set_highlight_syntax(True)\n buf.set_language(language)\n buf.set_text(response.body.decode(response.encoding))\n \n def __on_resposne_read(self, response):\n buf = gtksourceview.Buffer()\n self.set_buffer(buf)\n buf.set_text(response.body.decode(response.encoding))\n \n\nclass RawResponseView(gtk.TextView):\n def __init__(self):\n gtk.TextView.__init__(self)\n self.set_wrap_mode(gtk.WRAP_WORD)\n self.set_editable(False)\n \n def set_response(self, response):\n buf = gtk.TextBuffer()\n self.set_buffer(buf)\n buf.set_text(response.to_string())\n \n def __on_response_read(self, response):\n buf = gtk.TextBuffer()\n self.set_buffer(buf)\n buf.set_text(response.to_string())", "sub_path": "grest/restview.py", "file_name": "restview.py", "file_ext": "py", "file_size_in_byte": 9546, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "gtk.VBox", "line_number": 15, "usage_type": "attribute"}, {"api_name": "gobject.SIGNAL_RUN_LAST", "line_number": 18, "usage_type": "attribute"}, {"api_name": "gobject.TYPE_NONE", "line_number": 18, "usage_type": "attribute"}, {"api_name": "gtk.VBox.__init__", "line_number": 23, "usage_type": "call"}, {"api_name": "gtk.VBox", "line_number": 23, "usage_type": "attribute"}, {"api_name": "grest.query.RestQuery", "line_number": 25, "usage_type": "call"}, {"api_name": "gtk.Entry", "line_number": 28, "usage_type": "call"}, {"api_name": "gtk.STOCK_EXECUTE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "gtk.ToolItem", "line_number": 34, "usage_type": "call"}, {"api_name": "gtk.combo_box_new_text", "line_number": 40, "usage_type": "call"}, {"api_name": "gtk.ToolItem", "line_number": 49, "usage_type": "call"}, {"api_name": "gtk.Toolbar", "line_number": 53, "usage_type": "call"}, {"api_name": "gtk.HPaned", "line_number": 62, "usage_type": "call"}, {"api_name": "gtk.Frame", "line_number": 68, "usage_type": "call"}, {"api_name": "gtk.SHADOW_NONE", "line_number": 70, "usage_type": "attribute"}, {"api_name": "gtk.STOCK_STOP", "line_number": 102, "usage_type": "attribute"}, {"api_name": "gtk.STOCK_EXECUTE", "line_number": 106, "usage_type": "attribute"}, {"api_name": "gtk.VBox", "line_number": 109, "usage_type": "attribute"}, {"api_name": "gtk.VBox.__init__", "line_number": 112, "usage_type": "call"}, {"api_name": "gtk.VBox", "line_number": 112, "usage_type": "attribute"}, {"api_name": "gtk.Frame", "line_number": 117, "usage_type": "call"}, {"api_name": "gtk.Frame", "line_number": 122, "usage_type": "call"}, {"api_name": "gtk.TextView", "line_number": 126, "usage_type": "call"}, {"api_name": "gtk.ScrolledWindow", "line_number": 127, "usage_type": "call"}, {"api_name": "gtk.POLICY_AUTOMATIC", "line_number": 128, "usage_type": "attribute"}, {"api_name": "gtk.Frame", "line_number": 130, "usage_type": "call"}, {"api_name": "gtk.TextView", "line_number": 134, "usage_type": "call"}, {"api_name": "gtk.WRAP_WORD", "line_number": 135, "usage_type": "attribute"}, {"api_name": "gtk.ScrolledWindow", "line_number": 136, "usage_type": "call"}, {"api_name": "gtk.POLICY_AUTOMATIC", "line_number": 137, "usage_type": "attribute"}, {"api_name": "gtk.Frame", "line_number": 139, "usage_type": "call"}, {"api_name": "gtk.TextBuffer", "line_number": 150, "usage_type": "call"}, {"api_name": "gtk.ScrolledWindow", "line_number": 164, "usage_type": "attribute"}, {"api_name": "gtk.ScrolledWindow.__init__", "line_number": 171, "usage_type": "call"}, {"api_name": "gtk.ScrolledWindow", "line_number": 171, "usage_type": "attribute"}, {"api_name": "gtk.POLICY_AUTOMATIC", "line_number": 172, "usage_type": "attribute"}, {"api_name": "gtk.ListStore", "line_number": 174, "usage_type": "call"}, {"api_name": "gtk.TreeView", "line_number": 175, "usage_type": "call"}, {"api_name": "gtk.CellRendererText", "line_number": 178, "usage_type": "call"}, {"api_name": "gtk.TreeViewColumn", "line_number": 181, "usage_type": "call"}, {"api_name": "gtk.CellRendererText", "line_number": 185, "usage_type": "call"}, {"api_name": "gtk.TreeViewColumn", "line_number": 188, "usage_type": "call"}, {"api_name": "gtk.VBox", "line_number": 208, "usage_type": "attribute"}, {"api_name": "gtk.VBox.__init__", "line_number": 211, "usage_type": "call"}, {"api_name": "gtk.VBox", "line_number": 211, "usage_type": "attribute"}, {"api_name": "gtk.HBox", "line_number": 215, "usage_type": "call"}, {"api_name": "gtk.Notebook", "line_number": 218, "usage_type": "call"}, {"api_name": "gtk.POS_BOTTOM", "line_number": 219, "usage_type": "attribute"}, {"api_name": "gtk.ScrolledWindow", "line_number": 223, "usage_type": "call"}, {"api_name": "gtk.POLICY_AUTOMATIC", "line_number": 224, "usage_type": "attribute"}, {"api_name": "gtk.Label", "line_number": 226, "usage_type": "call"}, {"api_name": "gtk.ScrolledWindow", "line_number": 230, "usage_type": "call"}, {"api_name": "gtk.POLICY_AUTOMATIC", "line_number": 231, "usage_type": "attribute"}, {"api_name": "gtk.Label", "line_number": 233, "usage_type": "call"}, {"api_name": "gtksourceview2.View", "line_number": 252, "usage_type": "attribute"}, {"api_name": "gtksourceview2.View.__init__", "line_number": 254, "usage_type": "call"}, {"api_name": "gtksourceview2.View", "line_number": 254, "usage_type": "attribute"}, {"api_name": "gtk.WRAP_WORD", "line_number": 255, "usage_type": "attribute"}, {"api_name": "gtksourceview2.Buffer", "line_number": 259, "usage_type": "call"}, {"api_name": "gtksourceview2.language_manager_get_default", "line_number": 261, "usage_type": "call"}, {"api_name": "gtksourceview2.Buffer", "line_number": 268, "usage_type": "call"}, {"api_name": "gtk.TextView", "line_number": 273, "usage_type": "attribute"}, {"api_name": "gtk.TextView.__init__", "line_number": 275, "usage_type": "call"}, {"api_name": "gtk.TextView", "line_number": 275, "usage_type": "attribute"}, {"api_name": "gtk.WRAP_WORD", "line_number": 276, "usage_type": "attribute"}, {"api_name": "gtk.TextBuffer", "line_number": 280, "usage_type": "call"}, {"api_name": "gtk.TextBuffer", "line_number": 285, "usage_type": "call"}]} +{"seq_id": "648719722", "text": "# coding: utf-8\n# by Satoshi Endo @hortense667\n#フォントデータ（フォントパターン　■と_でできてる）を16進化する\n# 縦は8ドットまで、横は128ドットまでの文字をbdf化\n# 文字は ★A とか表現する。\n# BDFファイルを作るための下処理 入力は _after_burner.txt\n# python _punchbdf0x.py _after_burner.txt で実行\nimport sys\nimport io\nimport codecs\nimport re\nimport serial\nimport struct\n\nglobal fdic\nglobal fmap\nglobal ntbl\n\nntbl = [\" \"] * 9\t# 漢字フォント読み出し字の保存テーブル\nfdic = {}\t# フォントテーブルへの辞書\n\n# アタリフォント（一部オリジナル）のフォントテーブル\nfmap = []\n\n# COMに１文字送る\ndef sendptn(ptn):\n\tif testf != \"1\":\n\t\twhile True:\n\t\t\tif ser.out_waiting == 0:\n\t\t\t\tbreak\n\t\ta = struct.pack( \"B\", ptn )\n\t\tser.write(a)\n\t\tser.flush()\n\ty = bin(ptn)[2:]\t\t\t#for test\n\twhile len(y) < 8:\t\t\t#for test\n\t\ty = '0' + y\t\t\t#for test\n\tz = ''\t\t\t\t\t#for test\n\tfor j in range(8):\t\t\t#パターンを裏返しに\n\t\tz = y[j:j+1] + z\t\t#for test\n\tz = z.replace('0','＿')\t\t\t#for test\n\ty = z.replace('1','■')\t\t\t#for test\n\tprint(y)\t\t\t\t#for test\n\treturn()\n\n#==========================================\n# ここからメイン処理\n#==========================================\n#標準出力のおまじない\n#sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='cp932'.lower())\nsys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8'.lower())\n#フォントファイルの読み込みとテーブルへの展開\nfilename = sys.argv[1]\ndatafile = codecs.open(filename, 'r' , 'utf-8'.lower())\nbitmap_f = 0\nfirstf = 1\nprint('CHARSET_ENCODING \"1\"')\nfor line in datafile:\n\tline = line.rstrip()\n\tif '★' in line:\n\t\tif firstf == 0:\n\t\t\tprint('ENDCHAR')\n\t\tfirstf = 0\n\t\ts = line.replace('★', '')\n\t\tbdf_f = 0\n\t\tif s == \" \" or s == \"\":\n\t\t\tprint('STARTCHAR '+ \" \")\n\t\t\tprint('ENCODING ' + str(ord(\" \")))\n\t\telse:\n\t\t\tprint('STARTCHAR '+ s)\n\t\t\tprint('ENCODING ' + str(ord(s)))\n\t\tbitmap_f = 1\n\t\tkidx = 0\n\telif '▲' in line:\n\t\tprint(line)\n\telse:\n\t\tfontw = len(line)\n\t\tline = line.replace('■', '1')\n\t\ty = line.replace('＿', '0')\n\t\tif len(y) < 8:\n\t\t\twhile len(y) < 8:\t#左に0をパディング\n\t\t\t\ty = y + '0'\n\t\telif len(y) > 8:\n\t\t\twhile len(y) < 16:\t#左に0をパディング\n\t\t\t\ty = y + '0'\n\t\tx = int(y, 2)\t#2進法として整数化\n\t\tz = hex(x)[2:]\t#16進表現に変換\n\t\tif (len(z) < 2):\n\t\t\twhile len(z) < 2:\t#左に0をパディング\n\t\t\t\tz = '0' + z\n\t\telif (len(z) > 2):\n\t\t\twhile len(z) < 4:\t#左に0をパディング\n\t\t\t\tz = '0' + z\n\t\tif bdf_f == 0:\n\t\t\tbdf_f = 1\n\t\t\tprint('BBX '+str(fontw)+' 8 0 -1')\n\t\t\tprint('BITMAP')\n\t\tprint(z)\nprint('ENDCHAR')\ndatafile.close()\n\n", "sub_path": "8-pixels-bdf-maker.py", "file_name": "8-pixels-bdf-maker.py", "file_ext": "py", "file_size_in_byte": 2692, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "struct.pack", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 50, "usage_type": "attribute"}, {"api_name": "io.TextIOWrapper", "line_number": 50, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 52, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "581938033", "text": "from flask import Flask, jsonify\nfrom sqlalchemy import create_engine, func\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy.ext.automap import automap_base\nimport sqlalchemy\nimport numpy as np\n\nengine = create_engine(\"sqlite:///stuff.db\")\n# reflect an existing database into a new model\nBase = automap_base()\n# make sure that I have a primary key or else this won't work\n# reflect the tables\nBase.prepare(engine, reflect=True)\n# Save reference to the table\nethnicity = Base.classes.nickdb\n# Create our session (link) from Python to the DB\nsession = Session(engine)\n#################################################\n# Flask Setup\n#################################################\napp = Flask(__name__)\n#################################################\n# Flask Routes\n#################################################\n@app.route(\"/\")\ndef welcome():\n \"\"\"List all available api routes.\"\"\"\n return (\n f\"Available Routes:
    \"\n f\"/api/v1.0/states
    \"\n \n )\n # this is the syntax I want to use to call API stuff\n\n\n@app.route(\"/states\")\ndef names():\n \"\"\"Return a list of all State abbreviations\"\"\"\n # Query all passengers\n results = session.query(ethnicity.STATE).all()\n # Convert list of tuples into normal list\n all_names = list(np.ravel(results))\n return jsonify(all_names)\n\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "sub_path": "static/js/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1374, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.automap.automap_base", "line_number": 10, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "379704905", "text": "import collections\nfrom abc import ABCMeta, abstractmethod\n\n\nclass AlgorithmInterface:\n \"\"\"\n Clinical scoring systems to predict organ failure in AP patients. \n\n This module includes scoring metric equations as well as helper \n functions for estimating lab tests, converting units, etc. \n \"\"\"\n __metaclass__ = ABCMeta\n\n name = ''\n required_fields = []\n optional_fields = []\n semi_req_fields = []\n score_range = {}\n\n @classmethod\n def __init__(self, request):\n self.request = request\n\n @classmethod\n def can_process(self):\n if not all([self.request.get(ii) not in [None, ''] for ii in self.required_fields]):\n return False\n semi_req = [all(self.request.get(jj) not in [None, ''] for jj in ii) for ii in self.semi_req_fields]\n return not self.semi_req_fields or any(semi_req)\n\n @classmethod\n def __repr__(self):\n return 'Required parameters: ({})\\nEither/Or parameters: ({})\\nOptional parameters: ({})'.format(\n ', '.join(self.required_fields),\n ', '.join(self.semi_req_fields),\n ', '.join(self.optional_fields))\n\n @classmethod\n def params(self):\n return { \"required\": self.required_fields,\n \"either/or\": self.semi_req_fields,\n \"optional\": self.optional_fields}\n\n @abstractmethod\n def evaluate(self):\n pass\n\n @classmethod\n def calculate_subscore(self, variable, score_range):\n sr = collections.OrderedDict(sorted(score_range.items()))\n res = None\n for k, v in sr.items():\n if variable >= k:\n res = v\n return res\n\n @classmethod\n def enforce_lower_bound(self, variable, lower_bound):\n \"\"\"\n If variable below lower bound, set to None\n\n Args:\n variable: variable in question, int or float\n lower_bound: int or float\n\n Returns:\n variable: returns same if above lower bound, else None.\n \"\"\"\n if variable is not None:\n if variable < lower_bound: \n variable = None\n return variable\n\n @classmethod\n def kg_to_lb(self, weight):\n LB_TO_KG = 0.453592\n if weight:\n val = weight / LB_TO_KG \n return float(\"{0:.2f}\".format(val))\n\n @classmethod\n def cm_to_inch(self, height):\n INCH_TO_METER = 2.54\n if height:\n val = height / INCH_TO_METER\n return float(\"{0:.2f}\".format(val))\n\n @classmethod\n def imperial_to_metric(self, height, weight):\n \"\"\"\n Convert imperial units to metric units for height and weight.\n\n Returns None if input is None. \n\n Args:\n height: height in inches\n weight: weight in pounds\n\n Returns:\n height: height in meters, or None\n weight: weight in kg, or None\n \"\"\"\n INCH_TO_METER = 0.0254\n LB_TO_KG = 0.453592\n if height is not None and weight is not None:\n return (height * INCH_TO_METER, weight * LB_TO_KG)\n elif height is not None:\n return (height * INCH_TO_METER, weight)\n elif weight is not None:\n return (height, weight * LB_TO_KG)\n else:\n return (height, weight)\n\n @classmethod\n def calculate_bmi(self):\n \"\"\"\n Estimate body mass index from height and weight in metric.\n\n Args:\n height: height in meters, float\n weight: weight in kg, float\n bmi: body mass index, kg/m^2\n\n Returns:\n bmi: body mass index, kg/m^2\n \"\"\"\n _ = self.request\n bmi = _.get('bmi')\n weight = _.get('weight')\n height = _.get('height')\n\n if not bmi:\n if height and weight:\n bmi = weight / height**2\n return bmi\n\n @classmethod\n def get_bicarbonate(self):\n _ = self.request\n bicarbonate = _.get('bicarbonate')\n hco3_arterial = _.get('hco3_arterial')\n hco3_serum = _.get('hco3_serum')\n\n if not bicarbonate:\n bicarbonate = hco3_arterial if hco3_arterial else hco3_serum\n return bicarbonate\n\n @classmethod\n def get_peritonitis(self):\n _ = self.request\n peritonitis = _.get('peritonitis')\n guarding = _.get('guarding')\n tenderness = _.get('tenderness')\n\n if not peritonitis:\n peritonitis = guarding or tenderness\n return peritonitis\n\n @classmethod\n def arterialbg_from_pulseox(self):\n \"\"\"\n Imputes PaO2 (from ABG) from SpO2 (from pulse oximeter reading).\n\n Brown, Samuel M., et al. Critical care medicine \n 45.8 (2017): 1317-1324.\n\n Args:\n paO2: arterial oxygen partial pressure \n spO2: SpO2 pulse oximetry measurement\n\n Returns:\n paO2: real part of nonlinear imputation of PaO2 from SpO2.\n\n NOTE: May choose not to approximate if PaO2 > 0.96 because\n approximation worsens at edges of sigmoid.\n \"\"\"\n _ = self.request\n paO2 = _.get('paO2')\n spO2 = _.get('spO2')\n\n if not paO2:\n if spO2:\n c1, c2, denominator = 11700, 50, (1/float(spO2) - 1)\n term1 = (\n (c1 / denominator) + \n (c2**3 + (c1 / denominator)**2)**0.5\n )**(1/3)\n term2 = (\n (c1 / denominator) - \n (c2**3 + (c1 / denominator)**2)**0.5\n )**(1/3)\n paO2 = (term1 + term2).real\n return paO2\n\n @classmethod\n def fahrenheit_to_celsius(self, temperature):\n \"\"\"\n Converts patient temperature from fahrenheit to celsius.\n\n Args:\n temperature: Temperature in Fahrenheit\n\n Returns:\n temperature: Temperature in Celsius\n \"\"\"\n if temperature: \n temperature = (temperature - 32) / 1.8\n return temperature\n\n @classmethod\n def glasgow_coma_scale(self):\n \"\"\"\n Compute Glasgow Coma Scale based on eye, verbal, and motor response.\n\n Args:\n eye_score: int, 4= open spontaneously, 3=open to verbal command\n 2=open in response to pain, 1=no response\n verbal_score: 5=talk-oriented, 4=confused speech oriented,\n 3=inappropriate words, 2=incomprehensible sounds,\n 1=no response\n motor_score: 6=obeys commands, 5=localizes pain,\n 4=flexion-withdrawal, 3=abnormal flexion,\n 2=extension, 1=no response\n\n Returns:\n glasgow_coma: int, used to assess comma status\n \"\"\"\n _ = self.request\n eye_score = _.get('eye_score')\n verbal_score = _.get('verbal_score')\n motor_score = _.get('motor_score')\n \n glasgow_coma = None\n if all(v is not None for v in [eye_score, verbal_score, motor_score]):\n glasgow_coma = eye_score + verbal_score + motor_score\n return glasgow_coma\n\n @classmethod\n def maintenance_fluid(self):\n '''\n Computes daily maintenance fluid for patients\n\n Args:\n height: patient height in m\n weight: patient weight in kg\n sex: male or female\n Returns:\n adjusted_weight: adjusted body weight in kg\n maintenance_fluid: average daily maintenance fluid volume in mL\n '''\n height = self.request.get('height')\n weight = self.request.get('weight')\n sex = self.request.get('sex')\n\n HEIGHT_THRES = 1.524 #in meters, equiv to 5ft\n M_TO_IN = 39.3701 # 1 meter = 39.37 in\n\n maintenance_fluid = None\n adjusted_weight = None\n if all(v not in [None, ''] for v in [height, weight, sex]):\n\n # Constants different depending on sex\n if sex.lower()[0] == \"m\": \n ideal_weight = 50\n else: #female\n ideal_weight = 45.5\n\n # Devine formula for ideal body weight\n if height > HEIGHT_THRES:\n ideal_weight = ideal_weight + (2.3 * ((height * M_TO_IN) - 60))\n\n # Adjust body weight for obese patients\n # Traynor AM. Antimicrob Agents and Chemother. 1995.\n # Hicks C. Ann Hematol. 2012. \n adjusted_weight = weight\n if weight > 1.25 * ideal_weight: \n adjusted_weight = ideal_weight + 0.4 * (weight - ideal_weight)\n\n maintenance_fluid = int(35 * adjusted_weight)\n \n return maintenance_fluid\n", "sub_path": "API/algorithms/interface.py", "file_name": "interface.py", "file_ext": "py", "file_size_in_byte": 8621, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "abc.ABCMeta", "line_number": 12, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 44, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "272278073", "text": "import pickle\n\nfrom pipdeptree import (req_version, render_tree,\n top_pkg_name, non_top_pkg_name,\n top_pkg_src, non_top_pkg_src)\n\n\nwith open('tests/pkgs.pickle', 'rb') as f:\n pkgs = pickle.load(f)\n\n\npkg_index = {p.key: p for p in pkgs}\nreq_map = {p: p.requires() for p in pkgs}\n\n\ndef find_req(req, parent):\n \"\"\"Helper to get the requirement object from it's parent package\n\n :param req : string\n :param parent : pkg_resources.Distribution instance\n :rtype : instance of requirement frozen set\n\n \"\"\"\n return [r for r in pkg_index[parent].requires() if r.key == req][0]\n\n\ndef test_req_version():\n sqlalchemy = find_req('sqlalchemy', 'alembic')\n assert req_version(sqlalchemy) == '>=0.7.3'\n mako = find_req('mako', 'alembic')\n assert req_version(mako) is None\n\n\ndef test_non_top_pkg_name():\n flask_p = pkg_index['flask']\n flask_r = find_req('flask', 'flask-script')\n assert non_top_pkg_name(flask_r, flask_p) == 'Flask [installed: 0.10.1]'\n\n markupsafe_p = pkg_index['markupsafe']\n markupsafe_jinja2_r = find_req('markupsafe', 'jinja2')\n assert non_top_pkg_name(markupsafe_jinja2_r, markupsafe_p) == 'MarkupSafe [installed: 0.18]'\n\n markupsafe_mako_r = find_req('markupsafe', 'mako')\n assert non_top_pkg_name(markupsafe_mako_r, markupsafe_p) == 'MarkupSafe [required: >=0.9.2, installed: 0.18]'\n\n\ndef test_render_tree_only_top():\n tree_str = render_tree(pkgs, pkg_index, req_map, False,\n top_pkg_name, non_top_pkg_name)\n lines = set(tree_str.split('\\n'))\n assert 'Flask-Script==0.6.6' in lines\n assert ' - SQLAlchemy [required: >=0.7.3, installed: 0.9.1]' in lines\n assert 'Lookupy==0.1' in lines\n assert 'itsdangerous==0.23' not in lines\n\n\ndef test_render_tree_list_all():\n tree_str = render_tree(pkgs, pkg_index, req_map, True,\n top_pkg_name, non_top_pkg_name)\n lines = set(tree_str.split('\\n'))\n assert 'Flask-Script==0.6.6' in lines\n assert ' - SQLAlchemy [required: >=0.7.3, installed: 0.9.1]' in lines\n assert 'Lookupy==0.1' in lines\n assert 'itsdangerous==0.23' in lines\n\n\ndef test_render_tree_freeze():\n tree_str = render_tree(pkgs, pkg_index, req_map, False,\n top_pkg_src, non_top_pkg_src)\n lines = set(tree_str.split('\\n'))\n assert 'Flask-Script==0.6.6' in lines\n assert ' - SQLAlchemy==0.9.1' in lines\n assert '-e git+git@github.com:naiquevin/lookupy.git@cdbe30c160e1c29802df75e145ea4ad903c05386#egg=Lookupy-master' in lines\n assert 'itsdangerous==0.23' not in lines\n", "sub_path": "tests/pipdeptree_tests.py", "file_name": "pipdeptree_tests.py", "file_ext": "py", "file_size_in_byte": 2623, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pickle.load", "line_number": 9, "usage_type": "call"}, {"api_name": "pipdeptree.req_version", "line_number": 29, "usage_type": "call"}, {"api_name": "pipdeptree.req_version", "line_number": 31, "usage_type": "call"}, {"api_name": "pipdeptree.non_top_pkg_name", "line_number": 37, "usage_type": "call"}, {"api_name": "pipdeptree.non_top_pkg_name", "line_number": 41, "usage_type": "call"}, {"api_name": "pipdeptree.non_top_pkg_name", "line_number": 44, "usage_type": "call"}, {"api_name": "pipdeptree.render_tree", "line_number": 48, "usage_type": "call"}, {"api_name": "pipdeptree.top_pkg_name", "line_number": 49, "usage_type": "argument"}, {"api_name": "pipdeptree.non_top_pkg_name", "line_number": 49, "usage_type": "argument"}, {"api_name": "pipdeptree.render_tree", "line_number": 58, "usage_type": "call"}, {"api_name": "pipdeptree.top_pkg_name", "line_number": 59, "usage_type": "argument"}, {"api_name": "pipdeptree.non_top_pkg_name", "line_number": 59, "usage_type": "argument"}, {"api_name": "pipdeptree.render_tree", "line_number": 68, "usage_type": "call"}, {"api_name": "pipdeptree.top_pkg_src", "line_number": 69, "usage_type": "argument"}, {"api_name": "pipdeptree.non_top_pkg_src", "line_number": 69, "usage_type": "argument"}]} +{"seq_id": "337553206", "text": "import logging\nimport os\nimport shutil\nimport numpy as np\nimport unittest\nimport doctest\nimport pysnptools.util as pstutil\nfrom pysnptools.pstreader import PstData\nfrom pysnptools.pstreader import PstMemMap\nfrom pysnptools.distreader import DistReader, DistData\nfrom pysnptools.util import log_in_place\n\n\nclass DistMemMap(PstMemMap,DistData):\n '''\n A :class:`.DistData` that keeps its data in a memory-mapped file. This allows data large than fits in main memory.\n\n See :class:`.DistData` for general examples of using DistData.\n\n **Constructor:**\n :Parameters: **filename** (*string*) -- The *\\*.dist.memmap* file to read.\n \n Also see :meth:`.DistMemMap.empty` and :meth:`.DistMemMap.write`.\n\n :Example:\n\n >>> from pysnptools.distreader import DistMemMap\n >>> from pysnptools.util import example_file # Download and return local file name\n >>> mem_map_file = example_file(\"pysnptools/examples/tiny.dist.memmap\")\n >>> dist_mem_map = DistMemMap(mem_map_file)\n >>> print(dist_mem_map.val[0,1], dist_mem_map.iid_count, dist_mem_map.sid_count)\n [0.43403135 0.28289911 0.28306954] 25 10\n\n **Methods inherited from** :class:`.DistData`\n\n :meth:`.DistData.allclose`\n\n **Methods beyond** :class:`.DistReader`\n\n '''\n\n def __init__(self, *args, **kwargs):\n super(DistMemMap, self).__init__(*args, **kwargs)\n\n @property\n def val(self):\n \"\"\"The 3D NumPy memmap array of floats that represents the distribution of SNP values. You can get this property, but cannot set it (except with itself)\n\n\n >>> from pysnptools.distreader import DistMemMap\n >>> from pysnptools.util import example_file # Download and return local file name\n >>> mem_map_file = example_file(\"pysnptools/examples/tiny.dist.memmap\")\n >>> dist_mem_map = DistMemMap(mem_map_file)\n >>> print(dist_mem_map.val[0,1])\n [0.43403135 0.28289911 0.28306954]\n \"\"\"\n self._run_once()\n return self._val\n\n\n @val.setter\n def val(self, new_value):\n self._run_once()\n if self._val is new_value:\n return\n raise Exception(\"DistMemMap val's cannot be set to a different array\")\n\n\n @property\n def offset(self):\n '''The byte position in the file where the memory-mapped values start.\n \n (The disk space before this is used to store :attr:`DistReader.iid`, etc. information.\n This property is useful when interfacing with, for example, external Fortran and C matrix libraries.)\n \n '''\n self._run_once()\n return self._offset\n\n @property\n def filename(self):\n '''The name of the memory-mapped file\n '''\n #Don't need '_run_once'\n return self._filename\n\n @staticmethod\n def empty(iid, sid, filename, pos=None,order=\"F\",dtype=np.float64):\n '''Create an empty :class:`.DistMemMap` on disk.\n\n :param iid: The :attr:`DistReader.iid` information\n :type iid: an array of string pairs\n\n :param sid: The :attr:`DistReader.sid` information\n :type sid: an array of strings\n\n :param filename: name of memory-mapped file to create\n :type filename: string\n\n :param pos: optional -- The additional :attr:`DistReader.pos` information associated with each sid. Default: None\n :type pos: an array of numeric triples\n\n :param order: {'F' (default), 'C'}, optional -- Specify the order of the ndarray.\n :type order: string or None\n\n :param dtype: {numpy.float64 (default), numpy.float32}, optional -- The data-type for the :attr:`DistMemMap.val` ndarray.\n :type dtype: data-type\n\n :rtype: :class:`.DistMemMap`\n\n >>> import pysnptools.util as pstutil\n >>> from pysnptools.distreader import DistMemMap\n >>> filename = \"tempdir/tiny.dist.memmap\"\n >>> pstutil.create_directory_if_necessary(filename)\n >>> dist_mem_map = DistMemMap.empty(iid=[['fam0','iid0'],['fam0','iid1']], sid=['snp334','snp349','snp921'],filename=filename,order=\"F\",dtype=np.float64)\n >>> dist_mem_map.val[:,:,:] = [[[.5,.5,0],[0,0,1],[.5,.5,0]],\n ... [[0,1.,0],[0,.75,.25],[.5,.5,0]]]\n >>> dist_mem_map.flush()\n\n '''\n\n self = DistMemMap(filename)\n self._empty_inner(row=iid, col=sid, filename=filename, row_property=None, col_property=pos,order=order,dtype=dtype,val_shape=3)\n return self\n\n def flush(self):\n '''Flush :attr:`DistMemMap.val` to disk and close the file. (If values or properties are accessed again, the file will be reopened.)\n\n >>> import pysnptools.util as pstutil\n >>> from pysnptools.distreader import DistMemMap\n >>> filename = \"tempdir/tiny.dist.memmap\"\n >>> pstutil.create_directory_if_necessary(filename)\n >>> dist_mem_map = DistMemMap.empty(iid=[['fam0','iid0'],['fam0','iid1']], sid=['snp334','snp349','snp921'],filename=filename,order=\"F\",dtype=np.float64)\n >>> dist_mem_map.val[:,:,:] = [[[.5,.5,0],[0,0,1],[.5,.5,0]],\n ... [[0,1.,0],[0,.75,.25],[.5,.5,0]]]\n >>> dist_mem_map.flush()\n\n '''\n if self._ran_once:\n self.val.flush()\n del self._val\n self._ran_once = False\n\n\n @staticmethod\n def write(filename, distreader, order='A', dtype=None, block_size=None, num_threads=None):\n \"\"\"Writes a :class:`DistReader` to :class:`DistMemMap` format.\n\n :param filename: the name of the file to create\n :type filename: string\n :param distreader: The data that should be written to disk. It can also be any distreader, for example, :class:`.DistNpz`, :class:`.DistData`, or\n another :class:`.Bgen`.\n :type distreader: :class:`DistReader`\n :param order: {'A' (default), 'F', 'C'}, optional -- Specify the order of the ndarray. By default, will match the order of the input if knowable; otherwise, 'F'\n :type order: string or None\n :param dtype: {None (default), numpy.float64, numpy.float32}, optional -- The data-type for the :attr:`DistMemMap.val` ndarray.\n By default, will match the order of the input if knowable; otherwise np.float64.\n :type dtype: data-type\n :param block_size: The number of SNPs to read in a batch from *distreader*. Defaults to a *block_size* such that *block_size* \\* *iid_count* is about 100,000.\n :type block_size: number\n :param num_threads: optional -- The number of threads with which to write data. Defaults to all available\n processors. Can also be set with these environment variables (listed in priority order):\n 'PST_NUM_THREADS', 'NUM_THREADS', 'MKL_NUM_THREADS'.\n :type num_threads: None or int\n :rtype: :class:`.DistMemMap`\n\n >>> import pysnptools.util as pstutil\n >>> from pysnptools.distreader import Bgen, DistMemMap\n >>> from pysnptools.util import example_file # Download and return local file name\n >>> bgen_file = example_file(\"pysnptools/examples/2500x100.bgen\")\n >>> distreader = Bgen(bgen_file)[:,:10] #Create a reader for the first 10 SNPs\n >>> pstutil.create_directory_if_necessary(\"tempdir/tiny.dist.memmap\")\n >>> DistMemMap.write(\"tempdir/tiny.dist.memmap\",distreader) # Write distreader in DistMemMap format\n DistMemMap('tempdir/tiny.dist.memmap')\n\n \"\"\"\n block_size = block_size or max((100_000)//max(1,distreader.row_count),1)\n\n if hasattr(distreader,'val'):\n order = PstMemMap._order(distreader) if order=='A' else order\n dtype = dtype or distreader.val.dtype\n else:\n order = 'F' if order=='A' else order\n dtype = dtype or np.float64\n dtype = np.dtype(dtype)\n\n self = PstMemMap.empty(distreader.row, distreader.col, filename+'.temp', row_property=distreader.row_property, col_property=distreader.col_property,order=order,dtype=dtype, val_shape=3)\n if hasattr(distreader,'val'):\n self.val[:,:,:] = distreader.val\n else:\n start = 0\n with log_in_place(\"DistMemMap writing sid_index \", logging.INFO) as updater:\n while start < distreader.sid_count:\n updater('{0} of {1}'.format(start,distreader.sid_count))\n distdata = distreader[:,start:start+block_size].read(order=order,dtype=dtype,num_threads=num_threads)\n self.val[:,start:start+distdata.sid_count,:] = distdata.val\n start += distdata.sid_count\n\n self.flush()\n if os.path.exists(filename):\n os.remove(filename) \n shutil.move(filename+'.temp',filename)\n logging.debug(\"Done writing \" + filename)\n return DistMemMap(filename)\n\n\n\n def _run_once(self):\n if (self._ran_once):\n return\n row_ascii,col_ascii,val,row_property,col_property = self._run_once_inner()\n row = np.array(row_ascii,dtype='str') #!!!avoid this copy when not needed\n col = np.array(col_ascii,dtype='str') #!!!avoid this copy when not needed\n\n DistData.__init__(self,iid=row,sid=col,val=val,pos=col_property,name=\"np.memmap('{0}')\".format(self._filename))\n\nclass TestDistMemMap(unittest.TestCase): \n\n def test1(self): \n old_dir = os.getcwd()\n os.chdir(os.path.dirname(os.path.realpath(__file__)))\n\n filename2 = \"tempdir/tiny.dist.memmap\"\n pstutil.create_directory_if_necessary(filename2)\n distreader2 = DistMemMap.empty(iid=[['fam0','iid0'],['fam0','iid1']], sid=['snp334','snp349','snp921'],filename=filename2,order=\"F\",dtype=np.float64)\n assert isinstance(distreader2.val,np.memmap)\n distreader2.val[:,:,:] = [[[.5,.5,0],[0,0,1],[.5,.5,0]],[[0,1.,0],[0,.75,.25],[.5,.5,0]]]\n assert np.array_equal(distreader2[[1],[1]].read(view_ok=True).val,np.array([[[0,.75,.25]]]))\n distreader2.flush()\n assert isinstance(distreader2.val,np.memmap)\n assert np.array_equal(distreader2[[1],[1]].read(view_ok=True).val,np.array([[[0,.75,.25]]]))\n distreader2.flush()\n\n distreader3 = DistMemMap(filename2)\n assert np.array_equal(distreader3[[1],[1]].read(view_ok=True).val,np.array([[[0,.75,.25]]]))\n assert isinstance(distreader3.val,np.memmap)\n\n logging.info(\"in TestDistMemMap test1\")\n distreader = DistMemMap('../examples/tiny.dist.memmap')\n assert distreader.iid_count == 25\n assert distreader.sid_count == 10\n assert isinstance(distreader.val,np.memmap)\n\n distdata = distreader.read(view_ok=True)\n assert isinstance(distdata.val,np.memmap)\n os.chdir(old_dir)\n\n def test2(self):\n from pysnptools.distreader import Bgen\n\n old_dir = os.getcwd()\n os.chdir(os.path.dirname(os.path.realpath(__file__)))\n\n bgen = Bgen('../examples/example.bgen')\n distmemmap = DistMemMap.write(\"tempdir/bgentomemmap.dist.memamp\",bgen)\n assert DistData.allclose(bgen.read(),distmemmap.read(),equal_nan=True)\n os.chdir(old_dir)\n\n def test_doctest(self):\n import pysnptools.distreader.distmemmap as mod_mm\n import doctest\n\n old_dir = os.getcwd()\n os.chdir(os.path.dirname(os.path.realpath(__file__)))\n result = doctest.testmod(mod_mm)\n os.chdir(old_dir)\n assert result.failed == 0, \"failed doc test: \" + __file__\n\ndef getTestSuite():\n \"\"\"\n set up composite test suite\n \"\"\"\n \n test_suite = unittest.TestSuite([])\n test_suite.addTests(unittest.TestLoader().loadTestsFromTestCase(TestDistMemMap))\n return test_suite\n\n\nif __name__ == \"__main__\":\n logging.basicConfig(level=logging.WARN)\n\n suites = getTestSuite()\n r = unittest.TextTestRunner(failfast=True)\n ret = r.run(suites)\n assert ret.wasSuccessful()\n\n\n result = doctest.testmod(optionflags=doctest.ELLIPSIS|doctest.NORMALIZE_WHITESPACE)\n assert result.failed == 0, \"failed doc test: \" + __file__\n", "sub_path": "pysnptools/distreader/distmemmap.py", "file_name": "distmemmap.py", "file_ext": "py", "file_size_in_byte": 12115, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pysnptools.pstreader.PstMemMap", "line_number": 14, "usage_type": "name"}, {"api_name": "pysnptools.distreader.DistData", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.float64", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pysnptools.pstreader.PstMemMap._order", "line_number": 180, "usage_type": "call"}, {"api_name": "pysnptools.pstreader.PstMemMap", "line_number": 180, "usage_type": "name"}, {"api_name": "numpy.float64", "line_number": 184, "usage_type": "attribute"}, {"api_name": "numpy.dtype", "line_number": 185, "usage_type": "call"}, {"api_name": "pysnptools.pstreader.PstMemMap.empty", "line_number": 187, "usage_type": "call"}, {"api_name": "pysnptools.pstreader.PstMemMap", "line_number": 187, "usage_type": "name"}, {"api_name": "pysnptools.util.log_in_place", "line_number": 192, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 192, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path", "line_number": 200, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 201, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 202, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 213, "usage_type": "call"}, {"api_name": "pysnptools.distreader.DistData.__init__", "line_number": 215, "usage_type": "call"}, {"api_name": "pysnptools.distreader.DistData", "line_number": 215, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 217, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 220, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path", "line_number": 221, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 221, "usage_type": "call"}, {"api_name": "pysnptools.util.create_directory_if_necessary", "line_number": 224, "usage_type": "call"}, {"api_name": "pysnptools.util", "line_number": 224, "usage_type": "name"}, {"api_name": "numpy.float64", "line_number": 225, "usage_type": "attribute"}, {"api_name": "numpy.memmap", "line_number": 226, "usage_type": "attribute"}, {"api_name": "numpy.array_equal", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.memmap", "line_number": 230, "usage_type": "attribute"}, {"api_name": "numpy.array_equal", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.memmap", "line_number": 236, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.memmap", "line_number": 242, "usage_type": "attribute"}, {"api_name": "numpy.memmap", "line_number": 245, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 246, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 251, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 252, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 252, "usage_type": "call"}, {"api_name": "os.path", "line_number": 252, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 252, "usage_type": "call"}, {"api_name": "pysnptools.distreader.Bgen", "line_number": 254, "usage_type": "call"}, {"api_name": "pysnptools.distreader.DistData.allclose", "line_number": 256, "usage_type": "call"}, {"api_name": "pysnptools.distreader.DistData", "line_number": 256, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 257, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 263, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path", "line_number": 264, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 264, "usage_type": "call"}, {"api_name": "doctest.testmod", "line_number": 265, "usage_type": "call"}, {"api_name": "pysnptools.distreader.distmemmap", "line_number": 265, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 266, "usage_type": "call"}, {"api_name": "unittest.TestSuite", "line_number": 274, "usage_type": "call"}, {"api_name": "unittest.TestLoader", "line_number": 275, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 280, "usage_type": "call"}, {"api_name": "logging.WARN", "line_number": 280, "usage_type": "attribute"}, {"api_name": "unittest.TextTestRunner", "line_number": 283, "usage_type": "call"}, {"api_name": "doctest.testmod", "line_number": 288, "usage_type": "call"}, {"api_name": "doctest.ELLIPSIS", "line_number": 288, "usage_type": "attribute"}, {"api_name": "doctest.NORMALIZE_WHITESPACE", "line_number": 288, "usage_type": "attribute"}]} +{"seq_id": "616834278", "text": "from pathlib import Path\nfrom textwrap import dedent\n\nfrom models.collection import SampleSolution, load_text_from_file, base_res_path, ExerciseFile, ex_resources_path\nfrom models.web import SiteSpec, HtmlTask, HtmlAttribute, WebSolution, WebExercise, WebExerciseContent\n\nex_res_folder: Path = ex_resources_path('web', 1, 3)\n\nhtml_tasks = [\n HtmlTask(\n id=1,\n text=\"\"\"Erstellen Sie eine passende h1 - Überschrift, die 'Ford Mustang' enthält.\"\"\",\n xpathQuery='/html/body//h1',\n awaitedTagName='h1',\n awaitedTextContent='Ford Mustang'\n ),\n HtmlTask(\n id=2,\n text=dedent(\n \"\"\"\\\n Erstellen Sie den Link auf der Seite, der auf Wikipedia verweist.\n Geben Sie als Ziel die URL 'https=//de.wikipedia.org/wiki/Ford_Mustang' an.\"\"\"\n ).replace('\\n', ' '),\n xpathQuery='/html/body//a',\n awaitedTagName='a',\n attributes=[HtmlAttribute(key='href', value='https=//de.wikipedia.org/wiki/Ford_Mustang')]\n ),\n HtmlTask(\n id=3,\n text=dedent(\n \"\"\"\\\n Erstellen Sie im Link das Bild des Ford Mustang.\n Geben Sie als Quelle des Bildes die URL\n 'https=//upload.wikimedia.org/wikipedia/commons/2/2d/1964_12_Ford_Mustang.jpg' und als alternative\n Beschreibung 'Ford Mustang' an.\n Geben Sie außerdem eine Breite von 250 und eine Höhe von 188 an, um das Bild zu skalieren.\"\"\"\n ).replace('\\n', ' '),\n xpathQuery='/html/body//a//img',\n awaitedTagName='img',\n attributes=[\n HtmlAttribute(\n key='src',\n value='https=//upload.wikimedia.org/wikipedia/commons/2/2d/1964_12_Ford_Mustang.jpg'),\n HtmlAttribute(key='alt', value='Ford Mustang'),\n HtmlAttribute(key='width', value='250'),\n HtmlAttribute(key='height', value='188')\n ]\n ),\n HtmlTask(\n id=4,\n text=dedent(\n \"\"\"\\\n Binden Sie die vorgegebene CSS - Datei 'mustangStyle.css' ein.\n Die entsprechende Datei ist unter der URL 'mustangStyle.css' zu finden.\n Setzen Sie auch den entsprechenden Wert für das Attribut 'rel'.\"\"\"\n ).replace('\\n', ' '),\n xpathQuery='/html/head//link',\n awaitedTagName='link',\n attributes=[\n HtmlAttribute(key='rel', value='stylesheet'),\n HtmlAttribute(key='href', value='mustangStyle.css')\n ]\n )\n]\n\nsampleSolution: SampleSolution[WebSolution] = SampleSolution(\n id=1,\n sample=WebSolution(\n files=[\n ExerciseFile(\n name='mustang.html',\n fileType='htmlmixed',\n editable=False,\n content=load_text_from_file(base_res_path / 'web' / 'coll_1' / 'ex_3' / 'sol_1' / 'mustang.html'),\n )\n ]\n )\n)\n\nweb_coll_1_ex_3: WebExercise = WebExercise(\n exerciseId=3,\n collectionId=1,\n toolId='web',\n title='Hyperlinks und Bilder in HTML',\n authors=['bje40dc'],\n text=load_text_from_file(base_res_path / 'web' / 'coll_1' / 'ex_3' / 'text.html'),\n topics=[],\n difficulty=2,\n content=WebExerciseContent(\n files=[\n ExerciseFile(\n name='mustang.html',\n fileType='htmlmixed',\n editable=True,\n content=load_text_from_file(base_res_path / 'web' / 'coll_1' / 'ex_3' / 'mustang.html'),\n ),\n ExerciseFile(\n name='mustangStyle.css',\n fileType='css',\n editable=False,\n content=load_text_from_file(base_res_path / 'web' / 'coll_1' / 'ex_3' / 'mustangStyle.css'),\n )\n ],\n siteSpec=SiteSpec(\n fileName='mustang.html',\n htmlTasks=html_tasks,\n jsTasks=[]\n ),\n sampleSolutions=[sampleSolution]\n )\n)\n", "sub_path": "data/web/coll_1/ex_3/web_coll_1_ex_3.py", "file_name": "web_coll_1_ex_3.py", "file_ext": "py", "file_size_in_byte": 3913, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pathlib.Path", "line_number": 7, "usage_type": "name"}, {"api_name": "models.collection.ex_resources_path", "line_number": 7, "usage_type": "call"}, {"api_name": "models.web.HtmlTask", "line_number": 10, "usage_type": "call"}, {"api_name": "models.web.HtmlTask", "line_number": 17, "usage_type": "call"}, {"api_name": "textwrap.dedent", "line_number": 19, "usage_type": "call"}, {"api_name": "models.web.HtmlAttribute", "line_number": 26, "usage_type": "call"}, {"api_name": "models.web.HtmlTask", "line_number": 28, "usage_type": "call"}, {"api_name": "textwrap.dedent", "line_number": 30, "usage_type": "call"}, {"api_name": "models.web.HtmlAttribute", "line_number": 41, "usage_type": "call"}, {"api_name": "models.web.HtmlAttribute", "line_number": 44, "usage_type": "call"}, {"api_name": "models.web.HtmlAttribute", "line_number": 45, "usage_type": "call"}, {"api_name": "models.web.HtmlAttribute", "line_number": 46, "usage_type": "call"}, {"api_name": "models.web.HtmlTask", "line_number": 49, "usage_type": "call"}, {"api_name": "textwrap.dedent", "line_number": 51, "usage_type": "call"}, {"api_name": "models.web.HtmlAttribute", "line_number": 60, "usage_type": "call"}, {"api_name": "models.web.HtmlAttribute", "line_number": 61, "usage_type": "call"}, {"api_name": "models.collection.SampleSolution", "line_number": 66, "usage_type": "name"}, {"api_name": "models.web.WebSolution", "line_number": 66, "usage_type": "name"}, {"api_name": "models.web.WebSolution", "line_number": 68, "usage_type": "call"}, {"api_name": "models.collection.ExerciseFile", "line_number": 70, "usage_type": "call"}, {"api_name": "models.collection.load_text_from_file", "line_number": 74, "usage_type": "call"}, {"api_name": "models.collection.base_res_path", "line_number": 74, "usage_type": "name"}, {"api_name": "models.web.WebExercise", "line_number": 80, "usage_type": "name"}, {"api_name": "models.collection.load_text_from_file", "line_number": 86, "usage_type": "call"}, {"api_name": "models.collection.base_res_path", "line_number": 86, "usage_type": "name"}, {"api_name": "models.web.WebExerciseContent", "line_number": 89, "usage_type": "call"}, {"api_name": "models.collection.ExerciseFile", "line_number": 91, "usage_type": "call"}, {"api_name": "models.collection.load_text_from_file", "line_number": 95, "usage_type": "call"}, {"api_name": "models.collection.base_res_path", "line_number": 95, "usage_type": "name"}, {"api_name": "models.collection.ExerciseFile", "line_number": 97, "usage_type": "call"}, {"api_name": "models.collection.load_text_from_file", "line_number": 101, "usage_type": "call"}, {"api_name": "models.collection.base_res_path", "line_number": 101, "usage_type": "name"}, {"api_name": "models.web.SiteSpec", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "403558890", "text": "from django.shortcuts import render, redirect\nfrom books_author_app.models import *\n\n# Create your views here.\n\n# Display views\ndef index(request):\n context={\n 'books':Book.objects.all()\n }\n return render(request, 'index.html', context)\n\ndef book_info(request,id):\n context={\n 'book': Book.objects.get(id=id),\n 'authors': Author.objects.all(),\n }\n return render(request, 'bookinfo.html', context)\n\ndef author_info(request,id):\n context={\n 'author': Author.objects.get(id=id),\n 'books': Book.objects.all(),\n }\n return render(request, 'authorinfo.html', context)\n\ndef authors(request):\n context={\n 'authors': Author.objects.all(),\n }\n return render(request, 'authors.html', context) \n\n\n\n\n\n# Action views\ndef add_book(request):\n Book.objects.create(\n title=request.POST['title'],\n desc=request.POST['description'],\n )\n return redirect('/')\n\ndef add_author(request):\n Author.objects.create(\n first_name=request.POST['first_name'],\n last_name=request.POST['last_name'],\n notes=request.POST['notes'],\n )\n return redirect('/authors')\n\ndef author_to_book(request,book_id):\n author_to_add = Author.objects.get(id=request.POST[\"new_author\"])\n book_added = Book.objects.get(id=book_id)\n book_added.authors.add(author_to_add)\n return redirect(f'/books/{book_added.id}')\n\ndef book_to_author(request,author_id):\n book_to_add = Book.objects.get(id=request.POST[\"new_book\"])\n author_added = Author.objects.get(id=author_id)\n author_added.books.add(book_to_add)\n return redirect(f'/authors/{author_added.id}')\n", "sub_path": "books_author_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1648, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.shortcuts.render", "line_number": 11, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 18, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 51, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 57, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "164759915", "text": "import socket\nimport numpy as np\nimport time\n\nfrom queue import Queue\nfrom threading import Thread\nfrom time import sleep, time\nfrom signalprocessing.peakfrequency.peak_frequency import PeakFrequency\nfrom signalprocessing import signal_tools\nfrom riemannianClassifier.classifier import riemannianClassifier\nfrom pylsl import StreamInfo, StreamOutlet\n\n# load .env file\nfrom os import getenv\nfrom dotenv import load_dotenv\nload_dotenv()\n\nn_chn = int(getenv('N_CHN'))\nn_sample_per_block = int(getenv('N_SAMPLE_PER_BLOCK'))\nwindowlength = int(getenv('WINDOWLENGTH')) # window length in seconds\nfs = int(getenv('FS')) # sampling frequency of NeurOne\npeak_frequency_o = PeakFrequency(channels=n_chn - 1, samples=(windowlength * fs), fs=fs)\nclf = riemannianClassifier()\nclf.load_self() #assuming that we have a pre-trained classifier\n\n#Set LSL\nLSLchannels = 4\nLSLinterval = 10\nLSLinfo = StreamInfo('Feedback', 'VIS', LSLchannels, LSLinterval, 'float32', 'myuid34234')\nLSLoutlet = StreamOutlet(LSLinfo)\n \n\ndef receive(sock, data_queue, trigger_queue):\n\n np_samples = np.zeros([n_sample_per_block, n_chn])\n prev_seq_nr = None\n while True:\n # connection, client_address = sock_tcp.accept()\n try:\n while True:\n\n cnt = 0\n while cnt < n_sample_per_block:\n\n data, addr = sock.recvfrom(2048) # buffer size is 1024 bytes\n np_samples[cnt], label, sequence_nr = convert_bytes(data, cnt)\n if prev_seq_nr is None:\n prev_seq_nr = sequence_nr\n elif sequence_nr != prev_seq_nr+1:\n print(\"package out of order!\")\n\n prev_seq_nr = sequence_nr\n cnt += 1\n data_queue.put_nowait(np_samples)\n trigger_queue.put_nowait(label)\n\n \n #print(np_samples)\n # print(len(np_samples))\n\n\n\n except():\n print(\"error receiving\")\n exit()\n # finally:\n # connection.close\n # pass\n\ndef process_window(data_queue, trigger_queue):\n cnt = 0\n window = np.zeros([windowlength * fs, n_chn-1])\n while True:\n while cnt < windowlength*fs:\n start = time()\n block = data_queue.get(block=True)\n print(data_queue.qsize())\n for sample in range(block.shape[0]):\n window[cnt, :] = block[sample, :-1]\n cnt += 1\n do_stuff(window.T)\n stop = time()\n print(stop - start)\n cnt = 0\n\n\n\ndef do_stuff(window):\n # signal processing\n peak_frequency_result = peak_frequency_o.transform(x=window)\n welch = signal_tools.extract_amplitudes_welch(window, (windowlength * fs))\n\n print('{} {}'.format(peak_frequency_result.shape, welch.shape))\n\n binary_probabilities = clf.predict_proba(window) # compute class probabilities for binary classification\n\n # TO-DO pass probabilities to feedback/UI\n # ...\n\n # push to visualization\n LSLoutlet.push_sample(binary_probabilities)\n\n return\n\ndef convert_bytes(data, cnt):\n np_samples = np.zeros([n_sample_per_block, n_chn])\n samples = data[28:]\n np_samples[cnt] = np.asarray(\n [int.from_bytes(samples[x:x + 3], byteorder='big', signed=True) for x in range(0, len(samples), 3)])\n\n label = np_samples[cnt][-1]\n\n sequence_nr = int.from_bytes(data[4:8], byteorder='big', signed=True)\n\n return np_samples[cnt], label, sequence_nr\n\n\n\nif __name__ == '__main__':\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # Internet\n sock.bind((getenv('UDP_IP'), int(getenv('UDP_PORT'))))\n\n # sock_tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # SOCK_DGRAM\n # tcp_server_address = (TCP_IP, TCP_PORT)\n # sock_tcp.bind(tcp_server_address)\n # sock_tcp.listen(1)\n\t\n\n packet_len = 28\n\n data_queue = Queue(maxsize=0) #setting the queue maxsize to 0 makes it \"infinite\". PriorityQueue could be interesting to maintain ordering of UDP packets.\n trigger_queue = Queue(maxsize=0)\n\n\n receiver_thread = Thread(target=receive, args=(sock, data_queue, trigger_queue))\n consumer_thread = Thread(target=process_window, args=(data_queue, trigger_queue))\n\n receiver_thread.start()\n consumer_thread.start()\n\n receiver_thread.join()\n consumer_thread.join()\n\n", "sub_path": "client_udpeeg.py", "file_name": "client_udpeeg.py", "file_ext": "py", "file_size_in_byte": 4423, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 16, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 18, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 19, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 20, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 21, "usage_type": "call"}, {"api_name": "signalprocessing.peakfrequency.peak_frequency.PeakFrequency", "line_number": 22, "usage_type": "call"}, {"api_name": "riemannianClassifier.classifier.riemannianClassifier", "line_number": 23, "usage_type": "call"}, {"api_name": "pylsl.StreamInfo", "line_number": 29, "usage_type": "call"}, {"api_name": "pylsl.StreamOutlet", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 72, "usage_type": "call"}, {"api_name": "time.time", "line_number": 75, "usage_type": "call"}, {"api_name": "time.time", "line_number": 82, "usage_type": "call"}, {"api_name": "signalprocessing.signal_tools.extract_amplitudes_welch", "line_number": 91, "usage_type": "call"}, {"api_name": "signalprocessing.signal_tools", "line_number": 91, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 108, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 120, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 120, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 120, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 121, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 131, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 132, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 135, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "59745435", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib import gridspec\n\nf=open('circuit56_9.txt','r')\nf1=open('circuit5695.txt','r')\nf2=open('circuit57_0.txt','r')\ndata=np.loadtxt(f)\ndata1=np.loadtxt(f1)\ndata2=np.loadtxt(f2)\n\nt=data[:,0]\nx=data[:,1]\n\nt1=data1[:,0]\nx1=data1[:,1]\n\nt2=data2[:,0]\nx2=data2[:,1]\n\nn11=750000\nn22=800000\npi=3.141516\n#plt.show()\nx=x\nprint(len(x))\nn1=1000\nn2=len(x)\nx=x[-50000:]\nt=t[-50000:]\n\nx1=x1[-50000:]\nt1=t1[-50000:]\n\nx2=x2[-50000:]\nt2=t2[-50000:]\n\ndxdt=np.diff(x)/np.diff(t)\nddxt=np.diff(dxdt)/np.diff(t[0:-1])\n#ax.plot(x[0:len(x)-1],dxdt)\n\ndxdt1=np.diff(x1)/np.diff(t1)\nddxt1=np.diff(dxdt1)/np.diff(t1[0:-1])\n\ndxdt2=np.diff(x2)/np.diff(t2)\nddxt2=np.diff(dxdt2)/np.diff(t2[0:-1])\n\n\n\nfig = plt.figure(figsize = (12,12))\n#fig.text(0.5, 0.04, c, ha='center',fontsize=16)\n#fig.text(0.0, 0.5, '$dU/dt$', va='center', rotation='vertical',fontsize=16)\n# set height ratios for sublots\ngs = gridspec.GridSpec(3, 2) \n\n# the fisrt subplot\nax0 = plt.subplot(gs[0])\n# log scale for axis Y of the first subplot\n\nline0, = ax0.plot(x[0:-1], dxdt, color='r', label='$56.90$ $V$')\n\n#the second subplot\n# shared axis X\nax1 = plt.subplot(gs[2], sharex = ax0)\nline1, = ax1.plot(x1[0:-1], dxdt1, color='b', linestyle='-', label='$56.95$ $V$')\nplt.setp(ax0.get_xticklabels(), visible=False)\n# remove last tick label for the second subplot\nyticks = ax0.yaxis.get_major_ticks()\nyticks[0].label1.set_visible(False)\n\nax5 = plt.subplot(gs[4], sharex = ax1)\nline2, = ax5.plot(x2[0:-1], dxdt2, color='m', linestyle='-', label='$57.00$ $V$')\nplt.setp(ax1.get_xticklabels(), visible=False)\n# remove last tick label for the second subplot\nyticks = ax1.yaxis.get_major_ticks()\nyticks[0].label1.set_visible(False)\n\n\n# put lened on first subplot\n#ax0.legend((line0, line1,line2), ('$56.90$ $V$', '$56.95$ $V$','$57.0$ $V$'), loc='lower left',fontsize=20)\n\nax2=plt.subplot(gs[1])\nax2.plot(x[0:-1], dxdt, color='r')\nax3=plt.subplot(gs[3], sharex=ax2)\nax3.plot(x1[0:-1], dxdt1, color='b')\nplt.setp(ax2.get_xticklabels(), visible=False)\nax4=plt.subplot(gs[5], sharex=ax3)\nax4.plot(x2[0:-1], dxdt2, color='m')\nplt.setp(ax3.get_xticklabels(), visible=False)\nyticks = ax4.yaxis.get_major_ticks()\nyticks[-1].label1.set_visible(False)\n\n\nax0.legend(loc='lower left',fontsize=35)\nax1.legend(loc='lower left',fontsize=35)\nax5.legend(loc='lower left',fontsize=35)\n\n#xticks=np.arange(10.0,14.0,1)\n#ax5.set_xticks(xticks)\n\nyticks = np.arange(-0.15, 0.15, 0.05)\nax0.set_yticks(yticks)\nax1.set_yticks(yticks)\nax5.set_yticks(yticks)\n\n# remove vertical gap between subplots\nplt.subplots_adjust(hspace=.0)\nplt.setp(ax1.get_xticklabels(), fontsize=25)\nplt.setp(ax1.get_yticklabels(), fontsize=25)\nplt.setp(ax0.get_xticklabels(), fontsize=25)\nplt.setp(ax0.get_yticklabels(), fontsize=25)\nplt.setp(ax3.get_xticklabels(), fontsize=25)\nplt.setp(ax3.get_yticklabels(), fontsize=25)\nplt.setp(ax2.get_xticklabels(), fontsize=25)\nplt.setp(ax2.get_yticklabels(), fontsize=25)\nplt.setp(ax4.get_xticklabels(), fontsize=25)\nplt.setp(ax4.get_yticklabels(), fontsize=25)\nplt.setp(ax5.get_xticklabels(), fontsize=25)\nplt.setp(ax5.get_yticklabels(), fontsize=25)\n\n\n\nax1.set_xlabel('QCL Voltage $U_{qcl}$ ($V$)',fontsize=30)\nax3.set_xlabel('QCL Voltage $U_{qcl}$ ($V$)',fontsize=30)\nax4.set_xlabel('QCL Voltage $U_{qcl}$ ($V$)',fontsize=30)\nax5.set_xlabel('QCL Voltage $U_{qcl}$ ($V$)',fontsize=30)\n\nax0.set_ylabel('$dU_{qcl}/dt$',fontsize=30)\nax1.set_ylabel('$dU_{qcl}/dt$',fontsize=30)\nax5.set_ylabel('$dU_{qcl}/dt$',fontsize=30)\n#ax1.xaxis.labelpad = 10\nax0.set_xlim([9.75,13.25])\n#ax0.set_ylim([0,950])\n#ax1.set_ylim([700,770])\nax1.set_xlim([9.75,13.25])\nax2.set_xlim([11,11.5])\nax2.set_ylim([-0.025,0.025])\nax3.set_xlim([11,11.5])\nax3.set_ylim([-0.025,0.025])\nax4.set_xlim([11,11.5])\nax4.set_ylim([-0.025,0.025])\n\n\n\n\npoint=11.19 ,0.001\ncircle_rad = 11.5 # This is the radius, in points\nax2.plot(11.19,0.001, 'o',\n ms=circle_rad * 2, mec='g', mfc='none', mew=4)\nax2.annotate('Saddle p.', xy=point, xytext=(50, -40),\n textcoords='offset points',\n color='g', size='large',\n arrowprops=dict(\n arrowstyle='simple,tail_width=0.3,head_width=0.8,head_length=0.8',\n facecolor='r', shrinkB=circle_rad * .5),fontsize=25\n)\n\n\n\n#####ARROW PLOTS AX2#######\n\nax2.annotate('', xy=(11.1, 0.005), xytext=(11.03, 0.01),\n arrowprops=dict(facecolor='black',lw=0.08, shrink=0.03))\n\nax2.annotate('', xy=(11.35, 0.015), xytext=(11.3, 0.01),\n arrowprops=dict(facecolor='black',lw=0.08, shrink=0.03))\n\nax2.annotate('', xy=(11.25, -0.01), xytext=(11.3, -0.015),\n arrowprops=dict(facecolor='black',lw=0.08, shrink=0.03))\n\nax2.annotate('', xy=(11.08, -0.013), xytext=(11.12, -0.008),\n arrowprops=dict(facecolor='black',lw=0.08, shrink=0.03))\n\n#####ARROW PLOTS AX3######\n\nax3.annotate('', xy=(11.15, 0.006), xytext=(11.08, 0.015),\n arrowprops=dict(facecolor='black',lw=0.08, shrink=0.03))\n\nax3.annotate('', xy=(11.40, 0.017), xytext=(11.32, 0.01),\n arrowprops=dict(facecolor='black',lw=0.08, shrink=0.03))\n\nax3.annotate('', xy=(11.22, -0.005), xytext=(11.29, -0.015),\n arrowprops=dict(facecolor='black',lw=0.08, shrink=0.03))\n\nax3.annotate('', xy=(11.07, -0.011), xytext=(11.15, -0.003),\n arrowprops=dict(facecolor='black',lw=0.08, shrink=0.03))\n\n####ARROW PLOTS AX4#########3\n\nax4.annotate('', xy=(11.15, 0.006), xytext=(11.08, 0.015),\n arrowprops=dict(facecolor='black',lw=0.08, shrink=0.03))\n\nax4.annotate('', xy=(11.40, 0.017), xytext=(11.32, 0.01),\n arrowprops=dict(facecolor='black',lw=0.08, shrink=0.03))\n\nax4.annotate('', xy=(11.22, -0.005), xytext=(11.29, -0.015),\n arrowprops=dict(facecolor='black',lw=0.08, shrink=0.03))\n\nax4.annotate('', xy=(11.07, -0.011), xytext=(11.15, -0.003),\n arrowprops=dict(facecolor='black',lw=0.08, shrink=0.03))\n\nplt.show()\n\n\n\n\n\n\n", "sub_path": "saddle_paper2.py", "file_name": "saddle_paper2.py", "file_ext": "py", "file_size_in_byte": 5919, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.loadtxt", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}]} +{"seq_id": "505664416", "text": "import pygame\r\nimport sys\r\nfrom pygame.locals import *\r\nfrom random import randint\r\nfrom Products import *\r\nimport time\r\nimport Character\r\nfrom Director import *\r\nfrom Builder import *\r\nfrom Character import *\r\nimport loadImages\r\nfrom Juego import Juego\r\n\r\n\r\n\r\n\r\n\r\nclass menu():\r\n def seleccion():\r\n i = 0\r\n width = 900\r\n height = 600\r\n colour = (227, 166, 162)\r\n personajes=0\r\n empezar=True\r\n\r\n pygame.init()\r\n\r\n posX = 100\r\n posY = 250\r\n screen = pygame.display.set_mode((width, height))\r\n pygame.display.set_caption(\"Prueba\")\r\n fuente = pygame.font.Font(None, 20)\r\n\r\n text = \"derecha kakaroto, izquierda Megaman\"\r\n mensaje = fuente.render(text, 1, (255, 255, 255))\r\n screen.blit(mensaje, (15, 250))\r\n pygame.display.flip()\r\n\r\n\r\n\r\n\r\n\r\n while empezar:\r\n\r\n for i in pygame.event.get():\r\n\r\n if i.type == pygame.KEYUP:\r\n if i.key == K_LEFT:\r\n print(\"0\")\r\n auxlist = mainFactory.moveRight()\r\n screen.fill(colour)\r\n screen.blit(auxlist[0], (posX, posY))\r\n pygame.display.update()\r\n time.sleep(0.1)\r\n pygame.quit()\r\n\r\n personajes = 0\r\n\r\n Juego.ventana(personajes)\r\n sys.exit()\r\n\r\n\r\n if i.key == K_RIGHT:\r\n print(\"1\")\r\n auxlist = mainFactory.moveRight()\r\n screen.fill(colour)\r\n screen.fill(colour)\r\n screen.blit(auxlist[1], (posX, posY))\r\n pygame.display.update()\r\n time.sleep(0.1)\r\n\r\n personajes = 1\r\n Juego.ventana(personajes)\r\n pygame.quit()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "menu.py", "file_name": "menu.py", "file_ext": "py", "file_size_in_byte": 2018, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.init", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 54, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 56, "usage_type": "call"}, {"api_name": "Juego.Juego.ventana", "line_number": 60, "usage_type": "call"}, {"api_name": "Juego.Juego", "line_number": 60, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 70, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 70, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 71, "usage_type": "call"}, {"api_name": "Juego.Juego.ventana", "line_number": 74, "usage_type": "call"}, {"api_name": "Juego.Juego", "line_number": 74, "usage_type": "name"}, {"api_name": "pygame.quit", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "156889781", "text": "## Bruno Vieira Ribeiro 03/2021\n\nimport streamlit as st\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import confusion_matrix,classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.tree import plot_tree\n\nsns.set(font_scale=1.5)\n\nst.set_option('deprecation.showPyplotGlobalUse', False)\n\nst.markdown('''\n#   The [mushroom dataset](https://archive.ics.uci.edu/ml/datasets/Mushroom) \n''', unsafe_allow_html=True)\n\ndf_mush = pd.read_csv('mushrooms.csv')\n\nselected_view = st.selectbox(\n 'Would you like to explore the data or do some modeling?',\n ('Let\\'s explore!', 'Model away!')\n)\n\nif selected_view == 'Let\\'s explore!':\n\n with st.beta_expander(\"Show intro?\"):\n st.markdown('''\n The [mushroom data set](https://archive.ics.uci.edu/ml/datasets/Mushroom) has been contributed to the UCI Machine Learning over 30 years ago. From the authors description:\n\n > This dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family Mushroom drawn from The Audubon Society Field Guide to North American Mushrooms (1981). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. **The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like \"leaflets three, let it be'' for Poisonous Oak and Ivy.**\n\n Each mushroom is characterized by 22 distinct features:\n\n * cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s\n * cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s\n * cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r,pink=p,purple=u,red=e,white=w,yellow=y\n * bruises: bruises=t,no=f\n * odor: almond=a,anise=l,creosote=c,fishy=y,foul=f,musty=m,none=n,pungent=p,spicy=s\n * gill-attachment: attached=a,descending=d,free=f,notched=n\n * gill-spacing: close=c,crowded=w,distant=d\n * gill-size: broad=b,narrow=n\n * gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e,white=w,yellow=y\n * stalk-shape: enlarging=e,tapering=t\n * stalk-root: bulbous=b,club=c,cup=u,equal=e,rhizomorphs=z,rooted=r,missing=?\n * stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s\n * stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s\n * stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o,pink=p,red=e,white=w,yellow=y\n * stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o,pink=p,red=e,white=w,yellow=y\n * veil-type: partial=p,universal=u\n * veil-color: brown=n,orange=o,white=w,yellow=y\n * ring-number: none=n,one=o,two=t\n * ring-type: cobwebby=c,evanescent=e,flaring=f,large=l,none=n,pendant=p,sheathing=s,zone=z\n * spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r,orange=o,purple=u,white=w,yellow=y\n * population: abundant=a,clustered=c,numerous=n,scattered=s,several=v,solitary=y\n * habitat: grasses=g,leaves=l,meadows=m,paths=p,urban=u,waste=w,woods=d\n\n All features are categorical (and not numeric), so it poses a good exercise for encoding.\n\n \n\n With a quick look at these features, we can notice 5 different kinds of features relating to the \"anatomy\" of the mushrooms: **cap, gill, stalk, veil and ring**. From the figure we can get an ideia of what each feature is.\n\n Besides this features, we have **odor**, which has a clear meaning as does **bruises** (you can check for an interesting article on why *magic mushrooms* turn blue when bruised [here](https://www.nature.com/articles/d41586-019-03614-0)). Also, we have **spore-print**, which, according to the [Wikipedia](https://en.wikipedia.org/wiki/Spore_print#:~:text=The%20spore%20print%20is%20the,fall%20onto%20a%20surface%20underneath.&text=It%20shows%20the%20color%20of%20the%20mushroom%20spores%20if%20viewed%20en%20masse.) page is defined as\n > The spore print is the powdery deposit obtained by allowing spores of a fungal fruit body to fall onto a surface underneath. It is an important diagnostic character in most handbooks for identifying mushrooms. It shows the color of the mushroom spores if viewed en masse.\n\n\n\n \n\n **population** relates to the way the mushroom grows. The image to the left shows examples of three kinds of population. And, finally, **habitat** refers to where the mushroom grows. According to the [Intermountain Herbarium](https://herbarium.usu.edu/fun-with-fungi/collect-and-identify#:~:text=Where%20they%20grow%2C%20such%20as,%2C%20is%20the%20mushrooms'%20substrate.) of the Utah State University:\n > Mushrooms are found almost everywhere, but not all mushrooms are found in all kinds of habitat. Where they grow, such as coniferous forest, oak forest, etc., is the mushrooms' habitat. Some mushrooms develop in only one kind of habitat, such as a bog, a forest, or an open lawn or meadow. What they actually emerge from, such as peat, a log, or soil, is the mushrooms' substrate.\n\n Before going to the data, we want to be very clear: this should not be considered as a guide for mushroom picking. Again, from the [Intermountain Herbarium](https://herbarium.usu.edu/fun-with-fungi/collect-and-identify#:~:text=Where%20they%20grow%2C%20such%20as,%2C%20is%20the%20mushrooms'%20substrate.):\n > People die every year from eating tasty but poisonous mushrooms. There are no so-called tests for telling a poisonous mushroom from a non-poisonous one.\n ''', unsafe_allow_html=True\n )\n\n '## The data frame:'\n\n st.dataframe(df_mush.head())\n\n '## Number of unique entries for every column:'\n fig1 = plt.figure(figsize=(12,4), dpi = 400)\n ax = sns.barplot(data=df_mush.describe().transpose().reset_index().sort_values('unique'),x='index',y='unique')\n plt.xticks(rotation=90)\n st.pyplot(fig1)\n\n '### The main objective is to classify mushrooms as edible or poisonous.'\n '### We can check for class balance in the data set:'\n fig2 = plt.figure(figsize=(8,4), dpi = 400)\n ax = sns.countplot(data = df_mush, y ='class')\n st.pyplot(fig2)\n\n # Utility function to plot distribution of selected feature\n def get_dists(feature):\n fig, axes = plt.subplots(1, 2, figsize=(21, 6), dpi = 400)\n fig.suptitle('Distribution of '+ feature + ' feature')\n\n sns.countplot(ax = axes[0], data=df_mush, x=feature, palette=\"dark\",\n order = df_mush[feature].value_counts().index)\n axes[0].set_title('Distribution')\n\n sns.countplot(ax = axes[1], data=df_mush, x=feature, hue='class',\n order = df_mush[feature].value_counts().index)\n axes[1].set_title('Distribution according to class')\n st.pyplot(fig)\n\n '## Exploring distributions'\n selected_feat = st.selectbox(\n 'Select feature to show distribution:',\n df_mush.columns[1:]\n )\n\n get_dists(selected_feat)\n\n############################################\n############################################ Modeling page\n############################################\n\nelif selected_view == 'Model away!':\n st.info(\n '''\n ### The modeling will be done using a [decision tree](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html).\n ### Test size, maximum number of leaf nodes and cross-validation splits can be chosen at the sidebar.\n ''')\n\n '''\n ## Data preprocessing\n '''\n\n col_exp, col_code = st.beta_columns(2)\n with col_exp:\n st.success('We drop \"veil-type\" feature as it has only one unique value '\\\n +'and get dummies for all remaining features (as they are all categorical!).')\n with col_code:\n with st.echo():\n X = df_mush.drop(['class', 'veil-type'], axis = 1)\n y = df_mush['class']\n X = pd.get_dummies(X,drop_first=True)\n\n t_size = st.sidebar.slider(\n 'Choose test-size:',\n min_value = 0.1,\n max_value = 0.5,\n value = 0.2,\n step = 0.1\n )\n\n max_ln = st.sidebar.slider(\n 'Choose max_leaf_nodes:',\n min_value = 2,\n max_value = 20,\n value = 5,\n step = 1\n )\n\n c_v = st.sidebar.slider(\n 'Choose number of cross-validation splits:',\n min_value = 2,\n max_value = 20,\n value = 5,\n step = 1\n )\n\n see_tree = st.sidebar.checkbox('Plot decision tree?')\n\n if st.button('Run model'):\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = t_size, random_state=42)\n\n model = DecisionTreeClassifier(max_leaf_nodes = max_ln)\n model.fit(X_train, y_train)\n\n def report_model(model, visual = False):\n model_preds = model.predict(X_test)\n acc = accuracy_score(y_test,model_preds)\n if acc == 1.0:\n st.balloons()\n st.write('Accuracy: ', acc)\n\n c_report = classification_report(y_test, model_preds)\n\n '## Confusion matrix:'\n col1, col2 = st.beta_columns(2)\n with col1:\n st.write('')\n st.write('')\n st.write(40*'=')\n st.text('Classification Report:\\n ' + c_report)\n st.write(40*'=')\n # plot_confusion_matrix(model, X_test, y_test)\n # st.pyplot()\n with col2:\n cf_matrix = confusion_matrix(y_test, model_preds)\n group_names = ['True Pos','False Neg','False Pos','True Neg']\n categories = ['edible','poisonous']\n group_counts = [\"{0:0.0f}\".format(value) for value in\n cf_matrix.flatten()]\n group_percentages = [\"{0:.2%}\".format(value) for value in\n cf_matrix.flatten()/np.sum(cf_matrix)]\n labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in\n zip(group_names,group_counts,group_percentages)]\n labels = np.asarray(labels).reshape(2,2)\n\n fig_c2 = plt.figure(figsize=(9,9), dpi = 400)\n ax = sns.heatmap(cf_matrix, annot=labels, fmt='',\n cmap = 'vlag', xticklabels=categories, yticklabels=categories,\n annot_kws={\"fontsize\":24})\n ax.set(xlabel=\"Predicted label\")\n ax.set(ylabel=\"True label\")\n st.pyplot(fig_c2)\n\n if visual:\n print('\\n')\n '## Visualizing the decision tree:'\n plt.figure(figsize=(12,8),dpi=150)\n plot_tree(model,filled=True,feature_names=X.columns)\n st.pyplot()\n\n report_model(model, see_tree)\n\n def get_cv(model, cv):\n st.markdown('## Cross-validation:')\n st.write('Model: ', model)\n st.write(50*'=')\n scores = cross_val_score(model,X_train,y_train, scoring='accuracy',cv=cv)\n\n st.info('Cross-validated accuracy scores: \\n \\n'+str(scores))\n # st.write('Cross-validated accuracy scores:')\n # st.text(scores)\n st.write(50*'=')\n st.info('Mean cross-validated accuracy score: \\n \\n'+str(scores.mean()))\n # st.write('Mean cross-validated accuracy score:')\n # st.write(scores.mean())\n st.write(50*'=' + '\\n\\n')\n\n get_cv(model, c_v)\n", "sub_path": "my_mushroom_app.py", "file_name": "my_mushroom_app.py", "file_ext": "py", "file_size_in_byte": 12350, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "seaborn.set", "line_number": 16, "usage_type": "call"}, {"api_name": "streamlit.set_option", "line_number": 18, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 26, "usage_type": "call"}, {"api_name": "streamlit.beta_expander", "line_number": 33, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 34, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "streamlit.pyplot", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 98, "usage_type": "call"}, {"api_name": "streamlit.pyplot", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 106, "usage_type": "call"}, {"api_name": "seaborn.countplot", "line_number": 110, "usage_type": "call"}, {"api_name": "streamlit.pyplot", "line_number": 113, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 116, "usage_type": "call"}, {"api_name": "streamlit.info", "line_number": 128, "usage_type": "call"}, {"api_name": "streamlit.beta_columns", "line_number": 138, "usage_type": "call"}, {"api_name": "streamlit.success", "line_number": 140, "usage_type": "call"}, {"api_name": "streamlit.echo", "line_number": 143, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 146, "usage_type": "call"}, {"api_name": "streamlit.sidebar.slider", "line_number": 148, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 148, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 156, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 156, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 164, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 164, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.checkbox", "line_number": 172, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 172, "usage_type": "attribute"}, {"api_name": "streamlit.button", "line_number": 174, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 176, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 178, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 183, "usage_type": "call"}, {"api_name": "streamlit.balloons", "line_number": 185, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 186, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 188, "usage_type": "call"}, {"api_name": "streamlit.beta_columns", "line_number": 191, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 193, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 194, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 195, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 196, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 197, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 213, "usage_type": "call"}, {"api_name": "streamlit.pyplot", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "sklearn.tree.plot_tree", "line_number": 224, "usage_type": "call"}, {"api_name": "streamlit.pyplot", "line_number": 225, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 230, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 231, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 232, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 233, "usage_type": "call"}, {"api_name": "streamlit.info", "line_number": 235, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 238, "usage_type": "call"}, {"api_name": "streamlit.info", "line_number": 239, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 242, "usage_type": "call"}]} +{"seq_id": "423815770", "text": "\nimport time\nimport requests\nimport urllib3\nimport socket\n\n\n\nclass ExternalIpQuery():\n def __init__(self, api):\n self.api = api\n self.url = api\n \n def is_connected(self, timeout = 3):\n try:\n request = requests.head(self.url, timeout=timeout)\n return True\n except requests.ConnectionError as ex:\n print(ex)\n return False\n \n def request_ip(self, timeout = 3):\n ip = None\n try:\n ip = requests.get(self.api).text\n except socket.gaierror as socket_error:\n print(socket_error)\n print(\"socket error!\")\n except urllib3.exceptions.NewConnectionError as urllib3_ex_new_conn_error:\n print(urllib3_ex_new_conn_error)\n print(\"new connection error!\")\n except urllib3.exceptions.MaxRetryError as url_maxtry_error:\n print(url_maxtry_error)\n print(\"Max retries error!\")\n except requests.exceptions.ConnectionError as req_ex_conn_error:\n print(req_ex_conn_error)\n print(\"connection error!\")\n return ip\n \n \ndef main():\n api = \"https://api.ipify.org\"\n\n\n p1 = ExternalIpQuery(api)\n ip = p1.request_ip()\n print(ip)\n \n\n \n\n\nif __name__ == \"__main__\": main()\n\n", "sub_path": "ip_query.py", "file_name": "ip_query.py", "file_ext": "py", "file_size_in_byte": 1316, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.head", "line_number": 16, "usage_type": "call"}, {"api_name": "requests.ConnectionError", "line_number": 18, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "socket.gaierror", "line_number": 26, "usage_type": "attribute"}, {"api_name": "urllib3.exceptions", "line_number": 29, "usage_type": "attribute"}, {"api_name": "urllib3.exceptions", "line_number": 32, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 35, "usage_type": "attribute"}]} +{"seq_id": "231667782", "text": "import unittest\r\n\r\nfrom testfixtures import LogCapture\r\n\r\nfrom dsciqcm.core import _MDLTableDMA\r\nfrom dsciqcm.prisource import _DMAName\r\nfrom dsciqcm.config import DMA_NAMES\r\n\r\nDMA = 501\r\n\r\n\r\nclass MDLTableDMAMod(_MDLTableDMA):\r\n\r\n @property\r\n def _dmas(self):\r\n return self._dmas_\r\n\r\n @_dmas.setter\r\n def _dmas(self, newdma):\r\n self._dmas_ = newdma\r\n\r\n\r\nclass TestDMATable(unittest.TestCase):\r\n\r\n def test_missing_dma(self):\r\n table = MDLTableDMAMod([DMA])\r\n table.name = DMA_NAMES\r\n table._dmacol = \"dma_code\"\r\n table._dmas = [DMA, 123]\r\n message = \"{}: One or more missing DMAs\".format(DMA_NAMES)\r\n with LogCapture() as log:\r\n self.assertFalse(table._check_dma())\r\n log.check_present((\"e2eqc\", \"WARNING\", message))\r\n\r\n def test_empty_dma(self):\r\n table = MDLTableDMAMod([DMA])\r\n table.name = DMA_NAMES\r\n table._dmacol = \"dma_code\"\r\n table._dmas = [123]\r\n message = \"{}: No records exist for specified DMAs\".format(DMA_NAMES)\r\n with LogCapture() as log:\r\n self.assertFalse(table._check_dma())\r\n log.check_present((\"e2eqc\", \"ERROR\", message))\r\n\r\n def test_pass(self):\r\n table = _DMAName([DMA])\r\n with self.assertLogs(\"e2eqc\", \"INFO\"):\r\n self.assertTrue(table.run_qc())\r\n\r\n\r\nif __name__ == '__main__':\r\n unittest.main()\r\n", "sub_path": "test/test_mdl_dma.py", "file_name": "test_mdl_dma.py", "file_ext": "py", "file_size_in_byte": 1412, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "dsciqcm.core._MDLTableDMA", "line_number": 12, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 23, "usage_type": "attribute"}, {"api_name": "dsciqcm.config.DMA_NAMES", "line_number": 27, "usage_type": "name"}, {"api_name": "dsciqcm.config.DMA_NAMES", "line_number": 30, "usage_type": "argument"}, {"api_name": "testfixtures.LogCapture", "line_number": 31, "usage_type": "call"}, {"api_name": "dsciqcm.config.DMA_NAMES", "line_number": 37, "usage_type": "name"}, {"api_name": "dsciqcm.config.DMA_NAMES", "line_number": 40, "usage_type": "argument"}, {"api_name": "testfixtures.LogCapture", "line_number": 41, "usage_type": "call"}, {"api_name": "dsciqcm.prisource._DMAName", "line_number": 46, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "357273933", "text": "\"\"\"\nCovalent Organic Framework\n==========================\n\nFor usage examples see :class:`.COF`.\n\n#. :class:`Honeycomb`\n#. :class:`Hexagonal`\n#. :class:`Square`\n#. :class:`Kagome`\n#. :class:`LinkerlessHoneycomb`\n\n\"\"\"\n\nimport numpy as np\nimport itertools as it\n\nfrom .topology_graph import TopologyGraph, VertexData, Vertex, EdgeData\nfrom ...utilities import vector_angle, flatten\n\n\nclass _COFVertexData(VertexData):\n \"\"\"\n Holds data for a COF vertex.\n\n Attributes\n ----------\n id : :class:`int`\n The id of the vertex. Must match the index in\n :attr:`TopologyGraph.vertices`.\n\n position : :class:`numpy.ndarray`\n The position of the vertex.\n\n edges : :class:`list` of :class:`.EdgeData`\n The edges connected to the vertex.\n\n cell : :class:`numpy.ndarray`\n The unit cell in which the vertex is found.\n\n aligner_edge : :class:`int`\n The edge which is used to align the :class:`.BuildingBlock`\n placed on the vertex. The first :class:`.FunctionalGroup`\n in :attr:`.BuildingBlock.func_groups` is rotated such that\n it lies exactly on this :class:`.Edge`. Must be between\n ``0`` and the number of edges the vertex is connected to.\n\n \"\"\"\n\n def __init__(self, x, y, z):\n \"\"\"\n Initialize a :class:`.VertexData` instance.\n\n Parameters\n ----------\n x : :class:`float`\n The x coordinate.\n\n y : :class:`float`\n The y coordinate.\n\n z : :class:`float`\n The z coordinate.\n\n \"\"\"\n\n self.aligner_edge = None\n super().__init__(x, y, z)\n\n @classmethod\n def init_at_center(cls, *vertex_data):\n obj = super().init_at_center(*vertex_data)\n obj.aligner_edge = None\n return obj\n\n @classmethod\n def init_at_shifted_center(\n cls,\n vertex_data,\n shifts,\n lattice_constants,\n aligner_edge=None\n ):\n \"\"\"\n Initialize at the center of shifted `vertex_data`.\n\n Parameters\n ----------\n vertex_data : :class:`tuple` of :class:`.VertexData`\n The vertices at whose center this vertex should be\n intialized.\n\n shifts : :class:`tuple`\n For every vertex in `vertices` the amount by which it is\n shifted along each axis. For example\n\n .. code-block:: python\n\n shifts = (\n (1, 0, -1),\n (0, 0, 0)\n )\n\n means that the first vertex in `vertices` is shifted\n up along the x axis, is not shifted along the y axis\n and is shifted down along the z axis and the second\n vertex is not shifted at all.\n\n lattice_constants : :class:`tuple` of :class:`numpy.ndarray`\n The a, b and c lattice constants, each written as a vector.\n\n \"\"\"\n\n positions = []\n for vertex, shift in zip(vertex_data, shifts):\n total_shift = 0\n for dim_shift, constant in zip(shift, lattice_constants):\n total_shift += dim_shift * constant\n positions.append(vertex.position + total_shift)\n\n position = np.divide(\n np.sum(positions, axis=0),\n len(positions)\n )\n return cls(*position)\n\n def clone(self, clear_edges=False):\n clone = super().clone(clear_edges)\n clone.aligner_edge = self.aligner_edge\n return clone\n\n def get_vertex(self):\n return _COFVertex(self)\n\n\nclass _COFVertex(Vertex):\n \"\"\"\n Represents a vertex of a :class:`.COF`.\n\n Attributes\n ----------\n id : :class:`int`\n The id of the vertex. This should be its index in\n :attr:`.TopologyGraph.vertices`.\n\n \"\"\"\n\n def __init__(self, data):\n \"\"\"\n Initialize a :class:`_COFVertex`.\n\n Parameters\n ----------\n data : :class:`_COFVertexData`\n The vertex data.\n\n \"\"\"\n\n # The edge which is used to align the :class:`.BuildingBlock`\n # placed on the vertex. The first :class:`.FunctionalGroup`\n # in :attr:`.BuildingBlock.func_groups` is rotated such that\n # it lies exactly on this :class:`.Edge`. Must be between\n # ``0`` and the number of edges the vertex is connected to.\n self._aligner_edge = data.aligner_edge\n super().__init__(data)\n\n def clone(self, clear_edges=False):\n \"\"\"\n Return a clone.\n\n Parameters\n ----------\n clear_edges : :class:`bool`, optional\n ``True`` if the clone should not be connected to any edges.\n\n Returns\n -------\n :class:`Vertex`\n The clone.\n \"\"\"\n\n clone = super().clone(clear_edges)\n clone._aligner_edge = self._aligner_edge\n return clone\n\n def get_aligner_edge(self):\n return self._aligner_edge\n\n def place_building_block(self, building_block, vertices, edges):\n \"\"\"\n Place `building_block` on the :class:`.Vertex`.\n\n Parameters\n ----------\n building_block : :class:`.BuildingBlock`\n The building block molecule which is to be placed on the\n vertex.\n\n vertices : :class:`tuple` of :class:`.Vertex`\n All vertices in the topology graph. The index of each\n vertex must match its :class:`~.Vertex.id`.\n\n edges : :class:`tuple` of :class:`.Edge`\n All edges in the topology graph. The index of each\n edge must match its :class:`~.Edge.id`.\n\n Returns\n -------\n :class:`numpy.nadarray`\n The position matrix of `building_block` after being\n placed.\n\n \"\"\"\n\n if len(building_block.func_groups) == 2:\n return self._place_linear_building_block(\n building_block=building_block,\n vertices=vertices,\n edges=edges\n )\n return self._place_nonlinear_building_block(\n building_block=building_block,\n vertices=vertices,\n edges=edges\n )\n\n def _place_linear_building_block(\n self,\n building_block,\n vertices,\n edges\n ):\n \"\"\"\n Place `building_block` on the :class:`.Vertex`.\n\n Parameters\n ----------\n building_block : :class:`.BuildingBlock`\n The building block molecule which is to be placed on the\n vertex.\n\n vertices : :class:`tuple` of :class:`.Vertex`\n All vertices in the topology graph. The index of each\n vertex must match its :class:`~.Vertex.id`.\n\n edges : :class:`tuple` of :class:`.Edge`\n All edges in the topology graph. The index of each\n edge must match its :class:`~.Edge.id`.\n\n Returns\n -------\n :class:`numpy.nadarray`\n The position matrix of `building_block` after being\n placed.\n\n \"\"\"\n\n building_block.set_centroid(\n position=self._position,\n atom_ids=building_block.get_bonder_ids()\n )\n fg_centroid = building_block.get_centroid(\n atom_ids=building_block.func_groups[0].get_bonder_ids()\n )\n start = fg_centroid - self._position\n e0_coord = (\n edges[self._edge_ids[0]].get_position(self, vertices)\n )\n e1_coord = (\n edges[self._edge_ids[1]].get_position(self, vertices)\n )\n target = e0_coord - e1_coord\n\n if self._edge_ids[self._aligner_edge] != self._edge_ids[0]:\n target *= -1\n\n building_block.apply_rotation_between_vectors(\n start=start,\n target=target,\n origin=self._position\n )\n start = building_block.get_centroid_centroid_direction_vector()\n building_block.apply_rotation_to_minimize_angle(\n start=start,\n target=self._position,\n axis=target,\n origin=self._position,\n )\n return building_block.get_position_matrix()\n\n def _place_nonlinear_building_block(\n self,\n building_block,\n vertices,\n edges\n ):\n \"\"\"\n Place `building_block` on the :class:`.Vertex`.\n\n Parameters\n ----------\n building_block : :class:`.BuildingBlock`\n The building block molecule which is to be placed on the\n vertex.\n\n vertices : :class:`tuple` of :class:`.Vertex`\n All vertices in the topology graph. The index of each\n vertex must match its :class:`~.Vertex.id`.\n\n edges : :class:`tuple` of :class:`.Edge`\n All edges in the topology graph. The index of each\n edge must match its :class:`~.Edge.id`.\n\n Returns\n -------\n :class:`numpy.nadarray`\n The position matrix of `building_block` after being\n placed.\n\n \"\"\"\n\n building_block.set_centroid(\n position=self._position,\n atom_ids=building_block.get_bonder_ids()\n )\n building_block.apply_rotation_between_vectors(\n start=building_block.get_bonder_plane_normal(),\n target=[0, 0, 1],\n origin=self._position\n )\n fg_bonder_centroid = building_block.get_centroid(\n atom_ids=building_block.func_groups[0].get_bonder_ids()\n )\n start = fg_bonder_centroid - self._position\n\n aligner_edge = edges[self._edge_ids[self._aligner_edge]]\n edge_coord = aligner_edge.get_position(self, vertices)\n target = edge_coord - self._position\n building_block.apply_rotation_to_minimize_angle(\n start=start,\n target=target,\n axis=[0, 0, 1],\n origin=self._position\n )\n return building_block.get_position_matrix()\n\n def assign_func_groups_to_edges(\n self,\n building_block,\n vertices,\n edges\n ):\n \"\"\"\n Assign functional groups to edges.\n\n Each :class:`.FunctionalGroup` of the `building_block` needs\n to be associated with one of the :class:`.Edge` instances in\n :attr:`edges`.\n\n Parameters\n ----------\n building_block : :class:`.Molecule`\n The building block molecule which is needs to have\n functional groups assigned to edges.\n\n vertices : :class:`tuple` of :class:`.Vertex`\n All vertices in the topology graph. The index of each\n vertex must match its :class:`~.Vertex.id`.\n\n edges : :class:`tuple` of :class:`.Edge`\n All edges in the topology graph. The index of each\n edge must match its :class:`~.Edge.id`.\n\n Returns\n -------\n :class:`dict`\n A mapping from the id of a functional group in\n `building_block` to the id of the edge in :attr:`edges` it\n is assigned to.\n\n \"\"\"\n\n if len(building_block.func_groups) == 2:\n return self._assign_func_groups_to_linear_edges(\n building_block=building_block,\n vertices=vertices,\n edges=edges\n )\n return self._assign_func_groups_to_nonlinear_edges(\n building_block=building_block,\n vertices=vertices,\n edges=edges\n )\n\n def _assign_func_groups_to_linear_edges(\n self,\n building_block,\n vertices,\n edges\n ):\n return {\n fg_id: edge_id for fg_id, edge_id in enumerate(sorted(\n self._edge_ids,\n key=self._get_fg0_distance(\n building_block=building_block,\n vertices=vertices,\n edges=edges\n )\n ))\n }\n\n def _get_fg0_distance(self, building_block, vertices, edges):\n fg_coord = building_block.get_centroid(\n atom_ids=building_block.func_groups[0].get_bonder_ids()\n )\n\n def distance(edge_id):\n displacement = edges[edge_id].get_position(\n reference=self,\n vertices=vertices\n ) - fg_coord\n return np.linalg.norm(displacement)\n\n return distance\n\n def _assign_func_groups_to_nonlinear_edges(\n self,\n building_block,\n vertices,\n edges\n ):\n # The idea is to order the functional groups in building_block\n # by their angle from func_groups[0] and the bonder centroid,\n # going in the clockwise direction.\n #\n # The edges are also ordered by their angle from aligner_edge\n # and the edge centroid going in the clockwise direction.\n #\n # Once the fgs and edges are ordered, zip and assign them.\n\n fg0_coord = building_block.get_centroid(\n atom_ids=building_block.func_groups[0].get_bonder_ids()\n )\n bonder_centroid = building_block.get_centroid(\n atom_ids=building_block.get_bonder_ids()\n )\n fg0_direction = fg0_coord-bonder_centroid\n axis = np.cross(\n fg0_direction,\n building_block.get_bonder_plane_normal()\n )\n func_groups = sorted(\n range(len(building_block.func_groups)),\n key=self._get_func_group_angle(\n building_block=building_block,\n fg0_direction=fg0_direction,\n bonder_centroid=bonder_centroid,\n axis=axis\n )\n )\n edge_ids = sorted(\n self._edge_ids,\n key=self._get_edge_angle(axis, vertices, edges)\n )\n assignments = {}\n for edge_id, fg_id in zip(edge_ids, func_groups):\n assignments[fg_id] = edge_id\n return assignments\n\n @staticmethod\n def _get_func_group_angle(\n building_block,\n fg0_direction,\n bonder_centroid,\n axis\n ):\n\n def angle(fg_id):\n func_group = building_block.func_groups[fg_id]\n coord = building_block.get_centroid(\n atom_ids=func_group.get_bonder_ids()\n )\n fg_direction = coord-bonder_centroid\n theta = vector_angle(fg0_direction, fg_direction)\n\n projection = fg_direction @ axis\n if theta > 0 and projection < 0:\n return 2*np.pi - theta\n return theta\n\n return angle\n\n def _get_edge_angle(self, axis, vertices, edges):\n\n aligner_edge = edges[self._edge_ids[self._aligner_edge]]\n aligner_edge_coord = aligner_edge.get_position(self, vertices)\n connected_edges = tuple(edges[id_] for id_ in self._edge_ids)\n edge_centroid = self._get_edge_centroid(\n centroid_edges=connected_edges,\n vertices=vertices\n )\n # This axis is used to figure out the clockwise direction.\n aligner_edge_direction = aligner_edge_coord - edge_centroid\n\n def angle(edge_id):\n coord = edges[edge_id].get_position(self, vertices)\n edge_direction = coord - edge_centroid\n theta = vector_angle(\n vector1=edge_direction,\n vector2=aligner_edge_direction\n )\n\n projection = edge_direction @ axis\n if theta > 0 and projection < 0:\n return 2*np.pi - theta\n return theta\n\n return angle\n\n def __str__(self):\n return (\n f'Vertex(id={self.id}, '\n f'position={self._position.tolist()}, '\n f'aligner_edge={self._aligner_edge})'\n )\n\n\nclass COF(TopologyGraph):\n \"\"\"\n Represents a COF topology graph.\n\n COF topologies are added by creating a subclass which defines the\n :attr:`vertices` and :attr:`edges` of the topology as class\n attributes.\n\n Attributes\n ----------\n vertex_data : :class:`tuple` of :class:`.VertexData`\n A class attribute. Holds the data of the vertices which make up\n the topology graph.\n\n edge_data : :class:`tuple` of :class:`.EdgeData`\n A class attribute. Holds the data of the edges which make up\n the topology graph.\n\n vertices : :class:`tuple` of :class:`.Vertex`\n The vertices which make up the topology graph.\n\n edges : :class:`tuple` of :class:`.Edge`\n The edges which make up the topology graph.\n\n Examples\n --------\n :class:`COF` instances can be made by supplying only\n the lattice size (using :class:`.Honeycomb` as an example)\n\n .. code-block:: python\n\n import stk\n\n bb1 = stk.BuildingBlock('NCCN', ['amine'])\n bb2 = stk.BuildingBlock('O=CC(C=O)C=O', ['aldehyde'])\n cof1 = stk.ConstructedMolecule(\n building_blocks=[bb1, bb2],\n topology_graph=stk.cof.Honeycomb((2, 2, 1))\n )\n\n Different structural isomers of COFs can be made by using the\n `vertex_alignments` optional parameter\n\n .. code-block:: python\n\n lattice = stk.cof.Honeycomb(\n lattice_size=(2, 2, 1),\n vertex_alignments={0: 1, 2: 2}\n )\n cof2 = stk.ConstructedMolecule(\n building_blocks=[bb1, bb2],\n topology_graph=lattice\n )\n\n The parameter maps the :attr:`~.Vertex.id` of a vertex to a number\n between 0 (inclusive) and the number of edges the vertex is\n connected to (exclusive). So a vertex connected to three edges\n can be mapped to ``0``, ``1`` or ``2``.\n\n You can also build COFs with multiple building blocks, but you\n have to assign each building block to a vertex with\n `building_block_vertices`.\n\n .. code-block:: python\n\n lattice = stk.cof.Honeycomb(\n lattice_size=(2, 2, 1),\n vertex_alignments={0: 1, 2: 2}\n )\n bb3 = stk.BuildingBlock('NCOCN', ['amine'])\n cof2 = stk.ConstructedMolecule(\n building_blocks=[bb1, bb2, bb3],\n topology_graph=lattice\n building_block_vertices={\n bb1: lattice.verices[:2],\n bb2: lattice.verices[4:],\n bb3: lattice.verices[2:4]\n }\n )\n\n \"\"\"\n\n def __init_subclass__(cls, **kwargs):\n for i, vertex in enumerate(cls.vertex_data):\n vertex.id = i\n for i, edge in enumerate(cls.edge_data):\n edge.id = i\n edge.lattice_constants = tuple(\n np.array(constant)\n for constant in cls._lattice_constants\n )\n return super().__init_subclass__(**kwargs)\n\n def __init__(\n self,\n lattice_size,\n periodic=False,\n vertex_alignments=None,\n num_processes=1\n ):\n \"\"\"\n Initialize a :class:`.COF`.\n\n Parameters\n ----------\n lattice_size : :class:`tuple` of :class:`int`\n The number of unit cells which should be placed along the\n x, y and z dimensions, respectively.\n\n periodic : :class:`bool`, optional\n If periodic bonds are to be made across the lattice,\n this should be ``True``. If ``False`` the functional\n groups on the ends of the lattice will be unreacted.\n\n vertex_alignments : :class:`dict`, optional\n A mapping from the :attr:`.Vertex.id` of a :class:`.Vertex`\n :attr:`vertices` to an :class:`.Edge` connected to it.\n The :class:`.Edge` is used to align the first\n :class:`.FunctionalGroup` of a :class:`.BuildingBlock`\n placed on that vertex. Only vertices which need to have\n their default edge changed need to be present in the\n :class:`dict`. If ``None`` then the default edge is used\n for each vertex. Changing which :class:`.Edge` is used will\n mean that the topology graph represents different\n structural isomers. The edge is refered to by a number\n between ``0`` (inclusive) and the number of edges the\n vertex is connected to (exclusive).\n\n num_processes : :class:`int`, optional\n The number of parallel processes to create during\n :meth:`construct`.\n\n \"\"\"\n\n if vertex_alignments is None:\n vertex_alignments = {}\n\n self._lattice_size = lattice_size\n self._periodic = periodic\n\n vertex_data = self._get_vertex_data(vertex_alignments)\n edge_data = self._get_edge_data(vertex_data)\n\n vertex_data = tuple(\n vertex\n for clones in flatten(vertex_data, {dict})\n for vertex in clones.values()\n )\n super().__init__(vertex_data, edge_data, (), num_processes)\n\n def _get_vertex_data(self, vertex_alignments):\n \"\"\"\n Create the vertex data of the topology graph instance.\n\n Parameters\n ---------\n vertex_alignments : :class:`dict`\n A mapping from the :attr:`.Vertex.id` of a :class:`.Vertex`\n :attr:`vertices` to an :class:`.Edge` connected to it.\n The :class:`.Edge` is used to align the first\n :class:`.FunctionalGroup` of a :class:`.BuildingBlock`\n placed on that vertex. Only vertices which need to have\n their default edge changed need to be present in the\n :class:`dict`. If ``None`` then the default edge is used\n for each vertex. Changing which :class:`.Edge` is used will\n mean that the topology graph represents different\n structural isomers. The edge is refered to by a number\n between ``0`` (inclusive) and the number of edges the\n vertex is connected to (exclusive).\n\n Returns\n -------\n :class:`list`\n A nested :class:`list` which can be indexed as\n ``vertices[x][y][z]``, which will return a :class:`dict`\n for the unit cell at (x, y, z). The :class:`dict` maps\n the vertices in :attr:`vertex_data` to its clone for that\n unit cell.\n\n \"\"\"\n\n xdim, ydim, zdim = (range(dim) for dim in self._lattice_size)\n # vertex_clones is indexed as vertex_clones[x][y][z]\n vertex_clones = [\n [\n [\n {} for k in zdim\n ]\n for j in ydim\n ]\n for i in xdim\n ]\n # Make a clone of each vertex for each unit cell.\n cells = it.product(xdim, ydim, zdim)\n vertices = it.product(cells, self.vertex_data)\n for cell, vertex in vertices:\n x, y, z = cell\n clone = vertex.clone(True)\n clone.cell = np.array(cell)\n clone.aligner_edge = vertex_alignments.get(vertex.id, 0)\n # Shift the clone so that it's within the cell.\n for axis, dim in zip(cell, self._lattice_constants):\n clone.position += axis * dim\n\n vertex_clones[x][y][z][vertex] = clone\n return vertex_clones\n\n def _get_edge_data(self, vertex_data):\n \"\"\"\n Create the edge data of the topology graph instance.\n\n Parameters\n ----------\n vertex_data : :class:`list`\n A nested :class:`list` which can be indexed as\n ``vertex_data[x][y][z]``, which will return a :class:`dict`\n for the unit cell at (x, y, z). The :class:`dict` maps\n the vertices in :attr:`vertex_data` to the clones for that\n unit cell.\n\n Returns\n -------\n :class:`tuple` of :class:`.EdgeData`\n The edge data of the topology graph instance.\n\n \"\"\"\n\n edge_clones = []\n # Make a clone for each edge for each unit cell.\n xdim, ydim, zdim = (range(dim) for dim in self._lattice_size)\n cells = it.product(xdim, ydim, zdim)\n edges = it.product(cells, self.edge_data)\n for cell, edge in edges:\n x, y, z = cell\n # The cell in which the second vertex of the edge is found.\n periodic_cell = np.array(cell) + edge.periodicity\n # Wrap around periodic cells, ie those that are less than 0\n # or greater than the lattice size along any dimension.\n dims = zip(periodic_cell, self._lattice_size)\n cell2_x, cell2_y, cell2_z = np.array([\n (dim+max_dim) % max_dim\n for dim, max_dim in dims\n ])\n # Make a vertex map which accounts for the fact that\n # v1 is in cell2.\n v0, v1 = edge.vertices\n vertex_map = {\n v0: vertex_data[x][y][z][v0],\n v1: vertex_data[cell2_x][cell2_y][cell2_z][v1]\n }\n # If the edge is not periodic if periodic_cell is did not\n # have to wrap around.\n dims = zip(periodic_cell, self._lattice_size)\n edge_is_not_periodic = all(\n dim >= 0 and dim < max_dim\n for dim, max_dim in dims\n )\n clone = edge.clone(vertex_map, True, True)\n edge_clones.append(clone)\n if edge_is_not_periodic:\n clone.periodicity = np.array([0, 0, 0])\n\n return tuple(edge_clones)\n\n def _before_react(self, mol, vertices, edges):\n if self._periodic:\n return vertices, edges\n return vertices, tuple(\n edge for edge in edges if not edge.is_periodic()\n )\n\n def assign_building_blocks_to_vertices(self, building_blocks):\n \"\"\"\n Assign `building_blocks` to :attr:`vertices`.\n\n Parameters\n ----------\n building_blocks : :class:`list` of :class:`.Molecule`\n The :class:`.BuildingBlock` and\n :class:`ConstructedMolecule` instances which\n represent the building block molecules used for\n construction. Only one instance is present per building\n block molecule, even if multiples of that building block\n join up to form the :class:`ConstructedMolecule`.\n\n Returns\n -------\n :class:`dict`\n Maps the `building_blocks`, to the\n :class:`~.topologies.base.Vertex` objects in\n :attr:`vertices` they are placed on during construction.\n The :class:`dict` has the form\n\n .. code-block:: python\n\n building_block_vertices = {\n BuildingBlock(...): [Vertex(...), Vertex(...)],\n BuildingBlock(...): [\n Vertex(...),\n Vertex(...),\n Vertex(...),\n ]\n ConstructedMolecule(...): [Vertex(...)]\n }\n\n Raises\n ------\n :class:`ValueError`\n If there is more than one building with a given number\n of functional groups.\n\n \"\"\"\n\n bb_by_degree = {}\n for bb in building_blocks:\n num_fgs = len(bb.func_groups)\n if num_fgs in bb_by_degree:\n raise ValueError(\n 'If there are multiple building blocks with the '\n 'same number of functional groups, '\n 'building_block_vertices must be set explicitly.'\n )\n bb_by_degree[num_fgs] = bb\n\n building_block_vertices = {}\n for vertex in self.vertices:\n bb = bb_by_degree[vertex.get_num_edges()]\n building_block_vertices[bb] = (\n building_block_vertices.get(bb, [])\n )\n building_block_vertices[bb].append(vertex)\n return building_block_vertices\n\n def _get_scale(self, mol):\n \"\"\"\n Get the scale used for the positions of :attr:`vertices`.\n\n Parameters\n ----------\n mol : :class:`.ConstructedMolecule`\n The molecule being constructed.\n\n Returns\n -------\n :class:`float` or :class:`list` of :class:`float`\n The value by which the position of each :class:`Vertex` is\n scaled. Can be a single number if all axes are scaled by\n the same amount or a :class:`list` of three numbers if\n each axis is scaled by a different value.\n\n \"\"\"\n\n return 5*max(\n bb.get_maximum_diameter()\n for bb in mol.building_block_vertices\n )\n\n def __repr__(self):\n vertex_alignments = ', '.join(\n f'{v.id}: {v.get_aligner_edge()}'\n # Only get the vertices in the first unit cell.\n for v in self.vertices[:len(self.vertex_data)]\n )\n\n x, y, z = self._lattice_size\n periodic = ', periodic=True' if self._periodic else ''\n return (\n f'cof.{self.__class__.__name__}('\n f'lattice_size=({x}, {y}, {z}), '\n f'vertex_alignments={{{vertex_alignments}}}'\n f'{periodic})'\n )\n\n\nclass Honeycomb(COF):\n \"\"\"\n Represents a honeycomb COF topology graph.\n\n Building blocks with three and two functional groups are required\n for this topology graph.\n\n See :class:`.COF` for more details and examples.\n\n Attributes\n ----------\n vertex_data : :class:`tuple` of :class:`.VertexData`\n A class attribute. Holds the data of the vertices which make up\n the topology graph.\n\n edge_data : :class:`tuple` of :class:`.EdgeData`\n A class attribute. Holds the data of the edges which make up\n the topology graph.\n\n vertices : :class:`tuple` of :class:`.Vertex`\n The vertices which make up the topology graph.\n\n edges : :class:`tuple` of :class:`.Edge`\n The edges which make up the topology graph.\n\n \"\"\"\n\n _lattice_constants = _a, _b, _c = (\n np.array([1., 0., 0.]),\n np.array([0.5, 0.866, 0]),\n np.array([0, 0, 5/1.7321])\n )\n\n _vertex_data = (\n _COFVertexData(*((1/3)*_a + (1/3)*_b + (1/2)*_c)),\n _COFVertexData(*((2/3)*_a + (2/3)*_b + (1/2)*_c))\n )\n\n vertex_data = (\n *_vertex_data,\n _COFVertexData.init_at_center(\n _vertex_data[0], _vertex_data[1]\n ),\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[0], _vertex_data[1]),\n shifts=((0, 0, 0), (0, -1, 0)),\n lattice_constants=_lattice_constants\n ),\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[0], _vertex_data[1]),\n shifts=((0, 0, 0), (-1, 0, 0)),\n lattice_constants=_lattice_constants\n )\n )\n\n edge_data = (\n EdgeData(vertex_data[2], vertex_data[0]),\n EdgeData(vertex_data[2], vertex_data[1]),\n\n EdgeData(vertex_data[3], vertex_data[0]),\n EdgeData(\n vertex_data[3],\n vertex_data[1],\n periodicity=(0, -1, 0)\n ),\n\n EdgeData(vertex_data[4], vertex_data[0]),\n EdgeData(\n vertex_data[4],\n vertex_data[1],\n periodicity=(-1, 0, 0)\n )\n )\n\n\nclass Hexagonal(COF):\n \"\"\"\n Represents a hexagonal COF topology graph.\n\n Building blocks with six and two functional groups are required\n for this topology graph.\n\n See :class:`.COF` for more details and examples.\n\n Attributes\n ----------\n vertex_data : :class:`tuple` of :class:`.VertexData`\n A class attribute. Holds the data of the vertices which make up\n the topology graph.\n\n edge_data : :class:`tuple` of :class:`.EdgeData`\n A class attribute. Holds the data of the edges which make up\n the topology graph.\n\n vertices : :class:`tuple` of :class:`.Vertex`\n The vertices which make up the topology graph.\n\n edges : :class:`tuple` of :class:`.Edge`\n The edges which make up the topology graph.\n\n \"\"\"\n\n _lattice_constants = _a, _b, _c = (\n np.array([1., 0., 0.]),\n np.array([0.5, 0.866, 0]),\n np.array([0, 0, 5/1.7321])\n )\n\n _vertex_data = (\n _COFVertexData(*((1/4)*_a + (1/4)*_b + (1/2)*_c)),\n _COFVertexData(*((1/4)*_a + (3/4)*_b + (1/2)*_c)),\n _COFVertexData(*((3/4)*_a + (1/4)*_b + (1/2)*_c)),\n _COFVertexData(*((3/4)*_a + (3/4)*_b + (1/2)*_c))\n )\n\n vertex_data = (\n *_vertex_data,\n _COFVertexData.init_at_center(\n _vertex_data[0], _vertex_data[1]\n ),\n _COFVertexData.init_at_center(\n _vertex_data[0], _vertex_data[2]\n ),\n _COFVertexData.init_at_center(\n _vertex_data[1], _vertex_data[2]\n ),\n _COFVertexData.init_at_center(\n _vertex_data[1], _vertex_data[3]\n ),\n _COFVertexData.init_at_center(\n _vertex_data[2], _vertex_data[3]\n ),\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[0], _vertex_data[2]),\n shifts=((0, 0, 0), (-1, 0, 0)),\n lattice_constants=_lattice_constants\n ),\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[0], _vertex_data[1]),\n shifts=((0, 0, 0), (0, -1, 0)),\n lattice_constants=_lattice_constants\n ),\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[0], _vertex_data[3]),\n shifts=((0, 0, 0), (0, -1, 0)),\n lattice_constants=_lattice_constants\n ),\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[2], _vertex_data[1]),\n shifts=((0, 0, 0), (1, -1, 0)),\n lattice_constants=_lattice_constants\n ),\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[2], _vertex_data[3]),\n shifts=((0, 0, 0), (0, -1, 0)),\n lattice_constants=_lattice_constants\n ),\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[1], _vertex_data[3]),\n shifts=((0, 0, 0), (-1, 0, 0)),\n lattice_constants=_lattice_constants\n ),\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[3], _vertex_data[0]),\n shifts=((0, 0, 0), (1, 0, 0)),\n lattice_constants=_lattice_constants\n )\n )\n\n edge_data = (\n EdgeData(vertex_data[4], vertex_data[0]),\n EdgeData(vertex_data[4], vertex_data[1]),\n\n EdgeData(vertex_data[5], vertex_data[0]),\n EdgeData(vertex_data[5], vertex_data[2]),\n\n EdgeData(vertex_data[6], vertex_data[1]),\n EdgeData(vertex_data[6], vertex_data[2]),\n\n EdgeData(vertex_data[7], vertex_data[1]),\n EdgeData(vertex_data[7], vertex_data[3]),\n\n EdgeData(vertex_data[8], vertex_data[2]),\n EdgeData(vertex_data[8], vertex_data[3]),\n\n EdgeData(vertex_data[9], vertex_data[0]),\n EdgeData(\n vertex_data[9],\n vertex_data[2],\n periodicity=(-1, 0, 0)\n ),\n\n EdgeData(vertex_data[10], vertex_data[0]),\n EdgeData(\n vertex_data[10],\n vertex_data[1],\n periodicity=(0, -1, 0)\n ),\n\n EdgeData(vertex_data[11], vertex_data[0]),\n EdgeData(\n vertex_data[11],\n vertex_data[3],\n periodicity=(0, -1, 0)\n ),\n\n EdgeData(vertex_data[12], vertex_data[2]),\n EdgeData(\n vertex_data[12],\n vertex_data[1],\n periodicity=(1, -1, 0)\n ),\n\n EdgeData(vertex_data[13], vertex_data[2]),\n EdgeData(\n vertex_data[13],\n vertex_data[3],\n periodicity=(0, -1, 0)\n ),\n\n EdgeData(vertex_data[14], vertex_data[1]),\n EdgeData(\n vertex_data[14],\n vertex_data[3],\n periodicity=(-1, 0, 0)\n ),\n\n EdgeData(vertex_data[15], vertex_data[3]),\n EdgeData(\n vertex_data[15],\n vertex_data[0],\n periodicity=(1, 0, 0)\n )\n )\n\n\nclass Square(COF):\n \"\"\"\n Represents a sqaure COF topology graph.\n\n Building blocks with four and two functional groups are required\n for this topology graph.\n\n See :class:`.COF` for more details and examples.\n\n Attributes\n ----------\n vertex_data : :class:`tuple` of :class:`.VertexData`\n A class attribute. Holds the data of the vertices which make up\n the topology graph.\n\n edge_data : :class:`tuple` of :class:`.EdgeData`\n A class attribute. Holds the data of the edges which make up\n the topology graph.\n\n vertices : :class:`tuple` of :class:`.Vertex`\n The vertices which make up the topology graph.\n\n edges : :class:`tuple` of :class:`.Edge`\n The edges which make up the topology graph.\n\n \"\"\"\n\n _lattice_constants = _a, _b, _c = (\n np.array([1., 0., 0.]),\n np.array([0., 1., 0.]),\n np.array([0., 0., 1.])\n )\n\n _vertex_data = (\n _COFVertexData(*((0.5)*_a + (0.5)*_b + (0.5)*_c)),\n )\n vertex_data = (\n *_vertex_data,\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[0], _vertex_data[0]),\n shifts=((0, 0, 0), (1, 0, 0)),\n lattice_constants=_lattice_constants\n ),\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[0], _vertex_data[0]),\n shifts=((0, 0, 0), (0, 1, 0)),\n lattice_constants=_lattice_constants\n )\n\n )\n\n edge_data = (\n EdgeData(vertex_data[1], vertex_data[0]),\n EdgeData(\n vertex_data[1],\n vertex_data[0],\n periodicity=(1, 0, 0)\n ),\n EdgeData(vertex_data[2], vertex_data[0]),\n EdgeData(\n vertex_data[2],\n vertex_data[0],\n periodicity=(0, 1, 0)\n )\n )\n\n\nclass Kagome(COF):\n \"\"\"\n Represents a kagome COF topology graph.\n\n Building blocks with four and two functional groups are required\n for this topology graph.\n\n See :class:`.COF` for more details and examples.\n\n Attributes\n ----------\n vertex_data : :class:`tuple` of :class:`.VertexData`\n A class attribute. Holds the data of the vertices which make up\n the topology graph.\n\n edge_data : :class:`tuple` of :class:`.EdgeData`\n A class attribute. Holds the data of the edges which make up\n the topology graph.\n\n vertices : :class:`tuple` of :class:`.Vertex`\n The vertices which make up the topology graph.\n\n edges : :class:`tuple` of :class:`.Edge`\n The edges which make up the topology graph.\n\n \"\"\"\n\n _lattice_constants = _a, _b, _c = (\n np.array([1., 0., 0.]),\n np.array([0.5, 0.866, 0.]),\n np.array([0., 0., 5/1.7321])\n )\n\n _vertex_data = (\n _COFVertexData(*((1/4)*_a + (3/4)*_b + (0.5)*_c)),\n _COFVertexData(*((3/4)*_a + (3/4)*_b + (1/2)*_c)),\n _COFVertexData(*((3/4)*_a + (1/4)*_b + (1/2)*_c))\n )\n\n vertex_data = (\n *_vertex_data,\n _COFVertexData.init_at_center(\n _vertex_data[0],\n _vertex_data[1]\n ),\n _COFVertexData.init_at_center(\n _vertex_data[0],\n _vertex_data[2]\n ),\n _COFVertexData.init_at_center(\n _vertex_data[1],\n _vertex_data[2]\n ),\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[0], _vertex_data[1]),\n shifts=((0, 0, 0), (-1, 0, 0)),\n lattice_constants=_lattice_constants\n ),\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[0], _vertex_data[2]),\n shifts=((0, 0, 0), (-1, 1, 0)),\n lattice_constants=_lattice_constants\n ),\n _COFVertexData.init_at_shifted_center(\n vertex_data=(_vertex_data[1], _vertex_data[2]),\n shifts=((0, 0, 0), (0, 1, 0)),\n lattice_constants=_lattice_constants\n )\n\n )\n\n edge_data = (\n EdgeData(vertex_data[3], vertex_data[0]),\n EdgeData(vertex_data[3], vertex_data[1]),\n\n EdgeData(vertex_data[4], vertex_data[0]),\n EdgeData(vertex_data[4], vertex_data[2]),\n\n EdgeData(vertex_data[5], vertex_data[1]),\n EdgeData(vertex_data[5], vertex_data[2]),\n\n EdgeData(vertex_data[6], vertex_data[0]),\n EdgeData(\n vertex_data[6],\n vertex_data[1],\n periodicity=(-1, 0, 0)\n ),\n\n EdgeData(vertex_data[7], vertex_data[0]),\n EdgeData(\n vertex_data[7],\n vertex_data[2],\n periodicity=(-1, 1, 0)\n ),\n\n EdgeData(vertex_data[8], vertex_data[1]),\n EdgeData(\n vertex_data[8],\n vertex_data[2],\n periodicity=(0, 1, 0)\n )\n )\n\n\nclass LinkerlessHoneycomb(COF):\n \"\"\"\n Represents a honeycomb COF topology graph.\n\n Building blocks with three functional groups are required\n for this topology graph.\n\n See :class:`.COF` for more details and examples.\n\n Attributes\n ----------\n vertex_data : :class:`tuple` of :class:`.VertexData`\n A class attribute. Holds the data of the vertices which make up\n the topology graph.\n\n edge_data : :class:`tuple` of :class:`.EdgeData`\n A class attribute. Holds the data of the edges which make up\n the topology graph.\n\n vertices : :class:`tuple` of :class:`.Vertex`\n The vertices which make up the topology graph.\n\n edges : :class:`tuple` of :class:`.Edge`\n The edges which make up the topology graph.\n\n \"\"\"\n\n _lattice_constants = _a, _b, _c = (\n np.array([1., 0., 0.]),\n np.array([0.5, 0.866, 0.]),\n np.array([0., 0., 5/1.7321])\n )\n\n vertex_data = (\n _COFVertexData(*((1/3)*_a + (1/3)*_b + (1/2)*_c)),\n _COFVertexData(*((2/3)*_a + (2/3)*_b + (1/2)*_c))\n )\n\n edge_data = (\n EdgeData(vertex_data[0], vertex_data[1]),\n EdgeData(\n vertex_data[0],\n vertex_data[1],\n periodicity=(-1, 0, 0)\n ),\n EdgeData(\n vertex_data[0],\n vertex_data[1],\n periodicity=(0, -1, 0)\n )\n )\n", "sub_path": "src/stk/molecular/topology_graphs/cof.py", "file_name": "cof.py", "file_ext": "py", "file_size_in_byte": 42126, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "topology_graph.VertexData", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.divide", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 122, "usage_type": "call"}, {"api_name": "topology_graph.Vertex", "line_number": 136, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 422, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 422, "usage_type": "attribute"}, {"api_name": "numpy.cross", "line_number": 448, "usage_type": "call"}, {"api_name": "utilities.vector_angle", "line_number": 484, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 488, "usage_type": "attribute"}, {"api_name": "utilities.vector_angle", "line_number": 508, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 515, "usage_type": "attribute"}, {"api_name": "topology_graph.TopologyGraph", "line_number": 528, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 616, "usage_type": "call"}, {"api_name": "utilities.flatten", "line_number": 673, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 721, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 722, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 726, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 758, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 759, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 763, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 767, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 788, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 930, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 931, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 932, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 958, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 959, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 961, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 962, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 968, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 969, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1005, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1006, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1007, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1072, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1073, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1075, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1076, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1078, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1079, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1081, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1082, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1084, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1085, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1087, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1088, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1094, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1095, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1101, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1102, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1108, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1109, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1115, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1116, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1122, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1123, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1129, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1166, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1167, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1168, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1190, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1191, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1196, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1197, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1233, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1234, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1235, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1277, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1278, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1280, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1281, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1283, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1284, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1286, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1287, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1293, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1294, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1300, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1301, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1337, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1338, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1339, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1348, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1349, "usage_type": "call"}, {"api_name": "topology_graph.EdgeData", "line_number": 1354, "usage_type": "call"}]} +{"seq_id": "90638539", "text": "from django.conf.urls import url\nfrom .views import *\n\nurlpatterns = [\n\t\turl(r'^$',index,name='home'),\n\t\turl(r'^Register/$',register,name=\"register\"),\n\t\turl(r'^LogIn/$',login,name=\"login\"),\n\t\turl(r'^LogOut/$',logout,name=\"logout\"),\n\t\turl(r'^Corte/$',cortes,name='corte'),\n\t\turl(r'^Servi/(?P\\d)/$',Servicio,name='servicio'),\n\t\turl(r'^Root/$',admin,name='admin'),\n\t\turl(r'^Galeria/$',Galeria,name='galeria'),\n\t\turl(r'^Reservar/$',reservacion,name='reservacion'),\n\t\turl(r'^Reservar/(?P\\d+)/$',BotonCambio,name='boton'),\n\n\t]\n\n", "sub_path": "peluqueria/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 534, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "148028556", "text": "# -*- coding: utf-8 -*-\n__author__ = 'manman'\n\n\"\"\"\n遍历文件夹jsons，获取所有文件内容，并保存文件title到一个list里面并打印。\n\"\"\"\n\nimport os\nimport json\n\nbase_path = '{base_path}\\\\jsons'.format(base_path=os.getcwd())\n\n\ndef get_content(filename):\n \"\"\"\n 从filename读取数据\n :param filename:\n :return:\n \"\"\"\n result = ''\n with open(filename) as f:\n for line in f:\n result += line\n return result\n\n\ndef get_json_data():\n \"\"\"\n 获取json数据的title的list\n :param content:\n :return: res_list\n \"\"\"\n res_list = []\n for item in os.listdir(base_path): # '.\\\\jsons'\n res = get_content('{base_path}\\\\{filename}'.format(base_path=base_path, filename=item))\n json_res = json.loads(res)\n res_list.extend(data.get('title') for data in json_res)\n return res_list\n\n\ndef print_data(res_list):\n \"\"\"\n\n :param res_list:\n :return:\n \"\"\"\n for title in res_list:\n print(title)\n\n\nif __name__ == '__main__':\n res_list = get_json_data()\n print_data(res_list)\n", "sub_path": "learn/011.py", "file_name": "011.py", "file_ext": "py", "file_size_in_byte": 1084, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.getcwd", "line_number": 11, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 34, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "624799164", "text": "#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n#__author__=='Yxn'\n'''\n脚本用于定时删除3天以前的日志并且将一天前的日志通过邮件发送至SEO邮箱中\n'''\n\ndef del_old_log(dirname):\n\timport os\n\timport time\n\tfrom sys import argv\n\timport datetime\n\tformat = datetime.datetime.fromtimestamp # 格式化文件时间\n\tnow = time.strftime(\"%Y%m%d\") # 获取当前系统时间\n\tlog_name = ['UzaiWebBJ','UzaiMobileList','UzaiWebSH','UzaiWebWWW'] # 只发送此列表中的日志\n\n\tfor dirname,pathname,filenames in os.walk(dirname): # 遍历目录\n\t\tfor filename in filenames:\n\t\t\ttoday_log = 'u_ex%s.log' % (time.strftime('%y%m%d')) # 今天新生成的日志\n\t\t\tmail_dir_name = dirname.split('\\\\')[2]\n\t\t\tresult_file = os.path.join(dirname,filename) # 拼接文件路径\n\t\t\tfile_time = os.path.getmtime(result_file) # 获取文件时间\n\t\t\tresult_time = format(file_time).strftime(\"%Y%m%d\") # 格式化文件时间\n\t\t\tif int(now) - int(result_time) > 3: # 3天前的日志文件会被删除\n\t\t\t\tos.remove(result_file)\n\n\t\t\t\t#如果不符合前一个条件的文件则需要匹配如下条件：\n\n\t\t\t\t#\t1. 发送的日志不是今天的\n\t\t\t\t#\t2. 项目名称必须和log_name里面的项目名称一样\n\t\t\t\t#\t3. 必须是前一天的文件\n\n\t\t\telif mail_dir_name in log_name and int(now) - int(result_time) < 1 and filename != today_log:\n\t\t\t\tzip_log(result_file)\n\t\t\t\tsend_log(mail_dir_name,\"%s.zip\" % (result_file),filename) # 通过邮件发送日志\n\t\t\t\tos.remove(\"%s.zip\" % (result_file))\n\n# 增加压缩功能\ndef zip_log(filename):\n\timport zipfile\n\tzip_log_file = zipfile.ZipFile(\"%s.zip\" % (filename),'a',zipfile.ZIP_DEFLATED,True)\n\tzip_log_file.write(filename)\n\tzip_log_file.close()\n\ndef send_log(dirname,log_file,filename):\n\thostname = get_hostname()\n\timport smtplib\n\tfrom email.mime.text import MIMEText\n\tfrom email.mime.multipart import MIMEMultipart\n\tfrom email.mime.application import MIMEApplication \n\n\tfrom_addr = '' #发信人账号\n\tpassword = '' # 发信人密码\n\tto_addr_list = ['',''] # 收信人账号，如果要发多人可使用'abc@abc.com,abcd@abc.com'\n\tsmtp_server = '' # 发信服务器\n\n\tmsg = MIMEMultipart()\n\tmsg['Subject'] = u'IISlog定时发送'.encode('utf-8')\n\tmsg['To'] = ','.join( to_addr_list )\n\tmsg['From'] = from_addr\n \n\t# 邮件内容\n\tContents = MIMEText('当前日志文件位于服务器%s的%s的%s' % (hostname,dirname,filename),'plain','utf-8')\n\tmsg.attach(Contents)\n\n\t# 带上二进制附件 \n\tpart = MIMEApplication(open(log_file,'rb').read()) \n\tpart.add_header('Content-Disposition', 'attachment', filename=\"%s.zip\" % (filename))\n\tmsg.attach(part)\n\n\t# 发送邮件\n\tserver = smtplib.SMTP(smtp_server, 25) # 连接邮件服务器\n\tserver.login(from_addr,password)\n\tserver.sendmail(from_addr,to_addr_list, msg.as_string()) # 发送邮件\n\tserver.quit()\n\n# 获取当前机器的主机名\n\ndef get_hostname():\n\tfrom socket import gethostname\n\treturn gethostname()\n\nif __name__ == '__main__':\n\tdel_old_log('E:\\\\iislog')\n", "sub_path": "个人脚本/Del_old_log.py", "file_name": "Del_old_log.py", "file_ext": "py", "file_size_in_byte": 2978, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime", "line_number": 13, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 14, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 17, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.getmtime", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 25, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 36, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 41, "usage_type": "call"}, {"api_name": "zipfile.ZIP_DEFLATED", "line_number": 41, "usage_type": "attribute"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 57, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 63, "usage_type": "call"}, {"api_name": "email.mime.application.MIMEApplication", "line_number": 67, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 72, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "75612468", "text": "#_*_coding:utf-8_*_\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn import metrics\nimport numpy as np\nfrom . import learning_methods\nfrom . import log_class\ntunned_n_estimators = [120,300,500,800,1200]\ntunned_max_depth = [None,5,10,15,25,30]\ntunned_min_samples_split = [2,4,10,15,100]\ntunned_min_samples_leaf =[1,2,5,10]\ntunned_max_features = ['sqrt','log2','auto',None]\n#add more feautre tunning in the future\n\nclass RandomForestRegression_CV(learning_methods.learning_methods):\n \"\"\"\n 随机森林交叉验证\n \"\"\"\n def __init__(self,x,y,metric,scoring = 'neg_mean_squared_error',n_jobs=3,save_model = False,processed_data_version_dir='./'):\n \"\"\"\n 初始化相关参数\n\n args:\n x: numpy array\n y: numpy array\n metric: sklearn 中的函数，用来在交叉验证中评估验证集上的效果，不过auc 不行，因为auc的参数 不是 (y_true,y_pred) 的形式\n optional: http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics\n metric_proba: False, 表示 metric 函数是否接受模型输出0-1之间的概率值\n scoring: 用sklearn自带的 GridSearchCV 时需要的评估函数, 一般是越大越好。默认为 neg_mean_squared_error\n 可选项: 'neg_log_loss' 'roc_auc' ,'neg_mean_squared_error' 等\n n_jobs: 多少个线程,默认为3\n save_model: True or False, 表示是否保存模型,保存路径为 processed_data_version_dir/modules/\n processed_data_version_dir: 存放log 或者保存模型的目录,默认为 ./ \n \"\"\"\n super(RandomForestRegression_CV,self).__init__(x,y,metric,scoring=scoring,save_model=save_model,processed_data_version_dir=processed_data_version_dir)\n self.model = RandomForestRegressor(n_jobs=n_jobs)\n self.n_jobs = n_jobs\n\n \n\n\n def cross_validation(self):\n scoring = self.scoring \n self.train_score()\n self.cv_score()\n\n params = {'n_estimators':tunned_n_estimators}\n gsearch = GridSearchCV(estimator=self.model,param_grid=params,scoring=scoring,n_jobs=self.n_jobs,iid=False,cv=3)\n gsearch.fit(self.x,self.y)\n self.model.set_params(n_estimators= gsearch.best_params_['n_estimators'])\n print('best n_estimators for rf:{}'.format(gsearch.best_params_['n_estimators']))\n\n self.cv_score()\n self.train_score()\n\n\n\n params = {'max_depth':tunned_max_depth}\n gsearch = GridSearchCV(estimator=self.model,param_grid=params,scoring=scoring,n_jobs=self.n_jobs,iid=False,cv=3)\n gsearch.fit(self.x,self.y)\n self.model.set_params(max_depth= gsearch.best_params_['max_depth'])\n print('best max_depth for rf:{}'.format(gsearch.best_params_['max_depth']))\n\n self.cv_score()\n self.train_score()\n \n\n params = {'min_samples_split':tunned_min_samples_split}\n gsearch = GridSearchCV(estimator = self.model,param_grid = params,scoring=scoring,n_jobs=self.n_jobs,iid=False,cv=3)\n gsearch.fit(self.x,self.y)\n self.model.set_params(min_samples_split = gsearch.best_params_['min_samples_split'])\n print('best min_samples_split for rf:{}'.format(gsearch.best_params_['min_samples_split']))\n self.cv_score()\n self.train_score()\n\n params = {'min_samples_leaf':tunned_min_samples_leaf}\n gsearch = GridSearchCV(estimator = self.model,param_grid = params,scoring=scoring,n_jobs=self.n_jobs,iid=False,cv=3)\n gsearch.fit(self.x,self.y)\n self.model.set_params(min_samples_leaf = gsearch.best_params_['min_samples_leaf'])\n print('best min_samples_leaf for rf:{}'.format(gsearch.best_params_['min_samples_leaf']))\n self.cv_score()\n self.train_score()\n\n params = {'max_features':tunned_max_features}\n gsearch = GridSearchCV(estimator = self.model,param_grid = params,scoring=scoring,n_jobs=self.n_jobs,iid=False,cv=3)\n gsearch.fit(self.x,self.y)\n self.model.set_params(max_features = gsearch.best_params_['max_features'])\n print('best max_features for rf:{}'.format(gsearch.best_params_['max_features']))\n self.cv_score()\n self.train_score()\n\n self.plot_save('RandomForestRegression')\n \n return self.model\n\n", "sub_path": "kagglemethods/random_forest_regression.py", "file_name": "random_forest_regression.py", "file_ext": "py", "file_size_in_byte": 4412, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 49, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 60, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 70, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 78, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 86, "usage_type": "call"}]} +{"seq_id": "580845202", "text": "\"\"\"\n Core Form\n ~~~~~~~~~\n\n :author: nanang.jobs@gmail.com\n :copyright: (c) 2017 by Nanang Suryadi.\n :license: BSD, see LICENSE for more details.\n\n forms.py\n\"\"\"\nimport colander\n\n\nclass SelectEnumInt(colander.String):\n def __init__(self, enum, msg=None):\n self.request = None\n self.enums = enum\n self.msg = msg or u'Object does not exists'\n\n def enum(self, val):\n try:\n values = list(map(lambda x: x[0], self.enums))\n if val not in values:\n return None\n return val\n except:\n return None\n\n def serialize(self, node, appstruct):\n if appstruct is colander.null:\n return colander.null\n\n val = self.enum(appstruct)\n if not val:\n raise colander.Invalid(node, self.msg)\n return str(val)\n\n def deserialize(self, node, cstruct):\n if cstruct != 0 and not cstruct:\n return colander.null\n\n val = self.enum(cstruct)\n if not val:\n raise colander.Invalid(node, self.msg)\n return val\n\n\n@colander.deferred\ndef csrf_token_validator(node, kw):\n request = kw.get('request')\n\n def validator(_node, value):\n if value != request.session.get_csrf_token():\n raise colander.Invalid(\n _node,\n u'Invalid CSRF token',\n )\n\n return colander.All(colander.Length(max=255), validator, )\n\n\nclass CSRFSchema(colander.Schema):\n csrf_token = colander.SchemaNode(\n colander.String(),\n validator=csrf_token_validator\n )\n\n\nclass APISchema(colander.Schema):\n csrf_token = colander.SchemaNode(\n colander.String(),\n validator=csrf_token_validator\n )\n\n\nclass BaseSchema(colander.Schema):\n pass\n\n\nclass BaseForm(object):\n _schema = None\n _controls = None\n _errors = None\n\n def __init__(self, request):\n assert self._schema, u'Set _schema class attribute'\n self.request = request\n self.schema = self._schema().bind(request=self.request)\n\n def validate(self):\n try:\n self._controls = self.schema.deserialize(\n self.request.params.mixed()\n )\n return True\n except colander.Invalid as e:\n self._errors = e.asdict()\n return False\n\n @property\n def errors(self):\n return self._errors\n\n def submit(self, obj=None):\n raise NotImplementedError(u'Submit method must be implemented')\n", "sub_path": "BahnMaze/form.py", "file_name": "form.py", "file_ext": "py", "file_size_in_byte": 2502, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "colander.String", "line_number": 14, "usage_type": "attribute"}, {"api_name": "colander.null", "line_number": 30, "usage_type": "attribute"}, {"api_name": "colander.null", "line_number": 31, "usage_type": "attribute"}, {"api_name": "colander.Invalid", "line_number": 35, "usage_type": "call"}, {"api_name": "colander.null", "line_number": 40, "usage_type": "attribute"}, {"api_name": "colander.Invalid", "line_number": 44, "usage_type": "call"}, {"api_name": "colander.Invalid", "line_number": 54, "usage_type": "call"}, {"api_name": "colander.All", "line_number": 59, "usage_type": "call"}, {"api_name": "colander.Length", "line_number": 59, "usage_type": "call"}, {"api_name": "colander.deferred", "line_number": 48, "usage_type": "attribute"}, {"api_name": "colander.Schema", "line_number": 62, "usage_type": "attribute"}, {"api_name": "colander.SchemaNode", "line_number": 63, "usage_type": "call"}, {"api_name": "colander.String", "line_number": 64, "usage_type": "call"}, {"api_name": "colander.Schema", "line_number": 69, "usage_type": "attribute"}, {"api_name": "colander.SchemaNode", "line_number": 70, "usage_type": "call"}, {"api_name": "colander.String", "line_number": 71, "usage_type": "call"}, {"api_name": "colander.Schema", "line_number": 76, "usage_type": "attribute"}, {"api_name": "colander.Invalid", "line_number": 96, "usage_type": "attribute"}]} +{"seq_id": "299021928", "text": "# First, create a vocab\n# Assume I have it\n\nimport json, pickle\nimport numpy as np\n\n\nx = np.zeros((20000,20000))\n\n\nimport pickle\nfrom scipy import sparse\nimport numpy as np\n\"\"\"Read in a wtcf pickle, and return a matrix of smoothed thingies\"\"\"\ndef make_wtcf(pname, vocab):\n with open(pname, 'rb') as file:\n wtcf = pickle.load(file)\n \n smoothed = sparse.lil_matrix(( len(wtcf['vocab']), wtcf['wtcf'][0].shape[0]))\n for i in range(smoothed.shape[0]):\n row = wtcf['wtcf'][i]\n total = row.sum() + smoothed.shape[1]\n smoothed[i] = row / total\n smoothed = smoothed.tocsc()\n bests = [[vocab[k] for k,_ in sorted(enumerate(smoothed.getcol(i).toarray()), key=lambda x: -x[1])[:20]] for i in range(smoothed.shape[1])]\n return smoothed, bests", "sub_path": "scripts.py", "file_name": "scripts.py", "file_ext": "py", "file_size_in_byte": 749, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.zeros", "line_number": 8, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 17, "usage_type": "call"}, {"api_name": "scipy.sparse.lil_matrix", "line_number": 19, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "207263535", "text": "import os\nimport sys\nimport json\nimport boto3\nimport logging\n\nfrom botocore.config import Config\n\nmy_path = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, my_path + \"/..\")\n\nLOG = logging.getLogger(__name__)\n\n\nclass FeatureToggle:\n \"\"\"\n FeatureToggle is the class which will provide methods to query and decide if a feature is enabled based on where\n SAM is executing or not.\n \"\"\"\n\n def __init__(self, config_provider):\n self.feature_config = config_provider.config\n\n def is_enabled_for_stage_in_region(self, feature_name, stage, region=\"default\"):\n \"\"\"\n To check if feature is available for a particular stage or not.\n :param feature_name: name of feature\n :param stage: stage where SAM is running\n :param region: region in which SAM is running\n :return:\n \"\"\"\n if feature_name not in self.feature_config:\n LOG.warning(\"Feature '{}' not available in Feature Toggle Config.\".format(feature_name))\n return False\n stage_config = self.feature_config.get(feature_name, {}).get(stage, {})\n if not stage_config:\n LOG.info(\"Stage '{}' not enabled for Feature '{}'.\".format(stage, feature_name))\n return False\n region_config = stage_config.get(region, {}) if region in stage_config else stage_config.get(\"default\", {})\n is_enabled = region_config.get(\"enabled\", False)\n LOG.info(\"Feature '{}' is enabled: '{}'\".format(feature_name, is_enabled))\n return is_enabled\n\n def is_enabled_for_account_in_region(self, feature_name, stage, account_id, region=\"default\"):\n \"\"\"\n To check if feature is available for a particular account or not.\n :param feature_name: name of feature\n :param stage: stage where SAM is running\n :param account_id: account_id who is executing SAM template\n :param region: region in which SAM is running\n :return:\n \"\"\"\n if feature_name not in self.feature_config:\n LOG.warning(\"Feature '{}' not available in Feature Toggle Config.\".format(feature_name))\n return False\n stage_config = self.feature_config.get(feature_name, {}).get(stage, {})\n if not stage_config:\n LOG.info(\"Stage '{}' not enabled for Feature '{}'.\".format(stage, feature_name))\n return False\n account_config = stage_config.get(account_id) if account_id in stage_config else stage_config.get(\"default\", {})\n region_config = (\n account_config.get(region, {}) if region in account_config else account_config.get(\"default\", {})\n )\n is_enabled = region_config.get(\"enabled\", False)\n LOG.info(\"Feature '{}' is enabled: '{}'\".format(feature_name, is_enabled))\n return is_enabled\n\n\nclass FeatureToggleConfigProvider:\n \"\"\"Interface for all FeatureToggle config providers\"\"\"\n\n def __init__(self):\n pass\n\n @property\n def config(self):\n raise NotImplementedError\n\n\nclass FeatureToggleDefaultConfigProvider(FeatureToggleConfigProvider):\n \"\"\"Default config provider, always return False for every query.\"\"\"\n\n def __init__(self):\n FeatureToggleConfigProvider.__init__(self)\n\n @property\n def config(self):\n return {}\n\n\nclass FeatureToggleLocalConfigProvider(FeatureToggleConfigProvider):\n \"\"\"Feature toggle config provider which uses a local file. This is to facilitate local testing.\"\"\"\n\n def __init__(self, local_config_path):\n FeatureToggleConfigProvider.__init__(self)\n with open(local_config_path, \"r\") as f:\n config_json = f.read()\n self.feature_toggle_config = json.loads(config_json)\n\n @property\n def config(self):\n return self.feature_toggle_config\n\n\nclass FeatureToggleAppConfigConfigProvider(FeatureToggleConfigProvider):\n \"\"\"Feature toggle config provider which loads config from AppConfig.\"\"\"\n\n def __init__(self, application_id, environment_id, configuration_profile_id):\n FeatureToggleConfigProvider.__init__(self)\n try:\n LOG.info(\"Loading feature toggle config from AppConfig...\")\n # Lambda function has 120 seconds limit\n # (5 + 25) * 2, 60 seconds maximum timeout duration\n client_config = Config(connect_timeout=5, read_timeout=25, retries={\"total_max_attempts\": 2})\n self.app_config_client = boto3.client(\"appconfig\", config=client_config)\n response = self.app_config_client.get_configuration(\n Application=application_id,\n Environment=environment_id,\n Configuration=configuration_profile_id,\n ClientId=\"FeatureToggleAppConfigConfigProvider\",\n )\n binary_config_string = response[\"Content\"].read()\n self.feature_toggle_config = json.loads(binary_config_string.decode(\"utf-8\"))\n LOG.info(\"Finished loading feature toggle config from AppConfig.\")\n except Exception as ex:\n LOG.error(\"Failed to load config from AppConfig: {}. Using empty config.\".format(ex))\n # There is chance that AppConfig is not available in a particular region.\n self.feature_toggle_config = json.loads(\"{}\")\n\n @property\n def config(self):\n return self.feature_toggle_config\n", "sub_path": "samtranslator/feature_toggle/feature_toggle.py", "file_name": "feature_toggle.py", "file_ext": "py", "file_size_in_byte": 5327, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 98, "usage_type": "call"}, {"api_name": "botocore.config.Config", "line_number": 114, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 115, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 123, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 128, "usage_type": "call"}]} +{"seq_id": "304931215", "text": "import os\nimport sys\npardir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nsys.path.append(pardir)\nfrom channel_mask_generator import ChannelMaskGenerator\nfrom dense_mask_generator import DenseMaskGenerator\nfrom dataset import *\nfrom result_save_visualization import *\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nimport time\n\ndata_dict = {'epoch': [], 'time': [], 'train_loss': [], 'train_acc': [], 'val_loss': [], 'val_acc': []}\n\n# パラメータ利用, 全結合パラメータの凍結\nnew_net = parameter_use('./result3/pkl1/original_train_epoch50.pkl')\n# 全結合層、畳み込み層のリスト\ndense_list = [module for module in new_net.modules() if isinstance(module, nn.Linear)]\nconv_list = [module for module in new_net.modules() if isinstance(module, nn.Conv2d)]\nconv_count = len(conv_list)\n# for dense in dense_list:\n# dense.weight.requires_grad = False\n\noptimizer = optim.SGD(new_net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n\n# マスクのオブジェクト\nch_mask = [ChannelMaskGenerator() for _ in range(conv_count)]\nde_mask = [DenseMaskGenerator() for _ in dense_list]\nfor i, dense in enumerate(dense_list):\n de_mask[i].mask = np.where(np.abs(dense.weight.data.clone().cpu().detach().numpy()) == 0, 0, 1)\ninv_prune_ratio = 10\n\n# channel_pruning\nfor count in range(1, inv_prune_ratio + 9):\n print(f'\\nchannel pruning: {count}')\n # ノルムの合計を保持\n channel_l1norm_for_each_layer = [list() for _ in range(conv_count)]\n\n # ノルムの取得, 昇順にソート\n for i, conv in enumerate(conv_list):\n channel_l1norm_for_each_layer[i] = [np.sum(torch.abs(param).cpu().detach().numpy()) for param in conv.weight]\n channel_l1norm_for_each_layer[i].sort()\n\n # 枝刈り本体\n with torch.no_grad():\n for i in range(len(conv_list)):\n threshold = channel_l1norm_for_each_layer[i][int(conv_list[i].out_channels / inv_prune_ratio * count)]\\\n if count <= 9 else channel_l1norm_for_each_layer[i][int(conv_list[i].out_channels *\n (9 / inv_prune_ratio + (count - 9) / inv_prune_ratio ** 2))]\n save_mask = ch_mask[i].generate_mask(conv_list[i].weight.data.clone(),\n None if i == 0 else conv_list[i - 1].weight.data.clone(), threshold)\n conv_list[i].weight.data *= torch.tensor(save_mask, device=device, dtype=dtype)\n\n # パラメータの割合\n weight_ratio = [np.count_nonzero(conv.weight.cpu().detach().numpy()) / np.size(conv.weight.cpu().detach().numpy())\n for conv in conv_list]\n\n # 枝刈り後のチャネル数\n channel_num_new = [conv.out_channels - ch_mask[i].channel_number(conv.weight) for i, conv in enumerate(conv_list)]\n\n for i in range(conv_count):\n print(f'conv{i + 1}_param: {weight_ratio[i]:.4f}', end=\", \" if i != conv_count - 1 else \"\\n\")\n for i in range(conv_count):\n print(f'channel_number{i + 1}: {channel_num_new[i]}', end=\", \" if i != conv_count - 1 else \"\\n\")\n\n accuracy = 0\n f_num_epochs = 10\n # finetune\n start = time.time()\n for epoch in range(f_num_epochs):\n # train\n new_net.train()\n train_loss, train_acc = 0, 0\n for i, (images, labels) in enumerate(train_loader):\n images, labels = images.to(device), labels.to(device)\n optimizer.zero_grad()\n outputs = new_net(images, False)\n loss = criterion(outputs, labels)\n train_loss += loss.item()\n train_acc += (outputs.max(1)[1] == labels).sum().item()\n loss.backward()\n optimizer.step()\n with torch.no_grad():\n for j, conv in enumerate(conv_list):\n conv.weight.data *= torch.tensor(ch_mask[j].mask, device=device, dtype=dtype)\n for k, dense in enumerate(dense_list):\n dense.weight.data *= torch.tensor(de_mask[k].mask, device=device, dtype=dtype)\n avg_train_loss, avg_train_acc = train_loss / len(train_loader.dataset), train_acc / len(train_loader.dataset)\n\n # val\n new_net.eval()\n val_loss, val_acc = 0, 0\n with torch.no_grad():\n for images, labels in test_loader:\n labels = labels.to(device)\n outputs = new_net(images.to(device), False)\n loss = criterion(outputs, labels)\n val_loss += loss.item()\n val_acc += (outputs.max(1)[1] == labels).sum().item()\n avg_val_loss, avg_val_acc = val_loss / len(test_loader.dataset), val_acc / len(test_loader.dataset)\n process_time = time.time() - start\n accuracy = avg_val_acc\n\n print(f'epoch [{epoch + 1}/{f_num_epochs}], time: {process_time:.4f}, train_loss: {avg_train_loss:.4f}'\n f', train_acc: {avg_train_acc:.4f}, 'f'val_loss: {avg_val_loss:.4f}, val_acc: {avg_val_acc:.4f}')\n\n # 結果の保存\n input_data = [epoch + 1, process_time, avg_train_loss, avg_train_acc, avg_val_loss, avg_val_acc]\n result_save('./result3/csv1/dense_conv_prune_parameter.csv', data_dict, input_data)\n\n # パラメータの保存\n if count == 6:\n parameter_save('./result3/pkl1/dense_conv_prune_conv60per.pkl', new_net)\n parameter_save('./result3/pkl1/dense_conv_prune_conv60per_copy.pkl', new_net)\n elif count == 8:\n parameter_save('./result3/pkl1/dense_conv_prune_conv80per.pkl', new_net)\n parameter_save('./result3/pkl1/dense_conv_prune_conv80per_copy.pkl', new_net)\n elif count == 13:\n parameter_save('./result3/pkl1/dense_conv_prune_conv94per.pkl', new_net)\n parameter_save('./result3/pkl1/dense_conv_prune_conv94per_copy.pkl', new_net)\n break\n else:\n pass\n", "sub_path": "scripts/prune_dense_conv.py", "file_name": "prune_dense_conv.py", "file_ext": "py", "file_size_in_byte": 5862, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 26, "usage_type": "name"}, {"api_name": "channel_mask_generator.ChannelMaskGenerator", "line_number": 29, "usage_type": "call"}, {"api_name": "dense_mask_generator.DenseMaskGenerator", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.count_nonzero", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 57, "usage_type": "call"}, {"api_name": "time.time", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 95, "usage_type": "call"}, {"api_name": "time.time", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "321891510", "text": "# coding:utf-8\n__author__ = 'cwang14'\n\nfrom collections import defaultdict\nfrom typing import List\n\n\nclass Solution:\n def findLadders(self, beginWord: str, endWord: str, wordList: List[str]) -> List[List[str]]:\n wordset = set(wordList)\n if endWord not in wordset:\n return []\n _m, size = defaultdict(list), len(beginWord)\n begin_q, end_q, b2e = {beginWord}, {endWord}, True\n alpha = ''.join([chr(ord('a')+i) for i in range(26)])\n while begin_q:\n if len(begin_q) > len(end_q):\n begin_q, end_q, b2e = end_q, begin_q, not b2e\n print(wordset, begin_q)\n wordset -= begin_q\n _q = set()\n for word in begin_q:\n for i in range(size):\n for l in alpha:\n neighber = word[:i] + l + word[i+1:]\n if neighber in wordset:\n print('hit')\n _q.add(neighber)\n if b2e:\n _m[neighber].append(word)\n else:\n _m[word].append(neighber)\n\n if _q & end_q:\n res = [[endWord]]\n while res[0][0] != beginWord:\n res = [[x]+i for i in res for x in _m[i[0]]]\n return res\n\n begin_q = _q\n return []\n", "sub_path": "Week_05/126_单词接龙II_双向遍历_交集终止.py", "file_name": "126_单词接龙II_双向遍历_交集终止.py", "file_ext": "py", "file_size_in_byte": 1426, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.List", "line_number": 9, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "500237236", "text": "#!/usr/bin/env python3\n\nimport argparse\nimport logging\nimport subprocess\n\nlog = logging.getLogger(__name__)\nlog.setLevel(logging.DEBUG)\n\ndef suricata(args):\n if args.alert_summary:\n subprocess.check_output(['alertsummary.sh', './out/fast.log'])\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n subparsers = parser.add_subparsers(help='sub-command help')\n\n # Suricata subparser\n suri_parser = subparsers.add_parser('suri', help='For parsing Suricata log outputs')\n suri_parser.add_argument('--alert_summary', action='store_true', help='Print list of alerts found.')\n parser_foo.set_defaults(func=surcicata)\n\n args = parser.parse_args()\n args.func(args)\n", "sub_path": "mtracks.py", "file_name": "mtracks.py", "file_ext": "py", "file_size_in_byte": 703, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 8, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 12, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "193632865", "text": "from datetime import datetime\n\n\nclass Pump:\n def __init__(self, id):\n self.id = id\n self.isPumpOn = False\n self.history = []\n\n def startTimer(self):\n if(self.isPumpOn == False):\n currentTime = datetime.now()\n self.history.append(HistoryItem(startTime=currentTime))\n self.isPumpOn = True\n else:\n print(f\"Pump: {self.id} is already on\")\n\n def stopTimer(self):\n if(self.isPumpOn == True):\n currentTime = datetime.now()\n self.history[-1].endTime = currentTime\n self.isPumpOn = False\n else:\n print(f\"Pump: {self.id} is already off\")\n\n def printAllLogs(self):\n for item in self.history:\n print(f\"startTime: {item.startTime} endTime: {item.endTime}\")\n\n\nclass HistoryItem:\n def __init__(self, startTime=None, endTime=None):\n self.startTime = startTime\n self.endTime = endTime\n", "sub_path": "Pump.py", "file_name": "Pump.py", "file_ext": "py", "file_size_in_byte": 952, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.now", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "179767949", "text": "from argparse import ArgumentParser, Namespace\nfrom typing import List\n\nfrom darker.argparse_helpers import NewlinePreservingFormatter\n\nISORT_INSTRUCTION = \"Please run `pip install 'darker[isort]'`\"\n\n\ndef parse_command_line(argv: List[str]) -> Namespace:\n description = [\n \"Re-format Python source files by using\",\n \"- `isort` to sort Python import definitions alphabetically within logical\"\n \" sections\",\n \"- `black` to re-format code changed since the last Git commit\",\n ]\n try:\n import isort\n except ImportError:\n isort = None\n description.extend(\n [\"\", f\"{ISORT_INSTRUCTION} to enable sorting of import definitions\"]\n )\n parser = ArgumentParser(\n description=\"\\n\".join(description), formatter_class=NewlinePreservingFormatter,\n )\n parser.add_argument(\"src\", nargs=\"*\")\n isort_help = [\"Also sort imports using the `isort` package\"]\n if not isort:\n isort_help.append(f\". {ISORT_INSTRUCTION} to enable usage of this option.\")\n parser.add_argument(\n \"--diff\",\n action=\"store_true\",\n help=\"Don't write the files back, just output a diff for each file on stdout\",\n )\n parser.add_argument(\n \"-i\", \"--isort\", action=\"store_true\", help=\"\".join(isort_help),\n )\n parser.add_argument(\n \"-c\",\n \"--config\",\n metavar=\"PATH\",\n help=\"Ask `black` to read configuration from PATH.\",\n )\n parser.add_argument(\n \"-v\",\n \"--verbose\",\n dest=\"log_level\",\n action=\"append_const\",\n const=10,\n help=\"Show steps taken and summarize modifications\",\n )\n parser.add_argument(\n \"-q\",\n \"--quiet\",\n dest=\"log_level\",\n action=\"append_const\",\n const=-10,\n help=\"Reduce amount of output\",\n )\n parser.add_argument(\n \"--version\", action=\"store_true\", help=\"Show the version of `darker`\"\n )\n parser.add_argument(\n \"-S\",\n \"--skip-string-normalization\",\n action=\"store_true\",\n dest=\"skip_string_normalization\",\n help=\"Don't normalize string quotes or prefixes\",\n )\n parser.add_argument(\n \"-l\",\n \"--line-length\",\n type=int,\n dest=\"line_length\",\n help=\"How many characters per line to allow [default: 88]\",\n )\n return parser.parse_args(argv)\n", "sub_path": "src/darker/command_line.py", "file_name": "command_line.py", "file_ext": "py", "file_size_in_byte": 2376, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.List", "line_number": 9, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 23, "usage_type": "call"}, {"api_name": "darker.argparse_helpers.NewlinePreservingFormatter", "line_number": 24, "usage_type": "name"}, {"api_name": "argparse.Namespace", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "11399717", "text": "import peewee as orm\nfrom flask_admin.contrib.peewee import ModelView\nfrom flask import url_for, redirect\nimport flask_login\nimport requests\n\nfrom curpud import db\nfrom curpud.auth.models import User\n\n\n# Translations\nissn_label = 'ISSN'\nname_label = 'Nombre'\nshort_name_label = 'Nombre Corto'\ndatabase_label = 'Base de Datos'\nweb_page_label = 'Página Web'\nrelevance_label = 'Relevancia'\ndoi_label = 'DOI'\nowner_label = 'Autor'\njournal_label = 'Revista'\nproofs_file_label = 'Pruebas'\n\n\nclass BaseModel(orm.Model):\n class Meta:\n database = db\n\n\nclass BaseModelView(ModelView):\n can_export = True\n details_modal = True\n\n def is_accessible(self):\n auser = flask_login.current_user\n if auser.is_anonymous:\n return False\n user = User.get(User.login == auser.id)\n return auser.is_authenticated and user.is_admin\n\n def inaccessible_callback(self, name, **kwargs):\n # redirect to login page if user doesn't have access\n return redirect(url_for('auth.login'))\n\n\nclass Relevance(BaseModel):\n name = orm.CharField(\n unique=True\n )\n\n def __str__(self):\n return self.name\n\n\nclass RelevanceView(BaseModelView):\n column_labels = {\n 'name': name_label\n }\n\n form_args = {\n 'name': {\n 'label': name_label\n }\n }\n\n column_searchable_list = ['name']\n\n\nclass DataBase(BaseModel):\n name = orm.CharField(\n unique=True\n )\n web_page = orm.TextField(\n null=True\n )\n relevance = orm.ForeignKeyField(\n Relevance,\n related_name='databases'\n )\n\n def __str__(self):\n return self.name\n\n\nclass DataBaseView(BaseModelView):\n column_labels = {\n 'name': name_label,\n 'web_page': web_page_label,\n 'relevance': relevance_label\n }\n\n form_args = {\n 'name': {\n 'label': name_label\n },\n 'web_page': {\n 'label': web_page_label\n },\n 'relevance': {\n 'label': relevance_label\n }\n }\n\n column_searchable_list = ['name', 'web_page', Relevance.name]\n\n\nclass Journal(BaseModel):\n issn = orm.CharField(\n unique=True\n )\n name = orm.CharField(\n unique=True\n )\n short_name = orm.CharField(\n null=True\n )\n sjr = orm.DoubleField(\n null=True\n )\n index = orm.IntegerField(\n null=True\n )\n database = orm.ForeignKeyField(\n DataBase,\n related_name='journals'\n )\n\n def __str__(self):\n return \"{} ({})\".format(self.name, self.issn)\n\n\nclass JournalView(BaseModelView):\n column_labels = {\n 'issn': issn_label,\n 'name': name_label,\n 'short_name': short_name_label,\n 'database': database_label\n }\n\n form_args = {\n 'issn': {\n 'label': issn_label\n },\n 'name': {\n 'label': name_label\n },\n 'short_name': {\n 'label': short_name_label\n },\n 'database': {\n 'label': database_label\n }\n }\n\n column_searchable_list = ['issn', 'name', 'short_name', DataBase.name]\n\n\nclass Teacher(BaseModel):\n user = orm.ForeignKeyField(User)\n\n\nclass Publication(BaseModel):\n doi = orm.CharField(unique=True)\n owner = orm.CharField()\n journal = orm.ForeignKeyField(Journal)\n proofs_file = orm.CharField(unique=True)\n\n @property\n def title(self):\n return self._get_json()['message']['title'][0]\n\n @property\n def indexed_date(self):\n date = self._get_json()['message']['indexed']['date-parts'][0]\n return \"/\".join(map(str, date))\n\n @property\n def pages(self):\n return self._get_json()['message']['page']\n\n @property\n def cites_number(self):\n return self._get_json()['message']['reference-count']\n\n def _get_json(self):\n url = \"http://api.crossref.org/works/{}\".format(self.doi)\n r = requests.get(url)\n return r.json()\n\n def to_dict(self):\n j = self.journal\n return {\n 'doi': self.doi,\n 'numero_citas': self.cites_number,\n 'revista': {\n \"nombre\": j.name,\n \"issn\": j.issn,\n \"relevancia\": j.database.relevance.name,\n \"ranking\": j.sjr,\n \"indexacion\": j.index\n }\n }\n\n\nclass PublicationView(BaseModelView):\n column_labels = {\n 'doi': doi_label,\n 'owner': owner_label,\n 'journal': journal_label,\n 'proofs_file': proofs_file_label\n }\n\n form_args = {\n 'doi': {\n 'label': doi_label\n },\n 'owner': {\n 'label': owner_label\n },\n 'journal': {\n 'label': journal_label\n },\n 'proofs_file': {\n 'label': proofs_file_label\n }\n }\n\n column_searchable_list = ['doi', 'owner', Journal.name]\n", "sub_path": "curpud/publications/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 4900, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "peewee.Model", "line_number": 24, "usage_type": "attribute"}, {"api_name": "curpud.db", "line_number": 26, "usage_type": "name"}, {"api_name": "flask_admin.contrib.peewee.ModelView", "line_number": 29, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 34, "usage_type": "attribute"}, {"api_name": "curpud.auth.models.User.get", "line_number": 37, "usage_type": "call"}, {"api_name": "curpud.auth.models.User", "line_number": 37, "usage_type": "name"}, {"api_name": "curpud.auth.models.User.login", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 42, "usage_type": "call"}, {"api_name": "peewee.CharField", "line_number": 46, "usage_type": "call"}, {"api_name": "peewee.CharField", "line_number": 69, "usage_type": "call"}, {"api_name": "peewee.TextField", "line_number": 72, "usage_type": "call"}, {"api_name": "peewee.ForeignKeyField", "line_number": 75, "usage_type": "call"}, {"api_name": "peewee.CharField", "line_number": 107, "usage_type": "call"}, {"api_name": "peewee.CharField", "line_number": 110, "usage_type": "call"}, {"api_name": "peewee.CharField", "line_number": 113, "usage_type": "call"}, {"api_name": "peewee.DoubleField", "line_number": 116, "usage_type": "call"}, {"api_name": "peewee.IntegerField", "line_number": 119, "usage_type": "call"}, {"api_name": "peewee.ForeignKeyField", "line_number": 122, "usage_type": "call"}, {"api_name": "peewee.ForeignKeyField", "line_number": 158, "usage_type": "call"}, {"api_name": "curpud.auth.models.User", "line_number": 158, "usage_type": "argument"}, {"api_name": "peewee.CharField", "line_number": 162, "usage_type": "call"}, {"api_name": "peewee.CharField", "line_number": 163, "usage_type": "call"}, {"api_name": "peewee.ForeignKeyField", "line_number": 164, "usage_type": "call"}, {"api_name": "peewee.CharField", "line_number": 165, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 186, "usage_type": "call"}]} +{"seq_id": "163252217", "text": "import cv2\nimport numpy as np\nimport os\nimport time\n\nshow_frames = False\n\ndef main():\n starting_bb = (478, 303, 844-478, 668-303)\n \n sizes = (\n (1920, 1080),\n (1366, 768),\n (640, 360),\n (320, 180),\n )\n \n trackers = {\n 'MOSSE': cv2.TrackerMOSSE_create,\n 'CSRT': cv2.TrackerCSRT_create,\n 'MedianFlow': cv2.TrackerMedianFlow_create\n }\n \n result_file = open('evaluation_result.txt', 'w')\n \n for tracker_name in trackers:\n print('\\n***Tracker: {}***'.format(tracker_name))\n result_file.write('\\n***Tracker: {}***\\n'.format(tracker_name))\n \n for size in sizes:\n print('Size: {}'.format(size))\n result_file.write('Size: {}\\n'.format(size))\n \n tracker = trackers[tracker_name]()\n tracker_initialized = False\n \n scale_x = size[0]/1920\n scale_y = size[1]/1080\n bb = (starting_bb[0] * scale_x, starting_bb[1] * scale_y, starting_bb[2] * scale_x, starting_bb[3] * scale_y)\n \n tp = 0\n fp = 0\n fn = 0\n tn = 0\n \n start_time = time.time()\n i = 0\n \n for frame_no in range(108, 438, 3):\n i += 1\n print('Processing frame {}'.format(frame_no))\n frame = cv2.imread(os.path.join('frames', '{}.jpg'.format(frame_no)))\n gt = cv2.imread(os.path.join('gt', '{}.png'.format(frame_no)))\n gt_bb = cv2.imread(os.path.join('gt_bb', '{}.png'.format(frame_no)))\n \n frame = cv2.resize(frame, size)\n gt = cv2.resize(gt, size)\n gt_bb = cv2.resize(gt_bb, size)\n \n if not tracker_initialized:\n tracker_initialized = True\n tracker.init(frame, bb)\n else:\n ret, bb = tracker.update(frame)\n \n print(bb)\n \n p1 = (int(bb[0]), int(bb[1]))\n p2 = (int(bb[0] + bb[2]), int(bb[1] + bb[3]))\n cv2.rectangle(frame, p1, p2, (255,0,0), 2)\n \n gt = cv2.cvtColor(gt, cv2.COLOR_BGR2GRAY)\n detection = np.zeros(gt.shape, dtype=np.uint8)\n cv2.rectangle(detection, p1, p2, 255, -1)\n \n tp += np.sum(np.logical_and((detection==255),(gt==255)))\n fp += np.sum(np.logical_and((detection==255),(gt==0)))\n fn += np.sum(np.logical_and((detection==0), (gt==255)))\n tn += np.sum(np.logical_and((detection==0), (gt==0)))\n \n if show_frames:\n cv2.imshow('', frame)\n cv2.waitKey(1)\n \n end_time = time.time()\n diff_time = end_time - start_time\n \n precision = tp / (tp + fp)\n recall = tp / (tp + fn)\n accuracy = (tp + tn) / (tp + tn + fp + fn)\n f1_score = (2*tp) / (2*tp + fp + fn)\n \n print()\n \n print('precision', precision)\n print('recall', recall)\n print('accuracy', precision)\n print('f1_score', recall)\n \n result_file.write('Precision: {:.3f}\\n'.format(precision))\n result_file.write('Recall: {:.3f}\\n'.format(recall))\n result_file.write('Accuracy: {:.3f}\\n'.format(precision))\n result_file.write('F1_score: {:.3f}\\n'.format(recall))\n result_file.write('Elapsed: {:.3f}s\\n'.format(diff_time))\n result_file.write('Avg frame took: {:.3f}s\\n'.format(diff_time / i))\n result_file.write('=======================\\n')\n \n print('Took {} seconds, avg frame processing: {:.3f} seconds'.format(diff_time, diff_time / i))\n \nif __name__ == '__main__':\n main()", "sub_path": "src/test_sequence/evaluate_trackers.py", "file_name": "evaluate_trackers.py", "file_ext": "py", "file_size_in_byte": 4016, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.TrackerMOSSE_create", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.TrackerCSRT_create", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.TrackerMedianFlow_create", "line_number": 21, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 73, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 83, "usage_type": "call"}, {"api_name": "time.time", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "181020922", "text": "import itertools\nfrom chan import utils\n\nDVACH_URL = 'http://2ch.hk'\n\n\nclass Page(object):\n\n \"\"\"\n Represents a board's page. It has a list of threads presented on the page.\n\n By default threads will not be fully loaded\n and will have only 3 posts from the head.\n \"\"\"\n\n def __init__(self, board_name, index):\n self._threads = []\n self.board_name = board_name\n self.index = index\n self._url = None\n self._json_url = None\n\n def _format_url(self, fmt):\n \"\"\"Return string representation of url.\"\"\"\n index = 'index' if (self.index == 0) else str(self.index)\n return '{}/{}/{}.{}'.format(\n DVACH_URL, self.board_name, index, fmt)\n\n def __getitem__(self, index):\n return self.threads[index]\n\n def __len__(self):\n return len(self.threads)\n\n def __repr__(self):\n return 'Page /{}/{}'.format(self.board_name, self.index)\n\n @property\n def url(self):\n \"\"\"Property which represents url of board page.\"\"\"\n if not self._url:\n self._url = self._format_url('html')\n return self._url\n\n @property\n def json_url(self):\n \"\"\"Property which represents url of json page.\"\"\"\n if not self._json_url:\n self._json_url = self._format_url('json')\n return self._json_url\n\n @property\n def threads(self):\n \"\"\"Property which represents list of board threads.\"\"\"\n if not self._threads:\n page_json = utils.load_json(self.json_url)\n self._threads = [Thread(self.board_name, thread)\n for thread in page_json['threads']]\n return self._threads\n\n\nclass Thread(object):\n\n \"\"\"\n Represents a 2ch.hk thread.\n\n If initialization is done by JSON then thread has only original post.\n Initialization by thread's number gathers all the data.\n \"\"\"\n\n def __init__(self, board_name, data=None, num=None):\n self.board_name = board_name\n self.num = None\n self.original_post = None\n self.posts = None\n self._url = None\n self._json_url = None\n self.title = None\n self.posts_count = None\n self.files_count = None\n\n if num:\n # initialization by thread number\n self.num = str(num)\n data = utils.load_json(self.json_url)\n elif not data:\n # no data, no num\n raise Exception('Invalid set of initial arguments')\n self._parse_json(data)\n\n def __len__(self):\n return len(self.posts)\n\n def __repr__(self):\n return 'Thread /{}/#{}'.format(self.board_name, self.num)\n\n def _parse_json(self, data):\n \"\"\"Get required fields from JSON and inits fields of the class.\"\"\"\n self.files_count = int(data.get('files_count'))\n self.posts_count = int(data.get('posts_count'))\n\n # understanding by unique keys what kind of json we are dealing with\n if data.get('posts'):\n # dealing with thread's data from page.json\n self.num = str(data.get('thread_num'))\n self.posts = [Post(self, data.get('posts')[0])]\n elif data.get('num'):\n # dealing with thread's data from catalog.json\n self.num = str(data.get('num'))\n self.posts = [Post(self, data)]\n else:\n # dealing with thread.json\n self.posts = [Post(self, post_data)\n for post_data in data.get('threads')[0]['posts']]\n\n self.original_post = self.posts[0]\n\n def _format_url(self, fmt):\n \"\"\"Return string representation of url.\"\"\"\n return '{}/{}/res/{}.{}'.format(\n DVACH_URL, self.board_name, self.num, fmt)\n\n @property\n def url(self):\n \"\"\"Property which represents url of board page.\"\"\"\n if not self._url:\n self._url = self._format_url('html')\n return self._url\n\n @property\n def json_url(self):\n \"\"\"Property which represents url of json page.\"\"\"\n if not self._json_url:\n self._json_url = self._format_url('json')\n return self._json_url\n\n def update(self):\n \"\"\"Update thread's content to the latest data.\"\"\"\n\n thread_json = utils.load_json(self.json_url)\n self.title = thread_json['title']\n self.files_count = int(thread_json['files_count'])\n self.posts_count = int(thread_json['posts_count'])\n\n posts_length = len(self) - 1 # OP is omitted\n gap = self.posts_count - posts_length\n if gap:\n missed_posts = thread_json['threads'][0]['posts'][-gap:]\n self.posts += [Post(self, data) for data in missed_posts]\n\n def __getitem__(self, index):\n return self.posts[index]\n\n @property\n def pictures(self):\n \"\"\"\n Return list of AttachedFile objects of all pictures in the thread.\n \"\"\"\n return list(itertools.chain.from_iterable(\n post.pictures for post in self.posts))\n\n @property\n def webms(self):\n \"\"\"\n Return list of AttachedFile objects of all wemb files in the thread.\n \"\"\"\n return list(itertools.chain.from_iterable(\n post.webms for post in self.posts))\n\n\nclass Post(object):\n\n \"\"\"\n Represents a single post in the thread.\n \"\"\"\n\n def __init__(self, thread, data):\n self.thread = thread\n self.message = data.get('comment')\n self.num = str(data.get('num'))\n self.url = '{}#{}'.format(thread.url, self.num)\n self.attachments = [AttachedFile(self, attachment)\n for attachment in data.get('files')]\n self._pictures = None\n self._webms = None\n\n def __repr__(self):\n return 'Post /{}/#{}'.format(self.thread.num, self.num)\n\n @property\n def pictures(self):\n if not self._pictures:\n self._pictures = [attachment for attachment in self.attachments\n if attachment.is_picture()]\n return self._pictures\n\n @property\n def webms(self):\n if not self._webms:\n self._webms = [attachment for attachment in self.attachments\n if attachment.is_webm()]\n return self._webms\n\n\nclass AttachedFile(object):\n\n \"\"\"\n Represents a file related to post.\n \"\"\"\n\n def __init__(self, post, data):\n self.name = data.get('name')\n self.size = int(data.get('size'))\n self.type = data.get('type')\n self.url = '{}/{}'.format(DVACH_URL, data.get('path'))\n\n def __repr__(self):\n return 'File {}'.format(self.name)\n\n def is_picture(self):\n return self.name.endswith(('.jpg', '.png'))\n\n def is_webm(self):\n return self.name.endswith('.webm')\n\n\ndef get_preview(board):\n \"\"\"\n Return a dictionary which represents light version of threads.\n\n Keys in result dictionary are thread numbers.\n Values are titles of original posts.\n \"\"\"\n # TODO: check if board is valid\n url = '{}/{}/threads.json'.format(DVACH_URL, board)\n data = utils.load_json(url)\n return {\n thread['num']: thread['subject'] for thread in data.get('threads')\n }\n\n\ndef get_all_threads(board):\n \"\"\"\n Return a list of Thread objects gathered from board.\n\n The list consists of all threads from board.\n Each element from this list has only original post.\n \"\"\"\n # TODO: check if board is valid\n url = '{}/{}/catalog.json'.format(DVACH_URL, board)\n data = utils.load_json(url)\n return [Thread(board, thread_data) for thread_data in data.get('threads')]\n", "sub_path": "chan/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 7576, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "chan.utils.load_json", "line_number": 56, "usage_type": "call"}, {"api_name": "chan.utils", "line_number": 56, "usage_type": "name"}, {"api_name": "chan.utils.load_json", "line_number": 85, "usage_type": "call"}, {"api_name": "chan.utils", "line_number": 85, "usage_type": "name"}, {"api_name": "chan.utils.load_json", "line_number": 140, "usage_type": "call"}, {"api_name": "chan.utils", "line_number": 140, "usage_type": "name"}, {"api_name": "itertools.chain.from_iterable", "line_number": 159, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 159, "usage_type": "attribute"}, {"api_name": "itertools.chain.from_iterable", "line_number": 167, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 167, "usage_type": "attribute"}, {"api_name": "chan.utils.load_json", "line_number": 236, "usage_type": "call"}, {"api_name": "chan.utils", "line_number": 236, "usage_type": "name"}, {"api_name": "chan.utils.load_json", "line_number": 251, "usage_type": "call"}, {"api_name": "chan.utils", "line_number": 251, "usage_type": "name"}]} +{"seq_id": "558275892", "text": "import pyomo.environ as pe\nfrom pyomo.environ import SolverFactory, value\nfrom pyomo.opt import check_optimal_termination\nimport pandas as pd\nfrom datetime import datetime\nfrom . import reconstruction as recon\nimport pyutilib.misc.timing as timing\nimport json\nimport matplotlib.pyplot as plt\n\ndef run_multinode_mobility_time_varying_decay_lsq(cm_rep_cases, populations, mobility, sigma, gamma, report_delay, reporting_factor, analysis_window, Cdates, verbose=False):\n \"\"\"\n This function solves the least-squares inference inference formulation\n using the decay-based reconstruction function.\n\n Parameters\n ----------\n\n cm_rep_cases : a dataframe of *new* cases reported in\n each time period; each column in the dataframe is a separate time\n series\n populations : a dataframe with a single column that represents the\n population for different columns in cm_rep_cases\n sigma : float\n the rate constant for cases leaving the E compartment (1/incubation period)\n gamma : float\n the rate constant for leaving the I compartment. This will be multiplied\n by 3 when applied to each of the three I compartments\n report_delay : int\n the number of days between when someone is infected and when\n they will become a reported case (This should only shift the data\n and not impact the inference results.)\n reporting_factor : float\n The reporting factor (>1). If set to 5 this means 1 in 5 cases is reported\n analysis_window : dict or None\n This is a dictionary indicating the window of time that should be used \n in the objective function. If None, then the full set of data will be used.\n The key \"days\" indicates the number of days from the end of the data that \n should be used in the objective function.\n verbose : bool\n If true, then more output is printed to the console when the analysis is run\n \"\"\"\n # check the inputs\n assert sigma > 0\n assert gamma > 0\n assert report_delay > 0\n assert (populations > 0).all().all() == True\n assert reporting_factor >= 1\n\n for i in [-1]: #range(-6,2):\n # create the Pyomo optimization formulation\n regu = 1*10**i\n m = create_inference_regu_formulation(\n Cdates=Cdates,\n cumulative_reported_cases=cm_rep_cases,\n populations=populations,\n mobility=mobility,\n sigma=sigma,\n gamma_1=gamma*3,\n gamma_2=gamma*3,\n gamma_3=gamma*3,\n report_delay=report_delay,\n reporting_factor=reporting_factor,\n delta_beta_regu=regu,\n analysis_window=analysis_window,\n verbose=verbose\n )\n\n # call the solver\n solver = SolverFactory('ipopt')\n solver.options['tol']=1e-8\n status = solver.solve(m, tee=True) #verbose)\n m.display()\n\n # Check that the solve completed successfully\n if check_optimal_termination(status) == False:\n return {'est_beta': None, 'status': 'failed', 'msg': 'Unknown solver error.'}\n\n results = dict()\n results['date'] = [m.DATES[t] for t in m.TIMES]\n for i in m.NODES:\n betas = list()\n transmissions = 0\n for t in m.TIMES:\n transmissions += m.T_data[i][t]\n if transmissions < 20:\n betas.append(None)\n else:\n betas.append(pe.value(m.beta[i,t]))\n results[i] = betas\n\n df = pd.DataFrame(results)\n print(df)\n pd.set_option('display.max_rows', None)\n print(df.std())\n df.plot(x='date')\n plt.show()\n\n return {'results': results}\n\n \"\"\" OLD RESULTS STRUCTURE\n # build the dictionary of results\n betas = list()\n wdates = list()\n status = list()\n fips = list()\n pops = list()\n est_transmissions = list()\n window_days = list()\n for i in m.NODES:\n for w in m.WINDOWS:\n wdates.append(m.DATES[w])\n fips.append(i)\n pops.append(populations[i])\n est_transmissions.append(m.window_transmissions[i][w])\n window_days.append(m.window_days)\n if m.beta[i,w].stale == True or m.window_transmissions[i][w] <= 2:\n status.append('invalid_insufficient_data')\n betas.append(None)\n else:\n status.append('ok')\n betas.append(value(m.beta[i,w])) \n\n ret = {'dates': wdates, 'est_beta':betas, 'status':status, 'population': pops, 'est_transmissions':est_transmissions, 'window_days': window_days, 'FIPS':fips}\n #df = pd.DataFrame(ret)\n #df.to_csv('foo.csv')\n return ret\n \"\"\"\n\ndef create_inference_regu_formulation(Cdates, cumulative_reported_cases, populations, mobility, sigma, gamma_1, gamma_2, gamma_3, report_delay, reporting_factor, delta_beta_regu, analysis_window, verbose=False):\n \"\"\"\n Creates a one-step-ahead inference model using a decay\n model with 3 I compartments. The model is written in terms of absolute\n numbers of cases (not ln-transform). The model combines estimates across\n multiple time series, one for each node.\n\n Parameters\n ----------\n Cdates: list of datetime objects\n The list of datetime objects that correspond to the dates for the\n cumulative_reported_cases\n cumulative_reported_cases : a dataframe of *new* cases reported in\n each time period; each column in the dataframe is a separate time\n series\n populations : a dataframe with a single column that represents the\n population for different columns in cumulative_reported_cases\n sigma : float\n the rate constant for cases leaving the E compartment (1/incubation period)\n gamma_1 : float\n the rate constant for leaving the I1 compartment.\n gamma_2 : float\n the rate constant for leaving the I2 compartment.\n gamma_3 : float\n the rate constant for leaving the I3 compartment.\n report_delay : int\n the number of days between when someone is infected and when\n they will become a reported case (This should only shift the data\n and not impact the inference results.)\n analysis_window : dict or None\n This is a dictionary indicating the window of time that should be used \n in the objective function. If None, then the full set of data will be used.\n The key \"days\" indicates the number of days from the end of the data that \n should be used in the objective function.\n reporting_factor : float\n The reporting factor (>1). If set to 5 this means 1 in 5 cases is reported\n\n \"\"\"\n if len(analysis_window) != 0:\n raise NotImplementedError('analysis_window is not yet implemented for multinode_decay_lsq')\n model = pe.ConcreteModel()\n\n # Cached data\n model.sigma = sigma\n model.gamma_1 = gamma_1\n model.gamma_2 = gamma_2\n model.gamma_3 = gamma_3\n model.eta = 0.5 # fraction of the day spent \"away\"\n model.report_delay = report_delay\n model.reporting_factor = reporting_factor\n model.delta_beta_regu = delta_beta_regu\n\n #model.NODES = pe.Set(initialize=list(range(len(cumulative_reported_cases.keys()))))\n model.NODES = pe.Set(initialize=list(k for k in cumulative_reported_cases.keys()))\n\n model.mobility = dict(mobility)\n model.MOBILITY_TUPLES = list()\n #model.mobility = dict()\n for i in model.NODES:\n if i not in model.mobility:\n model.mobility[i] = {}\n for j in model.mobility[i]:\n if i in model.NODES and j in model.NODES:\n model.MOBILITY_TUPLES.append((i,j))\n model.populations = dict()\n \n model.T_data = dict()\n model.I_data = dict()\n model.S_data = dict()\n for nodeid in model.NODES:\n model.populations[nodeid] = float(populations[nodeid]) # overall population\n cm_rep_cases_node = [v for v in cumulative_reported_cases[nodeid].values]\n\n rdates, rcases, dates, T, S, E, I1, I2, I3, R = \\\n recon.reconstruct_states_deterministic_decay(\n Cdates=Cdates,\n cumulative_reported_cases=cm_rep_cases_node,\n population=model.populations[nodeid],\n sigma=sigma,\n gamma=gamma_1/3,\n reporting_factor=reporting_factor,\n report_delay=report_delay\n )\n model.T_data[nodeid] = T\n model.I_data[nodeid] = dict()\n model.I_data[nodeid]['I1'] = I1\n model.I_data[nodeid]['I2'] = I2\n model.I_data[nodeid]['I3'] = I3\n model.S_data[nodeid] = S\n \n if not hasattr(model, 'TIMES'):\n model.TIMES = pe.Set(initialize=[i for i in range(len(T))], ordered=True)\n if not hasattr(model, 'DATES'):\n model.DATES = dates\n\n model.beta = pe.Var(model.NODES, model.TIMES, initialize=1.0, bounds=(0,None)) # transmission parameter\n # for now, alpha is not used\n # model.alpha = pe.Var(initialize=1.0)\n # model.alpha.fix(1.0)\n\n # define the variable for estimated transmissions\n model.T_hat = pe.Var(model.NODES, model.TIMES, initialize=1.0)\n # infection process\n def _infection_process(m, i, t):\n percent_mobile = 0\n if i in m.mobility:\n percent_mobile = sum(m.mobility[i][j]/m.populations[i] for j in m.mobility[i] if j in m.NODES)\n\n return m.T_hat[i,t] == m.beta[i,t] * (m.I_data[i]['I1'][t] + m.I_data[i]['I2'][t] + m.I_data[i]['I3'][t]) / m.populations[i] * m.S_data[i][t] * (1-m.eta*percent_mobile) \\\n + sum(m.beta[j,t] * (m.I_data[j]['I1'][t] + m.I_data[j]['I2'][t] + m.I_data[j]['I3'][t]) / m.populations[j] * m.S_data[i][t] * (m.eta*m.mobility[i][j]/m.populations[i]) for j in m.mobility[i] if j in m.NODES)\n\n model.infection_process = pe.Constraint(model.NODES, model.TIMES, rule=_infection_process)\n\n model.delta_beta = pe.Var(model.NODES, model.TIMES, initialize=0)\n def _delta_beta_con(m, i, t):\n if t == m.TIMES.first():\n return pe.Constraint.Skip\n return m.delta_beta[i,t] == m.beta[i,t] - m.beta[i,t-1]\n model.delta_beta_con = pe.Constraint(model.NODES, model.TIMES, rule=_delta_beta_con)\n\n # least squares objective function\n def _lse(m):\n return sum( (m.T_hat[i,t] - m.T_data[i][t])**2 for i in m.NODES for t in m.TIMES)\n model.lse = pe.Expression(rule=_lse)\n\n def _regu(m):\n return sum(m.delta_beta[i,t]**2 for i in m.NODES for t in m.TIMES)\n model.regu = pe.Expression(rule=_regu)\n \n def _total_lse(m):\n return m.lse + m.delta_beta_regu * m.regu\n model.total_lse = pe.Objective(rule=_total_lse)\n\n return model\n\n", "sub_path": "epi_inference/formulations/attic/multinode_mobility_time_varying_decay_lsq.py", "file_name": "multinode_mobility_time_varying_decay_lsq.py", "file_ext": "py", "file_size_in_byte": 10703, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pyomo.environ.SolverFactory", "line_number": 70, "usage_type": "call"}, {"api_name": "pyomo.opt.check_optimal_termination", "line_number": 76, "usage_type": "call"}, {"api_name": "pyomo.environ.value", "line_number": 89, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 89, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 92, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "pyomo.environ.ConcreteModel", "line_number": 170, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 170, "usage_type": "name"}, {"api_name": "pyomo.environ.Set", "line_number": 183, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 183, "usage_type": "name"}, {"api_name": "pyomo.environ.Set", "line_number": 221, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 221, "usage_type": "name"}, {"api_name": "pyomo.environ.Var", "line_number": 225, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 225, "usage_type": "name"}, {"api_name": "pyomo.environ.Var", "line_number": 231, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 231, "usage_type": "name"}, {"api_name": "pyomo.environ.Constraint", "line_number": 241, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 241, "usage_type": "name"}, {"api_name": "pyomo.environ.Var", "line_number": 243, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 243, "usage_type": "name"}, {"api_name": "pyomo.environ.Constraint", "line_number": 246, "usage_type": "attribute"}, {"api_name": "pyomo.environ", "line_number": 246, "usage_type": "name"}, {"api_name": "pyomo.environ.Constraint", "line_number": 248, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 248, "usage_type": "name"}, {"api_name": "pyomo.environ.Expression", "line_number": 253, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 253, "usage_type": "name"}, {"api_name": "pyomo.environ.Expression", "line_number": 257, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 257, "usage_type": "name"}, {"api_name": "pyomo.environ.Objective", "line_number": 261, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 261, "usage_type": "name"}]} +{"seq_id": "201094005", "text": "import argparse\nimport logging\nimport os\nimport time\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom data_cleaning import ParkingDataLoader\nfrom torch.utils.data import TensorDataset, DataLoader\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--network', type=str, choices=['resnet', 'odenet'], default='odenet')\nparser.add_argument('--tol', type=float, default=1e-3)\nparser.add_argument('--adjoint', type=eval, default=False, choices=[True, False])\nparser.add_argument('--downsampling-method', type=str, default='conv', choices=['conv', 'res'])\nparser.add_argument('--nepochs', type=int, default=100)\nparser.add_argument('--data_aug', type=eval, default=True, choices=[True, False])\nparser.add_argument('--lr', type=float, default=0.01)\nparser.add_argument('--batch_size', type=int, default=256)\nparser.add_argument('--test_batch_size', type=int, default=256)\n\nparser.add_argument('--save', type=str, default='./experiment1')\nparser.add_argument('--debug', action='store_true')\nparser.add_argument('--gpu', type=int, default=0)\nargs = parser.parse_args()\n\nloader = ParkingDataLoader()\n\ntrain_data, validation_data, test_data = loader.get_train_validation_test_datasets(validation_split=0.16,\n test_split=0.2)\n\nif args.adjoint:\n from torchdiffeq import odeint_adjoint as odeint\nelse:\n from torchdiffeq import odeint\n\n\ndef norm(dim):\n return nn.GroupNorm(min(32, dim), dim)\n\n\nclass ODEfunc(nn.Module):\n\n def __init__(self, dim):\n super(ODEfunc, self).__init__()\n self.relu = nn.ReLU(inplace=True)\n self.lin1 = nn.Linear(dim, dim)\n self.lin2 = nn.Linear(dim, dim)\n self.lin3 = nn.Linear(dim, dim)\n self.nfe = 0\n\n def forward(self, t, x):\n self.nfe += 1\n out = self.relu(x)\n out = self.lin1(out)\n out = self.relu(out)\n out = self.lin2(out)\n out = self.relu(out)\n out = self.lin3(out)\n return out\n\n\nclass ODEBlock(nn.Module):\n\n def __init__(self, odefunc):\n super(ODEBlock, self).__init__()\n self.odefunc = odefunc\n self.integration_time = torch.tensor([0, 1]).float()\n\n def forward(self, x):\n self.integration_time = self.integration_time.type_as(x)\n out = odeint(self.odefunc, x, self.integration_time, rtol=args.tol, atol=args.tol)\n return out[1]\n\n @property\n def nfe(self):\n return self.odefunc.nfe\n\n @nfe.setter\n def nfe(self, value):\n self.odefunc.nfe = value\n\n\nclass Flatten(nn.Module):\n\n def __init__(self):\n super(Flatten, self).__init__()\n\n def forward(self, x):\n shape = torch.prod(torch.tensor(x.shape[1:])).item()\n return x.view(-1, shape)\n\n\nclass RunningAverageMeter(object):\n \"\"\"Computes and stores the average and current value\"\"\"\n\n def __init__(self, momentum=0.99):\n self.momentum = momentum\n self.reset()\n\n def reset(self):\n self.val = None\n self.avg = 0\n\n def update(self, val):\n if self.val is None:\n self.avg = val\n else:\n self.avg = self.avg * self.momentum + val * (1 - self.momentum)\n self.val = val\n\n\ndef get_mnist_loaders(batch_size=128, test_batch_size=256):\n # train_data, validation_data, test_data\n\n train_loader = DataLoader(\n TensorDataset(\n torch.tensor(train_data.drop('Occupied', axis=1).values.astype(np.float32)),\n torch.tensor(train_data['Occupied'].values.astype(np.float32).reshape((len(train_data), 1)))\n ),\n batch_size=batch_size, shuffle=True, num_workers=2, drop_last=True\n )\n\n train_eval_loader = DataLoader(\n TensorDataset(\n torch.tensor(validation_data.drop('Occupied', axis=1).values.astype(np.float32)),\n torch.tensor(validation_data['Occupied'].values.astype(np.float32).reshape((len(validation_data), 1)))\n ),\n batch_size=test_batch_size, shuffle=True, num_workers=2, drop_last=True\n )\n\n test_loader = DataLoader(\n TensorDataset(\n torch.tensor(test_data.drop('Occupied', axis=1).values.astype(np.float32)),\n torch.tensor(test_data['Occupied'].values.astype(np.float32).reshape((len(test_data), 1)))\n ),\n batch_size=test_batch_size, shuffle=True, num_workers=2, drop_last=True\n )\n\n return train_loader, test_loader, train_eval_loader\n\n\ndef inf_generator(iterable):\n \"\"\"Allows training with DataLoaders in a single infinite loop:\n for i, (x, y) in enumerate(inf_generator(train_loader)):\n \"\"\"\n iterator = iterable.__iter__()\n while True:\n try:\n yield iterator.__next__()\n except StopIteration:\n iterator = iterable.__iter__()\n\n\ndef learning_rate_with_decay(batch_size, batch_denom, batches_per_epoch, boundary_epochs, decay_rates):\n initial_learning_rate = args.lr * batch_size / batch_denom\n\n boundaries = [int(batches_per_epoch * epoch) for epoch in boundary_epochs]\n vals = [initial_learning_rate * decay for decay in decay_rates]\n\n def learning_rate_fn(itr):\n lt = [itr < b for b in boundaries] + [True]\n i = np.argmax(lt)\n return vals[i]\n\n return learning_rate_fn\n\n\ndef one_hot(x, K):\n return np.array(x[:, None] == np.arange(K)[None, :], dtype=int)\n\n\ndef accuracy(model, dataset_loader):\n losses = []\n for x, y in dataset_loader:\n x = x.to(device)\n y = y.to(device)\n logits = model(x)\n loss = criterion(logits, y)\n losses.append(np.sqrt(loss.cpu().detach().numpy()))\n return (1 - sum(losses) / len(losses)) * 100\n\n\ndef count_parameters(model):\n return sum(p.numel() for p in model.parameters() if p.requires_grad)\n\n\ndef makedirs(dirname):\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n\n\ndef get_logger(logpath, filepath, package_files=[], displaying=True, saving=False, debug=False):\n logger = logging.getLogger()\n if debug:\n level = logging.DEBUG\n else:\n level = logging.INFO\n logger.setLevel(level)\n if saving:\n info_file_handler = logging.FileHandler(logpath, mode=\"a\")\n info_file_handler.setLevel(level)\n logger.addHandler(info_file_handler)\n if displaying:\n console_handler = logging.StreamHandler()\n console_handler.setLevel(level)\n logger.addHandler(console_handler)\n return logger\n\n\ndef getModel(size=64, layers=1):\n global model\n feature_layers = [ODEBlock(ODEfunc(size)) for _ in range(layers)]\n fc_layers = [nn.Linear(size, 1)]\n model = nn.Sequential(nn.Linear(16, size), *feature_layers, *fc_layers).to(device)\n return model\n\n\nif __name__ == '__main__':\n\n logger = get_logger(logpath=os.path.join(args.save, 'logs'), filepath=os.path.abspath(__file__))\n logger.info(args)\n\n device = torch.device('cuda:' + str(args.gpu) if torch.cuda.is_available() else 'cpu')\n\n criterion = nn.MSELoss().to(device)\n\n train_loader, test_loader, train_eval_loader = get_mnist_loaders(\n args.batch_size, args.test_batch_size\n )\n\n data_gen = inf_generator(train_loader)\n batches_per_epoch = len(train_loader)\n\n lr_fn = learning_rate_with_decay(\n args.batch_size, batch_denom=128, batches_per_epoch=batches_per_epoch, boundary_epochs=[60, 100, 140],\n decay_rates=[1, 0.1, 0.01, 0.001]\n )\n\n best_acc = 0\n batch_time_meter = RunningAverageMeter()\n f_nfe_meter = RunningAverageMeter()\n b_nfe_meter = RunningAverageMeter()\n end = time.time()\n\n for dims in [768, 512, 256, 128, 64, 32]:\n for layers in [12, 10, 8, 6, 4, 2]:\n try:\n model = getModel(dims, layers=layers)\n optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=0.9)\n logger.info(model)\n logger.info('Number of parameters: {}'.format(count_parameters(model)))\n print(args.nepochs * batches_per_epoch)\n with open(\"results2\", mode=\"a\") as f:\n f.write(\"-------------------------------------------------\")\n f.write(\"layers: \" + str(layers) + \"\\n\")\n f.write(\"dims: \" + str(dims) + \"\\n\")\n for itr in range(args.nepochs * batches_per_epoch):\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr_fn(itr)\n\n optimizer.zero_grad()\n x, y = data_gen.__next__()\n\n x = x.to(device)\n y = y.to(device)\n logits = model(x)\n loss = criterion(logits, y)\n loss.backward()\n optimizer.step()\n\n batch_time_meter.update(time.time() - end)\n\n end = time.time()\n\n if itr % batches_per_epoch == 0:\n with torch.no_grad():\n train_acc = accuracy(model, train_eval_loader)\n val_acc = accuracy(model, test_loader)\n if val_acc > best_acc:\n torch.save({'state_dict': model.state_dict(), 'args': args},\n os.path.join(args.save, 'model.pth'))\n best_acc = val_acc\n print(\"------------------------\")\n print(\"loss:\", 100 - np.sqrt(loss.cpu().detach().numpy()) * 100)\n logger.info(\n \"Epoch {:04d} | Time {:.3f} ({:.3f}) | NFE-F {:.1f} | NFE-B {:.1f} | \"\n \"Train Acc {:.10f} | Test Acc {:.10f}\".format(\n itr // batches_per_epoch, batch_time_meter.val, batch_time_meter.avg,\n f_nfe_meter.avg,\n b_nfe_meter.avg, train_acc, val_acc\n )\n )\n with open(\"results2\", mode=\"a\") as f:\n f.write(\"Epoch {:04d} | Time {:.3f} ({:.3f}) | NFE-F {:.1f} | NFE-B {:.1f} | \"\n \"Train Acc {:.10f} | Test Acc {:.10f} \\n\".format(itr, batch_time_meter.val,\n batch_time_meter.avg,\n f_nfe_meter.avg,\n b_nfe_meter.avg, train_acc,\n val_acc))\n\n except Exception as error:\n print(error)\n pass\n", "sub_path": "parking_prediction/odenet_mnist.py", "file_name": "odenet_mnist.py", "file_ext": "py", "file_size_in_byte": 10856, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "data_cleaning.ParkingDataLoader", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.GroupNorm", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 69, "usage_type": "call"}, {"api_name": "torchdiffeq.odeint", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 85, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.prod", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 120, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 127, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 128, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 135, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 136, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 191, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 195, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 197, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 199, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 202, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 215, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 216, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path", "line_number": 222, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 225, "usage_type": "attribute"}, {"api_name": "torch.nn.MSELoss", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 227, "usage_type": "name"}, {"api_name": "time.time", "line_number": 245, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 251, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 251, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 273, "usage_type": "call"}, {"api_name": "time.time", "line_number": 275, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 278, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 282, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 283, "usage_type": "call"}, {"api_name": "os.path", "line_number": 283, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 286, "usage_type": "call"}]} +{"seq_id": "510297001", "text": "\n#import文\nimport inspect, sys, os\nfrom os import device_encoding\nimport ply.yacc as yacc\nfrom lex import tokens\n\n#各種リスト格納用変数初期宣言\nintd = {}\nstrd = {}\nfuncd = {}\nclos = {}\nifsd = {}\n\niflis = []\nfunclis = []\nast_2 = []\nclasslist= []\n#--------\n\n#str型定義\nkataw = \"\"\nnowvar = \"\"\nnamestr = \"L-1\"\nnowvar2 = \"\"\nfuncname = \"\"\nvar = \"\"\n#--------\n\n#int型定義\niflisc = 0\nifc = 0\nlisc = 0\nicj = 0\n#--------\n\n#何これ\nprecedence = (\n ('left', 'OPTASU'),\n ('left', 'OPKAKERU'),\n)\n\n#文法一括管理\ndef p_teigilist_0(p):\n '''\n teigilist : teigi\n | teigilist teigi\n '''\n if (len(p) == 2):\n p[0] = (p[1])\n elif (len(p) == 3):\n p[0] = (p[1],p[2])\n\ndef p_paramlist(p):\n '''\n paramlist : param\n | paramlist CONMA param\n '''\n if (len(p) == 2):\n p[0] = [p[1]]\n elif (len(p) == 4):\n l = p[1]\n l.append(p[3])\n p[0] = l\n\ndef p_param(p):\n '''\n param : TYPE ID\n | TYPE\n '''\n if (len(p) == 2):\n p[0] = ('PARAM',p[1])\n elif (len(p) == 3): \n p[0] = ('PARAM',p[1],p[2])\n\ndef p_lamda(p):\n \"\"\"\n bun : ID EQUALS LAMDA KAKKO paramlist KOKKA LBRACE bunlist RBRACE SEMI\n \"\"\"\n p[0] = ( \"LAM\", p[1], p[5], p[8])\n\ndef p_exit(p):\n \"\"\"\n bun : EXIT KAKKO shiki KOKKA SEMI\n \"\"\"\n p[0] = ( \"EXIT\", p[3] )\n\ndef p_call(p):\n '''\n bun : ID KAKKO shiki KOKKA SEMI\n '''\n if p[1] == \"write\":\n p[0] = (\"WRITE\", p[3])\n elif p[1] == \"close\":\n p[0] = (\"CLOSE\", p[3])\n else:\n p[0] = ( \"CALL\", p[1], p[3] )\n\ndef P_shiki_dins(p):\n \"\"\"\n shiki : ID PIRIOD ID KAKKO shiki KOKKA\n \"\"\"\n p[0] = ( \"CLINS\", p[1], p[3], p[5])\n\ndef p_bun_inline(p):\n \"\"\"\n bun : INLINE KAKKO shiki KOKKA SEMI\n \"\"\"\n p[0] = ( \"INLINE\", p[3] )\n\ndef p_comment(p):\n \"\"\"\n teigi : STR\n \"\"\"\n p[0] = (\"PASS\", p[1])\n\ndef p_instance(p):\n \"\"\"\n bun : ID EQUALS ID SEMI\n \"\"\"\n p[0] = ( \"INS\", p[1], p[3])\n\ndef p_teigi_0(p):\n '''\n teigi : TYPE ID KAKKO paramlist KOKKA yaji TYPE LBRACE bunlist RBRACE\n '''\n p[0] = ('DEF',p[7],p[2],p[4],p[9])\n\ndef p_teigi_void(p):\n \"\"\"\n teigi : TYPE ID yaji TYPE LBRACE bunlist RBRACE\n \"\"\"\n p[0] = ('DEF',p[4],p[2],\"void\",p[6])\n\ndef p_teigi_1(p):\n \"\"\"\n teigi : STRUCT ID LBRACE teigilist RBRACE\n \"\"\"\n p[0] = ( \"STRUCT\", p[2], p[4] )\n\ndef p_class_f(p):\n \"\"\"\n class : classf\n \"\"\"\n p[0] = p[1]\n\ndef p_class_f_2(p):\n \"\"\"\n class : class classf\n \"\"\"\n p[0] = p[1],p[2]\n\ndef p_class(p):\n \"\"\"\n teigi : CLASS ID LBRACE class RBRACE\n \"\"\"\n p[0] = ( \"CLASS\", p[2], p[4])\n\ndef p_include(p):\n \"\"\"\n teigi : INCLUDE shiki PIRIOD shiki\n \"\"\"\n p[0] = ( \"include\", p[2], p[3], p[4] )\n\ndef p_classfunc(p):\n \"\"\"\n classf : ID KAKKO paramlist KOKKA yaji TYPE LBRACE bunlist RBRACE\n \"\"\"\n p[0] = list( (\"CLDEF\", p[1], p[3], p[6], p[8] ) )\n\ndef p_class_fc(p):\n \"\"\"\n bun : ID PIRIOD ID KAKKO shiki KOKKA SEMI\n \"\"\"\n p[0] = ( \"CLINST\", p[1], p[3], ( \"ID\", p[5] ) )\n\ndef p_pass(p):\n \"\"\"\n bun : PASS SEMI\n \"\"\"\n p[0] = ( \"PASS\", p[1] )\n\ndef p_bunlist_0(p):\n '''\n bunlist : bun\n '''\n p[0] = [p[1]]\n\ndef p_bunlist_1(p):\n '''\n bunlist : bunlist bun\n '''\n l = p[1]\n l.append(p[2])\n p[0] = l\n\ndef p_bun_type(p):\n #TODO : shikiかSTRか決める\n '''\n bun : TYPE ID EQUALS shiki SEMI\n '''\n p[0] = ('SENGEN',p[1],p[2],p[4])\n\ndef p_bun_lis(p):\n '''\n bun : TYPE ID EQUALS LISL shiki LISR SEMI\n '''\n p[0] = ( 'LISTD', p[2], p[5] )\n\ndef p_bun_deflis(p):\n \"\"\"\n bun : TYPE ID EQUALS ID LISL NUMBER LISR SEMI\n \"\"\"\n p[0] = ( 'LISTARG', p[2], p[4], p[6], p[1] )\n\ndef p_bun_newlis(p):\n \"\"\"\n bun : ID EQUALS LISL LISR SEMI\n \"\"\"\n p[0] = ( \"NEWLIS\", p[1])\n\ndef p_add(p):\n '''\n bun : ID EQUALS ID OPTASU NUMBER SEMI\n | ID EQUALS ID OPKAKERU NUMBER SEMI\n | ID EQUALS ID OPMIN NUMBER SEMI\n | ID EQUALS ID OPWARU NUMBER SEMI\n '''\n p[0] = ( 'DAINYU', p[1], p[3], p[4], p[5] )\n\n\ndef p_shiki_type(p):\n \"\"\"\n shiki : TYPEF KAKKO shiki KOKKA\n \"\"\"\n p[0] = (\"TYPEF\", p[3])\n\ndef p_shiki_int(p):\n \"\"\"\n shiki : TYPE KAKKO shiki KOKKA\n \"\"\"\n p[0] = ( \"CONVT\", p[1], p[3] )\n\ndef p_shiki_input(p):\n \"\"\"\n shiki : RESERV KAKKO shiki KOKKA\n \"\"\"\n p[0] = ( \"INPUT\", p[3])\n\ndef p_shiki_fib(p):\n \"\"\"\n shiki : FIB KAKKO shiki KOKKA\n \"\"\"\n p[0] = ( \"FIB\", p[3])\n\ndef p_bun_return(p):\n '''\n bun : RETURN shiki SEMI\n '''\n p[0] = ('RETURN',p[2])\n\ndef p_bun_if1(p):\n '''\n bun : IF hikaku LBRACE bunlist RBRACE \n '''\n p[0] = ('IF',p[2],p[4])\n\ndef p_bun_if2(p):\n '''\n bun : IF hikaku LBRACE bunlist RBRACE ELSE LBRACE bunlist RBRACE\n '''\n p[0] = ('IF-ELSE',p[2],p[4],p[8])\n\ndef p_bun_while(p):\n '''\n bun : WHILE hikaku LBRACE bunlist RBRACE \n '''\n p[0] = ( 'WHILE', p[2], p[4] )\n\ndef p_hikaku_1(p):\n '''\n hikaku : shiki\n '''\n p[0] = p[1]\n\ndef p_hikaku_2(p):\n '''\n hikaku : hikaku EQOP shiki\n | hikaku dainari shiki\n | hikaku syounari shiki\n '''\n p[0] = ('HIKAKU1',p[1],p[2],p[3])\n\ndef p_shiki_num(p):\n '''\n shiki : NUMBER\n '''\n p[0] = ('NUM',p[1])\n\ndef p_shiki_id(p):\n '''\n shiki : ID\n '''\n p[0] = ('ID',p[1])\n\ndef p_shiki_void(p):\n \"\"\"\n shiki : TYPE\n \"\"\"\n p[0] = ( \"TYPE\", p[1] )\n\ndef p_shiki_str(p):\n '''\n shiki : STR\n '''\n p[0] = ('STR',p[1])\n\ndef p_shiki_pass(p):\n '''\n bun : PASS \n | PASS KAKKO KOKKA\n | PASS KAKKO PASS KOKKA SEMI\n '''\n p[0] = ('PASS',p[1])\n\ndef p_shiki_len(p):\n \"\"\"\n shiki : LEN KAKKO shiki KOKKA\n \"\"\"\n p[0] = ( \"LEN\", p[3] )\n\ndef p_bun_append(p):\n \"\"\"\n bun : ID LISL shiki LISR EQUALS shiki SEMI\n \"\"\"\n p[0] = ( \"APPEND\", p[1], p[3], p[6] )\n\ndef p_shiki_MINNUM(p):\n \"\"\"\n shiki : OPMIN NUMBER\n \"\"\"\n p[0] = \"-\"+p[1]\n\ndef p_shiki_conma(p):\n \"\"\"\n shiki : shiki CONMA shiki\n \"\"\"\n if type(p[3]) == str:\n p[0] = (\"ID\", p[1][1]) + (p[3])\n else:\n p[0] = (\"ID\", p[1][1], p[3][1])\n\ndef p_shiki_enzan(p):\n '''\n shiki : shiki OPTASU shiki\n | shiki OPKAKERU shiki\n | shiki OPMIN shiki\n | shiki OPWARU shiki\n '''\n p[0] = ( \"OP\", p[1], p[2], p[3] )\n\ndef p_shiki_kakko(p):\n '''\n shiki : KAKKO shiki KOKKA\n '''\n p[0] = ('exp',p[2])\n\ndef p_shiki_args(p):\n \"\"\"\n shiki : shiki CONMA ID \n \"\"\"\n if len( p[1][0][0] ) > 1:\n p[0] = list( p[1] ) + [ ( \"ID\", p[3] ) ]\n else:\n p[0] = ( p[1] ), (\"ID\", p[3])\n\ndef p_bun_insert(p):\n \"\"\"\n bun : RESERV ID LISL NUMBER LISR EQUALS STR SEMI\n | RESERV ID LISL ID LISR EQUALS STR SEMI\n | RESERV ID LISL NUMBER LISR EQUALS ID SEMI\n | RESERV ID LISL ID LISR EQUALS ID SEMI\n \"\"\"\n p[0] = ( \"RES\", p[1], p[2], p[4], p[7] )\n\ndef p_bun_reserv (p):\n '''\n bun : RESERV KAKKO shiki KOKKA SEMI\n '''\n p[0] = ( \"RES\",p[1],p[3] )\n\ndef p_shiki_func_ret(p):\n \"\"\"\n shiki : ID KAKKO shiki KOKKA\n \"\"\"\n if p[1] == \"open\":\n p[0] = (\"OPEN\", p[3])\n elif p[1] == \"read\":\n p[0] = (\"READ\", p[3])\n else:\n p[0] = (\"CALL\", p[1], p[3])\n\ndef p_shiki_clinst(p):\n \"\"\"\n shiki : ID PIRIOD ID KAKKO shiki KOKKA\n \"\"\"\n p[0] = ( \"CLINST\", p[1], p[3], ( \"ID\", p[5] ) )\n\n\ndef p_shiki_call_void(p):\n '''\n shiki : ID KAKKO KOKKA\n '''\n p[0] = ( \"CALL\", p[1], \"void\" )\n\ndef p_shiki_list(p):\n \"\"\"\n shiki : ID LISL shiki LISR\n \"\"\"\n p[0] = ( \"LISARG\", p[1], p[3])\n\ndef p_lets(p):\n \"\"\"\n bun : TYPE LBRACE letlist RBRACE SEMI\n \"\"\"\n p[0] = ( \"LETS\", p[1], p[3] )\n\ndef p_letsglobal(p):\n \"\"\"\n bun : GLOBAL TYPE LBRACE letlist RBRACE SEMI\n \"\"\"\n p[0] = ( \"GLOBAL\", p[2], p[4])\n\ndef p_letslis(p):\n \"\"\" \n letlist : ID EQUALS STR SEMI \n | ID EQUALS NUMBER SEMI \n | letlist ID EQUALS STR SEMI\n | letlist ID EQUALS NUMBER SEMI\n \"\"\"\n if type(p[1]) != tuple: \n p[0] = ( p[1], p[3] )\n else:\n p[0] = [ p[1], (p[2], p[4])]\n\ndef p_str_add(p):\n \"\"\"\n bun : ID EQUALS ID OPTASU STR SEMI\n | ID EQUALS STR OPTASU ID SEMI\n \"\"\"\n p[0] = ( \"STRADD\", p[1], p[3], p[5] )\n\ndef p_add_add_num(p):\n \"\"\"\n bun : ID OPTASU OPTASU SEMI\n | STR OPTASU OPTASU SEMI\n \"\"\"\n p[0] = ('OPSTR',p[1],p[2],p[1])\n\n# syntax error\ndef p_error(p):\n print ('SyntaxErr : すみません、 %sに当てはまる文法作るのやめてもらっていいすか？' % p)\n\nparser = yacc.yacc(debug=0, write_tables=0)\n\nclass CodeGenartor:\n #file書き込みやそのための文字列作成など\n mList = []\n\n global icj, funclis\n\n def __init__( self ):\n self.ifcj = icj\n\n def append(self , line):\n self.mList.append(line)\n \n def out_put( self ):\n filename = sys.argv[1]\n wfile = open ( filename+\"s\", \"a\", encoding=\"utf_8\")\n wfile.truncate(0) \n for item in self.mList:\n print (\"\".join( item), file=wfile)\n \n def add_define( self , info ):\n self.append( [\"\\n\"+info['funcname']+':'] )\n \n def add_return( self ):\n self.append( ['','end;\\ncb;\\n'] )\n #TODO : end;cb;\n global namestr\n for name in iflis:\n if namestr == name or int( name.split( \"L\" )[1] ) < int( namestr.split( \"L\" )[1] ) :\n pass\n else:\n self.if_write( name, funcd )\n namestr = name\n for name in funclis:\n self.f_write( name, funcd )\n \n def f_write( self, nameandarg, funcd ):\n global funcname \n name = nameandarg.split( \":\" )[0]\n funcname = name\n arg = nameandarg.split( \":\" )[1]\n if arg != \"void\":\n args = \"\"\n self.append( [ '',\"\\n\"+name+\"(\"+arg.replace( \" \", \"\" ).replace( \"and\", \"\" )+\"):\" ] )\n arg = arg.split( \" and \" )\n for item in arg:\n if item != \"\" :\n if item.startswith(\"!\"):\n self.append( [ '',\"fode>\"+item.split(\"!\")[1]+\";\" ] )\n else:\n self.add_vall( item.split( \" \" )[0], item.split( \" \" )[1] )\n args += item\n else:\n self.append( [ '',\"\\n\"+name.split(\"!\")[0]+\"():\" ] )\n self.append( [ '',\"\\nfode>\"+name.split(\"!\")[1]+\";\" ] )\n walker.step2( funcd[name] )\n self.append( ['','end;\\ncb;\\n'] )\n\n def if_write( self, name, dic ):\n self.append( [ '',\"\\n\"+name+\"():\" ] )\n for item in funcd[name][6:]:\n walker.step2( item )\n\n def add_num( self , info, arg, mode ):\n self.append( ['',\"mode>\"+mode+\";\\n\"+\"mov \"+arg+\" \"+info[arg]+\";\" ])\n \n def add_deflis( self, arg, vall, mode, index, mold ):\n self.append( ['',\"mode>\"+mode+mold+\";\\n\"+\"mov \"+arg+\", \"+vall+\"[\"+index+\"]\"+\";\" ])\n\n def add_sym( self , arg, funcname ):\n #print(funcname)\n self.append( [\"ret \"+funcname+\", \"+arg+\";\" ])\n \n def add_fode( self, fode ):\n self.append( [ '',\"fode>\"+fode+\";\" ] )\n\n def add_call ( self, name, vall ):\n if type(vall) == tuple:\n self.append( [ \"\", \"call \"+name+\"[\"+vall[1]+\"];\" ] )\n\n else:\n self.append( [ \"\", \"call \"+name+\"[\"+vall+\"];\" ] )\n\n def add_msg ( self, word ):\n if (word[0] == \"ID\"):\n self.append( [ \"\", \"msg \"+word[1]+\";\" ])\n else:\n self.append( [ \"\", \"msg \"+word+\";\" ])\n\n def add_input ( self, word ):\n self.append( [ \"\", \"input \"+word+\";\" ])\n\n def add_dainyu( self, ast ):\n if ast[3] == \"+\":\n self.append( ['','mode>int;\\nadd ',ast[2],\", \",ast[4],\";\\nmov \", ast[1], \", \", ast[2],\";\"] )\n \n if ast[3] == \"-\":\n self.append( ['','mode>int;\\nsub ',ast[2],\", \",ast[4],\";\\nmov \", ast[1], \", \", ast[2],\";\"] )\n \n if ast[3] == \"*\":\n self.append( ['','mode>int;\\ndev ',ast[2],\", \",ast[4],\";\\nmov \", ast[1], \", \", ast[2],\";\"] )\n \n if ast[3] == \"/\":\n self.append( ['','mode>int;\\nmul ',ast[2],\", \",ast[4],\";\\nmov \", ast[1], \", \", ast[2],\";\"] )\n \n def add_ifcall( self, word ):\n self.append( ['', word ] )\n \n def add_if ( self, name ):\n self.append( ['', name +\"():\\n\"+funcd[name] ] )\n \n def add_while( self, loopn ):\n if type(loopn) != tuple:\n self.append( ['', \"jmp \"+str( loopn )+\", L\"+str( ifc )+\";\" ] )\n else:\n self.append( ['', \"jmp \"+str( loopn[1] )+\", L\"+str( ifc )+\";\" ] )\n\n def add_vall( self, mode, data ):\n self.append( ['',\"mode>\"+mode+\";\\n\"+\"pop \"+data+\";\" ] )\n\n def add_lis( self, ast ):\n if ast[2][0] == \"STR\" :\n self.append( [ '', \"mode>lis;\\nmov \"+ast[1]+\", \"+str( ast[2][1:] )+\";\" ] )\n else:\n self.append( [ '', \"mode>lis;\\nmov \"+ast[1]+\", \"+ast[2]+\";\" ] )\n \n def add_insert( self, lisn, index, STR ):\n self.append( [ '', \"mode>lisin;\\nmov \"+lisn+\"[\"+index+\"], \"+STR+\";\" ] )\n\n#codegenインスタンスを作成\ncodegen = CodeGenartor()\n\ndef if_j( x, y, mode, kata ):\n if kata == \"int\":\n if mode == \"==\":\n if x in intd and y in intd and intd[x] == intd[y]:\n return 0\n elif x in intd and intd[x] == int( y ):\n return 0\n elif y in intd and intd[ y ] == int( x ):\n return 0\n elif mode == \">\":\n if x in intd and y in intd and intd[x] > intd[y]:\n return 0\n elif x in intd and intd[x] > int( y ):\n return 0\n elif y in intd and intd[ y ] > int( x ):\n return 0\n elif mode == \"<\":\n if x in intd and y in intd and intd[x] < intd[y]:\n return 0\n elif x in intd and intd[x] < int( y ):\n return 0\n elif y in intd and intd[ y ] < int( x ):\n return 0\n elif kata == \"str\":\n if x[0] == \"ID\":\n x = strd[x[1]].replace( \"\\\"\", \"'\" ).replace( \"' \", \"\" ).replace(\"'\", \"\")\n y = y.replace( \"\\\"\", \"'\" ).replace(\"' \", \"\").replace( \"'\", \"\" )\n if x == y:\n return 0\n if y in strd:\n x = x.replace( \"\\\"\", \"'\" ).replace( \"'\", \"\" )\n y = strd[y].replace( \"\\\"\", \"'\" ).replace( \"'\", \"\" )\n if x == y:\n return 0\n else:\n return 1\n\nclass Calc:\n #各種四則演算計算用\n def __init__( self, ast ):\n self.ast = ast\n \n def first( self ):\n\n if self.ast[2] == \"+\":\n ret = self.plus()\n elif self.ast[2] == \"-\":\n self.min()\n return ret\n \n def plus( self ):\n if self.ast[1][0] == \"NUM\" or self.ast[3][0] == \"NUM\":\n formula = int(self.ast[1][1]) + int(self.ast[3][1])\n return formula\n \n def min( self ):\n if self.ast[1][0] == \"NUM\" or self.ast[3][0] == \"NUM\":\n formula = int(self.ast[1][1]) - int(self.ast[3][1])\n return formula\n\nclass Walker:\n\n def walk(self,ast):\n #step2に移動用。\n self.step2( ast )\n\n def step2(self,ast):\n\n global funcname, mode, funclis, kataw, ifc, iflisc, lisc, ast_2, funcd, classlist, clos, nowvar, nowvar2, var\n if ast[0] == 'exp':\n self.step2(ast[1])\n \n elif ast[0] == 'LISTD':\n codegen.add_lis( ast )\n\n elif ast[0] == \"PASS\":\n pass\n\n elif ast[0] == 'DEF':\n\n funcname = ast[2]\n size = 0\n vallw = \"\"\n\n try:\n for item in ast[3]:\n vallw += item[1]+item[2]\n size+=1\n except IndexError:\n pass\n \n funcd[ast[2]] = ast[2:]\n codegen.add_define({'funcname':ast[2]+\"(\"+vallw+\")\", 'localvarsize':size })\n\n codegen.append( [\"\", \"fode>\"+ast[1]+\";\"] )\n for item in ast[3]:\n self.step2(item)\n for item in ast[4]:\n self.step2(item)\n if funcname == \"main\":\n codegen.add_return( )\n\n elif ast[0] == 'ID':\n var = ast[1]\n self.step2(ast[1])\n \n elif ast[0] == \"WHILE\":\n add = []\n codegen.add_while( ast[1][1] )\n if ast[2] == \"<\":\n pass\n for item in ast:\n add += item\n funcd[\"L\"+str( ifc )] = (add)\n iflis.append( \"L\"+str( ifc ) )\n ifc+=1\n\n elif ast[0] == \"HIKAKU1\":\n if ast[2] == \"==\":\n jmode = \"je\"\n elif ast[2] == \"<\":\n jmode = \"ja\"\n elif ast[2] == \">\":\n jmode = \"jne\"\n ifword = jmode+\" \"+ast[1][1]+\", \"+ast[3][1].replace( \"\\\" \", \"\\\"\" )+\", L\"+str( ifc )+\";\"\n codegen.add_ifcall( ifword )\n\n elif ast[0] == 'IF':\n add = []\n self.step2(ast[0:2][1])#TODO : HIAKAKU\n for item in ast:\n add += item\n funcd[\"L\"+str( ifc )] = (add)\n iflis.append( \"L\"+str( ifc ) )\n ifc+=1\n\n elif ast[0] == 'DAINYU':\n codegen.add_dainyu( ast )\n\n elif ast[0] == \"LISTARG\":\n codegen.add_deflis(ast[1], ast[2], \"ind\", ast[3], ast[4] )\n \n elif ast[0] == 'PARAM':\n try:\n if ast[1] == \"int\":\n codegen.add_vall( ast[1], ast[2] )\n \n if ast[1] == \"str\":\n codegen.add_vall( ast[1], ast[2] )\n except IndexError:\n pass\n\n elif ast[0] == \"CALL\":\n size = 0\n vallw = \"\" \n self.step2(ast[2])\n funcname = ast[1]\n if ast[2][0] == \"OP\":\n codegen.add_call( ast[1], ast[2][1][1] )\n else:\n if ast[2][0][0] == \"ID\":\n codegen.add_call( ast[1], ast[2][0][1]+\", \"+ast[2][1] )\n else:\n nowvar = ast[1]\n codegen.add_call( ast[1], ast[2] )\n \n try:\n try:\n for item in funcd[ast[1]][1] :\n if item[1] != \"void\":\n vallw += item[1]+item[2]\n else:\n vallw += \"void\"\n except IndexError:\n for item in funcd[ast[1]]:\n if type(item) != tuple:\n vallw = \"\"\n else:\n vallw = item[1]+item[2]\n except KeyError:\n print(\"DefineErr：\"+ast[1]+\"？なんすか、\"+ast[1]+\"って\")\n\n elif ast[0] == \"LISARG\":\n if ast[2][0] == \"NUM\":\n index = ast[2][1]\n\n codegen.append( [\"\", \"mode>indstr;\\nmov \"+nowvar+\", \"+ast[1]+\"[\"+index+\"];\"] )\n\n elif ast[0] == \"RES\":\n if ast[1] == \"put\":\n if type(ast[2]) == tuple:\n try:\n if type(ast[2][1]) == str:\n nowvar = ast[2][1]\n else:\n nowvar = ast[2][1][1]\n self.step2(ast[2])\n codegen.add_msg( ast[2][1] )\n codegen.add_msg( \"\\\"\\\\n\\\"\" )\n except:\n if ast[2] == \"STR\":\n codegen.add_msg( ast[2][1] )\n codegen.add_msg( \"\\\"\\\\n\\\"\" )\n else:\n self.step2( ast[2] )\n codegen.add_msg( nowvar )\n codegen.add_msg( \"\\\"\\\\n\\\"\" )\n\n else:\n codegen.add_msg( ast[2] )\n codegen.add_msg( \"\\\"\\\\n\\\"\" )\n elif ast[1] == \"msg\":\n codegen.add_msg( ast[2] )\n elif ast[1] == \"input\":\n codegen.add_input( ast[2] )\n elif ast[1] == \"insert\":\n codegen.add_insert( ast[2], ast[3], ast[4] )\n\n elif ast[0] == \"RESF\":\n l = []\n l.append( \"CALL\" )\n l.append( ast[1] )\n l.append( ast[2] )\n self.step2( l )\n l = []\n l.append( \"RES\" )\n l.append( \"put\" )\n l.append( ast[1] )\n self.step2( l )\n\n elif ast[0] == 'SENGEN':\n mode = ast[1]\n if mode == \"int\":\n if ast[3][0] == \"shiki\":\n intd[ast[2]] = Calc(ast[3]).first()\n codegen.append( ['',\"mode>int;\\n\"+\"mov \"+ast[2]+\" \"+str(Calc(ast[3]).first())+\";\" ])\n elif ast[3][0] == \"LEN\":\n if ast[3][1][0] == \"ID\" and ast[1] == \"int\":\n codegen.append( [\"\", \"mode>len;\\nmov \"+ast[2]+\" \"+ast[3][1][1]+\";\"] )\n \n else:\n if ast[3][0] == \"NUM\":\n intd[ast[2]] = int( ast[3][1] )\n codegen.add_num({ast[2]:ast[3][1]}, ast[2], ast[1])\n else:\n\n if type( ast[3] ) == tuple:\n nowvar = ast[2]\n if ( ast[3][0] == \"ID\" or ast[3][0] == \"STR\" or ast[3][0] == \"NUM\") :\n codegen.append( [\"\", \"mode>int;\\nmov \"+ast[2]+\" \"+ast[3][1]+\";\"] )\n else:\n self.step2(ast[3])\n codegen.append( [\"\", \"mode>int;\\nmov \"+ast[2]+\" \"+nowvar+\";\"] )\n else:\n codegen.append( [\"\", \"mode>int;\\nmov \"+ast[2]+\" \"+ast[3]+\";\"] )\n if mode == \"str\":\n nowvar = ast[2]\n strd[ast[2]] = ast[3]\n try:\n self.step2( ast[3] )\n codegen.add_num({ast[2]:ast[3][1].replace( \"\\\"\", \"\")}, ast[2], ast[1])\n \n except:\n pass\n\n elif ast[0] == \"TYPEF\":\n codegen.append( [\"\", \"mode>type;\\nmov \"+ast[2]+\" \"+ast[3][1][1]+\";\"] )\n \n elif ast[0] == \"CONVT\":\n self.step2( ast[2] )\n codegen.append( [\"\", \"mode>con\"+ast[1]+\";\\nmov \"+nowvar+\" \"+nowvar+\";\"] )\n \n elif ast[0] == \"INPUT\":\n codegen.append( [\"\", \"input \"+ast[1][1]+\";\"] )\n\n elif ast[0] == \"APPEND\":\n if ast[2][0] == \"NUM\":\n codegen.append( [\"\", \"mode>append;\\nmov \"+ast[1]+\" \"+ast[2][1]+\" \"+ast[3][1]+\";\"] )\n\n elif ast[0] == \"LEN\":\n codegen.append( [\"\", \"mode>len;\\nmov \"+ast[1][1]+\" \"+nowvar+\";\"] )\n\n elif ast[0] == 'RETURN':\n self.step2( ast[1] )\n if type( ast[1][1] ) == tuple:\n codegen.add_sym( ast[1][1][1], funcname )\n else:\n codegen.add_sym( ast[1][1], funcname )\n\n elif ast[0] == \"LAM\":\n add = []\n arg = \"\"\n for item in ast[2]:\n if item[1] == \"void\":\n break\n else:\n arg += item[1]+\" \"+item[2]+\" \"\n funcd[ast[1]] = ast[3]\n funclis.append( ast[1]+\":\"+arg )\n \n elif ast[0] == \"OP\":\n \n item = ast[1:]\n for item2 in item:\n self.step2(item2)\n if ast[2] == \"+\":\n op = \"add \"\n\n elif ast[2] == \"-\":\n op = \"sub \"\n \n elif ast[2] == \"*\":\n op = \"dev \"\n \n elif ast[2] == \"/\":\n op = \"mull \"\n nowvar = ast[1][1]\n codegen.append( [ op+nowvar+\", \"+ast[3][1]+\";\" ] )\n\n elif ast[0] == \"OPSTR\":\n codegen.append( [ \"add \"+ast[1]+\", 1;\" ] )\n\n elif ast[0] == \"NEWLIS\":\n codegen.add_lis([\"\", ast[1], \"\\\"\\\"\"])\n\n elif ast[0] == \"LETS\":\n for msd in ast[2]:\n item = ( \"SENGEN\", ast[1], msd[0], msd[1] )\n self.step2(item)\n\n elif ast[0] == \"GLOBAL\":\n\n if type( ast[2][0] ) != str:\n for msd in ast[2]:\n item = ( \"SEG\", ast[1], msd[0], msd[1] )\n self.step2(item)\n else:\n self.step2( ( \"SEG\", ast[2][0], ast[2][1] ) )\n \n elif ast[0] == \"SEG\":\n codegen.append( [\"\", \"mode>global\"+ast[1]+\";\\nmov \"+ast[2], \" \"+ast[3]+\";\"] )\n\n elif ast[0] == \"STRADD\":\n codegen.append( [ \"stradd \"+ast[2]+\", \"+ast[3]+\";\" ] )\n\n elif ast[0] == \"STRUCT\":\n codegen.append( [ \"\", \"mode>struct;\\nname \"+ast[1]+\";\" ] )\n for item in ast[2]:\n self.step2(item)\n codegen.append( [ \"\", \"end;\" ] )\n\n elif ast[0] == \"CLINST\":\n arg = \"\"\n try:\n self.step2(ast[3])\n for item in ast[3]:\n if item[0] == \"ID\":\n arg = item[1]+\", \"+item[2]\n elif item == \"ID\":\n arg = ast[3][1][1]\n else:\n arg = item\n except:\n pass\n nowvar = ast[2]\n codegen.append( [\"\", \"class>\"+ast[1]+\" \"+ast[2]+\"[\"+arg+\"];\"] )\n \n elif ast[0] == \"CLASS\":\n codegen.append( [\"\", ast[1]+\"():\"] )\n if type ( ast[2] ) != list:\n for item in ast[2]:\n self.step2( item )\n else:\n self.step2( ast[2] )\n codegen.append(\"end;\\ncd;\")\n\n elif ast[0] == \"INS\":\n codegen.append( [\"\", \"inst \"+ast[1]+\", \"+ast[2]+\";\"] )\n \n elif ast[0] == \"CLDEF\":\n hikisu = \"\"\n for item in ast[2]:\n hikisu = hikisu + item[2] + \" and \"\n codegen.append( [\"\", \"in>\"+ast[1]+\"[\"+hikisu+\"];\"] )\n\n size = 0\n vallw = \"\"\n\n try:\n for item in ast[2]:\n if item[1] == \"void\":\n vallw = \"void\"\n else:\n vallw += item[1]+\" \"+item[2]+\" and \"\n size+=1\n except IndexError:\n pass\n funclis.append(ast[1]+\":\"+vallw+\"!\"+ast[3])\n funcd[ast[1]] = ast[4]\n funcname = ast[1]\n\n elif ast[0] == \"INLINE\":\n if type( strd[ast[1][1]] ) == str:\n codegen.append( [\"\", strd[ast[1][1]].replace(\"\\\"\", \"\")+\";\"] )\n else:\n codegen.append( [\"\", strd[ast[1][1]][1].replace(\"\\\"\", \"\")+\";\"] )\n\n elif ast[0] == \"EXIT\":\n codegen.append( [\"\", \"exit;\"] )\n \n elif ast[0] == \"FIB\":\n codegen.append( [\"\", \"mode>fib;\\nmov \"+nowvar+\" \"+ast[1][1]+\";\"] )\n\n elif ast[0] == \"include\":\n try:\n main( os.getcwd()+\"/include/\"+ast[1][1]+\".\"+ast[3][1] )\n except FileNotFoundError:\n main( ast[1][1]+\".\"+ast[3][1] )\n\n elif ast[0] == \"OPEN\":\n self.step2(ast[1])\n codegen.append( [\"\", \"open>\"+nowvar+\" \"+var+\" \"+ast[1][2]+\";\"] )\n \n elif ast[0] == \"WRITE\":\n self.step2( (\"ID\", ast[1][1] ) )\n v1 = var\n self.step2( (\"ID\", ast[1][2] ) )\n codegen.append( [\"\", \"mode>file;\\nmov \"+v1+\" \"+var+\";\"] )\n \n elif ast[0] == \"READ\":\n #TODO : 実装\n print(\"read\", \":\", ast)\n \n elif ast[0] == \"CLOSE\":\n self.step2(ast[1])\n codegen.append( [\"\", \"close>\"+var+\";\"] )\n elif type(ast[0]) == tuple:\n for item in ast:\n self.step2(item)\n\nwalker = Walker()\n\ndef main( filename ):\n file = open ( filename, \"r\", encoding=\"utf_8\" )\n data = \"\"\n for item in file:\n if item.startswith(\"class \"):\n item = item.replace( \"class \", \"fnclass \" )\n elif item.startswith( \"include \" ):\n item = item.replace( \"include \", \"fninclude \" )\n data += item\n file = data.replace( \"\\n\", \"\" )\n retcount = 0\n for i in range ( len( file.split( \"fn\" ) ) ):\n if file.split(\"fn\")[i] != \"\":\n result = parser.parse( \"fn\"+file.split(\"fn\")[i] )\n if result != None:\n walker.walk(result)\n codegen.out_put()\n \nif __name__ == '__main__':\n funcname = sys.argv[1]\n main( funcname )\n", "sub_path": "src/compile.py", "file_name": "compile.py", "file_ext": "py", "file_size_in_byte": 28807, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ply.yacc.yacc", "line_number": 464, "usage_type": "call"}, {"api_name": "ply.yacc", "line_number": 464, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 479, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 1012, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 1058, "usage_type": "attribute"}]}